Powering the retail wars

Remember a time when the most advanced piece of technology in the retail industry was the cash register? Big data, analytics and real-time are the new playthings of retail magnates, and as a result the rules of the industry have been completely rewritten. Eleanor Reader reports.

 

In 2013 retailers are using data generated from RFID tags, mobile devices, GPS-enabled tablets, web transactions and embedded sensors to learn more about their customers than ever before. Add SAP HANA real-time capabilities to that mix and you have an industry that will, quite simply, never be the same again.

SAP introduced HANA to the retail technology fray in October 2012 with a new solution based on NetWeaver Cloud, SAP Precision Retailing. The cloud-based application uses the consumer’s location, social profile, needs and the time at which they are conducting their transactions to generate relevant content (including discounts and special deals) designed to get that particular consumer to make the purchases the retailer desires.

The extreme edge of retail

Capgemini is the latest SAP partner in the retail space to take advantage of what it refers to as the ‘extreme’ capabilities of HANA. The company has run with this theme and created Extreme Applications for Retail, built on SAP’s in-memory HANA platform.

The IT services supplier is no stranger to the opportunities presented when big data, analytics and retail come together.

In a video released on 18 June 2013, Capgemini India’s VP, business information management, Venkat Iyler, explains that it’s about connecting the dots between existing customer data with other customer data that’s available – such as consumer behaviour, buying patterns and social media analysis – to be able to gain a 360-degree view of the customer.

“There is a science behind it. It’s based on your buying patterns, it’s based on your behaviour, not just in that store but over all your behaviours across websites or social media channels or what your friends could have bought,” Iyler says.

“So there’s a whole bunch of data that’s being collected, analysed and offered to you in real-time, which is what makes it very specific to your needs.”

He adds that these capabilities, in conjunction with using big data to combat the $37 billion lost to retailers in fraud and to manage competitor pricing, are making some retailers quite formidable.

Extreme Applications for Retail are pre-built and pre-packaged, incorporating Capgemini’s intellectual property (IP) in a core data model, analytics, and predictive algorithms so retailers can “buy, not build”.

The application includes all the usual suspects: intuitive dashboards, mobile integration, real-time analytical information, and instant insight into detailed data.

“When we designed these applications, we asked ourselves, ‘What could we change and improve for retailers if we could – at any point in their business processes – launch a very complex calculation on their data and have the answer in a second?’” says Capgemini’s VP of retail, Mohit Jain.

“With Extreme Applications for Retail, we want to unlock extreme capabilities, like the unlimited ability to exploit volumes of enterprise data along with external data sources to get insights on decisions that need to be made immediately – right when sales teams are in front of customers in the stores, right at the moment customers are navigating the store’s online shop.”

Jain highlighted three business modules included in the first release of Extreme Application for Retail:

  1. Market Basket Analysis module, which will take into account buying patterns to analyse in real time, as transactions happen in the stores, such as which articles are purchased together.
  2. Next Best Action module, that will allow, in real time, personalised purchase or promotion recommendations for a specific customer identified on any interaction channel.
  3. Markdown Management module, which will use current and past sales trends to determine the best promotion strategy so that the state of the inventory at the end of the season is optimal from a profitability point of view.

Capgemini is currently in discussions with Australian customers about potential pilot projects. The company didn’t name names, but did say the applications can benefit retailers from smaller national operators to large, global players.

“Harnessing the wealth of information available at retailers’ fingertips has been very challenging, and most of them have had little choice but to implement cross-sell and retention strategies that are static, with limited efficiency, considering predictions are based on historical purchase behaviors and are updated very infrequently – on a monthly basis, for example,” Jain says.

Taking HANA beyond SAP

SAP and Capgemini have forged a strong, strategic partnership over the past 20 years. But how do these applications differ from SAP’s retail industry solutions?

Jain says that the main differentiator is that Extreme Applications for Retail are packaged products with periodic new releases that can support other, non-SAP back-end systems.

“We are not tied to any technical constraints coming from SAP BW or SAP ERP, which is important considering we want to truly integrate our customer’s data in a business-oriented way, whether their data is included in SAP or non-SAP applications,” he says.

For the Market Basket Analysis module, differentiators include the fact that it’s a native SAP HANA application and includes stock data. The Next Best Action module is different because it’s a combined application for clientele and customer-targeted promotions that can be used for smartphone apps, email campaigns, the web store, and store clerk apps (for supporting customer advice).

The real hero of the applications, though, is HANA – which has been the driver of one of Capgemini’s key growth initiatives since 2011.

“Because we believe the capabilities of the SAP HANA platforms are really game-changing, we want to bring the value of these capabilities to all data and all retailers, whether or not they have a pre-installed SAP landscape,” Jain says.

This article first appeared in the Inside SAP Yearbook 2014. 

 

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