Machine Learning Model for Predicting Customer Interest
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Solution Overview
Problem
Merchants face challenges in maintaining customer interest throughout the sales process, often wasting resources producing and promoting products or services that customers are not interested in, while customers also waste resources reviewing uninteresting offers.
Innovation Solution
A machine learning model is trained with third-party data from merchants and customer interest data, including location and birthdate information, to predict customer interests and provide personalized recommendations, offers, and restrict website access to relevant products or services, thereby optimizing resource allocation and customer engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If merchants produce and promote products or services without predicting customer interest, then they can offer a wide range of products, but they waste resources on products that customers are not interested in
Solution Approach 1:
The system performs preliminary action by training a machine learning model to predict customer interest before merchants produce and promote products. The model analyzes customer data (demographics, behavior, preferences) and third-party data to generate interest predictions, allowing merchants to allocate resources to products with high predicted interest and avoid wasting resources on uninteresting products.
Solution Approach 2:
The system enables self-service by allowing the machine learning model to automatically analyze customer data and generate interest predictions without manual intervention. The model continuously learns from customer interactions and updates predictions, enabling automated resource allocation decisions that adapt to changing customer preferences.
2Loss of energy
If customers review multiple offers to find relevant products, then they can discover suitable products, but they waste time and resources reviewing uninteresting offers
Solution Approach 1:
The system performs preliminary action by pre-analyzing customer data and predicting interests before presenting offers. The machine learning model processes customer demographics, behavior patterns, and preferences to generate personalized recommendations, so customers receive only relevant offers without needing to review unrelated options.
Solution Approach 2:
The system extracts and presents only the most relevant offers to each customer based on predicted interests. By filtering out uninteresting offers and presenting only those with high relevance scores, the system eliminates customer waste of time reviewing irrelevant options while ensuring product discovery through targeted recommendations.
3Productivity
If merchants target only interested customers, then they improve sales efficiency, but they need accurate prediction of customer interest which increases system complexity
Solution Approach 1:
The system introduces a machine learning model as an intermediary between customer data and interest prediction. The model acts as a mediator that automatically processes complex customer data patterns and translates them into actionable interest predictions, reducing the complexity burden on merchants while improving sales efficiency through accurate targeting.
Solution Approach 2:
The machine learning model performs self-service by automatically analyzing customer data, learning from patterns, and generating predictions without requiring complex manual configuration. The model continuously improves its accuracy through self-learning from customer interactions, maintaining high sales efficiency while keeping the system relatively simple to deploy.
Data Source
AI summary
A device may receive third-party data associated with merchants and may receive customer interest data associated with customers of the merchants, wherein the customer interest data includes data identifying locations of the customers and birthdates of the customers. The device may train a machine learning model, with the third-party data and the customer interest data, to generate a trained machine learning model. The device may receive, from a user device, data identifying a location and a birthdate of a particular customer of a particular merchant, wherein the particular merchant is one of the merchants, and may process the data identifying the location and the birthdate of the particular customer, with the trained machine learning model, to determine a predicted interest of the particular customer. The device may perform one or more actions based on the interest of the particular customer.