Customer Research & Voice of Customer Automation
Make every interview, review, and call transcript move the marketing program, with AI doing the synthesis at scale.
Most companies sit on a goldmine of customer language: calls, interviews, support tickets, reviews, churn notes. They never turn it into anything useful. The synthesis work is too slow to do by hand. I design and build voice-of-customer workflows that pull customer language out of every surface, synthesize it into themes, surface ICP signals, and feed the output into positioning, content, and sales enablement.
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What you get
Source Mapping
Every place customer language lives: calls, interviews, reviews, support, churn, win/loss, community. I map them and pick the highest-impact ones to instrument.
Ingestion and Synthesis Workflows
AI workflows that ingest transcripts, reviews, and notes and synthesize them into themes, language patterns, and ICP signals.
ICP and Positioning Feedback Loop
The synthesis output feeds positioning, ICP definition, and messaging work, so the marketing program is grounded in the language customers use.
Content and Sales Enablement Outputs
Customer language pulled into content briefs, sales decks, objection handling, and proof points, automatically.
What we put in place together
- Source map and instrumentation plan
- Ingestion and synthesis workflows (with prompt libraries)
- Theme and language libraries (refreshed on cadence)
- ICP and positioning feedback loop
- Content and sales enablement outputs
A marketing program grounded in the language customers use, refreshed continuously, instead of opinions and stale interview notes.
Book a Discovery CallQuestions buyers ask about Customer Research & Voice of Customer Automation
The objections I hear most often before a discovery call, answered up front.
A win/loss study is a snapshot. This is a continuous loop. The workflows refresh every week or month so the language stays current as the market moves.
Sales call recordings, customer interviews, support tickets, public reviews, churn notes, win/loss interviews, and community conversations. The mix is tuned to where your ICP talks.
I design the pipeline around your data classification policy, scrub PII where appropriate, and use enterprise-tier providers with no-training policies for the sensitive surfaces.
Yes. I feed researchers and PMs higher-quality, more frequent input so their qualitative work goes deeper, instead of replacing it.
First useful output usually inside 30 days as the workflows go live. The continuous loop and the impact on positioning and content keep building from there.