Recently, 4R Systems introduced the all-new 4R Navigator – a suite of machine learning-powered analytics services designed to help retailers solve critical business challenges. 4R Navigator consists of four scientifically designed solutions that provide actionable insights with very fast results, and without the typical overhead costs. These solutions can be deployed and provide quick – but substantial – wins in as little as 3-6 weeks.
We already explored SKU Rationalization and how it can help you navigate your assortment. To better comprehend what clustering is, it’s best to get an aerial view of your organization so you can see the bigger picture.
What is Clustering Analysis?
Clustering has been around in retail for a long time. There are several reasons a retailer may want to utilize clustering. For example, products can be clustered to identify distinct annual seasonal behavior. Stores can also be clustered based on the strengths of certain product groups for use in segmenting stores for better assortment planning. The resulting clusters can then be used to provide insight, improve forecast accuracy, optimize assortments, and more.
4R’s Clustering Analysis uses a machine learning process to group stores or products that behave similarly. It can also isolate the unique patterns for those groups, providing even more valuable insight than traditional clustering methods.
How Does Clustering Analysis Work?
Ideally, each store should have its own unique assortment, pricing, promotions, and more, based on its specific customer base. This is impractical for a couple reasons. For one, data for individual locations may contain too much “noise” to discern meaningful patterns. It may also be operationally impractical to maintain distinct assortments and store plans for every location. By grouping stores or products together that behave similarly, some of this noise can be eliminated, allowing meaningful patterns to emerge.
Consider a supply chain that spans northern and southern regions of the U.S. Locations in the north will see increased demand for sweaters in the winter compared to stores in the south, which may sell flip-flops throughout the year. Clustering Analysis can identify and isolate these behaviors. Additionally, it may uncover individual locations that buck the trend. For example, a store located near a mountain ski resort in the southwest might more closely resemble stores further north than others in the southwest region.
While store clustering is a common retail practice, 4R’s Clustering Analysis takes it beyond the basics. With machine learning and AI, we are able to take a scientific approach to grouping your stores and products. This ensures the groupings are as accurate as possible and that each cluster will use the right attributes. 4R’s Clustering Analysis can help you find the best store and/or product clusters to optimize your supply chain.
After a certain point, creating more store clusters may actually not be beneficial. But how can you know at what point you start to lose benefits by adding more clusters? With 4R’s machine learning and AI-powered Clustering Analysis, you’ll know exactly how many and what type of clusters you need for your stores. The machine learning model can adapt as your chain opens more stores or relocates others.
What factors should be used to group stores or products? How many distinct groups should be used? How much does all this benefit my supply chain? With 4R’s machine learning and AI-powered Clustering Analysis, you will have answers to all these questions.
Get on the Path to Profit
Ready to improve your bottom line in as little as 3 to 6 weeks? To get started with Clustering Analysis, or to learn more about the other 4R Navigator solutions, call us at (610) 644-1234 or email email@example.com.