Categories: Business

Smart Ways To Use Consumer Behavioral Data In 2025

Marketing has historically been focused on what you can tell your audience— how you can educate them about your product or service. However, if you still think “content is king,” move over, because there’s a new sheriff in town. In today’s world, the companies to win are those poised to collect the best data and wield it strategically. In 2025, there will be an even stronger push toward what you can learn and understand about your customers.

This isn’t news, exactly: behavioral data collection has been on marketers minds for decades, in the form of surveys and focus groups. Then it was customer loyalty programs, in-store observation, and direct mail.

Now, as data regulation does away with the last morsel of the third-party cookie, it’s all about first-party data: what customers tell you directly. Here are some of the best ways to capture and leverage consumer behavioral data in 2025.

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Analyze Interactions

As third-party cookies become a less viable option, more companies are turning to first-party data collection. This can come in the form of all information you collect directly from your customers, via things like surveys, purchase history, and website interaction.

A plethora of software tools exist to enable marketers to collect and analyze data on customers and prospects. You can collect everything from voluntary email signups to clicks and mouse-tracking data.

A powerful tool within this category is the implementation of interactive content, such as 3D product models that customers can explore. Consider an automotive sales software platform that enables an automaker’s website to display a 3D car model, manipulate it, and zoom in.

For the customer, this allows for a fun, engaging, and informative experience as they discover the car’s features. The more important opportunity, however, arguably lies on the data collection side.

When a business deploys an interactive model on its website, it can study the customer’s every move and interaction. Recalling the car example, again, the automaker can learn which features the customer hovers over, zooms in on, or clicks.

This gives them incredibly important insight on factors like which features the customer considers important, or which customizations to upsell. Someone who lingers for a long time on the head unit might be a candidate for a stereo upgrade.

Create Better Conversations

AI-based conversational agents can offer a similar degree of insight into customer behaviors, wants, and needs. At the most basic level, they can aggregate large volumes of demographic, query, and event-based data. For example, they can collect information about how frequently customers ask to make returns, and why.

From there, companies can glean insight into which products or services need to be improved or removed from their catalog.

More advanced AI models can interpret rhetoric and detect sentiment, to make deeper predictions about customer desires and pain points. They can understand if a customer is angry or sad and consider whether to escalate them to a human agent.

Analyzing large samples of behavioral data in this way can help them strengthen future customer support and offer proactive solutions. Over time, it can improve customer retention, loyalty, and overall brand sentiment.

AI can also use predictive analytics to make personalized product recommendations or service improvements based on past interactions with each customer. It can even factor in data collected from other touchpoints to make these predictions.

For example, imagine that a car customer goes to the dealership and takes a test drive of the model they were looking at. The AI chatbot can follow-up to ask them how they enjoyed their visit, and what could’ve improved the experience.

Detect And Prevent Fraud

Fraud is on the rise, with one source predicting e-commerce fraud alone will cost the global economy as much as $107 billion in 2029.

In large part, this is due to the rising use of AI to spoof identities, hack security tools, and stage more sophisticated attacks. In other words, fraud is getting easier: you don’t have to be an expert to do it.

With access to the right AI model and a little bit of reading and coding, virtually anyone can learn how to commit fraud.

One of the most insidious types of data-based fraud is what’s called a deep fake. A deep fake is when a hacker uses AI to mimic a person’s voice, face, gait, or other biometric details — or create a new “person” all together. This can enable them to manipulate a victim’s friends or family and gain access to personal information. Or it can be used to create or access financial accounts illegitimately, so they can withdraw funds, apply for credit cards, and more.

As scary and powerful as deep fakes might sound, they can’t quite get everything right about an individual, real or fake. There will always be behavioral inconsistencies, speech irregularities, and strange digital artifacts.

Let’s revisit that car customer: data software can analyze how the customer moves their mouse or how fast they type. If another user accesses their account, the system can detect anomalies in their movement and alert fraud prevention teams.

Behavioral Data – A Drop In The Bucket

These are just a few of the ways that companies can leverage consumer data to improve loyalty, increase profits, and fortify their cybersecurity. The actual list of possibilities is virtually limitless, and growing with each new startup or research paper.

As you consider new use-cases for your industry and company, remember to be highly selective. With millions of uses for data out there, you’ll want to invest in the most relevant — and safest — ones.