Moving top-brand consumer goods products off store shelves is the chief priority for a North American sales and marketing giant. More than 30,000 employees across 100-plus offices work directly inside retailers and behind the scenes to keep products selling.
On the front lines, close to 16,000 staff members keep stock visible, organized, and accurately priced across North American retail stores. Backstage, nearly 600 analysts focus on metric-based measurements to gauge product success.
Instituted new advanced analytics and machine learning capabilities in a single unified platform, allowing for more complex modeling than previously possible.
Improved capabilities to investigate retail at greater scale will improve product sales.
Designed a platform that makes use of the marketer’s existing skill sets.
Uncovered opportunities, including a way to look for additional prospects or strategic influencers within a client’s data, and integration with a management application.
Created a solid platform for future machine learning and AI capabilities.
Here are some insightful blog posts from the BlueGranite team for retail topics and scenarios.
Retail victory requires expertly juggling supply and demand. When this retailer began grappling with too many products at some locations and not enough at others, the corporation set out to gain a deeper understanding of its customers’ evolving needs and reduce disruption in sales. To better predict adequate future supply, the company enlisted BlueGranite to help it uncover underlying factors influencing sales.
It’s been decades since a billion-dollar American clothing retailer began in a single location. Despite its now far-reaching brick-and-mortar and online presence, the merchant is committed to continuing the quality and service that made it a success. Part of that process includes a major, multiyear deployment of a company-wide SAP enterprise resource planning (ERP) system.The new technology touches, and is expected to improve, every facet of the business.
Less than a decade ago, a U.S. direct-to-store marketer and distributor of international foods had just one warehouse and five employees. The booming business now employs more than 100 people who supply products to over 7,000 stores. This supplier faced some data challenges as it grew. Using a best of breed approach for choosing business information systems obtained the best individual IT applications but left the data created in silos.
A common data source among retailers are online reviews from ecommerce sites - what buyers have been saying about their products after a purchase. This example scenario includes users who make a purchase from ABC Apparel are asked whether they recommend the product, how they would rate the product on a scale of 1 to 5, and to review the product in a free text form.
This Power BI example report was made to analyze this information. It contains a summary of reviews, ratings, and recommendations sliced by the most important categories for ABC Apparel. In addition, we can also see ratings distributions for the most important categories, and we can drill down to see the individual reviews. ABC might be having a problem with repeat customers, so they are turning to reviews to see if it exposes any product issues. Or they might just be doing a brand health check and want to be sure that there are mostly positive reviews left on their website.