A new series presented by Analytics for Humans
What It Means and Why It is the Future of Data Analytics
Gartner, one of the top marketing research companies in the world, publishes an annual “hype cycle” graph. It describes the status and maturity of all up-and-coming technologies ranging from brain chips to self-driving cars.
In July, Gartner published the 2017 edition of the report (shown above). Notably, the report introduced a new concept called “Augmented Analytics”(“Augmented Data Discovery” in the graph), which they claimed to be the “future of data analytics”.
In the report, Gartner describes Augmented Analytics as:
“an approach that automates insights using machine learning and natural-language generation, marks the next wave of disruption in the data and analytics market.”
While it is a great definition for data experts, it’s not detailed enough for most people to understand what “Augmented Analytics” really means.
So, today let’s talk about what “Augmented Analytics” really is — and why you should care.
To understand “augmented analytics”, we must first understand the problem it solves. That is, we must understand why generating insights from data remains a huge challenge for almost all businesses.
At this point in the business world, everyone agrees that data analytics, like vitamins, is good for a business, and has the potential to drastically increase traction and revenue (if done properly).
However, the problem is that data analytics is not exactly the easiest thing to pull off in the real world.
You see, data is not like a vitamin that you can just pop in your mouth and be done with it.
In fact, data, by itself, is totally useless to your business.
For example, your online data might reveal that your revenue is decreasing by 10% from last month.
But what does that really mean to you? Is this decline an industry trend? Is it because one of your advertising channels is not working? Or is it because of other reasons?
That’s why you need to go deeper into your web analytics, ecommerce, and social media data to uncover what resulted in the decline of your revenue.
Then you need to place those changes into a business context, and identify the ones that you can act on immediately.
Going back to the declining revenue example, you might end up realizing that your social ads are 10% less effective than the previous month, and that you need to hire an agency to optimize your ad spend.
Now that insight is actionable because it connects directly to an action you can take to solve your business problem. These actionable insights are extremely helpful because they serve as a guiding light for what you should prioritize in your business.
However, to go from raw data to insights, you need to go through many technical steps, including:
1) Collect data from multiple sources
2) Clean data so it is ready for analysis
3) Conduct the analysis
4) Generate insights, and
5) Communicate those insights with the organization and convert them into action plans
If these steps sound complex, they are even more complicated to implement in practice, and you need to hire dedicated individuals (usually data scientists or data analysts) to perform these steps for your business.
But here’s the thing.
First of all, data scientists and analysts are REALLY scarce and expensive to hire, making it extremely cost-prohibitive for smaller businesses (100M and below) to leverage analytics.
The McKinsey Global institute projected that the U.S. economy could be short as many as 250,000 data scientists by 2024. And that’s after accounting for the recently growing supply of data scientists from colleges and education programs.
Second, no matter how good data scientists are, they are not business experts.
This means that the executives need to work very closely with the data scientists to make sure the analytical results actually make “business sense,” taking precious time away from the owners’ busy schedule.
Third, in practice, data scientists spend over 80% of their time doing simple mechanical things such as labeling and cleaning their data.
This makes their lives miserable and wastes the investment by the company (read more here).
Finally, data scientists are still human, meaning that their attention span and ability to do repetitive work are limited.
Therefore, a typical data scientist only analyzes a small portion (probably 10%) of your data that they think has the most potential of bringing you great insights. This means you may miss out on valuable insights in the remaining 90% — insights that may be mission-critical for your business.
For these reasons, almost all small and medium-sized businesses (SMBs) we interviewed (and by now we’ve interviewed more than 100) are still in the early stages of analytics adoption, despite a strong desire to leverage their data.
The challenge faced by businesses here is not going away anytime soon. We can’t expect a sudden spur of data scientists airdropped by Martians to solve the talent shortage. Nor can we expect analyzing this data to magically become easier to do for non-technical business people.
That’s where augmented analytics comes in.
What augmented analytics does is to relieve a company’s dependence on data scientists by automating insight generation in a company through the use of advanced machine learning and artificial intelligence algorithms.
An augmented analytics engine can automatically go through a company’s data, clean it, analyze it, and convert these insights into action steps for the executives or marketers with little to no supervision from a technical person. Augmented analytics therefore can make analytics accessible to all SMB owners.
Wait, aren’t those the problems solved by analytical software tools like Tableau and SAS?
This is the common misconception I hear from everyone outside (or even inside) the analytics industry.
Software tools like Tableau and SAS are tools that “support” analysis.
What this means is that those tools make analytics and communicating results easier for analysts in your company.
However, it doesn’t do the analyses FOR you, and it certainly does not eliminate the need of a business analyst or data scientist.
Augmented Analytics, on the other hand, is designed to conduct analyses and generate business insights automatically with little to no supervision, and can be used directly by marketers and business owners without needing the assistance of a business analyst or data scientist.
Therefore, its application is far more advanced and powerful than tools like SAS and Tableau.
On the technical side, tools like SAS and Tableau focuses on offering extremely flexible interfaces. This way, analysts can easily conduct any type of analysis on the platform and beautifully present the results.
On the other hand, augmented analytics focuses significantly more on the end goal of those analyses — insights.
For example, whereas Tableau enables you to create beautiful bar graphs (without telling you what the bar graph actually means for your business), an augmented analytics engine might simply tell you what your data is saying about your business. It may show only the relevant information that led the system reach that conclusion.
Therefore, augmented analytics software is much more focused on creating a knowledge base of business information to detect business trends, and using machine learning algorithms to detect those trends in a company’s data.
So how mature is Augmented Analytics right now?
Short answer, not very mature but it is going to grow VERY fast in the next couple of years.
Personally, as someone who is working on an augmented analytics company (Humanlytics, check it out!), I measure the maturity of all augmented algorithms in three stages, with each stage being significantly more advanced than the stage before it.
Stage 1: Data Preparation and Discovery
This is the stage most of the existing augmented analytics technologies are in. Key players include IBM Watson Analytics, Tableau Insights, and Qlik Sense.
At this stage, augmented analytics algorithms serve as a great complement to existing data scientists or analysts, but does not have the ability to completely substitute them.
Here, the algorithm’s primary purpose is to automate boring data preparation tasks such as data cleaning, data labeling, and data collection.
It may be able to detect some correlations and anomalies in the data, but most of these detections are noise, and data scientists still need to parse out real signals manually.
Stage 2: Signal Detection
At this stage, the augmented analytics algorithm can detect true signals in a company’s data with extreme reliability. However, it is unable to connect these discoveries with business situations or business actions.
Assistance is still needed from data analysts or data scientists to convert those discoveries into concrete business insights, but the time they need to spend on each insight is reduced dramatically.
Many companies will likely reach this stage in 2–3 years.
Stage 3: Actionable Insight Generation
This is the stage in which the augmented analytics engine can directly interface with executives in the company with little or no need for input from a business analyst or data scientist.
A large knowledge base of past business cases will be developed to help the augmented analytics systems connect trends in the company’s data to the larger context of the business. It can then offer concrete action steps based on these insights.
More importantly, the system will be able to track the implementation of these actions and provide additional insights on what the company can do better next time to maximize its operational effectiveness.
Here, the augmented analytics engine is not only a substitute for business analysts, but can also do a lot of things current analysts cannot do.
This stage is definitely a significant leap compared with the previous stages. I believe many businesses will start to reach this stage in 5–10 years.
Get excited about the coming data revolution!
As both a data scientist and an entrepreneur working in this field, I am super excited about the prospect of augmented analytics becoming adopted in the business world.
I like to compare the evolution of data analytics technologies with the evolution of the automobile.
To start, both are extremely complex systems that have thousands of if not millions of parts in them to make them function properly.
However, almost everyone in the country right now can drive an automobile, despite how complex the technology is.
This is because most of the complexity is abstracted away by the technology. Users only need to know the “data” that are relevant to them (eg how to use the steering wheel) to make decisions while driving.
Now, we are progressing even farther to make the task of “driving” unnecessary so that users can simply care about what’s really important to them: moving from point A to point B with as efficiently as possible.
In the data world, we are not even close to the design and technology achievements accomplished by the automobile industry (given we are only 10 years into the data revolution, I am not surprised).
But to quote Gartner, augmented reality is really the future of data analytics because it moves us closer than ever to that vision of “democratized analytics” because it will be cheaper, easier, and better. At last, we will see more and more businesses of all sizes using and benefiting from analytics for years to come.
We are driven by this vision of a future where data benefits everyone, not just the select few. If you are too, join our Facebook community here.
And what action steps your business can do at each step
Marketing is simple in theory: you follow your customers along their decision process as they search for a product or service that can satisfy their needs.
To help with their decision process, the marketer provides relevant information to help them make a decision, and in doing so, nudges them every so often so they will choose their company to satisfy their needs in the end.
Well, this marketing theory is very easy to explain, but extremely difficult to accomplish in practice.
This is because the customer decision journey is getting more and more complicated every day, enhanced by modern technology such as social media and smartphones.
As we explained in the previous article, the traditional concept of a customer funnel can no longer accurately capture the iterative, lengthy nature of the modern customer journey. We, as marketers, need a new way to think about our interaction with our customers so we can serve them better.
This article is designed to help you solve that problem.
Here, we will present a simple 3-stage framework to help you understand how your customers are interacting with your business in this modern age, and show you actions that you can take in each of the three steps presented to increase their chance of becoming your loyal customers.
While the traditional “funnel” framework focuses on conversion as the sole purpose of your business actions, this 3-stage framework focuses on helping you create a holistic customer experience at each stage of the model. This framework also provides you with the right metrics to measure your success at each stage.
Stage 1: Search
Search is the start of your customer’s journey.
Triggered by some event in their lives (advertisements, recommendations from friends, or simply a need they want to met), your customers decided to embark upon a journey to find a service or product that can satisfy their needs.
“Teddy told me the most important idea in advertising is ‘new’. Creates an itch. You simply put your product in there as a kind of calamine lotion.” -Don Draper, Mad Men
Because modern customers are empowered by the internet and smartphones, they are likely to go onto the internet and find a wide range of offerings that have the potential of solving their problems.
Based on all these offerings, your customer forms a “consideration set” in their mind — a set of products or services that they are potentially interested in purchasing.
Your task, as a firm, is to be in that “consideration set.”
Company Actions: Acquire customers so they add your brand to their “consideration set”
So how do you get into the “consideration set” of your customers?
1) The first step is to know them really well.
Ask yourself the following question:
Who are my target audiences?
- What channels and platforms do they frequent if they are searching for a solution that satisfies their needs?
- What kind of information will they be interested in?
After considering these questions, you will have a fairly good idea of what online platforms and channels are the best ones for you to reach your audiences.
2) Then comes the second step: you craft messaging based on your assumptions about your customers.
Create messaging for each of the various channels you can reach them with, and then wait for the data to come in to evaluate how well you actually reached them in each channel.
Modern advertising platforms like Facebook and Twitter all provide ample data for you to examine the effectiveness of your ads on different audiences, and your job is to find the audience that resonates with your message the most. Once you identify the best audiences, double down on reaching them.
But how do you define “success” in this step?
Based on the customer story I described earlier, most customers are probably not ready to make a purchase yet. So what you really need to do here is to first attract them. Make them interested in your brand and product first, before worrying about actually converting them.
Therefore, your success metric should be “customers acquired.” This metric can be measured by the amount of customers visiting your website without bouncing, or even better, those that visited a product page or brand page on your site (this is your decision based on the stage of your company).
Stage 2: Consideration
Okay, let’s continue with the story of our customers.
Now that our customers have established a “consideration set,” they now conduct research on all options in their set to determine which company most accurately and reliably satisfies their needs.
We can divide up the information gathered by your customers roughly on a 2x2 matrix presented below.
Primary product information includes the features and details of the product as described by the companies themselves. This could be information from the company website, their Amazon profile, or other third-party platforms.
Primary brand information is about what the company stands for from the company’s point of view, whether that’s through the company’s social media page, their blog, or a press release.
Secondary product information, on the other hand, is product information from other users of the product, including friends of the potential customer. This could be from Amazon ratings and reviews, or review sites like Yelp.
Secondary brand information is the reputation of the brand from other sources, such as social media. This brand image, usually termed doppelganger personality, is the reputation that it does not have total control of, such as what others say about the brand on social media.
Increasingly, secondary brand and product information have become more and more important in a customer’s decision process. However, if the company does not have a brand message or a good product in the first place, that secondary information will not exist.
Modern consumers take secondary brand and product information very seriously, as evidenced by review sites like Yelp.
During the customer’s journey, she may be engaged with brands in the consideration set at different levels depending on how attractive each of the brands are to her. Your goal as a marketer in this stage is to make these customers as engaged as possible.
Company Actions: Deepening your engagement with your current audiences
The first step to increasing customer engagement is to define the various levels of engagement customers may have with your company.
For example, a customer that has signed up for your newsletter should receive significantly different treatment (both in messaging and channels) than customers that merely visited a product page.
I like to consider customers’ engagement with your website in three levels, but feel free to make up your own levels:
- View: This is the group of customers that has viewed multiple pages of your website, but did not proceed with actions such as checkout or newsletter signup. These customers are at the lowest engagement level.
- Act: This is the group of customers that has performed some actions on your website, whether that’s entering checkout, signing up for your newsletter, filling out a contact form, etc.
- Convert: This is the group of customers that has converted previously on your website (e.g. purchased a product). Because of their past history of conversion or purchase, this is the customer group with the highest level of engagement.
Understanding how many customers are at each stage of engagement can help you determine what type of engagement strategy you should focus on when interacting with your customers, which is the essence of the next step.
Notice that in this step, customers may still not be so certain they want to make a purchase yet. If you, for example, send them a coupon for your product during this stage, they may have a high likelihood of making a purchase, but only because of the product’s cheaper price point.
However, being associated with being the cheapest option is often one of the worst impressions a brand can have in a customer’s’ mind. This is because as soon as another cheaper alternative is available, your customer will leave.
