Sentiment Footprint and Sentiment Shaping are the AI Search subset of reputation management. The Footprint is the measurement sub-area that reads brand sentiment across four distinct layers and across the prompt clusters that matter for the business. Shaping is the action sub-area that changes what AI Search retrieves and what humans already believe about a brand across those same four layers. The operational order is fixed: measure first with the Footprint, then act with Shaping. You cannot shape what you have not measured.
The discipline runs continuously alongside the Narrative Coherence Discipline, the Crisis-Response Pattern, Narrative-Drift Detection, the Quarterly Narrative Audit, the Brand-Evolution Triggers, and the Narrative Coordination Calendar. The narrative artifacts keep the brand story consistent across surfaces. The Footprint and Shaping methods move sentiment across the layers where AI Search reconstructs it.
The chapter sits in Transformation because sentiment work compounds slowly, requires sustained organizational commitment, and rewards programs that convert one-off projects into permanent operating rhythms.
Why This Technique Matters
Brand trust can build for years and erode in months when narrative drift or a crisis hits unmanaged. AI Search compresses that timeline further because the same retrieval pass that reconstructs the brand for a buyer also reconstructs it for the next thousand buyers asking the same question. A single dragging source can carry across an entire prompt cluster.
Three observations make Sentiment Footprint and Sentiment Shaping necessary as named sub-areas.
First, sentiment in AI Search is not one number. The aggregate vendor score is a roll-up of four distinct layers of sentiment formation, and the operator who cannot tell which layer is dragging the readout cannot move it.
Second, measurement vendors are converging on a layered view without naming the underlying discipline. Peec AI exposes topic-level breakdowns alongside the aggregate score. Profound surfaces overall, theme-level, and per-prompt sentiment. HubSpot AEO Grader weights sentiment up to 40 points of its composite, the heaviest single component. Visiblie treats sentiment as categorical and pairs it with mention and recommendation rates. Four conventions, one underlying signal, no named integrating discipline.
Third, AI retrieval aggregates the whole narrative. ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews fan out a single prompt into ten to twenty sub-queries and pull from review platforms, community threads, owned domains, third-party comparison pages, news outlets, and entity sources in the same pass. The aggregated answer reads as one synthesis. The signal underneath comes from coherence (or its absence) across every surface AI touched. Programs that treat reputation as a PR function cannot keep up with that aggregation. Programs that run a named Footprint and a named Shaping practice can.
The cadence is permanent rather than campaign-shaped. The artifacts (the Quarterly Narrative Audit, the Coordination Calendar, the monthly Footprint refresh) are durable operating rhythms. The organizational change required to run it well, covered in Chapter 15, is structural rather than project-shaped.
The Sentiment Management Umbrella
Sentiment Management is the AI Search subset of reputation management. The phrase is lowercase in prose because it names a broad operating discipline, not a Searchbloom-coined term. Two Searchbloom-coined sub-areas sit underneath it.
Sentiment Footprint is the measurement sub-area. It reads the brand-aggregate Sentiment Score, the per-prompt-cluster Sentiment Scores, the source contributions for the weakest clusters, and the inferred state of Layer 1 (brand awareness) and Layer 2 (parametric) baselines. The Footprint produces a one-page report at a point in time and refreshes monthly. Every Sentiment Shaping engagement begins with one.
Sentiment Shaping is the action sub-area. It is the technique-set that changes what AI retrieves and what humans already believe about a brand across the four layers. Sentiment Shaping is the application of Corpus Engineering to brand sentiment.
Both are Searchbloom-coined methods, named and codified rather than invented. Review velocity, earned press, Reddit engagement, owned content, knowledge graph work, sponsorships, and founder brand work have been practiced for years. What Searchbloom is first to do is name the two sub-areas, stratify the layers of sentiment they operate on, and integrate them inside a 15-chapter AI Search framework.
The order is fixed. Measure first with the Footprint. Act second with Shaping. The Footprint is the prioritization tool; without it, Sentiment Shaping runs on instinct and chases whichever cluster the team noticed last. The Footprint refresh produces the monthly reprioritization. Layer 1 and Layer 2 inferences move slowly, and reading them quarterly across four refreshes is what surfaces the broader brand-work pattern.
The Narrative Coherence Discipline, the Crisis-Response Pattern, Narrative-Drift Detection, the Quarterly Narrative Audit, the Brand-Evolution Triggers, and the Narrative Coordination Calendar all run inside the Sentiment Management umbrella alongside the Footprint and Shaping work.
The Four Layers of Sentiment
A Sentiment Score is the output of a four-layer pipeline. Each layer is shapeable by a different category of work. The first move in any engagement is reading which layer is producing the gap.
