Behavioural analytics examines the “what’s” and “how’s” of consumer behavioral data to inform the “why’s” of consumer behavior. Thus, if the purpose of market research is to understand your target market, then online behavioural data collection and analysis has to become an essential ingredient in the research process.
Of course, market researchers have been using behavioral data for years, mostly in the form of qualitative techniques, including observations or ethnographies. However, it is the advances in technology and data science that have allowed researchers to combine traditional survey data with behavioural metrics that provide a more accurate and holistic understanding of the consumer.
Why Use Behavioural Data?
Instead of thinking of humans as fully rational economic actors, behavioural science acknowledges that our time and attention are limited, and we don’t have the mental resources to process all the information around us at all times. So in order to make efficient decisions, we use heuristics, or mental shortcuts, which can lead us to decisions that deviate from the predictions of rationality.
Using behavioral data to learn about consumer preferences and behavior is a very hot topic for marketing researchers. As discussed by InfoSurv here, surveys aren’t perfect: samples may not be representative, respondents’ memories may be flawed, survey questions may be badly designed, or there may be an incorrect analytical technique used. Moreover, even if all of those factors work perfectly, the fact is that survey data is limited to a sample of respondents at one point in time. Researchers have always struggled with these limitations, so it is no wonder that behavioral data, with its promise of ongoing, inexpensive, accurate data about what consumers actually do, is very compelling.
The science of analysing online behavioral data is now widespread in businesses of all sizes. However, what is missing from this data is the descriptive data (demographics and attitudes/beliefs) that help us understand who is behaving this way and why the behavior is happening. That’s where surveys come in. Thanks to the ease of tagging survey data with online identification, combining online behavior data and survey data becomes easier and faster for marketing researchers. If this can be achieved, that is where the most powerful consumer insights will be generated from.
Benefits for Market Research
By combining what we learn in surveys and what we learn from web analytics, we can create and test business decisions to appeal and motivate consumers at the individual level. Especially as the Internet of Things grows, you will be able to gain a true multi-faceted view of your customer – on the internet, on their phone, and other connected home appliances that will allow researchers to analyse every conscious and subconscious decision made by their target audience.
Some marketing researchers believe the days of survey research are numbered. They argue that as technology and interconnection develop and proliferate, surveys will inevitably succumb to the greater richness and lower costs of these other methods. That remains to be seen, but for now, combining behavioral data with survey data is an important advance for marketing. Omni-channel intelligence and data is going to be an essential part of understanding consumers into the future. By combining authentic survey data with behavioural data collection at scale, companies will be able to deliver real-time insights to influence decision making each day.
Challenges for Market Research
Despite this, there will always be challenges. As technology and ‘Big Tech’ has transcended regulatory boundaries in the last decade, our fascination as consumers with personalisation, flawless customer experience and an array of products that have fundamentally changed how we live our lives has led to many consumers casting a blind eye to the data driving such experiences.
However, a growing proportion of the population have started to take notice of the potential privacy infringements taking place each day by these data processors. This is most prevalent in Europe, thanks to the work carried out in Brussels and the implementation of GDPR in 2016. A key challenge moving forward will be the careful consideration of consumers and the ethical collection of quantitative, qualitative and behavioural data to effectively deliver these experiences into the future.