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Behavioral & Reported Data: When You Should Focus On Your Audience’s Attributes vs. Actions

by AnalyticsIQ, on November 15, 2022

This is a guest post contributed by predictive marketing data innovator AnalyticsIQ

Brands have a seemingly endless number of options when it comes to audience data attributes, especially via data collaboration platforms like Narrative. As a data innovator ourselves at AnalyticsIQ, not only do we deliver B2C data and B2B data, but within each of these respective databases, we focus on three primary categories:

  1. People
  2. Behaviors
  3. Predictors

Each of these data types plays an important role in identifying a target audience. But a question we hear often from marketers, data scientists, and partners is, “When is the right time to use these different types of data?”.  

So we’re here to share a quick guide of top use cases and times when it makes the most sense to use one, two, or all three of these types of data points. 


Consider the ‘People’ category to be the one-dimensional facts about an individual. This could be their age, income, or even where they live. They are core to understanding someone’s life stage and buying power. But it’s a bit like painting a picture in black and white.

Just think of yourself. I’m sure there is at least one friend your age that comes to mind who has very different wants, needs, interests, and motivations than you. Perhaps you’re a sneaker-head and don’t mind spending $500 on a pair of hard-to-find shoes. While your friend is more motivated to put that same $500 into their crypto account. So while People-focused attributes do a solid – and essential job – of drawing the outline of who someone is, layering in behaviors and predictors is key to developing the full color picture that will drive true results. 


As the old adage goes, “Actions speak louder than words.” No surprise that so many marketers turn to behavioral data. Behavioral data can be both historical and predictive. For instance, brands can utilize past behaviors like previous purchases to retarget users. They can also then use this past data, in combination with People attributes, to build predictive models and answer the question, “Who is most likely to take this same action in the future?”.

As in the case of People data, however, looking at behaviors only tells one part of the story. Someone may have visited the Range Rover website, potentially indicating interest. Does that necessarily mean they are a qualified potential buyer? Without applying People attributes, brands would not realize that the site visitor is actually an 18-year-old recent high school grad who loves cars but doesn’t have the means to afford a $100k SUV at this time.

Let’s flip the script. What if the site visitor was a qualified buyer? What other information could be helpful to move them along the customer journey? ‘Predictors’ around attitudes and motivations.


If people-data are the cake and behavioral data are the frosting on top, then predictors are the side of ice cream that makes for a complete experience. As we mentioned in the example above, by understanding what drives someone to take the actions they do, you can help move them through the journey by providing them with personalized products and experiences.

For example, if you knew that the individual interested in Range Rovers (behavior) was actually a qualified buyer (people) and was a risk-averse person (predictors), then you could ensure your message lined up to tout the vehicle’s safety features as well as the fact that financing options are the lowest they will be for the year, another safe bet for the risk-averse buyer.  Once again, however, there may be times when predictive attitude and motivation data can be powerful on its own, like when you’re selling luxury products or trying to drive donations, and must really understand the hearts of your audience. But when combined with People and Behavior data, attitudinal can be even more powerful.


Did you skim this article and just want to see the Cliff’s notes version? We’ve summarized these best practices in the simple chart below. Of course, there is no one-size-fits-all approach, but by letting this framework guide you, you’ll be in a position to know when it makes the most sense to use them on their own and also when to blend them together for an even more sophisticated, customized approach.

Data Type


Top Use Cases

Usage Details


Age, Gender, Marital Status, Income, Children, Employment

  • Mass media targeting with broad reach goals
  • Targeting commoditized, universal products (ex: toilet paper)
  • Building an audience on a small data budget
  • May be used alone to maintain scale but may not provide a competitive edge. 
  • More powerful in combination with Behaviors & Predictors.


Past Purchases, Lifestyle Interests, Social Media Usage, Health & Wellness

  • Digital retargeting
  • Identifying potential in-market shoppers
  • Planning cross-channel media mix
  • Building predictive models
  • When used alone, okay for smaller, less scalable executions.
  • More powerful when used in combination with People data.


Spontaneous vs Conscientious, Internal vs External Motivations, Future Actions & Behaviors

  • Reaching audience for emotional or highly personal products (non-profits, financial services, luxury goods)
  • Personalizing products and messaging to align
  • Least common to be used alone.
  • More often paired with other data so that messaging and promotional offers can be tailored based on motivations and attitudes.