By Adele Gritten
The impact of data science on the market research industry has been transformational. It has taken us beyond the confines of the traditional survey and into rich new territory.
A machine-led approach enables even those without advanced PhDs to identify consumer insights that would previously have gone unnoticed. This should be great news – particularly for clients. However, at the same time it could also be viewed as the root cause of a semi-existential crisis around the future of market research, namely the value of insight.
Is data science the future? If so, does this leave traditional market research in the past? Should we dive headfirst into data science and rely on Big Data, Artificial Intelligence (AI) and analytical modelling to lead the way?
Alternatively, is there a risk that the hype around data science could side-line the invaluable and irreplaceable role that human intuition and more traditional market research techniques play in research?
The most tech-averse of us must surely acknowledge the significant role AI and machine learning have to play in future-proofing the industry. However, their value is limited in isolation. Given the deluge of data at our finger tips, the onus is now more than ever on human interpretation rather than analysis and data preparation.
That means the sector should aspire to a true synthesis between market research and data science. The next generation of research must look beyond the data and will be shaped by humans and technology working together to meld hard data with softer intuition.
In this synthesised world, we are able to focus on business-critical information as the key outcome. It’s not about asking what we want to research, but about understanding what the business needs are and how we can use research to provide the intelligence to fulfil them.
After all, the ‘survey’ plays a single, and diminishing, element in interpreting the vaster and wider internal and external data sets available to businesses. The traditional focus on market research as a tool for ‘storytelling’, though still important, needs to be replaced with a more rigorous focus on the bottom line.
Resultantly, research firms should become much more focused on delivering business-centric, commercially-minded output to counter the research technology providers, which threaten to take market share from the incumbent players.
Ultimately success can only result from blending data ‘artistry’, the legacy qualitative skills upon which the industry has relied for decades, with data science. For example, ‘always-on data’ (continuous digital data collection) offers one way to ensure a consistent and platform-based approach from which to collect and collate data. At the same time, the value lies in interpretation, which remains a human-led activity.
To put this in context, Future Thinking is working with a leading UK healthcare provider, leveraging machine learning in addition to more traditional brand tracking and ‘always-on’ customer experience data collection (both structured and unstructured sources) to answer business crucial questions.
Looking at data more longitudinally and horizontally, we are able to answer typically ‘difficult’ business questions for the client. These include how to increase subscriptions by building awareness and consideration of a particular category, how to achieve this (alongside other core funnel metrics) through advertising and promotion investment; and, crucially, what investment needs to be maintained for to have the most impact?
Adding predictive analytics into the mix allows us to look at “what if” scenarios much more clearly. What would new subscriber numbers look like if the client was to invest in promotion at the same time as last year? What would it look like if they invested more?
The power of combining brand tracking, customer satisfaction, social and internal business data (CRM, POS, EMR, etc.) lies in allowing us to zoom in on customer and prospect needs to identify business opportunities and advise on follow-up strategy.
In this way, the application of data science coupled with human-led interpretation benefits the industry because it draws upon the best of both worlds. It offers a faster, more efficient and cost-effective means of information collation and curation.
Thanks to the availability of multiple information touchpoints, this approach allows for more balanced conclusions through the considered interpretation of data. This accepts that data collection is just a hygiene factor these days, not least because more often than not, it is readily available.
This means that the value (and the profit margin) for the future of the legacy marketing research industry will be based on interpretation and consultation, rather than the actual data gathering (where many of the bigger agencies still add significant mark ups and smoke and mirror margins).
The new world also moves market research away from siloed skillsets to a more universal and democratised access to data and information. Data science alone requires highly specialist capabilities but the ensuing analysis still needs the more contextual and commercially-focused approach brought by those who exist outside the Big Data bubble.
By Adele Gritten, UK MD, Future Thinking