Therefore, while conversion should probably be a metric you watch during this stage, measuring the engagement level of your customer pool is even more important. For example, you should track whether more people visited your product page as a result of your campaigns (pageviews), or if people’s overall time spent on your website increased (average session duration).
These engagement-deepening metrics are essential in telling you whether the messaging you used is resonating with your audiences (even more essential than just conversion rate).
Stage 3: Purchase
Now we are at the final stage of the customer decision journey. After researching all the products in the “consideration set,” it is time for your customer to make a decision of who to purchase from.
Behavioral science studies have shown that small “nudges,” or changes in “choice architecture,” can have a big effect on decisions like purchases.
While this step seems to be straightforward, small “hiccups” such as additional shipping costs, hidden clauses, or even memory lapses might prevent customers from completing the purchase from your company, even if they decided to purchase.
Your job, as the company, is to make a final attempt to nudge customers to choose your product and to prevent these hiccups, in order to maximize your conversion rate.
Company Actions: Convert your engaged customers
You might have noticed that in this framework, we are holding off on “hard conversion,” i.e. trying to convert customers with techniques such as coupons or promotions.
This is intentional. As we mentioned, if your customers convert simply because you offer a cheaper option, it is less likely that they will become loyal customers and advocates of your product in the future.
However, more often than not, your engaged customers need that final “promotion nudge” to make up their mind to purchase your product, and that’s what we will do in this stage.
In this stage, you should perform two primary actions:
- First of all, run multiple remarketing campaigns via various channels that focuses on converting your customers who have been exposed to your brand.
- Secondly, use checkout recovery and other funnel abandonment recovery techniques to maximize conversions.
The analysis you should conduct in this step is very similar to the traditional funnel analogy. In this stage, you should focus on conversion rate and the ROI of your campaigns (account for your cost of acquiring and engaging your customers when calculating this).
If you laid your foundation well, I have seen companies with 200% to 300% ROI at this stage, which is essentially free money.
Questions to consider
This concludes the introduction to our “Search-Consider-Purchase” model of the customer journey. You can see the full picture of the model below.
As you can see, while it is only three steps and simple in theory, it is in fact fairly complex if you try to implement it across different channels in your organization.
In the end, I would like to leave you with few key questions to consider when implementing this model:
- Given all the time you have, which stage should you spend most of your money on?
- How fast and frequently do you need to engage with your acquired customers before they disappear for good?
- What is the conversion rate of your customers at various engagement levels if you push them to convert today? Is it better to run engagement ads to push them to a deeper engagement level before converting them?
All these are more advanced and complex questions you need to answer to truly master this framework, and at Humanlytics we are trying to automate these analyses so you can answer these hard questions without having deep analytical expertise.
Finally (and most importantly), there is one question you can answer immediately about your marketing efforts that you must consider:
“Am I running ads to make sales, or to create a truly great customer group that can not only benefit from my product, but also advocate it?”
The answer should always be the latter, and this model can help you accomplish it.
Feel free to shoot me an email @ email@example.com or leave a comment below if you have any questions or concerns with anything explained in this article. Thanks!
And Three Reasons Why
Right now, when most business owners and marketers think about how customers interact with their businesses, they think in terms of a funnel that looks something like this:
The idea behind a marketing funnel is very simple - a large volume of customers get to know your product, and some of them become interested. Then some of these interested people consider buying. Finally, some of these people buy and become your loyal customers.
Along the way, some people may “drop off” from the funnel, and marketers need to recover these “drop-outs” as much as possible via advertising.
The simplicity of the funnel makes perfect sense even to a layperson. Personally, it became one of my guiding lights in my consulting engagements to help my clients improve their customer experiences.
However, as digital channels become one of the main mediums for customers to find information for products and services, their decision-making process have become significantly more iterative and complex.
This increasing complexity makes the funnel analogy (which was developed way back in 1924) more and more insufficient in fully capturing the dynamic nature of the modern customer experience.
I will go so far as to say that if you are still using the funnel to measure the effectiveness of your digital strategy, you are probably getting inaccurate insights about how customers actually behave - and you are probably making incorrect business decisions as a result.
In this article, I will show you three reasons why this is the case.
Reason 1: The funnel is not compatible with the new, digital customer experience
While the original purpose of the funnel was to provide business analysts with a simplified model of customer experiences, it is now too simple to support marketing decisions in a digital world.
In recent years, because of the overwhelming amount of information made available to the customers, the power of consumer decisions has shifted significantly from firms to customers.
This means that firms can no longer produce a product, run advertisements, and expect customers to flood through the door, as a traditional marketing funnel would suggest.
Instead, they have to design the product, branding, and messaging with their customers’ specific needs and wants in mind. Firms must now constantly engage with the customers with not only their product offerings, but also relevant information to keep customers interested in their brand.
The complexity in modern marketing is the main reason that funnels are no longer the best model for firms to use to make decisions. While the funnel is great at providing an overarching view of the customer decision process, it does not accurately capture the new, customer-centric model for three main reasons:
1) First of all, when customers today are making a purchase decision, they usually compare and contrast offerings from multiple companies.
This means that, as a marketer, you will be competing with many other companies for the customer’s attention and money, and the funnel fails to capture the dynamics of this competition.
2) Secondly, modern customers, especially millennials, want to be more involved with the business and the values it represents (you can read more about this here).
This means that companies should start to focus more and more on engaging customers with a variety of content on their website, instead of merely convincing them to make a purchase. The traditional funnel does not incorporate these engagement objectives into consideration.
3) Last but not least, with the onset of the digital age, massive amounts of information became available to customers via channels such as social media and review websites.
The vast improvement in the availability of information has made the customer journey to purchase significantly longer, and much more iterative (more on this in the next two reasons). These factors can no longer be captured by a linear conception of the marketing funnel.
For all of these reasons, the firm-centric funnel analogy can no longer meet the need of a consumer-centric, iterative framework. As such, this model must be modified or changed.
Reason 2: The funnel does not capture the full customer journey
The second reason the funnel is no longer sufficient stems directly from the first reason. Funnels usually paint an extremely simple picture of how customers interact with your company through digital channels, and they miss key components of the complete customer journey.
For example, a classical digital marketing funnel for ecommerce might look like the following:
Customers visit the homepage of your ecommerce store
Some of these customers then visit a product page in your store
Some of these customers enter checkout
Some of those customers completes the checkout process and buys your product
While this funnel captured the essence of a customer’s purchase experience in your store, it does not capture their full experience.
For example, the funnel cannot capture a customer journey like this one:
A customer visits an ecommerce store
The customer visits a product page because she is interested in the product
The customer added the product to her cart just to see how expensive it is
The customer exits the website because she does not completely trust the product yet
The customer compares alternatives on Amazon and other review platforms
The customer visits the website again to read more about the story of the company
The customer makes a decision to finally purchase a product
Even this journey I described above is a simplified version of how customers actually make a purchase in the real world, and the restrictive nature of the funnel will inevitably fail to capture the multi-session, cross-platform nature of your online customer journey.
When making business decisions, a simplified model of your customer behavior based on the traditional funnel may lead to drastically different conclusions for why your customers do what they do.
Let’s use cart abandonment as an example.
In a funnel-centric world, the best way to avoid funnel abandonment is through checkout recovery mechanisms such as follow-up emails and retargeting campaigns with Facebook and Google Display ads.
However, as illustrated in the more realistic customer journey described above (and the article linked here), people may abandon their cart simply because they were comparing prices or looking at a product. They may never have had any intent of purchasing in the first place.
That’s why, while you can recover some customers who experienced checkout issues or just simply got distracted, what you really need to do is to convince those people that are still in their comparison and evaluation stage that your product is the best one in their consideration set - a tactic that is not supported by the funnel. This leads to Reason 3.
Reason 3: The funnel doesn’t capture the iterative nature of the customer experience
The final reason ties very closely with the previous one. Not only does the funnel not fully capture your entire customer experience, it also does not cover the “iterative” nature of their interaction with your business.
The funnel view of the customer journey describes customer experiences in a linear fashion: the customer comes in, becomes more and more engaged, and ultimately purchases.
In reality, however, the decision processes of your customers are not so linear. In fact, it is possible for a customer to fall back to a previous stage of engagement. Even if a firm is actively running ads to reach that specific customer, more ads are not always better if an ad does not give the customer the right information they need at the right time to make their decision.
This concept of the iterative customer journey is thoroughly discussed in an article published by McKinsey couple of years ago, and I would strongly recommend that you read it.
The funnel’s inability to represent iterative experiences will result in two major decision problems for businesses.
First of all, it makes companies believe that more advertisements are always better than less because you will always be able to push customers through the funnel and convert.
However, when you start to consider the iterative nature of the customer journey, you will realize that bad or misplaced advertisements will actually decrease customer’s tendency to purchase, therefore harming the revenue potential of the company.
Secondly, the concept of funnels favors converting customers (pushing them through the funnel) much more than engaging with customers.
In reality, the decision to engage versus convert should be made based on a sophisticated ROI analysis taking into account two factors:
1) The current engagement levels of your customers, and
2) The conversion rate for customers at each of the engagement levels
Only with this kind of consideration can marketers truly optimize their messaging to their customers - and funnels do not provide you with a good framework to analyze this.
The way out - an iterative framework of customer journey
As we illustrated in this article, the funnel is a marketing framework that is so deeply ingrained in the minds of almost all marketers - but it is outdated and insufficient for the modern needs of a more customer-centric and interactive marketing world.
However, criticizing an existing solution without offering an alternative is never very helpful.
That’s why in the next few articles I publish, I will present a new marketing framework that we use at Humanlytics that solves many of the challenges of the funnel model that I presented in this article.
To stay updated on those articles, please follow our publication at Analytics for Humans, or sign up for our monthly newsletter. You can also follow us on social media channels such as Facebook and Twitter to get more news and updates about what is coming next.
Until next time!
Part 2: Creating a human-centered algorithm that is both flexible and rigorous
As explained in the previous article of this series, when designing a self-driving car, the purpose of the artificial intelligence is clear - move the passenger from point A to point B most efficiently without violating any traffic laws.
However, when designing an artificial intelligence to automate insight generation in digital consulting, defining a clear objective is significantly more difficult.
This is because the objective of a business is very much subjective to the owner of the businesses and could range from maximizing revenue to serving as a lifestyle business for owners to have a steady stream of income.
Even if the objectives are clearly defined, the strategies to achieve these objectives may vary greatly across industries.
For example, for the same purpose of maximizing revenue, an ecommerce store may run online ads to bring people to the website, whereas a government contractor may invest in an expensive face-to-face sales team.
For this reason, when tackling the challenge of automating the generation of insights and recommendations for digital marketing, we at Humanlytics immediately realized that our solution needed to have two important attributes:
On the one hand, the system needs to operate under a strict business and measurement framework that will enable the machine to solve specific business problems in structured ways.
On the other hand, there must be some degree of flexibility built into the business framework that allows the algorithm to interact with the users, and create a solution that is tailored specifically to their needs.
In other words, the algorithms we design must be human-centered.
This article will go more in-depth on how we at Humanlytics are working on a human-centric algorithm that creates harmony between flexibility and structure to automate insight generation in digital marketing.
Principle 1: Restricting analyses to only the metrics that are concrete and actionable
Human minds are designed for thinking, connecting, and creating. While those attributes are extremely important and valuable for businesses, the human brain is horrible at processing a large volume of information mechanically.
Unfortunately, this is what marketers have to do in the new digital age.
Let’s take your digital presence for example. Even if you are not running any advertising via Adwords or Facebook, you will receive data on a daily basis regarding the performance of your website (via Google Analytics), your social media platforms (via Facebook insights), and organic search rankings (via Google Search Console).
In each of these datasets, you will receive hundreds if not thousands of data points on the most nitty gritty details of your performance, including how many people visited your about page, how many people watched your videos, and how many people actually made a purchase (if you are an ecommerce store).
While all these data are overwhelming business owners across all company sizes and industries, there currently lack sa rigorous analytics and measurement framework to tell the business owners what they should measure in their business and why these data are important.
Therefore, in order to avoid data points that are not important to your business objective, the algorithm we designed at Humanlytics employs a customer-centric framework that tracks the effectiveness of your efforts in:
Acquisition: Bringing customers into your business website via channels such as Facebook and Organic Search
Engagement: Engaging with your customers so that they remain interested in your content
Re-engagement: Re-engaging with your potential customers via email and social media to further their interest
Conversion: Pushing your customers through the conversion funnel to persuade them to add value for your business.
In each step, our algorithm will use 4-5 key metrics (around 20 total), and as many dimensions (e.g. channels, demographics, etc) as possible, to identify the “missing pieces” that are preventing businesses from achieving their objectives. Our algorithm will also identify what the business should do to resolve these challenges.
Compared to the algorithms powering self-driving cars, the techniques employed in our solutions are much more primitive in the sense of feature engineering (self-driving cars use thousands if not millions of features).
However, while the algorithms for self-driving cars are designed for accuracy, our algorithms are designed to interpret data for even the most ordinary business owner. In other words, our algorithms are designed for humans.
Principle 2: Interacting with users to define objectives and metrics at each step
The centralized, restricted framework explained above is designed to resolve the chaotic nature of business data, but it doesn’t resolve the problem of varying business objectives depending on the preferences of business owners and marketing managers.
In order to accommodate the unique need of each business owner as much as possible, we built a series of experiences that are specifically designed to interact with business owners to understand their business needs and challenges.
This user experience uses AI to make recommendations on what objectives the owner should choose depending on their preferences, but leaves the ultimate decision power to the owners themselves.
After interpreting and understanding the needs of the owners, the system converts these requirements into concrete metrics that can be measured. The AI then fires up the data processing algorithm to produce the best ways forward for the owners to achieve their objectives.
However, the interactive experience between machine and user does not stop there.
