Investigate Declining ROI and Propose Effective Solutions

Quick Overview

This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Investigate Declining ROI and Propose Effective Solutions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Investigate Declining ROI and Propose Effective Solutions

Company: TikTok

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Technical Screen

##### Scenario E-commerce platform evaluating advertising effectiveness and diagnosing performance issues. ##### Question Describe one of your resume projects: which success metrics did you define, how did you evaluate them, and what were the key details of your A/B experiment design? Case 1 – The business wants to increase merchant advertising revenue or merchant GMV. How would you analyze the situation and propose actions? Case 2 – ROI on ads has recently declined. Walk through how you would investigate root causes and recommend fixes. ##### Hints Frame objectives, choose north-star and supporting metrics, outline experiment setup, segmentation, funnel and cohort analyses, and hypothesis-driven root-cause investigation.

Quick Answer: This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Investigate Declining ROI and Propose Effective Solutions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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Investigate Declining ROI and Propose Effective Solutions

E-commerce Ads Effectiveness and Diagnostics (Analytics & Experimentation)

Context

You are a data scientist on an e-commerce platform responsible for measuring advertising effectiveness and diagnosing performance issues across a two-sided marketplace (users and merchants). You work with paid placements/ads that affect merchant GMV and ad ROI.

Tasks

  1. Resume Project Walkthrough
  • Describe one relevant project end-to-end:
    • Objectives and success metrics (north-star and supporting/guardrail metrics).
    • How you evaluated success (incrementality, attribution, and statistical evaluation).
    • Key details of your A/B experiment design (unit of randomization, power/MDE, duration, interference handling, guardrails).
  1. Case 1 — Grow Merchant Ad Revenue or Merchant GMV
  • The business wants to increase either merchant advertising revenue (ad spend on the platform) or merchant GMV. How would you:
    • Frame objectives and select metrics.
    • Analyze current performance (funnel, segments, cohorts).
    • Propose data-driven actions and experiments.
  1. Case 2 — ROI on Ads Has Declined
  • ROI/ROAS has recently fallen. Walk through how you would:
    • Diagnose root causes with a structured metrics tree and analyses (segmentation, cohorts, time-series, experiment logs).
    • Recommend short-term mitigations and longer-term fixes.

Consider Including

  • Clear north-star metric and supporting metrics.
  • Experiment setup for marketplaces with potential auction/interference effects.
  • Funnel, cohort, and segmentation analyses.
  • A hypothesis-driven root-cause framework and validation plan.

Constraints & Assumptions

  • Preserve the scope, facts, inputs, and requested outputs from the prompt above.
  • If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
  • Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask Guidance

  • Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
  • State assumptions about instrumentation, randomization, sample size, and data quality.
  • Separate descriptive analysis from causal claims.

What a Strong Answer Covers Guidance

  • A metric framework with primary, guardrail, and diagnostic metrics.
  • A credible analysis or experiment design with clear assumptions and bias checks.
  • SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
  • An actionable recommendation that explains trade-offs and next steps.

Follow-up Questions Guidance

  • What sanity checks would you run before trusting the result?
  • How would you handle novelty effects, seasonality, or selection bias?
  • What decision would you make if metrics disagree?
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