Published On : December 06, 2016
For any candidate on today's job market, the offer of an interview is also an endorsement of the resume and cover letter that took so much time and effort to polish to perfection. The fact of the matter, though, is that these documents only open the door for your opportunity to close the job offer. While they're crucial, getting the job you want means competing against a pool of applicants whose documents may be at the same level yours are.
To set yourself apart in the next phase, you will need to be memorable but not overbearing, knowledgeable but not a know-it-all. Walking that line requires preparation, and the ability to recognize both general interview questions and also specific marketing data analyst interview questions. It also means going in with some refined and practiced answers, to ensure you tell prospective employers exactly what they need to know. This means being able to answer "Why do you want to work for us?" but it also means knowing what they might ask a data analyst that they wouldn't ask, say, a public relations representative.
5 Marketing Data Analyst Interview Questions & Answers
1. How do you define "Big Data"?
Big Data, as it is called, is the organization and interpretation of large data sets and multiple data sets to find new trends and highlight key information. In the case of your company, that means identifying trends in consumer tastes and behaviors that marketing strategists can take advantage of when they are planning a brand's next moves. For example, one use of Big Data would be looking at both market share and market growth together, then breaking them down by demographics to highlight both the most common demographics for products and the users with growing interest who might represent opportunities for growth.
2. When someone has trouble understanding your data model, how do you effectively communicate the major points it is trying to get across?
Explaining a data model is always a challenge, because we like to think that they are clear when we make them, but it is important to also remember that our job serves a wide variety of people in the company. I like to approach this situation by finding out what confuses people the most, then stepping through that part of the model. Once they believe they are getting it, I ask them to explain how that part fits with the rest, so I can hear that they are understanding and discussing it. As we go, if there is anything in the model that they missed or start to misinterpret, then I help guide them back.
3. Can you describe a big data project you have worked on?
As you can see from my resume, this would be my first analyst position after school. I did my internship for a larger data analysis firm, though, and my part of our projects was the organization and codification of the data sets we would pull in. I had to sort through the various different variables we were prepared to model, highlight missing information that would require assumptions, and generally organize the information for the full-time analysts so they could build their models more effectively. This taught me how to prioritize during my own assessments of data sets.
4. Tell us about your marketing experience. What made you interested in marketing data analysis specifically?
Before I went back to school, I mostly worked in a call center. We would go back and forth between handling warranty claims and customer service for some companies and conducting market research for others. That was where my interest started to grow. I learned in that job how the different ways of phrasing questions yielded different insights and responses from clients, and I started to get a sense for when questions were going to be more or less productive. As I came to understand how the design of these questions reflected the level of engagement certain brands had in the market, I started getting interested in how I could use this kind of understanding to move into the industry.
5. How does social media fit into what you do?
Social media is an ongoing sample set with live results that can be used to inform a brand's approach, but it is also volatile, and analysts can easily lose track of the fact that it is a world of its own. I view it as a treasure trove of information, but it is not necessarily more or less important than other indicators of consumer behavior.