Agenda

Predictive Analytics World for Business Las Vegas 2018

June 3-7, 2018 – Caesars Palace, Las Vegas



This page shows the agenda for PAW Business. Click here to view the full 7-track agenda for the five co-located conferences at Mega-PAW (PAW Business, PAW Financial, PAW Healthcare, PAW Manufacturing, and Deep Learning World).

TOPICS – The sessions across this two-day, three-track conference are grouped into the following five topics:
BUSINESS
Analytics operationalization & management
Track 1
WORKFORCE
Retaining & optimizing HR with analytics
Track 2, Day 1
TECH
Predictive modeling & machine learning methods
Track 2, Day 2
CASE STUDIES
Manifold business applications of machine learning
Track 3, Day 1
MARKETING
Analytics for customer acquisition & retention
Track 3, Day 2
TOPICS – The sessions across this two-day, three-track conference are grouped into the following five topics:
BUSINESS
Analytics operationalization & management
Track 1
WORKFORCE
Retaining & optimizing HR with analytics
Track 2, Day 1
TECH
Predictive modeling & machine learning methods
Track 2, Day 2
CASE STUDIES
Manifold business applications of machine learning
Track 3, Day 1
MARKETING
Analytics for customer acquisition & retention
Track 3, Day 2
Session Levels:

Blue circle sessions are for All Levels
Red triangle sessions are Expert/Practitioner Level

Workshops - Sunday, June  3rd, 2018

8:30 am
Room: Sicily
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

This one day workshop reviews major big data success stories that have transformed businesses and created new markets. Click workshop title above for the fully detailed description.

Leader
Marc Smith
Chief Social Scientist
Connected Action Consulting Group
4:30 pm
Workshop end (Big Data: Proven Methods You Need to Extract Big Value)
6:30 pm
Room: Sicily
Pre-Conference Training Workshop

Two and a half hour evening workshop:

This 2.5 hour workshop launches your tenure as a user of R, the well-known open-source platform for data analysis.  Click workshop title above for the fully detailed description.

Leader
Max Kuhn
Software Engineer
RStudio
9:00 pm
Workshop end (R Bootcamp: For Newcomers to R)

Workshops - Monday, June  4th, 2018

8:30 am
Room: Augustus I
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning).  Click workshop title above for the fully detailed description.

Leader
John ElderElder Research
Founder & Chair
Elder Research
Room: Sicily
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

Gain experience driving R for predictive modeling across real examples and data sets. Survey the pertinent modeling packages.  Click workshop title above for the fully detailed description.

Leader
Max Kuhn
Software Engineer
RStudio
Room: Emperors II
Pre-Confernce Training Workshop:

Full-day: 8:30am – 4:30pm

Dive in hands-on with crucial data prep steps, including cleaning, missing value imputation, feature creation/selection, and sampling.  Click workshop title above for the fully detailed description.

Leader
Dean AbbottSmarterHQ
Co-Founder and Chief Data Scientist
SmarterHQ
Room: Pompeian IV
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

This one-day introductory workshop dives deep. You will explore deep neural classification, LSTM time series analysis, convolutional image classification, advanced data clustering, bandit algorithms, and reinforcement learning. Click workshop title above for the fully detailed description.

Leaders
Ricky LoyndMicrosoft
Deep Reinforcement Learning Research Group
Microsoft
James McCaffreyMicrosoft
Senior Scientist Engineer
Microsoft
4:30 pm
Workshop end

Predictive Analytics World for Business - Las Vegas - Day 1 - Tuesday, June  5th, 2018

8:00 am
Registration & Networking Breakfast
8:45 am
Room: Augustus II
Eric SiegelPredictive Analytics World
Conference Founder
Predictive Analytics World
8:50 am
Room: Augustus II
KEYNOTE

Applied deep learning has fast become a standard tool for many industry machine learning applications. New advances in neural network techniques have opened the doors to solving problems at scale that were out of reach until recently.  Because of these advances, applications such as in image recognition for self-driving cars, medical image classification, text translation, and fake news detection are both tractable and often the industry standard.  In this keynote, Mike Tamir, who heads the data science teams at Uber ATG—the self-driving cars division—reveals how two key application areas of deep learning signal the broad importance of this emerging technology.

Session description
Speaker
Michael TamirUber
Head of Data Science, Advanced Technologies Group
Uber
9:15 am
Room: Augustus II
KEYNOTE

Data science, if judged as a separate science, exceeds its sisters in truth, breadth, and utility.  DS finds truth better than any other science; the crisis in replicability of results in the sciences today is largely due to bad data analysis, performed by amateurs.  As for breadth, a data scientist can contribute mightily to a new field with only minor cooperation from a domain expert, whereas the reverse is not so easy.  And for utility, data science can fit empirical behavior to provide a useful model where good theory doesn’t yet exist.  That is, it can predict “what” is likely even when “why” is beyond reach.

