Understand results
Chats and answer evidence
Inspect the collected AI answers that form the denominator and evidence for every Selfwin analytics result.
A chat is one stored answer for a prompt, engine, and run date. Chats are the evidence layer beneath every dashboard metric.
Chat list
The chat list can be filtered by the shared analytical dimensions and searched through the application’s global search. Each row identifies the prompt, model channel, run date, detected brands, and relevant performance details.
Answers with no detected brand remain visible. They are important because they contribute to the visibility denominator.
Chat detail
Open a chat to inspect:
- The complete prompt and answer.
- Model channel and underlying model ID when available.
- Country and collection date.
- Detected brands with occurrence count, first position, sentiment, and snippet.
- Retrieved and cited sources.
- Observed fanout queries.
- Structured response features captured by the collector.
Use the original answer when a metric looks surprising. The stored evidence should make it possible to explain why a brand was counted, where it appeared, and which sources influenced the answer.
Mention analysis
One mention record is stored per detected brand per answer, but it includes the number of textual occurrences. This supports two different calculations:
- Visibility counts the answer once.
- Share of voice counts all occurrences.
Position records the order of the brand’s first appearance among detected brands. Sentiment records contextual tone on a 0–100 scale when analysis produces a score.
Sources and citations
A retrieved source is not necessarily an explicit citation. Selfwin stores both the retrieved URL and the number of times it was explicitly cited where the collector can distinguish them.
Source pages aggregate this evidence by domain or URL. See Sources, domains, and URLs.
Failed answers
Pending and failed collection attempts can retain status and an error message, but they do not enter completed-answer metric denominators. Run history shows whether a thin data set came from skips, failures, or a small prompt set.
Externally collected answers
An authenticated MCP client can submit an externally collected anonymous answer for analysis. Selfwin processes it through the same brand, citation, fanout, and metric pipeline as native tracking evidence.