The surge in social media usage and the proliferation of mobile devices has led to consumers producing data at an alarming rate. There is already more than 4 million gigabytes of information per internet user aged 45-59, according to IDC, and each year those same consumers will produce another 1.8 million gigabytes of data covering spending habits, influencers, and demographics. But what is 'Big Data' and how much of it is actually useful to brands, and what benefit can we gain compared to what it costs to gather and use?
What is big data?
There is still a significant amount of uncertainty surrounding the concept of big data and how it is defined. According to Gartner, big data encompasses high-volume, high-velocity and high-variety information that can be processed innovatively for enhanced insight and decision making. In essence, the vast amount of data generated in real-time across so many channels requires a more sophisticated management approach in order to extract any meaningful intelligence. It also presents inherent challenges as highlighted in a recent study by IBM where they found that less than half of the organisations engaged in active big data initiatives are currently collecting and analysing external sources of data, like social media (figure 1).
Arguably many brands have been grappling with the challenge of big data for well over a decade. Buzzword aside, it all comes back to a marketing concept that has been around for decades. By collecting customer information from disparate sources and then connecting the dots across all that data, you can answer complex marketing and business questions. Better still, you can work towards an individualised single customer view allowing you to deliver value back to them in a unique and more rewarding way. Major food, retail and pharmacy chains with loyalty programmes such as Tesco in the UK and CVS in the US, have been successful in translating high volumes and frequencies of transactional data into a more relevant and personalised relationship proposition for its members.
Why big data?
Along with the ability to create a single view of the customer, brands can leverage 'big data' to understand their interactions across channels, who their loyal customers are, where they are in the purchase lifecycle, and listen to their needs. Figures from Aberdeen Group show that companies whose marketing teams use data analytics to enhance the targeting and relevance of their messaging, channels and timing are seeing sales growth of 30%, and marketing response rates from big data-enabled campaigns were up 100% over non-big data counterparts (6% campaign response rate versus 3% from a non-big data campaign).
Furthermore, those brands who have embraced big data successfully have grown substantially over the past 5-10 years. Take Amazon for example, who has seen revenue and profit levels completely outstrip those of its competitors (figure 2).
The essence of big data can still be linked to support critical marketing objectives - such as understanding current and future customer behaviour and motivation. Eventually brands can embrace more sophisticated techniques, such as price sensitivity or predicting future behaviour to determine the most appropriate action. Take the Discovery Drive reward programme, for example, which uses in-car telematics to gather data about driving styles to dynamically adjust the customer's future insurance premiums.
However big data is an incredibly complex thing to manage and it can quickly become overwhelming for marketers and analysts who are not properly equipped to handle it. It's easy to see how Google has the tools and expertise to combine data from disparate sources such as maps, search, social networks and more, and make sense of it, but that takes both serious technology and a robust data strategy. In fact some companies have consciously elected not to 'go big' but instead to keep their data collection, analysis and customer insight strategies far simpler. A brand doing just this is Kimpton Hotels in the US who use big data only to profile its typical guests, but then actively leverages 'Little Data' - a proportionally small subset of segmented data that relates to each customer - in a more traditional sense to proactively individualise each guest's stay. Taking this dual approach may mean that marketers' approach to data processing and analysis has to be more sophisticated but the end result caters for individual customer needs much more accurately and intelligently.
What are the benefits and the challenges?
Despite a number of operational concerns - such as the advanced marketing and analytical talent and technologies required, and the budget to fund them - for most businesses, putting a big data strategy in place offers several practical benefits as well as some significant challenges to consider:
- An ever-narrower segmentation of customers (micro-segmentation), allowing precisely tailored products, services and marketing messages, although there is an undeniable cost in terms of marketing resources;
- Making information more transparent and usable at much greater frequency;
- Improving and informing product and service development;
- The use of sophisticated analytics to improve decision-making;
- The collection of more accurate and detailed performance data, exposing variability and boosting performance, and supporting better management decisions.
But the benefits of big data extend far beyond segmentation, targeting, personalisation, marketing relevance and business process improvement. It directly addresses the prediction of future customer behaviours, needs, relationships and even customer lifetime value (CLV). All of these come together to build a stronger, more profitable, mutually beneficial customer relationship that can withstand not only the competition, but even hard economic times. However measuring the success of following a big data strategy is still critically important if any investment is to be justified to wider stakeholders, and this may not be straightforward as it often cuts across multiple systems and organisational silos.
How does big data connect with loyalty, and the business ?
There are other, arguably more important, uses for big data that are customer centric: generating new business value from personalisation, greater customer engagement, and supporting the loyalty programme, whether through improving the personalisation of points, offers or informing decisions about reward offerings or potential programme partner synergies.
Although 'big data' has recently become something of an industry cliché, the size of the data does not change the importance of getting your strategy right from the outset. The data is a means to several ends: it should enable not only a single view of the customer that provides practical and actionable insights, but it should also help make loyalty initiatives more valuable as they can further personalise the relationship approach. Big data's role is not to simply know everything; it is to enable smarter decisions and improve the ability to respond potentially in real-time to maximise the value and impact of the insight. For those brands responding in real time, the challenge is how to organise the business to manage and respond in a timely and relevant way to improve commercial performance.
GlaxoSmithKline (GSK) is a brand who is really using big data to their advantage. GSK, which owns brands such as Sensodyne and Lucozade, is already tracking consumers online and repurposing the data to benefit particular brands and drive stronger customer relationships. GSK is aiming to build direct relationships with 1 million consumers in a year, using social media as a base platform and driving people to brand websites where external data is integrated with information already held by the marketing teams. By tracking people who reference one of their particular brands and linking it to everything else they are talking about in the public domain, they are creating a much richer customer profile.
In the end the game itself has not really changed - only the scale of the data that is now available to brands. The big question is whether or not they invest to optimise, analyse and utilise that data to gain new insights that ultimately deliver more profitable and loyal customers. Whether you use small or big data, the goal of better understanding your customers and actioning insights to deliver greater value back to them is really what counts.
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