Traditional (Brick & Mortar) retailers spend significant resources trying to perfect the store layout for an optimal shopping experience for a customer. It is believed that a good layout increases time spent per visit and the overall basket size of the customer, thus driving the bottom line for the retailer. Most of these decisions related to placement of specific categories next to each other were previously primarily driven by observation and instinct. Access to point-of-sale data has transformed the retail industry by exploring new insights from data that were not captured. Insights from this data have empowered retailers with an ability to understand their customer’s buying behaviour accurately and improve their decision-making capabilities. While online retailers have been successful in leveraging this data at a promptly, traditional retailers are yet to completely adopt the dynamic technological advances to improve their decision-making capabilities and agility.
The client is a billion-dollar retailer based in the US. The client wanted to optimize its product portfolio and existing sales strategies. An important part of the mandate was to identify complimentary product categories and to recommend a data-driven blueprint for the future.
The client wanted to categorize customers by the products and services they choose, identify patterns to plan cross-selling campaigns, analyse and target customers based on product-centric purchase histories and patterns, plan multi-product promotions based on customer response, arrive at customer probability to buy additional products, and measure shifts in customer behaviour.
Impact Analytics arrayed a comprehensive suite of the following approaches, techniques, and solutions to address the client’s challenges: Impact Analytics employed Market Basket Analysis (Product Affinity) – a modelling technique, to identify items and categories that complement each other in a consumer’s basket. Product affinity analysis studies patterns and behaviors of customers to determine links in purchases so that stores can increase their cross-selling potential. Apart from identifying supporting products, the analysis also helped identify preferred products and categories that define customer behavior. Impact Analytics deployed the process of analysing product affinity in the following step-wise pattern:
- Collated, cleaned and analysed the POS/ transaction data for the previous 3 years.
- Studied this data to identify affiliated trends and the associated net margin impact.
- Employed a statistical model to find relationships between purchases and then delineated interactions between product categories.
- Simulated future sales using advanced data models including but not limited to the effect of product association.
Impact Analytics’ data-driven solutions generated the following business-critical insights:
- Product categories that don’t sell well by themselves sell better when they are a part of a product basket.
- Associated product placements increased the customers’ basket size by more than 15%.
- Personalized product recommendations based on customer’s purchase history increased the average number of visits by 25%.
- Product affinity analysis revealed categories that do not have any significant impact on store sales. Optimizing them freed up retail space and reduced inventory costs.
The client observed and realised the outcomes of the product affinity solution proposed by Impact Analytics. They then implemented the following measures to achieve their objectives:
- Associated products/categories were re-organized and placed close to each other/in nearby aisles through the revision of in-store planograms to drive sales.
- A product recommendation engine was created for in-store associates.
- The client’s e-commerce site was revamped to include a section on “Recommended Products”.
- Combined offers and product coupons were developed to drive sales of low-selling products by combining them with high-selling products.
Impact Analytics’ product affinity solution enabled the client to streamline its product offering by identifying categories that drive sales, provide customized product recommendations and insights for designing the most profitable store planogram. Together, these initiatives generated an incremental $3 million in FY 2017.