A current hot market research topic is the blending of ‘real’ data from observations or behaviour with survey and market research data in order to build pictures and patterns and from that try to forecast what customers will want into the future.
Statistical analysis of behavioural or observational data – say website data, tracking app use, e-commerce data or even just plain old-school sales and database data has turned into the massive data science industry, far larger than the older and more established market research industry.
In simple terms, the aim is to classify data in different ways, test for relationships and to use these statistical views of the data to identify and act on predictors in order to make the offer more relevant, or more likely to be chosen, by customers. For instance, identifying the characteristics of purchasers of different products, so as to better focus advertising, or linking data about the customer together in order to present a more relevant offer.
Simple targeting has always been done. Small, lower priced cars for young singles, larger but more staid and practical cars for older people with families. The difference now is the ability to mine for hidden relationships, and to mix the data sources together, and then to target at the individual level. Someone who posts about environmental issues would then get advertising more focused on the product’s environmental benefits and energy efficiency, than someone who perhaps tracks Formula One on their feed.
If you apply a time element too, then someone looking at houses or new accommodation now, might be a prime target for furniture sales a little down the line. Or, at the point that children reach 18, suddenly there are a whole set of life changes that impact the 18-year-old and their parents. Both get a mass of financial decisions, changes to lifestyle and outlook and both get more independence.
Understanding and mining the data therefore helps businesses identify new potential customers and customers at decision points, where the company can best offer new solutions, new services or new products.
However, the challenge with behavioural and correlational data, such as website data, e-commerce data, or just old school sales and database data is that it is typically backwards looking, and correlation can’t always be explained. It tells you want people did in the marketplace at some time in the past, but it can’t tell you why, and it can’t necessarily help with views about the future, where that future is different from now – for instance how the market will react to a new product.
For a practical example, an observation from data might show that car purchase by residents of Barcelona are typically smaller than those from outside the city. However, this doesn’t necessarily explain that part of the reason might be because Barcelona has large amounts of underground parking which makes larger cars more of a liability than small ones. The ‘why’ explains the data.
Similarly, if a company is planning to introduce a new car sharing service, say, the backwards looking data will show the relationships between trips and travel and individuals, but as it’s looking backwards, it can’t quite project forwards to the new service without going and asking – the data has to be created from scratch.
This is where blending of real data and survey data becomes massively useful. Not only can existing patterns be explained, and from those explanations will come new ideas about how to build the links, but also views of the future can be melded with actual older data to estimate not just the impact of the change, but also where and how that change will affect existing data patterns (and so perhaps lead to competitor responses and further change).
Now ‘blending’ is something of a new catchword, but actually companies have been blending research and real data for a long time. In most B2B studies existing customer data is often used to enhance the market research data, or used as keys for modelling research findings back to the database for tasks such as customer segmentation. In consumer markets, surveys and real data can be ‘fused’ statistically to build propensity models – a little like credit scoring.
The interesting aspect now though, is the ability to do this in near real time. So we might track a (willing) customer through their use of an e-commerce facility and then follow up immediately with questions about their behaviour and what they looked for. One interesting finding is that it seems customers often remembered how they looked – the journey – more than what they actually bought.
Once this is overlaid with the ability to conduct sample-size experiments where features and offers are modified to selected customer groups, who are then followed up with survey research to refine and iterate the offer.
This blending of research, behaviour and forecasting is the next frontier for customer insights.
dobney research 