In the past, I have written about the shift from demographics to personas. For decades marketers targeted people using census categories—age, income, gender, geography. Then along came personas, which at least forced us to think about motivations instead of birth certificates.

Personas were an improvement. And they’re still useful in many contexts—particularly when planning legacy media campaigns, crafting influencer programs, ideating creative experiential ideas and, of course, for all manner of marketing storytelling.

Personas haven’t stopped working. But the platforms delivering our marketing don’t really use them.

When you run campaigns today on Google, Meta, Amazon or TikTok, you can upload targeting parameters and audience definitions. But behind the scenes those systems are mostly reacting to behavioral signals—search activity, browsing patterns, content engagement, purchase history and dozens of other variables.

The ad isn’t shown because someone matches a persona. It’s shown because the system has detected behavior suggesting that person might soon need what the brand sells.

This means something subtle but important has happened in marketing strategy.

We still write plans that begin with the sentence: “Our target audience is…” But the systems delivering our campaigns increasingly operate on a different logic entirely. They respond to signals. And signals tell you not who someone is—but what they might be about to do.

The First Wave: Demographics

For most of the twentieth century marketing strategy began with demographic segmentation. Television advertising bought audiences such as “Women 25–54.” Print publications were segmented by income, gender or household composition and media buying decisions largely followed those categories.

Research published in the Journal of Advertising Research estimates that more than 70% of media planning decisions in the 1990s relied primarily on demographic segmentation. 

The system worked because media itself was segmented. If you bought ESPN you were buying men. If you bought a home magazine you were buying homeowners. It was tidy. It was measurable. It was also wildly imprecise.

Two people of the same age and income can have almost nothing in common besides the fact that they were born in the same decade. Demographics helped marketers find audiences. They did not necessarily help them understand people.

The Second Wave: Personas

Personas emerged as an attempt to fix that problem.

Instead of targeting “Men 35–50,” marketers created narrative archetypes describing motivations and lifestyle—urban professionals, ambitious founders, health-conscious parents.

Today 93% of companies report using buyer personas in their marketing strategy according to research from ITSMA and Cintell, and 56% of marketers say personas improve campaign effectiveness according to HubSpot’s State of Marketing report.

Personas improved how marketing teams think about messaging, storytelling and creative strategy. They remain particularly useful in areas such as influencer partnerships, experiential marketing and social storytelling where understanding motivations and identity still matters.

A good persona helps a creative team understand how a brand should sound, what it should say and where it should show up culturally. But personas describe who someone is. They don’t capture what someone is doing right now.

The Third Wave: Signals

Signals are behavioral clues that reveal curiosity, interest or emerging demand that are derived from search queries, browsing patterns, content engagement, product comparisons, location data, purchase history, etc. Instead of inferring behavior from identity, signals allow marketers to infer intent from behavior.

Research from Google Consumer Insights shows that 70% of consumers now use multiple channels during the path to purchase, while Think with Google reports that more than 60% of purchase journeys begin with online research rather than advertising exposure.

In other words, people often begin making decisions long before brands know they are in the market.

Consider travel. Someone searching for flights, comparing luggage sizes and reading packing guides is sending a pretty clear signal about what might be coming next. 

Travel platforms have learned to respond to those signals quickly. Booking.com, for example, runs more than 1,000 concurrent experiments across its platform, continuously adjusting recommendations based on search behavior, browsing patterns and booking signals. The system is responding not to a demographic profile but to behavior.

Signals and the Rise of AI Discovery

AI search and recommendation systems accelerate this shift. Algorithms interpret signals across thousands of behavioral variables simultaneously.

Salesforce’s State of the Connected Customer reports that nearly 60% of consumers now discover new brands through social feeds or algorithmic recommendations. Google research shows more than 40% of Gen Z consumers begin product discovery on TikTok or Instagram rather than traditional search engines.

These systems operate less like advertising channels and more like behavioral radar. When someone watches multiple running shoe reviews, reads marathon training articles and compares footwear specifications, platforms infer a high probability of interest in running gear.

Retailers that understand those signals can respond quickly. The sporting goods retailer Decathlon, which operates a digital ecosystem with more than 100 million active users globally, analyzes browsing behavior and content engagement to detect emerging sports interests before purchases occur.

Similarly, Zalando reports that recommendation systems influence more than 60% of transactions on its fashion platform, largely driven by behavioral signals such as browsing patterns and saved products.