Layer 1: Brand awareness
What a human asker already feels about the brand before they type the prompt. Built by paid media, sponsorships, breakthrough creative, founder fame, ESG initiatives, awards, books, prior product interactions, and word of mouth. The layer most marketing leaders have managed for decades and the layer most AI Search content ignores.
Layer 1 cannot be touched by source-shaping. Someone who watched a Super Bowl ad in February brings positive baseline to the "is X reliable" prompt in May, and no amount of Reddit AMA work changes that. Layer 1 reaches the human asker directly and reaches the LLM indirectly by feeding the broader content ecosystem the LLM trains on. Today's Layer 1 awareness work is tomorrow's Layer 2 parametric baseline.
Layer 2: LLM parametric
What the model "knows" about the brand from training. Slow to change. Updated only at each new training cutoff (six to twenty-four months depending on the model). For a brand the model has seen heavily, Layer 2 is the heaviest weight in a non-grounded response and a measurable contributor in grounded ones. For a brand the model has seen lightly, Layer 2 is thin and the model leans almost entirely on Layer 3 retrieval.
Layer 2 is shapeable, but only indirectly and on a delay. The parametric layer is updated only at training cutoffs, and what appears at the next cutoff is the aggregate of everything Layer 3 produced in the interval. The single most useful Layer 2 technique is the long-running, high-cadence Layer 3 program.
Layer 3: Retrieved-source per prompt-cluster
What the model pulls when it fans out queries for a specific intent bucket. Where most operational Sentiment Shaping work lives. Different intent clusters retrieve from different sources and produce different Sentiment Scores. "Brand reviews," "Brand awards," "Brand vs competitor," and "is Brand legit" each fan out into a different set of sub-queries pulling from a different mix of sources. Layer 3 work is shapeable in months.
Layer 4: Source-contribution per individual prompt
Which exact URLs contribute which signal to one specific prompt. The narrowest layer and the fastest to move. A single negative Reddit thread can drag a per-prompt score even when the brand-aggregate readout is healthy. A surgical intervention on the source contributing the drag can lift the per-prompt score in weeks. Layer 4 is also where entity work compounds: a clean Wikipedia article, a complete Wikidata entity, and a well-disambiguated Google Knowledge Graph entry all sit at Layer 4 and route to the model on specific prompts.
How vendor scores map to the layers
The brand-aggregate Sentiment Score reads all four layers blended. The per-prompt-cluster Sentiment Scores read Layer 3 and Layer 4 directly and pick up Layer 1 and Layer 2 echoes through parametric blending. The per-prompt Sentiment Scores read Layer 4 most cleanly, with Layer 3 and Layer 2 echoes attenuated by how much the model leans on retrieval versus parametric memory for that question.
The aggregate value summarizes. The cluster breakdown diagnoses Layer 3. The per-prompt source contribution diagnoses Layer 4. Branded search volume, Google Trends, and unprompted parametric language samples diagnose Layer 1 and Layer 2 indirectly. The Footprint integrates all of those reads into one artifact.
The Sentiment Footprint
The Sentiment Footprint turns a single vendor Sentiment Score into an actionable picture across the four layers and the prompt clusters that matter. It runs five components in order and produces a one-page report. Every Sentiment Shaping engagement passes through this gate.
1. Brand-aggregate Sentiment Score with trend
The starting reading. The brand-aggregate value across whichever vendor convention the customer is reading: Peec AI 0 to 100, HubSpot AEO Grader -100% to +100%, Profound percentage breakdown, or Visiblie categorical. The Footprint is vendor-neutral; whichever score is in place becomes the input.
Cadence: weekly readout, four-week rolling trend window for stability, monthly board-level summary. Display: current value, four-week delta, six-month trajectory, vendor convention noted alongside the value. The Peec AI documented band of 65 to 85 is the most quotable rubric in market. A brand under 65 has surfaces dragging the readout. A brand above 85 is rare and worth investigating to confirm the prompt panel is representative.
2. Per-prompt-cluster Sentiment Scores with trend
The diagnostic. Six to twelve clusters per brand. Standard cluster set: "[Brand] reviews," "[Brand] complaints," "is [Brand] legit," "[Brand] vs [competitor]" (one per primary competitor), "best [category]," "[Brand] alternatives," "[Brand] pricing," "[Brand] awards," "[Brand] [feature or product line]," "what does [Brand] do," "is [Brand] reliable," and brand-specific high-value clusters.
