Predictive Data Analytics in Retail Industry

Advanced Analytics in Retail Industry: Enhance the Inventory, Improves Engagement and Personalization for Consumers

Retail is becoming an increasingly data rich environment as more of the business goes digital, creating many more data capture opportunities digital for the retail industry is no longer an option, with the complexity of the market place requiring an omni channel presence for both brick and mortar and ecommerce players. With the advent of multiple channels, the channels of communication with customers also expand exponentially to include digital advertising, social media, and email. This results in the generation of a huge amount of internal and external data. As such, many retailers are now left with the problem of managing and leveraging unstructured “big” data from social media, third-party sources, and global entities. The challenge for retailers is to capture the right data, process at the right speed and take appropriate action. Advanced analytics represent a portfolio of tools, techniques, and organisational capabilities that can be applied to specific decisions across a wide range of business concerns. Ultimately the availability of date coupled with advanced analytics tools will change the decision making process in the retail industry.

Customer Engagement

Improved customer experience requires building customer profiles that leverage a wide variety of data across multiple channels. This multichannel marketing uses a mix of customer profiles, historic data, real-time events, loyalty programs, social interaction, online behaviour and presence to drive predictive analytics and customer insights. By learning where, when, and how buyers are most likely to shop, and which offers and products will appeal to them, retailers can determine product offerings and personalized marketing campaigns to extract maximum value from both high- and low-ticket customers.

An interesting application is the well-known concept of the ‘share of wallet’. This requires a retailer to identify the customers with the propensity to increase spending and the corresponding opportunity categories for that category of goods. Uncovering where the share is being lost, to whom, and why is an important first step in increasing sales throughput.

In a dynamic marketplace saddled with a huge cost of attracting new customers, it becomes vital to retain existing ones. Advanced analytics is extremely useful in this instance. By using a tool known as customer churn prediction, it is able to build models to identify the customers that are prone to attrition and determine the drivers and predictors of attrition to enable pre-emptive intervention.

Supply Chain Efficiency

Supply chains are also growing in complexity, with an influx of data and a demand for more-responsive relationships with customers who can choose to interact with the business across multiple sales channels. Merchandise assortments are becoming customised and tailored to suit individual requirements. Besides, operations are expanding globally. As a result, retailers have a tremendous opportunity to leverage analytics to manage inventory, reduce transportation costs, and increase collaboration with customers, merchants, marketing, and suppliers. Sales and inventory data from multiple source systems can be integrated to identify items at risk of under/overstocking. This can decrease inventory carrying costs substantially.

Additionally, predictive analytics can be used to allocate incoming inventory to high offtake outlets and to shift excess inventory to alternate locations as needed. Order fill rates and delivery metrics can also be analysed to evaluate and balance working capital and service levels. Real-time visibility into transportation operations and costs can allow retailers to more effectively manage and streamline this complex and important piece of the supply chain. Opportunities to improve productivity and increase asset utilisation can be identified by analysing carriers, lanes, modes, ship-to locations, contact details, and other key metrics. Category Management is a key area where the use of analytics in the supply chain is expanding to include collaborative systems that integrate marketing and merchandising and more closely connect retailers to their suppliers and distribution partners. This collaboration facilitates required product development, improved customer service, JIT inventory, and improved display at the store level.

Shelf Storage & Merchandise Planning

Product inventory and shelf space have always been a retailer’s most valuable resources. Now analytics can be used to determine which products in what quantities offer optimum shelf utilisation. Advanced analytics utilizes powerful optimization tools to leverage products, shelf storage, store fixture and performance data, enabling retailers to plan localized assortments based on the store clusters and maximize the space utilization. This can help retailers to plan a variety of assortment mixes and create balanced merchandise planning strategies, with unique market-based, customer-based, fashion-based, and price-based assortments. What creates further excitement is that the assortment can be customised to accommodate the seasonal requirements of the region.

Your digital future is beckoning. Are you ready?