For marketers seeking true scale and high performance, success hinges on blending historical facts with future likelihoods. Let’s demystify predictive consumer intelligence starting with what most people already know: deterministic data. Deterministic data is built from observed, verified truths about individuals. Things like age, gender, address, purchase history, or survey responses. It’s easy to understand and trust.
Think of it like this: you know Tiffany Smith is a 36-year-old vegetarian who drives a Honda Civic and lives in Chicago. That’s helpful. But that kind of data only helps if you want to talk to Tiffany. What if you want to reach the thousands of people like her—the ones who share her values, behaviors, and brand affinities?
That’s where predictive data comes in. It uses machine learning to uncover hidden behavioral similarities across the full population. It tells you not just who someone is—but what they care about, why they act, and what they’re likely to do next.
Instead of marketing to just Tiffany, predictive intelligence lets you reach everyone who acts like Tiffany—and that means better scale, better outcomes, and smarter strategy.
Why predictive data ≠ guesswork
Predictive data is often misunderstood. Some assume that if it isn’t 1:1 truth, it must be unreliable. But that’s a myth. Here’s the reality: Predictive insight is built on real, validated truth sets. It doesn’t try to confirm identity—it’s designed to uncover patterns: motivations, preferences, intent, and values. It gives marketers statistically reliable, future-facing insight that drives performance across channels.
Yes, a predicted trait may not align with an individual’s literal truth in every case—and that’s expected. Predictive intelligence isn’t about knowing everything about someone. It’s about knowing enough about many to act with confidence and precision.
There’s still a place for deterministic data
Let’s be clear: deterministic data plays a vital role. When you need 1:1 certainty—like issuing a credit card, verifying someone’s identity, underwriting a mortgage, or managing compliance—deterministic data is essential.
But when it comes to modern marketing, those moments are the exception.
Most marketers today need:
- Scalability — to reach beyond known users
- Behavioral insight — to personalize to the why, not just the who
- Future focus — to act on what’s coming next, not what already happened
Deterministic data is excellent for grounding and identity resolution. Predictive intelligence is your edge for understanding and anticipating at the scale of millions.
Deterministic data alone isn’t enough anymore
Here’s where deterministic data starts to have tradeoffs when it comes to advanced marketing:
- It’s backward-looking — based on past actions, transactions, or information provided in the past, not what they are about now or intend to do next.
- It’s limited — you can only target the people you already know, not the millions you want to reach.
- It’s shallow — it misses values, intent, emotional drivers, and psychographics
And consumers? They’re not static. Their preferences, priorities, and purchase intent shift faster than deterministic datasets can keep up. Predictive intelligence gives you a living, breathing understanding of who your audience is becoming—not just who they were yesterday.
Predictive data isn’t “bad data”—it’s better data
Yes, predictive data is probabilistic. But it’s not a gamble—it’s a strategy. You don’t check the weather to get an exact temperature. You check it to decide what to wear or how to pack for an upcoming trip. Predictive data is the same: it helps you prepare smarter, personalize better, and perform with more agility.
At Resonate, we build predictive intelligence from the ground up using rAI, our deep learning predictive framework. We start with a foundation of consented deterministic insight, which rAI links and scales predictions to the addressable population. That foundation includes:
- Declared truth from our US Consumer Study, the largest study of its kind resulting in 15K+ attributes that are then weighted to US Census data to ensure representation of the population.
- Verified offline data We source offline deterministic data encompassing demographics, geographics, voter affiliation and history, financing, public record, employment, and more.
- Observed digital behavior And we also curate 30B+ daily online interactions and signals like encompassing 30B+ daily consented online interactions and signals like topics viewed online, IP and location data, engagement with websites and media
And we don’t just trust the output—we have rigorous structured validation processes:
- We constantly monitor and validate all incoming data to ensure you keep pace with fast- changing consumer behavior.
- We retrain and evaluate our models every six weeks using both machine learning benchmarks and human logic checks.
- And, we assess the demographics against a top three US credit bureau to ensure alignment to the population.
Not all modeled data is the same—the methodology is important to ensure quality and reliability at scale.
Methodology isn’t the only measure of predictive quality
When it comes to evaluating modeled data, methodology is critical—but it’s also not the whole story. Even the most advanced AI models can underdeliver if you don’t consider other critical factors impacting the real-world effectiveness of your predictive intelligence:
- Coverage or completeness of your predictive data
- How closely you can target individuals
Let’s break those down.