Cadence: weekly readout, four-week rolling trend per cluster. Display: cluster name, current value, four-week delta, source set count, band indicator (0 to 100 scale: 65 to 85 healthy (Peec's benchmark band), below 65 flags the cluster as dragging the readout, above 85 rare). Calibrate bands to vendor convention. Sort ascending so the weakest cluster sits at the top.
3. Source-contribution analysis for the weakest two or three clusters
The surgical input. Per-prompt source citation captures across ChatGPT, Claude, Perplexity, and Gemini for the prompts the cluster work surfaces as weakest. Use Peec AI or Profound source captures where available; fall back to manual captures otherwise. Per prompt: list contributing URLs with signal carried (positive, neutral, negative), publication date and recency weight, and displacement option (response, fresh competing content, third-party correction, removal).
Flag URLs recoverable through response separately from URLs requiring displacement. Cadence: monthly refresh of the weakest two or three prompts; quarterly refresh of a broader sample. This component is what tells the operator whether the next move is Layer 3 cluster work or Layer 4 surgical work.
4. Inferred Layer 1 and Layer 2 baseline read
The broader context. Direct measurement of Layer 1 and Layer 2 is not possible from vendor sentiment tooling. The Footprint carries an inferred read. Layer 1 indicators: branded search volume in Google Search Console trended twelve months, Google Trends interest score, branded mention frequency across Reddit and X over ninety days, share of voice in tracked AI prompts against named competitors, brand-awareness survey data where available. Layer 2 indicators: unprompted language the model uses when describing the brand category (sample fifteen to thirty broad prompts), parametric-bias check (compare ungrounded to grounded responses; large divergence indicates weak Layer 2), Wikipedia citation density on adjacent topics.
Cadence: quarterly refresh. Display: a narrative paragraph paired with three or four justifying indicators. Do not present as a precise score.
5. Layer prioritization for next moves
The output. A ranked list of which layer to attack first, second, and third based on components 1 through 4. Format: ordered list of one to three priority layers with rationale. Cadence: monthly refresh paired with source contribution analysis.
Reading the report and artifact format
A trained operator identifies, in order: whether the aggregate sits in the healthy band, which clusters drag it, whether the weakest clusters are driven by specific recoverable URLs or diffuse patterns, whether Layer 1 and Layer 2 indicators are trending with the cluster work or independent of it, and what the prioritized next move is.
The artifact is one page or one screen. Grayscale per Searchbloom deliverable rules with brand accent reserved for the layer-prioritization callout and the band indicators for problem or critical clusters. Layout: top band carries the brand-aggregate value and trend microline; middle band carries the cluster table sorted weakest first; lower-middle band carries the source contribution panel for the weakest two clusters; lower band carries the inferred Layer 1 and Layer 2 paragraph; footer band carries the layer prioritization callout. The Footprint is delivered as the opening phase of a Sentiment Shaping engagement.
The Sentiment Shaping Practice
Sentiment Shaping is organized by which layer the technique moves. The Footprint dictates which layer and which cluster get attention first; the techniques below are the operational vocabulary the Footprint draws from. The companion Guide walks each technique in depth; this section frames how the work feeds the layered sentiment readout.
Layer 1: Brand awareness shaping
Slow. Compounds. Feeds Layer 2 parametric memory over training cycles. The first category most marketing leaders already manage. Layer 1 work that does not produce downstream retrievable surfaces is invisible to AI Search.
Key categories: paid media and breakthrough creative that produce organic discussion the model retrieves later (the model retrieves what people say about the impression, not the impression itself); founder and executive brand on X, LinkedIn, newsletters, podcasts, panels, conference stages, and books; awards and industry recognition (Gartner Peer Insights, Forrester Now Tech, G2 Grid, vertical "best of" lists) plus analyst relations with Gartner, Forrester, IDC; sponsorships, ESG, and community work that produces Layer 3 retrievable signal on "is X ethical" prompt clusters; employer brand through Glassdoor presence, employee advocacy under named voices, and public operating handbooks. The integration with MERIT is that all Layer 1 work eventually produces Layer 3 retrievable surfaces (recap coverage, transcripts, award announcements) that compound into Layer 2 parametric memory at the next training cutoff.
Layer 3: Retrieved-source shaping at the cluster level
The bulk of operational Sentiment Shaping. Each category corresponds to a class of retrievable sources AI pulls when fanning out for specific prompt clusters.
