MRS Analytics Spotlight : Michael Martin
14 September 2023

Interview with Michael Martin - This month's MRS Analytics Spotlight
Interviewed by Lisa Cowie, Freelance Researcher, and a member of the MRS Data Analytics Council.

Michael Martin is Senior Data Strategist at The Mix. With the tagline "Market research for people," they combine quant, qual, strategy and creative for global research campaigns. In recent years, the business has focused more on advanced data analytics, shifting to greater use of client data and data science. As a result Michael’s role has evolved to lead this function, with a particular focus on utilising secondary data. Here he talks about the use of data analytics for their clients.   

How do you think about research at The Mix?

Through primary and secondary research we operate as a strategic partner for clients who are looking to answer key business challenges through research, most often looking at future growth opportunities. We hate wasting time and money on primary research (if we don’t need to) and we know it's difficult for clients to commission if their stakeholders believe that the answer belongs within data already. 

We've created a bespoke solution working as an extended workbench to our client partners answering complex questions across various data sets. This data often already exists within the business and helps to bring together sprawling stakeholder groups, galvanising them behind one single data narrative.  

How did you start in the market research industry?
I started working in market research due to an obsession with human behaviour but also TV! My first research role was at Kantar working with TV data followed by a few different positions across multiple broadcasters/production companies such as Discovery Networks as well as other more traditional research agencies.

What's the role of data analytics or data science for research purposes within your company?
We regularly conduct segmentations for our clients across multiple categories, utilising cluster analysis. We also use other analysis techniques such as conjoint analysis, gap analysis and correspondence mapping, for other project types. Even if a project isn't commissioned as a quantitative methodology, we endeavour to still include data through secondary sources to back up our strategic recommendations.

How are you combining 'traditional' market research with data analytics?
Some of our more recent projects have involved not only growing a category but also play by play accounts of how to grow and reposition the brand in question. We have done this heavily backing our strategy with consumer interviews, shop alongs, expert interviews and quantitative surveys. We find 'traditional' market research asks people to self-report behaviour, whereas we design our questionnaires in a way that allows people to answer more accurately and then we can draw conclusions at analysis stage through analysing multiple questions together to create a complete picture of behaviour or attitudes.

Have the research questions that your clients bring to you changed over time? Have the solutions changed? .   

We are finding more and more that our clients have vast amounts of data and don't know what they know and what they don't. So we are helping them identify that. In this climate a lot of categories are shrinking and so there are a lot of questions about growing share in declining categories. Our solution to this has evolved and improved over time and we are now experts at helping clients grow share by using our data analytics solutions.

What are the key benefits and challenges of data science / advanced analytics being used in market research and your company specifically?

We are seeing data science becoming increasingly important to our clients. A key benefit is that we are informing our clients based on the most robust forms of analysis possible, utilising complex cluster analysis with extensive iteration options for segmentation as an example. Additionally we are seeing this result in more buy-in from senior stakeholders from all corners of the business. This leads to a key challenge that is how to communicate these technical aspects of data science in a simple way that everyone can understand. This is something we take pride in at The Mix, the ability to take complex data solutions and explain them in a clear concise way, simplifying but still communicating the full story.