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Retail SolutionsAssortment Optimization
Assortment Optimizationgo to top
Business Problem: The number of SKUs that retailers carry has increased exponentially over the years. The average grocery store carries between 40,000-60,000 SKUs. While this increased choice is valuable to consumers, it also causes confusion in their minds. SKU proliferation is a big source of cost escalation for manufacturers and retailers because of the complexity it creates in manufacturing and supply chain management. Retailers and manufacturers both want to understand which SKUs add incremental sales and whether the incremental sales justify continuing the SKU. Solution: By applying analytics to POS and panel data, companies can measure the incremental demand that a SKU addresses. This is also known as demand transferability. In other words, retailers and manufacturers are trying to understand if a particular SKU is not present, will consumers buy another product (transfer the demand), or will they go to another store to purchase the product. Deliverables:
Shopper Segmentationgo to top
Business Problem: Retailers want to understand the different segments of shoppers who shop at their stores, so that they can target their pricing, promotional, and marketing strategies to cater to each specific group. Solution: By applying analytics to POS data, loyalty card data, and demographic data for the store's catchment area, we can identify the common attributes of shoppers who shop at a particular retailer, including the differences in these attributes by region. Shoppers can be clustered along specific attributes, and pricing and promotional strategies can be targeted for each group. Deliverables:
Proactive Retention for Club Shoppersgo to top
Business Problem: Loyalty card data provides retailers with detailed information about the preferences, past frequency, and amount of purchases of each of their loyalty customers. Retailers would like to understand how they can use this information to create strategies to retain these customers. Solution: By analyzing historical POS data at the basket-level, in conjunction with customer loyalty data, we can model historical loyalty customer spend behavior. Using these models, we can forecast how each customer would behave in the future. This forecasted purchase information can be used to create strategies for retaining loyalty customers. Deliverables:
Design of Experimentsgo to top
Business Problem: Retailers want to introduce new concepts, products, ideas, pricing and promotion strategies, merchandizing strategies etc. within their stores. Typically, they would introduce these over a subset of the stores and depending on certain success criteria they would make a decision on whether to scale these across all their stores or not. Retailers want to understand how to identify a representative sample from a set of stores. Also, the sales of a store depends on a lot of different variables and it is hard to single out the impact of a specific strategy to overall sales. Retailers want to be able to measure the impact of tests conducted on key KPIs and to define the success or failure of the test. Retailers want to introduce new concepts, products, ideas, pricing and promotion strategies, merchandizing strategies etc. within their stores. Typically, they would introduce these over a subset of the stores and depending on certain success criteria they would make a decision on whether to scale these across all their stores or not. Retailers want to understand how to identify a representative sample from a set of stores. Also, the sales of a store depends on a lot of different variables and it is hard to single out the impact of a specific strategy to overall sales. Retailers want to be able to measure the impact of tests conducted on key KPIs and to define the success or failure of the test. Solution: We use historical POS data in conjunction with demographic data to identify test and control stores which trend very similarly and which are also representative of the market being studied. Comparison of sales data pre and post test between test and control stores would give a measure of the impact of the test. Demand models can be created against historical POS data which would enable adjustment of the sales data for differences in pricing, promotions, merchandizing etc between test and contol stores.We use historical POS data in conjunction with demographic data to identify test and control stores which trend very similarly and which are also representative of the market being studied. Comparison of sales data pre and post test between test and control stores would give a measure of the impact of the test. Demand models can be created against historical POS data which would enable adjustment of the sales data for differences in pricing, promotions, merchandizing etc between test and contol stores. Deliverables:
Markdown Optimizationgo to top
Business Problem: Short lifecycle product retailers (for example, fashion retailers or electronics retailers) have products which go out of trend/fashion/usability very quickly, and as such, they have a very short timeframe to sell their products. They want to achieve the best possible results on the relevant sales KPIs (i.e. units, revenue, average unit price, GM %, sell-thru %) within this duration. One of the key decisions is to determine the optimal markdown price for the products, so that the products run out of inventory within the specified out dates, while maximizing sales KPIs during the sales lifecycle of the product. Solution: We model historical POS data to identify key product parameters such as price sensitivity, seasonal decay of demand, promotional effectiveness, etc. These model parameters can forecast future sales KPIs, which can be used to find the optimal markdown prices which will maximize these KPIs. Deliverables:
Market Basket Analysisgo to top
Business Problem: Retailers want to understand which products/brands sell together (affinity) and which products/brands cannibalize each other. They also want to quantify the impact of these affinity and cannibalization results on financial metrics, and identify how they can use this information to price and promote products effectively. Solution: Applying analytics to historical POS data at he basket-level, we can track affinity and cannibalization relationships between various products/brands/categories across different countries/regions/stores. We can quantify the financial impact of these relationships, and also recommend promotional and pricing strategies specific to a product relationship. Deliverables:
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