Reviews and customer advocacy. Raving fan customers are the upstream driver. AI Search systems retrieve review platforms heavily on consideration-stage prompts, and reviews carry both rating and language signal, with language doing most of the work. Drive review velocity and recency on Trustpilot, G2, Sitejabber, BBB, Google, and Capterra. Trustpilot carries weight in ChatGPT retrieval; G2 carries weight in B2B SaaS evaluation. Aim for the Review Cohort Index health band from Chapter 1 of Mentions: above 20 percent annualized velocity is healthy, 10 to 20 percent is maintenance, below 10 percent is decay. Respond publicly to every negative review with a substantive branded reply; the response becomes retrieved content.
Earned media and PR. Earn press placements in Forbes, TechCrunch, the New York Times, the Wall Street Journal, and relevant industry trades. Tier 1 outlets carry five to ten times the citation weight of Tier 3, per Chapter 3 of Mentions, and Tier 1 coverage often surfaces unprompted in category-defining queries. Place executive bylines, original research, and data reports in third-party publications. Bylined work beats a press release by ten to fifty times on citation lift per piece. Contribute expert quotes through HARO, Qwoted, Connectively, and Featured.
Community and forum presence. Build sustained Reddit organic engagement under a named operator account, working to the 90/10 Rule and the Karma Velocity Index from Chapter 2 of Mentions. Reddit appears in 1.2 percent of ChatGPT responses and 6.3 percent of Perplexity responses (Profound, October 2025), and the language is unfiltered and disproportionately weighted on consideration prompts. Answer Quora questions from official accounts and from satisfied customers; Quora carries strong weight in Google AI Overviews and Gemini and has the slowest citation decay of any community surface. Participate in niche Slack and Discord communities using the Negative Thread Triage Framework. Reach the 500 subreddit-specific karma floor on Reddit, 100 Credits and 30 strong topic answers on Quora, and a sustained LinkedIn cadence before introducing branded mentions. The Cross-Platform Citation Stacking Effect (one platform baseline, three platforms 2.5x, five-plus diminishing returns) governs prioritization.
Creator, influencer, and podcast. Partner with creators who describe the brand in their own words on YouTube, TikTok, and Instagram. Creator content is retrieved at materially higher rates than brand-handle content. Branded creator content surfaces in YouTube citations on Google AI Overviews (29.5 percent of which cite YouTube). Sponsor or appear on podcasts where the host genuinely uses the product. Launch a branded podcast so the brand owns a discoverable, transcribed, retrievable surface on its own domain plus Apple Podcasts, Spotify, and YouTube.
Owned content and comparison footprint. Build a glossary and category-defining content footprint so the brand becomes the source LLMs cite when explaining the category. Develop a comparison page footprint for the brand against each major competitor; "X vs Y" prompts retrieve comparison content heavily. Build branded SERP control across "X reviews," "X complaints," and "X vs Y" clusters with favorable owned and third-party sources leading rather than negative aggregators. Pursue inclusion in "best of" listicles using the Listicle Hijack Method (25 to 40 percent hit rates on existing roundups). Optimize for AI Overviews and ChatGPT or Perplexity citations through the Answer-First Content Architecture in Chapter 7, with FAQ schema where applicable (40 percent citation lift).
Partnerships and integrations. Acquire complementary mentions through partnerships, integrations, and joint webinars. Integration directories on partner platforms (Salesforce AppExchange, HubSpot Marketplace, Slack App Directory, Shopify App Store) are retrieved heavily for category prompts and carry the partner's trust signal.
Layer 4: Surgical source-contribution shaping
The narrowest layer. The fastest to move.
Entity and knowledge graph. Maintain a clean Wikipedia presence. Wikipedia is in every major model's training corpus; the Spearman correlation between Wikipedia citation density and AI Overview visibility runs as high as 0.577 (Wills, March 2026). Build a Wikidata entity with canonical name, description, and P-properties linking to trusted directories. Develop a Google Knowledge Graph entity with consistent attributes across surfaces. Implement Organization, Person, Product, and Service schema with deep sameAs arrays pointing to Wikipedia, Wikidata, Crunchbase, LinkedIn, and industry directories. Run the Entity Authority Score discipline from Chapter 10 across brand, people, products, and topical entities.
High-value prompt source displacement. For a specific prompt where one bad source is dragging the per-prompt score, identify the source, identify what would displace it, and execute. The displacement plan options are bounded: response (contact the publisher or platform with substantive correcting information), fresh competing content (publish work substantial enough to outrank or out-cite the contributing source), third-party correction (get a reputable outlet to address the question more accurately and earn the citation in place of the original), or removal in the narrow cases where defamation, factual inaccuracy on a takedown-eligible surface, or terms-of-service violation applies. Most engagements run the first three; the fourth is rare and requires legal review.
