Cross-Channel Analytics for New Product Sales Prediction
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Solution Overview
Problem
Retailers face challenges in accurately predicting the sales performance of new products due to reliance on erroneous data and limited availability of sales data from similar channels, leading to poor sales and profitability issues.
Innovation Solution
The use of cross-channel analytics to estimate performance metrics by comparing data from focus and benchmark markets, clustering similar products, and adjusting sales ratios based on product similarity, allowing for precise predictions of new product sales without relying on product hierarchies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sales data from limited channels is used to predict new product performance, then data availability is constrained, but prediction accuracy deteriorates due to erroneous and insufficient data
Solution Approach 1:
The patent combines sales data from multiple different channels (online, offline, direct-to-consumer) into a unified dataset for training the machine learning model. This merging of diverse data sources increases the volume and variety of available information, enabling more accurate predictions for new product performance while reducing reliance on limited single-channel data
Solution Approach 2:
The machine learning model is designed to universally process and analyze sales data across multiple channels and product types. The system can handle diverse data formats and sources, making it adaptable to different retail contexts and product categories, thereby improving prediction accuracy without being constrained by channel-specific limitations
2Adaptability or versatility
If product hierarchies are used for data organization, then data structure is simplified, but prediction flexibility deteriorates due to inability to capture cross-channel relationships
Solution Approach 1:
The patent segments the sales data by channel type (online, offline, direct-to-consumer) while maintaining the ability to analyze cross-channel relationships. This segmentation allows the machine learning model to process each channel's unique characteristics separately while still capturing overall patterns, providing both structured organization and analytical flexibility
Solution Approach 2:
The system adds a channel dimension to the traditional product hierarchy structure. Instead of organizing data solely by product categories, the patent incorporates channel as an additional organizational dimension, enabling the model to capture relationships across different sales channels while maintaining data structure manageability through multi-dimensional indexing
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed for determining new product metrics using cross-channel analytics. An example apparatus includes processor circuitry to at least compare first products data associated with a first channel and second products data associated with a second channel to identify a product of interest corresponding to a product present in the first products data and not in the second products data, and third products corresponding to products present in both the first products data and the second products data, cluster the third products based on at least one metric to generate product clusters, for ones of the product clusters in the cluster output, calculate a ratio of a performance metric of the third products, and determine a value of a performance metric for the product of interest based on the first products data and a ratio of the performance metric.