But only if we do it right!  The most vital data scientist skill is recognizing analytic hazards.  With that, we become indispensable.

Session description
Speaker
John ElderElder Research
Founder & Chair
Elder Research
9:40 am
Room: Augustus II
Platinum Sponsored Session
The Session Description will be available shortly.
Session description
10:00 am
Exhibits & Morning Coffee Break
10:30 am
Room: Augustus I
Track 1: BUSINESS - Analytics operationalization & management

Operationalizing predictive models

This PAW track – "BUSINESS - Analytics operationalization & management" – is kicked off by James Taylor of Decision Management Solutions, an industry leader in the operationalization of predictive models.

Many organizations still struggle to get a business return on their investment on advanced analytics. The biggest barrier? An inability to integrate analytics, especially predictive analytics, into frontline systems and business processes. Work with a number of global companies has revealed three critical success factors. By adopting a more decision-centric approach to analytics, changing the way requirements and business understanding are defined, and considering advanced analytics as one of a set of decision-making technologies, organizations can tie their analytics investments to business results and deliver the business value they are looking for.

Session description
Speaker
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
Room: Augustus II
Track 2: WORKFORCE - Retaining & optimizing HR with analytics

Workforce optimization and retention

Drawing on a unique database consisting of workforce (HRIS) data from multiple organizations, Mercer's Workforce Analytics team has applied predictive modeling to examine to what extent systematic differences exist between what millennials actually value in employment as compared to older employees. The results challenge "popular press" stereotypes about millennials at work. By identifying the predictive antecedents of employee turnover, i.e., what employees do, as opposed to relying on survey results, i.e., what millennials say they value, this research provides insight into actual behavior in a way that has practical relevance to the design of effective employee value propositions (EVPs) and total rewards.

Session description
Speakers
Haig NalbantianMercer
Senior Partner, Co-leader Mercer Workforce Sciences Institute
Mercer
Tauseef RahmanMercer
Principal, Workforce Strategy & Analytics
Mercer
Room: Emperors II
Track 3: CASE STUDIES - Manifold business applications of machine learning
Infrastructure planning; telecom
10:30 - 10:50 am
Case Study: Charter Communications (Spectrum)

Charter Communications (Spectrum) is the second largest cable service provider in the nation, and every investment to expand fiber network is based on maximizing the ROI. Charter makes an extensive use of Predictive Analytics based on Logistic Regression, Survival Analysis, GIS analysis to determine the best go-to prospects and assign sales territories to maximize return.

The marketing team has been able to improve campaigns' performance by over 30%, and the network construction team has been able to identify thousands of new buildings with the best ROI (with only 5% rejection rate) which were deemed unreasonable till now.

Session description
Speaker
Nishant SharmaCharter Spectrum
Director of Predictive Analytics
Charter Communications
TV ad targeting
10:55 - 11:15 am
Case Study: Turner

Turner has developed a machine learning algorithm that is capable of predicting granular audience targets. This innovation, called Competitive Audience Estimation, enabled advertisers/agencies to buy and execute audience targeted marketing campaigns on linear TV. This talk will overview the methodology and attained results. 

Session description
Speaker
Wes ChaarTurner
Senior Vice President of Analytics, Data and Decision Sciences
Turner Broadcasting System, Inc.
11:15 am
5-minute transition between sessions
11:20 am
Room: Augustus I
Track 1: BUSINESS - Analytics operationalization & management
Operationalizing analytics
11:20 - 11:40 am
Case Study: Northwestern Mutual

Getting models and analytic products ‘over the line’ and implemented or operationalized can be challenging due to factors often outside the direct control of a modeling or analytics team.  I’ll share some of the obstacles that we’ve met in our journey maturing as an Enterprise Data and Analytics organization.  Some are business and organizational challenges, others are technology related. At Northwestern Mutual, we’ve successfully navigated a number of these challenges and deployed models and other analytic products for mission critical business functions.  I’ll talk through some of the challenges and share our approach to overcoming them.

Session description
Speaker
Dave PahlNorthwestern Mutual
Sr. Director - Enterprise Data and Analytics
Northwestern Mutual
Analytics management
11:45 am - 12:05 pm
Case Study: Quicken Loans

Data Science’s massive potential can only be unleashed if it’s aligned with, and empowered by, senior leadership in an organization. Even the most sophisticated, transformative and well-executed projects can be for naught if the right people don’t trust how it works, its value or haven’t developed a culture that values Data Science. But while senior leaders speak business, Data Science teams often speak in math or code, and increasingly do so as the field evolves. How can Data Science teams bridge this gap to elevate their place and impact within an organization? In this talk I will share the story of Quicken Loans’ Data Science team and how some simple but insightful best practices can build the trust, relationships and understanding with senior leaders to transform organizations.

