Insight-driven merchandise planning gives retailers a competitive edge
Retail has obviously undergone seismic shifts over the past few years. We’ve witnessed the rise of the disrupters and the downfall of some of retail’s most established brands. Shopping habits of customers have been permanently altered, and their expectations have risen exponentially to the point that a seamless shopping experience is a requirement, regardless of the proliferation of choice.
In the recent Shoppers disrupted – Retailing through the noise consumer survey, the IBM Institute of Business Value (IBV) showed that 36 percent of shoppers prefer online purchases, which is up 23 percent from the 2011 study. The recent study also shows that consumers are looking for their shopping experiences to be seamless and personalized, regardless of touch point or the technology they use.
Evidence of this finding is demonstrated in the importance shoppers place on inventory visibility, both before going to the store and while in the store. And 46 percent of consumers say the ability of an employee to solve an out-of-stock issue through a mobile device represents an important differentiation in selecting a retailer, which is up 40 percent from last year. The same study also revealed that 71 percent of consumers still prefer stores as their primary shopping channel, though this result is down from 86 percent from the 2013 study.
These results indicate that retailers are at an advantage when they have physical stores. However, the key question is, are they doing enough to understand which merchandise and customer segments matter most to them? They now have the opportunity to use advanced analytics that can deliver on differentiated store experiences to innovate their merchandise planning and assortment decisions. And they have opportunities to cross-sell and up-sell to customers while they fulfill their customer’s online orders from stores.
Imagine a scenario in which a retailer knows by store and location exactly which product category and product lines achieve the highest sales. Moreover, suppose the retailer has the ability to predict which products generally sell along with a specific product. The retailer can provide that insight to buyers for procurement decisions and use it to influence marketing communications that can drive demand. The end result can be a sales lift association with customers and stores that can provide a highly satisfied customer base.
Possessing vast knowledge in the retail industry and drawing upon experiences from more than 50,000 analytics engagements across the globe, IBM recently announced the release of an advanced analytics solution that can specifically address these challenges for retailers, marketers and planners. The IBM Lift Analytics solution for retail helps merchants and marketers understand direct and indirect incremental performance impact—the lift—from individual products or categories to help improve assortment, placement, pricing and promotional decisions. Using this solution, retailers can quickly address and act on questions such as what is the likely impact on sales of other products or product lines if makeup brushes are removed from the assortment? Or, which merchandise lines present the greatest opportunities for cross promotion with women’s clothing to midtier customer segments?
Lift analytics extends the analysis perspective beyond the sale of a specific product by quantifying sales of products that are often sold with the product. Insights are delivered using a number of familiar metrics such as sales dollars, units sold, gross margin and customer visits. Additionally, affinity relationships can be identified and quantified across numerous dimensions including time period, customer groups, locations and channels. These relationships give merchants and marketers the opportunity to find variances in customer behavior that can be exploited by using merchandising, assortment and promotional activities to drive lift.
Lift Analytics for Retail Version 1.0 delivers a prepackaged solution. It includes the IBM Predictive Customer Intelligence platform, an industry-specific data model, a set of real-time affinity models that cover both point-in-time and trend-over-time perspectives and an easily accessible, interactive insight delivery layer that spans both descriptive and predictive analytics. By identifying purchasing affinities, the solution helps retailers improve their decision making related to merchandising, assortment management and promotion—areas in which decisions are made frequently that have a direct impact on overall retail sales and profitability.
Learn more about IBM analytics for the retail industry. And for more details on this solution, attend the webcast, “How insight-driven retail merchandising and marketing decisions drive profitable growth,” that takes place September 17, 2015 at 2:00 PM ET.