Historical tracking is the longitudinal measurement of brand mentions, citations, and sentiment in AI-generated answers. By storing full responses over time, teams can analyze trends, attribute improvements to content changes, and forecast volatility across platforms.
Why historical tracking matters
- Attribution: Connect visibility lifts to specific releases or PR moments.
- Seasonality: Separate cyclical patterns from lasting improvements.
- Forecasting: Anticipate platform behavior and plan content updates.
What to store
- Full responses with timestamps, platform, prompt, and citations.
- Mention, sentiment, and positioning annotations.
- Tags for topics, products, regions, and campaigns.
LLM Pulse provides auditable response history and time‑series views across platforms and tags to support robust analysis.
Table of Contents
How to implement a durable archive
- Define a stable prompt corpus: Include discovery (best‑for), education (what is), and comparison (X vs Y) prompts for each category you care about.
- Normalize metadata: Always capture platform, timestamp, prompt, project/brand, locale, and competitor set so data remains comparable.
- Store raw and derived data: Keep the full answer text and citations plus derived fields such as mention flags, sentiment scores, citation position, and positioning phrases.
- Version content: Note when your own pages changed to correlate cause and effect with visibility shifts.
Analysis playbook (from snapshot to story)
- Establish baselines (week 0–2): Measure mention frequency, citation frequency, share‑of‑voice, and sentiment per platform.
- Annotate moments: Product launches, pricing changes, PR hits, and major content releases.
- Compare deltas by platform: Expect Perplexity/Google AI to react faster (retrieval), and ChatGPT / Claude to shift slower (training‑led).
- Identify durable wins: Pages that sustain earlier citation positions or improved positioning phrases for 4+ weeks.
- Feed the roadmap: Convert findings into content updates (refreshes, new comparisons, added FAQs) and seeding placements.
KPIs and heuristics
- Stability: 4‑week moving average of mentions by platform and tag.
- Momentum: Week‑over‑week change in citation‑weighted visibility.
- Sentiment drift: Net positive/negative shift over rolling periods.
- Resilience: How quickly visibility recovers after platform volatility.
LLM Pulse workflow
- Time‑series dashboards: Trend mentions, citations, and sentiment by platform and tag.
- Answer archive: Click into any datapoint to read the exact response and citations.
- Annotations: Tag events (launches, updates, PR) to explain trend lines.
- Exports: Share monthly roll‑ups with execs and partner teams.
Common pitfalls
- Changing prompts too often (breaks comparability)
- Focusing only on one platform (misses cross‑platform shifts)
- Ignoring position in citations (early citations carry more weight)
- Not versioning your own content changes (hard to attribute impact)
Example insight to action
- Observation: Perplexity citations for your “pricing” page drop after a policy update; sentiment turns cautiously negative.
- Action: Refresh pricing page with clearer tiers, update comparisons, and add a dated change log. Re‑seed to third‑party hubs. Monitor recovery over the next 2–3 cycles.