Session description
Speaker
James CarsonQuicken Loans
Data Science Team Leader
Quicken Loans
Room: Augustus II
Track 2: WORKFORCE - Retaining & optimizing HR with analytics
Workforce analytics
11:20 - 11:40 am
Case Study: Twitter

Attracting and retaining the right talent continues to be a struggle, especially in the Silicon Valley. Advances in statistical modeling have allowed us to predict and understand in depth and breadth why employees leave companies. This presentation will walk through some of the analytic techniques being used at Twitter, such as survival analysis, decision trees, etc. being used to predict employee attrition.

Session description
Speaker
Menghan ChenTwitter
People Data Scientist
Twitter
Workforce optimization
11:45 am - 12:05 pm
Case Study: Atlassian

Companies worldwide make massive investments to make their employees more productive. This endeavor has become more critical than ever as access to a qualified workforce is becoming increasingly challenging. While engaging users with relevant content is certainly a desired outcome, most product implementations still focus on optimizing click-through rate or engagement rather than productivity. As a workforce and management software market leader, Atlassian holds a unique position to determine how project documentation and team communication actually impact the throughput of employees. This talk addresses the intriguing correlations between the usage patterns observed in workforce collaboration tools, and the productivity of the user.

Session description
Speaker
Jennifer PrendkiAtlassian
Head of Data Science
Atlassian
Room: Emperors II
Track 3: CASE STUDIES - Manifold business applications of machine learning
Student registration acquisition modeling
11:20 - 11:40 am
Case Study: American Career College

Our overarching goal was to determine the underlying phenomena that create market demand for educational programs. The central question was: Is it the type of program, or the lead to enrollment process, or some identifiable behavior of the leads, or lastly would the demographic attributes turn out to be the chief drivers of enrollment. Rather than model the likelihood of enrollment with all the available variables, we methodically determined the product or school program variables that made them attractive; then the process of enrollment and finally to the behavioral characteristics of prospective students.  

Session description
Speaker
Peter Karpiuk
Consultant
Roundrock Management
Student registration acquisition modeling
11:45 am - 12:05 pm
Case Study: Becker College

Predictive modeling has gained popularity in studying college enrollment due to fierce competition in higher education. To make informed decisions and allocate limited resources to improve enrollment, predictive modeling has been applied to challenge and change the traditional recruitment process. This session is intended for two learning outcomes: Participants who are not familiar with predictive modeling will learn how to lay out a plan to collect and build a comprehensive data infrastructure and conduct predictive modeling. Participants who have run predictive modeling will learn how to critically examine the quality of their predictive analyses.

Session description
Speaker
Feyzi BagirovHarrisburg University
Data Science Advisor at Metadata.io and Analytics Instructor, Harrisburg University of Science and Technology
12:05 pm
Lunch & Learn

Coming soon!

Session description
Sponsored by
Pitney Bowes
1:30 pm
Room: Augustus II
KEYNOTE
Enterprise-wide deployment
Case Study: Dell EMC

Looking for tips and pointers on how and where to use machine
learning? Look no further. In this session, Theresa Kushner - SVP of Dell EMC's
Performance Analytics Group - will cover best practices as they apply for
medium to large enterprises, including:

- Tips for multinationals to effectively apply predictive
analytics in their business – where, how and why?

- Hottest techniques in predictive analytics for enterprises

- Role of start-ups in boosting predictive analytics techniques;
applying external expertise

- How to effectively partner with your IT organization to get
the most out of machine learning

Session description
Speaker
Theresa KushnerDell EMC
Sr Vice President, Performance Analytics Group
Dell EMC
2:15 pm
Diamond Sponsored Session
The Session Description will be available shortly.
Session description
Sponsored by
DataRobot
Speaker
Paul O'RourkeDataRobot
Sales Director
DataRobot
2:35 pm
5-minute transition between sessions
2:40 pm
Room: Augustus I
Track 1: BUSINESS - Analytics operationalization & management

Value-driven project strategies

Decisions are never made in a vacuum. They are the product of how decision-makers perceive their policy, political, business, and organizational environment. Shaping their decisions means shaping their environment. 

In this talk, Daniel Porter, Chief Analytics Officer of BlueLabs, will discuss his team’s approach for using data science to shape environments by influencing influencers. The approach pinpoints influencers across different industries and then dynamically scores them on their elasticity and level of influence within a given domain, so that organizations can target the most relevant influencers with ads and outreach, online and offline. 

Using examples from influencer outreach for major organizations, Porter will talk about how his team's approach to identifying and scoring influencers, from Fortune 100 executives to government officials, staff, major donors, and policy elites. Through a combination of data science tools and algorithms ranging from NLP classification to uplift modeling and non-proportional sampling methods, this approach provides organizations with an unparalleled ability to achieve their brand and policy objectives. 

