Market Entry and Consumer Behavior: An Investigation of a Wal-Mart Supercenter
Bibliographic record
Abstract
This paper provides an empirical study of entry by a Wal-Mart supercenter into a local market. Using a unique frequent-shopper database that records transactions for over 10,000 customers, we study the impact of Wal-Mart’s entry on consumer purchase behavior. We develop a joint model of interpurchase time and basket size to study the impact of competitor entry on two key household decisions: store visits and in-store expenditures. The model also allows for consumer heterogeneity due to observed and unobserved factors. Results show that the incumbent supermarket lost 17% volume—amounting to a quarter million dollars in monthly revenue—following Wal-Mart’s entry. Decomposing the lost sales into components attributed to store visits and in-store expenditures, we find that the majority of these losses were due to fewer store visits with a much smaller impact attributed to basket size. We also find that Wal-Mart lures some of the incumbent’s best customers, and that retention of a small number of households can significantly reduce losses at the focal store. Finally, certain observed household characteristics such as distance to store, shopping behavior, and product purchase behavior are found to be useful in profiling the defectors to Wal-Mart. Implications and strategies for supermarket managers to compete with Wal-Mart are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".