By Dietmar Walter

 

It has been a year now that with the foundation of QuenchTec I returned to the Market Research industry after spending five years in the fascinating world of Advanced Analytics, Machine Learning and Business Intelligence.

So what have I learned about the dynamics of the Market Research Industry since my return?

The industry is finally embracing new technologies and other multiple forms of data collections. Agile research and automation are not buzzwords any more, but are real and are major forces of change.However, it seems that the Market Research industry is still somewhat at unease with the inevitable reality that automation will change the industry, fast. Automation still seems often to be associated with the ‘cheap, quick and dirty’ way of doing research on a DIY basis. It’s often looked at it as an inferior way of doing research Well, I could not disagree more.

Three reasons why agile research and automation are major forces of change

Firstly

Automation and quality research based on proven methodologies do not have to be mutually exclusive. Here the Market Research industry can learn a lot from its cousins in the Advanced Analytics and Business Intelligence space. We wouldn’t say that IBM Watson is not a clever system, not based on scientifically proven and tested principles, would we?

Why would we take a different view for Market Research?

Of course, automation efforts come in all shapes and forms and at different level of sophistication. From simplistic and limited costumer feedback solutions to rather over-complicated difficult to use, old school research applications. What seems to be missing is the logic that by using thoughtful process re-design and encoding proven research methodologies into easy to use applications, automation can become the changing force in market research.

Secondly

Tech start-ups from outside market research bring innovation to our industry.

I have experienced this first hand when working with such outsiders”. They don’t start with a particular industry in mind, but use all their talent and energy on very specific tasks to either obtain deeper insights or speed up the insight generation process. Artificial Intelligence based analytics applications that are designed to understand and analyse unstructured data like text, pictures and video at scale are great examples.

The lack of market research expertise of such solutions has led to a lower adoption of such technologies within our industry, but rest assured: they learn fast. In fact, learning is intrinsically built into such technologies by design. Think about learning algorithms. Such tech companies start by adding an ontology layer on top of their analytical engines by encoding market research expertise into their solutions. Think about Google Translate. A few years ago the Google Translate system was clunky and had limited use. However, as a statistical based self-learning algorithm the improvement over the recent years have been phenomenal, and it keeps learning…

Thirdly

Technology driven developments are leading to a democratisation of the market research process that puts the corporate marketers in control. I experience that corporate clients, not Market Research agencies, are becoming the driving force for innovation.

Today’s marketers demand agile (automated, fast, frequent, cost effective) research processes to keep up with ever-changing business needs

They also require an integrated view of the consumers including other forms of data analytics next to surveys – social media, text analytics, and other internal and external data sources.

However, at corporate level the vast majority of typical research budget is still spent on executing and managing data collection, leaving very little to generate the insights that shape business decisions. Current research practices suffer from replication of processes, projects and data and limited reusability of prior market research projects. The end result is limited ROI on research spending and too slow an insight generation process, as required by the business.

These issues need addressing by shortening research processes through automation, but without compromising on proven research methodologies. And that is precisely my mission in my 2nd  life in Market Research.