Problem
More outreach created more chances for conversations, but more activity did not automatically make the company smarter.
Product management proof
Customer Intelligence And KPI Adoption
I designed discovery KPIs so Pier's customer conversations measured learning velocity instead of outreach volume.
What I owned
I defined customer-intelligence KPIs around deep discovery conversations, ICP refinement, pain-point validation, and budget-authority confirmation.
Result
The team had a clearer operating model for turning conversations into reusable product, GTM, and investor-facing learning.
Customer records and workflow details have been summarized for public use.
The situation
Pier had a discovery problem that looked like a productivity problem at first. More outreach created more chances for conversations, but more activity did not automatically make the company smarter.
The question became: what would count as evidence that we understood buyers, their pains, their objections, their budget authority, and their willingness to pay?
That question was the important move. A lead was not valuable because it added volume. It was valuable if it produced a conversation that could change our product, GTM, messaging, or investor-facing story.
The approach
I shifted the sprint goal from outreach volume to validated customer learning. The actual KPIs were concrete: 2-3 deep discovery conversations per week, one buyer-persona or ICP refinement cycle per week, an 8+/10 average pain-point validation score, and budget authority confirmed in every conversation.
Then I shaped the operating system around those metrics: conversation capture, automated extraction into HubSpot fields, a repository for pains, objections, and language patterns, and documented thresholds for how the learning would affect prioritization.
The metrics were concrete because the decisions were concrete: who is the buyer, what pain is real, who has budget authority, and whether the ICP is getting sharper or weaker.
What I built
- customer-discovery KPI definitions
- pain-point validation and budget-authority fields
- ICP refinement cycle logic
- transcript-to-CRM extraction requirements
- a learning repository for pains, objections, and buyer language
Why it matters
Pre-product-market-fit progress should mean learning velocity, not activity volume. The team needed evidence that could change product, GTM, messaging, or investor communication.
Result
Customer conversations became part of a reusable decision system. Lead sourcing served discovery quality instead of only increasing volume.
What I learned
Customer intelligence is useful when every conversation has a path back into a decision.