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Boost App Installs: Analyze and Experiment with Conversion Funnel

Last updated: Mar 29, 2026

Quick Overview

DoorDash analytics prompt on improving mobile web to app-install conversion, covering funnel measurement, attribution, cohorts, install and activation experiments, incentives, guardrails, and long-term retention impact.

  • medium
  • DoorDash
  • Analytics & Experimentation
  • Data Scientist

Boost App Installs: Analyze and Experiment with Conversion Funnel

Company: DoorDash

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Onsite

##### Scenario Many users place orders through the mobile web but never install the native app. The company wants to increase app installs among this segment. ##### Question How would you assess the current conversion funnel from web order to app install? Propose and design experiments to boost install rate. ##### Hints Define cohorts, measure baseline funnel, consider in-product prompts, incentives, retargeting; outline experimental groups, metrics, and duration.

Quick Answer: DoorDash analytics prompt on improving mobile web to app-install conversion, covering funnel measurement, attribution, cohorts, install and activation experiments, incentives, guardrails, and long-term retention impact.

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|Home/Analytics & Experimentation/DoorDash

Boost App Installs: Analyze and Experiment with Conversion Funnel

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DoorDash
Jul 12, 2025, 6:59 PM
mediumData ScientistOnsiteAnalytics & Experimentation
23
0

Mobile Web Order to App Install Funnel and Experiments

Many users place orders through mobile web but never install the native app. The company wants to increase app installs among this segment without harming web ordering or customer trust.

Constraints & Assumptions

  • Focus on users who place an order on mobile web and have no prior app install tied to their account or device.
  • Measure install and activation within a defined attribution window.
  • Separate app-store click-through, install, first app open, login, and first app order.
  • Use randomized experiments where possible, and protect the core web checkout funnel.

Clarifying Questions to Ask

  • Why does the business want app installs: retention, order frequency, lower marketing cost, notifications, or better user experience?
  • Can we reliably link a web user to a later app install and login?
  • Where in the funnel can we intervene: pre-checkout, post-order, email/SMS, delivery tracking, or retargeting?
  • Are incentives allowed, and what budget or abuse constraints apply?

What a Strong Answer Covers

  • Baseline funnel definition from mobile web order to app-store click, install, app open, login, activation, and repeat order.
  • Cohorts and segments: first-time versus repeat customers, iOS versus Android, geography, acquisition channel, order size, delivery experience, and app-install history.
  • Instrumentation and attribution: deferred deep links, account matching, event logging, exclusion of existing app users, and intent-to-treat analysis.
  • Experiment ideas such as post-order prompts, delivery tracking nudges, incentives, personalized value props, retargeting, QR/deep links, and reduced friction.
  • A/B design with treatment/control, randomization unit, eligibility, primary metric, guardrails, sample size, duration, and ramp.
  • Guardrails: web checkout conversion, cancellation, support contacts, refund rate, notification opt-outs, app uninstall, and incentive abuse.
  • Long-term impact: activation quality, next-order conversion, retention, incremental orders, and contribution margin.

Follow-up Questions

  • What if install rate rises but repeat order rate does not?
  • How would you handle users who install but never log in?
  • Which prompt placement would you test first?
  • How would you measure incrementality for retargeting campaigns?
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