A Mosaic Data Science Case Study
A leading outdoor lifestyle retailer needed to find a data science firm who could help design and develop predictive models for their merchandising teams to use in making buying decisions for size profiles in their apparel and footwear lines. Previously, all decisions were made manually based on historical sales data, and the customer data science team believed that automating much of the process, integrating new data sources, and running predictive analysis on these data streams would help the merchandising team make better decisions in a shorter timeframe.
Size profiles establish store-level and national target distributions of sizes (S, M, L, etc.) at the product-color level for apparel items and are used to guide purchase and inventory movement decisions. Improved decision support that accounts for more than simply historical sales improves in-store and warehouse inventory balance, leading to reduced markdowns and stockouts and higher overall revenue-per-unit.
Mosaic Data Science, a premier analytics firm, was contracted to work iteratively and collaboratively with the customer data science team to assess potential approaches and aggressively implement the one with the biggest impact.
The first phase of the project was exploratory to validate hypotheses and answer questions about the current size profiles, quantify the potential business opportunity in improving the profiles, and develop insights that would guide the modeling activities in Phase 2.
The customer team brought Mosaic an initial list of questions to be answered during the first phase of work that were refined and added to as early insights started flowing:
Mosaic’s data science consultants, in collaboration with the customer data science team, determined that an automated tool that generated a recommended size profile for any product within the customer’s catalog would be the best way to implement the predictive model. The tool determines a level of aggregation based on each product’s color, brand, and product category. Based on the store assortment plan for the product, the tool then aggregates historical demand from the appropriate time period across the assorted stores for each size of a product and returns a relative (percentage) size distribution for the style-color. The size distribution is written to a database table that can be read by the various tools in the merchandising toolset.
Mosaic built the predictive model in R. The tool takes its input data from tables in the Business Intelligence database and writes its outputs to a table in the same database. The size profiles are refreshed daily to adjust for any changes to the product catalog or store assortment plans.
Mosaic designed the model with three key components
Figure 1 Daily Stockouts for a specific product group
Mosaic brought cutting-edge techniques from machine learning, predictive analytics, and data engineering to help this retailer optimize their merchant buying decisions. The client now better understands their apparel and footwear demand, allowing them to make decisions that decrease stockouts, reduce end-of-season markdowns, and increase revenues at the item level.
The tool Mosaic designed and implemented provides automated recommendations for product-specific order distribution across sizes. This brought the merchandising team from a spreadsheet-based approach with substantial limitations into a world where much more data can be utilized to make better decisions. This saves the merchandising team much needed time and allows them to place the right product at the right store at the right time.
Figure 2 Statistical difference analysis of size profile performance