AI-based behavior analysis & sales forecast for retail
Client
A retail giant with 3 million customers sought to leverage their user data for sales forecasting. We used AI to analyze customer behavior and store insights, delivering actionable sales forecasts.
- Location: USA
- Industry: Retail
- Client since: 2023
Highlights in figures
- 7%visitors-to-buyers conversion rate increase
- 15%volume of data collected increase
- 35%monthly infrastructure costs decrease
Challenge
Our Client had a vast database of user activity, so they wanted to take advantage of it. Primarily, they were interested in sales forecasting capabilities. With AI, we analyzed customer data, purchase patterns, and store insights to provide actionable recommendations to the Client.
Requirements
Systematize and analyze Client’s data.
Implement predictive models to forecast buyer conversion rates, product interest, and future sales.
Help to optimize product placement on shelves in stores.
Develop recommendations based on identified patterns and suggest next-best offers.
For optimizing product placement recommendations, we needed to know:
Product dimensions.
Shelf dimensions.
Layout rules.
While getting all the data, our team cleaned and refined it to the required granularity. Using Light GBM as a pre-existing algorithm capable of identifying data patterns, we generated initial predictions. Following meetings with the client and receiving feedback, we fine-tuned additional parameters and proceeded with deployment.
Solution
Our team started working on the project by building the structure and architecture of the system, thus making it the basis of the client’s business. The framework for the future solution relied heavily on the data provided by the Client. To ensure the accuracy of forecasts and eliminate temporal errors, seasonality, and other factors, we requested the following two years of data:
Number of sold items.
Revenue generated from these items.
Prices of items.
Data Collection & Analysis Module
Developed a centralized AI platform to collect and analyze data in near-real-time.
Processed clickstream data, mobile data, server events, and email engagement metrics.
AI-Powered Personalization
Implemented a recommendation engine using collaborative filtering for personalized user experiences.
Utilized alternating least squares (ALS) algorithm for scalable predictive modeling.
Computer Vision Integration
Applied computer vision for automated product attribute detection and image similarity search.
Leveraged ResNet-50 CNN for multi-attribute image classification.
Besides, our software solution underwent rigorous QA & testing procedures, ensuring its stability and quality. We conducted ongoing performance testing to identify and resolve any potential issues.
To optimize our technology stack, we chose to host the solution on Amazon Web Services (AWS) using a serverless architecture. This approach allowed for scalability and fault-tolerance while minimizing infrastructure costs.
Tools & technologies
Back-end
Libraries
- MS SQL
- Pandas
- NumPy
Team
- x1Business analyst
- x1Data scientist
- x1Back-end developer
Results
We developed an AI solution that helps our client optimize sales. The tool does behavior analysis and forecasts sales for a retail company.
By leveraging our AI-powered solution, our client gained deeper insights into buyer behavior, increased sales through personalized experiences, and achieved significant cost savings in infrastructure management.
Increased conversion rate. Personalized communication based on AI-driven insights led to an 7% increase in visitors-to-buyers conversion rate.
Enhanced data collection. The volume of user data collected for predictive modeling increased by 15%, providing more accurate forecasts and insights.
Cost savings
Monthly infrastructure costs decreased by 35% due to optimized resource utilization and serverless architecture implementation.