For decades retailers have been managing their merchandise assortment, planning, and allocations based on prior sales numbers, emerging trends, store profiling, and overall demand. With continued access to richer data, retailers have never been more informed about their business.
Analyzing baskets and prior purchase behaviors is going to a new level. The amount of available proprietary, inherent, and market/demographic data including weather patterns, health/fitness activity, cultural trends, and geopolitical and economic indices can all be used for more accurate planning.But more data doesn’t necessarily mean it’s the right data. What if retailers could more accurately pinpoint by location not only what sold, but also what is most likely to sell in the future using variables including predictive triggers? And all that before the planning process even begins. No need for a magic eight-ball here. Reverse engineering your planning focuses on specific levers that prompt the process from the endpoint of the consumer basket all the way back to sourcing. Then layer on the capabilities of today’s artificial intelligence and you have a new model for end-to-end business planning. Is it too good to be true? Not really. While not all retailers are ready to take advantage of this inside-out approach, many do have the ability to strategically migrate to reverse-engineered planning.
How It Works: The Six-Step Process
- A retailer establishes a key performance lever that serves as a behavioral indicator. Here is where AI/ML can be utilized early in the process to elevate and identify key strategic levers.
- The planning process starts when that lever is triggered.
- The associated values assigned to that lever feedback across functional teams provide a single real-time view of the opportunity.
- Design, merchandising, planning/allocation, finance, marketing, and store operations are notified and aligned. A single source of truth/KPIs is shared from a unified/centralized source.
- A business baseline is reestablished for proactive planning.
- The process repeats and with machine learning and AI, gets faster, smarter, and more accurate.