How Can Behavioral Data Help You? (2025 Update)

By Kevin Novak

Originally published 2016, updated 2018. This new 2025 update reflects significant advances in privacy regulations, AI/ML capabilities, and first-party data strategies that have transformed how organizations collect and leverage behavioral data.

Behavioral data is the capture of interaction, click, engagement, movement through a website, actions taken via email marketing across the buyer journey and user movement through digital content. The evolution and growth of digital and technology have given businesses and organizations a great opportunity to record, collect and gain a higher level of intelligence from behavioral data.

Behavioral data can be amended by third-party data and other data you may have in your organization.

Tracking and collecting the data can aid an organization in producing new insights and prioritizing actions, product development and fixes. It can also enable effective and authoritative segmentation and persona development to ensure an organization understands the personas of customers, members or subscribers and how to tailor messages and services to each.

Behavioral data differs significantly from the traditionally collected and managed transactional data most organizations have. Transactional data focuses on sales (conversion) at the end of the customer journey funnel.

Transactional data is impactful and important to understand who has converted, the frequency of conversion across products and services and for forecasting. It is anecdotal and historical data about one aspect of a customer and one aspect of their relationship with you.

Behavioral data can also be defined as historical and descriptive but has a higher value given that it reflects overall behavior and the relationship you have with that customer, member or subscriber. Additionally, it is data that exists without bias.

Behavioral data reveals what we really do, not what we say.

How Is Behavioral Data Valuable?

• It informs decision-making and priority-setting by giving an organization a view into how all of its current and prospective customers, members or subscribers behave across digital properties, sites, mobile, communications and platforms, including learning management solutions (LMS).
• Collected data is applied against a specific customer or prospect by name. You don’t have to guess who the customer or prospect is and what segment they fall into.
• It allows migration away from taking action on survey data, which is often only based on a sampling of a few respondents and may not be reflective of all of your audiences.
• If you have survey data, behavioral data allows you to rationalize and compare survey findings with real-time actionable behavioral data.
• It provides the opportunity to move beyond transactional (conversion) data and reveal who is getting lost in the sales, relationship and engagement funnel process.
• It generates real-time objective data of challenges and problems with digital experiences and products and suggests prioritization of fixes and iterations that produce high customer satisfaction — and of course more revenue.
• It enables the creation of personas (personal profiles) to classify users into segments. Segments can be used to target and hyper-target messages and information reaching those who have particular needs and wants.
• It enables an organization to identify any problem your audiences have and target personal solutions. Resolutions generate higher loyalty and promote a longer-term relationship. Brand loyalty and equity are critical in a hyper-competitive market.
• It lays the foundation for effective and targeted use of an organization’s limited personnel and financial resources for maximal profit.
• It creates the edge an organization needs to compete against those stealing market share by offering similar content, services or products.

Where Does Behavioral Data Come From?

Behavioral data is collected through marketing automation and digital analytics platforms and tools and it can incorporate behaviors observed in a physical setting.

In a digital world it is preferable to find a platform that aggregates all your data. Marketing automation platforms that include the integration of email marketing, customer information and profiles, sales, digital analytics and general capture of all user activity will lead to the most fruitful results and gains in intelligence.

Single-focused platforms like email marketing lead to an isolated collection of elements of a customer’s, member’s or subscriber’s behavior. Oftentimes this data is hard to extract and difficult to correlate to data collected by other systems.

If you don’t have a holistic solution, single-focused systems can be connected via services like Zapier. This connective tissue is cloud-based and enables quick sharing of transactional and behavioral data.

For those seeking more complex systems and outputs, Customer Data Management Platforms (CDP) and Data Management Platforms (DMP) are also available.

Marketing Advantage

Audience preferences for purchasing, engagement and interaction have shifted as a result of digital and technology.

Mass marketing approaches like “spray and pray” aren’t working–particularly given the amount of electronic communications everyone receives. We only have so much attention and time to give to all of the communications and opportunities presented to us to interact or take some action.

As such, users (customers, members or subscribers) are seeking to interact only with marketing, communication and brands that are relevant.

We are more selective in how we spend our time; communication must be customized and seamless.

Segmentation and Personas

An organization can use behavioral data to create segments of its audiences.

Segmentation describes the many different types of people with their interests, challenges and needs that comprise your audiences.

Personas describe each personality set and can then be grouped into segments.

• The segmentation and definition of different types of people can be used to customize and target messages and communications.
• The segmentation and definitions can be used to create digital and mobile personalization that is relevant.
• Matching your audiences’ problems to solutions leads to more frequent conversions, which, of course, increases your revenue.
• Segmentation using personas and behavioral data enables improved validation from A/B testing and allows for defining and executing multi-variant tests against different customer types. The results produce a greater understanding of what works across segments or within a particular segment.
• Segmentation also ensures you are spending limited resources effectively and can defend the use of marketing dollars by directly correlating the spend to resulting sales.