After making recommendations for next steps based on the data processing algorithm, the decision-making process falls back on the business owners.
The front-end “wizard” is going to come back and work with the owners again to consolidate the final action steps the owner will take, depending on the preferences and resource constraints of the company.
Finally, the tool will create concrete implementation schedules for the owners to follow so they know exactly what they should do next and monitor their progress.
Due to the interactive and iterative nature of our tool, we like to say that we're building a “personal trainer” or “personal assistant” for digital analytics - it doesn't achieve your goals inside a black box, but rather asks for your feedback along the way. It's got the best of both worlds between "Do It Yourself" and "Do It For Me"
In this article, we examined the processes we used at Humanlytics to create a more human-centric AI that works with business owners to both (1) monitor the decisions made by the owners, and (2) make recommendations based on the results of their actions.
However, I understand most of our discussion remains surface-level, and I do not want to stop our introduction to the Humanlytics framework here.
As we develop our product, we will continue this series with concrete examples of how we are accomplishing each of the features and functions described in this article. We will also give in-depth explanations of the design decisions we make.
At that time, we will also offer you opportunities to try out our tools and offer feedback on how we can make the tools better for your needs as a business owner. This way, we can build something together that is truly valuable to improve your business.
Meanwhile, please comment below if you have any comments or questions about what we are doing!
We are also looking for companies to beta test the product for us and help us grow our consulting knowledge base. If you’re interested, please contact us @ firstname.lastname@example.org or sign up here: bit.ly/HMLbetatest
If you are interested and passionate about what we are doing, please contact us or sign up for our weekly newsletter below. Thanks!
Choosing how frequently you check your business analytics and metrics can be surprisingly tricky.
On the one hand, while monitoring changes to metrics on a daily basis can help you identify anomalies and major events very quickly, you may see your metrics jump significantly up or down depending on what day of the week it is. Like the stock market, these daily fluctuations might mean nothing in the long run, but you might latch onto a false signal.
On the other hand, while you are less likely to see these drastic changes when you are analyzing your metrics monthly, such long delays between analytical cycles will prevent your business from rapidly testing out different ideas.
You need to find a happy medium.
I recommend keeping track of your metrics on a weekly basis for three simple reasons:
1. People behave similarly from week to week
While the “week” is a completely arbitrary unit of time invented by our ancient ancestors, it is surprisingly well-suited for modern analytics needs.
Let’s take the “views” metrics for our medium publication as an example:
Starting mid-September, we ramped up our content schedule from posting 1-2 pieces per week to consistently posting 3 pieces a week.
If we looked at the effectiveness of this posting schedule by comparing day-by-day, we would most likely miss a clear indicator of whether our strategy resulted in an overall increase in our publication views.
However, when we compare the total number of views across weeks, it is very clear that our shift in strategy resulted in an increase in our readership.
This is because unlike days, hours, months, or any other time period, people tend to behave very consistently from one week to another, making it easier to compare metrics across weeks.
For example, most people tend to check their email less on Friday afternoons because they're wrapping up work and getting ready for the weekend, so your email marketing metrics will probably be lower on Fridays, which you can expect to see when you compare metrics between weeks.
Therefore, when you are comparing your metrics changes from one week to another, it is much more likely that those changes are due to your efforts, instead of random noise caused by seasonality across weeks.
For this exact reason, using weeks as a unit of analysis in your organization can help you accurately identify what initiatives worked, and what didn’t work. This enables you to leverage these ideas that did produce results, and iterate upon those that didn’t.
2. Week-to-week analysis helps you focus on long term strategy while not losing sight of short-term changes
One of the biggest problems I see from companies that have just begun utilizing analytics is that they make decisions from their data too quickly.
Very often, I would see a business owner completely crossing out a channel (such as Facebook ads) after spending less than $100 on the platform across only two or three weeks.
However, the reality is that ad platforms such as Facebook require a lot of time and energy for optimization before it starts showing results. More likely or not, you will not see drastic contributions from these channels to your revenue in the first month - or even the first quarter.
While not completely eliminating the “hasty conclusion” issue, analyzing campaign metrics weekly can serve as a buffer that will lessen the chances of making rash decisions, such as terminating campaigns that may be either under-optimized or still gathering momentum.
On the other side of the coin, tracking weekly changes can offer you a good idea of whether a channel is getting better or worse in a reasonably speedy time frame, enabling you to identify weak ads and channels quickly without wasting too much money on them.
3. Analyzing on a weekly basis will align with your decision cycle
The last reason is fairly practical — it is simply impossible to come up with a coherent action plan and execute on it if you track your metrics in any time period shorter than a week.
A well-executed digital marketing plan requires not only a coherent strategy, but also ample time for the digital marketing team to build aesthetically-pleasing assets such as images and videos that resonate with your target audience. All of these steps take time.
Making data-driven decisions on a weekly basis can help you accomplish two things. On one hand, it gives your digital marketing team ample time to strategize and prepare content to engage your audiences. On the other hand, it provides them with enough time pressure to be motivated and ensure rapid execution of your decisions.
As we demonstrated, analyzing your metrics on a weekly basis can offer you many advantages not offered by viewing metrics on a daily or monthly basis.
In additional to the benefits I mentioned, many analytics and dashboard tools on the market (such as Google Analytics) track your metrics on a week-to-week basis by default. As a result, tracking metrics weekly is also simpler to setup than other options.
However, I do like to mention here that as your analytics capabilities grow, you may find that analyzing your metrics weekly isn’t quite enough to satisfy your more complex analytics need s. In that case, you can expand to a more frequent analytics schedule that meets those needs — but that’s a problem that’s relatively far away for most small and medium sized businesses.
Even if you are using a schedule different than the one recommended in this article, it is still worth your time to answer the three questions I brought up in this article:
Is the period I selected stable enough to be compared over time?
Can the time period enable me to focus on both long-term and short-term changes in my metrics?
Can the time period enable my team to execute an action plan within each decision cycle?
I hope these questions can provide you with a framework for thinking about the role of analytics in your business. If properly approached, these questions can help you to grow your digital presence much faster than you thought was possible!
Comments or Questions? Leave a message below.
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AI will not steal our jobs. It will result in more meaningful work.
Okay, here are the hard facts: AI will bring dramatic changes to our society in the next few decades and take away many, if not the majority, of the jobs that exist today.
Terrifying, right? But wait a minute.
Let’s say AI does displace most of the jobs in the world. Then its impact on the structure of our society will be so fundamental that even the way we define the term “job” will be drastically changed.
In other words, after the “age of AI” dawns, rather than thinking about how we should find new jobs for people who were displaced by AI, we should be thinking in a much broader scope about our role as humans in this new world — a world in which most of us will not need to work as much (or as hard) as we are now working.
Therefore, let’s go beyond talking about AI displacing jobs, and instead have a conversation about how we as a society should define “jobs” in the first place in the age of AI.
Jobs will no longer be necessary for daily bread, but will instead be pursued for their own enjoyment
For most people right now, when we talk about jobs, we are really talking about something that we have to do for 8 hours (or more) a day to put food on the table for ourselves and our families.
For the majority of us, jobs are not something that we do for its own enjoyment, but rather a duty that we must fulfill to justify our existence as a productive member of society. One report, for instance, finds that more than half of Americans are unhappy at work.
In the age of AI, this conception of “joyless jobs” will change dramatically, because most of the them will be replaced by AI in the very near future.
In 2013, a research study by Oxford found that around 47% of percent of existing jobs are at risk of being automated by AI in the 20 years.
While many see this number as a threat to a large segment of society, I see this as a liberation for many to pursue what they really want to do and to unleash their full potential.
One of the biggest industries this point applies to is finance. The aforementioned study projected that over 40% of work in finance will be replaced by AI in the next two decades.
As graduates from a top-ranked business school, many of my peers went into the finance industry after finishing college.
What greets them are grueling 80+ hour work weeks, soul-crushing tasks such as crunching numbers on Excel every day, and the overwhelming pressure to deliver (or risk getting weeded out in the competitive corporate ladder in finance).
While most of them did not really enjoy their job (in fact, one study showed that over 50% of junior analysts quit after the first three years), my finance friends saw these jobs as the necessary evil to get the jobs they really wanted, such as jobs in private equity and startups.
What AI will do to the finance industry is remove the need of these necessary evils to college graduates. By eliminating the option of these passionless but well-regarded finance jobs, AI will open the opportunity for them to pursue the work they are passionate about in the first place, whether that’s work in startups, private equity, or otherwise.
For example, with the onset of the internet age, jobs such as e-commerce owners are created that greatly simplified the selling process for most producers, making them focus on their core enjoy of their job — making products, instead of worrying about convincing distributors to distribute their products.
The interesting thing about these new, creative industries enabled by AI (like startups) is that no matter how many jobs AI can replace, there will always be new ones created to fill in the gap (such as , therefore not displacing anyone in the process. Thus at the end of the day, AI will only make jobs more enjoyable and less demanding.
AI will not only elevate the enjoyment of work, but also the wellbeing of the working class
Many are quick to ask, “What about working class jobs like manufacturing? People there really do not have alternatives like young college graduates, and their jobs are also at risk due to AI.”
The point I want to make here is that this population we are talking about is a significant portion of our society. Therefore, some kind of mechanism has to be in place in the age of AI to make sure these people are not left behind.
The ultimate solutions we use may vary. It might be a universal base income (UBI). Just this week, Hillary Clinton revealed in her campaign memoir What Happened that she had worked with staffers on a campaign proposal for a universal basic income for Americans, funded by carbon and financial transaction taxes.
Once considered politically improbable, universal basic income is receiving more and more traction on the left and the right as a serious policy solution to large-scale job automation. As Andrew Yang (founder of Venture for America) points out in his upcoming book The War on Normal People, UBI is “rapidly gaining popularity among forward-thinking politicians and economists. It represents a critical step toward a new kind of economy he calls “human capitalism.”
Or it might be European-style education and retraining programs to help those workers adopt AI-based work. European social democracies such as Germany have spearheaded government programs to retrain workers during times of high unemployment.
Or it might simply be (I personally am banking on this) the development of AIs with advanced interaction design that enable workers to take on higher-level jobs even without a high level of education (you don’t really need to know how a car works to drive).
But the bottom line is that these people will not be left behind by the AI revolution. They will only be elevated by AI in this new societal structure shaped by the AI revolution.
In fact, I will go so far as to argue that this group of people will contribute unprecedented levels of productivity to society that they were never able to under the old “job” system.
To support this argument, let’s look back at the history of human evolution.
As someone who comes from a psychology background, what amazes me about the human species is that, although our genome has not altered drastically for the past 5000 years, we managed to elevate our well-being to a level that is unimaginable even with millions of years of natural evolution.
What this means is two things.
First of all, the rapid advancement of humans stems not from an increase in our raw brain power, but rather our ability to make sophisticated tools that support our work, enable us to communicate rapidly, and build better tools through innovation.
Secondly, there is not that much variance among humans in terms of intelligence and ability to innovate. After all, the privileged are not privileged because they have better genes than the underprivileged.
This means that if we pair the least intelligent person in our society today with the most intelligent person from 2000 years ago, the less intelligent person is still significantly more productive than the smartest ancient person, despite their differences in intelligence because they have access to resources and tools that makes them so productive.
So if the variance in the ability to innovate is not very large among the general population, AI will free up the minds of working class people to be much more creative and innovative, rather than leaving them with nothing to do.
What this means is that working class people can focus more of their time and energy to education and training (which are made much more affordable by the internet), or simply doing work that they themselves consider worthwhile.
And when they work in projects and fields that are actually enjoyable to them, their level of contribution to society is going to increase, enabling a better society for us all. After all, operating a cash register usually does not inspire much fulfillment or motivation to innovate.
AI enables humans to be more fully human
One of the major concerns you may have at this moment is “What if AI takes all of our jobs away without giving us all the benefits that you have mentioned? Won’t this create a world of oppression by those who own the AIs?”
I strongly believe this will not be the case.
As I wrote quite extensively in the article I published last week (linked below), to make the perfect AI that can replace human jobs, the AI needs to interact with humans and learn from us.
What this means is that AIs will never be able to push us aside and do everything by itself without our input.
Instead, the determinant of a great Artificial Intelligence should (and hopefully will) be its ability to understand and ask questions about human needs, and provide humans with the right information at the right time to make the right decision.
Eventually, a division of labor will occur between humans and AI, in which humans will only be responsible for tasks that are human in nature — making decisions based on our moral standards and free will. AI, as our compliments and co-workers, will be responsible for collecting data and running analytical processes to empower us to make better decisions.
This will eventually make “inhuman” jobs, such as manufacturing and bagging groceries, unnecessary. This will bring out the true advantage of the human being — human creativity and innovation — to its full potential in our new economy.
Like all innovations, AI will be a net good for society and human ingenuity will control the side effects
Andrew Smith once said, “People fear what they don’t understand and hate what they can’t conquer.” This perfectly describes our fearful perception of AI right now.
At its current trajectory, the AI revolution is inevitable in the next couple of decades, and the changes it will bring worry many people whose jobs will be impacted.
However, as explained in this article, the changes brought by AI to our society will be much more fundamental than we currently realize. Therefore, it is does not make sense to judge the changes AI will bring in the future with the lenses of our current culture, where jobs are the ultimate good and the displacement jobs the ultimate evil.
In the end, just like all human innovations in the past (including the steam engine, electricity, and the internet), new innovations that add value, no matter how feared in their time, will on balance create a net positive impact on our society and make our lives better.
It is hard to imagine an AI future that is not better than the status quo. While some people might have hardships in the short-term due to mismatches between advancements in AI and advancement in institutional policies to control its negative effects, this pain will be at most short-lived and temporary.
As it has always done, human ingenuity will kick in and find solutions for any negative side effects that appear. Institutions like OpenAI (Elon Musk’s AI research nonprofit) are established (and more will continue to be established) to trailblaze a path to safe AI that improves society.
