Micro-Audience Intent and Fit Scorecard
Rank audience clusters by signal prevalence and customer-size fit
- Difficulty
- Easy
- Time to result
- ~days to results
- Steps
- 5
- Confidence
- 97%
This scorecard evaluates a prospective micro-audience on two separate axes. Intent reflects the prevalence of a newly observed signal: when more qualified companies independently mention the same metric, the cluster receives a higher intent rating. Fit measures alignment with the desired customer profile, demonstrated in the episode through employee count and SMB or mid-market tiers. Keeping the axes separate prevents a frequent targeting error: a highly active market signal may come from companies the product cannot serve, while an ideal company profile may show no evidence of a current priority. The scorecard guides campaign sequencing, with high-intent, high-fit clusters receiving attention first and weaker clusters remaining candidates for monitoring, further research, or lower-cost tests.
Origin
Extracted from Marketing Against The Grain as the scoring mechanism used to rank KPI-based micro-audience clusters.
Core principles
- 01Evaluate buying signals separately from customer fit.
- 02Use repeated signals across companies as evidence of stronger intent.
- 03Define fit with explicit ICP attributes.
- 04Prioritize clusters that are strong on both dimensions.
- 05Keep lower-intent clusters available for monitoring rather than treating every signal equally.
How to run it
- 1
Define the intent evidence
Choose the repeated observable signal that qualifies companies for the cluster and establish how occurrences will be counted.
Pro tip Count only independently verified, currently relevant company signals.
Watch out Duplicate job listings or copied descriptions can inflate apparent intent.
- 2
Set intent thresholds
Map signal prevalence to consistent tiers, such as five or more qualifying companies for a high-intent group.
Pro tip Calibrate thresholds to market size and the rarity of the signal.
Watch out A universal threshold may distort results across very different markets.
- 3
Define fit criteria
Select explicit ICP attributes such as employee count, market tier, industry, geography, or technical requirements.
Pro tip Use exclusion flags alongside positive fit criteria.
Watch out Do not let a convenient proxy such as headcount stand in for every aspect of fit.
- 4
Score each cluster
Assign and display intent and fit independently so the reason for prioritization remains visible.
Pro tip Attach supporting evidence to each score.
Watch out Avoid collapsing the dimensions too early into an opaque composite number.
- 5
Choose an action
Prioritize high-intent, high-fit audiences; research ambiguous clusters; monitor low-intent clusters; and reject poor-fit groups.
Pro tip Use campaign results to recalibrate the scorecard over time.
Watch out The scorecard ranks hypotheses and does not guarantee sales.
In the wild
Three mid-market companies mention lead prioritization and pipeline velocity in recent job advertisements. The cluster receives an intent rating of three out of five because only three companies support the signal, while its mid-market employee profile supplies the fit assessment.
→ The campaign team can see both the strength of the emerging signal and the cluster's alignment with the target market.
A marketer defines five independently matching companies as the minimum for the top intent tier. A cluster with six strong-fit companies is prioritized over a cluster supported by only two postings.
→ Campaign selection follows a repeatable rule instead of subjective enthusiasm.
Common mistakes
Conflating intent with fit
A company can strongly express a need while remaining unsuitable for the product, or fit the ICP without showing a current need.
Counting duplicate evidence
Repeated copies of the same posting should not be treated as independent company signals.
Using unexplained scores
Scores lose decision value when reviewers cannot see the thresholds and evidence behind them.
Is it for you?
Best for
Teams comparing several evidence-backed audience clusters for limited campaign capacity or budget.
Not ideal for
Situations where cluster evidence is too sparse or inconsistently collected to support meaningful comparison.
From the transcript
“And then I have a simple score that is an intent and fit score.”
“And so if five or more companies all have the same metrics, they're in my high-tier group, high intent group, because we found a new…”
“And the fit is basically, you know, are they SB at mid-market? It's just employee count.”
From the episode
How I Turned Perplexity Labs into a Marketing Machine