Evaluate smart cart idea and design experiment

Question

Instacart is teaming up with a neighborhood grocery store to launch a smart cart inside the physical location. Customers shopping at that partner store can use the smart cart interface to:

1. search or browse products and in-store prices for that store, and
2. at the same time, view products and prices from other nearby stores available on the Instacart app.

You're a Data Scientist asked to judge whether this product idea is worthwhile and to plan how to measure its impact. Assume you can instrument events on the cart and tie them to Instacart account activity, though the feature launches in only some partner stores at first.

1. Is this a good idea? Give your reasoning, the objective the feature should support, and the main risks and tradeoffs.
2. Hypotheses. Put forward clear, directional, testable hypotheses—primary and secondary—about how the smart cart might affect the business. Include both desired positive effects and possible negative ones (for example, cross-store switching or partner-store cannibalization, choice overload, and price-perception or trust issues).
3. Metrics. Define a measurement plan that includes:
   • a primary success metric,
   • 2–4 diagnostic metrics (to explain why the primary metric changed), and
   • 1–3 guardrail metrics (to make sure no harm occurs). Be explicit about attribution windows, and whether outcomes are measured at the trip or store-visit level or at the user level.
4. Experiment design. Design an A/B test (or another approach) to measure causal impact, covering:
   • the unit of randomization (user, trip, cart, store, store-day/time-block) and why you choose it,
   • how to handle interference/spillovers (shoppers seeing others use the cart, the shared store environment, staff behavior),
   • necessary logging/instrumentation,
   • power/MDE considerations and how you would estimate them, and
   • key threats to validity (selection bias, noncompliance, novelty, SRM) plus mitigations, and how you would reach a ship/no-ship decision.
5. Quasi-experimental fallback. If a clean randomized experiment isn't possible, propose a credible quasi-experimental alternative (such as difference-in-differences or synthetic control) and the key assumptions you would need to validate.

Overview:

A PayPal data science onsite case: assess an Instacart smart-cart feature that displays in-store and competitor prices, then form hypotheses, define primary/diagnostic/guardrail metrics, and design a causal experiment. It tests product sense, experiment design, causal inference, and metric definition, including how to handle interference through store-level or switchback randomization and a difference-in-differences fallback.