I grew up in this industry during the early emergence of online video – when on the agency side we would spend many late nights in the office working to make the case to pull single-digit percentages of media budgets from the “TV” upfronts and move them into “Digital”.
We were doing this for obvious reasons – media consumption was evolving, eyeballs were sometimes shifting, and time spent was constantly increasing. The hurdle was that influencing business decisions with anecdotal data (ex. my cousin just got something called an iPad) didn’t happen easily when the status quo was so empirical (ex. weekday primetime on this network will deliver us 150 GRPs) *note: anyone “asking for a friend” can click here for a media math refresher from the Video Advertising Bureau
What allowed this trend, in those early days, to quickly evolve into the “who cares which screen it’s on” media world we now operate in, where consumers choose where, how and oftentimes, when, they consume entertainment – was measurement. “Digital” media, particularly peoples’ consumption of it, had to be measured, counted and reported in a way that made it comparable on paper to its traditional counterparts
The explosion of media and advertising measurement and the information that it unleashed resulted in a healthy amount of new data that the industry needed, and a whole lot of new data that subsets of the industry wanted.
As “currency measurement” became essential to all media platforms (as best it could) the new shiny object became using data, often collected from real-time or near real-time signals, for media targeting. Once upon a time a media buyer would have to buy that whole weekday primetime spot mentioned above, knowing that only 72% of the time it would be reaching their target audience. Now, you can serve an ad specifically to that cousin who just got something called the iPad because they had recently searched for “pre-owned midsize SUV’s” or spent five minutes researching new running shoes – indicating they may be in the market for either of those.
But in the seeking of instant gratification – targeting ads toward people who have recently taken some type of measurable action or behavior – the advertising community may be missing out on reaching the right people based on core marketing principles: focusing on consumers’ attitudes, opinions and interests to truly understand who they are (beyond age/gender), what they plan on doing next, and why. Media planning always starts this way, so why wouldn’t the media buy follow accordingly?
A Case For Attitudes Over Behaviors For Media Targeting
The concept of advertising is pretty straightforward – people seek entertainment and companies are happy to subsidize the cost of that entertainment by paying for a sliver of time to promote their products and services. That said, not all advertising experiences are created equal, and ensuring a positive outcome for brands starts with finding the right audience.
The debate between attitudinal and behavioral targeting continues to shape how brands connect with audiences. While behavioral inferences—based on observable actions like clicks, purchases, or website visits—have long been the default in digital targeting, they often fall short of capturing the why behind consumer behavior.
Enter attitudinal inferences: insights derived from consumers’ values, beliefs, motivations, and preferences. These are not just abstract ideas—they form the qualitative foundation of effective media planning and should be prioritized when targeting audiences too. Here’s why.
Media Planning Starts with Attitudes, Not Clicks
Ask any seasoned media planner where they begin their strategy, and you’ll hear the same thing: qualitative insights. Before a single impression is booked or a budget is allocated, planners seek to understand the mindset of the consumer. This process includes conducting ethnographies, analyzing focus groups, and studying psychographic data. Why? Because knowing what your audience cares about—what motivates their decisions, how they perceive your brand, what their aspirations are—is the cornerstone of relevance.
Behavioral data can tell you what someone did. Attitudinal data tells you why they did or will do it.
This is more than a philosophical difference. It’s a practical one. If a brand only targets people who have clicked on a shoe ad, they may miss the broader audience of people who love fashion, care about sustainability, or value comfort over brand prestige—key attitudinal drivers that influence purchase but don’t always leave a digital breadcrumb.
Behavioral Targeting Is Often Backward-Looking
Behavioral data is inherently historical. It is based on past actions, which may or may not be indicative of future behavior. Just because someone browsed for a hybrid SUV last month doesn’t mean they’re still in the market today. More importantly, it tells us little about how they make decisions—whether they prioritize environmental impact, cost savings, family safety, or brand loyalty.
Attitudinal data, by contrast, enables forward-looking targeting. It helps advertisers anticipate needs, aspirations, and preferences. It aligns messages with values, not just past behaviors. That’s how you reach consumers before they search, when influence is still possible.
Contextual Relevance > Transactional History
Media planning best practices emphasize contextual relevance—placing the right message in front of the right person at the right time. Attitudinal insights allow for more emotionally resonant, creative messaging because they reflect the consumer’s mindset. Behavioral targeting can feel clinical, impersonal, and even intrusive. Attitudinal targeting respects context and intent, leading to ads that fit the consumer’s current journey. This ensures the best user experience possible – which is the goal all along.
It’s (Now) More Scalable Than You Think
One common misconception is that attitudinal targeting is too niche or hard to scale. But with advancements in AI and psychographic modeling, it’s now possible to infer attitudes at scale using consumer survey panels. Media platforms are increasingly integrating attitudinal signals into their planning tools, allowing brands to reach segments like “eco-conscious consumers” or “experience-driven travelers” without relying solely on declared data. At Dynata, we’re excited to be pioneering ways to make these approaches easier for marketers every day, and we look forward to following along this never-ending journey to keep advertising a welcomed and relevant component to the media and entertainment landscape