Stop asking only whether your messages landed. Narrative Analysis tells you what is actually being said about you, in the words the market is using, whether you intended it or not.
What it does
Where message tracking measures your intent against coverage, narrative analysis sets your intent aside and reads the coverage on its own terms. It surfaces the dominant narratives organically forming around your brand or topic, groups them into clusters, and ranks them by how much of the conversation they own and how positive or negative they are. This is the analysis to reach for when your positioning may have drifted from reality, when unfamiliar themes are surfacing, or when you are in a contested narrative environment and need to see the whole field, not just the part you scripted.
The questions it answers
- What narratives are actually dominating our coverage right now, beyond the ones we set out to land?
- Which narratives are growing, which are stable, and which are fading within the period?
- Where are we sitting in sentiment terms, and which narrative is both large and negative?
- Is each narrative driven by a single repeat voice we can address, or by many independent voices we cannot?
- Are we caught up in any off-strategy narratives that pull us into topics outside our remit?
What's in the report
The report opens with a narrative landscape view: a bubble chart plotting the top narratives by prominence, sentiment and audience reach, so the shape of the conversation is visible at a glance. It then identifies five to eight narrative clusters, each named in plain language, not phrase fragments.
- A profile for every cluster covering its share of coverage, reach, net sentiment, intensity and trajectory, with the distinctive phrases and voices that define it.
- A dominance ranking that singles out the largest narrative, any high-intensity cluster with low share, and any cluster where high share meets negative sentiment.
- A sentiment-by-narrative breakdown that separates settled narratives from contested ones, where high positive and high negative coexist.
- An intensity view that classifies each cluster as a crisis seed, advocacy opportunity, active concern, active strength or background hum.
- Voice carriers per cluster, distinguishing addressable voice-led narratives from diffuse ones.
- Narrative interaction showing which clusters reinforce each other and which compete.
Where you supply a prior period, the report adds an emerging-versus-entrenched view classifying themes as new, growing, entrenched or fading. Every cluster is grounded in representative items cited back to source.
Who it's for
It serves communications and corporate-affairs leaders who need to understand the real narrative environment before they respond, brand strategists checking whether stated positioning matches what the market is saying, and issues and reputation managers watching for narratives that are quietly turning negative or pulling the brand off course.
Why Truescope Analyst
Mapping the real narrative landscape by hand means reading hundreds of articles, spotting the patterns, and resisting the urge to see only the story you hoped for. It is slow, it is subjective, and it usually stops at the coverage you happened to notice. An agency narrative audit gives you a considered read, but on their timetable and at their price. General AI tools can theme a handful of pasted articles, but they cannot cluster across your full dataset or weight a narrative by audience reach and intensity. Narrative Analysis reasons across all of your coverage, builds the clusters from co-occurrence rather than guesswork, weights them by reach, and cites the examples, in minutes, inside your subscription.
Frequently asked questions
What period does the narrative analysis cover?
It works across up to the last 13 months for coverage in your Truescope inbox, or the last 90 days for global data not yet matched to your workspace. Supplying a comparator period unlocks the emerging-versus-entrenched view that shows how narratives have drifted over time.
How are the narrative clusters decided?
Clusters are built from the phrases that actually co-occur in your coverage, then named in plain language. The grouping is computational and approximate, so it is best read critically: some clusters may be facets of one story, and one story may fragment across several. The named clusters are a starting point for judgement, not a verdict.
Can it tell which narratives we seeded versus which emerged on their own?
No. It names the narratives present in your coverage but cannot separate the ones you placed from the ones that arose organically. That context comes from you, and the analysis is most useful when you read it alongside what you know about your own activity.
