Marketing Analytics Case Study Guide for 2024

Case study questions in marketing analyst interviews are scenario-based questions that mirror the day-to-day work of analysts.

In marketing analytics case studies, the interviewee is provided with marketing data or a specific scenario, then must develop a detailed solution for the provided case question. For example, in a marketing analytics case interview, you might be asked, “How would you measure the effectiveness of a marketing channel?”

You would then propose marketing analytics metrics that you would be most interested in, like cost per acquisition (CPA) or the return on ad spend (ROAS). Ultimately, the most common types of marketing analytics case study questions include:

• Measuring effectiveness - These questions ask you to gauge the effectiveness of marketing campaigns based on the provided data.
• Marketing analysis - These questions provide data that you can first analyze and then propose marketing strategies based on your analysis.
• Marketing metrics - These questions ask you to propose metrics to assess performance or investigate a problem. An example would be: “Campaign A and Campaign B have the same spend. However, Campaign A is converting at a much higher rate. What metrics would you use to investigate the discrepancy?”

Marketing analytics use data to inform marketing decisions. By integrating data into marketing decisions, businesses can refine their marketing campaigns, better understand what drives customer action and increase ROI on their marketing spend.

Marketing analytics has numerous applications for businesses, including:

• Determining the ROI for marketing campaigns.
• A/B testing marketing messages to find what works best.
• Identifying which messages, advertisements, and marketing activities drive customer action.
• Optimizing and personalizing marketing messages for customers.

Marketing case study questions within interviews mirror the job responsibilities of marketing analysts. For example, you could be provided with data and asked to make an analysis on how the company should allocate marketing spend.

At their core, marketing case studies are scenario-based questions that ask you to present a well-constructed solution to a potential or real-world marketing problem. These questions allow you to apply your marketing expertise to a real case, as well as use your problem-solving and analytical thinking skills to address it.

Here we will review a deep dive into a solution for one of the most common marketing analyst case study questions:

1. How would you measure the effectiveness of different marketing channels?

More context. Say you are running paid advertisements for an online learning business, to drive customers to your curriculum. The business only sells a single course, which costs $100. You have spent $1,000 on Facebook Ads and Google Ads in order to increase sign-ups. What metrics would you be most interested in reviewing your decision and investment?

A version of this question is asked in nearly every marketing analyst interview. Your goal should be to define what “effective” means in this context, and then talk about the most important metrics for measuring it.

Example Solution:

First, start with some clarifying questions like:

• What are the goals of the two marketing campaigns? Is it to increase sales of the course? Generate awareness of the public to your product? Drive engagement from existing enrollees?
• Are these the first two campaigns the company has run? How long have they invested in paid advertising?

For the purposes of this example, assume the goal is to increase sales and that the company already has an established marketing presence on Google and Facebook.

If the goal is sales, we would be interested in return on investment (ROI). That is, if we invest more money into marketing channels that have a higher ROI, we are effectively pursuing the options that maximize our returns.

To understand ROI, there are two main marketing analytics metrics we should focus on:

1. Cost Per Acquisition (CPA) - The total ad spend for a marketing channel divided by the number of customers. Ideally, we want CPA to be as low as possible so that we’re spending less to acquire customers than they’re bringing in.
2. Customer Lifetime Value (CLV) - The amount of revenue brought in by customers from a specific marketing channel. For SaaS companies, acquiring one customer may yield an average of five or six years of annual subscription renewals, such that the lifetime value of one customer is quite a bit higher than the upfront cost to get them to sign up.

Let’s focus on breaking down the cost per acquisition metric. CPA is the average cost to acquire a customer for each marketing channel spent. Here is the following data we are given for this situation:

• We spend $1000 on Facebook Ads.
• 10 individuals converted to customers.
• To calculate CPA, we divide the ad spend of $1,000 by the 10 customers acquired (¹⁰⁰⁰⁄₁₀), for an expected CPA value of $100 per customer.

Next, we want to look at the CLV and how it relates to CPA. For our example, the customer lifetime value is $100 (because the company only sells one course, and does not expect customers to purchase multiple times).

• If my course costs $100 and I convert 10 customers (with a CPA of $100 each), then I am breaking even on my ad spending and revenue.
• But if the business adds another course, and on average all customers from Facebook purchase 1.5 courses, then my customer lifetime value is $150, which gives me a $50 profit per customer (the CPA has not increased in this scenario).

Note that CLV is particularly important for subscription-based products because if one channel results in long-term customers with a higher CLV versus a larger payoff upfront but drastically worse long-term value, we would likely want to target the option with the greater long-term outlook.

1. Reviewing Funnel Metrics

Since most marketing is about getting the company brand and mission in front of customers, many times it is up to the internal product team to work on converting customers down the line.

So in marketing analytics, we focus on breaking that CPA number down a bit more into a funnel:

• How many people saw my Facebook ad?
• How many people clicked the ad and viewed the course landing page?
• How many people actually converted?

By reviewing the funnel metrics for CPA, we can learn which channels are the most efficient at turning ad impressions into conversions. This will also help us identify where we need to improve in the funnel.