Reach is important—but so is coverage
When marketers evaluate data partners, match rate often gets the spotlight. But fill rate—the percentage of matched records that are actually populated with the attributes you need—is just as important, if not more so.
Two providers might both match 85% of your records, but if one only fills 40% of those with meaningful data while the other fills nearly all, the difference in downstream performance will be dramatic.
Fill rate is what determines the actionability of your data. High fill rates result in:
- Richer segmentation opportunities
- More complete personalization inputs
- Better-trained predictive models
- More accurate measurement and insight
It’s not just about how many IDs are recognized—it’s about how many profiles are usable. More complete profiles = more consistent marketing intelligence across your entire audience.
When comparing providers, marketers should ask:
- How many attributes are filled per matched record?
- How consistently are key traits like intent, values, and preferences populated?
- Are fill rates transparent and measurable across different IDs?
The more complete the file, the more confident your decisions—and the better your outcomes.
More right answers, more of the time
Another factor to consider is the first-party identifiers you can match on and the industry-wide tradeoff between reach and precision. When you prioritize higher match rates to maximize addressable audience size, some of that scale may come from resolving IDs at the household level instead of the individual.
This isn’t a flaw—it’s a standard, widely accepted practice across the industry.
Identifiers like IP address, postal address, or ZIP+4 frequently resolve at the household level. While they don’t offer 1:1 personal identity, they still provide highly directional value—especially in contexts like anonymous website traffic or data enrichment where individual-level identifiers aren’t always available.
If two adults share a household, and one is exploring home improvement content or comparing health insurance options, it’s reasonable to infer relevance for both. These matches often fuel strong performance when paired with predictive or behavioral insight.
What’s important is knowing what level of resolution you’re working with. When evaluating data providers, marketers should ask:
- What types of identifiers do you accept (e.g. HEM, MAID, IP, ZIP11)?
- Do they resolve to individuals or households?
- Is the resolution level made transparent in the matched output?
This clarity allows you to balance scale with signal strength and apply the right strategy for each match type—whether you’re activating media, building lookalike models, or analyzing audience behaviors.
While individual-level IDs offer maximum precision, household-level matches meaningfully extend reach while preserving relevance. Used strategically, they help marketers navigate today’s fragmented identity landscape without sacrificing impact.
Wrapping it up: ask smarter questions, get better data
As predictive data becomes more central to modern marketing, the smartest teams aren’t just asking what’s in the model—they’re asking:
- How often is the data refreshed?
- How much of my file will be fully populated?
- What types of identifiers are accepted?
- Do I know what level of resolution I’m working with?
It’s not enough to chase high match rates. It’s about balancing reach with fill, understanding how resolution impacts your strategy, and partnering with data providers who offer transparency, quality, and breadth.
Predictive data doesn’t replace your first-party data—it builds on it. It gives marketers the ability to scale insight, uncover intent, and act ahead of the curve. And in a privacy-first, signal-fractured, identity-constrained world, that forward-looking intelligence is no longer a nice-to-have—it’s a necessity.
Frequently Asked Questions
Is predictive data compliant with privacy standards?
Resonate is privacy-first by design. Our data is strictly developed in compliance with the ever-shifting US privacy laws. Resonate models use fully pseudonymous identifiers and ethically sourced behavioral data—so your teams can move fast without adding risk.
How “fresh” is predictive data?
Resonate’s predictive data is more forward-looking and intent focused than other providers. Additionally, our models are refreshed on an ongoing basis to ensure you keep pace with fast-changing consumer behavior, with formal ongoing retraining and validation cycles to maintain performance and data quality.
Does predictive replace deterministic?
No—these are different tools for different jobs. Use deterministic data when you need person-level certainty (e.g., mortgages, credit issuance, fraud prevention, or compliance workflows). Use predictive data when you need scale, depth, and forward-looking insight for marketing—targeting, personalization, modeling, and audience understanding across millions. The best outcomes come from layering predictive on a deterministic first-party identity spine: deterministic grounds who someone is; predictive reveals why they act and what’s next.
What’s the biggest mistake in vendor evaluation?
Treating match rate as the finish line. Prioritize depth and breadth of data for your specific need, data recency, fill rate, and ID transparency.
Ready to put better consumer data to work?
Schedule a consultation with a Resonate data expert now to learn more about unlocking the value of your first-party data with Resonate’s predictive intelligence.
