Product analytics

Interaction analytics is the analysis of full conversation transcripts to surface demand drivers, friction, and risk across every contact rather than a sampled few. Because it reads what customers actually said, it identifies emerging issues that structured ticket fields and survey scores are too coarse to reveal.

Voice of customer is the discipline of turning reviews, support tickets, surveys, and conversations into operational decisions. The distinguishing feature of a working programme is the closed loop: findings reach the team that can fix the cause, and the resulting change is measured against contact volume.

Topic clustering groups conversations by theme to size the drivers behind contact volume, without requiring an analyst to define categories in advance. Because clusters emerge from what customers actually said, the method surfaces new issues that a fixed intent taxonomy would file under a generic other.

A feature request is a customer-originated ask for new or changed product functionality. These arrive through support tickets, sales calls, app reviews, NPS comments, and community forums. Product teams rarely act on raw counts alone. Requests are weighted by frequency, revenue at risk, and the segment asking, since one enterprise account’s blocker can outweigh a hundred casual mentions. A stated request often masks the underlying job the customer is trying to get done, so teams probe past the literal ask before scoping work. At volume, near-duplicate requests worded differently need consolidation to avoid double-counting demand. OnClarity’s Voice of Customer classifies requests by topic across 100+ sources automatically. Booking.com uses this feed to spot bugs in feedback quickly and get the right engineers what they need to fix them.

A north star metric (NSM) is the single measure a company uses to capture the value its product delivers to customers. It is chosen because it predicts long-term revenue better than any other number available. A valid NSM passes three tests: it reflects real customer value, it moves before revenue does, and a team can actually influence it through their work. Airbnb tracks nights booked. Spotify tracks time spent listening. The common failure mode is choosing a volume metric like signups or pageviews, which can rise while retention quietly falls. Beneath the NSM sit input metrics, the specific actions that drive it. These give teams a measurable, causal chain from daily work to the number that matters.