Amazon accounts for 39% of all CPG e-commerce revenue, nearly 7x more than Walmart.1 But as an e-commerce retailer, Amazon has different data elements than brick & mortar retailers, which poses a challenge for traditional forecasting methods.

Antuit’s Amazon Channel Forecasting improves forecasting accuracy by incorporating Amazon’s unique data elements, including lost buy box, ASIN ranking, glance views, replenishable out of stock, and customer reviews. By combining these with competitor price matching and promotion behavior, the solution delivers a highly accurate forecast that accelerates your performance on Amazon.

1 Neilsen E-commerce Measurement

Case Study - Mastering Amazon for a Consumer Electronics Brand

The Problem - A multinational consumer electronics (CE) brand regards Amazon as a critical channel, yet forecasting for it was complicated. Amazon’s business practices exacerbated the problem. Despite having large warehouses, Amazon doesn’t carry large inventory volumes. Consequently, they submit purchase orders as frequently as twice a week. Due to all of this, the CE brand was only achieving 25% forecast accuracy on average. This low accuracy was leading to lost sales and lower placement on Amazon.

The Result - Amazon shares a wealth of valuable information with their suppliers, but it is only useful if you know how to translate that into meaningful information. Understanding this, the company chose Antuit to address its forecasting challenges. After a swift implementation, Antuit’s solution more than doubled the consumer electronics’ forecast accuracy, exceeding the expectations of the CE company.

Amazon is harder than other accounts to predict. Antuit’s forecasts give us an unbiased, data-driven view that we never had before.

Analytics Executive, Consumer Goods Company

Our solutions are built upon’s world-class AI Demand Forecasting

Unified Demand Signal

Control for the differences between regions, stores, online, and even the fulfillment type, and serve as the connective tissue across financial, assortment, allocation, size, and pricing decisions.

Dynamic Aggregation

An analytic methodology to address data sparsity, avoid the impact of fringe sizes, handle new items, and protect unit minimums.

Omnichannel Profiling

Delivering demand profiles that consider store and online sales independently, but optimize for BOPIS and ship-from-store (SFS) aspects of inventory location.

Seamless Integration

Delivers pricing and forecasting results through API integration, feeding either’s application suite or existing ERP solutions.

Scalable Data

AI models capable of digesting data that accounts for every demand driver - including seasonality, price, product lifecycle, trends, and local events.

Cloud Native

Built natively in the cloud with scalable distributed processing.

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