Common DoorDash Case Study Interview Questions and Examples

 

Based on Glassdoor reviews and candidate reports, these are the most frequent case types you'll encounter.

 

Market Expansion Cases

 

Example: "DoorDash is considering launching in Charlotte, NC. How should we approach this decision?"

 

How to approach it:

 

First, clarify the objective. Are we optimizing for revenue growth, market share, or profitability? Different goals lead to different recommendations.

 

Structure your analysis around four key areas:

 

Market Attractiveness

 

• Population: Charlotte metro is ~2.8M people
• Demographics: Median household income, age distribution, urbanization rate
• Food delivery penetration: What % of restaurant spending happens via delivery?
• Estimated TAM: If 2.8M people × 30% delivery adoption × $200/year spending = $168M annual market

 

Competitive Landscape

 

• Who's already there? (Uber Eats, Grubhub)
• What's their market share?
• Are they profitable, or subsidizing growth?
• What's their merchant selection and delivery time?

 

Operational Feasibility

 

• Geography: Charlotte is sprawling. Will we need more Dashers per order than in dense cities?
• Transportation: Do Dashers need cars, or can bikes work in Uptown?
• Merchant density: Are restaurants clustered or spread out?
• Dasher supply: Can we recruit enough drivers? What's the gig economy like?

 

Financial Viability

 

• Customer acquisition cost in a competitive market
• Expected order frequency and AOV
• Unit economics: Contribution margin per order
• Time to profitability

 

Strong answer example: 

 

"I recommend we launch in Charlotte, but with a focused strategy. 

 

We should start with the Uptown and South End neighborhoods, which have high population density and strong restaurant clusters. This limits our initial Dasher needs and reduces delivery distances. We can acquire customers at an estimated $35 CAC through local partnerships rather than expensive digital advertising. 

 

Based on comps from similar metros like Nashville, I estimate we'd reach 100,000 MAU within 12 months with a 2.5 order frequency, generating $60M in annual GMV. At a 23% take rate, that's $13.8M in revenue against approximately $10M in year-one costs, reaching profitability in month 16."

 

Product Launch Cases

 

Example: "We're testing DashMart convenience stores in three cities. What should we analyze before expanding to 20 more cities?"

 

How to approach it:

 

Define success metrics first:

 

• Customer metrics: Orders per store per day, basket size, customer retention
• Unit economics: Revenue per store, contribution margin, payback period
• Operational metrics: Inventory turnover, out-of-stock rate, delivery time

 

Analyze the pilot results:

 

• Which city performed best/worst, and why? (Demographics? Existing customer base? Marketing?)
• What's the customer overlap with restaurant orders? Are DashMart customers new or existing?
• What items sell best? (This informs inventory strategy)
• Are we cannibalizing restaurant orders or creating incremental demand?

 

Identify risks before scaling:

 

• Supply chain complexity: Can we source inventory reliably in 20 cities?
• Real estate: Can we find suitable store locations in each market?
• Capital intensity: Each store requires ~$200K investment. Do we have $4M to deploy?
• Operational bandwidth: Do we have regional managers who can oversee expansion?

 

Strong answer example: 

 

"The pilot data shows strong performance in Atlanta (180 orders/day) and moderate performance in Phoenix (95 orders/day), but Seattle is struggling (40 orders/day). 

 

The key difference is existing DoorDash customer density. Atlanta had 300K MAU before DashMart; Seattle had only 85K. I recommend we only expand to cities with 200K+ existing MAU, strong grocery delivery demand signals (high Instacart usage), and available real estate near residential areas. 

 

This filters our list from 20 cities to 8 high-probability markets. We should launch 3 stores in each city over 6 months rather than launching all 20 markets simultaneously, which allows us to refine operations and reduce risk."

 

Operations Optimization Cases

 

Example: "Delivery times increased from 32 to 37 minutes in San Francisco over the past month. Diagnose the problem and recommend solutions."

 

How to approach it:

 

Break delivery time into components:

 

• Restaurant prep time (time to cook food)
• Dasher assignment time (time to find available Dasher)
• Dasher transit to restaurant (drive time to pickup)
• Wait time at restaurant (Dasher arrives before food ready)
• Delivery time (restaurant to customer)

 

Ask for data on each component. If data isn't available, make explicit assumptions.

 

Generate hypotheses for each component:

 

Restaurant prep time increase:

 

• Did we add new restaurant partners who are slower?
• Are existing restaurants getting more complex orders?
• Is there a seasonal effect? (More complex orders in winter?)