The Importance of Behavioral Data to Organizational Leaders

If you are a CEO, CMO, marketing director, operations leader, or other decision-maker, behavioral data can give you action-oriented insights and intelligence that challenges institutional knowledge and opinion. It is real and actionable data that proves how members, customers and subscribers interact and engage with you.

For Marketing Leaders: Behavioral data enables you to move beyond campaign metrics and understand the complete customer journey. You can identify which touchpoints drive conversions, where prospects drop off, and which messages resonate with different audience segments. This intelligence allows you to optimize spend, improve ROI, and demonstrate marketing’s impact on revenue.

For Operations and Product Teams: Behavioral data reveals how customers actually use your products and services, where they encounter friction, and which features drive value. This insight informs product roadmaps, prioritizes fixes, and ensures development resources are allocated to what matters most to your audiences.

For Sales Leaders: Understanding behavioral signals—which content prospects consume, how frequently they return to your site, which features they explore—enables sales teams to prioritize outreach, personalize conversations, and close deals faster.

For Customer Success and Service Teams: Behavioral data identifies customers at risk of churning, reveals common problems before they escalate, and highlights opportunities for upsells and cross-sells based on actual usage patterns.

Transactional data and biased survey-based information can only tell part of the story.

In this hyper-competitive market, you need to have your finger on the pulse of your customers. And behavioral data reflect real-time trends and shifts in customer preferences. Gut instinct is relegated to the past. Take advantage of the sophisticated intelligence tools at hand.

Your competitors may already be two steps ahead of you.

2025 Update: The New Era of Behavioral Data

Since 2018, the landscape of behavioral data has undergone a seismic transformation. What was once primarily a marketing advantage has become a strategic imperative shaped by regulatory change, technological advancement, and fundamental shifts in how consumers interact with digital properties. Organizations that have adapted to this new reality are seeing unprecedented returns, while those clinging to 2018 methodologies are falling behind.

The Privacy-First Revolution and Tracking Fragmentation

The most significant change since 2018 has been the global shift toward privacy-first data collection and the resulting fragmentation of the tracking landscape. The implementation of GDPR in 2018, followed by CCPA in 2020 and a cascade of privacy regulations worldwide, fundamentally changed how organizations can collect and use behavioral data.

The Cookie Reversal and What It Really Means

In July 2024, Google made a surprising reversal of its long-standing plan to eliminate third-party cookies in Chrome. After years of delays and industry pushback, Google announced it would not deprecate cookies but instead introduce a user-choice model where Chrome users can decide whether to enable or block third-party cookies across their browsing experience.

However, this reversal doesn’t mean we’re returning to 2018. Instead, we’re entering an era of tracking fragmentation:

The Fragmented Reality:

• Safari and Firefox have blocked third-party cookies by default for years and continue to do so
• Chrome users can now choose whether to allow cookies, similar to Apple’s App Tracking Transparency framework
• Early signals from similar choice-based systems (like Apple’s ATT) show only 25-30% of users opt in to tracking
• Privacy regulations (GDPR, CCPA, and others) still require explicit consent regardless of browser capabilities
• Different regions have different tracking restrictions based on local privacy laws

Why First-Party Data Is Now Essential

This fragmented landscape makes first-party and zero-party data strategies more critical than ever before. Organizations must now:

• Build Direct Relationships: Collect data directly from your audience through owned channels (email, apps, websites, loyalty programs, accounts) that work consistently across all browsers and privacy settings
• Implement Consent Management: Deploy robust consent management platforms (CMPs) that respect user choices while maximizing opt-in rates through transparent value exchanges
• Create Compelling Value Exchanges: Give customers clear reasons to share data—personalized experiences, exclusive content, better service, rewards programs
• Ensure Compliance Everywhere: Navigate a patchwork of privacy regulations that vary by region, industry, and context

The Advantage of First-Party Data

The fragmented tracking environment has actually strengthened the competitive advantage of organizations that invest in first-party relationships. First-party data collected with consent is:

• More accurate than third-party data
• More actionable because you control collection and activation
• More trusted by customers who voluntarily share it
• More reliable because it works regardless of browser, device, or privacy setting
• More compliant with evolving regulations

The organizations winning in 2025 aren’t those who held onto third-party cookies—they’re those who recognized that direct customer relationships deliver better data and better outcomes.

