Sentiment analysis for reviews across many locations

The challenge

Sentiment analysis for reviews at one store is straightforward. Across hundreds, the hard parts are consistency, fairness and getting the right detail to the right people. Without a shared approach, every region reports differently and nothing can be compared.

One taxonomy

Use the same themes and sub-themes for every location, built from reviews across the network. Local differences show up as differences in topic sentiment, not in different categories.

Store-level measures

• Negative share per topic, per store, per month.
• Mention counts beside every share.
• Change since last period.
• Strengths: topics with high positive share.

Rolling up

• Region: are problems local or regional?
• Format: do mall stores differ from high-street stores?
• Network: what is changing everywhere?

Issues that rise everywhere usually have central causes; issues at a few stores usually have local ones.

Fair comparison

• Peer groups by format and location type.
• The same window for all.
• Small samples flagged, not ranked.

Views for each level

• Leadership: network trends and decisions needed.
• Theme owners: their topic across stores.
• Regional managers: their stores against peers.
• Store managers: their own reviews and top issues.

An example

A chain of 250 stores finds network billing sentiment steady, but the regional roll-up shows one region worsening while others improve. Within that region, ten stores account for most of the change, all on a new payment terminal. The fix goes to the terminal supplier, not to store teams.

Common mistakes

• Different categories per region.
• Ranking stores with very different volumes.
• Network averages only.
• No evidence for store teams.

Common questions

Checklist

• One taxonomy everywhere.
• Store-level topic sentiment.
• Regional and format roll-ups.
• Peer comparisons with sample flags.
• Views per level.

In short

Use one taxonomy, measure topic sentiment per store, roll up to regions and the network, compare fairly, and give each level the view and evidence it needs.

Cadence by level

Store managers look at new reviews weekly and their monthly topic view. Regional managers review their stores monthly against peers. Theme owners review their topics monthly across stores. Leadership reviews network trends and decisions monthly or quarterly. Matching cadence to level keeps everyone looking at the right detail.

A second example

A pharmacy chain finds staff-friendliness sentiment highest in its smaller neighbourhood stores and lowest in large flagship stores. Reading the reviews, flagship customers mention not being able to find anyone to ask. The fix is a floor greeter at peak hours, copied from what the neighbourhood stores do naturally.

Keeping it consistent

Name one owner for the taxonomy and the method, record changes with dates, and check a sample monthly across regions and languages. Consistency is what lets a regional manager trust that a store's number means the same as another's.

Doing this with Falcon

Falcon measures sentiment for every theme a review mentions rather than one score per review, keeps the exact words that support each label, and reports negative share by topic, store and month with small samples flagged and unclear texts kept visible. Every number opens the reviews behind it.