If your intent feeds are full but qualified pipeline isn’t moving, the constraint probably isn’t “more data.” It’s that nobody can explain—on one page—which signals matter, how they combine, and where they get activated.

If your intent feeds are full but qualified pipeline isn’t moving, the constraint probably isn’t “more data.” It’s that nobody can explain—on one page—which signals matter, how they combine, and where they get activated.

That gap is showing up in the numbers. In a Bombora webinar recap, 72% of sophisticated B2B marketers reported layering two to five audience data types most or all of the time. Yet the same recap frames the real problem as clarity: not a lack of signals, but confusion about which ones to trust, how to weight them, and how to activate them across channels.

Here’s the uncomfortable part: stacking signals can make teams feel more precise while getting less incremental. More knobs, fewer answers.

If you only change one thing, change this: treat your targeting like a recipe with explicit ingredients, weights, activation rules, and a measurement plan. Not an “audience.” Not a dashboard screenshot. A recipe you can run, audit, and improve.

Why “data recipes” matter in 2026 (and why they’re easy to get wrong)

Buyers are doing more of the work without vendors. The research brief cites that up to 75% of early-stage evaluations can happen without vendor interaction. That’s a brutal constraint for pipeline teams: the buying committee is forming opinions while marketing is still arguing about which intent topic list to use.

So teams reach for more signals—intent, review-site behavior, partner audiences, first-party engagement, AI scoring. The intent is rational. The failure mode is also predictable: every new source arrives with its own definition of “in-market,” and nobody reconciles them.

In the Bombora session, Kristy Derby-Strauss (VP of Omnichannel at Just Global) put it plainly:

“Access to data is not the problem. It’s understanding how to make the most of the signals that we have.”

That line is the nut graf for 2026. Privacy constraints and fragmented journeys aren’t going away, and measurement is still a pain point (the research brief notes 25% of marketers cite ROI as a top barrier in the context of scattered data and visibility challenges). If the recipe isn’t explicit, attribution becomes vibes.

The primary tactic: build one “Two-Signal Recipe” and force an overlap readout

This piece sticks to one move because that’s what teams can actually ship: build a two-signal recipe (not five), then run overlap math and a holdout-based measurement plan. Add complexity later—only if the readout earns it.

Bombora’s recap gives a clean reason to start here. In one example shared, only 25% of accounts overlapped between Bombora Curated Ecosystem Audiences (partner-based audiences, including signals from platforms like G2) and intent-based audiences. That’s not a rounding error. It suggests either (a) real, untapped in-market pockets, or (b) mismatched definitions of “in-market” across sources.

Both possibilities are useful. But only if the team treats overlap as a diagnostic, not a victory lap.

Step 1: Define the outcome in pipeline terms (before picking signals)

Most “recipes” fail because the outcome is vague. “More engagement.” “More awareness.” That’s how teams end up optimizing clicks while Sales complains about junk.

Pick one pipeline-adjacent outcome for the next 30 days. Examples that map cleanly to measurement:

Christine Li (VP, Global Partnerships and Data Solutions at G2) described why signal choice should follow the buyer’s research stage: intent data like Bombora Company Surge® reflects open-web topic research, while review-site behaviors can indicate evaluation depth (category browsing versus comparisons, reviews, alternatives). Different stage. Different job.

Step 2: Pick exactly two signals with different “time horizons”

Good recipes mix signals that answer different questions:

Why two? Because once you go past two, you can’t explain performance changes without lying to yourself. Directional attribution is hard enough already.

Step 3: Run overlap math and decide what the non-overlap means

Build three cohorts:

That 25% overlap example from Bombora is the pattern interrupt: most teams assume the overlap is huge. It often isn’t. The non-overlap is where the opportunity (or the trap) lives.

Here’s the operational interpretation:

Activation: “meet buyers where decisions are actually happening”

A recipe that can’t be activated is just segmentation theater. Kevin Mallon (Head of Third-Party Partnerships at Reddit) framed the activation problem like this:

“The opportunity isn’t just to add more data. It’s figuring out how to layer those signals and meet buyers where decisions are actually happening.”

In practice, that means the recipe has to turn into addressable audiences in the channels where research behavior shows up. The Bombora recap describes Curated Ecosystem Audiences being deployable across 100+ destinations (examples listed include The Trade Desk, LiveRamp, Google DV360, and Reddit). The specific platform matters less than the rule: the same cohort definitions must travel across channels, or measurement collapses.

One more constraint: buyers self-educate. The research brief also cites that 70% of buying journeys are pre-sales and require strong content. So activation can’t just be “show ads.” It needs proof-heavy assets mapped to stage (category explainer versus competitive comparison versus implementation detail).

Run it this week: a 14-day Two-Signal Recipe test (with a real stop-loss)

Setup (Day 0–2)

Budget range: whatever your team considers “testable.” The key is consistency across cohorts, not hero spend. Directional, not definitive.

Launch (Day 3)

Readout (Day 10–14)

What to measure (and what not to over-interpret): clicks and conversions can be directional, but don’t claim causality from platform dashboards alone. The holdout is the point. Use it to estimate incrementality, even if the answer is messy.

The trade-off: this approach can reduce volume before it improves quality. That’s normal. It’s also why teams need Sales alignment on the handoff—otherwise the organization panics and resets to broad targeting.

When this is wrong: if first-party data is thin or CRM hygiene is poor, layering third-party signals can produce false confidence. Fix the foundation, then add ingredients.

Kicker: the recipe is the governance

Bombora describes its Data Cooperative as analyzing billions of monthly consumption events across thousands of B2B sources and supporting 18,000+ intent topics. The raw material is not the bottleneck in 2026. Not even close.

The bottleneck is whether a team can write down—clearly—what the signals mean, how they combine, where they run, and how they’ll be judged. In the Bombora webinar recap, Kristy Derby-Strauss warned against chaos testing:

“You can’t launch a thousand new things at once. You need to set up tests so you can actually compare tactics.”

That’s the quiet promise of “data recipes.” Not more data. More discipline. And discipline is what turns a pile of signals into pipeline.