1. The power and potential of Big Data is well understood, but businesses
across financial services, retail, media, high technology and health care
industries are challenged to unlock the value of it. We hope this guide
will inspire you with practical ideas about how to unlock the business
value of Big Data for your company to drive innovation and gain a
competitive edge.
Big Data includes a broad range of information from transactional data about what happened
in the past to unstructured, future-looking data, including sensor, digital, text and social. This
amalgamation of data holds the key to understanding your customers’ preferences, behaviors,
tendencies, interests and passions.
The power of Big Data Analytics lies
in its ability to deliver insights into
customer behavior and intent. Using
Big Data Analytics, vast amounts of
information are transformed into
actionable intelligence that can be
used to change business outcomes,
rather than just report on them.
www.fico.com Make every decision countTM
A Guide to Optimizing the Customer
Lifecycle with Big Data Analytics
Unlock the Business Value of Hadoop
Summary
Get to the Value of Big
Data with Democratized
Analytics

2. December 2014 ©2014 Fair Isaac Corporation. All rights reserved. page 2
In the era of digital business, your Customer Engagement Lifecycle cannot be fragmented,
inconsistent or slow. Regardless of your industry, Big Data can be used to overcome these
challenges as well as to optimize, integrate and personalize every step of a fast moving
Customer Engagement Lifecycle.
By understanding each and every customer interaction, very deep insights into how customers
buy, use and get support for your products and services can be discovered and applied. This
will improve the critical business metrics of Customer Life Time Value (LTV) and Customer
Likelihood to Recommend (LTR), which drive customer satisfaction and competitive advantage.
Optimize Your Customer
Engagement Lifecycle
with Big Data Analytics
A Guide to Optimizing the Customer
Lifecycle with Big Data Analytics
PURCHASE USAGE
SUPPORT
Personalize
Cross- & Upsell
Deep
Customer
InsightsChannel
Analytics
Deliver
Competitive
Products
Telemetry
Analytics
Ensure
Proactive
Support
Response
Analytics
Big Data Customer Engagement Lifecycle
Below are use case ideas for applying Big Data Analytics to optimize ways customers purchase,
use and receive support—the key phases of the Customer Engagement Lifecycle.
An expected benefit of the use cases is to increase the key business metrics of Customer
Lifetime Value (CLV) and Customer Likelihood to Recommend (LTR).
Customer Purchase
Interactions
Enable richer and highly-personalized
cross-selling and upselling.
Use Big Data Analytics for:
• Customer micro-segmentation
• Dynamic product categorization
• Personalized and auxiliary product
recommendations
Customer Usage
Interactions
Ensure highly-competitive products and
services.
Use Telemetry Analytics on
Big Data for:
• Product usage patterns
• A/B feature testing
• Failure detection and diagnostics
Customer Support
Interactions
Deliver proactive and cost-effective
customer service.
Use Response Analytics on
Big Data for:
• Churn reduction
• Self-service help
• Customer loyalty improvement
Use Cases for the
Customer Lifecycle

3. ©2014 Fair Isaac Corporation. All rights reserved. page 3
Use Channel Analytics on Big Data for
Highly Personalized Selling
Acquiring the right customers, who are a good fit for your business
and will become long-term customers, is critical in today’s fast-
moving markets. Customer acquisition costs are very high and 25%
of new customers will actually deliver negative profits.
Use Big Data to analyze all transactional, behavioral and social data
to more accurately identify, profile and segment customers. Then,
recommend the most relevant and engaging products and services.
A Guide to Optimizing the Customer
Lifecycle with Big Data Analytics
Optimize Purchase
Interactions to Increase
Revenue and Profit
Personalize
Cross- & Upsell
Channel
Analytics
PURCHASE
Use Case Exploratory and predictive analytics Big Data sources
Customer Micro-Segmentation • Sessionization
• Ranking
• Aggregate
• Collection
• Grouping
• Clustering
• Logs
• Product categories
• Reviews
• Social
• Profiles
• Orders
Dynamic Product Categorization • nGram
• Regex
• Ranking
• Statistical
• Math
• Classification
• Search terms
• Product descriptions
• Clickstreams
• Search terms
• Product descriptions
• Clickstreams
Personalized and Auxiliary
Recommendations
• URL parsing
• Collection
• Grouping
• Aggregate
• Collaborative filtering
• Users
• Products
• Ratings
“Make no mistake: Big Data is the new definitive
source of competitive advantage across all industries.
Enterprises that dismiss Big Data as a passing fad do so
at their peril.
” —Wikibon, February 2014

4. Use Telemetry Analytics on Big Data for
Highly Competitive Products and Services
Gaining insight into how customers use and experience your
products and services is one of the most time-consuming and
complex issues businesses face today. The advent of sensor,
RFID and digital technologies makes this easier, yet it remains an
untapped, game-changing opportunity.
Use Big Data to understand product usage patterns and customer
response by analyzing all the instrumentation, monitoring,
incident and social data. Then, enhance product features, adjust
product roadmaps and prevent quality issues from happening.
©2014 Fair Isaac Corporation. All rights reserved. page 4
A Guide to Optimizing the Customer
Lifecycle with Big Data Analytics
Optimize Usage
Interactions to Gain
Market Share
Deliver
Competitive
Products
Telemetry
Analytics
USAGE
Use Case Exploratory and predictive analytics Big Data sources
Product Usage Patterns • Histogram
• Aggregate
• Multi-structured
• Trending
• Neural network
• nPath
• Correlation
• Web logs
• Search terms
• Products
• Users
• Demographic
• Geographic data
A/B Feature Testing • Scoring
• Aggregate
• Math
• Transformation
• Clustering for user segmentation
• Profiles
• Features
• Time
Failure Detection
and Diagnostics
• Scoring
• NLP
• XML/JSON parsing
• Time-series
• Aggregate
• Linear/polynomial regression
• Instrumentation logs
• Product
• Product state information
• Error code

