The convergence of artificial intelligence, ubiquitous connectivity, biotechnology, data, and computing has heralded the fourth industrial revolution. Where the first industrial revolution created the machine manufacturing economy and the second saw the introduction of electricity and mass production, the third industrial revolution brought the digital computing era and, for the first time, machines began to encroach on mental work. The fourth industrial revolution now brings a blurring of the distinctions between digital and physical worlds, evolving at exponential speeds and enabled by technologies like machine learning that can automate automation itself!
What does this new paradigm mean for Market Research (MR) and Insights teams? The fusion of data and new technologies with business processes and human effort is redefining the way market research operates, and thereby its relevance to the business organization.
As new technologies change the way data is sourced, managed, and consumed, access to insights is increasingly expedited and democratized, revolutionizing the way information is operationalized and supports decision-making.
Some key trends impacting the use of research data within organizations emerging today are:
- Changes in privacy regulations and the permissible use of Personally Identifiable Information (PII)
- Democratization of research data as part of a larger trend of data becoming a mainstream dimension across job roles – this means largely open access to internal organizational data such as aggregated customer data, financial data, supply chain data, and so on
- Rise of easy-to-use self-service BI tools and platforms for all employees across levels and functions
- The use of Artificial Intelligence (AI) and automation across the enterprise
The trend that is likely to have the greatest impact for MR and Insights departments is the application of AI and automation at critical pain-points across the MR operations value chain. We know from data over the last decade that the market research industry has been experiencing flat growth rate and has been relatively less agile in terms of adapting to fast-changing business requirements. There is constant tension from operations and insights perspectives in terms of how to balance speed, cost, and depth of insights in MR, and this is where AI plays a crucial role.
Opportunities for AI in Market Research
Let’s look at the classic research value chain, all the way from research problem definition to the final insights that are delivered to your business partner, depicted in the visual below.
Today AI and other advanced digital technologies have created opportunities at several points in the value chain where we can increase agility for MR businesses, either by driving speed and cost efficiencies in operations or by democratizing data and providing deeper insights. Some immediate areas of transformation are:
- Survey programming – We can now reduce the time to get a survey link to go live by 50%. Additionally, MR teams can leverage the knowledge gained from executing hundreds of thousands of surveys to make recommendations to a researcher about which questions they can select as part of their research program.
- Data quality – Despite several measures that panel companies are taking to ensure data quality, there is still opportunity to automate the cleaning process and get data into a ready-to-use state for your data analysis team, reducing the time required for this by at least a third.
- Analytics – Researchers constantly need to unearth insights by referencing and using data-points across studies, which is easier said than done considering the effort required to merge datasets to produce such insights. Here again, companies can merge datasets much faster using a combination of human and machine intelligence.
There are several other ways to increase efficiencies along the MR value chain using artificial intelligence and digital analytics. Some use cases directly contribute to a hard business metric (e.g. increased sales revenue), while others bring indirect benefits – for example, time and cost savings can be reinvested in transformation initiatives, better decision-making, enhanced customer experience, and innovation.
In the new world, research teams are looking for opportunities to become thought leaders and strategic influencers to the business. For the foreseeable future too, human intelligence will still be required to synthesize different data points into actionable insights. Using smarter technologies, we believe researchers will be able to explore their data more deeply across data points and add value to their business. With this advanced new research management and the entry of insights into everyday decision-making, research teams once considered only for delivering insights have begun to play a more strategic role in organizations. This is a shift in the right direction.
About the Author: Anil Damodaran is Product Head for two products at Course5 Intelligence – the Ad Creative Testing platform and LINK, our market research data integration environment. In his current role, he is focused on Strategy, Go-to-market, Partnerships and delivering great Customer Experiences. He has extensive operational experience in his prior roles, having built and led research consulting teams for various Fortune 500 companies in the Technology, Telecom, and Media space. Anil has an MS in Marketing from Lancaster University, UK and an MBA from University of Mumbai.