Get fresh updates from Hortonworks by email

Once a month, receive latest insights, trends, analytics information and knowledge of Big Data.

cta

Get Started

cloud

Ready to Get Started?

Download sandbox

How can we help you?

closeClose button
May 22, 2015
prev slideNext slide

The Big Data and Analytics Opportunity in Retail: Where’s Your Business?

In this guest blog, IDC Program Director for Retail Insights Greg Girard shares his insights how retailers employ big data and analytics to drive decision and action across myriad industries. 

Big data and analytics (BDA) have become top agenda items for a growing number of retail executives, and rightly so in the broader social and economic context of data-enabled decision and action. While “data-driven,” as a term, has been around for quite some time, the ability to act on insight has taken on new urgency. We see evidence of data-driven decisions across industries — from energy, utilities, and manufacturing, to clinical care and government.

Retailers are no strangers to massive, time-sensitive data volumes. The industry is at the epicenter of emerging BDA opportunities in enterprise data, social media, digital and mobile advertizing, mobile metadata, instrumented store operations, and item-level RFID.

BDA is spawning new businesses and new business models — many indicative of transformation retail business models. A textbook case is Uber, essentially an open community taxi sourcing and dispatching service with the analytic twist of variable pricing. Uber is already spawning competitors, displacing an established industry, and rapidly expanding into same-day delivery of merchandise.

We’ve seen a specialty big box retailer increase revenue from its email promotion campaigns by up to 5%. The retailer achieved these results by integrating store, catalog, and online transactions and online behavioral data, applying data science and advanced analytics personalizing emails to align promoted products to each shopper’s website behaviors and recent purchases.

Another national retailer in the DIY (do it yourself) segment used local market data to surgically set prices against nearby stores—improving key metrics and avoiding a race to the bottom discounting program. Daily automated collection of online data about competitors’ assortments, products, inventory positions, and promotions combined with predictive analytical insights delivered benefits through better assortments and pricing and promotion architecture.

Monsanto provides an example of a BDA-based value-added aftermarket service — a cloud platform for on-demand sensor-based agricultural husbandry. Then there’s the rapidly evolving biometric wearables segment, with integration to smartphones and other connected devices, providing a platform for health and wellness services.

In some cases, data is becoming a medium of exchange for the next generation of economic interactions, most notably in retail as consumers show an increased willingness to exchange data about themselves for services and guidance. Retailers must grow more adroit at discerning these customers and honing services that match expectations.

Which retailers are poised to capitalize on the BDA opportunity (or are doing so already), which are not, and why? To answer these questions, IDC surveyed 200 North American retailers. Some 40 – 60% of retailer respondents told us they are pursing BDA initiatives for one or more of the following growth drivers:

  • Customer acquisition
  • Pricing and promotion
  • Marketing and competitive intelligence
  • Customer service and support

The survey results revealed basically two types of retailers — high achievers and low achievers. High achievers report that their quantified results in their BDA efforts meet or exceed their expectations. Low achievers fail to meet their expectations.

In general, high achievers skew toward higher levels of BDA maturity in all five dimensions of the IDC Maturity Model, while low achievers skew toward lower levels of maturity. This pattern points to a correlation between increasing levels of maturity and higher rates of success in meeting or exceeding expectations.

For example, in data maturity, just over 50% of retailers remain at “opportunistic” and “ad hoc” stages of maturity. If you’re at either of those stages, data is incomplete and requires substantial manual effort to prepare for consumption. Some multi-sourced structured data and unstructured content may exist, but lack timeliness and veracity. Data collection, monitoring, and integration processes are not yet in place and consistent data governance and security practices have not been established.

So, as a retailer, what’s your own technology situation? Does your technology stack include storage and computational technology fitted for BDA, or is it generic or otherwise limited? If it’s the latter, you’re at ad hoc maturity. If your technology is appropriately — workload by workload — fit for purpose but not integrated, you’re at the opportunistic stage. At the “repeatable” stage, adoption of deployed and integrated fit-for-purpose technologies remains selective.

It’s a marathon and not a sprint, an evolution not a revolution. BDA requires a plan and strategy to get from where you are now, to where you want to be — the “optimized” stage of maturity in IDC’s model. This is where enterprise-wide efforts are subject to continuous optimization, aligning BDA programs to rapidly emerging priorities.

About the Author

As Program Director, Merchandise Strategies, Greg Girard is responsible for setting and delivering IDC Retail Insights’ authoritative perspective on how retailers should use information technologies to achieve key operational, tactical, and strategic objectives in the sourcing, buying, planning, assortment, allocation, replenishment, and pricing of merchandise. Mr. Girard has extensive experience in retail technology. He launched the AMR Research Retail Advisory Service and led it for several years. He was the first analyst to cover technologies that have become mainstays of today’s best practices in retail; e.g., price and revenue optimization, store execution and task management, as well as advanced assortment planning, multi-echelon replenishment, and constraint-based supply chain planning.

Mr. Girard holds a master’s degree in urban planning from Hunter College, City University of New York, and a BA in philosophy from Boston College. He has lived in the Middle East and worked internationally.

Mr. Girard is a frequent speaker at industry and technology vendor events and contributor to retail technology media outlets. His Twitter handle is @gregorydgirard.

Tags:

Leave a Reply

Your email address will not be published. Required fields are marked *