Customer feedback analysis examples from retail, restaurants, hotels and apps

How to read these examples

Every customer feedback analysis example below follows the same shape: a business question, the feedback used, the analysis, what it found and what happened next. They are illustrative scenarios built to show the method, not case studies of named companies, and the numbers are examples. The steps are what transfer to your own data.

1. Fashion retail: why is one region's rating falling?

• Question: ratings at 14 stores in one region fell over a quarter. Why?
• Data: 2,300 Google Maps reviews from those stores and 40 comparable stores elsewhere.
• Analysis: classify mentions by theme; compare negative share by theme against the other stores; look at the trend.
• Finding: product and staff themes were unchanged. Trial-room complaints had doubled, concentrated on weekends, after a refit reduced the number of trial rooms.
• Action: weekend trial-room staffing and a queue system; trial-room complaints fell back over the next two months.

2. Jewellery: what drives five-star reviews?

• Question: what do customers at the best-rated stores praise that others do not?
• Data: positive mentions from the top and bottom quarter of stores by negative share.
• Analysis: compare positive share by sub-theme between the two groups.
• Finding: both groups praised designs equally. Top stores were far more often praised for patient help with selection and for explaining purity and pricing clearly.
• Action: a training module on consultative selling, piloted at the weaker stores.

3. Restaurant chain: delivery or dine-in?

• Question: is a falling app rating about food or about delivery?
• Data: app reviews and Google Maps reviews over six months.
• Analysis: classify both; separate food quality, packaging and delivery; compare channels.
• Finding: food-quality mentions were stable in both channels. App reviews showed rising complaints about missing items and cold food on long deliveries.
• Action: packing checklist and delivery-radius changes, owned by operations, not the kitchen.

4. Hotel group: check-in or rooms?

• Question: which part of the stay costs us most reviews?
• Data: Google reviews for 22 hotels over a year.
• Analysis: negative volume and negative share by sub-theme; split by weekday and weekend.
• Finding: room comfort was mostly positive. Check-in waits on Friday evenings were the largest single source of negative mentions.
• Action: an extra front-desk shift on Fridays and pre-arrival online check-in.

5. Shopping app: did the last release break something?

• Question: the Play Store rating dipped after a release. Is it the release?
• Data: Play Store reviews for four weeks before and after.
• Analysis: classify by issue; compare before and after; check the app version mentioned or recorded.
• Finding: a new complaint, payment failing with one wallet, appeared only after the release.
• Action: a hotfix within a week; the complaint disappeared from new reviews.

What the examples have in common

• A specific question, not "what do customers think?".
• Classification to specific issues, not broad categories.
• A comparison: other stores, other channels, before and after.
• Reading the reviews behind the number before deciding.
• An owner and a follow-up to check the change worked.

Running your own analysis

To turn one of these examples into your own, write the business question first, in one sentence, and decide what comparison will answer it: other stores, another channel, or before and after. Then gather only the feedback that comparison needs. Most analyses go wrong at the start, by collecting everything and hoping a finding appears.

• Question: what decision will this analysis inform?
• Comparison: against what will you judge the result?
• Measure: which topic, at what level, with what sample?
• Evidence: which reviews will you show?
• Owner: who will act on it, and when will you check?

What not to conclude

• That a store is bad because its rating is lower, without checking sample size and peer group.
• That a fix worked because the rating went up, without checking the issue itself.
• That an issue is everywhere because it is in the overall numbers; it may be a few stores.
• That customers do not care about something because they rarely mention it. Absence of complaints can mean it works.

Doing this with Falcon

Falcon does this for your Google Maps and Google Play reviews. Every review is classified into themes, sub-themes and issues from your own taxonomy, with the sentiment of each mention; specific issues are found by clustering what customers wrote, each with a short summary; and every count opens the reviews behind it, with the supporting quote highlighted.