My UX Toolkit: Behavior Analytics

My UX Toolkit is a series of posts exploring different tools and techniques used in the user experience design and research process, my understanding of them, and when they can be applied. UX is a broad and varied space that can range from quantitative statistical analysis to graphic design, from branding and content strategy to storyboarding. Here I am trying to scratch the surface of how UXers UX, share my knowledge and further my own understanding of this vast career field.

Behavior Analytics

What is conversion?

All digital interfaces, products, and services are created with a specific goal, a specific task that the target user needs to carry out. This is true even for the most altruistic of user products. Take a website for a non-profit organization that provides food and shelter for the homeless population. The purpose of the site is probably to inform visitors on the work the non-profit does and how the visitors themselves can help. The action they would like the users to take is to donate money to the organization or sign up to volunteer with them. Successfully facilitating a user completing this task is what marketers and UX-ers would call conversion.

What is conversion? It is the point at which a user completes a desired action. Conversion can be done on a micro or macro level. For the non-profit described above, getting a user to sign up for a newsletter is a micro conversion. Getting them to donate cash or sign up to volunteer their time is the ultimate goal, and the ultimate conversion.

For a more common example, take a big retail corporation with a well known online presence like REI.

If a user

• Opens an email they received from REI
• Clicks a link in the email that brings them to REI’s website
• Clicks on a product that interests them
• Adds the product to their cart
• Purchases the product

Each step of that process is considered conversion.

What does all this have to do with Behavior Analytics?

In order to increase conversion, something called conversion rate optimization (CRO), it is important to understand how users are interacting with your product. How are they finding your website? What brought them their in the first place? Where are you losing or leaking users? This is where behavior analytics can be a valuable tool.

Behavior analytics is a quantitative analysis and research tool that tells you what is happening when users interact with your product. Once you have data on how users use your product and where you tend to lose them, you can start to hypothesize how to increase user engagement and conversion.

Key information to consider

Traffic Sources — The origin through which people found your site. Are they coming to your website from a direct link? Are they finding it after searching google? What were the keywords searched? The answers to questions like these can give clues as to what the user was looking for when they came to your product, and whether or not they found it.

Heat Maps — These maps show the user behavior on individual pages. Heat Maps are a data visualization tool that show how users click, scroll or move the cursor on a website page. Using the color spectrum, red signifies a popular area and blue signifies a cold area. This tool can be used to analyze which calls to action are effective, any spots where users expected a link or button that doesn’t exist, and how far users tend to scroll on a page.

Bounce Rate — The percentage of people that enter a site and leave without viewing other pages. When a user “bounces” it can indicate that they did not find the content they were seeking on your site. This is another clue, directly related to traffic sources, that can indicate that your site does not provide the content that a user believes it will.

Conversion Rates — This is the percentage of users that your product successfully converts over a specific period of time compared to the overall number of users.

Behavior analytics is a quantitative evaluation of user behavior, and has pros and cons. It is a quick and inexpensive way to gain insight into user behavior. It reveals in black and white exactly how users interact with a product. Qualitative research methods such as user interviews or diary studies are self reported, and while users may believe they are answering truthfully about their own behavior, oftentimes their recollections can be over exaggerated or faulty.

The main drawback of behavior analytics is that it tells you the “what”, what users are doing on your product, but not the “why”, why they behave the way they do. When short on time and resources it is a great way to gain some insight into user behavior. Even better, when used as a jumping off point and combined with qualitative research it is a solid foundation to a deeper more nuanced understanding of the user.