AI and Machine Learning: From Descriptive to Predictive

In 2018, behavioral data was primarily descriptive and historical. Today, artificial intelligence and machine learning have transformed behavioral data into a predictive and prescriptive tool.

Modern AI Applications

Predictive Analytics: Machine learning models can now analyze behavioral patterns to predict future actions with remarkable accuracy. Organizations can identify which customers are likely to churn, which prospects are most likely to convert, and which segments will respond to specific offers—often before traditional signals appear.

Next-Best-Action Decisioning: Real-time AI engines process behavioral signals to determine the optimal next interaction for each individual. This goes far beyond segmentation; it’s true 1:1 personalization at scale.

Behavioral Anomaly Detection: AI systems identify unusual patterns that may indicate problems, opportunities, or fraud. A customer whose engagement suddenly drops, a prospect whose research behavior intensifies, or a user whose navigation patterns suggest confusion—all can trigger automated responses or alerts.

Natural Language Processing (NLP): Behavioral data now includes analysis of how customers interact with chatbots, voice assistants, and support channels. NLP extracts intent, sentiment, and needs from conversational interactions, adding a rich layer of behavioral intelligence.

Propensity Modeling: Organizations build models that predict propensity to buy, likelihood to engage with specific content types, optimal send times for communications, and product affinity—all based on behavioral patterns across your entire customer base.

The key difference: In 2018, you analyzed what happened and made decisions. In 2025, AI analyzes what’s happening and makes decisions automatically, while you focus on strategy and optimization.

The Modern Tech Stack Evolution

The platforms and tools for collecting and analyzing behavioral data have matured significantly since 2018.

Google Analytics 4 (GA4): Google’s complete rebuild of its analytics platform reflects the privacy-first era. GA4 uses event-based tracking instead of session-based, can operate without cookies through server-side tracking, and includes built-in machine learning for predictive insights. Organizations still using Universal Analytics (sunset in 2023) are operating with outdated intelligence.

Customer Data Platforms (CDPs) Have Matured: In 2018, CDPs were emerging. Today, they’re essential infrastructure for any serious behavioral data strategy. Modern CDPs like Segment, mParticle, Treasure Data, and Adobe’s Real-Time CDP unify behavioral data from every touchpoint—web, mobile, email, CRM, support, point-of-sale, and more—into a single customer view that updates in real-time.

Composable Architecture: The “all-in-one platform” approach mentioned in 2018 has evolved into composable architecture—best-of-breed tools connected through APIs and integration layers. Organizations now select specialized tools for different functions and connect them through CDPs, reverse ETL tools, and modern data warehouses.

Data Warehouses as Marketing Platforms: Tools like Snowflake, Google BigQuery, and Amazon Redshift are no longer just data storage—they’re becoming central to marketing operations. Behavioral data is stored, analyzed, and activated directly from the warehouse, giving organizations complete control and flexibility.

Real-Time Activation: The lag between behavioral signal and response has collapsed from hours or days to milliseconds. Server-side tracking, edge computing, and streaming data pipelines enable organizations to respond to behavior as it happens.

Journey Orchestration: Beyond Segmentation

The segmentation and persona approach from 2018 remains valuable, but it’s been augmented by journey orchestration—a more sophisticated way to leverage behavioral data.

How Journey Orchestration Works

Instead of placing customers into static segments, journey orchestration tracks where each individual is in their unique journey and delivers the right experience at the right moment. This approach:

• Maps all possible paths through your customer experience
• Identifies critical decision points and drop-off risks
• Automatically triggers interventions based on behavioral signals
• Adapts the journey in real-time based on each interaction
• Optimizes across channels for seamless experiences

Example: A prospect visits your pricing page three times in two days, downloads a product comparison guide, but doesn’t start a trial. Journey orchestration might trigger a personalized email with answers to common objections, followed by a targeted ad highlighting your free trial, and a chatbot offer for a live demo—all coordinated and timed based on that specific behavioral pattern.

Platforms like Braze, Iterable, Salesforce Marketing Cloud, and Adobe Journey Optimizer enable this level of sophistication.

Post-Pandemic Digital Acceleration

The COVID-19 pandemic accelerated digital transformation by an estimated 5-10 years. This acceleration fundamentally changed behavioral data in several ways:

Expanded Digital Footprints: Customers, members, and subscribers now interact with organizations across more digital touchpoints than ever. Virtual events, online communities, digital service delivery, telehealth, virtual consultations, and hybrid experiences all generate behavioral data that didn’t exist (or was minimal) in 2018.

Changed Expectations: The pandemic normalized personalized digital experiences. Customers now expect organizations to remember their preferences, anticipate their needs, and deliver seamless experiences across all channels. Behavioral data is the foundation for meeting these elevated expectations.