Ultimately, we will live in a world that is more creative, productive, and enjoyable, and we will never want to return to the world we have now. So get ready, and enjoy the ride.
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A How-To Guide on Building User Segments to Add Value to Your Business
This article is part of our Google Analytics tutorials series. Check out our previous post on How to Set Up Goal Funnel Visualization Reports on Google Analytics.
In the last few weeks, we’ve been publishing step-by-step tutorials on how to set up Google Analytics and Google Tag Manager properly (without coding) as part of our Google Analytics tutorials series.
So far, we’ve covered how to configure important features to unleash the full potential of Google Analytics, including event tracking, goals, and goal conversion funnels.
Beginning this week, we’ll start to look at analysis features and techniques that you can start using today to distill actionable business insights from your digital marketing data on Google Analytics.
Today we’ll focus on one of the most basic, yet most powerful analysis tools in Google Analytics: segmentation. In this article we’ll cover:
- How Segmentation Adds Value to Your Business
- How to Use Segments in Google Analytics
- Configuring Default Segments
- Configuring Custom Segments
- Example Business Questions You Can Answer with Segmentation
- Shortcomings of Segmentation in Google Analytics
- Next Steps
How Segmentation Adds Value to Your Business
Whether you call it audience segmentation, user segmentation, or customer segmentation, the question behind segmentation is actually very simple — breaking down aggregate data by different dimensions (e.g. age, gender, income, location, etc.) to identify patterns (e.g. clusters of customers with similar characteristics). This is the heart of all data analysis.
In business (and many other fields of applied data science), the motivation behind segmentation can be boiled down to this: identifying which few key factors are driving the majority of business outcomes, such as an increase or decrease in revenue.
Management consulting, for example, focuses on isolating “the key driver causing the bulk of the problem.” From Victor Cheng’s
So the first reason to use segmentation is to identify your core audience and buyer persona for your business. For example, you may find that 80% of your website sales is driven by one customer segment (e.g. young urban professional women between 25–34 who live in NYC, Boston, and DC). That would be an incredibly valuable (and actionable) insight for your business.
Now you can tailor your messaging and target your digital ads to that buyer persona. For instance, you might build a segment of users who viewed your product pages for female yoga pants on Google Analytics. You can then target this segment with a Google Adwords remarketing campaign that showcases new items on those product pages.
Secondly, you can better identify specific trends in your business. If you notice that product sales for your core audience of young professional women in New York City has dipped in the last month, you can investigate to see if there is a new competitor that is undercutting your prices in that local market.
If there is such a competitor, you can offer a discount or rewards program in order to lower your prices in that region and retain those customers.
Now that we understand the meaning and value of data segmentation, let’s look at how to use the segmentation tools in Google Analytics.
How to Use Segments in Google Analytics
In Google Analytics, “segments” are simply a way to look at a data subset in a report.
The default “All Users” segment at the top of every Google Analytics report.
There are two types of segments in Google Analytics: user segments and session segments.
User segments are subsets of your website visitors who may visit your site across several sessions over the course of 90 days. You can create user segments based on a combination of dimensions such as age, gender, session date, traffic source, on-site behavior, and more.
As we mentioned in our previous post, 3 Fundamentals To Know About Google Analytics Before Doing Analysis, here are some dimensions that Google Analytics offers:
- Demographics: Includes the attributes of a user such as his/her age, gender, and interests.
- Location: The geographic location the website is accessed from including everything from the city to the continent.
- Behavior: Whether the users are new or returning and how engaged they are with your website in terms of repeated visits and session length.
- Devices: The devices the sessions were conducted on; including Mobile, Desktop, or Tablet.
- Channels: The channel source of those visits; including Direct, Referral, Social Media, Organic Search, etc.
Session segments, on the other hand, represent user behavior during a single session. For instance, you can build a session segment for all sessions originating from a specific marketing campaign, or all sessions during which a visitor completed a purchase.
You can create both user and session segments using metrics (e.g. pageviews), dimensions (e.g. age), session date ranges, and sequences of user interactions (e.g. played a video and then visited a product page). For more detailed information about session and user segments, check out this Google Analytics help page.
The “All Users” segment, which includes every website visitor in your date range, will be the default user segment for every report.
To add and compare segments, click the “Add Segment” button at the top of a report (e.g. the Audience Overview report).
The segment builder will expand and show both system segments and custom segments. System (or default) segments are automatically available in Google Analytics, while custom segments are the segments that you have built or imported.
You can share custom segments that you have built or import segments built by others from the Analytics Solutions Gallery. If you choose to share your segment, you only share the segment, not any of your data.
I would recommend starting off by downloading two segments:
- Occam’s Razor Awesomeness by Avinash Kaushik
- New Google Analytics User Starter Bundle by the Google Analytics team
These custom segments (and custom reports/dashboards) are very useful. They include SEO segments, loyal visitors, and comment submitters.
If you haven’t already, you should check out Avinash Kaushik’s book, Web Analytics 2.0. It is currently one of the most authoritative frameworks on web analytics for business decision-making.
Now let’s talk about how to configure default and custom segments.
Configuring Default Segments
To view a default (or system) segment, click the “System” tab in the segment builder. Select the segments you wish to compare and click the “Apply” button. These segments will appear in every report until you change them or leave Google Analytics.
Click on the down arrow for a segment to access the drop-down menu. To remove a segment, click “Remove.” To copy and edit a segment, click “Copy.” To create a remarketing audience, click “Build audience.”
To add additional segments, click the plus icon.
Configuring Custom Segments
To build a customized segment, click “Create New Segment” below the applied segment fields. As you can see in the side tabs, you can create custom segments based on demographics, technology, behavior, date of first session, traffic sources, and (if configured) ecommerce.
For example, under Demographics you can filter for users who are 25–34, female, and located in New York City. You can also use Affinity Categories and In-Market Segments from Google Adwords. Affinity categories are used to increase awareness with potential customers at the top-of-the-funnel. In-Market segments represent traits that make users more likely to purchase near the bottom-of-the-funnel.
The sidebar on the right will preview what percentage of your total users this segment represents, so you can see how broad or narrow this segment is.
Lastly, you can segment users by sequences of user actions, which include both pageviews and events. For instance, you can build a segment of users who played a video and then submitted a contact form.
Example Business Questions You Can Answer with Segmentation
The most useful feature of segments is the ability to compare multiple segments in a report (you can compare up to four at a time). Here are some business questions you can investigate by comparing user segments:
- What factors are related to whether website visitors make a purchase? Create a custom segment called “Made no Purchase.” Compare the “Made a Purchase” user segment with your “Made no Purchase” user segment.
As you can see in this example report, non-purchasers overwhelmingly tend to be new visitors.
2. What traffic sources and channels drive the most highly-engaged traffic to my website? Compare the bounce rate and session duration of the “Paid Traffic,” “Organic Traffic,” and “Referral Traffic” segments.
In this example report, referral traffic has a significantly lower bounce rate (and thus higher engagement) than paid traffic for most of the past month. Perhaps this company’s marketer should focus more of their resources on driving referral traffic rather than on paid search campaigns.
3. How do my new users and returning users compare in terms of on-site engagement? Compare bounce rate of the “New Users” segment with that of the “Returning Users” segment.
For instance, if I have a spike in new user traffic from a new Adwords campaign but the bounce rate is much higher than that of my returning users, then perhaps this Adwords campaign did not target the right users (i.e. highly engaged users) to direct to my site.
Shortcomings of Segmentation in Google Analytics
As we discussed in our previous post, 3 Fundamentals To Know About Google Analytics Before Doing Analysis, slicing and dicing data across dimensions is good for discovering “known unknowns” — i.e. the business questions you already know you want to answer.
For example, you may have an idea of your buyer persona. Let’s say you sell yoga mats, and you think your ideal customer is a young female urban professional. You can use Google Analytics to confirm or disprove that hypothesis.
But there are also unknown unknowns — i.e. patterns, anomalies, and clusters that you didn’t know about. For example, one of the core audiences for your yoga mat ecommerce store might be baby boomer men who need yoga mats for physical therapy.
You might have never discovered that if you didn’t slice and dice your data correctly. Because there are so many dimensions to segment your data by, and you only have so much time, there are bound to be dozens of actionable business insights that are missed by human analysts.
That’s why at Humanlytics, we’re building an AI-based digital analytics tool that breaks down the data in hundreds of different combinations on the back-end to automatically detect the patterns and insights for you.
We are launching our beta next month (October 2017), so sign up for our newsletter here to receive updates and to try our beta: bit.ly/HMLnewsletter
Today we’ve discussed how segmentation adds value to your business’ bottom line, how to compare segments in the Google Analytics user interface for analysis, and how to customize segments. We’ve also covered the shortcomings of customer segmentation in Google Analytics, and how we’re building an AI digital marketing tool to address this pain point.
But we’ve only scratched the surface of what you can do with segmentation in Google Analytics. We’ve provided a few examples of business questions you can start to explore by comparing user and session segments across time.
1. To learn more about our business framework for how to identify your core website audience using Google Analytics audience reports, check out our previous post:4 Steps of Understanding Your Core Audiences Using Google Analytics.
It was a part of our series to answer the first of the four business questions you can answer with Google Analytics: who are my ideal users?
2. If you found this tutorial helpful, or if you want to get updates on our AI-based digital marketing platform (which will automate all of this analysis for you), then follow us on Medium and sign up for our newsletter using the button below!
The challenge is human-computer interaction design, not technology
These are not the AI challenges you’re looking for
Artificial intelligence (AI) is one of the most hyped terms in the 21st century, and yet one of the most misunderstood.
Very often, when talking about AI, we like to automatically couple it with other terms such as Machine Learning, Deep Learning, and Neural Networks. This makes it sound like over 90% of AI is this kind of statistical algorithm that only PhDs can understand.
This is where we are dead wrong about AI.
After many years of building data and AI products as a data scientist and entrepreneur, I realized that the primary challenge of building AI solutions lies not in building an efficient system, but rather in human-centered design.
While automated learning and classification algorithms are vital to the development of an artificially intelligent system, they only serve as enablers of true intelligence. These algorithms are necessary to develop AI, but not sufficient.
What this means is that the improvement of machine learning algorithms over the years has made it possible for machines to perceive the world almost like humans do. However, in order for a machine to think and act like a human, we must look elsewhere for the answer.
Over the thousands of years of evolution, humans developed unique ways of interpreting and thinking about the world. In order for AI to have a significant impact on our society, it must understand not only how to act like a human, but also how to think like us.
Unfortunately, while the information revolution has enabled us to collect petabytes of data on how we act in a certain situation, not much data has been collected on how we think. This makes it impossible to properly train an AI system.
That’s why we must shift our focus from technology to interaction design. We need to start working on large-scale interaction systems that enable machines to rapidly communicate and collaborate with humans. Machines need to start learning how we conceptualize the world.
What this means for AI researchers and companies, is that the true future of AI lies in design, in an AI’s ability to interact with and learn from humans, and in understanding human contexts — not in more powerful CPUs and algorithms.
This also means technical prowess will become less and less important in building a great AI, relative to deep empathy toward the needs and challenges of the end users who will be interacting with these AI systems.
In this article, I will take an in-depth dive into modern AI technology systems, and demonstrate where AIs today fall short at compared to human intelligence.
Then, I will discuss why learning by interaction has been one of the best ways, if not the only way, for both AIs and humans alike to learn about our world.
Finally, I will offer some suggestions on how we can create these interaction systems that will ultimately enable true artificial intelligence.
Current AI closely mimics the human thinking process, but falls short in learning on its own
Most of the AI systems on the market mimics the human information processing model in psychology. Therefore, it is vital to start our discussion with how an intelligent system like our brain processes information (you can read more about information processing here).
In general, an intelligent system processes information in three very distinctive stages: reception, interpretation, and learning.
Reception is the process in which some receptors (e.g. eyes or ears of the human body) receives signals from the environment, and send those signals to a processing agent (i.e. the brain) in formats that are interpretable by the processing system (i.e. electromagnetic signals).
In AI, examples of these receptors include the cameras on Tesla’s self-driving cars, the Amazon Echo for Alexa, or the iPhone for Siri.
Then comes the interpretation process, in which the processing agent (i.e. the brain) performs three operations to the data sent by the receptors:
- First, it identifies several objects of relevance from the data (i.e. recognizing that there is a red round shape).
- Then, it goes into a library of references (i.e. the human memory), searches for references that will help it identify the objects, and then identifies them (i.e. recognizing that the shape is an apple).
- Finally, based on the current state of the entire system (i.e. how hungry you are), the processing agent (i.e. the brain) determines the importance of each piece of information it receives, and present to the users only the information that passes a certain threshold (in humans, this is called attention). That’s why when you are hungry, you are more likely to see apples and food compared to other objects.
In AI, interpretation usually happens in a large information processing system on the cloud, using sophisticated machine learning algorithms such as neural networks.
With recent developments in machine learning and game-playing algorithms (especially in deep neural networks), AI systems can identify objects based on a body of reference exceptionally well, enabling amazing innovations such as self-driving cars to develop.
However, we cannot stop here, since the library of reference used by the processing agent is limited, especially in the beginning of its life cycle (a baby might not know what an apple even is).
That’s why learning needs to occur to continuously to expand this library of references for the system to reach its full potential.
For modern AI systems, this is where the real challenge lies.
As the current technology stands, AIs are really good at classifying a situation into categories and optimizing based on the parameters provided. However, it cannot create these categories or parameters from scratch without help from human developers.
This is because AI “sees” the world as multiple, purely mathematical matrices, and does not have the intrinsic ability to empathize with human experiences unless we teach it to.
Furthermore, during the training of these classification models, AIs are only given the outcomes of each specific situation, instead of the entire thinking process and rationale that led to that specific outcome, which makes comprehension impossible.