5. Use Response Analytics on Big Data for More Proactive
and Cost-Effective Customer Service
Losing customers is very expensive, so developing more long-term
customers is important. Loyal customers are more profitable; they
understand your brand, buy more products and recommend you to
others while you become efficient at servicing them.
Use Big Data to deeply understand customer behavior by analyzing
patterns across a much broader set of interactions, including
channels, social media and product usage, to reduce churn and
take proactive action.
©2014 Fair Isaac Corporation. All rights reserved. page 5
A Guide to Optimizing the Customer
Lifecycle with Big Data Analytics
Optimize Support
Interactions to Increase
Long-Term Customers
Ensure
Proactive
Support
Response
Analytics
SUPPORT
Use Case Exploratory and predictive analytics Big Data sources
Churn Reduction • Sessionization
• Transformation aggregate
• Multi-structured
• Logistic Regression
• Naïve Bayes
• Social
• User
• Orders
• Cases
• Reviews
• Log
• Demographic
Self-Service Help • nGram
• Context nGram
• Aggregate
• Grouping
• Clustering
• User
• Case
• Search terms
• Knowledge base
• Logs
Customer Loyalty
and Advocacy
• Ranking
• Statistical
• Timeseries
• Aggregate
• Scoring
• nPath
• Clustering
• Social
• User
• Orders
• Cases
• Reviews
• Log
• Geographic data
• Sentiment

6. ©2014 Fair Isaac Corporation. All rights reserved. page 6
A Guide to Optimizing the Customer
Lifecycle with Big Data Analytics
Top 10 Success Factors for Big Data Analytics
With Big Data all the rage and Hadoop-based infrastructure solutions increasingly a go-to solution for aggregating, storing and accessing
data, it’s critical that businesses consider how to prioritize requirements for aligned analytics solutions that will leverage this new
infrastructure. After all, it’s the power of the analytics tools that will glean the Big Data insights and not the repository itself. Analytics
solutions, for example, should be standards-based, easy-to-use and couple natively to operational applications.
Critical Success Factors What to Look For What to Avoid
Analytic component discovery
and re-use
✓ Allows users to discover and reuse available
analytic assets.
✗ Lacks ability to house and search for
analytics assets.
Native Hadoop integration ✓ Performs exploration and discovery directly
on Hadoop.
✗ Replicates data on Hadoop or is retrofitted to
Hadoop.
Re-use of existing skills ✓ Supports data scientists, data analysts and
business users existing skills.
✗ Uses command line interface, proprietary
tools or exotic technologies that require
special skills or training.
Power ✓ Can reuse and be extended with advanced
analytics.
✗ Solutions limited to simple interfaces without
analytic power.
Project-based environment ✓ Organizes users to be more efficient. ✗ Requires users to use a technical interface
with limited GUI features.
Collaboration ✓ Supports team and project-based
collaboration to share and reuse work as
well as communicate about project
activities and status.
✗ Limits usage to single-user silos.
Standards compliance ✓ Complies with industry standards including
Hadoop projects and PMML.
✗ Uses proprietary techniques and creates
proprietary analytic assets.
Integrates with operational
environments
✓ Insights can be actioned in operational
systems.
✗ No solution for implementing insights in the
business.
Existing analytic models, built
in SAS, SPSS and R, can be
leveraged
✓ Can ingest and manage standard UDF
versions of predictive analytics models.
✗ Converts predictive analytics models into
proprietary code.
Ready integration with Big
Data applications and existing
BI tools, such as Tableau
✓ Seamlessly integrates with traditional BI tools
to add value to native Hadoop analytics.
✗ Re-implements BI in a proprietary manner on
Hadoop.

7. A Guide to Optimizing the Customer
Lifecycle with Big Data Analytics
For more information North America Latin America Caribbean Europe, Middle East Africa Asia Pacific
www.fico.com +1 888 342 6336 +55 11 5189 8222 +44 (0) 207 940 8718 +65 6422 7700
info@fico.com LAC_info@fico.com emeainfo@fico.com infoasia@fico.com
FICO and“Make every decision count”are trademarks or registered trademarks of Fair Isaac Corporation in the United States and in other countries. Other product and company names herein may be trademarks of their
respective owners. © 2014 Fair Isaac Corporation. All rights reserved.
4079WP 12/14 PDF
FICO® Big Data Analyzer
Drives Business Value from
Big Data on Hadoop
FICO® Big Data Analyzer is the purpose-built analytics environment for business users, analysts
and data scientists to gain valuable insights from the exploration and analysis of any type and
size of data on Hadoop. Making Big Data accessible, masking Hadoop complexity, it allows all
users to drive business value from any data.
Combined with FICO® Analytic Modeler and FICO® Decision Management Platform, the
FICO Big Data Analyzer is the industry’s only Big Data application designed for the entire Big
Data Analytics lifecycle, spanning data exploration, model building and operational decision
implementation. It makes Big Data readily accessible to business analysts and data scientists,
enabling them to solve pressing business problems and deliver value more quickly.