Top 3 strategies for CPG companies to enhance Amazon demand forecasting
CPG companies face a significant challenge in achieving accurate forecasts for Amazon’s purchase orders, with industry leaders often operating at less than 50% accuracy. This issue is exacerbated by Amazon’s unique marketplace dynamics, where products are identified by Amazon Standard Identification Numbers (ASINs), distinct from the UPCs used by CPG companies. Furthermore, Amazon’s competitive marketplace means multiple sellers may offer the same ASIN, with only one seller featured in the buy box at any given time, determined by factors like price, stock availability, and seller reputation.
To enhance demand forecast accuracy for Amazon, the top 3 levers are:
1. Consider Requested Delivery Date (RDD) for Demand Forecasting
Unlike traditional shipment date-based forecasting, which may not align with customer demand timelines, focusing on requested delivery dates provides a clearer picture of actual demand. This approach considers the time dimension of demand forecasting, essential for maintaining high order fill rates and customer satisfaction.
2. Cleanse (Impute) Order History to Address Amazon’s Reordering System
Amazon’s automated reordering system triggers additional orders when CPG companies fail to fulfill initial orders promptly. This can lead to inflated demand forecasts if not correctly managed. To mitigate this:
Identify reorder instances by analyzing order quantities that deviate from historical patterns.
Treat these reorders as outliers in forecasting models and use statistical techniques to adjust forecasts accordingly.
Implement imputation strategies leveraging machine learning models to smooth out the impact of these outliers and improve forecast accuracy.
3. Leverage Rich Data Sets Provided by Amazon
Amazon offers a wealth of data that can significantly enhance forecasting capabilities:
Glance Views and Unique Visitors: These metrics provide insights into product visibility and customer interest, which are crucial for understanding demand drivers.
Customer Reviews and Rankings: Weekly updates on reviews and rankings help gauge customer sentiment and product performance relative to competitors.
Buy Box Metrics: Lost Buy Box (LBB) and Rep OOS (out of stock) indicate pricing competitiveness and stock availability dynamics, influencing demand forecasts.
Sellable and Unsellable Units: Detailed inventory data aids in adjusting forecasts based on actual stock availability.
CPG companies can use artificial intelligence and machine learning to process Amazon's complex data sets and generate more accurate forecasts.
By focusing on these strategies, CPG companies can mitigate the challenges posed by Amazon’s marketplace dynamics and achieve higher forecast accuracy, ultimately improving operational efficiency and customer satisfaction. These efforts enhance supply chain management and strengthen competitive positioning in the evolving e-commerce landscape.
Partner with TrueGradient to unlock your full potential.
Click for a demo today or email us at info@truegradient.ai
Jasneet Kohli
Co-Founder
I thrive at the intersection of business, technology, and data science to create value for CPG and Retail companies. Well-rounded experience in the entire spectrum of Supply Chain - Forecast to Ship.
Now part of an incredible journey at TrueGradient. Drawing from our experience with Amazon, Walmart, Mondelēz, and IBM, the team is committed to democratizing advanced modelling techniques. The platform drives end-to-end planning decisions (Demand, Inventory, Price, Promo, Assortment), helping companies improve service levels while minimizing costs.
In the past, i have served Fortune 500 clients. Held leadership roles in large organizations and start-up environments, such as Head of Operations, Solution Architect, Head of Customer Success, and Go-To-Market leader; worked in Asia (India and Singapore), Europe, and North America. Passionate about grooming talent and building high-performing teams.
I am an active sportsperson who plays both individual and team sports – soccer, golf, and cycling.
 (1).svg){kind=icon}
