Intent Data Gap Audit
Use existing performance data to discover and source missing intent signals
- Difficulty
- Easy
- Time to result
- ~days to results
- Steps
- 6
- Confidence
- 97%
Connect an LLM to relevant sales and marketing records, then ask it to analyze the current personas, fields, activity, and outcomes for missing context that could improve performance. Review the suggested signals with human judgment, rejecting fields that are irrelevant, intrusive, unavailable, or unsupported by evidence. Prioritize a small set of gaps and run a separate research task to identify reliable sources or vendors for each one. Test enrichment on a limited sample before making a large purchase or operational commitment. Finally, compare whether the new fields improve targeting, scoring, campaign performance, or sales decisions. The sequence deliberately separates deciding what data is useful from deciding where to buy it, reducing the risk of adopting a vendor simply because its dataset is easy to obtain.
Origin
Extracted from Marketing Against The Grain when Kip and Kieran proposed a short action item using CRM connectors and deep research.
Core principles
- 01Existing sales and marketing data can reveal missing context
- 02Data needs should be defined before vendors are selected
- 03Research sources only after identifying useful fields
- 04Enrichment should improve a specific persona or result
How to run it
- 1
Connect existing records
Give the analysis system access to the relevant sales and marketing fields, activities, personas, and outcomes.
Pro tip Use a bounded, permissioned dataset that represents recent performance.
Watch out Do not expose unrelated sensitive data to the model.
- 2
Diagnose missing context
Ask which additional signals could improve persona enrichment, targeting, timing, or conversion results.
Pro tip Require the model to explain the expected decision value of each field.
Watch out Treat suggestions as hypotheses, not established causal drivers.
- 3
Prioritize the gaps
Rank proposed fields by likely predictive value, availability, legality, cost, and operational usefulness.
Pro tip Start with fields that change a concrete marketing or sales decision.
Watch out Avoid enriching data merely because it is commercially available.
- 4
Research sources
Run a separate deep-research project to find suitable first-party methods, public sources, APIs, or vendors for the selected fields.
Pro tip Compare coverage, refresh frequency, provenance, usage rights, and price.
Watch out A polished vendor claim is not proof of data accuracy.
- 5
Test the enrichment
Acquire or derive a small sample and verify accuracy, match rates, and workflow compatibility.
Pro tip Manually inspect representative records before automating.
Watch out Do not sign a large contract before validating the sample.
- 6
Measure improvement
Evaluate whether the enriched data improves prioritization, personalization, or downstream conversion.
Pro tip Maintain a control group where practical.
Watch out Do not retain fields that add cost without decision value.
In the wild
A B2B team connects its CRM to an LLM and asks which missing signals could improve its personas and results. The analysis identifies hiring activity and recent funding as promising gaps, after which a deep-research task compares sources and vendors for those fields.
→ The team obtains a shortlist of testable data sources without beginning from a vendor catalog.
Common mistakes
Starting with the vendor
Choosing a provider before defining the decision need can create a large dataset that does not improve performance.
Accepting every suggested field
LLM recommendations require review for relevance, privacy, feasibility, and actual predictive value.
Skipping sample validation
Coverage claims can conceal stale, inaccurate, or poorly matched records.
Is it for you?
Best for
It is best for teams beginning an intent-data program or unsure which enrichment fields would materially improve targeting.
Not ideal for
It is not ideal when source CRM data is unreliable, unlawfully collected, or too sparse to support a meaningful diagnosis.
From the transcript
“Well, at HubSpot, we have our connector with chat GPT, as well as we just launched Claude this past week.”
“And I think, Kieran, I would go in and I would do a prompt for Chat GPT or Cloud and say, look at my sales…”
“And then I'd have them have it do a deep research project of the best ways and vendors to get that data.”
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