"Why does the outlet in Ankara score worse than the mall store in İzmir?" comes up at every regional meeting and is usually answered with anecdotes. Store comparison ties the question to data: it lines up the stores, cities or regions you choose over the same period on the same metrics and shows where each stands against the brand average. This post covers which metrics the comparison screen in BranchVoice offers and how it is used in a regional review.
Why store comparison deserves its own screen
The store list shows every branch on its own; the dashboard shows the chain as a whole. The gap between them is the question "how do these three stores stand relative to each other". Sorting the list by rating answers it only partly, because it ignores review volume, the period and what the reviews actually say. Ranking a store with a 4.8 from five reviews next to one with a 4.3 from three hundred reviews in the same column is misleading.
The comparison screen fixes this in three ways: every group is restricted to the same period, every metric is shown together with its volume, and AI metrics derived from review text sit next to the star rating.
What you can compare with what
There are three comparison modes:
- Store: up to four stores side by side. Typical uses are similar-format branches in the same city, or a newly opened store against a mature one.
- City: pooled values for all stores in the cities you pick, for city-level marketing or logistics decisions.
- Region: Marmara against the Aegean against Central Anatolia, for regional managers comparing with each other.
The period can be the last 30 days, 90 days, one year or all history. Alongside each group the screen returns a brand baseline in the same shape, so "is the Aegean region doing well" becomes "is the Aegean region doing well compared with the brand overall".
The metrics that are compared
Metrics come from two layers. The first is platform data, computed only from sources that carry a rating (Google Maps, Yandex Maps): average rating, negative rate (2 stars and below), response rate, star distribution and a six-month star trend.
The second layer comes from the AI analysis and also covers sources without ratings:
- Experience score (0–100): derived from the sentiment of the review text; when it disagrees with the stars, this is the real signal.
- Positive and negative content rates, high-risk rate, critical and urgent review counts.
- Churn-intent and recommend-intent rates, plus the share of reviews that mention staff or contain a suggestion.
- Emotion distribution (anger, disappointment, satisfaction and so on) and the topic breakdown: review count, negative count and share per topic.
- Platform breakdown: which sources the group's reviews come from.
The topic breakdown is the most explanatory part of the comparison. Two stores can have the same experience score, but if "staff attitude" dominates in one and "stock and sizing" in the other, the action to take is entirely different. See analysis groups and topic taxonomy for how topics are defined.
When stars and content disagree
The view that provokes the most debate is a star rating and an experience score pulling apart at the same store. A high rating with a low experience score usually means text-free 5-star reviews are in the majority while the customers who do write are complaining. The reverse can mean a historically low rating is recovering under new management. The six-month star and experience trends let you tell those two stories apart.
The comparison uses genuine customer reviews only: lead records captured from social media and duplicate reviews repeated across platforms are excluded.
A workflow for the regional meeting
In practice a regional manager works through this sequence before the meeting:
| Step | Screen | Question asked |
|---|---|---|
| 1 | Region comparison (90 days) | Where does my region stand against the brand? |
| 2 | Store comparison (worst two and best two branches) | Does the gap come from stars or from content? |
| 3 | Topic breakdown | Which topic explains the gap? |
| 4 | Store detail | What changed at this store over six months? |
The store detail page continues the comparison: city and brand benchmarks for the chosen period, the six-month trend and the AI metrics appear on one page. From there you can pull the latest reviews with "sync now" or drill into the store's review list.
If a gap revealed by the comparison persists, turn it into a monitoring rule: define a 7-day negative-rate threshold for the lagging store and follow it with critical review alerts.
Next steps
Store comparison turns "which store is good" into "which store is good on which topic, compared with whom". Once your stores are connected the comparison screen works without extra setup. To see your own regions side by side, request a demo.