What is behavioral analytics?

What is behavioral analytics?

Behavioral analytics is the practice of collecting and analyzing both qualitative and quantitative user data to understand customers' behavioral patterns and how they engage with a product or service. This includes interactions on a company’s website, social media accounts, and more.

Behavioral analytics can be applied across various marketing and customer engagement channels, which we’ll explore in the next section. Its primary purpose is to answer specific questions about the customer experience, enabling businesses to optimize their strategies.

Behavioral analytics in marketing and customer engagement

Behavioral analytics enables marketing and customer engagement teams to strengthen the business’s relationship with users and convert them into customers. It also provides insights into why users act the way they do before making a purchase or performing an action beneficial to the business.

For marketing teams, the focus is often on guiding users through the customer journey, moving them along the sales funnel. (We’ll explore this concept in greater detail later in this guide.) The goal is to identify where and how users transition from prospects to sales leads and, eventually, to customers. Armed with this information, marketing teams can collaborate to create strategies that accelerate the conversion process based on user behaviors.

For example, users may drop off in the middle of a blog post before clicking on a link that can take them to a useful guide on retail trends. The goal of the content piece is to generate downloads and gain user data for follow-ups about products and services. The content marketing team would work with a demand generation team to make sure you reach the CTA link faster, via better copy and placement.

Imagine a user exploring an apparel website, browsing various products but ultimately leaving without making a purchase. To re-engage this user, an automated abandoned cart email could be triggered, reminding them about the items they viewed and encouraging them to return to complete their purchase. This follow-up creates an additional opportunity to convert the user into a customer.

What is behavioral analytics strategy

At its core, behavioral analytics contains the data used to understand user behaviors, while behavioral analytics strategy becomes the process to improve the user experience. This is done through understanding current user data and needs in order to create efficiencies and better the overall customer experience.

Creating and executing on a strategy includes setting benchmarks and defining goals. Without any structure, it’s difficult to understand what, if anything, needs to be done to improve customer experience. It’s important, too, to use different types of behavior analysis for data collection.

How to measure behavioral analytics

Understanding how to measure behavioral analytics is crucial for identifying customer behaviors and refining strategies. Some examples of metrics for behavior analysis include:

6 best practices for working with behavioral analytics

To maximize the effectiveness of behavioral analytics, follow these best practices:

Understand the current experience and issues for users

The first step is to evaluate the current user experience (UX). Businesses cannot expect users to convert without addressing friction points or usability issues.

Define goals

Every behavioral analytics strategy should connect to defined business goals. Set measurable targets and prioritize them based on urgency or potential impact.

Start with a manageable number of goals and focus on the most critical issues, such as improving site speed, enhancing page copy, or ensuring proper product loading.

Data collection

Data collection is essential for understanding user behavior, but not all data will be equally relevant. Focus on metrics tied directly to your goals and collect data over a meaningful period.

Data analysis

Once data is collected, compare it to your benchmarks to assess performance and identify trends or areas for improvement. Look for patterns in user behavior, such as frequent drop-off points or pages with high engagement.

Implement necessary changes

After identifying areas for improvement, use both qualitative and quantitative insights to guide your strategy. Collaborate with relevant teams to make targeted changes based on your findings.

Monitor and measure, repeat

User behavior evolves, so regular monitoring is essential to ensure long-term success. Continually measure the effectiveness of changes and refine strategies based on new data.