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As marketers, we have more data at our disposal than ever before.
So why are we still trying to guess who will be interested in our next big campaign?
All of this information is available – we just need to know what to look for.
Understanding what consumer data is, where it comes from, and how to use it strategically is the foundation of effective, personalized marketing.
Whether you’re trying to reach new audiences, improve your campaigns, or drive better ROI, data isn’t just helpful—it’s essential.
In this post, we’ll break down the different types of consumer marketing data, explore why it matters, and show you how the right data can transform your marketing efforts from guesswork to precision.
What is Consumer Data?
Consumer data includes insights on consumers’ demographics, interests, behaviors, and purchase intent. It’s used to help your brand reach the right people when it matters most.
Unlike customer data, which focuses on your EXISTING buyers, consumer audience data helps you understand and reach potential NEW customers—even if they haven’t interacted with your brand yet.
For example, a few months ago I started researching home office upgrades. I wasn’t buying anything yet, just browsing articles about ergonomic chairs and watching YouTube reviews. But within days, I started seeing ads for standing desks, monitor arms, and even wellness tips for remote workers across my social feeds and search results.
I hadn’t made a purchase, so I wasn’t part of anyone’s customer data, but my behavior and interests clearly put me in a “work-from-home ” audience. The brands targeting me were using consumer audience data to connect with people like me: not yet buyers but showing signals of intent.
So, what is the real purpose of consumer audience data? It helps you reach people before they’ve become customers (more importantly, before they become someone else’s customers) —with messaging that’s timely, relevant, and feels helpful instead of pushy.
Why Does Consumer Data Matter?
Consumer audience data gives marketers insights to help them think bigger. It’s not just about who’s already buying, it’s about who could be.
According to Exploding Topics, 89% of marketers see a positive ROI when they use personalization in their campaigns
It allows for precise targeting and smarter strategizing.
For example, let’s say you sell high-end fitness equipment. Your customer data tells you who has already purchased from you, but based on look-alike modeling, you use consumer audience data to target millennials within a certain income bracket and lifestyle interests who haven’t yet engaged with your brand.
That insight can fuel targeted prospecting campaigns that match your current customer profile.
If you’re only looking at who already buys from you, you might be leaving huge opportunities untapped.
5 Types of Consumer Data
Let’s identify 5 types of consumer audience data:
1. Demographic Data
Demographic data forms the backbone of consumer audience insights. It contains the most important details; such as age, gender, income, household size, and marital status. These variables are important insights for tailoring messaging, segmenting audiences, and predicting buying behaviors.
For example, understanding the age of your target audience helps you create relevant content and offers that resonate. Media habits, values, and purchasing priorities of Gen Z generally differ vastly from Baby Boomers. Age also helps you identify life stage moments, such as entering college or retirement, which often trigger distinct consumer needs.
The number of people in a household is another great example of a trait that influences buying behavior. A family of five will shop, eat, and entertain themselves differently than a single professional living alone. These differences affect product preferences, pricing sensitivity, and brand loyalty.
When you understand how demographics intersect, you can refine audience segmentation and craft intentional messages that resonate.
For me, my demographic data would indicate that I’m a male in my early 20s who recently graduated college. Receiving advertisements for apartments in my area has been common as I would be more inclined to purchase an apartment close to me based off my demographic data.
2. Psychographic Data
These are insights into attitudes, values, interests, and lifestyle.
Unlike demographic data, which tells you who people are, psychographic data helps explain why they behave the way they do. It’s the difference between knowing your audience is women aged 35-44, and knowing that these women prioritize sustainable living, enjoy outdoor activities, and value experiences over material goods.
Psychographic data can include:
- Personal values (e.g., environmentalism, family-first mindset, ambition)
- Hobbies and interests (e.g., marathon running, gourmet cooking, gaming)
- Lifestyle choices (e.g., frequent travelers, urban professionals, DIY enthusiasts)
- Opinions and beliefs (e.g., preference for brands supporting social causes)
Psychographic insights are critical for developing messaging and creativity that resonate on a deeper emotional level. Demographics alone rarely drive engagement—people want to feel understood.