Defensive and reputation protection. Monitor for coordinated negative campaigns and review bombing through Alertmouse, Ahrefs Firehose, and Sprout Social. Coordinated campaigns produce dense temporal clusters of negative signal the model retrieves heavily in the moment. Target detection within 48 hours of the cluster forming, upstream remediation within 96 hours, and per-prompt sentiment recovery within 30 days. Manage anti-fraud and impersonator sites through DMCA takedowns and trademark enforcement.
A note on Layer 2
Layer 2 is shapeable, but only indirectly and on a delay. The parametric layer is updated only at training cutoffs, and what appears at the next cutoff is the aggregate of everything Layer 3 produced in the interval. Set expectations against the timeline of the training cycle, not the engagement. Wikipedia and high-authority third-party publications compound especially well into Layer 2 because they feed both retrieval and training; operators who care about Layer 2 prioritize those two surfaces inside the broader Layer 3 program.
The Narrative Coherence Discipline
The Narrative Coherence Discipline is the cross-surface narrative-keeping work that runs continuously inside Sentiment Management. It holds owned, third-party, community, and review surfaces consistent so the AI retrieval pass reconstructs a coherent brand story instead of a fragmented one. The discipline operates at three levels and across four surfaces, and it is the operational firewall against the narrative drift that defeats Sentiment Shaping over time.
Brand-Level Narrative
The canonical brand story. One-sentence pitch, category framing, founding story, value claim. Three nested versions: a 30-word pitch for elevator and headline use, a 100-word overview for short-form bios and press kit boilerplate, and a 250-word boilerplate for longer-form attribution and contributed-piece end-blocks. The substance is consistent across all three; the difference is length, not framing.
Product and Service Narrative
What the offering does, who it serves, how it differentiates, and what outcomes it produces. Tighter cadence than the Brand-Level Narrative because product changes more frequently. Quarterly alignment is the minimum, triggered out of cycle by feature releases, repositioning, pricing changes, and discontinuations. The discontinued-feature support article that the model is still retrieving despite a feature relaunch is a common Product and Service Narrative drift pattern.
Expert Narrative
Background, role, expertise area, topical authority. Each named expert (founders, executives, principal practitioners) carries a canonical bio with consistent positioning across the brand site, LinkedIn, byline attribution lines, Wikidata person entities, and any third-party speaker bios. Expert narratives drift faster than brand narratives. Quarterly drift detection catches the drift early; Brand-Evolution Triggers handle step-change events. The Expert Narrative is also where named-operator citation lift compounds with entity work.
The four surfaces of narrative coherence
The same surfaces AI retrieves from when reconstructing brand sentiment.
Review-platform surface. G2, Capterra, TrustRadius, Trustpilot, Clutch, Gartner Peer Insights, BBB, Sitejabber, Yelp, Google Business Profile, the Apple App Store and Google Play, plus Glassdoor and Indeed for employer sentiment. 4+ star baseline with active brand reply on every cited platform. Active monitoring for new category entrants the brand has not yet claimed. Every visible negative review carries a substantive brand reply within 48 hours.
Community surface. Reddit, Quora, Hacker News, Stack Exchange, Nextdoor, LinkedIn, industry-specific Slack and Discord, and niche forums. 90/10 Rule governs participation. Operator-led posting under named accounts with sustained Karma Velocity Index above per-platform healthy bands. Negative threads triaged using the Negative Thread Triage Framework.
Social surface. LinkedIn, X, Facebook, Instagram, TikTok, YouTube, Threads, Bluesky, Pinterest, Snapchat, Twitch, and the platforms the audience occupies. LinkedIn carries disproportionate weight in Microsoft Copilot retrieval, and YouTube is the single most-cited surface across AI engines, so video belongs here as a first-class surface rather than an afterthought. Weekly monitoring cadence. Executive bios, company page descriptions, and pinned content carry the same canonical wording as the source-of-truth documents.
Search surface. Brand-name SERP ownership across the first page and AI Overview real estate. The brand owns homepage, key product pages, About, founder bios, key resources, social profiles, and review site profiles. National SEO motion for multi-location brands. Branded SERP control extends to "X reviews," "X complaints," "X vs Y," and "is X legit" clusters.
Coherence across the four surfaces is what AI retrieval reads as a coherent brand story. Drift on any one surface drags every layer of the Sentiment readout.
The owned-to-third-party alignment pattern
Every contributed piece, every community reply, every awards application uses brand and product wording that matches the source-of-truth document. When the editor of a Tier 1 outlet asks for a paragraph of background, the paragraph is the canonical 100-word overview. When the conference organizer asks for a speaker bio, the bio is the canonical Expert Narrative. When the customer support team writes a help article, the article uses the same product wording the marketing site uses. Editors and analysts copy boilerplate. The model retrieves the editor's piece, the analyst's note, the help article, and the marketing page in the same retrieval pass. When all four carry the same canonical wording, the synthesized response reads coherent. When they carry four versions, it reads confused.
