The most useful review is the one that names what they left.

Over 20,000 competitor reviews, across eight platforms. More than 1,900 of them name the product the reviewer switched away from, and many give the reason. That is the closest thing to a win-loss interview that exists in public data.

20,000+
Competitor reviews tracked
8
Review platforms
1,900+
Reviews naming a product switched away from
150+
Industries represented

Who their customers left, and why.

A reviewer explaining what they moved off is telling you which displacement argument worked on them. Aggregate a few hundred of those and you have the objections that actually shift a decision — sourced, dated, and attributable to a named platform rather than to a hunch.

Switching mentions · a competitor's review corpus
Switched away from
Incumbent suite
Reason given
Price rose at renewal, usage had not
Switched away from
Point tool
Reason given
Support response time on a production issue
Switched away from
Legacy platform
Reason given
Could not integrate with the rest of the stack
In the product, each row carries the reviewer's role, company size, industry and country, and links back to the review it came from.
Fig. 1 — Illustrative. Real rows name the platform; these deliberately do not.

We do not publish a sentiment score, and here is why.

Public review platforms default their listings to their highest-rated reviews. Any corpus collected from them — ours included — skews positive as a matter of structure, not of product quality. An aggregate sentiment number built on that would be a confident, misleading figure, and putting one on a dashboard would make it look like a measurement.

So we report themes, complaint categories and movement instead. Those survive the bias: a theme rising month over month is meaningful even when the absolute level is flattering, because the flattery is roughly constant.

Complaints kept apart, not averaged together.

Reviews are flagged by the kind of complaint they carry. Blend them into one score and a pricing objection gets cancelled out by a compliment about the interface — which is exactly the sale you were about to lose.

Price complaints
A pricing objection, stated as such.
500+
Bug reports
Something is broken, and someone wrote it down.
1,700+
Feature requests
The gap the market keeps asking them to close.
2,000+

The same competitor reads differently by segment.

Every review carries the reviewer's role, company size, industry and country, so you can cut the corpus down to the buyer you are actually up against instead of the average of everyone.

Reviewer role

The complaint an admin files is not the complaint a practitioner files. Both are in there, tagged.

Company size

A tool that delights a ten-person team can be the same tool that a five-hundred-person team calls unusable.

Industry

Across more than 150 industries, so you can read a competitor inside the vertical you actually sell into.

Country

Where a frustration is concentrated is often a support-coverage or localisation story, not a product one.

Movement over level

Because the absolute level is biased upward, the reading that matters is the direction. A support theme climbing across consecutive weeks tells you something is degrading, whatever the headline rating says. A price-complaint theme falling away after a repackaging tells you the repackaging worked.

Eight platforms, public reviews only.

G2TrustpilotGoogleCapterraBBBGoogle PlayClutchApp Store

Everything here comes from reviews that anyone can read. Nothing behind a login or a paywall, nothing scraped from a private community. And it is not a substitute for running your own win-loss programme — talking to the buyers who chose against you will always tell you things a public review cannot.

Review themes feed the customer-evidence stream in Your Position and the customer-intelligence section of every battlecard. The full collection map lives in data sources.

Reviews and sentiment: common questions

Why does IndustryLens not publish a sentiment score?

Public review platforms default their listings to their highest-rated reviews, so any corpus collected from them — ours included — skews positive as a matter of structure, not of product quality. An aggregate sentiment number built on that would be a confident, misleading figure. IndustryLens reports themes, complaint categories and movement instead, because those survive the bias: a theme rising month over month is meaningful even when the absolute level is flattering.

What is switching data in competitor reviews?

Switching data is the set of reviews that name the product the reviewer moved away from. More than 1,900 of the 20,000+ competitor reviews IndustryLens tracks name a product switched away from, and many give the reason. Aggregate a few hundred of those and you have the objections that actually shift a decision — sourced, dated and attributable to a named platform rather than to a hunch.

Which review platforms does IndustryLens track?

Eight platforms: G2, Trustpilot, Google, Capterra, BBB, Google Play, Clutch and the App Store. Everything collected comes from reviews that anyone can read — nothing behind a login or a paywall, and nothing scraped from a private community.

How is review mining different from social media competitor analysis?

Social media competitor analysis reads what a competitor and its audience post on social platforms. This page is about a different surface: the eight public review platforms — G2, Trustpilot, Google, Capterra, BBB, Google Play, Clutch and the App Store — where a buyer writes the product up after living with it. Nothing on this page collects social posts, and reviews are not a stand-in for them. The two sit alongside each other as competitor-analysis surfaces: social carries what a vendor says and how it lands, reviews carry what its customers say once the sale is done. IndustryLens collects social separately, and the full collection map is in data sources.

How are competitor complaints categorised?

Reviews are flagged by the kind of complaint they carry — price complaints, bug reports and feature requests are kept apart rather than averaged together. Blend them into one score and a pricing objection gets cancelled out by a compliment about the interface, which is exactly the sale you were about to lose.

Can competitor reviews be filtered by segment?

Yes. Every review carries the reviewer’s role, company size, industry and country, across more than 150 industries, so the corpus can be cut down to the buyer you are actually up against instead of the average of everyone. The same competitor frequently reads differently by segment: a tool that delights a ten-person team can be the one a five-hundred-person team calls unusable.

Read the reviews your competitors hope nobody aggregates.

Add the companies you sell against and we start collecting, tagging and tracking their public reviews.