Manual monitoring frequency varies with situational intensity, but the underlying platforms run continuously regardless. For active situations – crisis windows, transactions, executive transitions, contested news cycles – account teams check the dashboards daily and adjust the work week by week. For established programs at steady state, weekly review is sufficient for SERP movement and AI narrative drift, with deeper audits quarterly. IMPACT™ polls Google continuously across the client’s full keyword set and geographies regardless of how often a human looks at the data – the daily resolution is the substrate, not the cadence of attention. AIQ™ polls the eight AI engines daily. WikiAlerts™ fires in real time on any watched page edit. The platforms cover the floor; the human cadence is about which signals to act on this week. The pattern is automation continuous, human attention scaled to intensity.

Reputation scores – single composite numbers aggregating multiple underlying signals – are popular in executive reporting because they compress complex pictures into a digestible figure. The honest read is that they are useful for high-level trend reporting and largely useless for operational decisions. A score that includes search composition, sentiment, AI accuracy, Wikipedia state, and peer benchmark might move from 72 to 78 over a quarter without telling the team which of the inputs actually changed or what to do next. The reporting that works at Five Blocks shows the composite score where the client wants it for board reporting, but always alongside the underlying signals: where the score moved, what specifically improved, what specifically deteriorated, and what work is producing the change. Methodology also varies dramatically across providers, so cross-provider score comparison is largely meaningless. Treat the score as a communication tool, not a decision tool.

Google has not produced a unified global SERP in over a decade. Results are personalized by country, city, language, and device, and the differences across markets can be substantial. The same name query that produces a clean SERP in New York can return a contested article in London and a different Wikipedia language version in Frankfurt. For multinational clients this matters constantly: an investor base across Europe, Asia, and the Americas sees different first impressions of the same executive, and a program that tracks only the US picture is missing most of the footprint. The discipline is per-market tracking through GeoSearch and IMPACT™, with regional intervention strategies where the picture varies meaningfully. AIQ™ layers on the AI engines, which themselves vary by user locale and language. Monthly reporting covers per-market progress separately rather than aggregating into a misleading global figure.

Sentiment analysis is the classification of content – a URL, an article, an AI response – on a positive, neutral, negative axis. For reputation work it serves as a tracking signal rather than a decision criterion, because the same content can register different sentiments depending on the model and the prompt. The practical use: each ranking URL on the priority SERPs gets a sentiment score during analysis, and the aggregate sentiment of page one is tracked monthly. Each AI engine response in AIQ™ gets scored per engine, with the trend over time more meaningful than any single snapshot. Sentiment movement is a leading indicator of stakeholder perception movement, particularly when combined with source-attribution data showing which sources are driving the picture. The failure mode is treating sentiment as a goal in itself rather than as a signal of underlying narrative state – the work is on the narrative, not on the score.

Monthly progress reports at Five Blocks follow a consistent structure tied to the goals set at the start of the engagement. The sections: SERP movement against priority queries with specific URL gains and losses, illustrated through IMPACT™ charts. AI narrative state across the eight engines AIQ™ monitors – sentiment trend, source attribution shifts, themes emerging or fading. Wikipedia activity including any Talk-page work filed or accepted and any edit notifications from WikiAlerts™. Peer benchmarks showing the client’s position relative to the named peer set across each layer. Work completed during the period – source-level interventions, content built, profile work, technical changes. Three to five prioritized recommendations for the coming period. A short list of wins worth noting and risks worth flagging. Visuals over text where useful. The report is the operational document for the engagement, not a marketing artifact.

Competitive reputation tracking is one of the higher-value outputs of a program because it converts the data into strategic action: where peers have advantages worth closing, where the client has advantages worth defending, where the category is shifting. IMPACT™ runs the client’s priority query set against named peers in identical geographies and languages, producing per-query SERP composition comparisons. AIQ™ runs the same prompts against each peer across the eight engines, producing model-by-model comparison of narrative state, source attribution, sentiment, and theme coverage. The aggregate view shows share of voice on each priority query and across the AI narrative as a whole. The output feeds two things: monthly reporting where peer comparison usually proves more actionable than absolute numbers, and strategic recommendations about which interventions to prioritize based on where the leverage is greatest relative to the competition.

The mechanics: for each priority query, IMPACT™ captures every URL on page one and page two at daily resolution. Each URL gets sentiment-scored – positive, neutral, negative – based on its actual content rather than just headline tone. The aggregate sentiment for the SERP is calculated as a weighted average accounting for ranking position (higher slots count more). The same data captured weekly or monthly produces trend lines that show movement over time. The same approach applies inside AIQ™ for the AI engines: every response gets sentiment-scored per engine, and the trend lines per engine track narrative state over time. Both views reveal narrative drift before it becomes obvious, validate intervention impact when sentiment moves in response to specific work, and feed monthly reporting. The discipline is consistency in the classification methodology so trend comparisons across time are valid.

Peer benchmarking is the framing that makes reputation data actionable. The methodology has to be rigorous to produce useful comparisons. The same query set, the same geographies and languages, the same time windows, the same classification criteria across the client and every peer in the named set. IMPACT™ runs the SERP comparison; AIQ™ runs the AI engine comparison with identical prompts against each peer. The aggregate views show where the client leads, where peers lead, and where the category is uniformly weak or strong. The peer set is defined deliberately at the engagement’s start – usually three to seven companies that are genuinely comparable on the dimensions clients care about (market position, size, geography, business model). Once set, the peer set runs continuously and the comparison feeds monthly reporting. Most clients find the peer benchmarks more useful than the absolute metrics, and most strategic decisions in our engagements get made against peer-relative data.

When a notable article publishes – positive or negative – measuring its actual reputational impact is more involved than tracking page views. The signals worth tracking. SERP placement: does the article rank for the priority branded queries, at what position, for how long. SERP feature presence: does it appear in Top Stories, news boxes, AI Overviews, knowledge panels. AI narrative adoption: do the AI engines start citing the article when answering questions about the brand, and does the article’s framing show up in engine responses. AIQ™ tracks this directly across the eight engines. Engagement and traffic signals from analytics where the article is on owned property, or from third-party tools where available for earned content. Downstream coverage: does the article get cited or referenced by other publishers, amplifying its reach. The combined picture tells you whether the article actually moved the needle or just generated a news-cycle blip.

Board-level reporting on reputation needs to be concise, executive-grade, and decision-oriented. The structure that works: a single-page summary of current posture against peers on the priority layers (Google, AI, Wikipedia). A short list of top risks the board should be aware of – usually three to five. A summary of the work completed during the reporting period that ties to the program objectives. Movement on the KPIs the board has agreed are the measurement standard. The AI narrative trend with specific examples showing what engines are saying and how it has moved. Three to five recommendations or decisions the board needs to make, sized to their scope of authority. Visuals over text where they communicate better – SERP composition charts, share-of-voice graphs, AI engine comparison views are typically more useful than paragraphs. Full operational detail sits in an appendix or backup deck for any director who wants to dig deeper.