Quantifying Emotions, Or, How To Automate The User Behavior Analysis

Recognizing user behavioral patterns and introducing UX behavior-based improvements is the future of all “user-friendly” activities.

Fortunately, we can improve and automate analytical processes to maximize their efficiency and minimize their time consumption.

However, to fully understand this breakthrough, we first need to answer one question.

What exactly is user behavior analysis?

The analysis of behavioral patterns is nothing more than extracting the essence of how our customers operate – or feel – while in contact with our digital product.

While observing their emotions and experiences, we look for repeatability.

These patterns allow us to predict where they will drop their purchase intention like a hot potato, abandon the almost-full cart, or exit the website.

And since there is repeatability, there is a place for its automation.

In an online world, it’s a real bed of roses.

We can employ many analytical tools to accompany users along the way of interacting with the product. These observations allow algorithms to be trained to look for a specific type of repetitive behavior.

UX automation is based on classic research explaining the correlations of users’ emotions with their purchasing decisions.

By translating this knowledge into the digital world, we can increasingly use automation tools, and thus – optimize analytical processes to make working with data more efficient.

By “handing over” parts of the analytics to automation tools, we’ll be able to focus on the websites’ bottlenecks.

Instead of browsing through all the available data, we’ll look for potential problems in the right places. Instead of analyzing each visit, we will only consider those directly related to the conversion.

Why do you need to analyze user behavior?

The most obvious answer: to build a market advantage by responding directly to the needs of your target group.

The analysis of behavior patterns allows mapping the REAL customer journey.

Why use REAL?

Because often in the customer journey mapping process, our projects resemble, to say the very least, wishful thinking.

We want customers to follow a specific path designed for them, forgetting that most purchasing processes – if they end up selling at all – are quite chaotic.

Customer journey mapping should always be preceded by an analysis of user behavior. Only this way can we create paths that match real consumers’ experiences.

Working with qualitative data comes down to optimizing a natural customer path – not the one we’ve pictured when creating our product.

Thanks to the conclusions drawn from behavioral patterns, you can improve your customers’ journeys on 3 levels: marketing, product, and conversion.

To properly optimize marketing activities, we need to know exactly what is happening to our users along their entire journey.

Where they land, what they see, how they react.

So we optimize based on observations.

Product optimization is everything that concerns our product. Changes in forms, more encouraging CTAs, redesigning the product page, facilitating the path to the checkout, etc.

How do we go about optimizing the customer journey in the context of the product?

We shorten it! Why?

Customers – those online in particular – do not always have a strong purchasing incentive. It is not uncommon for them to be driven to your product or service by impulse.

The longer the journey to convert, the more time for the user to change their mind!

Conversion optimization does not only consist of the last steps of the sales funnel (cart, checkout).

For improvements to make sense, you need to take a few steps back and look at the entire customer journey – from marketing communication to product interactions.

How to automate user behavior analysis?

Recognition of behavioral patterns

By recognizing specific user behavior patterns, we can “teach” analytical tools to detect and define them.

The information filtered through the prism of user experience allows focusing only on the data – customer journeys, recordings, heatmaps – which are relevant to your product or service.

This is the ultimate way to finally get answers to the question “why do users get stuck/drop out/are not converting?” etc.

Scale

For years, UX experts have dealt with the lack of “statistical significance” in the presented results of qualitative research.

Attempts to quantify UX make it easier and more effective to monitor introduced changes. In addition, we eliminate the chronic problem – how to convince stakeholders to UX research?

Thanks to the scale of behavioral patterns and the trends we observe, we can finally see if the implemented changes eliminate a given frustration.

This allows us to check whether certain events result from temporary errors and downtimes, e.g., with the connection speed, or whether they relate to permanent issues with the product’s usability.

Goal

Focusing analytical activities – especially automated ones – on a specific business goal allows for a quick diagnosis of issues and immediate recommendations for improvements.

With a clearly defined goal, you do not waste time watching all visits recordings or going through individual sessions.

Automating behavioral analysis allows you to focus on those customer journeys (and their experiences!) that directly affect your business.

Author

Kamila Kotowska

Head of marketing at cux.io

For years associated with the creative industry. Conducts classes with students and training in effective communication. At work, she focuses the most on an unconventional approach to solving communication problems and building creative strategies. She calls herself a Creative MacGyver.