DoorDash Data Scientist Interview Question - Solving a Merchant Acquisition Problem

Summary

In this interview, the candidate discusses how they would build a predictive model to identify which merchants DoorDash should target for acquisition when entering a new market. They focus on factors such as merchant characteristics (menu diversity, pricing, customer reviews), market data (demographics, competition, location), and the merchant's potential for success on the platform. The candidate emphasizes the importance of clearly defining success metrics, selecting the right features, and choosing an appropriate model, while also considering the business context. They highlight the need for iterative evaluation, combining technical and business insights to ensure the model aligns with DoorDash’s goals for growth and customer satisfaction.

Takeaways

• 😀 The main task is to build a model to predict which merchants DoorDash should acquire when entering a new market.
• 😀 Defining a 'new market' is critical; in this case, it refers to a new city where DoorDash hasn't yet launched its food delivery service, like Miami.
• 😀 The success of acquiring new merchants can be defined in various ways, including the number of orders per week, revenue, or customer satisfaction (reviews and ratings).
• 😀 Short-term success metrics (e.g., first few months' order volume) are typically prioritized over long-term value for model building in new markets.
• 😀 Merchant acquisition focuses on predicting which merchants would succeed on DoorDash, but the company also needs to consider which merchants are interested in joining the platform.
• 😀 Important features for the model include demographic data (e.g., income, age), merchant characteristics (e.g., menu diversity, pricing), and competitive landscape in the target area.
• 😀 Location-based features are crucial, such as population density, proximity to demand hubs (residential areas, office buildings), and customer sentiments from online reviews.
• 😀 Data sources should include historical data from current markets, demographic insights, competition analysis, and merchant-specific details like business hours and cuisine type.
• 😀 When building the model, a few potential algorithms are suggested, including multi-variable regression and random forests, with performance benchmarked based on accuracy.
• 😀 The model must be validated, potentially with leading indicators (e.g., daily/weekly average orders), due to the lag in outcome metrics like monthly order volume or revenue.

Q & A

What is the primary objective of building a model for merchant acquisition in a new market like Miami?

The primary objective is to predict which merchants DoorDash should target for acquisition when entering a new market, such as Miami. The goal is to identify merchants that will be successful on the platform based on metrics like monthly order volume or revenue, particularly in the short-term after they join.

How should success be defined for a new merchant joining DoorDash in a new market?

Success can be defined in several ways, including metrics like the number of orders per week, monthly revenue, and customer satisfaction. The focus is primarily on short-term success (ramp-up period) rather than long-term value, though long-term factors like customer retention may also be considered.

What factors should be considered when evaluating which merchants to acquire in a new city?

Key factors include demographic data (income, age distribution), merchant-specific data (menu diversity, pricing, hours of operation, and cuisine type), competitive landscape (number of competitors in the area), location-based data (proximity to high-density areas), and customer sentiment (reviews from platforms like Yelp).

Why is it important to collect data on the population and demographics of a new market?

Understanding the population and demographics of the new market is critical because it helps to match merchant offerings with local preferences. Data like income distribution and age groups can help identify which types of cuisine or menu items are likely to be popular in that area.

What are some challenges that might arise when collecting data for merchant acquisition models?

Challenges include ensuring the data is accurate and comprehensive, avoiding biases in data collection, and dealing with hard-to-gather data like customer sentiment or competition density. There is also the challenge of integrating diverse data sources, such as demographic and technographic data.

What kind of predictive models are suggested for evaluating the success of a merchant on DoorDash?

The conversation suggests using models like linear regression for simplicity, or random forests for more flexibility. Random forests can handle non-linearity and reduce the risk of overfitting. Different models can be benchmarked to determine which one provides the most accurate results.

How would you evaluate the performance of a model in this context, given that success metrics are lagging indicators?

To address the lag in success metrics (like monthly orders or revenue), it’s suggested to use leading indicators, such as weekly or daily averages, to provide earlier feedback on the merchant’s performance. This allows for faster decision-making while still tracking longer-term success.

How can a model avoid overfitting when predicting the success of new merchants?

Overfitting can be avoided through techniques such as cross-validation, hyperparameter optimization, and ensuring that the data is split properly between training and testing datasets. Additionally, using regularization techniques and evaluating models on out-of-sample data can help ensure that the model generalizes well.

Why is it important to define the scope of the problem before diving into modeling?

Defining the scope upfront helps ensure that the model focuses on the most relevant aspects of the problem. The scope should clearly define what inputs (features) will be used, what success metrics will be evaluated, and how the model will be applied in the business context. This prevents unnecessary complexity and ensures that the modeling efforts are aligned with business goals.

What role does business collaboration play in building a predictive model for merchant acquisition?

Collaboration with business stakeholders is crucial in defining success metrics, understanding business priorities, and ensuring that the model aligns with company objectives. It’s important for data scientists to work closely with the business team to ensure that the model outputs are actionable and that everyone is aligned on the goals and scope of the project.