Customer Franchise—A Hidden, Yet Crucial, Asset
Bibliographic record
Abstract
Abstract We introduce a measure of customer franchise value for subscription‐based companies—a fast growing and vital sector of the economy. This measure is based on information voluntarily disclosed by some, but not all, firms. Controlling for self‐selection, we examine the measure's information content and find that customer value is significantly positively associated with stock price and this association is incremental to both GAAP and a set of non‐GAAP variables typically considered in valuation tests. Furthermore, we show that the customer value measure is positively associated with future earnings and analysts' forecast errors. Importantly, we find that the documented results are robust to controlling for the individual inputs used to derive the measure, highlighting the need to consider the interaction between stand‐alone value drivers in assessing a firm's performance. These findings indicate that the proposed measure of customer value is an important valuation tool that quantifies and summarizes the main trends and factors underlying the performance of subscription‐based enterprises. This study informs researchers and investors, as well as accounting policymakers, about a major value‐generating asset currently missing from corporate financial reports.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".