Catalina Marketing found that 10 percent of customers receiving coupons at the grocery checkout redeemed them on a future visit – not bad. But by moving analytics to where the data lived, the company could refresh models much faster. Scoring for millions of customers, which previously took 4.5 hours, is now done in 60 seconds. The result is more tailored coupons based on fresher information – and a 250 percent increase in coupon redemption.

Problems that were difficult or impossible to solve before are now manageable. Organisations can analyse all their data – not just a subset of it – and are empowered to analyse it more extensively, iteratively and frequently. The end result is better business decisions in a fraction of the time.

That’s the promise of big data analytics – advanced analytics applied in a high-performance computing infrastructure to address business questions that are best answered with a vast amount of diverse data sources.  The concept of high-performance analytics is about using these high-performance computing techniques specifically with analytics in mind.   This accelerated processing with huge data sets is made possible by four primary technologies:

High-performance computing makes it possible to analyse all available data, for cases where analysing just a subset or samples would not yield as accurate a result. High-performance computing enables you do things you never thought about before because the data was just way too big.

In-database analytics, an element of high-performance computing, moves relevant data management, analytics and reporting tasks to where the data resides. This approach improves speed, reduces data movement and promotes better data governance.

In-memory analytics can solve complex problems and provide answers more rapidly than traditional disk-based processing because data can be quickly pulled into memory.

The Hadoop framework stores and processes large volumes of data on grids of low-cost commodity hardware.

What benefits can be gained when retailers realise the value of their “big” data?

A number of retail companies already are reporting impressive results with the speed and efficiency at which the information is made available via big data analytics and high performance optimisation. Business users are now able to implement profitable changes using near-real-time data. They are better able to take advantage of quickly changing trends. Lacking this type of decision-making speed, many merchants will risk lost sales today and in the future. Some recent results include: Using customer insights to develop individual offers, one leading retail organisation improved its offer redemption rate by 6%. A major U.S. retailer reported a margin basis point improvement of 10 to 40 points with high performance markdown optimisation. An international department store saw revenue jump 3% to 10%, and a North American supermarket chain saw increased unit sales, a margin increase of 2% to 7%, and revenue increases of 3%. Previously merchants had to run markdown optimisation at the regional level, but now it can be accomplished at the store level. While they can still look at weekly data, now they also can use daily data to help with promotion optimisation, especially during the holidays. They could even conduct hourly analysis when it comes to a day like “Black Friday”*.    

React quickly to stay competitive
In today’s fast-changing retail & consumer products environments, retail and CPG companies must react just as quickly in order to stay competitive. While optimisation, planning and analytics have been important components of the retail equation, they become significantly more powerful when fueled by high performance computing and the “big data” paradigm. 

Would you like to know more?

SAS in partnership with Capgemini have developed a “Value Discovery” (link) offer which will provide companies that sneak peek to the value and benefits of the data an organisation holds. It will help to quantify the value and provide a clear roadmap to help understand how they can transform their organisation to be truly data driven.
* Black Friday is the day following Thanksgiving Day in the United States, often regarded as the beginning of the Christmas shopping season

This week’s blog was guest written by Caroline Cull, Alliance Manager at SAS UK