Cohort-Based Inventory Prediction for Small Business Cold Start
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Solution Overview
Problem
Small businesses face the 'cold start' problem when trying to use predictive models due to the lack of historical data, and existing solutions using noisy historical data from other small businesses often result in inaccurate models.
Innovation Solution
The method involves identifying product categories using a categorization model applied to product data from internal and external sources, creating a knowledge graph for business entities, generating cohort clusters through clustering, and using these clusters to learn an extrapolation model for predicting inventory levels, which is then accessible via an online merchant platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predictive models are trained using historical data from other small businesses, then the cold start problem is addressed, but the model accuracy deteriorates due to noisy data
Solution Approach 1:
The patent segments businesses into distinct cohorts based on industry, size, and other relevant characteristics. By dividing the heterogeneous pool of small business data into homogeneous segments, the noisy data problem is addressed while still leveraging external historical data for cold start scenarios.
Solution Approach 2:
The patent applies local quality by creating cohort-specific predictive models rather than a single generic model. Each cohort receives tailored predictions based on its specific characteristics, improving accuracy by addressing the unique patterns and noise profiles of different business types.
2Reliability
If predictive models are based on single business historical data, then model accuracy is maintained for large businesses, but the cold start problem occurs for small businesses
Solution Approach 1:
The patent merges data from multiple small businesses within the same cohort to create sufficient training data for predictive modeling. This combination allows small businesses to benefit from aggregated historical patterns without requiring long individual data accumulation periods.
Solution Approach 2:
The patent performs preliminary actions by pre-segmenting businesses into cohorts and pre-training models on aggregated cohort data before individual businesses need to accumulate sufficient historical data. This preliminary model training enables immediate predictive capabilities for small businesses.
Data Source
AI summary
A method identifies a plurality of product categories by applying a categorization model to product data that includes product names and product descriptions. The product data is obtained from internal sources and external sources. The method creates a knowledge graph for a plurality of business entities from inputs that include the plurality of product categories, published data that includes merchant categorizations, and geographical data. The method generates cohort clusters of similar business entities by applying a clustering algorithm to the knowledge graph. The method uses the cohort clusters to learn a first extrapolation model for predicting inventory. And the method provides access to the first extrapolation model to predict inventory level at a specific point in time. The access is provided via a merchant application that runs on an online merchant platform.