The reputation of a chain with hundreds of stores is not written in one place. In the same week a branch's rating slips on Google Maps, a returns problem becomes a thread on Şikayetvar, a campaign is picked apart on Ekşi Sözlük and a customer on X complains naming the store. A team watching these sources separately is both late and blind to the same problem repeating across them. This multi-platform reputation management guide walks through the model that ties five platforms into one flow: collection, AI classification, alerts, reporting and lead capture.
Why multi-platform reputation management needs a single flow
Each platform speaks a different language and sends a different signal. Map platforms carry a star rating and a store listing, and the rating is on the shop front. Complaint sites attract structural problems: returns, product quality, customer service. Forums and social media reveal brand perception, campaign reactions and, sometimes, the first sign of a crisis.
The rule of a single flow is simple: whatever its source, every review is linked to the same store model, classified with the same questions and reported with the same metric definitions. A "checkout queue" complaint about the mall store in İzmir lands on the same store scorecard whether it was written on Google, Şikayetvar or X.
One flow in five steps
Step 1: Collecting reviews from five platforms
BranchVoice connects five sources and treats each according to its nature. Two of them need a word of introduction for readers outside Turkey: Şikayetvar is the country's main consumer complaint site, and Ekşi Sözlük is a long-running collaborative forum where brands are discussed under topic headings.
| Platform | How reviews are linked | Key signal |
|---|---|---|
| Google Maps | Deterministically via the store listing | Star rating, owner reply |
| Yandex Maps | Deterministically via the store listing | Star rating, second map source |
| Şikayetvar | Brand level, AI moves to the store | Structural complaints, tags |
| Ekşi Sözlük | Brand level, AI moves to the store | Brand perception, threads |
| X | Brand level, AI moves to the store | Crisis signal, leads |
Setup starts by importing the store list; Google and Yandex listings are searched, candidates confirmed and historical reviews fetched, as described in importing your store list. From then on scheduled sync adds new reviews regularly and a failing source shows up on the data health strip. For platform-specific setup see Google Maps review management, Yandex Maps reviews, Şikayetvar monitoring and Ekşi Sözlük brand monitoring.
Step 2: AI classification and store matching
Every collected review is classified in a single AI call. The core questions are the same for all reviews: sentiment, a 0 to 4 risk score, response urgency, churn and recommendation intent, responsible unit, whether a suggestion is made, whether an employee is named and whether a competitor is mentioned. On top come topics specific to your sector and brand. You define your own topics through analysis groups; AI review classification explains the question logic.
For non-map sources the second job is store matching. Şikayetvar, Ekşi Sözlük and X reviews start in a virtual "General" store; when the AI finds a mall name, district or store alias in the text it moves the review to the real branch. If the match is not confident the review goes to a manual matching queue. The mechanism is covered in matching reviews to stores.
Step 3: Alert rules and anomaly detection
Classified reviews are the raw material of the alert engine. Alert rules are defined at store, region or brand scope and watch two kinds of metric: star-based ones (1-star count, negative rate, average rating, Google store rating, age of the oldest unanswered negative) and content-based ones (experience score, critical review count, urgent unanswered count, churn rate, the share of a specific topic). A condition runs over a window of 6 hours to 30 days; when the threshold is crossed an alert opens, an in-app notification appears and, if the rule names recipients, an e-mail goes out. Rule design is covered in critical review alerts.
Deviations that rules cannot anticipate are caught by anomaly detection: a volume spike against a store's own baseline, a sudden jump in a topic's share, a 7-day rating falling below the 90-day average, or suspicious reviews clustering in a short period. Problems are flagged even where no threshold is defined; see review anomaly detection.
Step 4: Dashboard, reports and the weekly digest
Daily follow-up happens on the dashboard: a five-card scorecard, a coverage strip, rule-based highlights, the topic impact matrix, a store leaderboard and operational indicators.
The management rhythm is set by reports. Scheduled reports are generated as PDF and Excel at store, region or brand scope, e-mailed to recipients and kept in an archive; the executive, regional and store templates serve different levels of the organisation. The weekly digest e-mail that arrives every Monday delivers the last seven days' scorecard and notable changes to the whole team. See automated PDF and Excel reports and the weekly digest email.
Step 5: From reviews to leads
The side of reputation management usually forgotten is demand. Posts on X that express a need in your product category without naming your brand are collected in a lead pass separate from the review pass, filtered by an AI gate and scored 0 to 100 on purchase intent. Leads are tiered hot, warm and cold and tracked in the Lead Center through new, contacted, qualified and converted statuses. The flow is described in capturing leads from social media.
A rollout roadmap
For a chain with hundreds of stores we suggest this order:
- Import the store list and connect Google. It is the highest-volume, most visible source.
- Review the analysis groups. Keep the sector defaults and add three to five brand-specific topics. Any new topic can be backfilled over historical reviews.
- Set up the first alert rules. One "critical review" rule per region and one brand-wide "unanswered negative" rule is a sufficient start for most chains.
- Add Şikayetvar, Ekşi Sözlük and X. Once brand-level sources are live, check the store matching queue weekly.
- Schedule reports by level. Weekly regional Excel for regional managers, monthly executive PDF for leadership.
- Enable the lead pass. Define your product categories and enter a few need-language keywords.
Data protection rules apply at every step of this flow; review authors are stored under pseudonyms and the details are explained in KVKK and data security.
A regional manager's week
The weekly digest arrives Monday morning; the store needing attention and the rising topic are identified. During the week alert e-mails deliver critical reviews instantly and the regional manager speaks to the store manager the same day. The impact matrix on the dashboard names the recurring problem in the region. On Friday the regional report arrives with an Excel attachment and becomes the agenda of the regional meeting. In the same week the marketing team calls the hot leads in the Lead Center.
The value of this cycle is that every level works from the same data at its own rhythm, and nobody has to build a spreadsheet.
Next steps
Multi-platform reputation management is as much a question of discipline as of tooling: the same store model, the same questions, the same metrics. BranchVoice provides that discipline ready-made for five platforms; starting from your store list, the full flow can be live within a few weeks. To see what it looks like on your own chain, request a demo.