The Retailer's Guide to Big Data

the retailer's guide BIG DATA Retailers already know how to use customer data to run their brick-and-mortar operations. And they've been digging through reams of website analytics for at least a decade now. But data didn't really get big until an explosion of smartphone use and social media addiction, resulting in the need for more agile, relevant marketing. WHAT MAKES DATA SO BIG ? 6. 1.01 400 90% billion mobile subscriptions billion Facebook users worldwide million tweets per day Of the world's data has been created in the past two years 87% 604 84 of the world's million users log-in monthly from mobile devices million users access Twitter via mobile population ... and data will only get bigger as traffic from smartphones and t ablets outpa ces traditional devices PERCENTAGE OF WEB VOLUME O F DIGITAL CONTENT TRAFFIC BY 2016 2.7 7.9 zettabytes in 2012 zettabytes in 2015 equals 9 million galaxies of stars equals 18 Libraries of Congress 39% 61% wireless devices wired devices TAKING THE MYSTERY OUT OF BIG DATA In order to leverage Big Data, retailers must first need to understand the different types of data generated, and then figure out which information they should collect and share. DATA DETAILS Retailers view Big Data along two different constructs. Top Data Challenges for Retailers Name Address 20% STRUCTURED • Date of Birth Velocity 46% Transactions Volume Loyalty Points, etc 34% • Amounts Variety Product Reviews "Likes" SEMI-STRUCTURED/ UNSTRUCTURED Tweets Images, etc. DATA DISCOVERY How much enterprise does your company store? How much of the data is unstructured? 1.6% • 500-999 terabytes 8.2% more than 1 petabyte 24.6% Know the percentage 32.8% less than 50 terabytes 75.4% 24.6% Don't know 32.8% the percentage 51-500 terabytes Don't know CHALLENGES OF USING BIG DATA Given that nearly one-third of retailers are in the dark about their available data, it makes sense that silos are the primary hurdle in using this information. 45% Not using data effectively to personalize marketing communications 39% Data collected too infrequently or not quickly enough 42% 29% Not able to link data together at the individual customer level Too little or no customer/consumer data 51% Lack of sharing data is an obstacle to measuring marketing ROI GOALS FOR USING BIG DAIA Based on where retailers are investing (or planning to invest) their resources, they see the values in creating more sophisticated omnichannel marketing efforts. Retailers plan to focus their Big Data initiatives on improving: But they expect to deploy their first Big Data projects in: Operations Store Operations commerce/Multichanne Merchandising Operations Merchandising Marketing 14% 28% 29% 44% 60% 62% 3.7% 9.3% 13.0% 20.4% 20.4% 29.6% HOW TO DEVELOP A BIG DATA GAME PLAN Keeping up with today's demanding customers and analytics-sawy competitors (it's not just Amazon and Expedia anymore) means putting data at the heart of the retail business. Get started with this five-step plan: Determine the maturity level of your company's approach to Big Data, then implement proof of concepts to guide your ongoing investments. Zero in on business functions for which Big Data can drive the greatest improvement, and create detailed use cases for these projects. Three key areas to investigate first are pricing, segmentation, and marketing effectiveness. Size up your data management and analytics capabilities, identifying gaps and developing the necessary recruitment and training plans. Make sure your data strategy encompasses customer data/master data management, policy and process rules, and data collection usage and sharing. Anticipate the hiccups that accompany business change, helping teams adjust to this new way of incorporating Big Data and analytics into decision-making. Supply Chain Marketing Store Operations Supply Chain Ecommerce/Multichannel