How Narrative Coherence Shows Up in the Footprint
Narrative coherence is not a separate score. It is a dimension the Sentiment Footprint already reads. When a prompt-cluster sits in the problem band, the source-contribution analysis tells you which kind of problem it is. A cluster dragged by one or two recoverable URLs is concentrated negativity, a Layer 4 surgical fix. A cluster dragged by a diffuse, contradictory set of sources, where owned, third-party, community, and review surfaces tell different stories, is narrative incoherence, a Layer 3 cluster-wide fix that runs through the Narrative Coherence Discipline above.
Two readings keep the operator honest. A brand can be coherent but unfavorable, where every surface tells the same story and the story is not good; the fix is sentiment work, not alignment. A brand can be favorable but incoherent, where positive coverage carries contradictory framings; the fix is alignment, not more positive coverage. The Footprint distinguishes the two through the source-contribution pattern, and the Sentiment Management disciplines (the coherence work above, drift detection, the coordination calendar) move whichever one the readout calls for.
The Crisis-Response Pattern
The Crisis-Response Pattern is the four-step pattern for handling crises that defeat the Footprint and the Narrative Coherence Discipline in the short term. Crises produce dense temporal clusters of negative signal across multiple surfaces in days, faster than the monthly Footprint refresh. The Pattern runs in four steps, executed quickly and in order.
1. Document
Capture wrong responses, the queries that produced them, and the cited sources across ChatGPT, Claude, Perplexity, and Google AI Overviews. Build the evidence base before remediation; documentation drives where remediation lands. Capture source URLs, dates of source content, and specific language the engines used in the synthesized response.
2. Remediate upstream sources
Fix the upstream third-party sources that produced the wrong content. The highest-impact step. Fixing the source fixes the downstream pull. Owned-domain fixes without upstream work produce a confused story; the engines still surface the upstream content alongside the brand correction and the synthesis carries the contradiction. Upstream remediation includes contacting publishers or platforms with substantive corrections, working with editorial teams on retractions where source content is factually wrong, and removal requests where defamation or terms-of-service violation applies.
3. Publish authoritative correction
Publish the correction on the owned domain with schema markup, a named byline, and IndexNow notification to push the correction into the index. The owned correction becomes the canonical source for the corrected facts.
4. Monitor recovery
Track recovery as indexes update. Run per-prompt source contribution analysis on affected prompts daily for the first week, weekly for the following month, until the retrieved set turns over. Run a post-event review to feed the learnings into the Narrative Coordination Calendar and the playbook.
Most crises are blocked by the Coordination Calendar work below. The Pattern is for the ones that get through.
Narrative-Drift Detection
Narrative drift is the slow accumulation of small inconsistencies across surfaces that compounds into fragmentation over time. The Footprint can show healthy clusters while coherence slides across surfaces because positive coverage produces contradictory framings; the drift is invisible until cluster scores start moving. The discipline runs two mechanisms: the Quarterly Narrative Audit on fixed cadence and the Brand-Evolution Triggers on out-of-cycle basis.
The Quarterly Narrative Audit
A fixed-cadence review across five surface types, run in the same week of the same month each quarter. Treat it as a permanent commitment, not a project.
- Owned-domain surfaces. Homepage, About, key product pages, key bio pages. Sample the top 20 to 30 pages by traffic. Flag outdated positioning, drifted product wording, deprecated category framing.
- Third-party-described surfaces. Wikipedia, Wikidata, Crunchbase, analyst reports, media archives. Flag drifted descriptions, outdated funding figures, deprecated leadership listings.
- Social surfaces. LinkedIn company page, executive LinkedIn profiles, X, Facebook, Instagram, TikTok, YouTube, Threads, Bluesky, and the platforms the audience occupies. Sample bios, headers, recent posts. Flag drifted positioning.
- Review-platform surfaces. G2, Capterra, TrustRadius, Clutch, Trustpilot, Gartner Peer Insights, Yelp, Google Business Profile, Glassdoor, and category directories. Sample brand-controlled descriptions on each platform.
- AI-retrieved surfaces. A 10 to 20 query sample across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. Synthesized responses are the live readout of how the model reconstructs the brand from the surfaces above.
The audit produces a punch list. Each flagged surface routes to the team owner with a target completion date. The next quarter re-checks previously flagged surfaces to confirm remediation landed.
