30% increase in qualified leads with conversational AI

IMPACT

  • 30% increase in qualified leads vs. traditional gated content

  • 50% improvement in completion rate (8% → 12%)

  • 33% reduction in cost per lead ($180 → $120)

  • 85% data quality rate (vs. 60% baseline)

Became HPE's most efficient early-funnel channel. The framework became the foundation for bot deployments across other business units.

Conversational AI named Hugo, provided Hewlett Packer 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 Packer 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

I sequenced this to de-risk the AI investment and generate training data from real usage.

PUSHBACK

  • Sales: Wanted all data upfront. I ran a pilot proving 60% more completions with progressive capture—reframed as "more qualified leads" not "slower capture."

  • Engineering: Wanted sophisticated NLP first. I argued we needed to understand pacing before building complexity. Release 1 would generate training data for Release 2.

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.

OPERATIONAL IMPACT

Built a custom CMS and analytics dashboard, reducing iteration cycles from 2 weeks to 2 days. 

COLLABORATORS

  • Product Owners

  • Engineers and QA

  • Client Partners

  • Advertising agency

  • Sales and Marketing