 

Dasher assignment time increase:

 

• Do we have fewer active Dashers? (Check Dasher supply trends)
• Are Dashers declining orders more often? (Check acceptance rate)
• Has order volume spiked beyond Dasher capacity?

 

Delivery time increase:

 

• Has traffic gotten worse? (Check against city traffic data)
• Are delivery distances longer? (Check average miles per delivery)
• Are Dashers making multiple stops? (Check multi-order delivery rate)

 

Prioritize solutions by impact and ease:

 

Quick wins (implement this week):

 

• Offer peak-hour bonuses to increase Dasher supply during dinner rush
• Adjust assignment algorithm to prioritize nearby Dashers, even if slightly less efficient

 

Medium-term (implement this month):

 

• Analyze slow restaurants and provide prep time feedback or pause new orders when kitchen is backed up
• Test higher base pay to improve Dasher acceptance rates

 

Long-term (implement this quarter):

 

• Expand Dasher recruitment in underserved SF neighborhoods
• Optimize kitchen partnerships to reduce prep time variability

 

Strong answer example: 

 

"I'd start by analyzing where the 5-minute increase is coming from. My hypothesis is that Dasher supply has dropped—perhaps due to recent gas price increases or a competing platform offering higher pay. I'd check our active Dasher count, acceptance rates, and earnings per hour over the past 60 days. 

 

If I'm right and we've lost 15% of our Dasher base, I'd recommend an immediate $3 peak-hour bonus from 5-8pm to increase supply. This costs roughly $3 × 50,000 peak orders = $150K/day, but prevents customer churn from slow deliveries.

 

In parallel, I'd investigate why Dashers are leaving and address the root cause—whether that's competitive pay, better routing tools, or gas subsidies."

 

Pricing and Economics Cases

 

Example: "We're testing a new Dasher pay model: $2.50 base + 15% of order subtotal + $50 bonus for every 5th delivery. Should we launch this?"

 

Calculate the economics compared to current pay structure. Assume current model is $3 base + $1/mile + $0.10/minute.

 

Example calculation for average order:

 

• Current model: $3 + ($1 × 3 miles) + ($0.10 × 20 minutes) = $8
• New model: $2.50 + (15% × $40 order) + ($50 ÷ 5 deliveries) = $2.50 + $6 + $10 = $18.50

 

Wait—this seems dramatically higher. That can't be right for every delivery. Dig deeper:

 

The $50 bonus for every 5th delivery averages to $10/delivery only if Dashers complete 5 deliveries. Many Dashers only do 2-3 deliveries per session. Let's recalculate:

 

• Dasher who does 3 deliveries: Gets $0 bonus, so average = $8.50/delivery
• Dasher who does 5 deliveries: Gets $50 bonus, so average = $18.50/delivery
• Dasher who does 10 deliveries: Gets $100 bonus, so average = $18.50/delivery

 

This creates a strong incentive for Dashers to complete at least 5 deliveries per session. But what about behavioral effects?

 

Impact on Dasher behavior:

 

• Positive: Encourages longer sessions (Dashers will try to hit 5 deliveries)
• Negative: Dashers might decline orders #1-4 if they're low-value, waiting for better orders
• Positive: The 15% of order value incentivizes Dashers to accept higher-value orders
• Risk: Could reduce acceptance rates for small orders (<$20)

 

Impact on DoorDash economics:

 

• If average Dasher completes 5 deliveries, we're paying $18.50 vs. $8 = +$10.50/delivery
• That's a 131% increase in delivery costs
• Would need massive efficiency gains or price increases to offset this

 

Impact on customers:

 

• If we pass costs to customers, delivery fees increase significantly → order volume drops
• If we absorb costs, unit economics break → unsustainable
• Faster delivery times (more available Dashers) could improve satisfaction

 

Strong answer example:

 

"This pay model would increase our costs by over 100% for frequent Dashers, which is financially unsustainable without major price increases. However, the core insight, incentivizing longer Dasher sessions, is valuable.

 

I'd recommend testing a modified version: $3 base + 10% of order subtotal + $15 bonus for every 5th delivery. This creates the session-length incentive while keeping costs closer to current levels.

 

We should A/B test this in one market for 4 weeks, measuring Dasher session length, acceptance rates, delivery times, and unit economics before deciding on a broader rollout."

 

Metric Design Cases

 

Example: "We're launching bike Dashers in NYC. What metrics should we track to measure success?"

 

How to approach it:

 

Organize metrics by stakeholder, then by metric type (input, output, outcome).

