“Analytics Powered by AI”: Is it Just a Buzz Phrase?

Ilan Eiland
By Ilan Eiland
Updated October 21, 2021

Today, the term AI is thrown around so often it seems as if every new platform and digital product on the market boasts AI capabilities.

The situation has escalated to the point where certain companies advertising AI have begun using humans (also known as “vintage intelligence”) to do the work of their so-called AI technology.

In this digital climate, it might feel difficult to trust that “AI” provides value, even when it is the real deal, companies tend to be vague about how it works.

So do you really need AI to power your analytics platforms? I decided to write this post breaking down how AI is used to power Digital Adoption Platforms (DAP) and improve the user experience.

Find out how a Digital Adoption Platform can boost your business.

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Analytics offer incredible visibility into the performance of web-based platforms

If you own a digital platform or a website domain, you are familiar with analytics platforms. Analytics can offer valuable insights into our web platform, such as: where are users coming from, most visited pages, most used features, ignored features and they even help map out the customer journey.

But alas, analytics platforms are not enough.

In order to understand the role AI plays in today’s business platforms, let’s examine the gap these platforms share.

Analytics systems help us to locate problem areas but do not help solve the underlying issue

Let’s use a common example: As a platform owner, you use analytics platform to monitor and optimize your user’s behavior. You log into the system and notice that an important feature on the platform is showing low usage.

To remedy the issue, you might increase exposure to the feature by manipulating the page’s UI — such as moving the button or changing its color to grab our users’ attention.

Checking our analytics system a few weeks later, you might encounter a couple of things:

  1. Increased the traffic to the feature
  2. The usage of another important feature has decreased

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What now? Repeating the process would require conducting further changes to the platform with no guarantee that the effect will be positive. Manipulating a web platform is a very costly process that requires a technical team of experts.

But most importantly, you don’t want to upset the user flow by tweaking various aspects of the platform. This could lead to user confusion and churn.

What we need are not just insights, but actionable insights

I want to introduce an important concept — we call it actionable insights, meaning insights that offer a clear recommended course of action.

Insight #1: Looking at the geographic breakdown of your users, you notice a gap in a certain area.

You can examine the marketing strategy — perhaps not enough resources were allocated to the team running campaigns, or the way you are targeting this country or region isn’t speaking to the target audience.

By increasing the budget and addressing that area specifically, you can work to increase traffic from the geographic area in question. The insight is innately actionable.

Insight # 2: Your platform has detected that the majority of users who churn, do so on their third visit.

This is a powerful insight. Not every analytics system is capable of giving this kind of feedback. But is it actionable? Let’s come back to this point later on.

Digital Adoption Platforms, the tool for putting actionable insights into action

artificial intellegence

A Digital Adoption Platform (DAP) is a toolbox which can empower your web platform. If you are not familiar with this category of digital aids, this summary of a Constellation research will help you get up to speed.

I started this post by talking about the changes you can make to your platform. A good DAP should provide the ability to perform these tweaks without touching the platform code. If your goal is to draw more attention to an underperforming feature, you have a range of options that will serve our purpose without disrupting the flow of the page.

With a DAP, you can also segment by users, adding changes only for specific users without interfering with the experience of others. But most important, Digital Adoption Platforms have all the capabilities to turn almost any insights about our own web platform to an actionable one.

How AI can boost your platform’s performance

There is another type of insight we have not yet discussed: Insights based on AI. By applying Machine Learning to your platform, you can harvest even deeper and more meaningful insights.

Remember insight #2, the example about users who churn on their third visit?

This type of insight is often a product of advanced analytical and technological capabilities —  such as AI. Now let’s try to solve it using the actionability of the DAP tool.

AI powered analytics

Reduce user confusion and churn with DAP capabilities

What are your options?

  1. You can address your users with some help or guidance when they arrive to our platform for the third time. However, if the guidance is too basic and does not address the root of the problem, it will do little to stop them from churning.
  2. You can create a popup which provides users with discounts on our product. However, doing this for every third visit by a customer might end up being very costly for us.

This is not an easy problem to solve, even with the powerful capabilities of a DAP.

But if you look at the problem from an entirely different angle you might realize that the insight itself is limiting us. More than one parameter may affect whether or not a user is likely churn.

In a real use cases, tens if not hundreds of other parameters might influence the user’s experience: number of visits, length of each visit, the number of different features he used, feature misuse, errors and even time spent with support.

Understanding each parameter may help the product owner to understand what’s really going on in a user’s mind as they use the platform, but at the end of the day, they’re all just indicators. What we really want is concrete feedback from the user.

The true meaning of AI is a system that monitors all of those parameters and uses the sum total to then predict our users behavior. Are they going to churn? Are they going to buy? Do they need some guidance?

At the end of the day, these are the question that you, as the web platform owner, really care about. You probably don’t care that many users don’t use one of our features if you know that they are happy with your product overall.

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If you aren’t integrating AI-based insights, you are not taking advantage of the true potential of AI

What happens when you integrate AI into your DAP? All these issues can become trivial. If the DAP system itself give us the power to ask questions such as, is this user likely to churn? Does the user need help with this feature? You can then decide to act in these specific moments, only for those specific users.

If manually you can segment by only a few parameters, AI segments your users by tens different parameters in one click. Not only that, but each parameter is dynamic — learning the user’s behavior over time.

When your DAP is powered by AI, you can make your users feel that your platform truly understands them. They no longer even need to ask a question because the right answers will always be provided when and where they need it.  

Closing the circle

I find the term “closing the circle” is the best description of how AI powers a DAP.

Step 1. Find the relevant issues through AI insights

Step 2. Address and solving with DAP tools

Step 3. Provide real-time solutions with the A.I engine

Back to step 1: Find other issues as our users and our platform develops

It is my belief that DAP powered by AI is the future of the web industry and can address and solve almost every issue a web platform might have. There is no doubt in my mind that it will soon become a mandatory tool for every platform owner.

Use AI and machine learning to empower your users on any software.

Ilan Eiland
By Ilan Eiland
Ilan has a decade of experience in the world of data and development and has been leading the data science team at WalkMe for the last 2.5 years. His true passion is orchestrating solutions for complex problems.