For example, an AI system might be able to programmed recognize the image of a baby, but it will not understand why recognizing the image of a baby is needed in the first place since that information was never given to the AI by the engineers who created it.
In a way, an AI is like a super intelligent newborn baby — while you can show it all the knowledge in the world, it cannot understand how the world really works unless the AI actually gets out into the world to learn from experience.
Because AI lacks the ability to create its own contexts, most of the commands we are asking Siri and Alexa are actually manually programmed by the engineers at Apple and Amazon. It’s also why Amazon spends so much effort to create an open eco-system around Alexa, to encourage companies to program skills on its Alexa platform.
Because they are so human-dependent, the current AI systems such as Alexa cannot really develop new context and learn like humans. Therefore, it is really not accurate to call them “artificial intelligence”.
Interaction can solve the learning problem in AI
So how can we create an AI system that can empathize with the human world?
The answer is rather simple — we teach it to interact with humans and ask questions.
As I mentioned earlier, the biggest challenge of training AI human interaction at the moment is the lack of detailed, interaction-level data about the human thought process.
In order to collect this level of data, we need to ramp up AI’s role not only as a data consumption (using data generated elsewhere) agent, but also data collection agent (generating its own datasets).
What does this mean? It means we need to design AIs such that they can interact with humans to understand not only what the human wants, but why they want it. Just like an apprentice to a master craftsman, AIs need to learn on the job.
In order for AI to have this level of interaction with human, we must first shift our perception of AI.
Right now, we perceive AI as this omnipotent black box that can solve all the problems automatically without any requirement of human input.
For most people we think that we can give AI a general command such as “Alexa, run my business for me,” and expect Alexa to run our businesses for us as we stay in bed and watch Netflix.
However, this is much too high a standard for artificial intelligence, even for intelligence in general.
Let’s use consulting as an example. When a human consultant interacts with clients, they never pull the client aside and tell them, “Hey we understand your business needs perfectly. We can do everything to improve your business for you, you just need to sit and watch.”
Instead, they spend hours and hours sitting down with the clients, asking carefully crafted questions to better understand the needs of their clients, and ultimately work with the client to create a solution that is tailored to the needs of the client.
The success of a consulting project really hinges on a consultant’s ability to draw out the needs of the client and to deliver the most value for their client based on their constraints.
If this is the standard that we hold ourselves to when interacting with one of the more intelligent segments of human society, we should not expect AI to automatically understand all our needs and provide the perfect solution without interacting with us.
How can we create interactive AIs?
In order to give AIs the ability to ask intelligent questions the way consultants do, we must place a lot less emphasis on creating the most powerful machine learning algorithm. Rather, we ought to focus on designing systems that enable maximum interaction between AI and human users while achieving the task the AI is designed for.
More practically, it means that AI product managers should focus less on hiring engineers that are good at algorithm design, and more on recruiting human-centered designers who can talk with the end users of the AI and facilitate interaction between the users and the AI. The task of these designers is to identify the best ways for AI to work with users to improve both the AI’s own intelligence, and the lives of human users.
Essentially, creating interactive AIs demands that AI product managers (like myself) build AIs with the goal of understanding and serving people, instead of replacing people.
At the same time, the development of AI must also be much more transparent than it is right now. As design firm IDEO points out in “A Message to Companies That Collect Our Data: Don’t Forget About Us,” businesses today must design for transparency and user control if they want to build trust with customers.
At the moment, many AI companies refuse to demystify what is actually happening under the hood to their users, not because they fear competitors stealing their technology secrets (honestly, there is not that much to steal in the first place). Rather, they often fear that if their users know how manual the AI is, users will lose trust in the company.
While this fear is valid, to achieve the highest level of intelligence possible for AI technologies, users must be intensively involved in the design iteration process. Therefore, while transparent might harm the early adoption of an AI product in the short term, the long-term benefit of more transparency to the entire AI system is limitless.
The fact is plain and simple: the AI revolution is inevitable. Like it or not, AI will play a large role in our workforce in the next few decades.
However, to make AIs truly useful for our society, they need to understand not only what we humans do, but also why we do it, and this learning requires AIs to jump out of the black box and interact with its users.
This means that for years to come, AI will be less of a technology and coding problem. AI will be more of a design problem in which human-centered designers who empathize with end users will play a critical role.
Ultimately, I can see a future world in which AIs and humans exist in harmony, with each party playing its unique role in human society. Only then will the AI revolution result in prosperity for humanity.
Find Funnel Drop Off Points to Maximize Your Conversion Rate
In last week’s article, we walked you through how to properly set up Google Analytics and Google Tag Manager. In that tutorial, we briefly explained how to set up Google Analytics goals in order to measure your business objectives for your digital channels.
However, although setting up individual Goals such as newsletter signups and product purchases are essential (after all, you can’t improve what you can’t measure), the next step we recommend to every business is to set up what Google Analytics calls “Goal Funnels.”
In marketing, goal funnels (or conversion funnels) are simply a sequence of action steps that your leads must go through in order to “convert” (e.g. buy one of your products).
For example, for a typical B2B company, a website visitor may first discover a couple of blog posts by the company, then click on a product page link, fill out a contact form, schedule a call with a sales representative, and then become a customer.
On Google Analytics, each step of a goal funnel represents a step on your website that must be completed to achieve a Google Analytics Goal. Each step must be a web page with its own URL.
This week, we’ll teach you how to properly set up conversion funnels and distill actionable business insights from your funnel visualization report. We’ll cover:
- Limitations of Google Analytics Goal Funnels
- Why Goal Funnels?
- Setting Up Goal Funnels on Google Analytics
- Using Your Funnel Visualization Report for Analysis and Insights
Limitations of Google Analytics Goal Funnels
It is worth noting that this is one of the limits of goal funnels on Google Analytics. You can only track page-to-page funnels (where each step is a web page). As of now, although Google Analytics does offer some event tracking, “event funnels” are not currently supported (you need to use tools like Heap Analytics or Mixpanel for that).
A second limitation is that events and pageviews are only tracked on Google Analytics after they have been set up — it cannot track data retroactively like Heap Analytics can (which tracks all the data just in case).
Lastly, goal funnels are not as useful for tracking multi-session engagement. For example, it is not very good at tracking how website engagement turns into conversion (e.g. Homepage >> Product Page >> Checkout) because your users may complete this action in multiple sessions. Funnels are only reliable in tracking drop-offs and completions in one session.
These three factors (no event funnels, no retroactive tracking, no multi-session tracking) are all limits that you must take into account when you use goal funnels to answer your business questions.
That having been said, Google Analytics goal funnels are still a powerful feature that is really good at one thing — tracking drop-offs for conversions.
Why Goal Funnels?
Goal funnels are especially valuable for ecommerce businesses (i.e. think Shopify websites) with a particular series of steps required to make a purchase. But goal funnels aren’t just useful for ecommerce companies. All sorts of businesses can use goals and funnels to track micro conversions (actions that move leads closer to a purchase) such as submitting a contact form or visiting certain pages that indicate interest.
For example, let’s say you have an ecommerce company that sells T-shirts. The checkout funnel might look something like this:
Product Gallery — Product Page — Add to Cart — Proceed to Checkout — Shipping Information — Billing Information — Review — Confirmation Page.
One of the most compelling reasons for tracking a goal funnel using Google Analytics is the ability to quickly identify “problem pages,” i.e. exit pages where potential customers drop off or abandon their cart.
The problem may be technical (e.g. there is an error on one of the pages in the payment process) or design-related (e.g. there are too many pop-ups with promotional offers), but either way, you can’t fix these problems unless you use a tool to track how your funnels are performing.
This is where the Google Analytics Funnel Visualization report comes in. In one glance, you can see user behavior for each step of the funnel. The funnel can help you see visually how many users make it to the next step toward the conversion goal, and how many drop off. If you see a big group of users dropping off on a specific page, you should drill down to see what is driving the high exit rate for that step of the funnel.
The Funnel Visualization report on Google Analytics shows you how many users make it to the next step of purchasing a product.
To learn more about other ways to optimize the pages and user flow of your website, check out our past post: How to identify and fix the problem pages on your website with Google Analytics
Now that we understand the value of configuring goal funnels, let’s take a look at how to set one up.
Setting Up Goal Funnels on Google Analytics
Set Up the Goal
To set up a goal funnel, you must first set up a Google Analytics goal. As we explained in last week’s post, navigate to Admin >> Views >> Goals >> New Goal.
Admin >> Views >> Goals >> New Goal
Either select a pre-set goal template or create a custom goal. In this example, let’s say we’d like to track how many website visitors make it to the purchase confirmation page for our ecommerce store. We’ll select “Make a payment.”
Name the goal something you’ll remember. Since we want to measure the number of purchase confirmations, we’ll call it “Purchase Confirmation.” You can only use the Goal Funnels feature with destination goals, so we’ll select “Destination” as our goal type.
Enter the URLs of Each Step of the Funnel
Once you get to this point, toggle the Funnel switch to “On” to set up the pages of the funnel. Each step represents a web page that your website visitors must pass through to reach your Goal (e.g. in this case, completing a product purchase).
In this example, we’ll need to include a unique part of the URL for each page the user has to view in order to check out and make a purchase. We can name each step in our funnel and add the unique part of the URL.
For Shopify websites: Shopify has implemented its own Analytics code so the URL for the funnel page may be different than your actual web page URL. You can go into All Pages report (Behavior >> Site Content >> All Pages) to see what URL Shopify assigned to each of the pages.
If a potential customer must complete a certain step in the funnel to complete the goal in our funnel visualization report, toggle the “Required” switch to “Yes.” In our example, we only want to track website users who began the funnel on the first “Checkout” (/CheckoutCart) page, so we’ll make that first step required.
Note: This “Required” switch will only affect the numbers on the funnel visualization report, not the Goal completion metrics in your Conversions report.
Finally, click on “verify goals” to make sure your Goals are tracking the correct data properly.
Warning: when a goal is verified, it only means that your Goals are working; it doesn’t necessarily mean tracking on each page of the funnel is working properly. To confirm each step of your funnel is working, you need to wait about a week for enough data to be collected. Then check the funnel visualization report to do a sanity check.
Congratulations, you’ve created your first goal funnel!
Using Your Funnel Visualization Report for Analysis and Insights
As always, you can find your Goal metrics in your “Conversions” reports (Reporting >> Conversions >> Goals >> Overview).
To view your funnel visualizations, go to Conversions >> Funnel Visualization.
Reporting >> Conversions >> Funnel Visualization
As you can see in this example, there is a huge cart abandonment issue between the Cart page and the Billing/Shipping page. Only about 43% of users made it past the Cart checkout to the Billing/Shipping step.
As you can see in the screenshot, most of the users that dropped off simply exited the site. However, you don’t have to worry about exits to “/signin.html” because it is simply the page asking the user to sign in to their Google account before they checkout.
This visualization is one of the most efficient ways to understand the typical user flow of your website for most of your visitors. For instance, if users are dropping off the funnel you set up in order to get to another page on your site (e.g. a product promotion page), perhaps you should figure out why they are drawn to that page. You may even want to incorporate that page into your funnel.
To further explore how to improve the design of your web pages to enhance the on-site experience and engagement of your users, check out: How to Create Great Website Experiences Without Fancy Designs
Tip: You can find and add Google Analytics Goals configured by other businesses in the Analytics Solutions Gallery. You can then adopt and modify these for your company’s business objectives.
For example, you can download Google Analytics Goals that are common in a certain industry, such as Shopify ecommerce funnel goals:
Thanks for learning how to properly set up a conversion funnel on Google Analytics with us. Stay tuned next week for a step-by-step tutorial on how to segment and analyze your audience data in Google Analytics.
1. To learn how to improve your Goal metrics, check out our previous article: Conversion Optimization — 3 Simple Steps to Improve Your Conversion Rate Using Google Analytics
2. Kissmetrics also has a great guide on configuring Google Analytics conversion funnels: The Google Analytics Conversion Funnel Survival Guide
3. If you found this helpful, please subscribe to our newsletter below (and follow us on Medium) to get our weekly digital marketing digest, more tutorials, and updates on our AI-based digital marketing tool (beta in progress)!
A step-by-step guide, no coding required
In the last article, we guided you through how to set up Google Analytics through Google Tag Manager to track all page visits by users on your website (pageviews).
However, in many situations, you want to track user behaviors within your pages, such as “add to cart” button clicks, form submissions, or video views.
All of these behaviors are called “events”, and today we are going to show you how to send these events to Google Analytics via Google Tag Manager.
WARNING: This tutorial will assume that you have Google Tag Manager and Google Analytics configured on your web property. If you haven’t done so, please check out our last article linked at the beginning of this post to set them up.
The Battle Plan
As usual, let’s start with a battle plan, so we know exactly what we are getting into:
- First, we will compile a list of events that we want to track.
- Second, we will set up event triggers for each event you want to track on your website using a tool called CSS Element Selectors.
- Third, we will set up tags for each event to send data related to those events to Google Analytics.
- Finally, we will use the “preview” feature in Google Tag Manager to make sure all the events are being tracked correctly.
In this tutorial, we recommend using Google Tag Manager for event tracking for the below three reasons:
- This approach does not require the addition of any code onto your website, making implementation significantly easier if you don’t have a technical person on your team.
- Google is constantly changing their tracking mechanism, and they are releasing their new tracking code library called gtag.js in the next couple of months. Using Google Tag Manager will help you avoid the hassle of going into your code base again and changing the tracking mechanism every time they have an update.
- Google Tag Manager gives you an easier interface to view which events are being tracked. This interface will help you stay organized if you track a lot of events on your website.
Step 1: Compile a list of events
The first step of setting up event tracking is to identify what events we want to track in the first place.
I used to be a huge fan of the “track everything” approach, which advocates for the tracking of every single event that happens on your website.
The benefit of this approach is that it helps you understand the complete picture of user experience on your website. As someone who comes from a data science background, I adore the idea of having all the data.