Omnichannel Complexity: The line between online and offline has blurred. “Showrooming” (researching online, buying in-store) and “webrooming” (researching in-store, buying online) are standard behaviors. Organizations must track and unify behavioral data across all touchpoints to understand the complete customer journey.

Zero-Party Data: The New Competitive Advantage

One of the most significant developments since 2018 is the rise of zero-party data—information customers intentionally share with you.

Why Zero-Party Data Matters

In the fragmented tracking environment where some users block cookies, some don’t, and different browsers have different capabilities, the data customers explicitly give you becomes invaluable. This includes:

• Preference center selections (communication frequency, content topics, channels)
• Quiz and assessment results (product finders, style profiles, needs assessments)
• Account profiles (interests, goals, preferences)
• Feedback and reviews
• Purchase intentions and wish lists
• Personal context (life events, milestones, circumstances)

The Value Exchange: Organizations successful with zero-party data create compelling reasons for customers to share. Netflix’s personalization, Spotify’s Discover Weekly, Sephora’s Beauty Insider profile, and Stitch Fix’s style quiz all demonstrate how providing value in exchange for data creates a positive cycle: better data enables better experiences, which encourages more data sharing.

Integration with Behavioral Data: The most powerful approach combines zero-party data (what customers tell you) with behavioral data (what customers do). When preferences align with behavior, you have validated intelligence. When they diverge, you’ve identified an opportunity to improve or a signal that needs or preferences have changed.

The Importance of Behavioral Data to Modern Leadership

The landscape for C-level executives has evolved:

For CEOs and Board Members: Behavioral data is no longer just a marketing tool—it’s a strategic asset that informs product development, customer experience, risk management, and competitive positioning. Organizations with sophisticated behavioral data capabilities can respond to market shifts faster, allocate resources more effectively, and build sustainable competitive advantages.

For Chief Data Officers (CDOs): This role barely existed in 2018. Today, CDOs are responsible for ensuring behavioral data is collected ethically, stored securely, governed properly, and activated effectively. The intersection of privacy compliance, data quality, and business value requires executive-level focus.

For Chief Customer Officers (CCOs): Behavioral data reveals the truth about customer experience. It identifies friction points, validates assumptions, and measures the impact of experience improvements with precision that surveys and focus groups cannot match.

For Chief Technology Officers (CTOs): The technical infrastructure for behavioral data—real-time pipelines, data warehouses, CDPs, and activation platforms—requires significant investment and ongoing optimization. This is core infrastructure, not a marketing tool.

Emerging Behavioral Signals

The types of behavioral data organizations collect have expanded significantly:

Digital Body Language: Micro-behaviors like scroll depth, mouse movements, hesitation patterns, form field completion time, and content consumption pace reveal intent and emotion that traditional click tracking misses.

Cross-Device Behavior: Understanding how the same individual behaves across phone, tablet, desktop, and connected TV provides a complete picture of their journey and preferences.

Voice and Conversational Interactions: As voice assistants and chatbots become standard, the behavioral data from these interactions—question patterns, problem-solving paths, emotion in voice tone—adds new dimensions of understanding.

Community and Social Behavior: How customers interact in your community, forums, or social channels reveals advocates, influencers, detractors, and needs that transactional or website data alone cannot capture.

Product Usage Telemetry: For software, apps, and connected products, behavioral data includes feature usage, workflow patterns, adoption curves, and friction points within the product itself.

Ethical Considerations and Trust

One critical addition to the 2018 perspective: Organizations must now navigate the ethical implications of behavioral data collection and use.

Building Trust Through Transparency: Customers are increasingly aware of data collection and concerned about privacy. Organizations that are transparent about what data they collect, how they use it, and what value customers receive in return build trust and see higher opt-in rates and engagement.

Avoiding Manipulative Practices: The power of behavioral data combined with AI creates opportunities for manipulation—dark patterns, exploitative personalization, and psychological manipulation. Organizations committed to long-term customer relationships avoid these tactics, even when they might drive short-term gains.

Data Minimization: Collect only the behavioral data you need and will actually use. Over-collection creates privacy risks, storage costs, and compliance burdens without delivering value.

Algorithmic Fairness: AI models trained on behavioral data can perpetuate or amplify biases. Organizations must audit their models and data practices to ensure fair treatment across all customer segments.

Getting Started or Advancing Your Behavioral Data Strategy in 2025

Whether you’re just beginning or advancing an existing program, here’s how to approach behavioral data in the current environment:

1. Audit Your Current State

• What behavioral data are you collecting today?
• Is it compliant with current privacy regulations?
• Can you unify behavioral data across all customer touchpoints?
• Do you have consent and proper governance?
• How does your data collection work across different browsers and privacy settings?