Signals Across Entire Customer Ecosystems

Behavioral signals are now embedded across many digital ecosystems.

When someone uses IKEA’s online room planner, saves furniture combinations or browses design inspiration, those actions signal an upcoming home furnishing project. IKEA reports that more than 60% of customers begin their home furnishing journey online before visiting stores.

Design platforms see similar patterns. Canva, which now serves more than 170 million monthly users globally, tracks template usage, design behavior and editing activity to recommend new products and templates based on signals of creative intent.

Retail platforms respond to signals in real time as well. Sephora reports that more than 80% of online purchases involve personalized recommendations triggered by browsing behavior, product comparisons and engagement with reviews.

Even transportation platforms rely on behavioral signals. The Southeast Asian super-app Grab, serving more than 35 million monthly users, triggers offers and service recommendations based on location signals, purchase patterns and time-of-day behavior.

These systems operate less like traditional advertising campaigns and more like behavioral detection engines.

Why Signals Move Marketing Upstream

Signals often appear long before explicit purchase intent.

Someone researching kitchen renovations may browse design ideas months before contacting a contractor. A traveler may explore destinations weeks before booking a flight.

A widely cited consumer study from GE Capital Retail Bank found that 81% of shoppers conduct online research before making major purchases.

Brands that detect these early signals have an opportunity to influence decisions before the final purchase stage.

Plant-based food brand Oatly has leaned heavily into this dynamic by focusing on content engagement signals related to plant-based diets and recipe exploration, using those signals to introduce its products during early stages of consumer curiosity rather than waiting for explicit dairy-alternative searches.

The difference is subtle but important. Traditional marketing often waits for consumers to declare intent. Signal-driven marketing identifies curiosity earlier.

That is how brands enter the consideration set before competitors even know a customer exists.

Where Most Marketing Teams Actually Are

The reality is that marketing departments now operate across multiple targeting models simultaneously.

Demographics still dominate legacy media planning such as television and outdoor advertising. Personas still guide storytelling, influencer partnerships and experiential campaigns. Signals increasingly shape digital distribution. That shift is reinforced by how digital advertising is bought.

According to the Interactive Advertising Bureau, more than 70% of global digital advertising is now purchased programmatically, meaning algorithms use behavioral signals to determine which ads appear to which users.

Marketing strategy still begins with audience definitions. But the systems distributing those messages increasingly rely on signals.

The New First Question in Marketing

So if most marketing plans begin with: “Our target audience is…,” signals suggest a different starting point: “What behaviors suggest someone may soon need what we sell?”

Demographics helped marketers understand populations. Personas helped marketers understand people. Signals help marketers understand moments. And in an era increasingly shaped by AI search, algorithmic discovery and behavioral data, moments are where marketing influence increasingly lives.

Sources: Journal of Advertising Research, HubSpot State of Marketing, ITSMA / Cintell Persona Research, Google Consumer Insights, Think with Google Research, Salesforce State of the Connected Customer, PwC Global AI Survey, IAB Programmatic Advertising Report, GE Capital Retail Bank Consumer Study, Booking.com Engineering Blog, Sephora Innovation Reports, Decathlon Digital Ecosystem Data, Mercado Libre Investor Reports, Canva Company Data

The business landscape is constantly evolving, and so is your target audience. To stay relevant and competitive, it’s essential to continuously review and understand who your ideal customers are, what they desire and how to inspire them to become or remain loyal to your brand.

One of the main reasons for this periodic reassessment is the dynamic nature of market conditions and consumer behavior. Demographic shifts, economic changes, technological advancements, and cultural trends all influence who your customers are and what they need. For example, the rise of remote work has changed purchasing patterns for many industries, while the increasing awareness of sustainability has created new market segments focused on eco-friendly products.

I know it may be frightening to think about. But as these changes occur, your original target audience may no longer be the best fit for your products or services. This means your marketing strategies, which once yielded great results, might start to falter. By regularly reviewing your target audience, you can identify these shifts early and adjust your marketing strategies accordingly – before it’s too late.

Understanding Your Current Audience

To stay ahead in the competitive market, a thorough understanding of your audience is essential. Begin by analyzing demographic shifts within your customer base. Have your core customers aged or perhaps shifted geographically? These changes can significantly impact how you should communicate and what you should offer.

Next, evaluate whether your products or services still meet the evolving needs of your audience. Even the most beloved offerings can become outdated if they fail to adapt to changing consumer preferences. Assessing this alignment ensures that your business remains relevant and valuable to those you aim to serve.

