There’s a torrent of customer and market data that’s continuously being generated for retailers and consumer packaged goods (CPG) companies. However, in many cases, line of business leaders (LOB) don’t often have access to the most current data regarding customer buying trends or market conditions.
As a result, this puts business leaders behind the curve when it comes to acting on real-time information. Having real-time data to analyze and act on can make a huge difference for business leaders.
A McKinsey & Co. study points to one CPG company that was able to capture $377 million in business benefits and improve on-time delivery from 97 percent to 99.5 percent over three to four years, in part, by adopting customer-driven demand planning.
Another large grocery retailer led a predictive ordering pilot with a CPG manufacturer that drove 15 percent growth in same-store sales for a flat category by improving assortment and eliminating stock-outs.
LOB leaders for CPGs and retailers can benefit from using a predictive analytics platform that can mash up real-time and historical data. This can help business leaders identify and respond to customer and market changes quickly, and improve the speed and accuracy of their decision-making.
For instance, the “must-have” brands that consumers will purchase – whether on sale or not – slipped from 33 percent in 2010 to 29 percent in 2012, according to the 2013 American Pantry Study conducted by Deloitte.
Retail leaders that sell private label brands can draw on these insights and other consumer data (for example, point-of-sale data) to determine the prices that consumers are willing to spend on private label products.
Meanwhile, CPG and retail leaders can analyze customer behavior to determine the types of offers that target customers are most likely to respond to at the highest possible profit margin.
Data discovery tools can help CPG and retail leaders detect key customer and market trends as they’re emerging. For instance, the Deloitte study notes that while millennials rank highest in their interest in mobile shopping technologies, baby boomers’ interest in mobile technology grew at a faster rate last year.
Business leaders can use data discovery tools and techniques to identify these types of trends and blend them with analytics to determine which customer segments are most likely to respond to mobile coupons during their in-store shopping experiences.
Next Steps:
- We invite you to watch our on-demand webcast, “Data Discovery and Advanced Analytics for CPG & Retail.” In this webcast, you will learn how TIBCO enables advanced analytics in CPG&R with TIBCO Enterprise Runtime for R (TERR), TIBCO’s enterprise-grade implementation of the popular R language for statistical computing.
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