Amazon SageMaker AI/ML capabilities fit seamlessly into the
• Amazon SageMaker has great functionality and is easy to use
• Excellent support team and documentation
• Purpose-built tools for every step of ML development
• Secure your data and code throughout the ML lifecycle
ElectrifAi’s pre-built and pre-structured ML models on Amazon SageMaker provide clients:
• Proven results for real business problems
• Models quickly deployed for quick time-to-value
• Trusted partner for enhanced data security
• Provide customers with faster ML adoption
Enticing customers to stay long-term with excellent customer service is important to keep them from jumping ship to the competition. Customers who feel neglected or that the experience has gone stale will begin to shop around. With ElectrifAi’s predictive analytics machine learning models, know in advance those customers likely to churn and target strategies to keep churn from happening.
Predicting demand on a product, service, venue, travel destination, and more can help companies prepare by keeping items in stock, having enough staff to assist customers, adding more showtimes for popular movies, advising customers on where to travel at specific times of year, the list goes on. Machine learning makes accurate recommendations by predicting the demand based on factors such as popularity of the item, inclement weather that would prevent attendance, sale history, etc.
ElectrifAi has a range of other use cases more focused on specific industries such as: Banking, Financial Services, Insurance, Telecommunications, Retail and Healthcare. With deep domain expertise in each industry, ElectrifAi’s experienced data scientists use pre-built models to bring quick time-to-value for successful business outcomes on predictable timeframes with known results.
Personalized offers are more likely to entice customers to purchase items or services. Machine learning uses behavioral, spending and demographic data to help predict those likely to opt-out of emails, optimize prices with dynamic pricing, find opportunities to upsell/cross-sell, acquire new customers with intriguing offers tailored tospecific desires, and much more.
Knowing the exact price for a product or service is a challenge companies face in the competition to win customers. With machine learning, you can optimize prices based on a deep understanding of customer reactions to price changes.
Many companies have supplier contracts and invoices to manage and doing so manually can be a tedious and expensive task. Optimize contracts and invoices with advanced technology to speed up processes and reduce errors. Save costs through supplier contract optimization with machine learning, such as finding duplicate entries for the same supplier to decrease difficulty comparing contract terms to invoices. Machine learning can also identify when contracts expire and ideal times to renegotiate with suppliers. Additionally, invoice errors can be caught and fixed before paying the supplier, reducing the chance of accidental overpayment.
Incremental Annual Revenues
$70M through incremental lines added
$50M in incremental revenue through rate plan / feature upgrade campaigns
$10M from churn saves