Brand-Evolution Triggers
Out-of-cycle audits triggered by events that change the brand's narrative state faster than the quarterly cadence catches. The seven standard triggers:
1. Product launches and major feature releases. Narrative impact review one to two weeks before launch. Confirm owned-domain pages, third-party descriptions, and review-platform listings reflect the new state.
2. Repositioning and category shifts. Full narrative refresh across all five surfaces. The highest-risk drift event because canonical narratives change at the source while dispersed surfaces lag by months.
3. Executive transitions. CEO, CMO, and publishing-expert transitions trigger bio updates across owned domain, LinkedIn, Wikidata person entities, and third-party speaker bios. Coordinated narrative for departing experts and accelerated entity-build for incoming senior hires.
4. Funding events. Updates to Crunchbase, Wikipedia, Wikidata, the brand site About and press pages, and analyst contacts. Funding figures lag for years across third-party sources if the trigger is not run actively.
5. Mergers and acquisitions. Entity boundary mapping across Wikidata, Wikipedia, Crunchbase, and the Knowledge Graph. Update sameAs arrays, merge or split Wikipedia articles as appropriate, run sequential post-news AI sampling.
6. Major partnerships. Partner-directory listings, joint announcements, and updates to the strategic-partner page.
7. Crisis events. Trigger the Crisis-Response Pattern immediately. The Pattern's documentation step feeds the next quarterly audit as a known drift driver.
The triggers are governance artifacts. The Narrative Coordination Calendar (next) holds the Program Lead's seat at the table for each trigger event so the narrative impact review runs before the announcement, not after.
The Narrative Coordination Calendar
The Narrative Coordination Calendar holds the Narrative Coherence Discipline, the Sentiment Footprint refresh, and the Sentiment Shaping operating priorities in sync across teams and surfaces. The operating rhythm that turns the Footprint from a one-time diagnostic into a permanent operating rhythm.
The calendar holds fixed-cadence rhythms that run regardless of what else is happening, and trigger-driven slots tied to the Brand-Evolution Triggers. The Program Lead owns the calendar and holds a permanent seat in the cross-functional meetings that produce the trigger events (product launch planning, executive communications, pricing committees, M&A communications). The seat is structural, not informational.
Fixed-cadence rhythms
Weekly: Sentiment Footprint scan. Quick scan of the brand-aggregate value and the cluster table for material movement. Read against the four-week trend window. The Program Lead's standing readout, not a meeting.
Monthly: Sentiment Footprint refresh. Full refresh with source contribution analysis on the weakest two or three clusters. Layer prioritization callout updated. Operating priorities for the next 30 days reset.
Quarterly: Narrative Audit. Same week of the same month each quarter. The five-surface audit above. Produces the quarterly punch list and the cross-team remediation routing.
Quarterly: Layer prioritization review and Layer 1 / Layer 2 indicator refresh. The indicator set (branded search volume, Google Trends interest score, unprompted parametric language samples, share of voice) refreshes on the same quarterly beat.
Annually: Brand narrative refresh and coherence review. The canonical brand narrative, named-expert canonical bios, and product narrative all get a refresh review. Accumulated small changes get captured and dispersed across surfaces. The annual review reads coherence across the surfaces against the canonical narrative.
Trigger-driven slots
Product launch planning integration. One-page narrative impact doc for every launch covering owned-domain refresh needs, third-party outreach, outdated contributed pieces queued for refresh, and post-launch AI sampling.
Executive communication review. Pre-release review of board letters, earnings talking points, all-hands updates, and major external news.
Pricing and positioning updates. Updates to G2, Capterra, Clutch, and category directories on pricing or positioning changes. Wikidata and Wikipedia updates when the change is material. Advance buffer timing so directory listings update before the announcement.
M&A and partnership announcements. Entity boundary mapping. Wikidata sameAs updates. Wikipedia merges or splits. Sequential post-news AI sampling.
Hiring and team transitions. Bio updates across owned domain, LinkedIn, and Wikidata. Schema and sameAs links updated.
Crisis response overlay. The Crisis-Response Pattern runs out of cycle inside the calendar. The post-event review feeds the next Quarterly Narrative Audit.
Roles and ownership
The Sentiment Footprint owner is the Sentiment Shaping program lead (most often the customer's marketing operations lead in coordination with Searchbloom or the engaged agency). The Calendar owner is the same Program Lead. Source contribution analysis sits with a Sentiment Shaping operator running capture across the four major engines. Layer 1 and Layer 2 indicators sit with a data analyst pulling from Google Search Console, Google Trends, branded mention monitoring, and survey instrumentation. The layer prioritization callout is decided by the Program Lead in consultation with the operating team.
