Conversational AI named Hugo provided Hewlett Packard Enterprise a new method of engaging with potential clients.
The bot used natural language to share information about 12 industry topics, such as security, quantum computing, artificial intelligence, etc.
Situation
Hewlett Packard Enterprise's gated content had a fatal flaw: 8% conversion rate and 40% fake data. At $180 per qualified lead, HPE was paying 2.5x the industry benchmark for low-quality leads.
Execution
- Release 1: Designed content architecture connecting 40+ enterprise topics with intelligent bridging. Mapped 150+ interaction flows. Interviewed 6 decision-makers to establish baseline metrics.
- Release 2: Redesigned for conversational AI with progressive capture—users consumed 3-4 pieces before sharing basic info, additional data only after continued engagement. This eliminated the "now we're selling" moment that triggered fake data.
Strategic bet
Instead of optimizing forms, I proposed flipping the model: give value first, capture data progressively. Users would share real information in exchange for genuine value rather than to bypass a gate.
Roadmap & stakeholder alignment
- Release 1 (3 months): Scripted bot to validate that users would engage conversationally
- Release 2 (6 months): AI chat with progressive lead capture to prove conversion mechanics
User research
I wrote the research plan and interviewed 6 decision-makers to evaluate Hugo’s premise, branding, and interaction design.
Participants overwhelmingly remembered the brand and sponsor. However, the data capture was not effective. I used the feedback to adjust the pacing of lead-generation data capture.