Shopping for insights – How retailers can take advantage of big data

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A retailer exploiting big data fully can potentially increase its operating margin by 60% says global consulting firm McKinsey. Without a doubt, the retail industry has much to gain with the mining of Big Data. The reason big data is of such relevance to retailers is the diverse ways in which it can add value. Big data software and systems can help retailers combine a variety of data- unstructured and structured- from a plethora of sources in real-time and in cost effective ways to provide insights that can enable swift yet informed contextual decisions.

Here are some examples of analysis that retailers can carry out to aid their business growth and operations:

  • Track customers across different outlets and understand their purchase preferences
  • Identify purchase trends whether it is in a single store, city, state, or country; or across the world, depending on the size of their business
  • Analyze influence of offers, sales, coupons, online ads etc. on customer purchase preferences and trends- in-store or online
  • Track products from the moment they leave distributor to the moment they are sold
  • Stock tracking and instant notification of ‘need to restock’ products in a store or warehouse, based on purchase trends.
  • Track every action taken by the customer from the moment she enters the retailer’s store- physical or e-commerce
  • Identify customer location and based on that identify regional trends
  • Track stock in warehouses in various regions, compare regional trends and evaluate need to restock products
  • Identify products that customers prefer buying online, in physical stores, and those which perform well no matter which environment is used
  • Identify trends and decide on moving products to the best suited warehouses/retail outlets
  • Test and identify which forms of promotion work best online and which forms work best offline

With the wealth of such analysis, how can retailers sharpen their decision making?


With better insights into competitive pricing and demand trends, retailers can take better context-aware decisions on pricing, promotions and assortment management. By enabling cross channel sales data analysis in real-time can help retailers take decisions on what to sell and promote across different channels. By mining data from sources such as social media, online transactions, in store transactions etc, retailers can fine tune local assortment decisions. And that’s not all- by studying customer sentiments by mining social chatter and feedback to promotions retailers can predict or anticipate demand for their products across channels. All of the above help retailers take smarter decisions on merchandizing and inventory. Consider this: during the last holiday season Amazon has reportedly made 3 million daily price changes through the month of November 2013, including changing prices on almost a full third of their sampled assortment on Black Friday alone. Such dynamic decision making would be just impossible without Big Data analytics.

Customer lifecycle management

Big data analytics can also help retailers help understand their customers better- and tailor each and every customer’s interaction with their brand by choosing the channel, message, time of communication with the customer at an individual level. Such a targeted approach dramatically improve chances of customer acquisition; in addition by closely tracking customer behavior on various online and social channels, any dissatisfaction can be stemmed quickly; retailers can be far more proactive in tailoring offers to existing customers and engender loyalty. Also, the analytics can be used to up-sell more profitable products offering a chance to improve margins.

These examples demonstrate the value of big data analytics to the retail industry. At a time when the industry is grappling with dealing with the challenges of a demanding customer, increasing competition, multiplication of channels, big data analytics can provide some real answers.

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