Most marketing analytics interview questions aim to assess:
Therefore, many marketing analytics questions in interviews are a combination of definition-based and scenario-based case study questions. These questions test your ability to apply your marketing and data analytics prowess towards a business goal, with the most common interview question categories being:
Basic marketing questions for Marketing Analysts qualify your baseline technical knowledge and problem-solving abilities. These questions aim to assess your understanding of the job role and if you have the essential skills to fulfill the position’s requirements.
The role of a marketing analyst is to act as an “analytics translator” and help companies make sense of their marketing data to drive impact. The insights they generate help companies determine which products to sell, how to optimize their marketing mix, and how to refine ad campaigns.
Want to learn more about the role of a marketing analyst, needed skills, how to become one, job outlook, and salary expectations? Check What is the role of a marketing analyst?
Social media networks are rich repositories of Voice of Customer (VoC) data (verbatim customer feedback). Additionally, many brands—especially airlines—provide customer care via social media. Social media analytics reveals how users engage with content and includes qualitative data from forums, review sites, and social networks.
Brands use analytics to measure the following:
Marketing key performance indicators (KPIs) are numerical metrics that organizations monitor to gauge their progress towards a defined goal. Ultimately, KPIs measure the ROI of marketing initiatives, including paid ads, lead acquisition, and social media engagement.
To answer this question, you could:
A competitive analysis evaluates your competitor’s strengths, weaknesses, opportunities, and threats (SWOT). The outcome is a better understanding of the strength of your organization’s brand, marketing initiatives, and social media strategy relative to competitors. Use the STAR (Situation, Task, Action Response) method to tell a story about your findings, recommendations, and the outcome.
Data validation is a form of data cleansing to verify the accuracy and completeness of your data. Start with a small sample of the data before testing on a large dataset. One option is to write scripts in SQL to compare the data values and structures against your organization’s defined rules and ensure the information adheres to the required quality parameters. You can also use software applications like Talend or Datameer.
Google Analytics is a rich data source for website analytics and advertising analytics. Within their analytics, you can investigate business problems like a high website bounce rate (which landing pages have the highest bounce rate?). You can also analyze low conversions (what are the hotspots on your landing pages? Is your call-to-action positioned within a hotspot?) or poor return on ad spend (which ad has the worst/best click-through rate?).
Based on the type of data required, we can classify the research into two broad categories:
Primary research involves the following tools:
Marketing Analytics SQL questions test your ability to query databases, write scripts, and clean data. Common SQL questions include:
An aggregate function performs a calculation on a set of values and returns a single value. Aggregate functions are often used with the GROUP BY clause of the SELECT statement.
‘Relation’ describes the relationships between the tables in a relational database—meaning a table has a foreign key that references the primary key of one or more tables.
In this scenario, you have a table representing search results from Facebook searches. The query column is the search term; the position column represents each position the search result came in, and the rating column represents the human rating from 1 to 5, where 5 is high relevance and 1 is low relevance.
Note: If the customer purchased two things on the same day, we do not count that as an upsell, as the customers purchased them within a similar timeframe.
Questions like this are common in Amazon SQL interviews.
Hint: An upsell is determined by multiple days by the same user. Therefore, we have to group by the date field and the user_id to get each transaction broken out by day and user.
, DATE(created_at) AS date
GROUP BY 1,2
Now we have to filter for the users that purchased on multiple dates. How can we do this?
Evaluating what we know, we have an initial query that shows the purchases on each date. So effectively, we need to count the number of specific dates on which a user purchases
The schema below is for a retail online shopping company consisting of two tables, attribution and user_sessions. Here are some details of the two tables:
How do we solve this one? First-touch attribution is the channel with which the converted user was associated when they first discovered the website. It is helpful to sketch out the attribution model for converting users:
How do we figure out the beginning path of the Facebook ad and connect it to the end-purchasing user?
We need to do two actions: 1) subset all the users that converted to customers 2) figure out their first session visit to attribute the actual channel. We can do that by creating a subquery that only gets the distinct users that have converted.
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 and use your problem-solving and analytical thinking skills to address it.
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 discuss the most critical metrics for measuring it.
You can, for example, talk about evaluation return on investment (ROI) by measuring unit economics like Cost Per Acquisition (CPA) and Customer Lifetime Value (CLV). Then break down these metrics per funnel and consider attribution while explaining the difference between single and multi-channel attribution. More detailed answer here.
