Get smart

Big data is a powerful tool to advance our knowledge of consumer behaviour. But contrary to what some would have you believe, it isn’t a universal panacea to the challenges businesses face understanding their customers.

Also, don’t let the size thing fool you. Data is only useful if used in a smart way, so if you are looking to get the most out of your data assets – however big they might be – you need to combine behavioural information with consumer survey data, and a social science perspective.

I call it “smart data”, and here’s why researchers need to be setting the smart data agenda.

The right skills

McKinsey estimates that in the US alone there is a shortfall of 190,000 analytics experts and 1.5 million data-savvy managers with the necessary skills to make decisions based on the analysis of big data.

But the skills associated with market research are very much in the right category: an ability to understand and manipulate large data sets, good analytical skills, an ability to interpret findings and apply them to business.

Indeed, some research agencies are now in a position where they are handling big data every day as the level of digital data being captured proliferates and agencies start aggregating this information.

Intelligent integration

The need to understand the context and meaning of the behaviour that is observed in big datasets will rapidly become critical as there is only so much nuance that a purely behavioural analysis will provide. The consumer intention that is driving activity can only be properly understood by talking to the consumer. This is an opportunity for a wide range of thinkers and practitioners – from ethnographers and qualitative researchers through to marketing scientists and quantitative analysts.

Without understanding the consumer mindset there is a danger that big data never becomes smart data. As such, the value for marketers is limited. Intelligent integration brings consumer and research knowledge together with big data to avoid the risk of chasing the wrong questions and finding false positives. Appreciation of underlying assumptions within the data, and of the motivations behind consumer actions are likely to improve the way findings are understood and communicated.

Big picture

The starting point for big data has often been for brands to analyse their own proprietary datasets, generated from their own digital assets, and then to use this information to optimise consumer interaction with their brand.

However, walled-garden approaches like this only give part of the story. They tell you nothing about the activity that led the consumer to encounter the brand in the first place – nor, indeed, which other brands that consumer is having conversations with.

Research agencies are in a strong position to “own” the smart data approach for a number of reasons:

• By becoming aggregators of different clients’ big datasets it is possible to provide syndicated services that reflect activity in the market as a whole
• By building panels of consumers that capture digital activity across multiple devices it is possible to build up a fuller picture of their online lives, not just those related to specific brands
• By accessing social media data through APIs or other agreements to undertake a much more detailed look at the nature of online social interactions
• By using life-logging approaches to build up large datasets for a relatively small number of individuals

Together these provide the opportunity for a much wider understanding of a brand’s performance and can be used to place the work done on the brands’ own data assets in a much wider context.

Eyes on the prize

The era of big data allows us to start exploring human behaviour, and specifically human social interaction, in a way that has never before been possible. The tools we use always shape our understanding, so if our tools suddenly grow and change then so will our understanding – and not just in size and scale but qualitatively.

Perhaps this is the real heart of smart data, where our understanding of consumers is embodied within the data itself, generating insights into the human condition that are hard to derive by other means. Creating this type of understanding will make a huge difference to the way brands use their datasets as they start to properly integrate models of human and social behaviour.

The challenge for our industry is making the case for smart data – rather than just big data – as the most profitable way for brands to unlock the value held in their data assets.

Colin Strong is managing director of GfK’s technology division in the UK