Novel-KPI Micro-Audience Hunt
Find emerging buyer KPIs in job ads and cluster companies around shared priorities
- Difficulty
- Moderate
- Time to result
- ~weeks to results
- Steps
- 6
- Confidence
- 99%
The Novel-KPI Hunt converts recent hiring language into micro-audience signals. It first parses the ICP for buyer roles, category terms, market tier, differentiators, and baseline success metrics. It then searches recent job advertisements from relevant companies hiring those buyer roles. Instead of rediscovering familiar metrics, the method extracts accountability phrases absent from the baseline. Companies mentioning the same new KPI are clustered together, turning a repeated emerging responsibility into a potential audience. Each cluster becomes a card with a memorable name, supporting signal, shared pain, product hook, campaign suggestion, and fit and intent ratings. The method is valuable because job advertisements reveal what organizations currently expect buyers to improve, giving marketers a more timely basis for tailored positioning than a static persona alone.
Origin
Extracted from Marketing Against The Grain, where the host calls this research pattern a 'hunt' and demonstrates it with HubSpot buyer job advertisements.
Core principles
- 01Use baseline KPIs to identify what is already known.
- 02Search specifically for accountability metrics absent from the baseline.
- 03Treat repeated new metrics as evidence of an emerging audience need.
- 04Cluster companies by the metric they share.
- 05Translate research into a usable signal, pain, hook, and campaign.
How to run it
- 1
Parse the ICP
Extract baseline KPIs, buyer roles, category terms, market tier, and differentiators from the structured customer profile.
Pro tip Store each field separately so it can serve as a search filter or comparison set.
Watch out Missing baseline KPIs will cause familiar metrics to be misclassified as novel.
- 2
Find relevant hiring companies
Search recent job advertisements for companies in the target category that are hiring roles associated with the product's buyers.
Pro tip Require a clear role and company match before extracting metrics.
Watch out A job title alone may not indicate that the employee would influence the purchase.
- 3
Extract novel KPI phrases
Identify measurable responsibilities in the postings and remove phrases already represented in the baseline KPI set.
Pro tip Normalize minor wording variations before deciding that two metrics are different.
Watch out Do not mistake generic duties for measurable accountability signals.
- 4
Cluster by shared KPI
Group companies that mention the same novel metric and preserve the source evidence for each member.
Pro tip Split a cluster when similar language points to materially different business outcomes.
Watch out Loose semantic clustering can produce an audience with no coherent need.
- 5
Build the audience card
Give the cluster a catchy name and document its signal, pain, product hook, suggested campaign, intent, and fit.
Pro tip Make the hook explain how the product influences the KPI rather than merely repeating it.
Watch out Omit clusters where the product-to-KPI connection is speculative.
- 6
Validate and activate
Review the sources and product relevance, then pass credible cards into campaign development.
Pro tip Test the strongest cluster with a small campaign before expanding the hunt.
Watch out Public job ads indicate organizational priorities, not guaranteed purchasing authority.
In the wild
The research finds several SMB and mid-market companies whose recent roles mention lead qualification and conversion optimization. Those companies are clustered as Conversion Rate Maximizers, with a common pain and a campaign hook tied to improving conversion performance.
→ A newly observed KPI becomes the organizing signal for a more specific campaign audience.
The same hunt identifies companies discussing attribution-related responsibilities and groups them into a separate audience rather than merging them with pipeline-velocity or conversion clusters.
→ Distinct metrics produce distinct audience cards and marketing approaches.
Common mistakes
Hunting baseline metrics
The distinctive value comes from discovering responsibilities not already captured in the ICP's known KPI list.
Grouping unrelated metrics
Companies should share a genuinely common accountability signal, not merely belong to the same broad category.
Ignoring product relevance
An emerging KPI is not useful if the product cannot credibly affect it.
Is it for you?
Best for
B2B products whose buyers' changing responsibilities are visible in public job advertisements.
Not ideal for
Markets where relevant employers rarely publish detailed roles or where job descriptions use generic boilerplate.
From the transcript
“But the cool thing it's doing to create the micro audience is it's looking for KPI phrases that are specifically not in the baseline KPIs.”
“What I'm going to do is cluster companies by the metric.”
“And I want you to split them into groups by the mention of that new KPI.”
From the episode
How I Turned Perplexity Labs into a Marketing Machine