However, the disadvantage of this approach is that it is really easy to get overwhelmed by the amount of data and to overestimate the actual business value of spending your (or your analyst’s) time on analyzing every single element on your page.
For small and medium sized businesses, I would not recommend this approach when tracking your events. For larger companies, I would still recommend investing in more sophisticated behavior tracking tools such as Hotjar (for event heat mapping) and Heap Analytics (for event funnel tracking), instead of using Google Analytics.
Recently, I started to become a huge fan of the “macro and micro conversion” approach, which advocates for only tracking macro and micro conversion events on your website.
This approach understands the potential overload caused by analyzing all website events and instead teaches analysts only to record a small list of events that have business value to you.
These events could be macro conversion events, i.e. the events that your customers have to go through to either make a purchase or submit a lead form. They could also be micro conversion events that indicate increased audience engagement of your audiences. These may include button clicks such as signing up for a newsletter or looking through product information on your website.
Compile a short-list of macro and micro conversion events for your website (ideally less than ten total) based on the description above. Then we will be ready to move on to the next step of actually setting up event tracking.
Step 2: Setting up event triggers
Let’s start with a brief recap of what we discussed about triggers last week.
By default, Google Tag Manager tracks all events and page visits on your web property automatically. However, it doesn’t record any of this data unless you tell it to do so. Triggers are the way that you can tell GTM to track the specific events and pageviews that you want to send to Google Analytics or other services.
Therefore, we need to tell Google Tag Manager that we want to send the events we compiled in the previous step to Google Analytics. To do this, we will use something called a CSS element selector.
A short primer on how to select events
To identify the events that you want to track, it is helpful to understand conceptually how these buttons are coded on your website, and how we will select these elements.
On your website, all elements are coded with HTML tags with the structure illustrated below:
An HTML tag has several key components:
- The Tag Name, specifying the function of this specific tag in the overall context of your website. In this case, “h1” means “first header.”
- The Attribute Name, specifying additional information about the header, such as its class (used for grouping tags with similar attributes), id (used as the unique identifier of tags), and styles.
- The Affected Content is the text that will be displayed on the website either in a button or in a paragraph.
With those components of the tag in mind, a CSS element selector is a search query language in which you can systematically identify specific HTML tags on your website.
For example, the HTML tag illustrated in the graphic can be selected via the following simple query “h1.primary”.
To learn more about the syntax of CSS selector, use the following link.
Enable The Element Selector in Google Tag Manager
Now that we have conceptually understood of how we can select our events, let’s get our hands dirty.
Google Tag Manager allows you to identify events on your website based on the events’ various attributes (they call these attributes “variables”). You can get a list of all its built-in variables under the “variable -> configure” button of Google Tag Manager.
As you can see from the screenshot above, Google Tag Manager offers a very comprehensive list of built-in variables for you to identify events, from clicking an element to submitting a form.
In this tutorial, we will focus on one of the most basic events — clicking.
To accomplish this, we are going to use the “Click Element” variable. You can enable it in the screen above by checking the box next to “Click Element” (you can also enable other boxes in the Clicks category to enable more options during event selection).
Identify the CSS Selector of your events
Now we need to identify the unique CSS selector of each of our events.
To do that, we will enlist the help of Google Chrome Web Development Tool, a series of tools that you automatically have access to if you have Google Chrome.
We are going to use the Google Merchandise Store as an example.
On the Google Merchandise Store, when a user clicks on the add-to-cart button of a specific product, the add-to-cart action is conducted on the page, instead of directing users to a separate page. Therefore this click needs to be tracked as a micro conversion event.
When on this specific screen (you can follow along by going to this link), right click and select “inspect” on the dropdown menu. A console side panel will open up on the right side of the screen that looks like the screenshot below.
As instructions on the screenshot explain, you should first click on the element selector icon on the top left corner of the console. Then you should hover over the element of interest with your cursor, click on it, and your CSS identifier can be found on the right side of the console.
Finally, use the console query document.querySelectorAll to make sure there is only one result returning, so the CSS selector is unique (this will usually be the case). Here, you have your CSS selector.
P.S. If your CSS selector is not unique, you try making your CSS selector longer by adding more parameters in step 3 (they are left of the oval annotator). If it still doesn’t solve the problem, you need to identify the overlapping element and exclude it in Google Tag Manager triggering (we will show you how later).
Setting up the Trigger
Now, we are ready to create the trigger.
Go to the “Trigger” section of Google Tag Manager and select “New.”
Click on the Trigger Configuration box and select Click >> All Elements.
In the following setup screen, select “Some Clicks”, meaning you only want to track certain clicks happening on your website.
In the configure options following, select Click Element, Matches CSS Selector, and type in the unique CSS selector you identified in the previous step.
If your CSS selector was not unique despite extending the parameters, you should identify the events that you do not want to track, and create another rule in this screen with the option “does not include CSS selector.”
With everything configured, it should look like the screen below.
Finally, save all configurations. Now we are ready to set the tag!
Step 3: Setting up the Tag
Good news, the most complex part of this process is over. Now it’s all non-technical smooth sailing from here!
In this step, we will setup a tag that sends the event trigger to Google Analytics.
As I demonstrated in our post last week, go to the “Gag” section of Google Tag Manager using the right navigation bar, and click on the red “New” button on the top of the screen.
Configure the Tag
Configure the tag with the exact same Google Analytics settings as last week’s post, but this time, select “Events” instead.
Selecting events will enable a few more options for you to tell Google Analytics what this event is. Here is my recommended way of organizing your events, but feel free to come up with your own structure:
- Category: This is the overall category of the event, whether it is a check out event, an engagement event, or so on.
- Action: This is the action that occurred during this event, such as “click_add_to_cart”, or “click_video.”
- Label: This is where you place additional information about the event, such as what product is being clicked, what video is being watched, etc.
- Value: If you are using sophisticated micro conversion value assignment in your analytics pipeline, you can assign a value to this event. Otherwise, I recommend leaving this blank for now.
After filling out all relevant information on this page, click save. Now let’s finish setting up the trigger.
Connect Tag with the Trigger
This step is simple. Select the “Trigger” box on the tag creation screen, and select the trigger we just set up in the previous steps. Hit save, and your tags are officially configured!
Step 4: Debugging with Preview Feature of Google Tag Manager
Technically, all of your events should be configured and firing correctly on Google Analytics. However, just like every technology, this almost never happens with your first try.
That’s why you need tools to help you identify whether the event tracking you just configured is working, and that’s where Google Tag Manager’s preview function comes in.
After you finish setting up all of the events you intend to track, click on the “Preview” button on the top right corner to enter the preview mode.
Then, with the Google Tag Manager tab open, open a new tab for your website. You will find a debugger section at the bottom part of your screen that looks like the screen below
The left side of this section describes all action you have taken on your website, while the right side tells you what tag fired (or did not fire) in each of the actions that you have taken.
Now, return to the list of events you have, and click on each of them on your website to check whether the tag you have configured is firing correctly. If it is not, go back and double check all the steps to make sure everything is correctly configured, and try again until it works.
After everything is correctly configured, go back once again to Google Tag Manager, and submit and publish all the changes you have made for event tracking. Congratulations, you are done!
If you get stuck at any point along the process, please feel free to email me at email@example.com. I am more than happy to help you take a look and identify potential errors.
Meanwhile, thank you so much for reading this article. If you liked this tutorial, don’t forget to follow us on our Medium publication at Analytics for Humans, and subscribe to our newsletter through the link on the top right corner of this page Thanks!
A Step-By-Step Tutorial
According to a 2017 survey by Clutch, about 71% of small businesses in the U.S. have a website and understand the value of having a digital presence.
However, having a website is not enough — you also need to make sure that your website is operated and designed in a way that brings in customers and create value for your business. That’s where Google Analytics comes in.
As a free and powerful web analytics tool, Google Analytics is more popular than ever. In fact, W3Techs estimates that about 53% of all websites today uses it — and so should you.
To use Google Analytics, you must set it up correctly, and we have found it to be one of the biggest obstacles preventing small and medium businesses from using the tool. This guide is designed to provide a step-by-step for you to alleviate this pain.
The Battle Plan
Let’s begin with an overview of the major steps that we are going to take.
- First, we are going to set up Google Tag Manager on our website.
- Then, we are going to set up Universal Analytics (the newest version of Google Analytics) through Google Tag Manager. This will enable Google Analytics to track all pageviews on your website.
- Finally, we are going to go back to Google Analytics to set up “goals” and “views.” This will make sure that the data you track is not only accurate but meaningful for your business.
One of the biggest differences between our approach and a conventional Google Analytics setup approach is we stress the importance of setting up Google Tag Manager right from the start.
Google Tag Manager is a free “tag management” solution offered by Google that essentially serves as a “data broker” on your website.
It takes all your website data, and send it to different services such as Google Analytics, Facebook Analytics, and beyond.
We strongly prefer the Google Tag Manager approach for two primary reasons:
- If you set up Google Tag Manager, it will be the only time that you touch the codebase of your website. In the future, whether you want to implement additional event tracking, or add a new analytical service such as Hotjar, you will not need to make any direct changes to your website code.
- Google Tag Manager provides you with the powerful “preview” function, which helps you make sure that tracking on your website is working correctly.
With all that said, let’s get into the first step — setting up Google Tag Manager.
Setting up Google Tag Manager
Note: In this step, you will need someone with access to the codebase of your website (such as your webmaster). Make sure they are available when you are doing this step.
Google Tag Manager Overview
Google Tag Manager operates on an account and container structure, with one account linking to multiple containers.
For example, if you own a sunglasses company (let’s call it SUN) with a mobile app and an online ecommerce store, you will create one account for your company, and two containers — one for the mobile app, and the other one for the ecommerce store.
Creating your Accounts and Containers on Google Tag Manager
To create your Google Tag Manager account, go to https://www.google.com/analytics/tag-manager/, and click the green button on the top right corner that reads “sign up now for free.”
Here, you will be prompted to sign in with your Google Account. If you don’t have one, create a new one with this link:
This Google Account should be a permanent Google account that only you can access. You can always grant viewing or editing access to other people later.
Even if someone else (such as a webmaster) manages your account, still make sure that you have your own account so they can share the ownership permission with you, or else you might risk losing your data when they stop working for your organization.
After signing in with your Google Account, follow the setup instructions on the screen to create your tag manager account and your first container. Agree with the Google terms of service.
Injecting tracking code onto your website
After all those steps explained above, you will see a screen like this with your Google Tag Manager code:
As the instructions point out, you need to place two snippets of code onto every single page of your website, one in the header, and another in the footer.
While this may sounds daunting, with modern platforms such as Shopify and Wordpress, you really just need to change only one file in your website directory.
If you use one of the content management systems mentioned above, or even if you have a more sophisticated web app structure, all you need to do is to tell your developers to paste those snippets in the header and body sections of your theme/template file, whether that’s theme.liquid for Shopify, or theme.php for Wordpress.
If you do not have one of those content management systems mentioned above, your developer need to physically put this code in every page of your website. Also, you should really look into a reliable content management system (CMS) for your website.
After your developers complete this step, you should be all set! Take a breather. Now let’s set up Google Analytics.
Setting up Google Analytics via Tag Manager
WARNING: If you are a Shopify user, Shopify has their own way of setting up Google Analytics, so please following the instructions in this link if you are setting up page tracking for a Shopify website:
Nevertheless, it is still recommended to setup Google Tag Manager on your Shopify website since it enables dynamic event tracking that is beyond the capabilities of the default Shopify Google Analytics setup.
Google Analytics Overview
Let’s begin with an overview of Google Analytics.
GA (Google Analytics) is organized by a hierarchy of Accounts, Properties, and Views. A property is a website, or mobile app, or a point of sale device (like an external checkin service). A view is a filtered version of your website data (e.g. a common view is one that filters out employees at your company because you want to track website visitors, not your employees’ sessions).
For example, if you only have one website, you only need one Google Analytics account with one website property. If you have two websites (e.g. a personal website and a website for your business), you can make two accounts (one for each website).
Set up your first Google Analytics account and property
Use the same Google Account you used for Tag Manager, go to Google Analytics and sign in.
After you sign up, you can set up your account and website information (e.g. account name, website name, website URL, industry category, etc). Make sure you use the right time zone.
Get your Google Analytics Tracking ID
After setting up your account, you will be directly sent to a page similar to the one that you see below. If not, simply go to the Admin >>> Tracking Info section (it’s under the Property column).
Here, what you are looking for is the Tracking ID of your Google Analytics account. You can either write this code down (or paste it to a word doc), or simply keep the tab open while we set up Google Tag Manager.
Set Up Analytics Tag with Google Tag Manager
Now, it is time to go onto Google Tag Manager to set up our Google Analytics tracking tag.
Go back to tagmanager.google.com, and select the “New Tag” button on the top left of the screen.
A tag has two components: its “configuration,” and its “triggering”. Even though those two terms might sound complicated, here’s an easy way to understand them.
Tag configuration is the destination of the data (in this case Google Analytics). Tag “triggering” is the specific data from your website you want to send to a specific destination (in this case, we are sending all pageview data).
Let’s begin with Tag configuration. Click on the Tag configuration button, and a screen like the one below will show up.
Here, select Universal Analytics, and in the resulting screen (shown below), select “New Variable” under the “Google Analytics Settings” option.
In the resulting screen, set up a variable called “Google Analytics” (or whatever you’d like to call it), and enter your Tracking ID in the corresponding field. Save both the variable and tag configuration, and you are done configuring the tag.
Then, it is time to set up the tag’s “triggering.” In this case, we want to send all pageview data to Google Analytics (the default Google Analytics setting).
Click on the “trigger” area of the tag, and simply select the “all page” option. Then, you are all set, and your tag should look like the following screen.
Save the tag, then click “Submit” on the top right corner of the tag manager dashboard. Write down whatever notes you’d like to describe the actions you took. Congratulations, you now officially have Google Analytics configured properly on your website!
