Building and Deploying Customer Behavior Models

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Building and Deploying

Customer Behavior

Models

David Smith, VP Marketing and Community, Revolution Analytics Paul Maiste, President and CEO, Lityx

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In Today’s Webinar

• About Revolution Analytics

• About Lityx

• Customer Behavior Lifecycle

• Classic Approach vs. Today’s Approach

• Demonstrations and Case Studies

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Revolution Analytics at a Glance

Who We Are

Only provider of commercial big data big analytics platform based on open source R statistical computing language

Our Software Delivers

Scalable Performance: Distributed & parallelized analytics

Cross Platform: Write once, deploy anywhere

Productivity: Easily build & deploy with latest modern analytics

Our Services Deliver

Knowledge: Our experts enable you to be experts

Time-to-Value: Our Quickstart program gives you a jumpstart Guidance: Our customer support team is here to help you

Global Industries Served Financial Services

Digital Media Government

Health & Life Sciences High Tech Manufacturing Retail Telco Customers 300+ Global 2000 Global Presence

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Exploding growth and demand for R

 R is the highest paid IT skill – Dice.com, Jan 2014

 R most-used data science language after SQL – O’Reilly, Jan 2014

 R is used by 70% of data miners – Rexer, Sep 2013

 R is #15 of all programming languages – RedMonk, Jan 2014

 R growing faster than any other data science language

– KDnuggets, Aug 2013

 More than 2 million users worldwide R Usage Growth

Rexer Data Miner Survey, 2007-2013 70% of data miners report using R

R is the first choice of more data miners than any other software

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Revolution R Enterprise

 High Performance, Scalable Analytics

 Portable Across Enterprise Platforms

 Easier to Build & Deploy Analytic Applications

is….

the only big data big analytics platform based on open source R

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Speaker Bio

Paul Maiste is President and CEO of Lityx. He has a Ph.D. in Statistics, with nearly 25 years of experience designing and delivering strategic analytic solutions for predictive modeling and marketing optimization to businesses of all sizes and across industries.

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Customer Behavior Modeling Click to edit Master title style

Building and Deploying

Customer Behavior Models

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Customer Behavior Modeling

Agenda

• Intro and Background

• Customer Behavior Lifecycle

• Classic Approach vs. Today’s Approach • Demonstrations and Case Studies

• Q&A

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Speaker Bio

Paul Maiste is President and CEO of Lityx. He has a Ph.D. in Statistics, with nearly 25 years of experience designing and delivering strategic analytic solutions for predictive modeling and marketing optimization to businesses of all sizes and across industries.

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Company Background

Lityx is a world-class analytic

solutions and services firm with a diverse set of clients across

multiple industries.

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We deliver a hosted advanced

analytics platform, and help our clients by applying deep expertise to complex analytic solutions.

Our focus is predictive modeling and optimization applications in marketing analytics and CRM.

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Lityx has worked with marketers in diverse markets such as non-profit, media, gaming, financial services, healthcare, and retail/CPG.

Our track record

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Poll Question #1

• What analytics platform are you currently using? - SAS - SPSS - R / Revolution R Enterprise - KXEN - Other 6

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Customer Behavior Modeling ₇

 Predict likely churners and reasons.  Determine customer potential value.  Determine best retention offer.

 Increase loyalty.

 Winback lost customers.

 Predict cross-sell and up-sell.  Determine natural product

affinities.

 Determine most profitable marketing offers / messaging.  Increase loyalty and share of

wallet.  Customer segmentation.

 Predict prospect future value.  Predict likely responders.

 Predict best product and best offer.  Determine best offer timing.

Customer Acquisition

Relationship Growth

Customer Retention

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Customer Behavior Modeling ₈

 Predict likely churners and reasons.  Determine customer potential value.  Determine best retention offer.

 Increase loyalty.

 Winback lost customers.

 Predict cross-sell and up-sell.  Determine natural product

affinities.

