Beyond Better Signals: The Next Evolution of Audience Intelligence

By Michael Rutzick, Audience Intelligence Partner at Allant

Amazon DSP demonstrates why better signals create better audiences. Choozle CEO Tina Starr put it well in a recent article: “Most advertisers don’t have an audience problem. They have a signal problem.” She’s right. Marketers have never had greater access to people, IDs, segments, platforms, and data, yet more access hasn’t automatically produced better targeting.

Amazon DSP is a strong proof point. Commerce activity, browsing, searching, streaming, purchasing, tells advertisers more about what someone may be preparing to do than a demographic characteristic alone. The shift Starr describes isn’t about having more audience segments; it’s about identifying which audiences show real indicators of future action.

That’s an important evolution. At Allant, we believe it’s also just the beginning.

For too long, reach has functioned as a proxy for success. It’s a vanity metric that’s easy to report and easy to buy against but disconnected from whether it actually moved the business. The real question was never how many people a campaign could reach. It’s which combination of signals shows someone is ready to act, and whether that connection can be proven all the way through to a measurable outcome. That’s the distinction that should be driving audience strategy now.

That’s where multi-source Precision Audiences become increasingly important.

Single-Source vs. Multi-Source Intelligence

Amazon’s commerce activity is genuinely valuable. Browsing, searching, and purchasing behavior can reveal what someone may be preparing to do in a way that demographics alone never could. But even the richest commerce data is still one signal type: behavior generated inside a single company’s own properties. It has no visibility into life events like a move or a new child, no visibility into property or household change, and no visibility into the financial indicators and offline behavior that shape many consumer decisions. That’s not a knock on any one platform. It’s simply the nature of relying on a single source, no matter how large.

Take the example Starr uses in her own piece: a woman in Denver researching swimwear in January. Viewed alone, that’s an odd data point. Viewed through a commerce lens, it starts to look like she’s planning a vacation, which is already a meaningful improvement over demographics alone. But it still leaves the advertiser guessing at the most important part: what kind of trip.

Layer in signals from other sources and the picture sharpens considerably. Recent transaction history might show spending consistent with an all-inclusive resort rather than a backpacking trip. Loyalty data might reveal a pattern of Caribbean travel rather than European. Household financial characteristics might indicate a family trip rather than a solo one. Location and seasonal patterns might suggest she books early or waits for last-minute deals. Individually, none of these signals says much. Together, they start to describe not just that she’s traveling, but where, how, and with whom, which is the difference between knowing someone is in-market and knowing which advertiser actually belongs in front of her: a luxury resort, a family cruise line, or a budget airline, rather than travel ads in general.

That’s what Allant means by finding consumers in motion, and it’s the idea Precision Audiences are built on. Allant’s AMP+ platform brings together demographic, lifestyle, transaction, life-event, property, identity, behavioral, digital-intent, location, and (when available) client customer data, evaluating them in combination to determine which characteristics and behaviors are most associated with the outcome a client wants to drive. A broad audience might tell you someone could be a customer, and a single behavioral signal might suggest they’re interested. It’s the combination that tells you someone is more likely to act now, and specifically enough to know which advertiser should be the one to reach them.

Precision Doesn’t Mean Smaller

A natural question follows once you can identify someone this specifically: does narrowing in on her actually shrink the pool, and cost scale? It doesn’t have to. Allant’s technology can evaluate matched consumers across roughly 2,000 available digital behavioral signals, then narrow to the 75 to 125 signals most associated with the outcome a campaign is built around. The point isn’t to find the smallest group of people who look exactly like her. It’s to find everyone who shares the combination of signals that made her worth reaching in the first place, and there are more of those people than a single source could ever surface on its own.

That’s the part single-source data misses. Across an analysis of six independent compiled sources, Allant found that roughly 63 percent of resolved individuals appeared in only one source. A targeting strategy built on a single dataset isn’t just choosing a source. It’s also choosing that source’s blind spots, and likely walking past a large share of the audience it’s actually looking for.

An Audience That Should Travel With the Marketer

Her decision doesn’t happen in one place. She daydreams about the trip scrolling social one evening. A few days later she reads a travel blog and sees a couple of ads for destinations she’s already considering. By the weekend, she’s googling the specific resort she has in mind. Three different environments, three different moments, and the same decision moving forward the whole time.

Most audience strategies treat those as three separate targeting problems, rebuilding the audience fresh in each channel. Allant’s approach is to build the audience once and carry it with her: the same understanding of who she is and what she’s ready to do, distributed to whichever channel she happens to be in, whether that’s programmatic, CTV, social, search, email, direct mail, or retail media. That consistency is what actually keeps a brand in front of her as the frontrunner, rather than one advertiser among several she saw once and forgot.

Getting Smarter With Every Campaign

She books the trip. The easy move is to go find more people who look exactly like her and start the process over. That’s not enough. The campaign itself is a source of new information: which signals actually showed up most often among the people who booked, which combinations predicted a real trip versus idle browsing, and which segments never converted despite looking promising on paper. That evidence gets applied to the next audience, not just the next lookalike.

That’s why Allant treats Precision Audiences as a closed-loop process: build, activate, analyze, refresh, and improve. Each trip booked, each response, each non-response sharpens the model for the next campaign. The audience doesn’t just get replaced. It gets smarter, and it keeps scale intact by continually finding the next real version of her rather than a smaller and smaller copy of the last one.

Where This Leaves Marketers

The industry has spent years chasing more data, more platforms, and more segments, and still ends up measuring the same old metric: reach. But reach was never the goal. It’s the easiest thing to report, not the thing that proves a campaign worked.

The real measure is whether a marketer can draw a straight line from a signal to an outcome: a resort booked, a flight purchased, a customer acquired. That’s a fundamentally different standard than how many people saw an ad, and it’s the one marketers should be holding audience strategy to going forward.

That’s the work in front of marketers now. Not collecting more signals for their own sake, but connecting the ones that matter into a single, coherent understanding of the consumer, one that follows her across channels instead of resetting every time she moves, and gets sharper with every campaign instead of starting over. Precision Audiences are built to do exactly that: replace reach as the metric that matters with outcomes as the standard that counts.

Allant: Precision Audiences. Marketing Outcomes.