Session description
Speaker
Daniel Porter
Co-Founder
BlueLabs
Room: Augustus II
Track 2: WORKFORCE - Retaining & optimizing HR with analytics

Workforce optimization; sales analytics
Case Study: Shell International

In 2017, the HR Analytics team in Shell collaborated with Shell Global Commercial, and connected Sales and HR data to improve the way we hire, reward, and develop sales account managers. The research project, called Project Spark, developed an Individual Sales Performance Model to identify key drivers of Sales Performance. Statistical modelling techniques (e.g. general linear modelling, logistic regression analysis, multi-level modelling) were applied to understand the effects of, for example, Personality, Career Types, and Team Leadership on Sales Performance. Based on the insights, Global Commercial and HR are co-designing practical solutions to get the best out of our people.  

Session description
Speakers
Tashi ErdmannShell Oil Company
HR Analytics Manager
Shell International
Linda van LeeuwenShell Oil Company
HR Analytics Analyst
Shell International
Room: Emperors II
Track 3: CASE STUDIES - Manifold business applications of machine learning
IP video player data analysis
2:40 - 3:00 pm
Case Study: Comcast Cable

Comcast data scientists need to use the vast resources of our in-house data lake in order to perform ML tasks such as anomaly detection. In the schema-onread model of data lakes, a data scientist first has to find datasets of interest, infer the meaning of each attribute, and clean up the data to enable joins across datasets and to ensure equivalent data (e.g., timestamp or geo location) is represented in the same format or vocabulary. Typically 80% of her time is spent on these tasks, before she can begin identifying features of interest and actually running the ML models.  

How does data governance make feature engineering easy?  Imagine a data lake in which datasets are represented via peer-reviewed Apache Avro schemas, using common sub-schemas, with the semantics and format of each attribute well-documented.  A metadata repository enables data discovery based on these schemas, and also stores end-to-end lineage  from streaming data capture through storage in the lake.  Governance also enforces schemas to be compatible when they evolve, so that data stored in different schema versions don’t need to be converted before use in feature engineering.  Also, by definition, the features are already defined in the datasets in a common Avro representation. Finally, it allows the data preparation for ML to become generic and therefore re-usable, e.g. extracting from any data set a time series of a single value (i.e. a feature) according to several dimensions.

Session description
Speakers
Gabriel CommeauComcast Cable
Software Engineer
Comcast Cable
Barbara EckmanComcast Cable
Principal Data Architect
Comcast Cable
Pricing
3:05 – 3:25 pm

The point of a business is to maximize profit. The best way is to always have the best price. But sometimes it makes sense to offer discounts to focus on the long term. This talk will focus on building and deploy such a tool, internationalization and dealing with a large number of Shiny users.

Session description
Speaker
Konrad PabianczykAppsilon Data Science
Key Account Manager
Appsilon Data Science
3:25 pm
Exhibits & Afternoon Break
3:55 pm
Room: Augustus I
Track 1: BUSINESS - Analytics operationalization & management

Thought leadership; research & development

The opportunities and challenges created by the expanding size and definition of data has become the catalyst for many universities across the country to think differently. The evolving discipline of data science is changing the way universities approach education, research and funding. In particular, the way universities collaborate with the private sector for research and development has fundamental changed - at least when it comes to data science. And, importantly, this new private sector/university collaborative framework has, in turn, contributed to the evolution of Data Science for both parties.

In this session, Dr. Jennifer Lewis Priestley, director of one of the country's first Ph.D. program in Data Science, will talk about how the relationship between universities and private sector companies has been impacted by the evolution of data science.

Session description
Speaker
Jennifer Lewis PriestleyKennesaw State University
Professor of Applied Statistics and Data Science
Kennesaw State University
Room: Augustus II
Track 2: WORKFORCE - Retaining & optimizing HR with analytics

Workforce optimization
3:55 - 4:15 pm
Case Study: Centric Consulting

Our consulting company, Centric, was struggling with low utilization - too many employees were not on billable projects. We designed and implemented a machine learning model to help us better understand the factors driving employee utilization. Although we had only a few hundred rows of data with which to work, the insights gained were used to redesign internal business processes, resulting in a 13% gain in utilization which in turn resulted in a nearly 20% improvement in company-wide profitability. In this session, we will cover the process we took from designing the machine learning model to implementing our findings in "real life," including tips, tricks, and lessons learned.

Session description
Speaker
Carmen FontanaCentric Consulting
Cloud & Artificial Intelligence Practice Lead
Centric Consulting
Workforce optimization
4:20 – 4:40 pm
Case Study: Baptist Health Medical Group

Engaging employees is critical for any organization.  It doesn't need to be difficult, and it doesn't need to be expensive.  In this case study, the Baptist Health Medical Group's Practice Optimization Team will demonstrate what it did (and what you can do) to measure employee engagement, and use statistics-based insights to take engagement to the next level. 

In this session, we will share our approach for: 

1) Asking the right questions,

2) Identifying engagement drivers, 

3) Communicating insights to leadership, and

4) Creating team-generated action plans. 

