What is Behavioral Data? A Complete Guide with Examples and Use Cases

Have you ever received a push notification from the streaming platform you are subscribed to, recommending a show in a genre you are sure to enjoy? Or an email from the e-store you frequent, offering a discount on a T-shirt you secretly considered buying? We’re sure you have. And what was your reaction when this kind of personalized messaging reached you? “Hey, how can they know what I want?!” The answer is simple: they are really good at interpreting customer intent signals from user behavioral data.

This article defines what behavioral data is, describes its types and benefits, explains the nitty-gritty of behavioral data collection, outlines the ways to improve marketing results with behavioral data, and showcases how major-league brands with a powerful digital footprint leverage customer behavioral data to reach out to their current clientele, re-engage inactive customers, and increase app revenue.

What Is Behavioral Data?

Your clients, both potential and actual, take various actions when engaging with your brand across multiple customer touchpoints (both virtual and physical). Useful information about these customer interactions is known as behavioral data. These behavioral signals are a sort of breadcrumbs people leave behind, which are then picked up by companies via user activity tracking routines to learn about each customer’s characteristics, tastes, and habits. The data is also supremely useful for customer journey optimization, user flow analysis, and launching personalized marketing campaigns.

According to the source of the behavioral data, it is classified into three types.

• First-party behavioral data. This interaction-based data comes directly from customers. Companies analyze website clicks, email opens, click-through rates, abandoned carts, purchases, app usage time, CRM customer profiles, and more to get it. Another type of behavioral data that comes directly from clients is zero-party data. Unlike first-party data, which organizations collect through websites and in-app behavior tracking or mobile analytics, zero-party behavioral data is information intentionally shared by people via surveys, account creation forms, quizzes, etc.
• Second-party behavioral data. Simply put, it is someone else’s first-party behavioral data. This is the data collected by other companies in the same or a similar niche (mostly partner organizations) and shared with or sold to the business to help tap into new audiences, discover customer preferences and engagement patterns, etc.
• Third-party behavioral data. It is amassed by external agencies, brokers, or marketplaces, and is usually broader in scope but less specific. As a rule, organizations purchase behavior data of this kind to conduct a wide-ranging market analysis and expose universal trends symptomatic of the entire industry. 

Let’s dig deeper into various behavioral data examples. 

Examples of Behavioral Data

The most common behavioral data examples are:

• Website interactions. Here belong all digital actions of users who browse your web page. Companies track navigation and engagement (page views, time spent, scroll depth, you name it), interactions proper (video plays, button clicks, file downloads, and more), conversion paths (creating an account, newsletter sign-ups, checkouts, etc.), and frustration signals (cursor abandonment or repeated clicks on unresponsive buttons).
• Application usage. App owners examine various activation events and assess usage metrics, such as session frequency and duration, navigation paths, feature engagement, and drop-offs.
• E-commerce store interactions. The most informative customer experience actions here include browsing patterns (clicks on a certain product category and specific search queries), engagement signals (when people zoom in on product pictures or read reviews), conversion stage indicators (customers create wishlists, add items to cart, choose shipment options, etc.), and post-purchase behavioral data (order tracking, leaving reviews, and more).
• Email metrics. These indices fall into three categories: customer engagement parameters (open rate, click-through rate, and click-to-open rate); conversion behavioral data (conversion rate and revenue per user/email); and retention behavior benchmarks (bounce rate, unsubscribe rate, spam complaint rate, etc.).
• Social media interactions. How do customers communicate with your brand on social networks? Do they display passive (watch videos or read comments) or active engagement (react, like, share, or comment), write direct messages to the brand, or post content (unsolicited product reviews, complaints, unboxings, you name it)?
• Customer service omnichannel interactions across touchpoints. Brands collect omnichannel customer data when their clients contact the support center, recording which channel they use (email, chat, phone, etc.), how they navigate the process, what issues they have with the product, the tone and sentiment of their messages, and more.
• Event, point-of-sale, and in-store interactions. These include both online and offline behavioral data. Event tracking helps understand customer journey stages and identify bottlenecks. POS data reveal purchase timing, typical product mix, payment methods, average check size, and more. In-store interactions describe customer behavior in brick-and-mortar outlets, namely, foot traffic flow, time spent in certain aisles, interactions with sales assistants, etc.

