Noise for the sake of noise has always been stupid. Now, in the era of LLMs as the primary gateway to the internet, it is flat-out dangerous.
We see a lot of consumer tech companies “doing good PR” and getting coverage. It appears as though everything is fine, yet, when you look at how they are described in aggregate, the story is fuzzy.
If PR in 2026 is about shaping memory, then narrative consistency is the most critical KPI of a program. What matters is whether your story holds together when it is told by other people, and the RAG bots who increasingly do their bidding.
To this end, here’s a stab at an early framework we are starting to use to assess clients’ narrative consistency. I imagine it will evolve and, of course, welcome feedback.
What Narrative Consistency Actually Measures
To be clear, Narrative Consistency Score is not a dashboard metric, but a diagnostic you can undertake without paid or fancy tools (though I would love to see such a thing integrated into tools like Scrunch at some point)..
If five different people encountered your company through five different pieces of coverage, would they describe it the same way? Obviously not word for word, but in overall gist and substance.
If the answer is no, your PR is fragmenting memory instead of building it.
As we all know now, AI systems behave the same way humans do here. They do not reconcile contradictory explanations. They look for stable patterns and reuse them.
Where Narrative Consistency Breaks Down
In practice, inconsistency shows up in a few predictable places.
Problem framing drift
Imagine this scenario: One article says you are solving for convenience. Another frames you as a performance solution. A third positions you as a price-centric player.. All of those may be true, but when they rotate without a clear hierarchy, the market, and especially LLMs do not know which one matters.
If the problem you exist to solve changes depending on who is writing, there is no anchor.
Category confusion
This one is common in consumer tech.
You get described as a smart home company in one place, wellness in another, productivity somewhere else. Categories are memory shortcuts. If you do not supply one that sticks, people will supply different ones for you. The result is dilution.
Language that never repeats
Strong narratives reuse language.
If every article sounds novel, that is not a win. You need themes and phrases to recur across coverage, so that Ai systems have something to latch onto.
Rotating proof points
Like the aforementioned areas, proof should not come from a million sources. Indeed it should compound. When it rotates endlessly, credibility resets every time for an LLM.
How to Assess Narrative Consistency
As I said, you don’t need software for this. You can assess it with a small, fixed dataset and a bit of discipline.
Start with a defined sample:
- 8–12 earned media articles from the last 12 months (yes, you could analyze a bigger sample, but this is a start)
- 3–5 reference or community surfaces, such as Wikipedia entries, subreddit threads, community boards, or major directories
- Your current homepage copy
You want to evaluate four dimensions. Each is scored independently.
1. Problem Definition Stability
Guiding Question: Do sources describe your company or product as addressing the same underlying problem?
Method:
- Extract the primary problem statement from each source
- Normalize the language
- Count distinct problem framings
Scoring:
- 0–1 dominant problem definition: strong
- 2–3 definitions: weak
- 4+ definitions: incoherent
2. Category Placement Variance
Question: Are you placed in the correct category consistently?
Method:
- Log category labels and implied buckets across all sources
- Include user-generated classifications, not just editorial ones
- Count unique category placements
Scoring:
- 1–2 categories used consistently: strong
- 3–4 categories: unstable
- 5+ categories: high narrative entropy
Community platforms are especially sensitive to category confusion. They surface disagreement quickly.
3. Phrase Reuse Frequency
Question: Do relevant explanatory phrases repeat across sources?
Method:
- Identify 3–5 sentences that explain what the company does or why it exists
- Track reuse or close paraphrase across media and community sources
- Try to ignore slogans and taglines
Scoring:
- ≥30% of sources reuse similar explanatory language: strong
- 10–30%: weak
- <10%: negligible signal
If language does not survive paraphrase, it will not survive AI summarization.
4. Proof Point Concentration
Question: Are the same proof points cited repeatedly?
Method:
- List all proof points referenced across sources
- Identify how often the top 2–3 appear
- Weight community citations slightly higher than brand-originated claims
Scoring:
- Top proof points appear in ≥40% of sources: strong
- 20–40%: weak
- <20%: scattered credibility
Interpreting the Output
You do not need to compress this into a single number. The pattern matters more than the total.
Remember that strong narratives show:
- A stable problem definition
- Low category variance
- Reusable explanatory language
- Concentrated proof points across third-party reference surfaces
If two or more dimensions score poorly, your PR program is likely producing activity without durable memory.
The New Way
It is high time we move from rewarding PR for activity and instead incentivize memory making as a chief goal of any program.
Get in touch if you are wondering about how to send clear signals in 2026 and maintain narrative consistency.