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Predict! Issue #42

Dear Readers,

It has been a long time since predictive analytics only inhabited the dark shadows of data science laboratories, and myriad examples appear every week illustrating how this core discipline of data science is working its way into mainstream business processes. Yes, we data scientists are still geeks, but just like John Grisham transformed the erstwhile dull legal profession into a seemingly dynamic and action-packed environment, data science geeks are finally getting their day in the sun. Zementis is happy to be doing its part to further the cause of analytics geekdom.

Predictive analytics can be applied to a wide variety of industries and business function use cases, but it probably makes sense to highlight just one application for the purposes of this newsletter. Years ago, Zementis got its start by focusing on just a single use case: risk and fraud scoring for credit analysis and lending. Over time, our focus here evolved into a broader category that we call “Risk Management Analytics”. Our business and technology partner FICO focuses extensively on this area for their core business, yet their FICO Analytic Cloud has much broader applicability as well. In addition to managing debt (i.e. lending) and preventing fraud, FICO cites a third use case: engaging and growing customers. In June, FICO and Zementis will hold both a meetup session in New York and a webinar (for New Yorkers and others alike) to explore the topic of “seeing the whole picture” via predictive analytics.

Another one of Zementis’ business and technology partners, IBM, shares this core focus on Customer Analytics. IBM has embraced big data analytics and in particular, predictive analytics as a cornerstone of their strategy for transforming businesses through data-driven insights. Zementis supports several key IBM solutions, including IBM PureData System for Analytics, IBM InfoSphere BigInsights and IBM SPSS. This month, Zementis is proud to announce a new integrated capability for IBM SPSS users – a Functional Accelerator to leverage predictive models from different modeling tools in real-time on IBM’s Predictive Customer Intelligence platform.

Full details on both of these exciting announcements are included below. We hope that you will continue to discover ways to apply predictive analytics to your Customer Analytics challenges, and that Zementis can help make your efforts simpler, more agile, more efficient and increasingly insightful.

Kind Regards,

Michael Zeller
CEO, Zementis Inc.

 

Zementis, FICO, and Caserta Concepts Use Predictive Analytics to Reveal “The Whole Picture”

For many years, Zementis and FICO have enjoyed a strong, collaborative relationship, structuring analytical solutions that help business and other organizations derive critical insights into their data and applying those insights to drive informed, timely business decisions. This month, the two companies are highlighting the way that they use predictive analytics to “see the whole picture” that big data can reveal. Together with Caserta Concepts, a professional services firm specializing in big data, data warehouse and business intelligence consulting, Zementis and FICO are holding two events and inviting members of the data science community to join the conversation.

The first event will take place on the evening of June 3 in New York City. This live event will feature Dr. Michael Zeller, CEO of Zementis, as well as Tom Traughber, Senior Director of Software Development at FICO, and Elliott Cordo, Chief Architect at Caserta Concepts. Mr. Cordo will open the evening with an introduction to predictive analytics, focusing on advanced concepts geared for a data science audience.

Dr. Zeller will then discuss the imperative of incorporating predictive analytics into an organization’s big data strategy, and the benefits of a vendor-neutral approach for data science. He will also cover enabling technologies that support streaming and rapid processing, as well as how Zementis and FICO collaborate to bring the power of predictive analytics to the FICO Analytic Cloud.

FICO Company LogoTom Traughber, Senior Director of Software Development at FICO, will explore in detail FICO’s approach to deriving critical business insights from an organization’s holistic data set – both real-time data streams and historical data sets. He will frame the discussion in the context of actual market use cases for engaging and growing the customer base, optimizing debt collection operations and more effectively mitigating fraud risk.

On June 18, Zementis, FICO and Caserta Concepts will pick up the conversation with a webinar on the same topic, incorporating the dialogue from the June 3 meetup session in New York, and evolving the dialogue to explore practical applications of predictive analytics in greater detail.

Caserta ConceptsJoe Caserta, Founder and CEO of Caserta Concepts, will discuss the latest technologies and trends that are gaining traction in the market and the business value that their customers are deriving from big data projects. Michael Zeller from Zementis will recap the topics that he covered at the New York meetup session, provide additional perspectives and highlight case study examples from Zementis’ customer engagements. Representing FICO will be Shalini Raghavan, Senior Director of Product Management. She will offer FICO’s perspectives on the benefits of mining the entirety of available data to develop a holistic and accurate understanding of business situations, discuss various predictive analytical methodologies and describe FICO’s approach to uncovering key insights that can deliver competitive advantage for businesses.

To register for the New York meetup event on June 3, please click here.

To register for the webinar on June 18, please click here.

 

IBM Releases Zementis Customer Churn Model for Predictive Customer Analytics

As part of its efforts to drive greater insight into customer dynamics via predictive analytics, IBM has developed a series of integrated predictive analytics solutions, one of which is called Predictive Customer Intelligence (PCI). Zementis and IBM are proud to announce the general availability of a Functional Accelerator that is integrated with IBM’s PCI solution.

predictive-analytics-how-to-anticipate-your-customers-needs-1-638IBM Predictive Customer Intelligence offers businesses critical information and insights that enable them to provide proactive service to their customers, develop a consistent customer contact strategy and improve their relationships with customers. The technology integrates information from multiple internal and external data sources, enabling holistic analysis that yields predictive insights. These insights, in turn, allow businesses to make informed decisions that actively shape and improve the customer experience while increasing customer lifetime value (CLV).

A key component of this solution is a library of accelerators that IBM makes available via IBM’s Analytics Zone, its portal for analytics solutions. Visitors to the Analytics Zone will find a variety of accelerators developed for specific industries and cross-industry / horizontal use cases (“functional accelerators”). A description of IBM’s Predictive Customer Intelligence offering and the various accelerators is accessible here, and a technical brief covering the integrated Zementis / IBM solution for predictive analytics is available here.

The Zementis / IBM functional accelerator includes an example that simulates a customer’s interactions through a channel, such as a call center. The call center retrieves a customer’s profile and evaluates it first for the propensity to churn and then for overall sentiment. The churn score from the first model is then used as an input for the second model, the sentiment analysis.

The functional accelerator demonstrates how businesses can use models from two different modeling tools in a real-time scenario. It combines the real-time scoring capability of the Zementis ADAPA (Adaptive Decision and Predictive Analytics) engine with the modeling of IBM SPSS. Users can extend the library of available analytical and predictive models to include any models that can be published in Predictive Model Markup Language (PMML).

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