No matter what kind of data you track, behavioral data analysis ushers in numerous perks. 

Benefits of Behavioral Data

When accurately collected, thoroughly examined, and properly interpreted, behavioral data will grant you the following benefits. 

Behavior-driven decision-making

Action-based insights not only describe what your users do but also explain why they behave this way. This information helps brands understand their clientele better and plan their marketing strategy accordingly by optimizing offerings, improving website navigation, fine-tuning campaigns, allocating resources, forecasting future trends, and delivering relevant customer experiences. 

Deeper segmentation

With behavioral data, you can rely not only on demographic attributes of the audience but also on behavior-based segmentation. For example, you can align your marketing initiatives with each client's customer lifecycle and implement real-time personalization, tailoring your approach to address the interests and pain points of each individual user. 

More fine-grained customer profile

Behavioral data is a key to turning a flat, static customer profile into a multi-dimensional, dynamic one. An in-depth profile is an excellent source of information for predicting customer intent, preventing churn, enhancing conversion, reducing marketing costs, and performing data-driven product development.

Precise targeting

Analyzing each customer's spending patterns, purchase frequency, and engagement levels allows marketers to single out individuals who bring more value than others. Then, you can channel marketing efforts toward nurturing clients who deliver greater value, thereby maximizing ROI. 

Higher customer retention

Behavioral data is the staple of customer retention in any lifecycle marketing strategy. It enables businesses to optimize onboarding, remove pitfalls and bottlenecks in the customer journey, anticipate client needs, and prevent churn, improving the overall customer lifetime value (CLV).

Conversion rate boost

By analyzing clicks, scroll depths, session replays, and other behavioral data, experts identify and eliminate friction points, making the conversion funnel smoother. 

To make the most of your customers’ behavioral data, you need to know where and how to get it. 

How to Collect Behavioral Data

The algorithm for garnering behavioral data varies depending on its source.

Website

A website’s quantitative metrics can be obtained from Google Analytics 4 and Google Tag Manager. These tools allow you to track sessions, pageviews, events, and conversions through your website and make tag management a breeze. The best source of qualitative data on a website is short pop-up surveys encouraging users to leave feedback. 

Application

Getting behavioral data from an app is not that straightforward. First, you must select a third-party analytics solution that matches the app’s platform (web, Android, or iOS). Then, install the solution's Software Development Kit (SDK) into the app's codebase, adding project API keys to securely transmit data to the analytics dashboard. Once that is done, you must define the behaviors (sign-ups, session duration, feature usage, you name it) you want to track. Finally, inject tracking snippets into the application's frontend logic to enable data sending each time an action is spotted.

Marketing tech

Typically, a marketing tech stack comprises multiple platforms. Make sure you implement event tracking through SDKs or embedded code snippets (tracking tags) across all of them. To get a complete picture from omnichannel behavioral data, you should pool it in a centralized hub. As a rule, companies do it by setting up customer data platforms (CDPs) or sending information to data warehouses (DWHs).

Social media and surveys

Most social networks come equipped with a feature that captures and presents behavioral data (such as LinkedIn Analytics or Meta Business Suite) and/or built-in polls (Instagram and X have these). Alternatively, there are various third-party tools you can use (like Brandwatch, Sprinklr, or Hootsuite) to monitor audience sentiment.

Surveys are also of great value for registering self-reported actions and attitudes. Businesses can send them via email or embed them in their app or website using specialized tools (Typeform and Qualtrics reign supreme among such solutions).

Customer support center

It's not only about NLP-powered interpretation of customers' verbal communications with your support team. The way they do it is equally vital. So, you should analyze the support channel they select, employ visual tools to see where users struggle when seeking help, map out their navigation paths, and track KPIs related to behavioral data (first-contact resolution, time to resolution, self-service abandonment rate, etc.).

Collecting all the behavioral data you can reach won’t take your marketing efforts far until you learn to use it. 

How to Boost Marketing Results with Behavioral Data

Here are the most effective uses for behavioral data.

UX improvement

The correct interpretation of behavioral data helps development teams detect users' friction points, gauge feature adoption, simplify workflows, and validate changes via A/B testing.

Content and offer optimization

By analyzing behavioral data, businesses design better website layouts, improve the text they contain, and make their real-time messages more effective. The same is true for offers, which can also be tweaked to become more relevant. Approaching clients with upselling and cross-selling initiatives at the right moment can make a world of difference. 

