AI-based behavior analysis & sales forecast for retail
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.
USA
Retail
Since 2023
Highlights in figures
| 7% | visitors-to-buyers conversion rate increase |
| 15% | volume of data collected increase |
| 35% | monthly infrastructure costs decrease |
Challenge
Requirements
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.
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.
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.
Screenshots
Tools & technologies
Project scope
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.
Team
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Business analyst x1
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Data scientist x1
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Back-end developer x1
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.