Narrative Tracker

/digital-marketing-pro:narrative-tracker

Purpose

Track and analyze the narrative that AI engines construct about the brand. Monitor what ChatGPT, Perplexity, Gemini, and others say when asked about the brand, compare to desired positioning, detect drift or misrepresentation, and identify when competitors are gaining narrative territory in AI responses. Unlike visibility monitoring (which measures whether the brand appears), narrative tracking measures what is said — the qualitative story AI engines tell about the brand, whether it aligns with intended positioning, and how it changes over time. This gives marketers the insight to proactively shape AI perception through targeted content strategy rather than reacting after damage is done.

Input Required

The user must provide (or will be prompted for):

• Desired brand positioning statement(s): The core positioning the brand wants AI engines to reflect — value proposition, market position, key differentiators, and target audience. If not provided explicitly, these are extracted from the brand profile's positioning and messaging sections
• Key brand attributes to verify in AI responses: Specific attributes, claims, or themes that should appear when AI engines describe the brand — e.g., "enterprise-grade security", "founded in 2015", "serving 10,000+ customers", "leader in [category]". These become the checklist for narrative alignment scoring
• Competitor brands to track narrative for: One or more competitors whose AI narratives should be monitored alongside the brand — enables detection of narrative territory shifts where a competitor begins owning themes previously associated with the user's brand
• AI platforms to monitor: ChatGPT, Perplexity, Gemini, AI Overviews, Copilot — default is all. The user can narrow to platforms most relevant to their audience or where they have observed issues
• Query types: Brand queries ("Tell me about [brand]"), comparison queries ("[brand] vs [competitor]"), category queries ("best [category] solutions"), and problem-solution queries ("how to solve [problem brand addresses]"). A balanced mix is recommended for comprehensive narrative coverage

Process

1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Extract brand positioning, key messages, differentiators, value propositions, target audience, and competitive claims — these form the reference narrative against which AI responses are evaluated. Also check for guidelines at ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json — if present, load messaging dos/don'ts and positioning guardrails. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with user-provided positioning statements.
2. Query AI platforms and record narratives: For each query on each platform, capture the full AI-generated response and extract the narrative — what does the AI say about the brand, how does it position it relative to alternatives, what attributes does it highlight, what does it omit, and what does it get wrong. Record the complete response text, not just scores, because narrative analysis requires the actual language and framing used by the AI engine.
3. Score narrative alignment: Compare each AI response against the desired positioning on key dimensions. For each key brand attribute, mark as present (AI includes it accurately), absent (AI omits it), distorted (AI includes it but frames it incorrectly or negatively), or outdated (AI references an old version of this attribute). Flag misrepresentations where the AI states something factually incorrect about the brand. Flag narrative drift where the AI's positioning of the brand has shifted from the previous check — even if not incorrect, the framing or emphasis has changed. Calculate a narrative alignment score per platform and per query type.
4. Track competitor narratives: Run the same query types for each competitor brand. Record what AI engines say about competitors — their positioning, highlighted attributes, and claimed differentiators. Identify narrative territory shifts — themes or attributes that were previously associated with the user's brand but now appear in competitor descriptions, or neutral territory that a competitor has begun to claim. Map which brand "owns" which narrative themes in AI responses.
5. Record all narratives: Store full narrative data via python "${CLAUDE_PLUGIN_ROOT}/scripts/geo-tracker.py" --brand {slug} --action track-narrative --platform {platform} --context "{what the AI said}" (add --query/--url where available). The payload captures timestamp, platform, query, response text, alignment score, attribute presence/absence/distortion flags, misrepresentation flags, and competitor narrative data.
6. Compare to previous snapshots: If previous narrative data exists, diff current narratives against the most recent previous check. Detect new themes the AI has started associating with the brand, lost themes that no longer appear, shifted framing where the same attribute is described differently, resolved issues where previously flagged misrepresentations have been corrected, and new issues that have appeared since the last check.
7. Generate narrative correction strategy: Based on all findings, produce a targeted content strategy to influence AI perception — content to create that establishes missing attributes in citable sources, content to update that corrects outdated information AI engines are citing, structured data and entity updates that reinforce correct positioning, citation opportunities on high-authority platforms that AI engines trust, and defensive content for queries where competitors are gaining narrative territory.