You already have the tools and budget to do what we did. We will show you how.

Session description
Speakers
Rebecca GuessBaptist Health Medical Group
Quality Analyst, Team Lead
Baptist Health Medical Group
Matt HayesBaptist Health Medical Group
Director, Practice Optimization
Baptist Health Medical Group
Room: Emperors II
Track 3: CASE STUDIES - Manifold business applications of machine learning
Customer service analytics
Case Study: Google

At Google, we developed analytical methods to evaluate the hypothesis that Universal Agents, who handle support calls across several products, would outperform Laned Agents, who are specialized to support only a single product. Here in Google's gTech group, initially operating under the assumption that Universal Agents would have higher utilization than the more prevalent Laned Agents, Google increased the number thereof, thereby creating the opportunity to collect the data needed to make this comparison. After several months, we compared the two strategies. To do so, however, required some innovation, since many factors impact agent performance. To normalize for these factors and get a clean adjusted estimate of performance, we applied a form of mixed modeling (an advanced form of ANOVA). Our findings were counterintuitive: Universal Agents under-perform Laned agents in overall cost and measures of customer satisfaction. This project corrected many misconceptions and resulted in a relative increase in Laned Agents.

Session description
Speaker
Tarek SoukiehGoogle
Head of Analytics - YouTube Trust and Safety
Google
4:45 pm
Room: Augustus I
Track 1: BUSINESS - Analytics operationalization & management

Analytics management/team-building

Organizations are looking at new approaches to finding data scientists due to the high demand, low supply economics of the talent pool. This is leading to a situation of the "haves" versus the "have-nots", where the larger, financially rich organizations in the "sexy" industries are most capable of attracting and hiring data scientists, while the lesser companies will have to make do without one. One approach being considered is building the data scientist function out of a team of people currently on staff or readily available in the marketplace, also known as the "DataScienceStein" approach.

Session description
Speaker
Bryan BennettNorthwestern University
Professor
Northwestern University
Room: Augustus II
Track 2: WORKFORCE - Retaining & optimizing HR with analytics

Legality and ethics in operationalization

Predictive analytics tools hold great promise for harnessing the power of data to make empirically-based decisions with significant increases in efficiency and effectiveness. However, predictive analytics tools come under special scrutiny when used for high-stakes decision-making such as employment selection. Join us for an overview of foundational laws, theories of discrimination, and approaches for measuring adverse impact and validity in the use of predictive analytics for employment purposes. 

Session description
Speakers
Eric DunleavyDCI Consulting Group
Director of the Personnel Selection and Litigation Support Services Group
DCI Consulting Group
Kelly TrindelEEOC
Chief Analyst
Formerly at Equal Employment Opportunity Commission
Room: Emperors II
Track 3: CASE STUDIES - Manifold business applications of machine learning
Customer service analytics
Case Study: IBM

IBM Technical Support handles over a million tickets per year and is committed to helping clients solve technical issues and creating optimal client experience. An always-on prediction system was recently developed to automatically forecast the daily client experience outcomes by ticket prior to ticket resolution. Support agents use it to prioritize their workload and take tailored actions to improve client experience of high risk tickets. In this session, I'll introduce the journey of developing the prediction system via iterations by combining classical statistical methods, machine learning algorithms, and human feedback to achieve significant improvement in model quality and user adoption.

Session description
Speaker
Dan YangIBM
Senior Data Scientist
IBM
5:30 pm
Networking Reception
7:00 pm
End of first Conference Day

Predictive Analytics World for Business - Las Vegas - Day 2 - Wednesday, June  6th, 2018

8:00 am
Registration & Networking Breakfast
8:45 am
Room: Augustus II
KEYNOTE

What does a 90-year old company have to do with predictive analytics? Quite frankly, everything. Through predictive analytics services and solutions, Caterpillar is increasing the value of its equipment and commitment to its customers by helping them predict potential outcomes and make better business decisions. Morgan Vawter, Caterpillar’s Chief Analytics Director, will share how this legacy company is combining domain expertise with vast amounts of data and advanced mathematical techniques to help its customers build a better world through better fuel productivity, increased safety, planned downtime and other predictable efficiencies.

