2015 Consumer Markets Trend: Analytics & the Data-driven Consumer

The Digital Essentials, Part 3

Developing a robust digital strategy is both a challenge and an opportunity. Part 3 of the Digital Essentials guide series explores five of the essential technology-driven experiences customers expect, which you may be missing or not fully utilizing.

Get the Guide

In a Connected Consumer World, Analytics Starts with Consumer Data
The data is in and it’s revealing that the data is really in! Tons of it.  Not long ago, simply getting the data on our customers, sales, sentiment, stores – anything else we felt would be relevant – was a challenge. But now we have so much data we think we need, we are struggling to figure out what to do with it. With the shift to the connected consumer, perhaps the first step in leveraging the volumes of data is understanding who the consumer actually is.  Today, retailers interact with customers through a wide variety of mediums including mobile, web and in-store – often at the same time. But identifying who, when and where those interactions took place is not very easy. We might know the regulars by name, some by their loyalty cards, others their email address, and many by their mobile phone’s machine address. Some we can only infer.  If a retailer knew that someone browsed a website for products and then stopped, but arrived at the store 30 minutes later, what could they do with that information if they knew it was the same person? Consumer data will become more and more critical as brands drive more and more relevance and personalization.
Make it Relevant and Personal
There has definitely been a push to collect and save everything, and sift through it later for a fresh insight on something – anything. Again, the trend towards relevancy and personalization is driving customer engagement, and thus we feel that a basic understanding of the shopper’s journey is also critical to building a foundation for customer experience and the analytics that drive and validate it. By segment and category, customers discover, engage, transact, use, and advocate your products and services differently. They research hotels through customer reviews online (webrooming) but check out televisions in-store (showrooming.) Traditional research and descriptive analytics can expose these variations to focus your prescriptive analytics toward relevancy and personalization.
Complex Event Processing Makes Big Data Actionable
In many ways, retailers have perfected the art of analyzing not just customer activity but their overall business – from loss prevention, to allocation planning, to customer segmentation. But these are really descriptive analytics: sorting out what happened in the past to make some guesses about the future. Of course that takes time and today’s consumers frankly can’t wait that long. The ramp-up now is to evolve from descriptive analytics to predictive and prescriptive analytics to forecast and optimize on the fly, with more data and ultimately to personalize the customer experience. The great news is that the technology is moving quickly to make this possible. The confluence of big data and complex event processing technologies is going to enable extremely responsive and personalized experiences.
It Takes a Village
To date, I believe the operational aspects of retailing have realized the most value from analytics.  The data is typically fixed and well-structured in accessible enterprise systems to ship, store, present, and return items. This is the heart of buy, move, and sell. Of course, omni-channel has changed all of this and the reporting and analytics have been slow to catch up. The classic omni-channel use case – buy online, pick up in store – required lot of operational effort to make this possible. But when a customer returns that item to the store, who is taking the hit? The store? The .com site? The operational, customer, and marketing analytics for the omni-channel retailer are evolving and expanding as we speak. To continue the omni-channel use, it took concentrated effort to orchestrate store ops, the online business, most likely both fulfillment arms, store training and many other teams and make it work. The impact of multi-function analytics will follow the same arc because the value of building on these correlated, complex insights across teams will be required to keep up with the nonlinear, always varied, always changing shopper’s journey. If eternal vigilance is the price of liberty, then eternal analytics is the price of loyalty.