Once again, we are pleased to announce that CMS, the Centers for Medicare and Medicaid Services, has chosen Opera Solutions for a three-year, $28 million contract that will enable Opera Solutions to provide the operational analytics that drive the Health Insurance Marketplaces of the Affordable Care Act. In this role, Opera Solutions will continue to build on the strong foundation it has built in the first three years of this relationship, adding more advanced analytics and ensuring data integrity across the widespread, diverse data environment within CMS.
Most companies realize they are sitting on a treasure trove of customer data that has the potential to deliver tremendous business benefits; however, most also have no idea how to realize those benefits. How can companies use their data to bring in more customers, increase the amount they spend, and make them more loyal? How can companies use data to turn unhappy customers into loyal champions of the brand? And perhaps most important, how can companies use that data to drive a significant increase in revenue?
Is now the right time to add machine-learning analytics into your charge-capture management system?
ICD-10 is here and chances are, as a provider, you’re as ready for it as you can be, knowing there could be some hiccups and impact on revenues. Most predict that the impact will be confined to inpatient revenues, driven by significant adjustment issues in grouper methodologies. But what if the impact extends well beyond that, to outpatient revenues?
Have you ever wondered how Google or Hotmail finds and blocks a single spam email out of billions of emails? Or how companies analyze tweets for customer sentiment? Or how questionable content is identified on a Website? Natural language processing (NLP) does this in real time — and it can be used for a lot more than spam filtering.
When it comes to analyzing a company's spend, the old adage "what gets measured gets managed" is definitely true. However, measuring an enterprise's spend when you have free-text fields, or fields where employees can type in any response they want, can be an insurmountable task. Rarely do two people use the same words and phrases to describe the same thing. Even spelling varies. These variations make measuring how much is being spent on a given category or with a given vendor a very common challenge.
Signals empower us to predict the future by learning from the past.
We all learn from the past. So if you failed a mathematics exam in school, you learned you had to study harder not to fail the next one. Events that happened in the past can be measured in many different ways. Measuring a past event puts it into focus. In data mining, this process translates into creating a descriptive feature to describe or tell a story about the past in some way, shape, or form.
We call these descriptive features Signals. Signals are the indicators extracted from raw data that have proven to be valuable for solving a particular problem. Signals can also be created from other Signals by transforming one piece of information into something more meaningful or interesting. For example, using the initial Signal extracted from raw data — in this case, a test score — we can create several Signals that capture past school performance in mathematics and science for a certain student.
Are your employees communicating effectively? Find out how one company got everyone speaking the same language.
The larger a company gets, the more siloed it can become, and relying on happy hours, retreats, or top-down mandates are not enough to break these silos down. No matter how hard leadership tries, people have a difficult time getting on the same page when they all have different business objectives. And no company is immune. Even Apple has struggled with silos. Steve Jobs, in an effort to deconstruct silos, set up Apple's headquarters to encourage "collisions," or interactions between people who may not have necessarily interacted.
Travel season is upon us, and what used to create excitement and anticipation now creates dread and anxiety. Flying is certainly not what it used to be, but thankfully, it’s also getting a little bit better, thanks to data science and advanced analytics. The world’s largest airlines generate and consume huge amounts of data on hundreds of millions of passengers, and that data is finally paying off for travelers. Of course, not all airlines use data the same way, and some of them hardly use it at all. But here, we take an inside look at those that do to give you the three best ways advanced analytics are improving the travel experience for over 200 million passengers.
Tags: Signal Hub Technologies
2015 could be the year that true personalization finally takes hold, but to achieve it, you’ll need to change the way you think about marketing.
Customers expect to be treated as individuals. They require relevancy and prefer having a relationship with a company that keeps track of their preferences, purchases, and correspondence. Customers are ready for personalized marketing, and now, finally, more and more companies are, too.
Most redemption programs suffer from the same challenge: delivering rewards that customers actually want. To make this possible, the programs offer ever-more rewards, which puts the onus on the customer to find desirable ways to spend their points. In the end, redeeming points can be more of a chore than a reward, ultimately diminishing the value of the very program that was supposed to create value and differentiation in a crowded space. But with millions of customers, no one (or even 100) reward(s) will meet the desires of everyone. So what are credit card issuers to do? How do they put the value back into these programs, so customers are incentivized to choose one card over another?
When to nudge a delinquent customer for a payment — that is the question. Or rather, that is one of many — many — questions that Signals are helping telecom companies (telcos) answer. Because when to call and ask for payment can actually help determine whether to call at all, and eliminating calls can lead to significant savings. So how do Signals do this? And more important, how can telcos, specifically, take advantage of these Signals to improve customer experience and maximize bottom-line impact?