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:
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:
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:
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 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.
First, start with some clarifying questions like:
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:
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:
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:
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:
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.
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:
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.
To investigate the revenue decline, you have access to such information as:
To investigate the revenue decline, you have access to such information as:
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.
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) would cost 5 (250 ($500 ad spend / 2 conversions).
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:
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?