Evaluate Chatbot's Retailer Value and Launch Viability
Overview
This question evaluates a candidate's skills in product analytics, causal inference and quasi-experimental methods, metric design across user/customer, merchant/platform, and model/operations perspectives, plus business case and launch viability assessment for a B2C chatbot.
Scenario
You are evaluating whether to launch a B2C chatbot for retailers on a commerce messaging platform. The chatbot automates merchant–customer responses (e.g., FAQs, order status, returns, product info), is available 24/7, and can hand off to humans when needed. Direct A/B testing is not feasible due to contractual or product constraints.
Assume you have a simple pre/post visualization showing trends such as average response time and resolution rate for early adopters versus the historical period, but not a full experimental control.
Task
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Articulate the value this chatbot provides to:
- (a) Retailers
- (b) The platform
- State whether it is worth launching and under what conditions.
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Define and justify key metrics from three perspectives:
- (a) User/customer
- (b) Business/merchant and platform
- (c) Model/operations
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Since A/B testing is not possible, describe how you would:
- (a) Size the opportunity
- (b) Identify launch signals and guardrails
- (c) Use quasi-experimental methods (e.g., synthetic control, matched markets, interrupted time series) to infer impact
- Given a simple visualization (as described above), critique its strengths and weaknesses and suggest improvements.
Hints
- Discuss retention, merchant response time, revenue lift, cost savings, quasi-experiments or synthetic controls when A/B is impossible.
Solution
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