Case Study

UST helps retailers predict daily sales six weeks in advance and improve product availability

After creating a robust, flexible AI-based analytics solution using xpresso.ai, the company’s retail clients can improve demand forecasting, lead time estimating, and the optimal product reorder point.

OUR CLIENT

With roots dating back several decades, this marketing solutions company has evolved from providing coupons to the retail and consumer packaged goods (CPG) industries to offering sophisticated digital advertising solutions and intelligence to clients worldwide.

THE CHALLENGE

Forecasting retail sales with quantitative data rather than qualitative opinions

Many factors, like promotions, competition, holidays, and the weather, influence retail store sales. However, this marketing solutions company relied on limited qualitative data, like expert opinions and special events, to help its retail clients predict daily sales. The company wanted to offer its clients a more reliable forecasting solution that analyzed historical data, quantitative metrics, and social factors to predict daily sales up to six weeks in advance. A robust solution was needed to ingest data from multiple sources and quickly identify retail sales patterns, trends, and growth opportunities to forecast daily sales at the local store level.

THE TRANSFORMATION

Using reliable AI-based analytics to forecast daily sales six weeks in advance

UST helped the marketing company design, develop, and deploy an AI-based retail forecasting solution using UST xpresso.ai. The solution utilizes historical store-level data, such as daily sales, customer headcounts, store type, product assortment, promotions, and distance from the nearest competitor, to predict daily, individual store sales up to six weeks in advance. Store managers can view the insightful forecasting data as an Excel file and make data-driven decisions to optimize sales, profitability, and customer satisfaction.

Meanwhile, administrators can manage the algorithms and analytics solution using integrated workflows, a visualization dashboard, a variety of productivity tools, coding language environments, and pre-configured notebooks with internal and external libraries.

THE IMPACT

Analyzing daily sales data to improve product availability, staffing, and customer satisfaction

Retail companies using the sales forecasting solution resulted in to improved demand forecasting, estimated lead time, and determined the optimal product reorder point. The granular, 6-week advanced notice data helped individual stores better manage staffing and inventory to meet shoppers' demands and boost customer satisfaction.

RESOURCES

https://www.ust.com/en/alpha-ai

https://www.ust.com/en/industries/retail-and-cpg