How you can use predictive behavioral data to improve customer experiences

Most online users rack up almost 5,000 digital interactions per day. The glut of data makes it challenging for marketing teams to pinpoint the most relevant information. But each reaction, from customer preferences to pain points, offers valuable insight that can help improve your online presence and satisfy customers. With the right approach, you can identify the best insight sources and make the most of behavioral data.

What’s behavioral data?

Behavioral data is the digital footprint users leave behind when they interact with online platforms. These actions include clicks, site visits, time spent on pages, newsletter subscriptions, email sign-ups, and more.

Marketing teams typically receive this data for behavioral analysis through various sources, such as websites, apps, customer relationship management (CRM) software, marketing automation systems, and data visualization tools.

After collecting data, marketers save each interaction as an “event” when a user takes action. Each event has “properties,” or metadata describing it. For example, an event may be a smartphone user tapping on a push notification, with the properties being the mobile’s brand, location, and time.

Understanding behavioral data

Consumer behavioral data comes from every corner of the internet. Here are the most common occurrences:

• Adding items to shopping carts. Monitoring shopping cart data and subsequent actions helps improve sales funnels and minimize abandoned cart rates.
• Choosing between options. Tracking users’ decisions between various options, such as making a selection on a platform or a survey, provides insights into individual choices and preferences. This knowledge lets you shape personalization and product offerings.
• Clicking links. Analyzing the links that users click on most (and least) can help you understand the content, products, and services that resonate with them.
• Completing forms. When users fill out forms, they’re willing to provide information like email addresses and geographical locations. Use this consumer data for lead generation, segmentation, and personalization.
• Contacting customer support. Users’ calls, emails, and chats with customer support reveal your consumer base’s pain points and demands. These offer insight that lets you improve customer service operations and tailor future interactions to your audience’s preferences.
• Entering search queries. Knowing what users actively search for, whether on websites or search engines, provides valuable insight into their needs and interests.
• Reading blogs and articles. How long people spend reading specific themes and topics indicates their interests and reading patterns. This helps dictate the kind of content you should include going forward.
• Watching videos. Video engagement metrics, such as views, watch time, likes, comments, and shares, measure the impact and success of visual content.

Why is behavioral data important?

Behavioral data is more than a byproduct of online activities — it’s a valuable resource that lets you refine marketing strategies, optimize user experiences, and meet customer expectations more precisely. Here are a few reasons why predictive behavioral analysis is essential to your marketing strategy.

Personalization and precision targeting

Behavioral data allows you to move beyond generic marketing strategies, like mass email campaigns, by comprehensively understanding user preferences to create targeted and personalized messaging.

For example, if a customer buys a product from your website, you might email them a personalized thank-you note and similar recommendations or discounts. With precise targeting, you improve your consumers’ experience and boost conversions.

Optimized user experiences

Create seamless, intuitive, and functional digital experiences by analyzing how users navigate websites, where they spend the most time, and what encourages them to click and convert. Behavioral data also shows you pain points and potential bottlenecks across your digital channels.

For instance, offering a one-click payment process over the standard procedure on your ecommerce store may increase revenue. Behavioral data helps you identify these issues, make data-driven improvements, and nurture target audiences through the customer journey by meeting their expectations and preferences.

Data-driven decision-making

Behavioral data allows you to move beyond guesses and assumptions. It provides quantifiable insights into user behavior and interests so you can validate hypotheses, test strategies, and adapt based on data and real-time feedback.

This way, you can create a structured, agile, and responsive workflow where every decision is based on observable trends through stats and numbers.

Customer loyalty and acquisition

All the data collected from analytics tools reveals patterns that let you predict and address various user preferences, demands, and pain points. Consistently delivering on those fronts offers value to your customers by meeting and exceeding their expectations, which fosters loyalty among your consumer base.

Precision targeting and optimized interfaces also allow you to create loyalty programs, proactive customer support, and personalized ads to build lasting relationships. Not only does this strengthen your bond with existing buyers, but it encourages them to recommend your brand through word-of-mouth marketing.

Types of behavioral data

Behavioral data comes in several forms based on source and ownership. Here are the main types.

• First-party data. First-party behavioral data is collected directly from audiences through your website, mobile apps, social media, and email. However, you can’t keep tabs on first-party data once users leave the source. For example, an ecommerce website tracks clicks, searches, browsing history, and purchases — these interactions are first-party data that provide valuable insights into user behavior across digital channels.
• Second-party data. Second-party data comes from an external organization that shares its collected information with you through an agreement or mutually beneficial partnership. For instance, a service provider can collaborate with a review website. The review company shares its behavioral data (with consent) to help the service provider understand customer demands and pain points.
• Third-party data. Third-party data is sourced or bought from external providers who aggregate and sell information through various platforms. For example, an advertising agency buys demographic information from a data provider, like a target audience’s age bracket, to segment customer profiles and offer personalized marketing campaigns.

Common sources of behavioral data

You can track behavioral data from various touchpoints where users interact with online platforms and digital channels. Here are seven common examples of behavioral data sources.

1. Data and analytics tools

Analytics tools like Google Analytics, Smartlook, and Mixpanel track user interactions across websites, mobile apps, email, social media, and event platforms.

They provide visual reports and insights into metrics such as click-through rates, session durations, bounce rates, etc. These numbers help you understand how users behave on platforms, with areas and opportunities for improvement.

2. Social media platforms

With billions of monthly active users, social media networks like Facebook, Instagram, TikTok, and YouTube generate a wealth of behavioral data.

You’ll find user engagement insights, like likes, shares, and comments, indicating how popular and relevant posts and content are. They also show you how effective your marketing efforts are and how you can tap into trends to improve reach and following.

3. Email marketing tools

Email marketing platforms, like Mailchimp, HubSpot, and ActiveCampaigns, provide behavioral analytics related to email campaigns. You can monitor how often recipients open emails, click on links, and interact with email content and personalized messaging. This lets you improve your email marketing strategy for better lead generation, engagement, and conversions.

4. CRM systems

Customer relationship management (CRM) systems let you store and organize customer-related behavior analytics, like interactions, communications, feedback, and purchase history. CRM tools provide a centralized location for team members to access information and help you effectively understand and manage customer relationships.

5. Ecommerce platforms

With a third of the world’s population shopping online, ecommerce website analytics are rich behavioral data sources. Whether people browse and view products, add items to their wish lists and shopping carts, or offer transaction details, you’ll find plenty of personalized marketing and lead generation opportunities.

You can segment target audiences, create in-depth customer profiles, and use this data to tailor your offerings and marketing strategies to improve conversion rates and revenue.

6. Customer support channels

Customer service and helpdesk platforms, like Zendesk and Freshdesk, collect behavioral analytics through customer interactions with support teams and chatbots. Data points from this source include the number and frequency of support requests, issue types, feedback, and ratings (typically from 1–5).

With these insights, you can quickly identify your consumers’ most common problems and address them to improve customer satisfaction and retention.

7. Heatmaps

Heatmaps visually represent user behavior by showing how and where people interact with a webpage. For example, you can see where users click, scroll, and spend the most time. High-activity areas usually show darker, warmer colors, like red and orange, while less-used areas are green or blue.

Tools like Hotjar and Microsoft Clarity generate heatmaps based on aggregated engagement. They provide you with behavioral data and an intuitive understanding of your site or app’s most appealing and navigated zones.

These visual insights can help you make design improvements, optimize call-to-action (CTA) placements, and improve the overall customer journey by aligning interfaces with their preferences.

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