Interview with Rónán Dowling-Cullen - This month's MRS Analytics Spotlight
Authored by Lisa Cowie, Research Consultant and a member of the MRS Data Analytics Council.

Rónán Dowling-Cullen is the Co-Founder and Co-CEO at Bounce, a consumer insights platform powered by AI. A computer scientist by trade, Rónán loves solving big problems, specialising in Technology and Finance. Rónán was recently recognised as one of '30 Under 30: Rising business stars' by the Irish Independent.

Here Rónán provides great insight into how data analytics and AI is firmly rooting itself in market research, and what data scientists need to consider when working in this industry. 

How did you start in the market research industry?

I was approached while studying computer science in university to co-found a consumer insights technology company and I've been building products for market research since.

What attracted you to work in market research?

Even from an outsider's POV, it felt clear that the industry was ripe for disruption via technology.

Have you seen a shift in the nature of market research during your career?

We consistently hear from clients that they are shifting away from DIY tools (which heavily rely on internal resources) towards end-to-end solutions (including the use of AI/automated analytics and insights).

Why is it important research uses data analytics/ science in your opinion?

It is impossible to understand what actions you should take or even questions you should ask based on research without data analytics/data science.

What are some successes and challenges you've encountered working across both an insight function and a data function?

The successes have come when the insight and data functions in our client's companies work closely together and have aligned goals. The challenges come when they do not!

What are the key challenges for data scientists working in market research rather than other industries?

I feel in some organisations there is a traditional way of doing market research to answer business questions that may be at odds with how a data scientist would answer those questions and that can sometimes lead to conflict.

What new perspectives have you been able to bring to your organisation because of your training in data science?

I would say a data-first mindset and an evidence-based approach. A data science background biases for speed and impact based on rapid assessment of trends and data points.

What new perspectives on data science have you learned through working on market research problems?

Data science is only as valuable as the question being asked (or business problem being solved). You can have the best data scientists in the world but if they are pointed in the wrong direction they won't drive value. Market Research can be the bridging step between business goals/strategy and the use of data science.

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

The biggest evolution has been how the process starts. Initially, we were receiving research questions, but today we are briefed on business questions/objectives. Having that context from our clients helps us to inform our technology but also our solutions at each stage of the research journey (from design to result delivery).

How do you see advanced data analytics (and perhaps even machine learning / AI) impacting market research in the future?

Machine Learning and AI is at the core of what we do at Bounce. Each distinct stage of the insights journey is underpinned by AI technology combined with deep research consultancy.

What three pieces of advice would you give to a data scientist entering market research?

  1. First principles - everything starts with a business goal.
  2. Understand the role of market research and data science as they relate to those business goals.
  3. Datapoints become insights when wrapped in the context of the business goal you started with.


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