Measuring Negative Reviews' Impact on Coupon Repurchase
Management wants to understand how negative customer reviews affect the probability that a user repurchases the same coupon.
Design an analysis to quantify the causal impact of negative reviews on repurchase behavior.
Constraints & Assumptions
-
Distinguish negative reviews written by the focal user from negative reviews visible to that user before the repurchase decision.
-
Define the treatment, outcome, unit, and time windows before modeling.
-
Address confounding from coupon quality, merchant quality, price, promotion, seasonality, and user preferences.
-
Avoid reverse causality and post-treatment leakage.
Clarifying Questions to Ask
-
What counts as a negative review: rating threshold, text sentiment, complaint category, or volume shift?
-
Did users actually see reviews before deciding whether to repurchase?
-
What is the coupon lifecycle and expected repurchase window?
-
Are we estimating the effect of review exposure, review availability, or overall coupon reputation?
Part 1 - Define the Estimand
Specify outcome, treatment, unit of analysis, and time windows.
What This Part Should Cover
-
Use a user-coupon or user-merchant decision unit.
-
Define repurchase within a fixed window after eligibility or first purchase.
-
Define treatment as exposure to negative reviews before the decision, or an intent-to-treat proxy based on review availability.
-
Specify pre-period covariates and exclude information observed after treatment.
Part 2 - Data Requirements
List the data needed to measure review exposure and repurchase.
What This Part Should Cover
-
Include review ratings, text sentiment, timestamps, review visibility, page impressions, and ranking position.
-
Include transaction history, coupon attributes, merchant attributes, prices, discounts, inventory, and campaign calendar.
-
Include user history, acquisition channel, location, device, and prior engagement.
-
Include whether a user had an opportunity to see the review content.
Part 3 - Causal Inference Approach
Propose modeling or causal methods to isolate the review effect.
What This Part Should Cover
-
Use matching, fixed effects, difference-in-differences, propensity scores, or doubly robust methods where appropriate.
-
Include coupon, merchant, user, and time fixed effects when data supports them.
-
Check covariate balance, overlap, parallel trends, and sensitivity to unobserved confounding.
-
Consider natural experiments such as review moderation timing or delayed review display if available.
Part 4 - Interpretation and Validation
Explain how you would validate and communicate the results.
What This Part Should Cover
-
Report effect size, confidence intervals, and business impact on repurchase.
-
Segment by review severity, user type, merchant quality, and coupon category.
-
Run placebo tests, pre-trend checks, negative controls, and robustness checks.
-
Explain limitations if exposure is inferred rather than directly observed.
Follow-up Questions
-
How would you design an experiment to test review-display changes ethically?
-
What if negative reviews reduce repurchase but improve long-term trust?
-
How would you handle users who read reviews on another device or platform?