WhyUser provides a pre‑launch stress test for B2B SaaS demand‑gen and marketing‑ops teams, simulating a buying committee built from real buyer reviews, calls, and community threads. In about 30 minutes, its adversarial buyer agents flag silent vetoes, missing proof, and bounce risk on ads, emails, or landing pages, delivering evidence‑backed recommendations that can be integrated via UI or API before spend.
Funding
Funding not disclosed
Founders
Product
Problem
Demand‑gen and marketing‑ops teams often launch B2B ad, email, or landing‑page campaigns without knowing how the full buying committee will react, leading to silent vetoes, missing proof points, and wasted spend that are only discovered after the budget is burned.
Solution
WhyUser adds a 30‑minute pre‑launch stress test that simulates a complete buying committee built from real buyer language found in reviews, call transcripts, and community discussions. Hundreds of adversarial buyer agents, each representing a specific role and mood, evaluate the campaign asset and surface the persona that would veto, the evidence that is absent, and the risk of bounce. Every finding is linked to its source evidence, producing a hypothesis report that can be forwarded to stakeholders for quick remediation. The test runs either in‑app or headlessly via API, fitting into the existing plan‑build‑review‑launch workflow without requiring additional data preparation. By exposing hidden objections before spend, teams can adjust copy, proof, or targeting to improve campaign performance and reduce wasted budget.
Target Audience
Primary customers are demand‑generation and marketing‑operations leaders at B2B SaaS companies who design and launch paid ad, email, or landing‑page campaigns.
Features
- Simulation of up to 300 adversarial buyer agents across five committee roles, each derived from actual buyer reviews, calls, and community threads
- Automatic identification of silent vetoes, missing proof points, and bounce risk with citations to the underlying buyer evidence
- 30‑minute headless API integration that accepts a URL and returns a conflict‑graph report without manual prompt engineering
- Deterministic, element‑level re‑run capability that isolates the impact of specific fixes while preserving other agent responses
- Evidence tracker that logs each hypothesis and its outcome for continuous accuracy improvement
- Conflict graph visualization showing which role blocks the campaign and why, enabling targeted remediation