ElectrifAi created data models, intelligence layers, and test-and-learn capabilities to identify where a customer was in their journey. The solution brought together over 4,000 signals and used 90 ML models to give the brand a 360-degree view of shopper behavior. As a result, the brand saw $130 million in incremental annual revenue. The solution also saved the brand an estimated $10 million by preventing customer churn.
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Compressing and Classifying Multiple Data Sources
A leading waste and energy management company used multiple systems in different currencies, which made it challenging to measure companywide spend in a single currency.
ElectrifAi gathered, validated, cleaned, and consolidated data from four separate sources to streamline the client’s data. As a result, it compressed the total number of suppliers by 55% and classified 99.5% of all company spend.
“We are helping energy and chemical companies drive substantial cost savings through comprehensive spend and contract analytics, leveraging machine learning-based vendor and spend categorization, classification, and compression,” Edward Scott explained.
Implementing Computer Vision To Speed Up Building Inspections
A New York City-based construction and inspection company spent too much time manually inspecting buildings. This led to more errors, wasted time, and expenses for the company.
