Earnix Receives Recognition for Data Science & Machine Learning by Gartner – 2018

For organizations to become truly customer centric – analytics is a prerequisite. As consumers become continually short on time and expect more choice than ever, brands are turning to analytics to enable optimal product offerings, price, and choice of delivery channel. At Earnix, we spend a lot of time working with organizations around the world – helping them to become more customer centric – and digitally transform – with data and analytics technology. Organizations realize that they must embed analytics throughout their organization -to become data-driven and better compete in a marketplace where customer-centricity is becoming table stakes for the best and most preferred providers. Providing analytical technologies, such as segmentation, forecasting, optimization, and machine learning enable our customers to succeed in this age of digital transformation that is being led by the concept of becoming more customer centric.

To that end, just last month Earnix was recognized by Gartner in the Hype Cycle for Data Science and Machine Learning 2018 report. We feel understanding the growth in data science and machine learning is important as global brands are moving up the analytics maturity ladder and building advanced analytic teams within their organizations. Organizations around the world use Gartner “Hype Cycles” to get educated on the promise of cutting-edge technology, within the context of their industry and individual readiness for risk.

Gartner has classified Earnix as one of their sample vendors in the Optimization category. Optimization techniques (traditionally leveraged by operational research groups) maximize benefits while managing business trade-offs by finding optimal combinations of resources given a strict number of constraints in a given amount of time. Optimization solvers often generate executable plans of action and are one type of prescriptive analytics technique. Common approaches to this type of problem solving include linear programming, integer programming, stochastic programming and constraint programming. Optimization as a category is placed on the Hype Cycle graphic with a benefit rating of high, a market penetration for the technique of 5-20% of the target audience, and as an emerging maturity. We believe all of these attributes point to Earnix being a key provider for the optimization technology moving forward.

The Earnix solution helps banks and insurers globally determine optimal price and product configurations down to the individual customer level. By taking often big data sources, applying analytical modeling to this data to determine risk and demand, and then simulate outcomes – Earnix can analyze and predict customer behavior and analyze its impact on business performance. Armed with this new level of insight, banks and insurance companies can better synchronize offers and prices to current and future market demand. These benefits make Earnix a solid bet for companies looking to adopt optimization. Optimization generates consistent and measurable improvements in customer loyalty, profitability, and growth — and delivers greater value to customers and higher profits to shareholders.

One of the most recent inputs to optimization is the use of machine learning. Using machine learning models in optimization programs often produces improved analytical accuracy, precision, and insight. Earnix’s Integrated Machine Learning™ technology is designed for insurers at all levels of analytical maturity, who want real-time market responsiveness. From companies that are new to the area to expert users, Earnix empowers all financial institutions to utilize machine learning. Earnix has recently seen machine learning being used for predicting customer product choice, to credit utilization, to the optimization of competitive rank on aggregator portals. Cost and policy cancellation prediction programs have also improved with Earnix Integrated Machine Learning™.

So if you are considering optimization and machine learning techniques and programs and want a trusted provider with two decades of experience, look no further than to Earnix. We have been assisting banks and insurance companies in meeting the analytical demands they have around price (insurance pricing software), product, risk, and demand – and are here to help you as well! For more detail on the July 2018 Gartner Hype Cycle for Data Science and Machine Learning, 2018 report, Gartner subscribers can access the report here.
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