Retail today is a very challenging environment, new brands, verticals, channels and competitors are emerging daily. This creates a lot of complications when planning and managing your business.
In my experience planning retail businesses, we always aimed to optimize by creating product groupings based on a set of criteria, typically leveraging historical data. This process helped us understand product performance, but it didn’t stop there.
We would take it a step further by creating store groupings, considering the unique needs of each location, which then influenced planograms (POG) and inventory decisions within each store. By marrying these insights, we made more informed decisions, using the best data we had available to optimize product placement and replenishment.
Fast forward to today, and the landscape has changed dramatically. Machine learning (ML) enables retailers to take these traditional insights and supercharge them with real-time, dynamic data analysis. Here’s how:
With dynamic product and store clustering, ML continuously updates groupings based on real-time data, allowing retailers to keep up with consumer trends and regional demand shifts.
Forecasting is no longer just about past sales. ML uses factors like social media trends, weather, and promotions to deliver more accurate, up-to-date demand forecasts.
Personalized replenishment means stock decisions are tailored to each store and region’s specific needs. Retailers can ensure the right products are in the right stores at the right time.
With real-time adjustments, ML detects issues immediately, allowing retailers to adjust inventory and product groupings without waiting for sales reports.
Predictive analytics helps retailers plan better for new products. By analyzing external signals, ML can predict demand more confidently, even for products without sales history.
By combining the foundational practices of retail management with modern machine learning technologies, today’s retailers have the tools to make even more informed decisions, adapting quickly to customer needs and market dynamics.
For anyone in retail, optimizing your business no longer means just looking at historical sales data—it’s about tapping into real-time insights and predictive analytics to stay ahead.
#RetailExperience #MachineLearning #DemandPlanning #RetailTech #AI #SupplyChain #BusinessOptimization
Software Development | Managed Team | Team extestion | AI/ML Development
5moAbsolutely crucial insights! How do you recommend businesses start their pricing strategy?