Improving your Customer Centric Merchandising with Location based in-Store Merchandising

With any transformation in industry or marketplace, there are leaders and losers. The winners know the fundamental pillars that are hidden to some and evident to others that drive and enable success. In 2020, where connected consumers and the turmoil with the pandemic driven supply chains are driving more and more of retail’s response, at Cloudera we believe that the underlying foundation to retail’s success is based upon real-time and streaming data from retail’s edge – the retail store. 

The four basic pillars of retail founded upon data are based upon personalized interactions, customer-centric merchandising, supply chain agility, and reimagining stores. In my first blog of this series, I spoke about how successful retailers are leveraging customer profiles producing higher customer engagement results, and reduced marketing costs by delivering targeted, relevant, contextual content and recommendations. Leaders are moving to “segments of one,” defined as tracking and understanding individual behaviors across all touchpoints using data to customize offers, products, or services to the individual customer. The blog highlighted a success of a company that sought to improve their customer offers and they did by reducing the time for response from 3-5 days to less than three hours – ensuring the offers were relevant.

The benefits of moving to a customer-centric merchandising strategy

In this blog, which is the second in the series of four, I’d like to examine the benefits of moving away from a product-centric merchandising strategy to a customer-centric merchandising strategy requiring a deeper understanding of customer tastes and preferences. Granular product attribution and retail execution information shared between trading partners is critically important to better respond through localized assortments, tailored promotions, dynamic pricing, and product development (national brands and private labels) that are more reflective of emerging trends, and an ever-changing, diverse consumer base. Obtaining and leveraging real-time, enterprise-wide data provides the following business capabilities:

  • Consolidated Product & Sales Data— the ability to build an enterprise view of product, sales, and inventory across all locations, channels
  • Price and Promotion Optimization— the ability to change prices dynamically automating business processes, while considering both competitive pricing and predictive customer response models 
  • Customer Driven Assortments—Tailor assortments to align product mix by location or channels to provide what your customer wants to buy
  • Product Development—shorten product development time and improve NPI by identifying consumer trends to create distinctive products and optimize the category and brand portfolio to meet consumer demand, providing competitive differentiation

Both data-in-motion and data-at-rest are leveraged to drive customer-centric merchandising. Streaming data from in-store sensors, streaming video, and sensors are leveraged along with historical archives of consumer purchase behavior, inventory stock levels, weather predictions, point of sale information, and competitive pricing, are examples. 

Real-time, Location-based insights to improve conversion 

Product placement within apparel and department stores is well documented in positively affecting upsell and conversion. ne leading US department store retailer known for their clothing, accessories, and jewelry was receiving sales data that was not specific enough to suggest actionable changes to the layout of their brick and mortar stores in order to maximize product sales. The online side of the house had the advantage as it could compare what shoppers view with what they buy, but the retailer lacked this insight from their brick and mortar stores.

This retailer leveraged Cloudera enterprise data management solutions that provided micro-data on shopper location enabling in-store analysis, similar to the insight gleaned from the online portion of their business. The business leveraged IoT data that captured in-store location data from shoppers that have the mobile app on their smartphones. Data then streamed from Cloudera DataFlow to the Cloudera Data Platform providing:

  • Intra-day insight on the flow of customers and traffic throughout their stores
  • Identification of merchandising ‘hot and cold’ spots
  • Enabled real-time, location-based (proximity) marketing capabilities

Early results demonstrated a 40% conversion lift using real-time delivery of personalized offers and this data was further leveraged to redesign and optimize product placements within stores. The key to success was their ability to stream real-time data using Cloudera Data Flow from multiple sensors and seamlessly integrate this new data into the retailers Cloudera Data Platform, where cleansing, further analysis, and advanced analytics could be run-producing further value for the retailer. 

Stay tuned to my next blog in the retail space that will take the data challenge to the supply chain and discuss the benefits of leveraging real-time and stream analytics will improve supply chain visibility, fulfillment route optimization, and even predictive asset maintenance.  Additional retail content can be found in our retail resource kit.

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