 

Customer Metrics:

 

• Delivery time (outcome): Bikes might be faster in traffic but slower on long distances
• Order completion rate (outcome): Weather affects bikes more than cars
• Customer satisfaction score (outcome): Are customers happy with bike delivery?

 

Dasher Metrics:

 

• Earnings per hour (outcome): Can bike Dashers make similar money to car Dashers?
• Deliveries per shift (output): Are bikes completing fewer deliveries due to distance limits?
• Safety incidents (outcome): Bike accidents, injuries
• Active Dasher retention (outcome): Do bike Dashers stay active at similar rates?

 

Operational Metrics:

 

• Average delivery distance (input): Bikes should get shorter routes
• Order assignment time (output): Can we match bike orders as quickly?
• Service area coverage (input): What % of NYC can bikes serve effectively?
• Orders per square mile (input): Bikes work best in dense areas

 

Business Metrics:

 

• Contribution margin per delivery (outcome): Are bike deliveries more profitable?
• Market share in bike-serviceable areas (outcome): Do bikes help us compete better?
• Incremental orders (outcome): Do bikes unlock new order types?

 

Strong answer example:

 

"The primary metric should be Dasher earnings per hour—if bike Dashers can't earn within 10% of car Dashers, we won't have supply. I'd set a target of $22/hour for bikes vs. $24/hour for cars. 

 

Secondary metrics include delivery time (target: under 30 minutes for 90% of orders within 2 miles) and safety incidents (target: zero serious injuries per 10,000 deliveries).

 

I'd also track service area coverage—we should map which neighborhoods bikes can serve effectively and ensure we have density of both orders and bike Dashers in those areas.

 

Finally, I'd measure customer satisfaction separately for bike vs. car deliveries to identify any quality gaps."

 

How to Approach DoorDash Case Study Interviews

 

Here's an approach that works for all DoorDash case study interview types.

 

Step 1: Clarify the Objective (2 minutes)

 

Don't jump straight to solving. DoorDash cases are intentionally ambiguous. Ask:

 

• What's the primary goal? Revenue growth? Profitability? Market share? Customer satisfaction?
• Are there specific constraints? Budget limits? Timeline? Geographic scope?
• What does success look like? How will we measure it?
• Which stakeholder should we prioritize? (All three matter, but one might be primary)
• What data or information is available?

 

Taking 2 minutes here prevents solving the wrong problem.

 

Step 2: State Your Assumptions Explicitly (1 minute)

 

You won't have perfect information. Be transparent about what you're assuming.

 

Stating assumptions demonstrates analytical maturity and makes it easy for interviewers to course-correct if your assumptions are off-base.

 

Step 3: Structure Your Approach (2-3 minutes)

 

Break the problem into logical components. Share your structure before diving in. Here are some examples:

 

For a market expansion case:

 

1. Market attractiveness (size, growth, demographics)
2. Competitive landscape (who's there, how strong are they)
3. Operational feasibility (can we execute well)
4. Financial viability (will it be profitable)
5. Strategic fit (does this align with our priorities)

 

For a problem diagnosis case:

 

1. Define the problem clearly and quantify it
2. Break it into components (for delivery time: prep, assignment, transit, delivery)
3. Generate hypotheses for each component
4. Analyze data to confirm or eliminate causes
5. Recommend solutions prioritized by impact

 

For a new initiative case:

 

1. Define success metrics
2. Estimate market opportunity
3. Assess unit economics
4. Identify key risks
5. Design test-and-learn approach

 

This keeps you organized and makes it easy for interviewers to follow your logic.

 

Step 4: Analyze with a Three-Sided Lens (15-20 minutes)

 

This is what makes DoorDash cases unique. Before making any recommendation, rigorously evaluate impact on all three stakeholders.

 

Step 5: Be Quantitative Wherever Possible (throughout)

 

DoorDash values data-driven thinking. Even without exact numbers, demonstrate quantitative reasoning:

 

Show the interviewer you think in terms of magnitude and impact, not just directionally.

 

Step 6: Keep Recommendations Practical and Testable (final 5 minutes)

 

DoorDash wants implementable ideas, not theoretical frameworks. Structure recommendations as experiments.

 

Include:

 

• Specific action (what exactly to do)
• Test design (where, how long, what to measure)
• Success criteria (what results would justify scaling)
• Timeline (when to decide)

 

Step 7: Acknowledge Trade-offs and Risks (final 2 minutes)

 

No solution is perfect. Strong candidates proactively identify downsides.

