App store review analysis

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.

Sentiment analysis scores the emotional tone of a conversation to flag risk and measure experience without waiting for a survey response. Applied across every contact rather than a sampled few, it covers the large majority of customers who never complete a satisfaction survey at all.

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.

Root cause analysis is the practice of tracing a symptom, such as a spike in complaints, back to the underlying process, policy, or product defect that produced it. The alternative, resolving each ticket in isolation, never stops the symptom from recurring. Established methods include 5 Whys (repeated questioning to reach the originating cause), fishbone (Ishikawa) diagrams (mapping possible causes by category), and Pareto analysis (isolating the small number of causes driving most of the volume). In CX, this starts with clustering tickets by theme, then tracing the largest theme to its source. OnClarity’s Voice of Customer automates this by surfacing root causes directly from feedback. Grubhub uses it to turn feedback into insights that shape its roadmap. The real test of success is recurrence: a fix that addresses the root cause should stop the issue from reappearing.

Unstructured data is information with no predefined schema: call recordings, chat transcripts, email bodies, open-ended survey comments, app reviews, and social posts. It contrasts with structured fields like ticket category, CSAT rating, or handle time, which record that something happened but not why. In CX, the diagnostic detail lives in the free text. A ticket disposition coded “billing” only becomes useful once the transcript shows customers describing a confusing invoice layout. Voice of customer analytics depends on reading this text at scale. OnClarity’s Voice of Customer does this for Grubhub, unifying diner feedback into one view. Before analysis, unstructured text typically needs data cleansing to remove duplicates and standardize formatting.