User Research Synthesis AI: Edison for B2B SaaS Product…

Most product orgs ship the wrong things 40% of the time. Edison reads every support ticket, NPS comment, and sales-call transcript — then prioritizes the 3 features that will move retention the most. For User Research Synthesis, that means turning a swamp of raw user data into a crystal-clear, ranked roadmap — without a single manual tag or spreadsheet.

The User Research Synthesis problem most teams have

Manual user research synthesis is a time-tax on your best people. Product managers spend 12 hours per week just reading and tagging feedback — that’s 624 hours a year per PM. For a team of three PMs, that’s $187,200 in salary burned on clerical work. Even then, 68% of user insights never make it into a decision document. The result? Teams launch features that only 23% of users actually wanted, while the top three requests sit ignored for quarters at a time.

How Edison owns User Research Synthesis end-to-end

Edison doesn’t just summarize — he owns the full synthesis lifecycle. Here’s how:

• User-feedback synthesis — Edison ingests every channel: support tickets, NPS verbatims, sales call transcripts, and customer interview recordings. He tags each piece of feedback by intent, sentiment, and urgency, then groups them into coherent themes.
• Feature impact scoring — Each theme gets a score based on how many users mentioned it, the revenue tied to those accounts, and the correlation with churn risk. Edison surfaces the top 3 features that will move retention — not the loudest stakeholder’s pet project.
• Roadmap prioritization — Edison drafts a ranked roadmap with confidence intervals. He automatically updates it as new data flows in, so your team never works from a stale list.

Edison works 24/7. He never skips a ticket. And he speaks the language of product discovery — not buzzwords.

A concrete Edison workflow

Scenario: Acme SaaS (500 customers, $8M ARR) wants to reduce churn from 6.5% to 4.0%.

BEFORE: The PM team manually reviews 1,200 monthly support tickets and 80 NPS comments. They run 3 customer interviews per month. The synthesis takes 10 days — by then, the insights are cold. The team bets on a “quick-win” UI refresh. Churn stays flat.

Edison’s actions:

1. Ingests all 1,200 tickets, 80 NPS comments, and 15 recent sales call transcripts in 12 minutes.
2. Identifies that 41% of churned users cited “onboarding complexity” as the primary reason. Only 8% mentioned the UI.
3. Scores a feature called “guided onboarding wizard” at a 0.87 retention impact score — highest in the backlog.
4. Drafts a roadmap: Build onboarding wizard (P0), improve in-app search (P1), add CSV export (P2).
5. Proposes an A/B test: new onboarding flow vs. existing. Expected lift: 1.8% reduction in 30-day churn.

AFTER: Acme builds the wizard in 3 sprints. Churn drops to 4.2% within 60 days. Retained revenue: $184,000 per year. Total Edison time invested: 12 minutes of ingestion, plus daily 2-minute updates.

Why Edison wins vs. hiring

Hiring a human VP of Product costs $220,000–$350,000 per year, plus 8–12 weeks ramp time. They take vacation (3–4 weeks/year), get sick, and can leave at any moment — 22% annual turnover rate in VP Product roles. Even a great VP can only read ~40% of user feedback in a given week.

Edison costs a fraction of that. He’s on day one from minute one. No ramp, no PTO, no attrition. He reads 100% of feedback, every day. The best setup? A human VP who uses Edison as their synthesis engine — freeing them to do the strategic thinking only people can do.

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