Using Analytics to Reimagine the ‘5 P's’ in Retail
The use of data analytics across customer touchpoints and retail channels provides an opportunity for data scientists to revisit the “five P's” of marketing — place, people, product, price and promotion — and deliver a seamless shopping experience.
New generation data analytics enable retailers to calibrate an optimal mix of the five P's and transform the producer-oriented business into a customer-centric model. Significantly, design thinking paves the way for a bespoke shopping experience while machine learning and algorithms enable rapid testing, thereby maximizing value.
Place
Retail and fast-moving consumer goods (FMCG) enterprises need to adopt an anytime, anywhere model to serve millennial shoppers. While omnichannel is a business imperative, the age-old question of “what to place where” needs to be addressed in physical stores, online and any other digital channels shoppers may prefer to engage with.
For example, shopper data and behavior can accelerate “path to purchase” using smart planogramming to help shoppers find what they're looking for at physical and online properties. Big data and web analytics track the online journey and physical trail of shoppers. Significantly, brick-and-mortar retailers can optimize and improve the usability of their stores by analyzing customer data and distilling business insights of shopping behavior in near real time.
People
Marketing campaigns revolve around people, including customers and the employees who serve them. Associates at retail stores can help shoppers with personalized service and tips on how to navigate the store on a tablet device, which has a repository of customer information. Global retailers can engage with customers meaningfully and offer proactive service when they gauge requirements correctly. Big data tools analyze demographic data, in-store shopping behavior, social interactions, online browsing trends, transaction history and post-purchase trends to understand customers and predict preferences. They help improve customer retention and, at the same time, cultivate customer loyalty.
Product
Advanced demand management techniques — powered by data — help retailers and FMCG enterprises increase return on investment by accurately matching demand with supply. Demand-driven forecasting models combine real-time data with historical sales data to make projections for future demand, thereby optimizing sourcing, supply chain management and inventory management. They help create prediction tools which require minimal sales data to predict store-specific sales across product categories. The accuracy of such predictions increases with the frequency of data updates and quality of inputs. Prediction tools reduce the likelihood of a stock-out incident significantly since stock replenishment is triggered automatically.
Data analytics is a catalyst for accelerated product innovation via product design support and streamlining of product testing. Early feedback on the product and market sentiment enables retail enterprises to make appropriate course corrections. Big data also automates market basket analysis. The analytical tools combine structured and unstructured data to empower merchandisers with visibility into the composition and size of shopping carts. They can then be integrated with historical sales data to customize product assortment for each store.
Price
Data science applied correctly can significantly optimize pricing strategies. Analytical models evaluate variables influencing the financial performance of retail and FMCG enterprises. These enterprises can correlate data and identify patterns between demand for a specific product, sales of complementary products, cost of sales and history of competing brands.
Data insights help enterprises adopt a dynamic pricing system which can maximize profit. For example, end-of-season sales can be replaced with price discounting approaches that capitalize on demand, while simultaneously providing value to customers. Since e-commerce thrives on price optimization, enterprises should use big data platforms to map real-time merchandising data with customer demand and buyer behaviors to uncover business insights for dynamic pricing.
Promotion
Put together a digital-first strategy to design customized promotions, which not only boost sales, but also help deliver a superior customer experience.
Smartphones, social media, geospatial technology and analytics have transformed advertising and campaign management. For example, smart analytical models enable enterprises to crunch product and customer data, and supply chain metrics to create targeted offers. They can help retailers and FMCG brands cultivate customer relationships by reaching out to the right customers, at the right time, through the right channel.
Big data and advanced analytics help retailers and FMCG enterprises address fundamental questions around whether new product lines are right for particular markets, the best channel to market those products, and how products will perform against expectations. The “five Ps” of marketing are still incredibly relevant and can be even more powerful with the right applications of analytics.
Opinder Sardana is associate vice president and managing client partner of Consumer Retail Logistics at Infosys, a technology services and consulting firm.
