Cracking the Case Study: Analytics Interviews in the Age of Taxi Aggregators

Introduction

Over the last decade, the taxi industry in India has been transformed by the emergence of app-based taxi aggregators like Ola and Uber. Gone are the days of haggling with auto-rickshaws or worrying about drivers fleecing you by tampering meters. With just a few taps on your smartphone, you can now book a cab at your doorstep and travel in comfort.

However, this convenience has come at the cost of increased complexity in taxi fares. Earlier, fares depended simply on the distance traveled or were a flat rate. But today, the invoice from your Uber or Ola ride has a bewildering array of components:

Base fare: The flat fee charged for availing the cab
Price per km: Distance-based fee, usually higher for the first few kms
Price per minute: Fare component based on time taken for the ride
Minimum fare: The least amount you‘ll be charged for short rides
Peak pricing multiplier: Premium charged during times of high demand
Tolls, taxes and fees: Additional charges passed on to the rider

As data science and analytics professionals, it becomes important to analyze this complex and dynamic pricing model to identify the cheapest option for the riders. Consultant firms like McKinsey, BCG and Bain also frequently use such case studies to evaluate candidates‘ problem solving skills during interviews.

In this article, we‘ll take up a hypothetical case study to compare the fares of different taxi aggregators in Bengaluru across varying distances and vehicle types. We‘ll also learn a structured thinking approach that can be applied to solve such case studies and other unstructured problems efficiently.

The Case Study

Let‘s dive into the specifics of our case study. We‘ll consider three major taxi aggregators operating in Bengaluru:

1. Ola Cabs

Ola offers 3 categories of cabs:

Micro: Compact hatchbacks like Tata Nano or Datsun Redi-GO
Mini: Spacious hatchbacks like Suzuki WagonR or Hyundai i10
Prime Sedan: Premium sedans like Toyota Etios or Maruti Dzire

Here are the fare components for each category:

The minimum fare for Ola Micro is fixed at Rs. 50.

2. Uber

Uber has similar offerings with different branding:

UberGO: Economical hatchbacks, comparable to Ola Mini
UberX: Premium sedans, comparable to Ola Prime Sedan
UberXL: Premium SUVs for large groups, no direct competitor from Ola

3. TaxiForSure (TFS)

TaxiForSure, a subsidiary of Ola, has a simpler categorization:

Tata Indica: Spacious hatchback
Tata Indigo: Compact sedan
Maruti Swift: Premium hatchback

With this background information, let‘s look at some questions we want to analyze:

Which is the cheapest MICRO cab for distances between 1-8 km?
Which is the cheapest MINI cab for distances between 1-10 km?
If you get a free upgrade from Ola Micro to Mini, will it be cheaper than UberGO for 2-6 km?
If Uber is charging a 2.1X peak multiplier and Ola a 1.4X multiplier on Sedan cabs, which will be cheaper?
You‘ve already booked an UberGO at a 1.5X multiplier. Now an Ola Mini without peak pricing is available. But canceling Uber incurs a penalty. At what distance is it worth switching to Ola after factoring the cancellation fee?

To simplify our analysis, we can make a few assumptions:

Average speed of cabs is 13.33 km/hr, i.e. every km takes 4.5 minutes
No additional tolls or fees need to be factored
Taxes are excluded from fare calculations
Cancellation fees cannot be waived for confirmed bookings

Solving using Structured Thinking

Whenever faced with an unstructured problem statement like this, it helps to use a structured thinking approach to break down the problem into smaller parts and arrive at the solution systematically. Here are the broad steps to follow:

1. Understand the key aspects of the problem

First, develop a clear understanding of the different aspects that need to be considered:

The taxi operators and their offerings
Pricing components and fare structures for each
Relevant factors for the specific questions asked

In our case, we have 3 taxi services, each with multiple vehicle categories. For comparing fares, we need to consider the base fare, per km rate, per minute rate, minimum fare as well as additional factors like free upgrades, peak multipliers and cancellation penalties as mentioned in the questions.

2. Break the problem into smaller parts

Trying to tackle the entire problem at once can be overwhelming. Instead, break it down into bite-sized pieces that can be solved independently.

