Modeling Credit Card Share of Wallet: Solving the Incomplete Information Problem
Bibliographic record
Abstract
Despite the powerful technologies that have enabled the assembly of transactional databases and the processing of information about individual customers and their buying patterns, database marketers have been limited by what is known as the “incomplete information problem”—that is, marketers have incomplete information about a customer's behavior in the product category of interest. A company's transactional database can only be built from customers' transactions with that company. Any transactions that have been made with other companies are missing. The authors present a modeling approach designed to solve the incomplete information problem. They use transactions conducted with a single supplier in that category to infer consumers' behavior with other suppliers in that category. In particular, armed with prior knowledge of the parametric form of consumer interpurchase time distributions, they uncover elements of the stochastic process that dictates which supplier a consumer chooses on a particular purchase occasion. The authors focus on interpurchase times because they form the core of the incomplete information problem. If a company uses its observed interpurchase times to estimate interpurchase times in the category for a specific consumer, its estimate will be biased upward. Combined with the model developed in this study, a familiar analysis of transaction profitability can be used to build a new type of lifetime value: lifetime category value of the customer.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".