 

The DoorDash Take-Home Assignment

 

The take-home case is intense. Here's what you need to know:

 

1. You'll get minimal guidance

 

The prompt will be vague on purpose. You need to define the problem yourself and make reasonable assumptions. Document everything.

 

2. Time commitment is significant

 

Most candidates report spending 5-10 hours. Don't underestimate this. Block out time over 1-2 days to do quality work.

 

3. Deliverable format matters

 

Usually you'll create a slide deck or written report. Make it professional:

 

• Executive summary upfront
• Clear structure with headers
• Data visualizations where helpful
• Specific, actionable recommendations
• Appendix with detailed analysis

 

4. They may not discuss it much

Some candidates report the hiring manager barely references their work in the follow-up interview. Don't let this throw you. Be ready to defend your approach and recommendations anyway.

 

5. It's primarily a filter

The take-home helps DoorDash filter candidates before investing in onsite interviews. Put in the effort to pass this gate.

 

How to Prepare for DoorDash Case Study Interviews

 

Start preparing at least 2-3 weeks before your interview:

 

1. Learn DoorDash's business model

 

You can't solve DoorDash cases without understanding how they work. Study:

 

• The three-sided marketplace dynamics
• Revenue model (commissions, delivery fees, DashPass subscriptions)
• Key products (DoorDash, DashMart, DoorDash Drive, Storefront)
• Recent launches and strategic priorities
• Competitor positioning (Uber Eats, Grubhub, Instacart)

 

Read DoorDash's blog and recent news articles. Watch their earnings calls if you can.

 

2. Understand DoorDash’s key metrics

 

You cannot solve DoorDash cases without understanding these metrics cold. Memorize them and understand how they relate to each other.

 

Core Business Metrics

 

• GMV (Gross Merchandise Value): Total value of all orders placed through DoorDash. If customers order $100M worth of food in a month, GMV = $100M.

 

• Take Rate: DoorDash's percentage of GMV, including commissions from merchants, delivery fees from customers, and other charges. If GMV is $100M and DoorDash collects $25M, take rate = 25%.

 

• Monthly Active Users (MAU): Number of unique customers who place at least one order per month.

 

• Order Frequency: Average orders per customer per month. If you have 1M customers placing 3M orders, frequency = 3.0.

 

• Average Order Value (AOV): GMV divided by number of orders. Critical for understanding customer behavior and unit economics.

 

Operational Metrics

 

• Average Delivery Time: Time from order placement to delivery. Industry benchmark is 30-40 minutes; anything above 45 minutes significantly hurts customer satisfaction.

 

• Order Completion Rate: Percentage of orders successfully delivered. Target is 95%+. Cancellations hurt all three stakeholders.

 

• Dasher Utilization Rate: Percentage of time Dashers spend on active deliveries versus waiting for orders. Higher utilization = better Dasher earnings and satisfaction.

 

• Time to Assignment: How long it takes to match an order with a Dasher. Under 2 minutes is ideal; above 5 minutes indicates Dasher supply issues.

 

Stakeholder Metrics

 

• Customer Acquisition Cost (CAC): Marketing spend divided by new customers acquired. Benchmark is $20-40 per customer in competitive markets.

 

• Customer Lifetime Value (LTV): Total gross profit generated by a customer over their lifetime. Strong marketplace businesses have LTV:CAC ratios of 3:1 or higher.

 

• Dasher Retention Rate: Percentage of Dashers still active after 30/60/90 days. Losing Dashers after one week indicates pay or experience problems.

 

• Merchant Churn: Percentage of restaurants that stop using DoorDash each month. High churn (>5% monthly) suggests commission or operational issues.

 

3. Practice marketplace cases

Standard consulting cases help, but you need marketplace-specific practice. Look for cases about:

 

• Two-sided or three-sided platforms
• Operations and logistics
• Pricing and economics
• Market entry and expansion

 

4. Do mock interviews

 

Get feedback on your thinking process. Have someone give you a DoorDash-style prompt and practice talking through it. Record yourself to catch filler words and unclear explanations.

 

5. Review SQL and data analysis

 

While not always required, some rounds include data analysis. Refresh your SQL skills and practice working with datasets quickly.

 

6. Prepare behavioral stories

 

You'll face behavioral questions alongside cases. Have STAR-format stories ready about:

 

• Complex problems you solved
• Cross-functional collaboration
• Conflict resolution
• Times you moved fast and iterated
• Failures and lessons learned

 

Your Next Step to a DoorDash Offer

 

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