A keen eye on competitor strategies is also crucial. Are your rivals attracting segments of the market you’ve overlooked? Understanding their tactics can provide insights into potential gaps in your own strategy and highlight opportunities for growth.

Finally, keep an eye on emerging demographics that show interest in your products or services. These could be younger consumers entering the market, different cultural groups, or entirely new segments driven by technological advancements or societal trends. Recognizing and catering to these emerging demographics can open up new avenues for expansion and innovation.

By regularly and systematically analyzing these aspects, you can refine your marketing approach to ensure it remains sharp, targeted, and effective. This isn’t just about keeping up with the market—it’s about leading it.

Rethinking Your Targeting Strategy

Crafting an accurate and dynamic buyer persona is crucial for effective marketing. A well-defined persona guides your messaging, ensuring it resonates with the right audience. But creating a buyer persona isn’t a one-time task; it requires regular updates.

Revisit your buyer personas every 4 to 6 months. Consumer behaviors and preferences evolve, and your personas should reflect these changes. Regular updates ensure your marketing efforts remain relevant and engaging.

Incorporate both qualitative and quantitative data to enhance your targeting. Qualitative data, like customer feedback and social media interactions, provides insights into motivations and preferences. Quantitative data, such as sales figures and web analytics, offers a broader view of trends and behaviors. Together, these data types create a comprehensive understanding of your audience, allowing for more precise and effective targeting.

By maintaining accurate, dynamic buyer personas and leveraging diverse data sources, you ensure your marketing strategy remains sharp and impactful. This proactive approach keeps your brand ahead of the curve, consistently engaging the right audience with the right message.

The Role of AI in Audience Insights

Of course, as in all things marketing, Artificial intelligence (AI) is now beginning to play a crucial role in this process.

AI’s prowess in analyzing massive datasets transforms audience segmentation from a guessing game into a precise science. By processing vast amounts of information, AI identifies patterns and nuances that traditional methods might miss. This leads to highly detailed audience segments, each with distinct characteristics and behaviors.

Behavioral and psychographic profiling take this a step further. AI delves into the motivations, preferences, and habits of consumers, creating profiles that go beyond basic demographics. This deep understanding enables marketers to craft messages that resonate on a personal level, increasing engagement and loyalty.

Consider Netflix, a brand that excels in AI-driven audience insights. Netflix uses AI to analyze viewing habits, preferences, and even the time of day users are most active. This data allows Netflix to recommend content tailored to individual tastes, creating a highly personalized user experience. This approach not only keeps viewers engaged but also drives continued subscription growth.

Another compelling example is Spotify. By leveraging AI to analyze listening habits, Spotify creates personalized playlists and recommendations that cater to individual musical tastes. This level of personalization has been instrumental in building a loyal user base that feels understood and valued.

Coca-Cola uses AI to analyze social media and customer feedback, identifying trends and preferences. This helps them refine their consumer personas and create personalized marketing campaigns…and even develop new products that cater to specific customer segments.

Personalization at scale is where AI truly shines. AI tailors ads and content to individual preferences, ensuring each interaction feels unique and relevant. This capability is crucial in today’s market, where consumers expect personalized experiences. By analyzing purchase history and browsing behavior, e-commerce brands like Amazon may recommends products that align with individual interests, driving higher conversion rates and customer satisfaction.

Future Trends in Audience Targeting

Emerging technologies are poised to revolutionize audience insights. Tools like augmented reality (AR) and virtual reality (VR) are creating new ways for brands to engage with consumers, providing immersive experiences that yield valuable data on user preferences and behaviors. Blockchain technology is also gaining traction, offering enhanced data security and transparency, which can improve the accuracy and trustworthiness of audience insights.

Looking ahead, we can expect AI to become even more sophisticated, with advancements in machine learning enabling more accurate predictions of consumer behavior. AI will likely integrate more deeply with other technologies, such as voice assistants and IoT devices, to gather real-time data and deliver hyper-personalized experiences.

To stay ahead, brands must prepare for shifts in audience demographics and behaviors. This involves staying informed about technological advancements and being agile in adopting new tools and strategies. For instance, as Gen Z becomes a dominant consumer group, understanding their digital habits and preferences will be crucial. Brands should also be ready to pivot their strategies in response to global events and changing societal values, which can significantly impact consumer behavior.

By always listening and observing, by staying adaptable and forward-thinking, brands can effectively navigate change and continue to engage their audiences in meaningful ways.