What is a reputation scorecard?

A reputation scorecard is the structured executive view that turns a program’s many signals into something leadership can read at a glance and act on. Instead of separate reports for search, AI, and Wikipedia, it aggregates them onto a single card, and adds two things a raw feed lacks: trend lines that show direction over time, and prioritized recommendations attached to each reading, so the report drives decisions rather than describing a moment.

The five inputs a scorecard aggregates

Search composition

The makeup of the branded result set, what share of the visible positions is held by the entity’s own and aligned content versus competitors, hostile sources, or unrelated material, and the sentiment and source quality of what ranks. This is the headline measure of control over the branded search page.

The AI narrative

What the AI engines say about the entity, with what sentiment, what accuracy, and drawn from which sources. Perception increasingly forms in the AI layer, so the narrative each engine returns is a distinct input rather than a footnote. Monitored via AIQ™, which tracks eight major AI engines: ChatGPT, Copilot, Gemini, Google AI Overviews, Perplexity, Grok, Claude, and Google AI Mode.

Wikipedia and Knowledge Panel status

Whether the Wikipedia article and the Google Knowledge Panel are present, accurate, and complete. These are high-authority surfaces that compound: the Knowledge Panel pulls its description and key facts from Wikipedia and Wikidata, and Wikipedia content also feeds the AI engines and search rankings, so weakness here propagates across the other inputs. Monitored via WikiAlerts™.

Peer comparison

How the entity’s posture compares with direct peers across the same measures. Reputation is relative: a given score means little without the competitive context, and peer comparison is what turns an absolute number into a position.

Crisis readiness

The entity’s exposure to a reputation event before one happens, the vulnerabilities across search, the AI layer, and Wikipedia where a crisis could take hold or an inaccurate narrative could spread. On the scorecard this is the forward-looking posture, not a current-sentiment reading.

What separates a scorecard from a data dump

The distinction is not the inputs but what is done with them. A data dump hands leadership raw feeds and dashboards to decode. A scorecard adds trend lines, direction over time, so a single reading becomes a trajectory, and attached recommendations, a prioritized point of view on what to do next. The discipline is synthesis and restraint: interpret the signals into a coherent picture rather than showing everything, since a card with every metric communicates nothing. The audience is executives and boards who need the posture distilled into priorities and choices.

We build scorecards from IMPACT™, AIQ™, and WikiAlerts™ data, with trend lines and prioritized recommendations, so reputation reaches leadership as decisions rather than noise.