Data stories
Data stories pitch original data to journalists and researchers, scored by mentions on the domains answer engines cite.
Data stories pitch original data to journalists and researchers. A story counts when it earns mentions on the domains answer engines cite.
Ratings
| Dimension | Rating | Reads |
|---|---|---|
| Scalability | 2 of 5 | low: higher grows further with spend or automation |
| Cost | 2 of 5 | low: lower costs less per Customer |
| Latency | 2 of 5 | low: lower answers sooner |
Budget
| Property | Value |
|---|---|
| Tier | $0 |
| Minimum effective budget | $0 a month |
How it runs
The Startup publishes the dataset and its method on its own Site first, with every figure dated. Pitches are outbound Messages and wait their delay window. Success is measured in mentions on cited domains, not in raw coverage.
Processes
Runs as Content marketing, accountable to the Marketing Team.
Fits
| Fits | Value |
|---|---|
| Family | demand |
| Shelf | Compound: builds an engine that keeps growing (Grow) |
| Moves | Organic share |
| Motions | b2b, b2c, b2d, b2a |
| Venues | ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews, Google Search |
| States | Live, Repeatable, Scale |
Loop
| Step | Value |
|---|---|
| Hypothesis | Original data pitched to journalists earns mentions on cited domains and raises organic share. |
| Measure | Organic share over 90d, at least 30 observations |
| Learn | The learn step reads the Readout, records Evidence and a Decision, and drafts the next Variant. |
Example
A Startup that prices freight lanes publishes a quarterly index of spot rates from the 80,000 quotes it served. Two trade outlets cite the index, and answer engines start quoting it for current freight rates.