Audience Intent Lab

Enter a book title and description. The page infers category, addressable audience, and funnel assumptions deterministically, then lets real-market signals reshape expected purchases in the next 90 days.

Funnel Assumptions
Estimated unit sales per 90 days given funnel assumptions
Funnel Breakdown
Market Constraints
User Comparables

Add up to five comparable books by ISBN and self-reported sales. These entries stay in this browser and are not submitted.

Public Benchmarks
Internal Benchmarks for Nimble Books LLC

Forecast Provenance

Static GCP/Bolt external view · Deterministic text-to-assumption rules · Source database: /Volumes/Bolt4TB/audience_intent/audience_intent.sqlite
Generated: 2026-06-02T09:33:34.751591+00:00

Sources and Design Notes

Sources

  • U.S. population baseline and demographic/economic estimates.
  • Reader-screen and book-reading behavior attributes, including adult reader segments and book-frequency reference cells.
  • Education and socioeconomic audience attributes used for broad addressable-audience filters.
  • Public book-sales benchmark anchors from reported sales in news articles, trade publications, author pages, and publisher pages.
  • Public market-signal inputs entered by the user, including Amazon reviews, recent reviews, author platform, newsletter reach, group membership, expertise, and campaign scale.
  • User comparables entered by ISBN and self-reported sales. These are browser-local and are treated as unverified context.
  • Nimble Books LLC internal velocity and lifetime-sales anchors, visible only in internal view.

Research Literature

  • Rogers, Everett M. Diffusion of Innovations. 5th ed. Free Press, 2003.
  • Bass, Frank M. "A New Product Growth for Model Consumer Durables." Management Science 15, no. 5 (1969): 215-227. doi:10.1287/mnsc.15.5.215.
  • Lavidge, Robert J., and Gary A. Steiner. "A Model for Predictive Measurements of Advertising Effectiveness." Journal of Marketing 25, no. 6 (1961): 59-62. doi:10.1177/002224296102500611.
  • Colley, Russell H. Defining Advertising Goals for Measured Advertising Results. Association of National Advertisers, 1961.
  • Ajzen, Icek. "The Theory of Planned Behavior." Organizational Behavior and Human Decision Processes 50, no. 2 (1991): 179-211. doi:10.1016/0749-5978(91)90020-T.
  • Sheeran, Paschal. "Intention-Behavior Relations: A Conceptual and Empirical Review." European Review of Social Psychology 12, no. 1 (2002): 1-36. doi:10.1080/14792772143000003.
  • Webb, Thomas L., and Paschal Sheeran. "Does Changing Behavioral Intentions Engender Behavior Change? A Meta-Analysis of the Experimental Evidence." Psychological Bulletin 132, no. 2 (2006): 249-268. doi:10.1037/0033-2909.132.2.249.
  • Granovetter, Mark. "Threshold Models of Collective Behavior." American Journal of Sociology 83, no. 6 (1978): 1420-1443. doi:10.1086/226707.
  • Kahneman, Daniel, Jack L. Knetsch, and Richard H. Thaler. "Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias." Journal of Economic Perspectives 5, no. 1 (1991): 193-206. doi:10.1257/jep.5.1.193.
  • Morwitz, Vicki G. "Consumers' Purchase Intentions and Their Behavior." Foundations and Trends in Marketing 7, no. 3 (2014): 181-230. doi:10.1561/1700000036.
  • Sun, Baohong, and Vicki G. Morwitz. "Stated Intentions and Purchase Behavior: A Unified Model." International Journal of Research in Marketing 27, no. 4 (2010): 356-366. doi:10.1016/j.ijresmar.2010.06.001.
  • Young, Martin R., Wayne S. DeSarbo, and Vicki G. Morwitz. "The Stochastic Modeling of Purchase Intentions and Behavior." Management Science 44, no. 2 (1998): 188-202. doi:10.1287/mnsc.44.2.188.
  • Cunningham, George B., and Hyunil Kwon. "The Theory of Planned Behaviour and Intentions to Attend a Sport Event." Sport Management Review 6, no. 2 (2003): 127-145. doi:10.1016/S1441-3523(03)70056-4.
  • Lee, Choong-Ki, James W. Mjelde, Tae-Kyun Kim, and Hye-Mi Lee. "Estimating the Intention-Behavior Gap Associated with a Mega Event: The Case of the Expo 2012 Yeosu Korea." Tourism Management 41 (2014): 168-177. doi:10.1016/j.tourman.2013.09.012.
  • Reysen, Stephen, Daniel Chadborn, and Courtney N. Plante. "Theory of Planned Behavior and Intention to Attend a Fan Convention." Journal of Convention & Event Tourism 19, no. 3 (2018): 204-218. doi:10.1080/15470148.2017.1419153.

Design Notes

  • The forecast is deterministic and rules-based. It does not call paid model APIs.
  • The funnel starts from a U.S. population frame, narrows to a maximum potential addressable audience, then applies awareness, interest, intent, reach, demand, channel, price, and capture constraints.
  • Expected purchases are stated for the next 90 days.
  • Internal benchmark share links remove the internal flag so private views are not shared accidentally.