Customer Research
Customer discovery that keeps up with how fast you ship
Step 1Gather real customer stories
Everything downstream depends on the quality of stories you capture. Not opinions, not feature requests, not survey scores. Real stories about specific experiences. The AI can synthesize, find patterns, and score opportunities, but only if you feed it real evidence from real customers.
Step 1.1
Get customers to tell stories, not opinions
Ask about specific recent experiences, not what they'd like in the future. "Tell me about the last time you..." produces evidence. "What would you want..." produces speculation the AI can't verify.
Step 1.2
Go where the stories already are
Support tickets, sales call recordings, NPS verbatims, community forums. You don't always need a scheduled interview. Real stories live in data you already have.
Step 1.3
Capture the details that matter downstream
The value is in specifics: what tools they used, where they got stuck, what they tried first, what they gave up on. These details become the raw material for experience maps, opportunity scoring, and competitive analysis. Summaries lose the signal.
Step 2Synthesize each story into a snapshot people will actually read
A 45-minute customer interview contains hours of potential synthesis work. Most teams skip it. The ones that don't produce a wall of text nobody reads. Revelica's AI synthesizes each conversation into a structured snapshot with the key evidence extracted and an experience map that shows the full customer journey. One page per story. Readable. Citable. Useful.
Step 2.1
Turn a conversation into a one-pager
The AI extracts key quotes, pain points, context, and opportunities from a raw transcript or notes. The output is structured evidence, not a summary. Every insight traces back to what the customer actually said.
Step 2.2
Map the experience as the customer told it
Each snapshot includes an experience map: the steps the customer took, the tools they used, where the friction was, and where they gave up. This is the story of how they get the job done today, from their perspective.
Step 2.3
Build a library of citable evidence
Each snapshot becomes a piece of evidence the AI can reference later. When it scores opportunities, builds segment-level maps, or recommends strategic actions, it cites the specific customer stories that support its reasoning.
Step 3Synthesize snapshots into customer segments with cross-cutting opportunities
One customer story is an anecdote. Ten stories with the same friction point is a pattern worth acting on. The AI synthesizes across your snapshot library to build customer segments defined by jobs to be done, each with a segment-level experience map and a count of opportunities identified across the evidence. Every claim cites the specific snapshots that support it.
Step 3.1
Build segments from evidence, not assumptions
Customer segments defined by jobs to be done, built from the bottom up. Each segment represents a group of customers with similar stories, not a demographic box you drew on a whiteboard.
Step 3.2
Create segment-level experience maps with citations
The segment experience map shows the shared journey across multiple customers, with citations back to the individual snapshots. Click any step to see the specific stories behind it. The evidence trail stays intact.
Step 3.3
Count opportunities across segments
Opportunities surface from the evidence and get counted across segments. Which pain points appear in every conversation? Which are isolated to one segment? Frequency and breadth across segments tells you where the strategic leverage is.
Step 4Decide which opportunity to pursue and start ideating solutions
This is where customer evidence meets strategy. You've identified opportunities across segments. Now you need to decide which one to pursue and start thinking about solutions. The right answer isn't always the most requested feature or the biggest pain point. It's the opportunity that, if solved, most advances your team's desired outcome. The AI evaluates each opportunity against your strategic context so you're making the decision with the full picture.
Step 4.1
Evaluate opportunities against your desired outcome
Your teamspace has a desired outcome set during the Strategy workflow. The AI scores each opportunity by how much solving it would advance that outcome. Strategic leverage, not just volume of complaints.
Step 4.2
Consider evidence strength, not just frequency
A loudly requested feature isn't always the right opportunity. The agent weighs evidence quality: how many segments, how deep the friction, how strong the citations. Thin evidence means the opportunity needs more research, not a commitment.
Step 4.3
Commit and start ideating solutions
Pick the opportunity with the most strategic leverage. With clear evidence and a strong connection to your desired outcome, brainstorm solutions. What are the different ways to solve this problem? The Validate workflow picks up from here to surface and test the assumptions in each solution.
Start validating
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