This week, Kyle Cockett takes a look at the growing trend of ‘big data’ in the research industry, and the potential future implications.
During my tenure as a Research Executive, I have found myself working with a growing number of data sources during quantitative research projects. With a growing appetite for actionable insights, I increasingly find myself working in partnership with clients to source internal data that will help to provide quantifiable and conclusive findings. The process of harnessing such data is becoming progressively easier due to the increasing number of clients with well managed CRM systems in place. Coupled with the quantity of data shared on social media sources – regardless of the ongoing ethical debate – this means there is often an abundance of data to be examined and analysed during the reporting stage of projects.
The ever expanding size of datasets is not a new phenomenon – datasets characterised by large amounts of complex data from disparate sources have been around for many years. Tesco are often cited as one of the forerunners of large scale data mining with their Clubcard scheme, which gathers data on the purchasing habits of millions of customers. Despite this, only recently has the term ‘big data’ risen to prominence within the industry to describe such datasets, perhaps prompted by the ever increasing number of data sources – social media, smartphones and blogs are just a few examples of relatively new data streams. Google Trends reveals that the use of the term ‘big data’ has been growing in use exponentially in the past few years – and it is expected to grow even further. Ray Poynter, of Vision Critical, positions big data as the ‘one big trend’ at the forefront of the market research industry, ahead of twelve other multiple strands of expected change. As Poynter indicates, this prediction is firmly backed by the latest industry reports:
The ESOMAR Global Market Research report shows that 50% of the revenue that is currently nominally called market research comes from activities that do not relate to asking respondents questions. This 50% relates to store audits, people meters, processing loyalty card data, web analytics and other data related services.”
This does not necessarily mean the end of traditional quantitative research techniques, such as telephone surveys. However, it is expected that the findings from such surveys will increasingly begin to be used in conjunction with data from other sources – they will become one of the many scores or metrics fed into the big dataset. This is not expected to be an easy transition – many researchers currently work with data that has a size in the order of megabytes, while most big datasets are in the order of terabytes or even greater. Poynter states a belief that the move towards big data will be a ‘bumpy and a not altogether pleasant one for many market researchers’. If this is the case, then what is the benefit of gathering such large data sets? According to a research report by McKinsey Global Institute, big data is:
The next frontier for innovation, competitive advantage and productivity”
In order to conquer this frontier, it is essential that clients exploit the full potential of big data – the rise of big data provides huge scope for actionable insight and predictive modelling. Through the use of big data analytics, it is possible to create models that can predict changes in revenue, develop targeted customer value propositions, develop advanced segments, and identify where to focus resources among customers amongst other possibilities. There are many examples of such data mining already present in the retail industry – Tesco has a successful strategy of sending targeted discount vouchers to customers based on their typical basket of goods, while many online retailers offer purchase recommendations based on the past purchase history of their customers. These personalised touches often give retailers the extra edge over their next best rival. Applications are not always limited to customer satisfaction or retention either – by developing bespoke offerings, businesses can extract the full willingness to pay from their customers by accurately targeting premium offerings to appropriate segments. McKinsey estimate that a retailer using big data has the potential to increase operating margins by 60 per cent.
While many of these examples are heavily focused on retail, big data analytics also have the potential to shift approaches in business to business markets. Though B2B companies do not have the potential to mine data from social media sources as consumer companies do, they often still have well developed CRM systems that can provide a wealth of information on their customers. Who knows, it may take only one extra measure to provide insight that leads to a competitive edge?
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