Customer feedback analysis tools: a buyer's guide

Types of tool

Customer feedback analysis tools come in several types, usually defined by the feedback they were built for.

• Survey platforms with text analysis: strong on survey comments.
• Support and conversation analytics: strong on tickets, chats and calls.
• Review analytics: strong on public reviews from Google, app stores and review sites.
• General text analytics platforms: flexible, often need more setup.
• General AI chat tools: fast for one-off analysis, inconsistent over time.

Start from your main source

If most of your feedback is survey comments, a survey platform's analysis may be enough. If it is support conversations, conversation analytics. If you have many stores or an app with thousands of public reviews, review analytics is built for that. Many organisations end up with two tools for two sources, sharing one taxonomy.

Capabilities that matter

• Consistent classification against your own categories.
• Sentiment per topic, not per comment.
• Specific issues discovered within themes.
• Trends by location, channel and month.
• Evidence behind every number.
• Language coverage for your customers.
• Workflow: owners, actions and checks.

Comparing on your own data

1. Export three months of your feedback.
2. Write five questions you need answered.
3. Give both to each vendor.
4. Compare answers with what your team knows, and trace numbers to evidence.

Budgeting

• Licence: per seat, per location or per volume.
• Setup: taxonomy design, integrations, historic data.
• People: analyst and owner time each month.
• Change: training store teams to read and act on findings.

Red flags

• No way to see the feedback behind a number.
• Categories you cannot change.
• Results that shift when nothing in the feedback changed.
• Pricing that becomes unaffordable as you add locations.

Common questions

An example decision

A restaurant group has three feedback sources: 40,000 Google reviews a year across 120 outlets, 15,000 app reviews and a post-order survey with a comment box. Its survey platform's text analysis handles the comments adequately. Reviews are the largest and least used source, in four languages, and the operations team wants outlet comparisons with evidence. The group adds a review analytics tool on the same topic list as its survey categories, rather than replacing the survey platform.

Who should be involved

• CX or insights: owns the taxonomy and reporting.
• Operations: the main users of findings by store.
• Product: for app and digital feedback.
• IT: data access, single sign-on and integrations with CRM or ticketing.
• A store or regional manager: tests whether findings make sense on the ground.

Signs you have outgrown your current tool

• Analysts spend more time exporting and cleaning than analysing.
• Different teams report different numbers for the same topic.
• Feedback in some languages is ignored.
• Nobody can show the feedback behind a headline number.

Evaluating Falcon

If you are evaluating Falcon: it analyses Google Maps reviews of your stores and Google Play reviews of your app; classifies every review against a taxonomy built from your own reviews, with an exact supporting quote behind each label; finds and summarises specific issues; shows negative share by topic, store and month with small samples flagged; lets your team assign, draft and track replies, which are posted in Google Business Profile; and opens the reviews behind every number.