2. Prioritize First-Party Data Infrastructure

• Implement a CDP if you don’t have one
• Move to GA4 if you haven’t already
• Build direct data collection relationships with your audiences
• Create value exchanges that encourage data sharing
• Ensure your tracking works in all environments (cookie and cookie-less)

3. Invest in AI and Machine Learning Capabilities

• Start with predictive models for your highest-value use cases
• Implement next-best-action decisioning
• Build propensity models for key behaviors
• Use AI for personalization at scale

4. Move Beyond Segmentation to Journey Orchestration

• Map customer journeys across all touchpoints
• Identify critical behavioral signals at each stage
• Automate responses to key behaviors
• Test and optimize continuously

5. Ensure Ethical Data Practices

• Be transparent about data collection
• Provide clear value in exchange for data
• Respect customer preferences and consent
• Audit for bias and fairness

6. Build Cross-Functional Alignment

• Behavioral data insights should inform product, marketing, sales, service, and strategy
• Break down data silos between departments
• Create shared KPIs based on behavioral metrics
• Establish governance that enables access while ensuring compliance

The Competitive Reality

The behavioral data advantage that was emerging in 2018 is now fully realized. Organizations that have built sophisticated behavioral data capabilities are operating with fundamentally better intelligence than their competitors.

They know what customers need before customers ask. They identify problems before they cause churn. They optimize experiences continuously based on real behavior. They allocate resources to high-impact activities with confidence. They build products customers actually want. They deliver personalized experiences at scale.

Most importantly, they’ve built direct relationships with their customers that work regardless of browser, device, privacy setting, or tracking technology. They’re not dependent on third-party cookies or any single tracking method—they have multiple sources of consented, high-quality behavioral data.

The gap between organizations with mature behavioral data strategies and those without is wider than ever—and it’s growing.

Your competitors aren’t just two steps ahead anymore. In many cases, they’re operating in a different universe entirely.

Conclusion: The Path Forward

Behavioral data has evolved from a marketing advantage to a strategic infrastructure. The privacy-first era, tracking fragmentation, AI transformation, and digital acceleration have fundamentally changed how organizations collect, analyze, and activate behavioral intelligence.

The principles from 2018 remain sound: behavioral data reveals what people really do, enables better decision-making, and creates competitive advantages. But the methods, technologies, and environment have advanced dramatically.

The cookie reversal doesn’t change the fundamental strategy—it reinforces it. Organizations that invest in first-party relationships, leverage AI, orchestrate journeys, and use behavioral data ethically will thrive regardless of what browsers or privacy regulations do next. Those who waited for cookie deprecation to force their hand have lost years of advantage.

The future belongs to organizations that own their customer relationships and the behavioral data that flows from them.

Resources and Further Learning

Ideas and Innovations Newsletter: Join over 5,000 leaders receiving weekly insights on organizational transformation, measurement, leadership psychology, and the human factors that determine success. Published every Thursday.

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Human Factor Podcast: Weekly conversations exploring the psychology behind transformation success. Real discussions with leaders who understand that transformation is about people, not technology.

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The Human Factor Method: Evidence-based methodology for transformation success. Complete framework addressing the psychological and behavioral factors that determine whether organizational change succeeds or fails.

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Related Articles on Behavioral Data and Analytics: Browse the Ideas and Innovations Newsletter archive for articles on:

• First-party data strategies
• Customer segmentation and personas
• Analytics and measurement frameworks
• Privacy-first marketing approaches
• AI and behavioral intelligence

Book: The Truth About Transformation

Kevin Novak’s comprehensive playbook for organizations navigating change. The book includes the 2040 construct to transformation, case studies, and frameworks for honoring the human factor in organizational change.

Understanding the human factor informs how to collect, manage and act on behavioral data.

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Work With 2040 Digital

As always, 2040 Digital stands ready to help you navigate the complex and rapidly evolving landscape of behavioral data. Whether you’re just starting your behavioral data journey or looking to advance your capabilities to the next level, we can:

• Assess your current behavioral data infrastructure and capabilities
• Design a roadmap for advancing your data strategy
• Implement the platforms and practices that give you competitive intelligence advantages
• Train your team on behavioral data analysis and activation
• Build AI and machine learning models tailored to your business needs

Contact 2040 Digital to discuss how we can help you leverage behavioral data to drive growth and customer success.

The question is no longer whether to invest in behavioral data—it’s whether you’re investing enough, fast enough, with the right approach for 2025 and beyond.