Set up Views and Goals in Google Analytics
Now let’s segue back to Google Analytics to configure a few more settings to make sure that your tracking is accurate and useful.
Setup views to filter your data
The first step is to set up your “Views.” Views allows you to look at your data with certain filters. The default Google Analytics view is “All Web Site Data,” which is unfiltered. You should probably keep this default view so that you can always access all your raw website data.
You can add new views according to the needs of your business. Go to Admin >>> View column dropdown >>> Create new view.
For example, you can add views to filter out certain pages on your website or traffic that does not count toward your goals. I recommend adding a view that filters out employees at your company because you want to track website visitors, not your employees’ sessions.
Set up Goals to track your business objectives
Goals allow you to track important events on your webpages, such as visitors filling out a contact form or spending a certain amount of time on a product page.
For example, let’s say you want to track purchase completions on your website, measured by the number of times a website visitor sees a purchase confirmation (or thank you) page after their purchase.
To go to Goals, click on Admin >>> View (column) >>> Goals.
Click on the “New Goal” button.
Look through the goal templates to see if any of them match the event you are trying to track (e.g. “Make a payment”). If not, click “Custom.”
Give your goal an easy-to-remember name. Select Destination and click “Next Step.”
Find the URL of your confirmation page. Copy the URL segment after the “.com” (e.g. “/confirmation.html”). Paste it in the Destination field and select “Equals to” in the dropdown menu.
If you want to quantify the monetary value of each time this goal is completed, type the dollar value of that action. Click Create Goal.
This is an example of tracking a simple conversion funnel.
You can create up to 20 goals for each website property. Common goals that Google Analytics can track for your business include:
- Lead contact form submissions
- Email list sign ups
- Purchase completions
- Visiting pages that suggest an intent to purchase
- A certain number of pageviews per session
- Spending a certain amount of time on your website in a session
You can then use these goals to add website visitors to retargeting lists for Google Adwords campaigns.
For example, I may want to set up a goal such that if a visitor visits more than 3 pages and spends more than 2 minutes on my websites, Adwords will add them to a retargeting list because I see these visitors as “hot leads.” Adwords can then automatically retarget them with display ads and Gmail ads.
Check out this Google Analytics support page to learn more about how to properly configure goal tracking on Google analytics.
Need Help Setting Up and Configuring Google Analytics? We’re Here to Help
We hope you found this short step-by-step walkthrough of setting up Google Analytics (and Google Tag Manager) helpful. If you have any questions or feedback, please shoot us an email at firstname.lastname@example.org.
Coincidentally, we are offering to properly setup and configure Google Analytics (including reporting and goal tracking) to our beta testers, so let us know if you’re interested in giving us feedback on our tool.
Tune in for our next step-by-step tutorial on how to setup additional event tracking for Google Analytics through Google Tag Manager, along with tips on using the “preview” function of Google Tag Manager to make sure all your trackings are functioning correctly on your website.
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Stephanie Trinh, CEO of Trinh International, on Why SMBs have Always Used “Influencer Marketing”
As you can see in the above Google Trends graph, “influencer marketing” has become a hot buzzword in the last two years.
The idea behind influencer marketing is simple. In a nutshell, it is simply focusing on key “influencers,” or leaders with influence, to deliver a message to your target market. Unlike traditional marketing, influencer marketing pays key influencers to do the marketing for you, instead of marketing directly to consumers.
So why has this concept taken off so much in the last few years?
Simply put, it relies on a simple insight about social psychology: consumers trust product recommendations from leaders they admire and want to emulate.
It’s also efficient — why spend hundreds of dollars and hours building trust and authority with an audience when there are thousands of influencers who already have massive audiences that trust them?
But while there are hundreds of agencies and consultants that specialize in influencer marketing for large brands and enterprises, there are unsurprisingly few that provide influencer marketing services for SMBs (small and medium sized businesses).
My cofounder Patrick and I decided to talk to Stephanie Trinh, CEO of Trinh International (a marketing consultancy for Fortune 100 brands), to learn if influencer marketing is a high-potential channel for SMBs. Here is a short recap of our conversation:
Patrick Han: I noticed larger corporate brands are really embracing influencer marketing, but SMBs have not really caught up to this trend. Do you think SMBs influencer marketing makes sense for SMBs?
Stephanie Trinh: To put it as succinctly as possible: SMBs (that manage to grow and scale) have only ever relied on influencer marketing. Though “influencer marketing” is just a buzz phrase that has popped up recently, every business in its formative years has always thrived on word of mouth.
The definition of a SMB today versus 50 years ago has changed immensely. A kid with a smartphone can run a business from virtually anywhere with no employees, little to no inventory, and generate the same revenue as, if not more than, a steel factory. We live in a gig economy, where power is lent to the individual more so than ever before.
The gap between large corporate brands and SMB is closing because we live in an era of more access to information than ever before — our society is prone to information overload. Advertising doesn’t work like it used to; consumers are now empowered to change trends in the marketplace.
More access means more options for consumers. More access means more competition for companies large and small. More access means everyone is vying to earn everyone else’s business — and keep it.
Technology has leveled the playing field, and businesses are now realizing that PRINCIPLES never change. Businesses are built on trust, value, service, and a sense of community — cornerstones for why influencer marketing works. For large corporations, that sense of loyalty from influencer marketing may be more visible through the lens of social media, but SMBs need only get into the hearts of their customers to have the same effect.
The only suggestion I can give to SMBs looking to influence their markets is to network, network, network, with the intention of finding a need and filling it. With time, and experience, the SMB can then fine tune its brand/identity/product/service to suit its target audience — to know what it stands for, who as an entity it is, and isn’t — and cut out all the fluff.
Bottom line, it’s mutually beneficial relationships that make a business thrive. Period.
PH: I think you make a great point that SMBs have always used “influencer marketing” in a sense, but I was curious, do you see them identifying internet personalities with a social media following, and then offering them free products/services in exchange for a shout-out on their channel?
ST: I think it would be an option to consider, but it would also depend on the SMB’s budget. My philosophy is that one cannot expect to receive without giving, but it is up to their discretion. It takes a bit of faith to reach out to an internet celebrity with promotional products with a hopeful anticipation of return, but sometimes not every seed sown grows as expected. It is also hard to track.
Why SMBs aren’t keeping up with large enterprises in data-driven decision-making — at their own peril
“For small and medium businesses, using data is like trying to rescue a piano from a burning room — it’s nice to have but I have higher priorities.” -Small Business Owner
We are living in a time where data is revolutionizing almost every aspect of human society. Businesses, for example, are now expected to use data to target their core customers down to individual addresses, and provide these customers with personalized promotions to maximize consumer spending.
However, despite all this hype around the commercial sector leading the “data revolution,” for most small and medium-sized businesses (SMBs), using data feels like it’s making life harder, not easier.
To identify factors holding SMBs back from embracing the “data revolution”, we at Humanlytics interviewed over 60 SMBs. We discovered that there are 5 key factors that are preventing them from converting data into actionable business insights.
This article will give you an overview of these 5 major obstacles, and will kick off a new blog series where we unpack one challenge at a time — and provide an action checklist for solving each challenge.
Without further ado, here are the 5 challenges.
1. Data Interpretation
“I just want to talk to someone about how to think and approach data in my business. I want to know what questions to ask the data.” -Startup founder and digital strategist
The most common problem we found plaguing SMBs is interpreting and making sense of the data they have in their organization.
Modern technology makes it possible for all SMBs to capture data to some extent. Unfortunately, merely having the data doesn’t mean they can use it to improve their business.
Here are some specific data interpretation challenges that we heard from SMBs:
- Understanding what kind of business insights their data can provide to them
- Understanding the value and ROI of implementing analytics tools in their organization
- Choosing the right metrics to track
- Placing analytics results into a business context and converting them into action items
- Doing sanity checks of the analyses to make sure they are accurately answering the right questions
Now I know what you’re thinking. Can’t all of these problems be solved just by hiring data analysts or consultants who know what questions to ask and how to answer them?
Yes, that may be true for larger enterprises. But the problem for SMBs is that data analysts tend to be prohibitively expensive, and leadership will often prioritize filling the “must-have” positions first — operations, sales, product managers, whoever is bringing in the revenue to keep the lights on — before they hire a data scientist.
To make things more difficult, not having a data analyst in-house means that smaller businesses have a harder time knowing how to properly make an informed decision when hiring for data analysts.
2. Data Collection
“Analyzing data is easy. Getting quality data is the problem because good data is either pricey or simply nonexistent.” -Operations Manager
Many tools have emerged in recent years to help businesses with web analytics (Google Analytics, Mixpanel), customer relationship management (Hubspot, Salesforce), and ecommerce (Shopify, Woocommerce). Even though these tools have solved some of the more pressing data collection problems, some specific needs remain unmet.
Instead of complaining about having no data at all, most companies we interviewed face data collection challenges in a few specific areas. These areas include:
- Collecting qualitative data about their customers
- Converting these qualitative data into quantitative data
- Collecting accurate data about customer behavior on their website and on their product
- Verifying that the data collected is clean, standardized, formatted correctly, and accurate
Overall, data collection needs have become more sophisticated, emphasizing more on quality, rather than quantity.
3. Data Integration
“Our four different data systems do not gel well. It is a pain to gather data points from all these systems.” -Operations and Marketing Specialist
Most SMBs use at least 3 SaaS tools to collect data across their businesses (e.g. Facebook Analytics + Google Analytics + Operation Database). Some companies we spoke with use up to 15 tools at the same time. Because SMBs are using more and more tools that only good at one kind of task, making these tools talk to each other becomes a daunting challenge.
In order to capture valuable information such as a customer’s complete journey from awareness to revenue, companies have to pull data from all of their platforms and merge them together.
However, there are major challenges preventing SMBs from integrating their data, including:
- Lack of structured guidelines and procedures for data management
- Technical inability to connect various data sources together via databases or API connections
- The lack of bandwidth to set up a logical, holistic data infrastructure
- The huge time cost of reformatting data so they are compatible for integration
The main two barriers to solving these problems among the SMBs we spoke with are (1) lack of management bandwidth to manage all data sources, and (2) lack of technical human capital to construct a sound data infrastructure ahead of time.
The biggest benefit to data integration, of course, is to be able to answer bigger business questions more accurately, which brings us to the next data challenge.
4. Analytics Automation
“I have the analytics abilities to dig into my data, but valuable insights take too long to uncover.” -Director of Operations
Even for companies with integrated data infrastructure and analytics expertise, data analytics can still be an extremely resource-intensive effort.
This is because data analytics tasks are extremely explorative, and analysts have to slice and dice various metrics across many different dimensions such as demographics, time, and product categories.
With each dimension added to analysis, the effort of analysis increases exponentially, with perhaps hundreds of variables needed to uncover only a few important insights. Without automation, sometimes it is physically impossible for analysts to conduct all these analyses within their time constraints.
Common automation challenges SMBs face are:
- Lack of tools to automate tedious data cleaning and repetitive analysis processes during analysis
- Lack of tools to efficiently advise analysts on what analyses to focus on for actionable insights
- Lack of automated reporting mechanism post-analysis
The primary barrier preventing those two challenges from being resolved is the lack of analytics tools on the market that automate data cleaning and basic exploratory analysis. Without those tools, it will take even the most experienced analyst a lot of time to uncover insights, especially when you don’t know where to look or where to start.
5. Analytics Adoption
“I really want to use analytics more, but I haven’t done so because I need to first focus on putting out fires before thinking about optimization.”
If we were to describe the previous four challenges as technical problems, the last one is very much human. To many SMB owners, even though they might know data can create value for them in some abstract way, they either don’t have enough time or bandwidth to leverage their data, or they don’t see the short-term benefits.
Next to data interpretation, the adoption problem is probably the most difficult one facing SMBs in their path to joining the “data revolution.” Here’s what was holding back the SMBs we interviewed:
- Inability to see the immediate value of data, which means indefinitely pushing back analytics to some point in the future that will never come
- Fear of long-term commitment to expensive analytics tools
- Fear and frustration with the time cost of setup and implementation
- Feeling like they could be using their time more efficiently by focusing on something more urgent, like operations or sales
For many SMBs, data analytics is a nice-to-have, not a must-have. They don’t see that it is exactly because they are more constrained on time, money, and bandwidth that they need data even more than larger enterprises, because data-driven decisionmaking is how they can optimize their limited resources. Data is how they can make each dollar stretch further.
However, with a lack of transparent and low-commitment ways to see the value data analytics can add to their businesses, SMBs will remain stuck in outdated business practices and hesitant to fully adopt the “data revolution” of the large enterprise sector.
There you have it. As mentioned in the introduction, this article is not the end , but rather the beginning. In the next part of this series, we are going to drill down into each of the 5 challenges, and propose a roadmap for SMB owners to overcome each of these roadblocks to joining the data revolution.
Meanwhile, comment below and tell us which challenge most resonates with you. If you want to read more content like this, follow us on Twitter and Facebook, or visit our website at humanlytics.co for updates. Email email@example.com if you have any questions about the article.
- Email us or comment below on which challenge is your biggest pain point! We will write more guides, tutorials, and best practices on the topics we hear the most about.
- Read “What 3 Small Businesses Learned From Big Data” from Inc.com to learn about 3 great examples of data-driven SMBs.
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5 Steps to Prepare for Every Negotiation for Entrepreneurs and Founders
In last week’s article, we presented a high-level overview of how to prepare for a business negotiation as an entrepreneur.
As we mentioned, these guiding principles were refined over decades of real-life business experience by Marty Finkle, the CEO/Founder of AscendU, a group of executives who provide sales and boardroom consulting. Marty is also a Board Director for Scotwork, a company that specializes in negotiation training and consulting in 38 countries. Amazon, Boeing, Merck, Dell, Starbucks, and Walmart are just a few of their clients.
