At its core, market research is a discipline designed to help us keep up with change. But at times, even market research itself must transform to keep up with the market.
The need for faster and more robust insights has been growing due to continued pressure on budgets and timelines; and has only been accelerated by the coronavirus pandemic. These factors have been critical in driving the transformation of market research – with technology at its core. The average market research toolkit has now expanded to include everything from automated, do-it-yourself market research platforms to artificial intelligence (AI)/machine learning and neuroscience.
These technologies have become an essential part of the global consumer insights machine, enabling brands to keep up with accelerating change in consumer behaviour.
Technology enhances what market research can already do
Technology has made it possible to accelerate traditional market research processes, making them more efficient and streamlining costs through innovative approaches that can be used as either an alternative or supplement to traditional methods. It is also helping brands find new ways to answer one of the fundamental questions that market research seeks to answer: what do consumers really think?
Here are some examples of the different technologies fuelling a transformation in market research practices.
Automation has benefitted insights-seekers by drastically shortening the time between putting a survey in-field and retrieving valuable consumer feedback. With automated market research, deliverables that once took weeks can now be completed in as little as a few hours. Automation has also streamlined the market research process by making survey research more repeatable. For example, brands can get feedback on innovation concepts or test their creative faster by inputting a few simple parameters into a templated online survey, rather than recreating the survey each time.
DIY market research platforms
Demand for DIY market research is exploding; in fact, last year ESOMAR predicted that the sector would grow to $1.3billion by the close of 2021. DIY market research platforms like Kantar Marketplace are democratising insights, making it possible to get consumer feedback much more quickly and cost-effectively than in the past.
DIY platforms also facilitate greater agility, allowing market research teams to bring smaller projects in-house and execute as soon as needs emerge.
One other area where technology has enhanced market research is using simulated environments. Take packaging tests, for example. By displaying your packaging on simulated store shelving, you get a better sense of how consumers would experience your product – at a much lower cost than using a mock shelf. You can still get an understanding of which packaging will stand out on shelf despite the nature of the simulated environment you’re exposing your sample audience to.
One of these growing in popularity approaches is the application of neuroscience to online market research, which enhances our understanding of consumers’ emotional responses. Within the neuroscience field, one of the key insights tools is facial coding. When applied to ad testing, facial coding technology allows marketers to observe how people react to their creative by analysing their facial expressions and eye movements – decoding these expressions to predict overall sentiment with a high degree of accuracy. The transformative power of this approach is that it overcomes implicit biases that are often found in self-reported measures of emotional response.
Another technological innovation is the ability to process vast quantities of unstructured data at scale. Because we now have so much data about consumer behaviour (often created by consumers themselves in the form of social media posts, online reviews and other digital content), market researchers can analyse larger and more complex datasets with much faster turnaround times.
Market research tools powered by AI can make fast work of this data, with the potential to deliver new insight into what consumers are thinking – and to identify trends that were previously difficult to pinpoint. When applied to a traditional market research task like ad testing, AI and machine learning can quickly compare creative to a database of ads, identifying whether the ad will perform based on what has performed well in the past. For example, Kantar’s Link AI can predict creative effectiveness in just 15 minutes, allowing you to analyse higher volumes of ads more cost-effectively. This offers new opportunities in terms of competitive research and meta-analysis.