 Determine most profitable marketing offers / messaging.  Increase loyalty and share of

wallet.  Customer segmentation.

 Predict prospect future value.  Predict likely responders.

 Predict best product and best offer.  Determine best offer timing.

Customer Acquisition

Relationship Growth

Customer Retention

Customer Behavior Lifecycle Modeling

Optimize Customer Communication

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Poll Question #2

• What area of customer behavior modeling are you most interested in leaning about/doing more of?

- Customer Acquisition - Relationship Growth - Customer Retention

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The Imperative for Advanced Analytics

Marketers have a lot to worry about to maintain relevant data, create and grow profitable customers, and be more

efficient with existing budget.

Forrester has recently said: Vendors need to create more analytic solutions that “customers can use out of the box”

… such as business-user-oriented interfaces. We Agree, BUT ALSO

Let’s use the opportunity to make data scientists and modelers more efficient as well!

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Iterate

• Iterate through multiple algorithms

• Iterate through multiple data cleaning approaches • Debug and re-run

Iterate

Classic Approach

Data Prep and Manipulation

Design Approach and Algorithm

Coding

Test and Validate Implement

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• Often re-code in different system for implementation

• Write code for performance metrics and charting

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Today’s Approach

12 • Design model using business

language

• Simply presented options for the advanced user

• Automated and intelligent data pre-processing

• Iterative processing of multiple algorithms and settings

• Handle computational workload • Pre-computed performance metrics • Automated charts and comparisons

• Built-in model management

• Automated scoring process without coding

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What about the data scientists?

• Like Me!

• It’s time to focus our attention on design and analysis instead of hacking, debugging, and iterating.

- Without losing the computation power and modeling flexibility we require

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Data, insights, predict, optimize

Cloud‐based platform for

advanced analytics

• Data Manager • InsightIQ • PredictIQ • OptimizeIQ 14 Powered By

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Live Demonstration

Retail Churn Modeling

Apparel Industry

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Poll Question #3

• My expertise is best described as: - Hard core data scientist

- Big Data guru

- Scientific programmer/coder - Business analyst - Consultant - Marketing / Business - IT 16

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Customer Behavior Modeling ₁₇

Case Study – Large Non-Profit Organization

Affinity / Cross-Sell Models

 Client outsourced building of over two dozen affinity models to vendor using classic tools and manual process (3-4 month effort).

 Rebuilt all models using LityxIQ in 2 weeks, and model results (such as lift) were 5%

better than manually built models.

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Q&A

For more information: www.lityx.com

www.revolutionanalytics.com Art Warren - [email protected]

Paul Maiste - [email protected]

18 Upcoming Virtual Course led by Paul Maiste

Customer Analytics for Marketers April 21, 23, 28, 30 (9-1 PT)

Register at: www.revolutionanalytics.com/customeranalytics

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More Information

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Data Manager

• Easily import and manage complex data sources.

• Append and join datasets together.

• Clean, transform, create new fields.

• Filter and aggregate.

• General data preparation for using in other

solutions.

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InsightIQ

• Interactive graphical

analysis for creating and sharing insights through charts and tables.

• Business intelligence, reporting, and executive dashboards.

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Customer Behavior Modeling ₂₂

PredictIQ

• Automated model building focused on business

objectives including churn, value, risk, and affinity

models

• Includes validation, model management and version control, scoring, and

implementation

• Business forecasting

models for sales, revenue, and other business metrics

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OptimizeIQ: marketing optimization

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OptimizeIQ

• Optimize marketing

budget/resources across customer segments,

products, channels, and other business dimensions • Optimize media spend within

and across channels

• Optimize individual customer communications to maximize profitability

• Easy to define objectives and business constraints for a non-technical user

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Version 3.0 – End Q1

• Integration with Revolution RRE 7.0 - Big data connectivity to Hadoop

- In-database analytics with Teradata

- Big data modeling using GLM, Tweedie, CART, and more

- Integration directly with existing Revolution R code for additional control (Ver 3.x)

• API connectivity