Here, we can consider each question separately. And for each question, we can calculate and compare the fares for the relevant cabs across different distances in the specified range.

3. Solve each part and combine the results

Solve each part of the problem one by one and then combine the intermediate results to arrive at the overall solution.

For instance, for the first question, we can calculate the fares for Ola Micro and TFS Indica for distances from 1 to 8 km in intervals of 1 km. We can then compare the fares at each distance to find the cheaper option.

Similarly for the other questions, we calculate fares with the additional constraints like free upgrades or peak multipliers and find the distance ranges where one cab is cheaper than the other.

4. Analyze the results to derive insights

Finally, look at the results from each part and try to draw meaningful conclusions and insights. Do you see any patterns or trends? Are there any surprising or counterintuitive findings? What are the key takeaways for the decision makers?

In our case, some of the key insights could be:

The cheapest cab varies with distance and vehicle type. There is no one-size-fits-all answer.
Small differences in per km or per minute rates can add up to significant amounts over longer distances.
Peak pricing can change the economic dynamics significantly. Ola can become cheaper than Uber during surge even with a higher base fare.

Detailed Working and Answers

Now that we have a framework to approach the problem, let‘s solve each question in detail.

Q1. Which is the cheapest MICRO cab for distances between 1-8 km?

Among the 3 services, only Ola and TFS offer a micro category. Here‘s how their fares stack up at different distances:

TFS Indica starts out cheaper but Ola Micro becomes more economical beyond 5 km due to a lower per km rate. So there is no definite answer valid throughout the range.

Q2. Which is the cheapest MINI cab for distances between 1-10 km?

For the mini category, we have Ola Mini, UberGO and TFS Indica as the contenders.

UberGO turns out to be the cheapest across all distances due to its low base fare and per minute rate. TFS Indica is significantly costlier than the other two.

Q3. If you get a free upgrade from Ola Micro to Mini, will it be cheaper than UberGO for 2-6 km?

Getting a free upgrade to a higher category in Ola is quite common during lean periods. If we compare the fares of Ola Mini and UberGO in the 2-6 km range:

Interestingly, even after the free upgrade, UberGO remains cheaper than Ola Mini at the lower end of the distance range. Ola Mini pulls ahead only past the 5 km mark.

Q4. If Uber is charging a 2.1X peak multiplier and Ola a 1.4X multiplier on Sedan cabs, which will be cheaper?

Cab fares during peak hours can get complicated due to surge pricing. Here‘s how Ola Prime and UberX compare with the respective multipliers:

Although Ola Prime is generally more expensive than UberX, it becomes the cheaper option across all distances with a lower surge multiplier. Highlights the impact of peak pricing.

Q5. You‘ve already booked an UberGO at a 1.5X multiplier. Now an Ola Mini without peak pricing is available. But canceling Uber incurs a penalty. At what distance is it worth switching to Ola after factoring the cancellation fee?

This is the most complex scenario. We need to calculate the fare difference between the cabs at each distance and see where it exceeds the Rs. 50 cancellation fee for UberGO.

As we can see, it‘s only at a distance of 14 km and beyond that the savings from switching to Ola Mini compensates for the Uber cancellation penalty. For any shorter ride, you‘re better off sticking to Uber even with surge pricing.

Conclusion

In this article, we took a deep dive into the surge pricing dynamics of app-based taxi aggregators and how it impacts the cheapest cab choice across different distances and vehicle types.

The key takeaways are:

With the advent of Ola and Uber, taxi fares have become quite complex with multiple components beyond just distance traveled
The cheapest option varies significantly based on the distance and type of vehicle chosen
Surge pricing during peak hours can change the relative economics between cab services
Structured thinking is a valuable tool to simplify complex problems by breaking them down into smaller parts

Cracking case study interviews at top consulting firms requires a combination of analytical thinking, attention to detail and clear communication. Practicing with mock case studies across different domains is the best way to develop these skills over time.

Here are some additional resources to build your structured thinking muscle:

Do you have any other tips or experiences to share about case study interviews? Let me know in the comments!

Cracking the Case Study: Analytics Interviews in the Age of Taxi Aggregators

Introduction