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 activity through advanced customer behavior analysis and applied ABA forecasting methods to generate accurate sales predictions and store insights.

Data science & AI Retail

USA

Retail

Since 2023

Summary
This case study describes how an AI-based solution was developed to analyze customer behavior and forecast retail sales. The system performs customer behavior analysis and processes large volumes of customer activity data to predict buying patterns and optimize product placement.

In this material, we explain the approach used, the technologies behind the system, and the business results achieved.

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. Using AI models built around behavioral science in ai sales, we analyzed customer data, purchase patterns, and store insights to provide actionable recommendations to the client.

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, making it the analytical base of the client’s retail operations. The solution relied on advanced predictive models and aba forecasting techniques to identify patterns in large datasets and produce reliable sales projections.

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 the items.
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.

Screenshots

Tools & technologies

Python

MS SQL

Pandas

NumPy

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

Business analyst x1
Data scientist x1
Back-end developer x1

Results

We developed an AI solution that helps our client optimize sales. The tool performs behavior analysis and forecasts sales for a retail company, applying insights from behavioral science in AI sales to better understand customer decision-making.

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.

Our insights

Retail data is rarely ready for prediction models
Although the client had millions of customer records, the data came from many sources and used different formats. A large portion of the work went into cleaning, aligning time periods, and removing inconsistencies before the models could produce reliable forecasts.
Small recommendation changes can impact revenue
The team found that even minor adjustments to product placement or promotional suggestions could influence customer decisions. Retail environments are sensitive to such changes, so accurate insights from behavior analysis quickly translated into measurable business impact.
Forecasting improves when different data signals are combined
Purchase history alone did not provide enough context for strong predictions. Better results appeared once clickstream data, mobile interactions, and engagement signals were analyzed together.

Key takeaways

	Data preparation is critical for retail AI projects Accurate forecasts depend on well-structured historical data.
	Use multiple behavioral signals for prediction models Combining purchase, browsing, and engagement data improves forecast accuracy.
	Test recommendations before full deployment Small experiments help verify that predicted actions produce real business results.
	Cloud-based architecture helps manage large datasets efficiently Serverless environments allow systems to scale while keeping infrastructure costs under control.

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AI-based behavior analysis & sales forecast for retail

Summary

Highlights in figures

Highlights in figures

Challenge

Requirements

Solution

Screenshots

Tools & technologies

Project scope

Data collection & analysis module

AI-powered personalization

Computer vision integration

Team

Business analyst x1

Data scientist x1

Back-end developer x1

Results

Increased conversion rate

Enhanced data collection

Cost savings

Our insights

Retail data is rarely ready for prediction models

Small recommendation changes can impact revenue

Forecasting improves when different data signals are combined

Key takeaways

Key takeaways

Interested in AI solution pricing?