How Vera Bradley Profits From Big Data

Big data was undoubtedly one of the most buzzed about topics in the retail industry last year. What's most interesting about that is that there's not one universally accepted definition for the phrase — it means different things to different brands. For Vera Bradley, an omnichannel retailer of quilted handbags, accessories and luggage, big data means insight into what its customer wants.

In a session yesterday at the National Retail Federation's Big Show in New York City, Scott Steever, senior director of strategic initiatives at Vera Bradley, discussed how the brand uses big data to make its marketing and merchandising decisions more profitable.

Email Marketing
Vera Bradley wanted to improve the productivity of its email program, Steever said. After years of declining effectiveness, in large part to a "spray and pray" strategy of sending the same message to everyone on its list, the retailer began to use big data customer insights — e.g., purchase history, average order size, types of products purchased — to send targeted messaging to its subscribers.

The results were predictable. In a A/B test where the same offer was sent to Vera Bradley's full list and a segmented list based on the style of bag purchased, the segmented list performed substantially better despite 63 percent less emails being sent. The segmented list recorded a 43 percent increase in open rate, 101 percent increase in clickhrough rate and a 275 percent increase in conversion rate. Overall, the campaign sent to the segmented list generated a 14 percent increase in total revenues compared to the unsegmented list.

Pricing Optimization
In addition to improving its email program, Vera Bradley is using big data to optimize its pricing. With the help of First Insight, a product testing platform provider, Vera Bradley has revamped its pricing strategy to focus on pricing products based on consumer value and competitive positioning. Big data insights that Vera Bradley now factors into its pricing include competitive set analysis (i.e., what are its competitors charging for similar products), margin analysis and "what would they pay" online consumer engagement campaign data. Within that consumer testing, Vera Bradley is also collecting the percentage of favorable and unfavorable responses to the MSRP, the model price the consumer would pay for the item, and qualitative data as well. All of this data helps Vera Bradley price a single item.