How do you measure the success of a reputation management campaign?

Reputation measurement starts with baselines captured at engagement launch, because the same SERP can look successful or unsuccessful depending on what the program was trying to achieve. The six standard KPI layers, what each measures, and what a healthy baseline or target looks like:

Branded SERP composition

The share of page-one slots across 15, 40 priority queries held by owned, earned, third-party-positive, and hostile content. Baseline is captured at engagement start by classifying every ranking URL; the target is typically to grow owned-plus-earned share and shrink hostile share over time. IMPACT™ tracks this at daily resolution across geographies and languages.

Knowledge Panel accuracy and completeness

Whether the panel exists, and whether its key fields (name, description, founding date, headquarters, leadership, sameAs links) reflect current reality. A complete, accurate panel is the target; any inaccuracy signals a gap in the entity layer that needs source-level correction, since the panel updates only once the underlying source (Wikipedia, Wikidata, or structured data) is fixed.

AI narrative quality

Sentiment, source attribution, and theme coverage in responses from the eight AI engines AIQ™ monitors: ChatGPT, Copilot, Gemini, AI Overview, Perplexity, Grok, Claude, and Google AI Mode. Baseline is the narrative each engine returns at engagement start; the target is positive or neutral sentiment, attribution to authoritative sources, and accurate theme representation. Trend over time is more meaningful than any single snapshot.

Peer share-of-voice

The client’s percentage of page-one slots and AI narrative coverage relative to a named peer set of 3, 7 comparable companies, tracked in identical query conditions. The peer benchmark is often more actionable than absolute numbers because it reveals where leverage is greatest relative to the competition.

Wikipedia article stability

Edit activity, Talk-page dispute volume, accuracy of current article content, and revert patterns. A stable article with few contested edits and accurate infobox content is the target; elevated Talk-page activity or repeated reverts indicate contested matters that may also surface in Knowledge Panels and AI engines, since both draw heavily on Wikipedia. WikiAlerts™ tracks edit notifications continuously.

Qualitative stakeholder signals

What investors, journalists, candidates, and partners are saying in actual interactions with the client, tracked through structured check-ins during the engagement. These signals capture perception shifts not yet visible in the quantitative layers and often identify emerging narratives before they appear in search or AI responses.

Monthly reporting walks through each metric against the agreed baselines and goals. The work that does not appear directly in metrics, source-level remediation, entity-layer engineering, Talk-page edits, is what produces durable change, so reporting covers both inputs and outputs.