For example, a travel brand targeting “millennials” could run generic beach vacation ads. But psychographic data might reveal a subset of this audience values authentic cultural experiences and sustainable tourism. Armed with that insight, marketers can craft campaigns showcasing local community tours or eco-friendly stays instead of standard resort packages.
3. Behavioral Data
Behavioral data is how people browse, buy, and engage. This includes website visits, purchase patterns, and content consumption.
It captures what people do rather than just who they are. It’s the digital trail consumers leave behind as they interact with brands across channels. This data is important because it provides real-time signals of interest, intent, and readiness to act.
Examples of behavioral data include:
- Website visits and page views
- Click-through rates on emails or ads
- Shopping cart activity and abandoned carts
- Video watch time or scroll depth on articles
- Past purchases and purchase frequency
- App usage patterns
- Social media interactions (likes, shares, comments)
Behavioral data allows you to segment audiences based on actions, not assumptions. Two people with the same demographic might behave completely differently. One might visit a website daily and engage deeply with content; another might bounce after a few seconds. Those differences can, and should, influence how you communicate with them.
For example, if someone repeatedly browses a specific product category but hasn’t purchased, you might serve them a personalized offer or reminder ad. Or, if a customer has consistently purchased eco-friendly products, you can highlight sustainable items in future recommendations.
I recently purchased a new computer, and I just so happened to be browsing desks because of that. A few days later, I went on Instagram and while scrolling I saw an ad for one of the desks that I was lingering on the most. A reminder like that is sometimes all it takes. And I did end up purchasing the desk.
4. Intent Data
Intent data signals that someone is actively researching a purchase or going through a life event.
Intent data gives marketers visibility into real-time signals that suggest a person or household is in-market or approaching a moment of need. Unlike demographic or psychographic data, intent data is dynamic. It reflects behavior happening now, making it especially valuable for time-sensitive campaigns.
Types of Intent Signals Include:
- Browsing product comparisons or reviews
- Repeated visits to category pages on eCommerce sites
- Engaging with content related to major life changes (e.g., moving, having a baby, starting a new job)
- Downloading whitepapers, signing up for webinars, or interacting with B2B solutions
- Abandoning a shopping cart
- Requesting quotes or demos
Intent data bridges the gap between awareness and action. It tells you who is actively considering a purchase.
For example, a home insurance brand could target audiences showing “new mover” intent signals; such as changing addresses, searching for moving services, or browsing mortgage content. These signals often occur before someone actively shops for insurance, giving marketers a valuable head start.
5. Location Data
Geographic insights are for local targeting or regional personalization. Location data tells you where people are, or where they’ve been. Whether you’re promoting a sale, opening a new location, or personalizing your message, location data connects your digital efforts to what’s happening in the real world.
Types of Location Data Include:
- GPS coordinates from mobile devices
- IP-based geolocation from web browsing
- Point-of-sale transaction data tied to store locations
- Check-ins or geotagged posts on social media
- Census and regional demographic data
Location data enables hyper-local targeting and personalization that can dramatically improve engagement and ROI.
For example, a fitness brand might notice high foot traffic near local hiking trails. Using location data, they could target ads for outdoor gear to people who frequently visit those areas, reaching consumers when they’re most receptive to that message.
I live in Florida, so brands wouldn’t advertise snow boots or snowplows to me. They would market products that make more sense, such as swim trunks and jet skis.
Conclusion
Consumer audience data is too valuable to not be used in your campaigns. It is how you reach potential new customers by understanding who they are, what they care about, and how to reach them.
Don’t rely on who you already know, expand to who you don’t.
Ready to take your marketing from guesswork to precision? Start exploring your consumer audience data today and see the difference it can make for your next campaign.
