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What is behavioral analytics?

Learn how to better understand user data and improve customer engagement

Understanding why users make the decisions, and how they transition from window shoppers to purchases, is incredibly valuable for businesses. Rather than predicting customer behavior, knowing exactly how customers act provides actionable insights that can generate higher-quality leads and increase revenue.

 

This approach is known as behavioral analytics.

 

In this guide, we’ll explore  what behavioral analytics is, how it works with customer engagement and marketing, along with strategy, and the best practices for implementing this tactic.

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 examples include:

  1. Abandoned carts
  2. Creating an account with a business 
  3. Purchasing a product or service, or even a subscription 
  4. Clicking a call-to-action (CTA)  link to a product or service page 
  5. Filling out and submitting a form

 

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.

Some questions behavioral analytics may answer include

  1. What are users most interested in clicking on?
  2. What are users ignoring?
  3. What actions do users take before making a purchase and becoming customers?
  4. What actions do users take before abandoning a website or cart?
  5. What are users searching for when they land on a business’s page?

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.

Types of behavioral analysis

Before implementing any strategy, marketing teams must first conduct thorough behavioral analysis. Below are some of the most common types of behavioral analysis used to gain actionable insights:

A/B testing

An A/B test analysis allows for variations to be tested in a group of users. These users are divided into groups with each exposed to a different element to test.

 

For example, a common A/B test in email marketing involves testing different subject lines to improve open rates. If a company wants to increase subscriptions to its newsletter, it might test two subject lines to see which drives higher engagement.

Segmentation

Audience segmentation involves dividing users into distinct groups to better understand their needs and tailor strategies accordingly. Not every user or buyer fits into the same category, so this practice ensures marketing efforts are relevant and effective.

 

Common segmentation categories include:

 

  • Common segmentation categories include: First-time buyers.
  • Returning customers: Loyal customers with prior purchase history.
  • Browsers: : Users exploring without making a purchase or clicking links.

 

Segmentation is a critical component of behavioral analytics. For example, what works to engage return customers may not resonate with browsers, so a personalized approach is key.

Funnel analysis

The customer journey often follows a funnel, with various stages where users might drop off. Funnel analysis helps marketers identify weak points in the journey and make improvements to ensure users progress toward becoming customers.


For example, an e-commerce website might notice low user interest at the top of the funnel—indicating a brand awareness issue. This could stem from reduced website traffic, low engagement on social media, or poor organic search visibility. Funnel analysis enables teams to pinpoint the issue and develop strategies, such as boosting social media activity or improving SEO, to attract and retain users.

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:

 

  1. Page Views: Tracks how many users visit a specific page.
  2. Conversion Rates: Measures the percentage of users completing a desired action (e.g., making a purchase, filling out a form).
  3. Click-Through Rates (CTR): Tracks the percentage of users clicking on a desired link.
  4. User Flow: Analyzes the paths users take to navigate through a website.
  5. Exit Rate: Identifies how often users leave a specific page.
  6. Daily Active Users (DAU): Measures how many users interact with a website or app daily.
  7. Session Duration: Tracks the average time users spend on a website or app.
  8. Bounce Rate: Measures the percentage of users leaving a site after viewing only one page.

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.

 

How to do it:

 

  • Navigate the website or app as a user to identify barriers to completing tasks.
  • Collect qualitative feedback from real users through surveys or usability tests.
  • Assess critical touchpoints like web pages, forms, and checkout processes for clarity and ease of use.

Define goals

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

 

Examples of goals:

 

  • Increase landing page conversions by 15% within three months.
  • Reduce cart abandonment rates by 10%.
  • Boost engagement on product pages by improving CTA visibility.

 

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.

 

Best practices for data collection:

 

  • Use tools like Google Analytics, Hotjar, or Mixpanel to gather data.
  • Segment data based on audience types (e.g., new vs. returning users).
  • Monitor real-time data to identify immediate opportunities or issues.

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.

 

Techniques for analysis:

 

  • Use heatmaps to visualize user interactions.
  • Conduct cohort analysis to track behavior over time.
  • Compare performance metrics before and after campaigns or changes

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.

 

Example adjustments:

 

  • Improve CTA placement and wording.
  • Optimize page load times to reduce bounce rates.
  • Introduce personalized content for returning users.

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.

 

How to maintain effectiveness:

 

  • Schedule regular reviews of behavioral analytics.
  • A/B test new strategies or features..
  • Update benchmarks as user expectations shift.

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