Big Data In Traditional Retail – Part I

Bill Schmarzo By Bill Schmarzo August 6, 2012

I just completed my outlook for Big Data in the healthcare industry, and thought it was time that I talked about the potential impact of Big Data on the retail industry.  I’m going to focus this two-part blog series on the traditional brick-and-mortar retail industry, and will save the on-line retail industry for a later blog series.

Retail is one of my favorite industries because this is an industry that has historically had lots of data, from the traditional brick-and-mortar retailers (point-of-sale scanner data, UPC, customer loyalty cards, RFID, inventory, and supply chain) to the on-line eTailers (e-commerce transactions, web logs, clickstream analysis).  Some companies have literally rewired the industry’s value creation processes by exploiting data, such as:

  • Walmart – who was one of the first retailers to jump on the market changing ramifications of point-of-sale (POS) scanner data in the late 1980’s to introduce innovations in their supply chain and create strategic relationships with the consumer package goods manufacturers (with things like Category Champions and Retail Link)
  • Amazon – who not only made online commerce safe and easy for the neophyte shopper (one-click purchase, online product reviews, and product ratings), but also integrated shopping recommendations (“Customers Who Bought This Item Also Bought”) into the very fabric of the user experience.

Unfortunately, this is also an industry with many companies who have dropped the ball in leveraging their wealth of customer data (from sources such as POS, customer loyalty programs, e-commerce, and RFID data) to tease out actionable and material customer, product, and operational insights.

Retail Big Data Challenges

I have talked to several large retailers in the past month and was shocked to learn that while they have been capturing customer loyalty data for almost a decade, they have done little to leverage that data to improve their customers’ in-store shopping experience (merchandising, assortment, product placement, in-store promotions, packaging, etc.).

Customer loyalty data has to be a retailer’s greatest asset. I seem to be a member of countless loyalty programs and few of them seem to do anything with my information that really benefits my shopping experience.  Yes, I get discounts on select products when I show my card, and others give me coupons and free drinks when I use my card, but there is so much more that could be done with this data.

Key Retail Applications

Customer loyalty data, when combined with point-of-sale (POS) scanner, store inventory, local store demographics data, and an integrated customer master file, can provide the customer and product insights necessary to deliver material business benefits, including:

  • Increase market basket revenues and profits at the store by season/holiday and/or by local events level by identifying and quantifying those products that sell in combination, either immediately in the same shopping cart, like hamburger and hamburger buns, or some time period after the initial purchase, like razors and razor blades.
  • Increase private label sales by identifying, benchmarking, and scoring those product categories, across stores and geographies, that are most conducive to private label products and supporting private label conversion campaigns.
  • Optimize store product assortment and shelf space allocation at the product category by store by season/holiday levels by leveraging store and customer buying patterns, coupled with local store demographics and local events.
  • Improve In-store merchandising effectiveness at the product category by store by season/holiday level by leveraging past performance data combined with current economic, weather and season/holiday data.
  • Optimize merchandise markdown by leveraging customer segment buying patterns to determine optimal pricing levels, and the pace of merchandising markdowns.
  • Optimize pricing and yield management by determining the optimum price of products and services through their respective lifecycles based upon local customer buying patterns and behaviors.
  • Reduce inventory, replenishment, storage, and transportation costs with more timely product demand forecasts fueled by insights from most current customer shopping patterns.
  • Improve workforce hiring and staffing effectiveness to optimize staffing by leveraging data from customer shopping patterns, seasonality, and local events.
  • Optimize marketing mix by analyzing and attributing sales and store traffic effectiveness across different marketing messaging, offers and marketing channels.
  • Reduce fraud and in-store shrinkage through more timely analysis and scoring of sales and inventory in-and-out of store movements.

New Retail Sources of Big Data

One of the immediate Big Data opportunities in the traditional retail industry is the integration of social media data to provide new customer, product, and market insights.  Social media data (Twitter, Facebook, Pinterest) and smartphone app generated data can provide customer, product, and market insights that could be leveraged in the following ways:

  • Improve customer prospecting and acquisition thru finer-grained, more actionable micro-segmentation
  • Refine cross-sell/up-sell/market basket effectiveness thru more thorough purchase behavioral insights, personalized offers, and recommendations
  • Identify new product, service, and market opportunities thru constant monitoring of customer and market feedback, interests, and sentiments
  • Preempt customer churn and flag at-risk customers by leveraging social media feedback and consumer comments into more thorough, customer retention scores
  • Identify competitive vulnerabilities (win-backs) through competitive sentiment monitoring and analysis

Part II of this “Big Data in Retail” series will look at some new, innovative developments in the retail industry being driven by Big Data.

Bill Schmarzo

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0 thoughts on “Big Data In Traditional Retail – Part I

  1. Bill,
    One additional factor in thinking about customer data is the customer. Customer’s today are more sophisticated than they have ever been and have expectations as to how the retailer could use their data to their benefit…eg. tracking purchases, pre filing out forms, personal sales, recommendations… and retailers who don’t deliver on these ideas are seeing customer defections.