2. Considering Multi-Channel Attribution

Everything we have covered so far assumes that we are evaluating each marketing channel individually. But actually getting the right data for situations in which it is possible a customer might have interacted with marketing material across several platforms and mediums is the hard part. To separate out the different influences, we are going to have to use tools like Mixpanel, Google Analytics, or internal data systems to measure attribution.

Attribution is defined as the way we allocate and tie a visitor to a marketing channel. And it is not easy. For example, we might see that a customer came from Facebook but then dropped off or got bored on my landing page before seeing a Google Ads campaign and finally converting. From all of this, we still have to choose a marketing channel to attribute the conversion.

If they saw a Facebook ad and didn’t convert but then came back and made a purchase from a Google Ads driven organic search, do we attribute it to Facebook Ads or Google Ads?

There are a few ways to allocate attribution when we run into multi-touch attribution issues:

• First touch attribution - This attributes the conversion to the first campaign, e.g., Facebook Ads for the above example.
• Last touch attribution - The conversion would be attributed to the last campaign, e.g., Google Ads.
• Regression model - This type of attribution model would be developed and may weigh the impact of both the Facebook Ads and Google Ads impression before deciding which deserves the attribution.

Many times we try to improve our marketing techniques by segmenting our paid channels by campaigns. For Google Ads I might run two campaigns: one targeting a certain demographic like younger users and another targeting older users.

If we can find the CPA and CLV by these demographics, we can then zero in on better ratios to target and optimize campaign performance.

3. Next steps

Analytics case studies are generally discussions. The above answer would show that you understand the fundamentals of marketing performance measurement. However, the interviewer may try to steer the question by asking follow-ups or providing new information. For instance, they might ask, “what if the goal had been different? How would the response change if the goal was brand awareness?” The interviewer will now evaluate how well you can pivot and adapt your thinking.

Here’s a video guide on Marketing Analytics for Online Businesses:

2. How would you determine how much a company should pay for advertisements on a third-party app?

Case study questions are vague by nature, and it is your responsibility to ask clarifying questions before you jump into an answer. With this case, there are a lot of questions you can ask like:

• How widely used is the app?
• Do we have any information about its user base?
• Are the advertisements click-based or impression-based?
• What placements does the app offer?
• What is the goal of the campaign? Awareness, sales, engagement, leads?
• Is there a target ROI for the campaign?

To best estimate possible costs, you would want to look at historical advertising data. What campaigns have the company run before? What were the funnel metrics for these campaigns, e.g. click-through rates and conversion rates? Besides those two questions, you would need to consider customer metrics like customer lifetime value, average order value, or lead-to-conversion rate.

With this information, you can begin to define the maximum CPC or maximum CPM for advertisements on the third-party app.

3. An e-commerce company is experiencing a reduction in revenue for the past 12 months. What would you investigate to understand exactly where the revenue loss is occurring?

To investigate the revenue decline, you have access to such information as:

To investigate the revenue decline, you have access to such information as:

• Date of sale.
• Amount paid by customers.
• Profit margin per unit.
• Quantity of item.
• Item category.
• Item subcategory.
• Marketing attribution source.
• Percent discount applied.

A question like this gets asked in marketing analyst 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 would help you understand if the decline is due to decreasing marketing efficiency, an overreliance on discounts, or if a particular category is declining. Another option would be to investigate changes in profit margin per unit, which could help identify if production costs are rising.

4. Use the provided data to calculate the overall advertising cost per conversion.

More context. You work for an e-commerce company that wants to invest in Facebook Ads. You learn that an ad placement is $0.05 per impression, and the click-through rate (click per impression) is 1%. Based on your historical data, you also know that you have an average of 2% conversion rate on your website.

With this question, model the data to help you calculate cost per conversion.

• 10,000 impressions (at $0.05 per impression) would cost $500.
• This would result in 100 clicks (because the CTR is 1% of 10,000 impressions).
• Therefore, the cost per click would be $5 ($500 ad spend / 100 clicks).
• Since our average conversion rate is 2%, 100 clicks would result in 2 conversions.
• Therefore, the cost per conversion would be $250 ($500 ad spend / 2 conversions).

5. How would you design an A/B test to utilize the marketing budget in the most efficient way possible?

More context. You want to test multiple new channels, including YouTube Ads, Google Search Ads, Facebook Ads, and direct mail campaigns.

Start with follow-up questions. You want to define what “efficient” means, you need to understand the total budget to ensure you could test each channel properly, you want to know about marketing performance to-date, and finally discover if any data exists.

With an A/B testing question, you should propose metrics for the test like:

• Confidence interval.
• Power (likelihood that the change will actually make a difference).
• Length of the test.

Similarly, you would also want to provide a high-level overview of how you would run the test, including gathering data, checking distributions and performing post hoc analysis.

Note: A/B testing questions are not widely asked in marketing analyst roles during their interviews. However, testing and marketing optimization questions are common. Therefore, a simpler version of this question might be: what metrics would you be interested in when testing new marketing channels?

Interviews for a marketing analyst role typically include a mix of data science SQL questions, product metrics questions, and sometimes, a data analytics takehome assessment, in addition to the marketing analytics case study. See our guides for more practice marketing analyst questions.