We thought the seminar provided some very useful and actionable best practices on negotiation that would be very helpful to small business entrepreneurs and startup founders, so with Marty’s permission, we wrote a blog post about what we learned.
As promised, here is an exercise you and your team should go through before any negotiation. The exercise will take you through a checklist to make sure your team is clear and aligned on what your party wants, and what you’re willing to give up to get it.
By the end of the exercise, you’ll have a cheat sheet to reference during the negotiation to make sure you stay on track to achieve your objectives.
But first, to review what we learned from our last article, here are Marty’s 8 top negotiation tips from working at Scotwork:
Summary: Marty’s Top 8 Negotiation Tips
- Preparation is not a waste of time
- Identify your objectives and prioritize them
- Define your Intends and Musts (this defines your flexibility during the negotiation)
- Have a Wish and Concession List (these are your tradeables)
- Think about information flow ahead of time (what are your needs, what do you want to know, and the timing of information disclosure)
- Keep your strategy flexible
- Script your opening statement with your imperative
- Establish who is doing what in advance
Now for the preparation exercise.
The Cheat Sheet: the Preparation Exercise You Should Do Before Every Negotiation
Now that you know some foundational principles of how to prepare for your next negotiation, here is a checklist to make sure you hit all the different steps of preparation we discussed today. I recommend getting out a sheet of paper and filling out each section with your team.
1) Write Down Your Objective
First, at the top of your sheet, write down in a sentence what you hope to achieve from this negotiation.
This is a good summary statement that you can look back at to keep you focused on the main goal and to prevent distractions.
2) Clarify Your “Nice-to-haves” and “Must-haves”
Secondly, create a list with 3 columns:
- Intend to get (your realistic expectations)
- Your non-negotiables (must-haves/must-avoids)
Fill this out as best you can with the information and research you have.
3) Create Your Wish List & Concession List
Thirdly, write down a wish list (what you would like) and a possible concession list (what you’re willing to give away).
Again, fill this out as best you can with the information and research you have. See the “Identify Potential Concessions” section of our last article for more details.
4) Outline Your Opening Statement
Fourthly, on the other side of your sheet, plan out your opening statement. Here are some things you may want to achieve in your opening:
- Propose the agenda
- Set the tone
- Outline your position
- Explain your goals and needs
- Explain the expected outcome
- Outline the must-haves
- Structure expectations
- Establish the timescale
5) Consider the Other Side’s Wish List
Lastly, create 3 lists with 2 columns, one for your team and one for the other party:
- What You Want vs What They Want
- What You Wish to Avoid vs What They Wish to Avoid
- You Need to Get vs You Need to Give
You’ll fill this out as you acquire new information from the other party during the negotiation.
Make sure you have this cheat sheet with you during your negotiation. This sheet will make sure you and the conversation stay on track toward achieving your team’s objectives.
Marty is a very in-demand trainer and speaker, so a big thanks again to Marty for letting us share his insights and advice from his decades of experience. Also thank you VFA for booking him as our negotiation seminar!
Sign up for our newsletter on the top right corner of this page. If you have questions about anything covered in this article, feel free to email me at firstname.lastname@example.org.
It can help you filter out the noise and find true signals
In an ideal world, the actions you take directly affect your business metrics by either pushing them up or drawing them down.
You could then base your next action on this response in order to optimize your results.
However, in the real world, this almost never happens.
In the real world, metrics often move up and down by themselves due to external factors such as seasonality, major events, and factors that you cannot control.
For this reason, your metrics will almost always go up and down on a weekly and daily basis with little regard or relation to the actions you take.
To remove this unwanted noise and gain a real grasp of how your actions are affecting your metrics you should use a rolling average.
In fact, almost all time-series analyses done by professional data scientists use rolling averages to identify past trends in their data and predict what is going to happen in the future.
There are multiple ways of doing this but the easiest method is to average your metrics for the 4 weeks with week as a unit.
Sadly, computing a running average isn’t possible in many free analytics and dash boarding tools on the market so very often your best option is to manually pull your data and calculate it in excel.
While it may take more effort, tracking the rolling averages of your business metrics can help you reduce the risk of identifying false signals and drastically accelerate your business growth and analytical maturity.
Plus, using rolling averages can help you focus on the long-term impact of your advertising campaigns or business actions without losing sight of the short-term changes caused by those campaigns.
So, please use rolling averages when tracking your metrics. It’ll pay off.
Comments or Questions? Leave a message below.
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“Introduction to Digital Analytics” Episode 1:
Why digital marketing is critical to YOUR business
Welcome to the first episode of our new “Introduction to Digital Analytics” series.
In this video, we kick off the series by explaining why digital analytics is important for your business.
As mentioned in the video, truly understanding the value of digital marketing analytics for your business is helpful not only for convincing your boss, but also for guiding your own digital strategy.
To make sure you can apply the principles I introduced in the video, I’ve created a worksheet for you to sit down and identify the most important factors impacting your company’s digital analytics decisions.
You can also find the slides from this video in the link below.
Please give us any feedback in the comment session. Your comments will be crucial for us to improve these video shorts and make sure they are truly adding value to you.
If you have any questions about anything covered in this video, feel free to email me at email@example.com. I am more than happy to setup a call with you to answer any questions, no strings attached. Thanks!
Practical Tips for Small Business & Startup Entrepreneurs from a Corporate Negotiation Expert
Stop me if you’ve heard this situation before. Jerry is a small business entrepreneur. His biggest supplier represents 80% of his business, so whenever they ask for something, Jerry has to bend over backwards to keep them happy.
Jerry feels like his supplier gets so much more out of this relationship than he does, but there’s not much he can do about it. In every conversation where they negotiate prices, Jerry feels like he’s getting a bad deal because the supplier has all the power in their relationship. Or do they?
Recently we attended a workshop hosted by Marty Finkle, a Board Director for Scotwork, one of the most popular negotiation training companies in the world. Scotwork does negotiation consulting for clients such as Amazon, Boeing, Merck, Dell, Starbucks, and Walmart.
We thought the seminar provided some very useful and actionable best practices on business negotiation that would be very helpful to small business entrepreneurs like Jerry.
That’s why, with Marty’s permission, we are sharing with you how to prepare for your next negotiation, whether it’s negotiating prices on a sales call, or negotiating for your next raise.
This time, we are going to focus on the principles of negotiation. In the next few weeks, we’ll publish a worksheet to help you turn the theory into practice for your next real-life business negotiation. Let’s begin!
Do I need to negotiate in the first place?
The first thing to consider is whether negotiation is needed in the first place.
Negotiation isn’t the only way to resolve conflict. There are a whole host of conflict resolution strategies, including:
- Problem solving: Can we engineer a solution to the problem that benefits both parties?
- Persuasion: Can I simply talk the other party into doing what I want without negotiating?
- Haggling: Can I just focus on lowering one variable (such as price) and end the conflict for good?
However, sometimes the alternatives fall through and negotiation is your best option. If that’s the case, preparation is key.
To prepare for a successful negotiation, you need to go through 4 main steps:
- Clarify Objectives
- Research Information
- Plan an Agile Strategy
- Identify Potential Concessions
1. Clarify Objectives
Clarity is power. If you’re not clear about what you want, you’re going to become soft on your objectives, and you’re more likely to give in on key goals. In other words, if you don’t know what you want, the other party will get what they want.
That’s why you should always start with clarifying your objectives of negotiation.
List all that you are trying to get from the negotiation, go through each item on the list, and ask yourself, is this:
- a wish (i.e. a nice-to-have)?
- an intend (i.e. a realistic expectation)?
- or a non-negotiable (i.e. a must-have or must-avoid)
If an item on your list is non-negotiable, protect it with your dying breath when you’re negotiating. Don’t let your opponent tempt you into giving these up!
However, being clear doesn’t mean that you can’t be creative. It is important to consider other potential categories of benefits you can gain from the negotiation.
For example, if you show up to your dinner reservation and find out that your table is going to be an hour late, ask for something creative like a bottle of wine and roses on your table. You may just get it (this is an real-life story from Marty)!
Lastly, also consider the other side’s potential objectives. It’s impossible to always know exactly what the other party wants, but you can create some educated hypotheses that you can test with questions during the negotiation.
2. Research Information
We’ve all heard that knowledge is power, but it’s especially true in negotiations.
The party with less information is less equipped to make a rational decision about how much they should ask for and what they can realistically expect from the negotiation.
Therefore, it is mission-critical to do your homework thoroughly and learn as much as you can about the other party.
The first thing you need to pay close attention to is the power balance between you and the other party.
If you are a small food supplier negotiating with Walmart, you will have a lot less leeway for demands than if you were on the other side of the negotiating table.
That’s why, if you can, try to do your research ahead of time on factors such as:
- market share
- risk of failure
- costs of change
After identifying the power balance, you need to think through what information you need from the negotiation, and prepare the questions you can ask to test the accuracy of your assumptions and positions.
You also need to think through what information you should give and not give to the other party.
For instance, one piece of information you may want to draw out from the other party is their supplier concentration — that is, what other vendors is the other party using besides your company, and what are their other options?
If you learn that you’re their main vendor and their other vendor options are significantly more expensive, that will greatly improve your negotiating position.
Alternatively, you are on the other side of the table, then you want to make sure not to disclose that information if it’s not necessary.
3. Plan an Agile Strategy
To paraphrase Thomas Jefferson, “In matters of strategy, swim with the current; in matters of non-negotiables, stand like a rock.”
To succeed in your negotiation, even though you should plan out your strategy to focus on achieving your objectives, you need to keep it simple and flexible.
Here are some tips to create an effective strategy for your team:
- Keep it simple so everybody understands the strategy
- Keep your strategy flexible — don’t confuse your objectives with your strategy
- Allow yourself to adjust your strategy during the negotiation
If you represent a small business or startup, your decision-making flexibility can be your advantage, relative to large enterprises that are more constrained by countless stakeholders and bureaucratic rules.
But what if your strategy doesn’t work?
If things are not going to plan, be prepared to take a “time out.” You should strongly consider taking an adjournment during the negotiation if:
- your objectives turn out to be unrealistic
- if important new information has emerged
- if you need time to think
For example, if, in the middle of the negotiation, your team learned some surprising news from the other party that you didn’t prepare for, you may want to consider taking a short break to readjust your strategy with your team.
4. Identify Potential Concessions
Just as you write out your objectives ahead of time, you should also write out your possible concessions to the other party.
If you don’t, you may find yourself offering concessions that are unwise to give away (e.g. giving away must-haves or giving away too much). It’s much easier to make good trading decisions when you’re clear what the tradables are.
When considering what to consider a must-have and what to consider a possible concession, ask yourself these questions:
- What can you offer to make their life easier?
- Where do you have flexibility?
- What is the value of the concession in the other party’s mind
- What will you ask for in return?
One useful rule-of-thumb is to “trade every time.” In other words, every time someone asks for something from you, ask for something in return.
You can say something like “if you agree to x (e.g. pay us $10,000), then we’ll agree to y (e.g. make you our exclusive service provider). This will increase the likelihood the other party will take your deal. Avoid “soft” language such as “I’d like to x.”
We hope this article will help you increase your chances of success in your next negotiation with your suppliers and clients.
If you like this article, check out Marty Finkle and the great work he’s done at AscendU and Scotwork. Marty is a very in-demand trainer and speaker, so a big thanks again to Marty for letting us share his insights from his decades of experience. Also thank you VFA for booking him as our negotiation seminar!
- Always trade: when you make a proposal, say “if you do x, I will do y”
- Control the information flow: ask questions to get information you need, and carefully consider what information you to disclose or not disclose
- Ask creatively: if they can’t give you something in one category (e.g. a lower price or a higher salary), ask for something in other categories (e.g. a discount on another service they provide or more vacation days)
- Negotiation skills = power: controlling information flow and committing to non-negotiables can give you real power in negotiations. That’s why the best poker players can win even when the opponent has a better hand.
Sign up for our newsletter below. If you have questions about anything covered in this article, feel free to email me at firstname.lastname@example.org.
A journey about making digital analytics videos extremely useful
Last week, we gave a presentation titled an “Introduction to Digital Analytics (And More)” to fellows at the Venture for America training camp.
As we were preparing for the presentation, the idea came to me to make the presentation so well-designed and user friendly that the fellows could take the slides I used and present them to their future bosses, surely impressing the heck out of them.
I communicated this idea to the fellows after the presentation and they were all are super thrilled about it.
For this reason, we have decided to revisit the presentations we have given to the fellows in order to refine and redesign the elements. We’ll also film videos that serve as sample presentations for those slides.
In addition to making slides designed well enough to be presented to their bosses (and yours), we are also going to attach worksheets, custom Google Analytics Dashboards, and other useful tools in order to make sure that you can use what we have presented in the day-to-day operations of your business.
In order to get this done (and because we’re spending most of our time on development at the moment) we have decided to break up the presentation into 15–20 manageable pieces. We’ll perfect and release one piece a week.
Hence, we are happy to announce our “Introduction to Digital Analytics” video series.
Since this series covers pretty much the same content as our Google Analytics video series it will replace it.
This series is going to be divided into four primary modules:
- Why is Digital Analytics Important — this section is geared towards convincing you and your boss that digital analytics is crucial for your business, B2C or B2B.
- An Introduction to the Customer Journey — this section offers you background on theories of the“customer journey”, and gives you an overview of popular analytics tools on the market .
- Introduction to Google Analytics — this section gives you an overview of how to setup Google Analytics, along with basic fundamentals of the tool such as metrics vs dimensions.
- Common Google Analytics Use Cases — this section shows you 5–6 common, and extremely useful Google Analytics analyses you can carry out to accomplish different goals.
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The first part of this video series (due next week) is going to discuss why digital analytics is important for your business.
Until then, I would like to say that I am extremely excited to create this series and hope it can provide you with some real value!
If you have questions about anything covered in this article, feel free to email me at email@example.com.
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