Digital technologies are transforming retail, whether through on-line shopping websites, smartphone apps, augmented reality, new payments systems or location detection. However, the way the Digital Age will convert into real dollars for retailers is through the data this technology is producing in such vast quantities.
It is estimated that 90% of all the data gathered throughout human history was collected in the last two years. This is the phenomenon of Big Data – data that is collected about every interaction between people, systems, devices and information. Not only is the data collected, but new tools and improved compute power allows this data to be analysed in ways that can seem either miraculous or downright creepy. Whether it is used to determine if a customer is pregnant or the location of Osama Bin Laden, data can now allow organisations to know more about specific individuals than ever before.
What does this mean for retailers?
Firstly, if you are a retailer and you are not gathering and storing information about all your interactions with your customers, then you are already losing the race. And interactions are a lot more than simply sales transactions: you need to know what web pages your online customers visit, what searches they perform, when and how often do they use your mobile app, when do they visit your store, what aisles or displays in the store do they visit, and when does this finally turn into the sales transaction which is after all the only time you can monetize your customer relationship.
Secondly, you need a strategy to identify your customer across all of these interactions. Accurate customer identification is the key to unlock the value inside all the data you are gathering. Whether through an effective loyalty program, analysis of credit card usage, detection of smartphone identifiers, use of face recognition technologies, or most probably a combination of all of these, identification of your customer is crucial. Of course, at all times the privacy of the customer is paramount, so all the above techniques need to be used intelligently.
Thirdly, to get value from your data you need analysis. For this there are two crucial ingredients: the right tool sets, and the right capability in your team. Data Scientists are the heroes in this new world, and you need to build capability in that area. You also need the tools to allow your data scientists to access the data efficiently, processing vast amounts of information. Today, such tools can quickly and cheaply be acquired and deployed, using cloud technologies and Hadoop.
Following these three principles will allow the retailer to understand their customers, and target unique offers for specific customers tailored to their needs and wants. However, I believe that the truly successful retailer of the future will also address a fourth aspect of Big Data.
As awareness of the implications of Big Data on privacy grow, and as the public become wary of targeted marketing campaigns, customers will increasingly become reluctant to participate. To keep the customer willing to participate in the Big Data game, the canny retailer will give that data back to the customer in ways that add true value to the customer.
For example, if I regularly buy my fuel from the same retailer, that retailer will have insight not only into my own fuel consumption, but into the fuel consumption of others. Therefore, the retailer can tell me “Do you know your car is 10% less fuel efficient than other customers driving the same model?”. This is valuable information to me, as I can use it to have my car’s engine examined, or to change my driving habits, and ultimately save on fuel. Of course, this only works for me as a customer if I continue to use the same retailer for my fuel purchases – a better incentive for loyalty, I would contend, than most current loyalty programs
Big Data is without a doubt the retail currency of the future. But the real gold for tomorrow’s successful retailers is determining how to make the Big Data of real value for the Customer as well as for themselves.