ElectrifAi Machine Learning Solutions Drive Revenue, Success

ElectrifAi Machine Learning Solutions Drive Revenue, Success

Machine learning might sound like science fiction, but companies across all industries rely on innovations in machine learning and artificial intelligence to find efficiencies and quickly drive high-value business outcomes. Machine learning saves time, reduces costs, and speeds up the time to value for real estate professionals, retailers, government agencies, manufacturers, and more.

Innovative machine-learning companies such as ElectrifAi allow organizations to mobilize their data and make data-driven decisions in less time. “[We’re creating] very cutting-edge solutions with multiple applications across many industries,” Edward Scott, Electrifai CEO, told online platform Authority Magazine. Thanks to pre-built AI models, the company’s clients see time to value in as little as six to eight weeks.

It sounds improbable, but innovations in machine learning mean this technology is more accessible to brands than ever before — and Edward Scott is happy to break down some of ElectrifAi’s latest AI applications and the clients that benefited from ElectrifAi’s expertise.

Consolidating and Classifying Spend Data

“We are helping retailers across the globe optimize cash flow and inventory through demand forecasting, dynamic pricing, and optimized store replenishment,” Edward Scott said.

ElectrifAi worked with a luxury retailer that was experiencing data overwhelm. With multiple data sources from its enterprise resource planning and suppliers, the retailer wanted to classify its companywide spend.

ElectrifAi took data from seven sources, validated it, cleaned it, and consolidated it. The team also created spend dashboards so the client’s employees could quickly see companywide spend.

As a result, the retailer normalized spend data from 50 sources to increase spend visibility. It also classified 98% of all spend and compressed the number of suppliers in its system by 65%. That’s practical AI.

Increasing Supplier Diversity for City Government

One of the largest city governments in the United States worked with ElectrifAi to identify small and underprivileged suppliers in its system. As part of a diversity initiative, the government wanted to support these businesses. However, the city used a decade-old application with limited reporting capabilities — and it couldn’t afford to replace this legacy application, either.

ElectrifAi built a machine learning model to categorize and classify spend by vendor and identify small minority businesses. As a result, the city reduced its costs, increased supplier visibility, and was to redirect spend to minority-owned firms fulfilling diversity, equity, and inclusion pledges..

Recovering $14 Million in Missed Medical Billing Charges

A small hospital system was losing money and didn’t know where it was going. It also experienced high costs, which meant the business lost millions of dollars annually. ElectrifAi leveraged machine learning to create a model that gave the client insight into potential missed charges. It also created user-friendly Ai dashboards to pinpoint billing issues. As a result, the hospital system spotted $14 million in confirmed missed charges, which both injected cash into the business and remedied its profitability challenges.

Reducing Procurement Spend

A card payment service needed to better understand its expenses, so it brought in ElectrifAi to leverage prebuilt spend analytics solutions. Its internal procurement team needed a tool for ongoing data maintenance so the business could improve its procurement abilities while reducing costs.

ElectrifAi consolidated and classified spend data so the client could manage its data internally. After classifying three years of historical business data, ElectrifAi successfully reduced procurement costs for the client. It also created a fully operational spend data set that the client’s team could manage internally.

Reducing Supply Chain and Customer Risks

A leading manufacturer in Asia wanted to better manage its cash flow and minimize supply chain risks. Its customers were chronically overstocking and understocking, which meant the manufacturer had significant problems with cash flow, delayed customer payments, and defaults.

The manufacturer tapped the ElectrifAi team to improve its cash flow stability. ElectrifAi used predictive metrics to identify at-risk clients in real time. As a result, the client reduced supply chain and credit risks across its 20,000 customers.

Defining Customer Journeys With Test-and-Learn Capabilities

ElectrifAi partnered with one of the top three wireless providers in the U.S. to improve its customer experience. The brand had plenty of customer data, but it was too siloed. As a result, its customer retention and product cross-sell/upsell programs weren’t working as well as planned.

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.

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.

It tapped ElectrifAi to create a computer vision solution that would detect building defects. The team used InspectionAi, a computer vision solution, to do building inspections in hours versus weeks. It produced annotated videos and inspection reports, which saved time and improved accuracy. In fact, the solution led to a 75% reduction in time spent on inspections.

Machine Learning Drives Short-Term Gains and Long-Term Success

Machine learning seems like a far-off reality for many businesses. However, ElectrifAi shows that it’s possible to rapidly implement machine learning and see real business results in just a few weeks. Prebuilt machine learning solutions make this technology accessible to both enterprises and small and medium-sized businesses across all industries. The future is never certain, but it looks brighter thanks to these innovations in machine learning and AI.