Emerline Helped an AI Company Optimize Store Layouts and Improve Sales Predictions for Large Retailers

Our customer is an AI development company that specializes in providing tailored solutions for the retail industry. They have positioned themselves as industry leaders, offering AI-driven systems, services, and strategies to accurately predict consumer behavior and demand.

The company's key services encompass assortment optimization, price and trade optimization, retail layout optimization, and predictive market analytics. The customer has established a strong presence in the US market, delivering reliable solutions to leading American retailers.

However, our customer has faced a significant challenge in meeting the growing demand for predictions due to their limited resources. As their client base expands, the company requires assistance in developing robust machine learning models to enhance their predictive capabilities and ensure the scalability of their services.

In our collaborative efforts with the customer, we've successfully undertaken various projects focused on developing machine learning models to predict sales and optimize store layouts for large retailers. The process involved key steps in ML model development.

Sales prediction

Data collection. The customer provided us with essential data, including inventory details, costs, sale dates, and sales revenue.
Data preparation. We meticulously cleaned and preprocessed the data to ensure its quality and relevance to the model's objectives.
Feature development. We identified and developed relevant features, such as seasonal trends, historical sale patterns, and promotional impacts.
Model training. Using advanced machine learning algorithms, we trained the model on historical data to learn patterns and relationships.
Validation and testing. The model underwent rigorous validation and testing phases to ensure its accuracy and generalizability to new data.
Deployment. Once validated, the model was deployed for real-time sales predictions.

Store layout optimization model

The store layout optimization model development process was the next step after the sales prediction model, with the key difference lying in the input data. Customers provided information such as inventory volumes and aisle sizes. The resulting model offers insights into optimal fixture arrangement, contributing to efficient retail store organization.

• SQL
• PySpark
• Python
• DataBricks
• Prophet
• LightGBM

ML models developed by Emerline play a pivotal role in predicting trends, identifying patterns, and offering forecasts. Ultimately, they contribute to strategic planning by aiding in delivery scheduling, warehouse optimization, logistics efficiency, and other key aspects of retail operations.

Our two-year collaboration stands as a testament to customer satisfaction and acknowledgment of the high accuracy demonstrated by our ML models in optimizing retail operations.

Furthermore, in line with our dedication to efficiency, we are currently in the process of developing a versatile library. This resource will empower our customer to swiftly generate baseline predictions from diverse datasets, streamlining their data-driven decision-making processes.