Session description
Speaker
Morgan VawterCaterpillar
Chief Analytics Director
Caterpillar
9:30 am
Room: Augustus II
PLENARY SESSION

In the spring of 2017, over a thousand analytic professionals from around the world participated in the 8th Rexer Analytics Data Scientist Survey. In this PAW session, Karl Rexer will unveil the highlights of this year's survey results. Highlights will include:

- key algorithms

- challenges of Big Data Analytics, and steps being taken to overcome them

- trends in analytic computing environments & tools

- analytic project deployment

- job satisfaction

Session description
Speaker
Karl RexerRexer Analytics
President
Rexer Analytics
9:40 am
Room: Augustus II
Diamond Sponsored Session
The Session Description will be available shortly.
Session description
Sponsored by
diwo
10:00 am
5-minute transition between sessions
10:05 am
Room: Augustus I
Track 1: BUSINESS - Analytics operationalization & management
Sourcing analytics staff
Case Study: Verizon

In a world where demand outpaces supply, finding and keeping analytics talent has become a real dilemma.  Identifying the right mix of business skills and analytics skills can feel like an impossible search.  With so many people looking for strong talent, it often becomes difficult to compete.  How do you attract the right skills to your team to ensure a strong analytics capability?  What types of levels, roles, and titles do you need?  What are some of the ways to ensure you retain your analytics talent?  This session will discuss different compositions of successful analytics teams, as well as titles, career paths, and tips to win at the salary game. 

Session description
Speaker
Anne G. RobinsonVerizon
Executive Director, Strategy, Analytics and Systems
Verizon
Room: Augustus II
Track 2: TECH - Predictive modeling & machine learning methods
Model interpretation
Case Study: YMCA

As data science captures more attention from decision makers, translating the models from the language of the analyst into a language of the decision maker has become an important topic at conferences and in journals. It used to be that the focus on data storytelling was on visualization techniques. While this is important, as analyses become more complex, the task of interpreting the models likewise becomes more complex. Before we can decide on visualization techniques, we first need to uncover what to visualize. In this presentation, Mr. Abbott will describe ways to unravel complex descriptive and predictive models so they can be explained and visualized using machine learning models and resampling techniques. 

Session description
Speaker
Dean AbbottSmarterHQ
Co-Founder and Chief Data Scientist
SmarterHQ
Room: Emperors II
Track 3: MARKETING - Analytics for customer acquisition & retention
Marketing analytics strategy
10:05 - 10:25 am
Case Study: John Hancock

By 2021, Digital Marketing spend will near $120B in US and will be 46% of overall marketing spend. People-based marketing is a foundation of improving targeting efficiency. Successful targeting requires a combination of sophisticated modeling, deployment and application of both think and learn and test and learn methods. In this presentation, we will go over some of the results and lessons learned by John Hancock in its Direct to Consumer (D2C) journey, that went from 0 - 60 in less than a year.

Session description
Speaker
Kirit KottamJohn Hancock Insurance
Data Science Director
John Hancock Insurance
Response modeling for marketing
10:30 – 10:50 am
Case Study: Central Pacific Bank

In 2017, our bank implemented a machine learning propensity model in our direct consumer campaign. As a result we saw a 40% reduction in marketing cost and a 70% increase in response rate. Building a machine learning model is hard; convincing the organization to implement it is harder. In this session, we will present a success story of how we convinced the management committee to implement a machine learning model that appears to them like a black box. As a result, we saw a culture shift; the organization is now more willing to explore data science solution in other products.

Session description
Speakers
Ryuichi UmedaCentral Pacific Bank
Data Scientist
Central Pacific Bank
Haifeng WuCentral Pacific Bank
Data Scientist
Central Pacific Bank
10:50 am
Exhibits & Morning Coffee Break
11:20 am
Room: Augustus I
Track 1: BUSINESS - Analytics operationalization & management
Analytics leadership
Case study: CA Technologies

After 3 years of building a global analytics team for a 4B software company from bottoms up, we are now the go-to analytics shop for CA Technologies. We are considered a foundational piece of all transformational initiatives especially around sales, marketing and customer success. We’ve been featured in Forbes, Forrester and just received Information Magazine’s Analytics 50 Award. Hear about the key secrets and learnings from a successful analytics team leader that can help you reduce trial by error and avoid inevitable pitfalls. 

Session description
Speaker
Jennifer BerteroCA Technologies
VP, Business Analytics
CA Technologies
Room: Augustus II
Track 2: TECH - Predictive modeling & machine learning methods
Machine learning automation; ensemble models
Case Study: Which? (UK consumer reports)

A predictive model used in production has an incredible amount of manual overhead. That restricts the “real work” that a data scientist can do. If you can identify and automate that 85/90% of the work, what can be achieved? In this presentation, the elements of “model process automation” will be broken down and discussed using open source software throughout to demonstrate the logic. A real world example will be shown with Which? (the UK equivalent of Consumer Reports and Consumer Advocates) using 29,000 pricing ensemble models that create, train, build, tune and score every day totally automatically. From this example, best practices will be discussed.

Session description
Speaker
Phil WintersCIAgenda
Senior Managing Partner
CIAgenda
Room: Emperors II
Track 3: MARKETING - Analytics for customer acquisition & retention
Optimizing fan engagement
11:20 - 11:40 am
Case Study: Viacom

With a portfolio of brands ranging from Nickelodeon to Comedy Central, Viacom distributes and markets content that appeals to all demographics across the globe.  Still, finding the right digital audiences for tune-in marketing is a challenge. In this session, Viacom will share the tools and models they have built to create smarter audience targets, leveraging digital content consumption by TV viewers.

