Six benefits that big data can bring to retailers

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In the last week, I have interviewed four retail executives about their predictions for big data and high-performance analytics in the retail industry. I hope to publish their thoughts here in the next few weeks, but in the meantime, here are six concrete benefits that big data can bring to retailers, if they apply high-performance analytics.

1.  Ask and answer more innovative questions and receive more precise answers.
High-performance retailing offers far more dimensions than ever before. Now companies can calculate cross-elasticities of demand for thousands and thousands of SKUs in the grocery store. Previously, without a hypothesis of a crosssell of beer and diapers, they could only use intuition to come up with that combination. Now the technology can calculate elasticities and come up with combinations category and brand managers never thought of before.

2.  Increase Speed of Analysis.
With high-performance retailing, revenue optimization for a large department store, for example, can be completed in less than two hours for the entire organization. Executives can generate a daily forecast and facilitate immediate decisions, which was not feasible earlier. Price optimization can be calculated for an entire organization, in multiple scenarios, in less than four hours vs. waiting days for results previously. Before, merchants were forced to run models overnight, look at them the next day and fine-tune them. That process could take up to a week. Now they can run multiple models in a matter of minutes within the same day.

3.  IT and business can work more strategically together.
Business users don’t want to be forced to have to take every need to the IT. Retail IT organizations are spread thin and face an exploding amount of new needs and technologies. High-performance retailing can help both. IT can build a strategic retail analytics foundation. Business users can explore, visualize, hypothesize and test hypotheses and put innovative ideas into action — using science to help merchants know and engage customers.

4.  Give real-time decision-making to retailers across the supply chain.
From inventory and assortment decisions to real-time offers — retailers need rapid, fact-based ways to make the best and quickest decisions. Merchants are now competing across segments and channels — drug stores and big-box merchants are selling groceries, and shoppers can buy their favorite apparel or hard-to-find foods on Amazon. Therefore, store managers and associates must be empowered to provide the best service when customers walk into the store. High-performance retailing enables game-changing decisions and actions.

5.   Encourage better collaboration between consumer packaged goods (CPG) and retail.
Today’s retail and CPG partners are realizing the value of using high-performance resources to collaborate rather than compete. The end goal is selling more of the products customers want. By sharing real-time data, retailers and CPG companies can stock the right product assortment and make better use of trade promotion dollars. High-performance computing can provide more accurate forecasts faster.

6.   Avoid offer spam.
High-performance computing can help companies avoid sending target shoppers too many messages, and too many of the wrong messages. By using a solution that has a memory of previous offers and shoppers’ responses to those offers, merchants can now send out the right messages at the right time — reinforcing brand loyalty

This list is excerpted from the e-book, “High-performance retail: The art of the possible.”  The bottom line is that high-performance retailing is really about the uninhibited use of analytics: being able to answer questions that you could never answer before because it took too long, and using uninhibited creativity to better leverage the data you have.

This is day 15 of our "HPA once a day" blog post series. To read more, see all of the high-performance analytics posts on this blog or follow the high-performance analytics rss feed. 

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Alison Bolen

Editor of Blogs and Social Content

+Alison Bolen is an editor at SAS, where she writes and edits content about analytics and emerging topics. Since starting at SAS in 1999, Alison has edited print publications, Web sites, e-newsletters, customer success stories and blogs. She has a bachelor’s degree in magazine journalism from Ohio University and a master’s degree in technical writing from North Carolina State University.

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  1. Pingback: Big data answers for your industry and your role - SAS Voices

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