FASTEST GROWING START-UP IN METALS & MANUFACTURING

The CEO had a hunch the old method of determining a market’s value wasn’t as accurate as it was in the past. Historically adding new markets meant adding new teams to understand each local market—and that kind of human capital investment wasn’t scalable. So, they needed localization and cluster analysis to help determine the future of the industry at scale.

There was no standardization across the industry. The business knowledge was all kept within a representative’s head which necessitated hiring many people at the regional level to fill knowledge gaps. And this knowledge was also heavily regionalized using different data sources, thus not making the business sustainable or scalable.

As a fast-growing start-up, the company had to learn how to make data-based decisions. Market recaps were slow and market-to-market comparisons were difficult based on the level of local knowledge.

4400

SERVICE CENTERS IN THE U.S. HANDLED THE NATION’S STEEL VOLUME

100

ATTRIBUTES WERE CREATED

20

ADDITIONAL ATTRIBUTES WERE CREATED WITH MACHINE LEARNING TO HELP DEFINE ONES IMPORTANT TO THE FUTURE OF THE INDUSTRY

TOP 20%

OF SERVICE CENTERS SET UP TREND ANALYSIS TO MONITOR NEW DEVELOPMENTS MONTHLY

EVOLVED THINKING

A 150—year-old industry needed to make decisions backed by data versus perceived opportunity from tenured vendors. A quick- win of $10M revenue increase in 90 days was just the proof the industry needed

The team knew changing the thought process of a150-year-old industry would require results—and as fast as possible. They quickly identified and created a consistent way to think about the industry that was digitally based using standardized nomenclature and attributes. The digitized data base was built with flexibility, data cleansing and machine learning with natural language AI integration. Data was pulled from IBIS, metal center news, CRU and other sources to help triangulate the relative volume of the business. Anything that could be defined as an attribute was extrapolated and put together for a 360-degree view.

In addition to 200+ defined attributes, the learnings created a consistent way to think about the industry and ultimately the products within. The output created was dynamic and continually updated for newness which helped understand supply and demand and new opportunities for logistics to be leveraged. The marketplace pricing also helped inform regionalize pricing opportunities to maximize profits.

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