2016 Big Data Analytics Conference

• 

  Jennifer Golbeck

  Director of the Human-Computer Interaction Lab
  University of Maryland, College Park

Increasingly companies are seeking to leverage their social media strategy to maximize customer engagement.  However, there is a desire to balance the need for the insight and the desire to respect customer privacy. Dr. Jennifer Golbeck, a world leader in social media research, tracks the rise of social networks and data analytics, how new computational techniques are revealing hidden traits of millions of people online, and how this impacts the future of business. Part creepy and part surprise, this opening keynote address looks at how scientists and companies are leveraging big social data to develop new insights into customers and what they want.

 What does it mean to capture experience? In this talk, Shelly Blake-Plock, CEO of Yet Analytics will describe how through a blend of the science of semantics and the application of tracking technologies to human performance metrics, a new branch of technology has developed which will have significant impact upon the way that individuals and businesses understand and leverage the data of experience. Focusing on how the experience of employees effects the outcomes of business, we’ll examine how the data of experience is collected and made sense of; and how a better understanding of employee experiences may boost the capabilities of an organization. Surveying the current landscape of data technologies, we’ll also look into the future and consider the consequences of what today’s decisions about experience-based data models may ultimately have both for business and society with regards to privacy, communications, and emergent forms of artificial intelligence.

Voya Financial is using predictive modeling to deliver targeted messaging that drives thousands of positive customer actions every year. Using data from millions of customers, Voya’s analytics engine helps to identify the best call to action for each individual, and then delivers that message by phone, web or email. In this session, you will hear: 

• How predictive modeling can be used to deliver targeted messaging that works
• How you can measure success, and use measurement to drive continuous improvement Lessons learned about getting started, building a team, and maintaining momentum

Big Data requires a mental shift: what we lack in cleanliness or completeness, we make up for in volume and variety.  In many respects, partial data can tell us more than seemingly complete data.  This paradigm shift lies at the heart of understanding how Big Data processing works and how Big Data yields business value.

Predictive models provide insight that can be used to transform the insurance business. The challenge is, how do you communicate the results of very sophisticated tools and gain understanding from an audience that finds the topic complex and confusing.

• Learn tools and techniques to explain the results of predictive models
• Hear examples of how to present to different end-users of predictive models, both internal and external to your organization

An increasing amount of data, affordable computational power and unprecedented access to a large number of algorithms to extract information from the data have increased the importance of analytics organizations like never before. The combination of the internet and a growing philosophy of sharing have led to open source implementation outpacing proprietary implementations. This session will provide a practical guide to adopting open source algorithms in an analytics organization. Using a publicly available dataset, attendees will learn how open source can be used to solve a business problem - end to end: from data exploration to model development and deployment.

The benefit of an ongoing, two-way dialog with highly engaged consumers is full of allure, but insurance marketers need to determine how to effectively measure ROI in this new model, which spans across online and offline marketing channels. In this session, we’ll discuss what you need to know to shape your omni-channel marketing program and clearly understand its success. And, we’ll answer some key questions:

• Do you have a 360-degree view of your customer, effectively tying on-line with off-line consumer attributes to engagement strategy? 
• Is your digital marketing helping to improve your KPI’s
• How well do your digital marketing channels work with other channels? Which audiences are responding? Can you correctly measure and attribute their behaviors? 
• Are valuable marketing dollars being wasted due to redundancy across channels?

The utilization of data analytics for social media and next-generation data is disrupting the traditional insurance process, changing the way carriers market to and engage with customers, assess risk, and process claims.  Traditional data sources, such as credit scores and contributory databases, no longer provide insurers with the whole story, particularly for millennials and individuals who typically fall into the “unscorable” category. Next-generation data targets this information gap, changing the way insurers conduct business and solving one of today's biggest industry challenges.

Most salespeople don’t have access to data or the tools to analyze it, manufacturers do. While many companies began their analytics work building lead generation and cross-sell programs, the types and sophistication of analytics being developed to support distribution continue to increase. Come to this session to hear how companies can leverage advanced analytics to support distribution.

 The frequency of the big data capture from mobile devices exposes the regularity of specific human behavior and represents a new frontier for Life Analytics, which hopefully can be used to adjust actuarial assumptions.    

•  The potential & speed bumps of innovation: [brief] presentations by Lexis Nexis, John Hancock, etc. 
• Introducing mobile device &  telematics data into the mix
•  Customer acquisition – potential and limits of prospect targeting 
•  Underwriting – advancing “traditional” UW using predictive analytics  and combining mobile data with current applicant data
•  4 Steps to Dealing with telematics Big Data - meaningful summaries of  data from mobile devices 
•  Data Privacy & Security: state and federal & regulatory environment 

Do you still have questions that have not been answered?  If so, join us at the LIMRA Analytics Town Hall where you will have a last chance to pose questions, discuss analytics strategies, and participate in a “Q&A hackathon”!