Session description
Speakers
Samantha LipsonViacom
Sr Dir. Measurement, Data Scientist & Data Engineer
Viacom
Theresa LocklearViacom
Vice President, Audience Science Analytics
Viacom
Targeting marketing
11:45 am – 12:05 pm
Case Study: Caterpillar

Caterpillar is implementing predictive models to identify customers that are most likely to purchase heavy equipment.  In this session, a data scientist and an analytics manager from Caterpillar will discuss these models.

Session description
Speakers
Andy JacobCaterpillar
Data Scientist
Caterpillar
Will ScheckCaterpillar
Analytics Manager
Caterpillar
12:05 pm
Lunch
1:15 pm
Room: Augustus II
EXPERT PANEL

Machine learning (aka predictive analytics) only delivers value when acted upon – that is, when deployed. Only a carefully designed management process ensures that the analytics' output is pragmatically viable for operationalization, and that company operations – your internal consumers of analytics – know best how to employ the product they're consuming.

Lead by moderator James Taylor, an industry leader in the operationalization of predictive models, this expert panel explores and expands upon PAW Business' Track 1 topic, operationalization, to provide insights as to how best to execute on the functional deployment of machine learning.

Session description
Moderator
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
2:00 pm
Gold Sponsored Session
The Session Description will be available shortly.
Session description
Sponsored by
PrADS
2:05 pm
Gold Sponsor Presentation
The Session Description will be available shortly.
Session description
Sponsored by
diwo
2:10 pm
5-minute transition between sessions
2:15 pm
Room: Augustus I
Track 1: BUSINESS - Analytics operationalization & management
Analytics management
2:15 - 2:35 pm
Case Study: Comcast

Decision-support systems based on machine-learning algorithms can require a significant investment to build. Ensuring the data pipeline is reliable, building the right models, and hiring the right staff can be a major undertaking. This presentation will cover best practices for building a business case to deploy machine learning algorithms for large scale product development or other production-level systems.

Session description
Speaker
Bob BressComcast Cable
Vice President of Analytics & Business Intelligence
Freewheel, a division of Comcast
Value-driven analytics management
2:40 – 3:00 pm

In this session, Robert Lanning will deliver an energetic and engaging discussion on the responsibilities of analytics professionals to ensure that the projects worked on actually accomplish meaningful change in the organization. Through the use of thought-provoking examples, the attendee will leave this session with an appreciation of the responsibility analytics professionals should bring to their projects. 

Session description
Speaker
Robert Lanning
Analytics Consultant
Room: Augustus II
Track 2: TECH - Predictive modeling & machine learning methods
Best practices

Preeminent consultant, author and instructor Dean Abbott, along with Karl Rexer of Rexer Analytics, field questions from an audience of predictive analytics practitioners about their work, best practices, and other tips and pointers.

Session description
Speakers
Dean AbbottSmarterHQ
Co-Founder and Chief Data Scientist
SmarterHQ
Karl RexerRexer Analytics
President
Rexer Analytics
Room: Emperors II
Track 3: MARKETING - Analytics for customer acquisition & retention
Sales and marketing; B2B
Case Study: Ingram Micro

We searched for matches between 10+ million end customers and 250,000+ resellers along the B2B technology supply chain. The line of sight of our high-volume business is broad, capturing around half a billion transactions of 5+ million technology products globally. We built matching models and product recommendation engines based on statistical and machine learning approaches, including random forests and neural networks. Our deployed product offerings include lead generation, sales forecasting and cross-selling. In this session, we cover this project and show that the insights gained that pertain to feature engineering, model building and validation exercises can be scaled to industries with similar lead generation and matchmaking challenges, including Education, Healthcare and Travel.

Session description
Speakers
Doug HermanIngram Micro
Head of Data Science
Ingram Micro
Haoan LiuIngram Micro
Ingram Micro
3:00 pm
Exhibits & Afternoon Break
3:30 pm
Room: Augustus I
Track 1: BUSINESS - Analytics operationalization & management
Industry trends

This talk will review recent research by the International Institute For Analytics that studied the analytics maturity level of large enterprises. A range of interesting findings and trends were discovered. The talk will cover how maturity varies by industry and some of the key steps organizations can take to move up the maturity scale. The talk will also cover some exciting research that correlates analytics maturity with a wide range of corporate success metrics including financial and reputational measures. The results reinforce that organizations with more success in analytics have more success overall. 

Session description
Speaker
Bill FranksInternational Institute For Analytics
Chief Analytics Officer
International Institute For Analytics
Room: Augustus II
Track 2: TECH - Predictive modeling & machine learning methods
Geospatial data

Machine learning on spatial data is hard. In this session, Peter Lenz, one of the few geographers in the machine learning world, will cover some of the unique issues when working with such data, how to treat it correctly, and why Dstillery ended up building two parallel spatial featurizers to maximize the utility of geo data for its machine learning stack.

