Şikayetvar is a Turkish public complaint platform where customers file cases against companies, and a complaint there is addressed to the brand, not to a store. Reading "Brand X's store in the mall refused my return", working out which branch that is and forwarding it to the right regional manager is a manual task that repeats at head office every single day. This post explains how Şikayetvar monitoring is set up in BranchVoice, how new complaints are collected and how AI moves each complaint to the store it belongs to.
Why Şikayetvar matters so much for chains
A Google review is usually a short summary of an experience; a Şikayetvar complaint is a detailed, public case file that expects a resolution, and every customer who searches for the brand name sees it. For a chain, the hard part is not volume but context: the complaint is addressed to the brand, yet the problem happened in one branch, and the branch name is buried somewhere in the text or missing altogether.
The second difficulty is repetition. When the same return-policy issue is filed five times in a month from different cities, a team answering each one separately never notices it is systemic. A feed grouped by topic shows the pattern at a glance.
Setting up the source
Şikayetvar needs no store list. In the store import wizard you pick Şikayetvar as the source and enter your brand's name as it appears on the platform; you can add several brand names or sub-brands. There is no candidate review step, because what you are tracking is the brand's complaint page rather than a store. After you set the maximum number of complaints and pages to fetch, the first run starts immediately and collects historical complaints up to that limit.
During setup you also choose the scheduled sync frequency; for Şikayetvar sources the default is a weekly run. Every run is visible on the sync jobs page, where you can see how many complaints came in and, if something failed, why.
Scheduled syncs fetch only new complaints
The first import brings in the brand's history; the runs that follow work differently. BranchVoice knows the date of the last successful sync and asks only for complaints written after it. The same old complaints are never downloaded again each week, data-provider cost is incurred only for genuinely new content, and every record that lands on the dashboard is a new customer voice.
You can also trigger a sync by hand, for example during a campaign period when you do not want to wait for the weekly run. The full scheduling logic is covered in scheduled review sync.
How a complaint reaches the right store
This is where Şikayetvar fundamentally differs from Google and Yandex. Map reviews are linked to a store deterministically through the listing they were written on; a Şikayetvar complaint is first attached, at brand level, to the company's virtual "General" store. Then the AI analysis takes over:
- The complaint text is compared against your store list, and candidate stores are extracted from mall names, districts, store names and any aliases you have defined.
- AI decides which of the candidates the complaint is about. If the decision is confident enough, the complaint is moved to that store and the move is recorded in the link history.
- If the text names a mall that does not appear in the candidate store's name or address, the link is rejected; staying in the General pool is better than landing on the wrong branch.
- Complaints that cannot be resolved with confidence wait in the General pool. A high-risk complaint whose store is unknown is additionally placed in the manual link queue, where a team member picks the store from a candidate list.
The result is that a "my return was refused" complaint shows up under the regional manager's own store, while complaints left in the General pool are tracked separately as brand-wide issues. The decision logic is explained in matching reviews to stores.
Classification and alerts
Every complaint goes through the same AI questions as reviews from any other platform. The tags that prove most useful for Şikayetvar are:
- Risk level: hygiene, safety, misleading pricing and other issues that need a fast response are flagged, and critical ones are handed to the alert engine.
- Responsible unit: store staff, store hygiene, e-commerce logistics, customer service and similar distinctions determine whose desk the complaint should land on.
- Channel: whether the complaint is about an in-store experience or an online order; both are filed on the same Şikayetvar page, so AI decides this per complaint.
- Topics and tags: returns, checkout, product quality and so on; the platform's own tags are stored as well.
Alert rules are built on top of these tags. Rules such as "email the customer experience director when the number of high-risk complaints in the General pool crosses the threshold" or "notify the regional manager when a Şikayetvar complaint linked to their store is critical" follow the approach described in critical review alerts.
Next steps
After Google, Şikayetvar is the easiest source to connect: no store matching is needed, the brand name is enough. The first fetch classifies historical complaints by topic and store, and in the following weeks only new complaints arrive. To see your brand's Şikayetvar feed broken down by store, request a demo.