Programs that run the Footprint without the Program Lead seat in trigger meetings produce remediation work that lags the events the Lead should have attended. The Calendar is what makes the seat permanent.
Common Mistakes
Eight patterns that defeat the work. Each carries a one-sentence counter-test.
1. Skipping the Footprint. Counter-test: ask the program lead which cluster the current work is moving and what the per-cluster delta is week over week. If the answer is the aggregate score, the Footprint is missing.
2. Treating the Sentiment Score as one number. Counter-test: ask whether the program reports the weakest cluster as well as the aggregate.
3. Working only on the strongest cluster. Counter-test: sort the cluster table ascending. The cluster at the top is the priority unless the source contribution analysis or Layer 1 indicator read says otherwise.
4. Neglecting Layer 1 for Layer 3 speed. Counter-test: ask which Layer 1 work has been commissioned in the last twelve months and what branded search volume and unprompted parametric language samples are reading.
5. Neglecting Layer 4 surgical opportunities. Counter-test: ask which specific URLs were displaced or remediated in the last 30 days and what the per-prompt sentiment movement was.
6. Owned-only narrative discipline. Counter-test: ask when the brand's Wikipedia article, top five analyst notes, and top three review-platform descriptions were last reviewed against the canonical narrative.
7. Owned-only crisis response. Counter-test: review the last crisis-response execution. If step 2 (remediate upstream) shows fewer contacts than step 3 (publish correction) shows owned pages, the pattern was inverted.
8. Treating reputation as a PR function. Counter-test: confirm the Program Lead holds a permanent seat at product launch planning, executive communications review, pricing committee, and M&A communications. If the Lead is invited per event, the function is operating as PR.
Questions and Answers
Why measure first and act second?
Sentiment Shaping runs across four layers and six to twelve prompt clusters; the operating choice the program lead makes every week is which layer and which cluster to attack next. The Footprint is the prioritization tool. Without it, the program runs on instinct. The single most common reason engagements stall is skipping the Footprint and pushing on the wrong layer.
What if the brand has no vendor instrumentation in place?
Run a 50 to 100 prompt panel manually across ChatGPT, Claude, Perplexity, and Gemini and score against a simple positive / neutral / negative rubric. The manual panel produces a usable starter Footprint for the first 30 days. The vendor convention does not matter; the method does.
How long does Sentiment Shaping take to show results?
Layer 4 source-contribution work moves a per-prompt score in weeks. Layer 3 cluster work moves per-cluster scores in months. Layer 1 awareness work moves the human-side baseline over years and feeds Layer 2 parametric memory over training cycles (six to twenty-four months). A realistic scenario for a mid-market B2B SaaS brand: two quarters of disciplined work organized by the Footprint can move a brand-aggregate from the bottom of the healthy band to the middle. Beyond that requires Layer 1 awareness investment over years or a step-change event (a major analyst inclusion, a Tier 1 outlet front-page treatment, a viral cultural moment) that lifts multiple layers at once.
How does narrative coherence relate to the Sentiment Footprint?
Coherence is a dimension the Footprint reads, not a separate score. When a cluster is dragged by a diffuse, contradictory set of sources rather than one bad URL, that is narrative incoherence, and the source-contribution analysis surfaces it. A brand can be coherent but unfavorable, where every surface agrees and the story is not good, so the fix is sentiment work. A brand can be favorable but incoherent, where positive coverage carries contradictory framings, so the fix is alignment. The Footprint tells you which through the source pattern.
Does the Crisis-Response Pattern replace traditional crisis communications?
No. The Pattern runs alongside traditional comms and feeds the AI-specific work the comms team would not otherwise do. Steps 1 and 4 (document and monitor) are AI-specific because source captures and retrieved-set monitoring are not part of traditional comms. Step 2 (remediate upstream) extends to platform and editor outreach the comms team may not have on its checklist. Step 3 (publish authoritative correction) is the step comms likely already runs.
What is the right cadence for review responses?
Within 48 hours on every visible negative review on every cited platform. The response itself becomes retrieved content. A negative review with no response is interpreted by the model as accurate and unaddressed. A negative review with a thoughtful response showing context, accountability, and resolution shifts the language the model picks up from the same source.
What is the first concrete step for a brand that wants to run this work?
Build the Sentiment Footprint. Pull the brand-aggregate Sentiment Score from whichever vendor is in place. Pull per-prompt-cluster scores for the brand's top six to twelve intent clusters. Identify the weakest two or three clusters and read the source contributions. Every other move runs through the Footprint.