Detailed segmentation

Having a 360-degree view of their clients’ purchase behavior, desired benefits, and customer journey, marketers can divide the audience into granular groups and use these insights in event-triggered campaigns and lifecycle management strategies.

Campaign personalization

As app marketing insights indicate, personalization is the bedrock of successful digital marketing. Behavioral data allows companies to predict customer needs, adjust content and messaging, set up action-based triggers, and issue real-time recommendations. 

Sentiment signals handling

Understanding what users feel allows organizations to spot signs of frustration (like rage clicks or closed pages), forecast intent, forestall churn, and allocate resources to features their customers actually care about.

Behavioral triggers automation

Based on customer actions, marketers set messaging rules to unleash perfectly timed, relevant interventions that drive conversions and enhance engagement. 

Let's see how business juggernauts use the customer behavioral data they collect.

Behavioral Data in Action: 4 Use Cases

Netflix recommendation algorithm

The system utilized by this streaming service analyzes users' watch history, their devices and locations, timing, search queries, genres, instances of fast-forwarding/rewinding, show abandonment, and other behavioral data points. Then, customers with similar behavioral patterns are grouped to streamline the recommendation routine. Moreover, the viewers' tastes are used to show them the most effective thumbnails and posters, as well as to inform users about the rationale behind content recommendations ("because you watched…"). 

Zara demand forecasting

Point-of-sale data (purchases, returns, fitting room information, abandoned items) captured by the company’s software, AI-processed browsing habits registered by the official website, and social media engagement are examined to understand clients' sentiment and expectations. The conclusions the brand arrives at are considered when producing new items and designs in small batches rather than large seasonal collections. Customer reactions to these products are then scrutinized again to determine whether manufacturing should be scaled up. If the reception is lukewarm, the item is discontinued. This way, the organization avoids overstocking and saves a pretty penny by discontinuing the products that are unlikely to be purchased.

FedEx logistics analytics

The AI-fueled supply management mechanism employed by this company has allowed it to shift from reactive package tracking to a proactive business model. By analyzing customer shipping behavior, post-purchase interactions, FedEx creates predictive delivery estimates and generates tariff schedule codes. As a result, FedEx’s customers enjoy simpler customs clearance and minimal delivery times, while the organization’s operating costs are dramatically reduced.

Spotify music recommendations

The company’s Release Radar and Discover Weekly predictive experiences rely on both implicit and explicit user behavioral data, including playlists, session lengths, replays, saved tracks, skips, and more. Besides, Spotify’s AI-driven engine evaluates multiple audio characteristics a song has (tempo, danceability, acousticness, etc.), while NLP mechanisms analyze lyrics. Taken together, these insights help find and recommend new compositions the user might like

No blue-chip brand would be able to leverage behavioral data so successfully without the robust software required for the task.

Behavioral Data + Reteno: Maximized User Retention

Reteno is an all-in-one, AI-driven messaging SaaS suite designed specifically for mobile-first businesses and honed to boost retention across all stages of the customer journey. The platform is an excellent tool for:

• Precision segmentation. It enables experts to divide the clientele into granular groups based on their demographic and behavioral data and to detect early signals of churn (declining product use, reduced engagement, incomplete flows, escalating support requests, etc.) within each category.
• Winning back your users. Behavioral data is the primary source of information about why people churn. Once you learn their reasons, you can send in-app messages, emails containing feedback surveys, and mobile push notifications with we-miss-you offers that give users valid reasons to return.
• Automated smart re-engagement. The platform’s AI mechanisms utilize behavioral data as triggers to launch a series of automated, hyper-personalized messages that encourage dormant or even churned customers to re-engage.

Turn your customers’ behavioral data into up to 15% of total app revenue through email and push notifications with Reteno.

Final Thoughts

Behavioral data is information about customer interactions (such as website and e-store actions, app usage, social media communications, email metrics, customer service contacts, and more) that is collected by businesses directly from clients, shared by their partners, or purchased from external agencies. When properly utilized, it can be highly instrumental for data-driven decision-making, deeper audience segmentation, improved targeting, customer retention, and conversions. You can make the most of behavioral data only if you employ high-end tools to handle it.