We can frame the concept specifically to this problem is to think about controllable inputs, external drivers, and observable output. Start with the primary goals of Netflix:
When you look at acquisition output metrics specifically, there are several top-level stats that we can look at, including:
We would also want to bucket users by cohort with these conversion metrics. These metrics would help us see the percentage of free users acquired and retained by the cohort.
Let’s assume that, for this question, the VP of Sales provides you a graph that shows agents in Month 3 receiving more than three times the number of leads than agents in Month 1 as proof. What might be flawed about the VP’s thinking?
Hint: The key is not to confuse the output metrics with the input metrics. In this case, while the question makes us think that more leads are the output metric, churn is the better-analyzed metric. If you break out customers in cohorts based on the number of monthly leads, we can see if churn goes down by cohort each month.
To investigate the revenue decline, you have access to such information as:
Marketing analyst interviews get asked a question like this to determine if you can propose vital metrics to investigate a problem. You might start by analyzing monthly revenue by marketing source, category/subcategory, or 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. Another option would be to investigate changes in profit margin per unit, which could help identify if production costs are rising.
The new channels include Youtube Ads, Google search ads, Facebook ads, and direct mail campaigns. To start, you’d want to follow up with some clarifying questions and make some assumptions. Let’s assume, for example, that the most efficient means the lowest cost per conversion and that we must spend evenly across all platforms.
Interested to learn more about marketing analytics case studies? Check the marketing analytics case study guide for 2022.
Behavioral questions assess soft skills (e.g., communication, leadership, adaptability), your skill level, and how you fit into the company’s marketing team. Behavioral questions are expected early in the marketing analytics process (e.g., recruiter call) and include questions about your experience. Examples of behavioral interview questions for a marketing analyst role would be:
“Big data” describes structured or unstructured data that is so high-volume, fast, or complex compared to traditional databases that it cannot process without machine learning. The three Vs. characterize big data:
Since data is only useful if it is accurate and complete, consider two additional criteria:
Businesses use big data to yield faster and better decision-making by predicting future outcomes.
You’ll find a lot of variations to this question, but the objective is always the same: to assess your ability to communicate complex subject matter and make it accessible. Marketing analysts often work cross-functionally, an essential skill they must possess.
Have a few examples ready and use a framework to describe them. You might say:
“The marketing team wanted to better segment customers, so, after understanding their motivations and goals for the project, I presented several segmenting options and talked them through trade-offs.
I presented potential strategies for visualizing the new segments, described vital benefits, and ultimately discussed potential trade-offs.”
When asked about a project, you should walk the interviewer through the project from start to finish. Begin with the business problem and conception. Describe your approach and how you executed it. And always end with the results. You might say: “In my previous position, I was in charge of identifying opportunities to optimize our marketing efforts. Specifically, I was analyzing the types of ads that were generating conversions. Through my analysis, it became clear that one type of ad worked on Platform A but not Platform B. I persuaded the marketing team to optimize the ads it created for Platform B, resulting in a 10% lift in conversions.”
Here is a sample answer to this question. Here is an excerpt, “I was working at a healthcare company. Our goal was to improve user acquisition, and one strategy we tested was adding a “Subscribe to Our Newsletter” button in the footer of blog posts. After rolling the feature out, the number of subscribers wasn’t growing. “My job was to understand why the feature wasn’t working. Diving into the analytics, I found that the page scroll depth was just 50-75% for most of our content. Additionally, the average session duration was just 2-3 minutes. I recommended the content marketing team shorten articles, so they were fully read and move the opt-in higher on the page. After these changes, the opt-in rate increased by 50%.”
Market analysts often have access to confidential information about their clients and competitors. Employers must ensure you understand the importance of keeping company secrets private. In your answer, explain that you would never share confidential information with anyone outside of work. You can also mention that you would only use the information for professional purposes. You might say: “I will never share confidential information about clients or competitors with anyone outside my job. I take my job very seriously, so I will only use this information for professional purposes. I will only use it to help my employers create better products and services.”
The best answers in behavioral interviews are like stories, framed from beginning to end, and include plenty of interesting detail. Your goal should be to leave the listener satisfied with their initial question and possibly provide material for further review.
Interview Query offers a variety of resources to level up your Marketing Analytic skills and prepare for your interview. Premium members get access to our data science course, which features an entire SQL module, as well as basic Python, statistics, and marketing analytics skills. See our SQL questions for data analysts and our marketing analytics case study guide. You can also check out our 23 data analyst behavioral interview questions.