As a consumer researcher, I’m fascinated by the growing application of machine learning and automation to our industry, but a lot of it still seems very theoretical for the typical research generalist. I had the opportunity to attend the NA MRMW (Market Research in a Mobile World) Conference this month and was excited by all the examples that people shared of how they are applying AI and automation in their businesses today.
Below, I’ve captured just a few highlights of some tangible examples that inspired me and my non-technical take on the technology involved, as well as who shared the use case or capability at the conference. If this piques your interest, I encourage you to dig further into some of the companies below, attend a future MRMW conference, and look for application opportunities in your own business.
Key themes for data and insights from the MRMW (Market Research in a Mobile World) Conference...and beyond.
This is the world we live in: ever-increasing complexity with an ever-increasing drive for speed. There has been an exponential increase in the types and sources of data, and decision-making is becoming more nuanced.
Derek Franks, Global Director of Insights for EA, talked about Gregory Treverton's concept of puzzles versus mysteries, explaining that a puzzle has one clear right answer and is solved by collecting more data. Mysteries, on the other hand, can be hard to know if you've really solved, and they usually require making sense of the data you already have. We live in a world of mysteries!
Things are changing at CPG behemoth Procter & Gamble as well. Julie Setser, VP of Innovation Capability, talked about how they look at data and insights as a "body of evidence" now versus the historic approach of one magic number (e.g. a purchase intent score).
Consumer demand is driving faster innovation cycles, particularly in categories like technology, beauty, and fashion, and arguably across every consumer category to some extent. And in today's hyper-connected, social media savvy world, brands can't afford to miss an opportunity to react to customer feedback, both amplifying bright spots and fire-fighting when needed.
From a data and insights perspective, AI (artificial intelligence) and machine learning can automate time-intensive tasks and processes. AI chatbots can conduct adaptive surveys and even qualitative probing. Machine learning tools, like text analytics and sentiment analysis, can help keep a pulse on consumer reviews, brand posts, and other customer (dis)satisfaction data.
For product development and innovation, many are embracing agile research approaches. Inspired by concepts like Lean Startup and Google's Design Sprints, this new breed of research is done in a compressed time period and is highly iterative. Instead of waiting for a near-final product and one big 'validation' test, agile research prioritizes the killer issues and tackles them in chunks, using an MVP (minimum viable product) approach or rough prototypes.
Sarah Faulkner, Principal, Faulkner Strategic Consulting