Best-Selling Author, Keynote Speaker and Leading Business and Data Expert
Big Data and Shopping: How Analytics is Changing Retail
Drones zooming through the skies to deliver us packages that we haven’t even ordered yet – a (somewhat clichéd, already) vision of how technology, Big Data and analytics will impact the retail landscape in the near future.
But flights of fantasy aside, the way we buy and sell is evolving rapidly. Both online and offline, retailers which are embracing a data-first strategy towards understanding their customers, matching them to products and parting them from their cash are reaping dividends.
Although we are not quite (yet) at the stage where drone delivery and mind-reading predictive dispatch are mainstream, things have moved on greatly from early Big Data retail experiments, such as Target’s infamous attempts to work out who was pregnant. Today, retailers are constantly finding innovative ways to draw insights from the ever-increasing amount of structured and unstructured information available about their customers’ behavior.
I have done a lot of work with leading retailers over the past 12 months and thought it would be a good to take a look at some of the cutting edge applications of analytics in the world of shopping – offline as well as online. Major bricks ‘n’ mortar chains have fought hard to keep up with, and in some ways better, the advances in technology driven by the online retail boom. And many have found that their model offers specific opportunities to monitor and understand customer behavior which their online competitors just can’t match.
Big Data analytics is now being applied at every stage of the retail process – working out what the popular products will be by predicting trends, forecasting where the demand will be for those products, optimizing pricing for a competitive edge, identifying the customers likely to be interested in them and working out the best way to approach them, taking their money and finally working out what to sell them next.
Today, retailers have a wide range of tools available to them in order to work out what will be this season’s “must have” items, whether that be children’s toys or designer dresses. Trend forecasting algorithms comb social media posts and web browsing habits to work out what’s causing a buzz, and ad-buying data is analysed to see what marketing departments will be pushing. Brands and marketers engage in “sentiment analysis”, using sophisticated machine learning-based algorithms to determine the context when a product is discussed, and this data can be used to accurately predict what the top selling products in a category are likely to be.
Once there’s an understanding of what products people will be buying, then retailers work on understanding where the demand will be. This involves gathering demographic data and economic indicators to build a picture of spending habits across the targeted market. Russian retailers, for example, have found that the demand for books increases exponentially as the weather gets colder. So retailers such as Ozon.ru increase the amount of book recommendations which appear in their customers’ feeds as the temperature drops in their local areas.
Giant retailers such as Walmart spend millions on their real time merchandising systems – in fact Walmart is currently in the process of building the “world’s largest private cloud” to track, as they happen, millions of transactions every day. Algorithms track demand, inventory levels and competitor activity and automatically respond to market changes in real time, allowing action to be taken based on insights in a matter of minutes.
Big Data also plays a part in helping to determine when prices should be dropped – known as “mark down optimization”. Prior to the age of analytics most retailers would just reduce prices at the end of a buying season for a particular product line, when demand has almost gone. However analytics has shown that a more gradual reduction in price, from the moment demand starts to sag, generally leads to increased revenues. Experiments by US retailer Stage Stores found that this approach, backed by a predictive approach to determine the rise and fall of demand for a product, beat a traditional “end of season sale” approach 90% of the time.
Deciding which customers are likely to want a particular product, and the best way to go about putting it in front of them, is key here. To this end retailers rely heavily on recommendation engine technology online, and data collected through transactional records and loyalty programs off and online. Although Amazon may not yet be ready to ship products directly to our doors before we order them, it is already pushing them in the general direction. Demand is forecast for individual geographic areas based on the demographics they have on their customers in those areas. This means that when they do receive the orders they can be fulfilled more quickly and efficiently. Data on how individual customers interact and make contact with retailers is used to decide which is the best way to get their attention with a particular product or promotion – be it email, SMS or a mobile alert from an NFC transmitter when they walk or drive by a store.
Attracting the right kind of customers to your bricks ‘n’ mortar stores is key – too, as US department store giant Macy’s recently realized. Due to their analytics showing up a dearth of the vital “millennials” demographic group, it recently opened its One Below basement at its flagship New York store, offering “selfie walls” and while-you-wait customized 3D-printed smartphone cases. The idea is to attract young customers to the store who will hopefully go on to have an enduring lifetime value to the business.
Taking the money
Analytics has revealed that a great number of customer visits to online stores fail to convert at the last minute, when the customer has the item in their shopping basket but doesn’t go on to confirm the purchase. Theorizing that this was because customers often can’t find their credit or debit cards to confirm the details, Swedish e-commerce platform Klarna moves its clients (such as Vista Print Spotify, and 45,000 online stores) onto an invoicing model, where customers can pay after the product is delivered. Sophisticated fraud prevention analytics are used to make sure that the system can’t be manipulated by those with devious intent.
Pushing out the little guy?
So, with all this reliance on technology and resource-heavy analytics, is all of this just another hurdle for the little guy, in the face of competition from multinational retailing giants? Well, not necessarily. As is the case with Klarna mentioned above, a growing number of middle men are specializing in providing Big Data “as a service” infrastructure. This allows smaller businesses and independent operators to take advantage of many of the same data-driven approaches to sales and marketing, without the need for implementing expensive hardware solutions and hiring in $100k-plus per year data scientists. Targeted advertising platforms of the type pushed by Google and Facebook offer businesses of all sizes the chance to benefit from Big Data-driven segmented marketing strategies. And a growing number of startups are offering social analytics to help anyone work out where their customers are waiting for them on social media.
Retailers – large and small – have been reaping the benefits of analyzing structured data for years, but are only just starting to get to grips with unstructured data. There is undoubtedly still a great deal of untapped potential in social media, customer feedback comments, video footage, recorded telephone conversations and locational GPS data. Great benefits will come to those who put it to best work, and in my opinion the best solutions will more likely come from innovative thinking and approaches to analytics, rather than those who simply try to collect as much data as possible and then see what it does.