Audience Evidence Buckets
Complete an audience profile with behavioral, institutional, and direct evidence
- Difficulty
- Advanced
- Time to result
- ~months to results
- Steps
- 6
- Confidence
- 99%
This research model starts with three explicit buckets: unstructured customer communications, organizational collateral, and fresh AI-assisted interviews. Discovery conversations and emails reveal why people entered the business and what pain points they expressed. Product-marketing materials and sales decks capture customer research already embedded in the organization. Structured interviews add current, purpose-built evidence and can include screen-based walkthroughs so the researcher observes confusing words, weak statistics, browsing behavior, and format preferences. A supplementary public-data layer gathers natural customer language from forums, social platforms, and model-accessible sources. AI then consolidates the mixed evidence into patterns that can update the audience profile and its operational content rules.
Origin
Extracted from Marketing Against The Grain when the hosts outlined three audience-research buckets and then added behavioral walkthroughs and publicly available data.
Core principles
- 01Use multiple evidence types to reduce blind spots
- 02Capture why customers enter the business
- 03Treat internal collateral as accumulated customer research
- 04Collect fresh evidence through direct interviews
- 05Observe behavior and language, not just stated opinions
- 06Supplement internal evidence with public customer conversations
How to run it
- 1
Collect unstructured communications
Gather the discovery portions of calls, relevant emails, and other customer communications that reveal entry triggers, pains, and desired outcomes.
Pro tip Prioritize early-stage conversations where customers explain the situation in their own words.
Watch out Apply consent, privacy, access-control, and retention requirements before processing communications.
- 2
Gather institutional research
Collect product-marketing collateral, persona studies, sales decks, and other materials created from prior customer research.
Pro tip Record the date, market segment, and methodology behind each source.
Watch out Internal collateral may repeat assumptions rather than independent evidence.
- 3
Conduct fresh interviews
Send a structured interviewer or AI voice agent to representative target personas with a clear research objective and adaptive questions.
Pro tip Pilot the interview guide with humans before scaling automated sessions.
Watch out An automated interviewer can lead participants or miss emotional and contextual cues.
- 4
Observe real interactions
Ask participants to walk through content, pages, or product flows on screen so the research captures behavior and granular reactions.
Pro tip Ask participants to think aloud while avoiding corrective guidance.
Watch out Stated preferences can conflict with observed behavior; preserve both signals.
- 5
Mine public conversations
Collect relevant language and recurring concerns from forums, Reddit, X, and other places where target customers naturally gather.
Pro tip Separate first-person customer evidence from commentary by vendors or spectators.
Watch out Public-platform users may not represent the broader customer population.
- 6
Synthesize recurring patterns
Consolidate the evidence, identify repeated needs and language, note contradictions, and feed supported findings into the audience guide.
Pro tip Track each major audience claim back to more than one evidence source where possible.
Watch out Do not let the AI erase disagreements or minority patterns during summarization.
In the wild
A team combines discovery-call excerpts about content bottlenecks, existing sales and persona decks, interviews with target revenue leaders, screen recordings of those leaders reviewing a report, and public forum conversations. AI groups repeated pains, trusted metrics, confusing terms, and preferred formats while preserving links to the evidence.
→ The audience guide reflects expressed needs, observed behavior, internal learning, and natural public language.
Common mistakes
Treating three buckets as three documents
Each bucket is an evidence class that may require many representative sources, not a token file added to a prompt.
Interviewing without observation
Conversation alone can miss confusing words, page behavior, formatting needs, and reactions visible during a screen walkthrough.
Scraping without representation checks
Large volumes of public data can amplify loud but unrepresentative groups unless sources are mapped to the intended persona.
Is it for you?
Best for
Teams seeking a comprehensive audience model for content, product messaging, sales, or AI personalization.
Not ideal for
Very early products that cannot yet access representative customers, internal research, or meaningful public discussion.
From the transcript
“So there's three buckets, right? I would categorize them this way.”
“The second one is just all of the collateral you have, right?”
“The third one is the most interesting one, right? Wouldn't you actually have an AI agent that's stood up like an avatar and actually can…”
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