Realizing the Potential of Retail Analytics

Introduction

Retailing today is changing at a much faster pace than any other industry. Retailing industry is trying to be competitive by changing rapidly. A still-turbulent economy, with new and innovative selling channels, advanced digital technologies, and increasingly demanding consumers all challenge retailers to find new ways of remaining relevant and competitive. Looking at the purchasing decision journey of consumers which involves multiple steps, almost of these are now being captured, digitized, and transformed into metrics and data. This data which are captured have become an implied derivative of essential retail and consumer technologies, and are the source of retail analytics which is how to acquire the data to how to extract insights from it. Retailers look to turn these insights into differentiation and competitive advantage for the them and at the same time provide better shopper experience for the end consumer.

But the main challenge of big data is just that—it’s not only big but also unstructured most of the times. Massive amounts of structured and unstructured information are piling up in retailer and supplier data warehouses, stores (both physical and online) and at the manufacturing location as well. Unstructured data holds the key insight as customer metrics derived from video and other sensors, social media and mobile devices have the power to provide unprecedented insight into the purchase decision process.

Retailing industry are among the first industries that recognized the benefits of using predictive analytics and started to employ it mostly to stay ahead of competition and putting understanding of the customer as a first-priority goal. Today’s competitive business environment understanding of your customer requirement and offering the right products at right time is the key of any successful business. Due to high growth of internet, online shopping is becoming most interesting and popular activities for the consumers. Online shopping is providing a variety of products for consumers and is increasing the sales challenges for e-commerce players.

Offline or the brick and mortar and the pure play online retailers are evaluating its performance in terms of point of sale (POS) transactions and working to address questions like how much the customer bought, the size of the purchase, impact of marketing, how to leverage online visitors’ data to improve the customer experience and the driving factors of online shoppers’ behaviors. Predictive analytics is used to identify an untapped sales opportunity for new items shoppers’ online behavior.

Using statistical analytical techniques like A/B/multivariate testing, visitor engagement, behavioral targeting can lead toward high propensity of a prospect’s willingness to buy a product. In this chapter, authors provide insights on how the online data can support customized targeting, resulting in incremental increases in e-commerce revenues by advanced predictive modeling on visitors’ behavior.

Almost all of ecommerce retailers want to understand their customer behavior and need to maximize customer satisfaction in order to maximize their sales. As retailers look to build an optimum marketing strategy to deliver the targeted advertising, promotions and product offers, their expectations from these strategies is to motivate the prospects to buy more. Still it’s a challenge for most of the retailers to digest all of the data, technology, and analytics that are available to them.