It’s Tuesday morning. A research lead needs to answer what should be a basic audience question before she moves on to her next task, but it’s become a complicated process.
Specifically, she wants to know whether a new product has an audience beyond the core buyers. She’d look herself, but, as the research lead, she’s dependent on the syndicated team for data pulls. She doesn’t have direct access, so instead, she submitted a ticket.
While she waits, she tries a few other methods to get the information she needs. The CDP shows who’s already purchased, but it skews older and male, which isn’t useful for an audience expansion question. She checks the community panel data, but it’s two years old.
By Thursday, she has four outputs written in four different methodologies. None of them agree on who the audience is. None of them say anything about why this person would buy.
She’s worked hard, but she hasn’t gotten closer to the answer.
This isn’t a research, expertise, or motivation problem. It’s a data architecture issue.
How to Compress the Process from Discovery to Activation
No company starts out intending to build a six-tool research stack. Instead, each piece is added because it makes sense and solves something real. You need a data provider for psychographics. Then you add a syndicated service because you need category trends. A CDP gets thrown into the mix for first-party purchase data, and a panel is added for qualitative texture.
All of these tools, individually, work fine. The issue is that they don’t connect, resulting in a coordination cost that accumulates over time and results in extra manpower, data gaps, and even lost revenue.
Getting from a business question to an audience insight to activation requires routing through data science, submitting requests to a syndicated team, waiting on platform access you don’t personally have, and stitching together outputs that were never designed to talk to each other. The work is slow. The outputs don’t agree. And by the time the insight reaches the strategist who needs it, it is late and approximate.
There is a way to compress discovery, analysis, audience development, and activation from a months-long process to something faster. It requires you to go back and reexamine your data architecture.
Closing the Gap: How to Solve Your Data Fragmentation Issue
It’s not a new problem: Most brands and agencies aren’t short on data, especially first-party data. It’s critical to have this at your fingertips. Behaviors, transactions, and demographics are the first layer of intelligence that needs to be accessible across your technology stack.
What’s lacking is the ability for one person, like the research lead, to go in, ask a question her data should be able to answer, and get the insights, without the whole thing turning into a huge project.
For this, you need what’s called predictive consumer intelligence. It advances your understanding of consumers from what they’ve done to who they are right now — the feelings and values that motivate purchases, the intent signals you can’t see in the last transaction, the life-event shifts that point toward a future need. It fills in the “why” piece that’s missing from all the other types of data.
With predictive consumer intelligence, you’ll know whether a customer is ready to buy something new based on the motivational profile that connects that person’s behavior to their next likely action.
When you have a fragmented data ecosystem, this level of insight is impossible. For one thing, delays introduced by additional steps and stakeholders compromise the vitality of the data. Predictive consumer intelligence shows us just how quickly customer sentiment can change, and the manual, repeated steps in your activation workflow mean you might miss the moment. On top of that, the fragmentation itself makes it difficult to ensure you have the access you need to this intelligence in the first place.
There are two ways to approach these challenges and incorporate predictive consumer intelligence into your marketing strategy to reduce data fragmentation and gaps. The one you choose simply depends on how your business is structured, your needs, and the direction you want to take.
Solution 1: Closing Data Gaps Without Changing Your Tech Stack
The first option is to simply ensure that the right kind of predictive intelligence is available natively across the systems you already use.
If you’re satisfied with your current tech stack, consider data enrichment, which layers predictive consumer intelligence directly onto the first-party data you already have without requiring you to rip out or replace anything. The tools you rely on stay in place. What changes is the depth of what they know about each person in your database.
Where your existing data tells you what someone bought and when, enrichment adds the motivational layer underneath: what they value, what’s driving their next decision, and how likely they are to act. Two customers who look identical on purchase history can respond completely differently to the same campaign, and enrichment is what explains why. It closes the gap between the data you have and the understanding you actually need to build audiences, personalize messaging, and activate with confidence.
For teams already running on a warehouse-first infrastructure, modern enrichment options remove the friction that has historically made the process slow. Rather than uploading files, waiting on batch processing, and re-ingesting outputs outside your core environment, enrichment can now happen directly inside the workflows you already use — at the point of lead capture, record update, or any triggered event — so the intelligence is current, not weeks behind.
A large independent agency put this into practice by enriching their customer records with Resonate’s predictive consumer intelligence and integrating it into their analytics and segmentation workflows. The result was a 130% improvement in research efficiency, time that had previously gone to data wrangling shifted to actual analysis. The tech stack didn’t change. The quality of what it produced did.
Enrichment circumvents your fragmentation problem to put intelligence everywhere you need it. But it’s also possible to consolidate your tech stack to remove the friction. Let’s examine what that looks like in practice.
Solution 2: Upgrading Your Tech Stack to a Single Platform
If you’re frustrated with the limitations of your current tech stack and ready for a wholesale change, switching to a single platform that also offers you predictive consumer intelligence is the way to compress that months-long process we talked about earlier and to give you the one answer you need, fast.
When a single platform builds the audience, shows you where to reach them, surfaces what messaging is likely to resonate, and connects directly to activation, the workflow stops fragmenting at every seam. The need for handholding from your data science team at every step disappears. The ticket queue shrinks. The creative team and the media planners are working from the same data set, not reconciling outputs from three different vendors.
That is what a platform built around audience intelligence actually changes: not just the speed of insight, but the integrity of the work from the audience definition all the way through to the campaign.
One agency replaced their disconnected research stack with the Resonate Ignite Platform and cut time to insight in half while saving $400K in third-party data costs. The speed improvement was real. But the more durable change was that the insights driving their new business pitches were grounded in a consistent, individual-level data set rather than a patchwork of sources their team had to manually reconcile. That consistency shows in the work and in client confidence, and it compounds over time as the same predictive consumer intelligence that built the audience feeds ongoing optimization rather than starting from scratch each cycle.
Back to Tuesday
Let’s go back to the scenario at the beginning of this article. This time, the research lead has predictive consumer intelligence and the Resonate Ignite Platform. She wants to know whether a new product has an audience beyond core buyers. She opens the Ignite Platform and types in a prompt. The AI audience builder surfaces 38M individuals who match the profile she’s been trying to reach. She can see where they spend time, what motivates their purchases, and what their media consumption habits are.
It’s still Tuesday, but she has an answer today, instead of a half-answer on Thursday. The research lead has one story, built on one consistent data set, ready to hand to the creative team and the media planners. She didn’t need to submit a single ticket.
The Resonate Difference
Want to learn more about the power of Resonate’s predictive consumer intelligence, the Ignite Platform, or our data enrichment capabilities? Want to talk about how better data and a single platform will streamline your business? Schedule a consultation with a data expert today.