Session description
Speaker
Peter LenzDstillery
Senior Geospatial Analyst
Dstillery
Room: Emperors II
Track 3: MARKETING - Analytics for customer acquisition & retention
Text analytics for marketing
Case Study: Barclaycard

Natural Language Processing (NLP) algorithms are a part of emerging field within Artificial Intelligence (AI) that focuses on the interactions between human language and computers. By utilizing NLP algorithms, we can organize and structure knowledge to perform tasks such as automatic summarization, translation, relationship extraction, sentiment analysis, speech recognition, and topic segmentation. We leveraged NLP algorithms to dive deeper into customer complaint narratives and to identify major complaint themes. Moreover, we quantified underlying consumer sentiment and ultimately develop actionable consumer insights. These insights were useful for developing more effective Marketing and Customer Experience strategies.

Session description
Speaker
Vishal MordeBarclaycard US
Vice President - Data Science
Barclaycard US
4:15 pm
Room: Augustus I
Track 1: BUSINESS - Analytics operationalization & management
Analytics talent
Case Study: Cisco

Data professionals expect a lot from their leaders and organizations, and they are in higher demand than ever. How do you build an organization that Data Scientists are proud to be a part of, one that leverages the network effects of the analytics community as a whole? How do you keep your team focused on the projects that drive organizational goals forward, while satisfying the insatiable hunger for technical knowledge and “interesting work?” And a leader’s work doesn’t stop there: How do you get the most out of the cross-functional skills that your team members possess (both technical and otherwise), and how do you ensure that the critical analytics roles in your organization are properly recognized and rewarded for their contributions? Join Cisco for an insider look at what’s working today…and what’s planned for tomorrow.

Session description
Speaker
Kristen BurtonCisco
Director, Enterprise Data Science Office and Digital Process Transformation CoE
Cisco
Room: Augustus II
Track 2: TECH - Predictive modeling & machine learning methods
Advanced methods

We've all heard the hype. Being Bayesian is cool, right? But does it really offer any clear benefits over the frequentist approach, or is it just a different way of thinking about the solutions we already have on hand? The Bayesian approach does enable a very powerful class of models: multi-level/hierarchical models. With a new generation of statistical modeling platforms like the free tool STAN, these models can be built with rich structures that enable more granular insights. We will learn about the power of multi-level models through three concrete examples: digital UX optimization, market response modeling, and programmatic media optimization.

Session description
Speaker
Curt BergmannElicit
Senior Data Scientist
Elicit
Room: Emperors II
Track 3: MARKETING - Analytics for customer acquisition & retention
Time series analysis
Case Study: Uber

Time series analysis has not benefited much from the data science halo effect compared to, say, statistical learning techniques. Yet time series model are often used to inform some of the most critical and fundamental business decisions.

Case in point: when evaluating the overall health of a business, we typically look at high-level metrics such as CAC, top-line growth and margin. And when it comes to forecasting these metrics and planning the underlying investments in marketing, inventory and promotions, you need time series analysis 

In this talk we’ll take a random walk through topics related to time series analysis as it pertains to planning:

  • Why having robust planning models is important, even though you’ll always be wrong
  • The skill set and attitude needed to be good at building the underlying models
  • Embracing uncertainty as opposed to trying to make it go away
  • How to think about promo/price versus top-of-funnel spending
  • When ML and time series come together
Session description
Speaker
Kim LarsenUber
Director of Marketing Analytics
Uber
5:00 pm
End of second Conference Day

Workshops - Thursday, June  7th, 2018

8:30 am
Room: Sicily
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm

This one-day session reveals the subtle mistakes analytics practitioners often make when facing a new challenge (the “deadly dozen”), and clearly explains the advanced methods seasoned experts use to avoid those pitfalls and build accurate and reliable models.  Click workshop title above for the fully detailed description.

Leader
John ElderElder Research
Founder & Chair
Elder Research
Room: Emperors II
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm

This workshop dives into the key ensemble approaches, including Bagging, Random Forests, and Stochastic Gradient Boosting.  Click workshop title above for the fully detailed description.

Leader
Dean AbbottSmarterHQ
Co-Founder and Chief Data Scientist
SmarterHQ
Room: Pompeian III
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm

This workshop demonstrates how to build uplift models (aka net lift models) that optimize the incremental impact of marketing campaigns, covering the pros and cons of various core analytical approaches. Click workshop title above for the fully detailed description.

Leader
Kim LarsenUber
Director of Marketing Analytics
Uber
Room: Pompeian IV
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm

Gain the power to extract signals from big data on your own, without relying on data engineers and Hadoop specialists.  Click workshop title above for the fully detailed description.

Leader
James Casaletto
Senior Solutions Architect
MapR Technologies
4:30 pm
Workshop end
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