It’s no secret that the world of e-commerce is booming. While brick-and-mortar retail is going through challenging times due to the COVID-19 pandemic, some e-commerce categories are seeing a surge in sales, including food and beverage, personal care, and home fitness. With that success comes a slew of employment opportunities in the e-commerce space.
These e-commerce case study questions are applicable to interviews for a variety of roles including marketing analytics, business intelligence, data science, and any type of strategic role that relates to attracting and retaining customers for an online brand.
E-commerce case studies are generally scenario-based questions that ask you to work through a solution to a proposed business or marketing problem for an e-commerce brand.
Your job is to ask the interviewer for more information, make assumptions about the case, propose a solution, and ultimately consider the trade-offs of your solution. Case study interview questions aim to assess five major qualities:
Case study interviews effectively screen candidates because they assess these qualities in just a 30- to 45-minute exercise.
Let’s take a look at some of the top answers to e-commerce case study interview questions:
To investigate the revenue decline, you have access to information like:
An e-commerce case study like this is asked during marketing analytics interviews to determine if you can propose strong metrics to investigate a problem. You might start by investigating monthly revenue by marketing source, category/subcategory, or by the percent of the discount applied.
This analysis will help you understand if the decline is due to decreasing marketing efficiency, an overreliance on discounts, or if a particular category is declining. You could also investigate changes in profit margin per unit, which could help you identify if production costs are rising.
Broadly speaking, sending a mass email blast to a list of customers is generally not a good idea, especially when the objective of the email is to increase sales.
A better solution is to segment the audience and personalize the messaging by the audience. For example, if a customer was about to reach their licensing limit, you could send a personalized offer to add more licenses, whereas a win-back campaign could be used for recently churned users.
For more context, you know that the product costs $100, there’s an average 10% monthly churn, and the average customer’s lifetime is 3.5 months.
Average lifetime value is defined by the prediction of the net revenue attributed to the entire future relationship with all customers averaged. Given that you don’t know the future net revenue, you can estimate it by taking the total amount of revenue generated divided by the total number of customers acquired over the same period of time.
However, there’s a catch: it’s only a 14-month-old company. As a result, the average customer length is biased, because the company hasn’t existed long enough to correctly measure a sample average that is indicative of the mean.
How would you calculate this? Try to find the expected value of the customer at each month as a multiplier of retention times the product cost.
See a full solution to this question on YouTube:
More context: the duplicates may be listed under different sellers, names, etc. For example, “iPhone X” and “Apple iPhone 10” are two names that refer to the same iPhone.
See a full mock interview solution for this e-commerce case study question on YouTube:
See a step-by-step solution to this problem on YouTube: