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As end users begin to understand the value of predictive analytics, the market itself is evolving to include solutions targeted at different kinds of users, and to deal with more data and different deployment models. Data artisans who already use Alteryx enjoy the great benefits of data management and BI functionality, with predictive analytics as the logical next step to get even more ROI from their Alteryx investment. In this session you will learn about the challenges and opportunities associated with adding predictive analytics capabilities, and how companies at any stage of Alteryx implementation can take advantage of these advanced analytics tools to address business issues with far more power and insight.
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Growing your Alteryx ROI
with Predictive Analytics
Chris Diener
Senior Vice President, Analytics
AbsolutData
@AbsolutData
Business Leadership track
March 6, 2013
4:00 – 4:45 pm
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2
Just Scratching The Surface...There Is So Much More
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27%
20%
53%
45%
Top performers Low performers
SOURCE: Analytics: The New Path to Value, MIT Sloan Mgmt. report
Use insights to guide day-to-day operations
Use insights to guide future strategies
Data driven insights lead to better performance
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Marketing Analytics pioneers are Outperforming peers in financial markets
SOURCE: Stock performance Tesco vs. Sainsbury vs. Morrisons
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2012
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Tesco (Pioneer) Morrison Sainsbury
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Major web publisher – INCREASED REVENUE by 15%
while maintaining the same marketing spend
CPG company – INCREASED ANNUAL REVENUE by
$50 million across 4 countries
Online music provider - a -3% annual subscriber
churn made to an +11% SUBSCRIBER INCREASE
Impact of working with AbsolutData
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Priorities CMO CFO CIO
Social Media BI & Analytics BI & Analytics
Customer
Analytics
Enterprise Business
Applications
Mobile
Technologies
CRM Data & Document
Management
Cloud
Computing
Mobile
Applications
Service-oriented
applications and
architecture
Collaboration
technologies (Workflow)
Content
Management
Mobile
Technologies Virtualization
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Marketing Analytics Is Top On The Agenda Of CXOs
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SOURCE: IBM CMO Study, Gartner CFO, CIO Survey 2012
CXO Technology Priorities
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To help forward
looking
organizations
excel through
optimal use of
data
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What Drives Us At AbsolutData
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Big Data and Analytics solutions provider
Founded in 2001 320+ Professionals $20 million invested by Fidelity for
expansion Headquartered in San Francisco and
additional offices in New Delhi, London, Dubai, and Singapore
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AbsolutData Facts
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AbsolutData Service Offerings
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Analytics
Big Data
Market
Research
Data
Visualization
Business Impact Delivered
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Strategic
Business
Questions
Organizational
Big Data
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Our Big Data/Analytics Offerings Combined With Alteryx Platform
Define
Analytical
Needs
Answers
your
Analytical
Needs
Answers
your
Business
Questions
Big Data
Environment
Disparate Sources of
Structured & Unstructured
data
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Building The Analytics Layer
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#
Stages Of Alteryx Implementation
Data Blending
Production
Descriptive & Predictive Portfolio strategy activation CRM Marketing Effectiveness & ROI Operational Analytics
Embedding analytics with strong internal acceptance
Application of analytics in day-to-day decisions
Analytics
Compile data from different sources
Use Alteryx SharePoint
Create a unified file
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Stages Of Alteryx Implementation
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Data Blending
Compile data from different sources
Use Alteryx SharePoint
Create a unified file
Help with the initial and continual lifting
Identify right business needs
Prioritize data sets
Connect and align data sets
Combine data sets into reporting
How Can ADT Help?
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Stages Of Alteryx Implementation
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Analytics
Descriptive & Predictive
Portfolio strategy activation
CRM
Marketing Effectiveness & ROI
Operational Analytics
Statisticians & Consultants
Effective implementation as per the business needs
Building analytic solutions
Propensity, Engagement and other CRM models
Marketing effectiveness, attribution and mix
Sales force management and effectiveness
Segmentation and targeting
How Can ADT Help?
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Stages Of Alteryx Implementation
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Production Embedding analytics with strong internal acceptance
Application of analytics in day-to-day decisions
Full utilization of analytical models
Integrating outputs into the business flow
Connecting analytics to processes & business rules
Making outputs consumable
Day-to-Day decision making
Translating one-off analytics or periodic analytic events into automatic processes
How Can ADT Help?
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How We Do It
Augmenting your team – adding skills, capability, experience
Communication with stakeholders to ensure internal acceptance
Thought Partnership In Implementing Analytics Models
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Key Challenges In Implementing Analytics
Who does it?
How to ensure stakeholder ownership?
How to internalize an analytics enabled culture?
Devising an analytics strategy that can be
integrated and trusted internally
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Key Challenges In Implementing Analytics
Extending BI into
Analytics
Incremental approach or new build?
How do we integrate
existing tools and
applications with analytics?
Build on existing analytics
applications and objectives?
OR Re-evaluate
analytics strategy in light of the new
capabilities?
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Challenge: Vision of What Can Be
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How do we set the vision for REAL-TIME ANALYTICS
applications -- a different way of thinking is required for
enhanced analytics ROI
Framing production as a continuous flow
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Case Study: BI/Reporting Analytics Solution For A Leading Global CPG Company
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Addressing Visualization Challenges
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Key Business
Questions
Business Requirement: Tool that enabled quick and
easy access to retail sales data
Key Challenge: Extremely large size of data Security of data Access of data to be filtered by
user Regular updating of data Flexibility and appeal in viewing
the reports
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Interactive Web Based Dashboard: Configuring Reports
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Brands
Flexibility
Category
Brands
Promotion
Different retailer information
captured in one platform
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Interactive Web Based Dashboard: Reports
Brand
Brand
Competition 1
Competition 2
Others
Brand Competition 1 Competition 2
Brand
Competition 1
Competition 2
Others Performance review along with
benchmarking
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Case Study: Reducing Churn For A Leading Online Company
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Effective
Churn
Reduction
Strategy
E
X
I
T
R
E
T
A
I
N
Which customer is likely to attrite?
Why are they leaving/What message should be given to the
customers to retain them? 26
From To
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Who Is Likely To Attrite? When? At What Cost?
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Using survival analysis to derive probability of
a customer remaining with time
Combined with past
transactional behavior
Pro
babilit
y
Time (in months)
Gives Lifetime Value of customers
Retention Strategy - Whom to retain?
+ 0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
1
0 3 6 9 12 15 18 21 24 27 30
Customer 1 Customer 2 Customer 3
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Tenure (in Months)
05
10152025303540
1 2 3 4 5 6 7 8 9 10 11 12 orMore
Churn Index Reduces Sharply
After Initial Months
Incre
asi
ng P
robabilit
y o
f
Churn
Churn Index Modeling Data
Models Show Critical Periods To Focus On Retention
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$23 $35
$46
$15
$242
Decile 1 Decile 2 Decile 3 Decile 4 Decile 5
Decreasing Probability of Churn
LTM Value Distribution
Lifetime Value Analysis Prioritize Customers That We Want To Retain
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… Identified “At-Risk High Valued” Customers
Low
pro
fit
pote
nti
al
Hig
h p
rofi
t pote
nti
al
Low Risk of Attrition
High Risk of Attrition
Lifetime Value Analysis Prioritize Customers That We Want To Retain
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… Identified “At-Risk High Valued” Customers
Low
pro
fit
pote
nti
al
Hig
h p
rofi
t pote
nti
al
Low Risk of Attrition
High Risk of Attrition
Choose to lose Aggressively
defend
Low cost maintenance Sustain and building
relationship
Lifetime Value Analysis Prioritize Customers That We Want To Retain
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Why Are They Leaving? What To Offer To Keep Them?
Retention Strategy - What to offer?
What will resonate with my customers?
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Segments with differentiated needs
Communication strategy(executed on entire customer base)
Product Enhancements
Why are customers leaving?
Whey they use the service?
What is the attitude towards music and life in general?
Online Survey Outcome
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Customized Messaging For Each Segment
Characteristics Seg1
Family Values
Seg2
Music Discoverers
Seg3
Music Lovers
Music Genre Comedy, Gospel, Oldies, Country,
Vocals, Children, Holiday
Alternative Punk / Obscure Music New Age, Folk, Soul, Electronica
Dance, Soundtracks, Classical
Attitudes
Multiple Pieces of Information that
would interest various Age Groups
Children’s Section
Info About Various New Released
Artists/ Albums/ Obscure Songs
Various Kinds of Genre Available
New Release / Information on Each
Schemes Incentives For Downloading More
Songs
Sharing Music Information Through
Quizzes With Rewards
Sharing Music Information Through
Quizzes With Rewards
Demographics
Male/Female
Age: 25 to 35
Full time professionals
Male/Female
Age: 18 to 24
Part time professionals
Male/Female
Age: 30 to 45
Senior industry professionals
Promotional Schemes
Provide Lyrics With Songs
Increase Perception That Money Is
Saved By Using Rhapsody
Advertise In Record Stores NA
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Case Study: Multi-Touch Attribution Model & Its Application For A Leading Online Company
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Where are my customers coming from?
How can I avoid attribution of leads being double
counted across channels?
Which channels to focus on?
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TV GI Search Clicks
Email Campaigns
Print Affiliates Display Clicks
Over all Signups
Primary Relationships
TV Impacts Display
Impressions
TV Impacts
TV Impacts
Secondary Relationships
Pathway Analysis
Marketing Mix Modeling
Multi Layered Modeling Approach Was Used To Attribute Signups
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Pathway Analysis
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TV
Radio
Online
Magazine
Weak Relationship
Strong Relationship
Affiliate Clicks
Paid Search Clicks
Display Clicks
Overall
Sales
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Actual Contribution Of TV Quantified
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11.4%
9.0%
2.5%
2.6%
Paid
Search Clicks Non
paid search Cable
Total Impact
7.5% Final
Attribution 5.7%
Primary Model
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Actual Contribution Of TV Quantified
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Actual TV Attribution taking
into account indirect
contribution of Search
11.4%
9.0%
2.5%
- 3.8%
- 1.0%
2.6%
- 0.1%
- 2.2%
3.8%
- 0.1%
2.2%
Paid
Search Clicks Non
paid search Cable
Total Impact
7.5% Final
Attribution 5.7% 11.1%
Primary Model
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Reallocation Of Spend Led To An Increase In Revenue By 15%
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Q1 - Pre optimization Q1 - Post optimization
Total Spend
$22 Million
$101,561,543
Q1 - Pre optimization Q1 - Post optimization
Marketing Budget Revenue
TV
Online
TV Initial
Allocation
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Reallocation Of Spend Led To An Increase In Revenue By 15%
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Q1 - Pre optimization Q1 - Post optimization
Total Spend
$22 Million $22 Million
$101,561,543
$116,694,212
Q1 - Pre optimization Q1 - Post optimization
Revenue Impact
Marketing Budget Revenue
TV
Online
TV
Online
Initial
Allocation
Re -
Allocation
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Case Study: Analytics Implementation For A Leading DTH Service Provider
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Increase The Top Line And Bottom Line Through Better Customer Engagement
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Create segments based on usage of the product
Communicate relevant material for upselling
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Increasing Effectiveness
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Should tell in
advance about
what customers
want to do
Large enough to
initiate
marketing
strategies
The Segments
should be
heterogeneous
amongst
themselves
The Segments
should be robust
over a time
frame
Divide customers into actionable segments
Substantial Differentiable Predictive Stable
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Prediction Period
01/01/2011 01/07/2011
Observation Period
1.5 years
01/07/2009 31/12/2010
6 Months
Segmentation Methodology
Observation period data was used to identify segments, profiles and triggers
Prediction period behavior was used to future predicted behavior of subscribers
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<6 6-12 12-24 >24
Age On Network
Holistic Lifecycle Segmentation Solution Was Used To Develop Engagement Plan For Each Segment
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A Complete lifecycle of customer was created which was further used to lay out the engagement plan for each segment
Engagement
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<6 6-12 12-24 >24
Age On Network
INFANTS
(22%, 4%)
CHURN/ PRE-CHURN (10%, 6%)
STARS (11%, 24%)
SPORTS ENTHUSIAST
(6%, 9%)
RISING STARS (13%, 17%)
STAGNANTS (10%, 10%)
ADOLESCENT DEFAULTERS (6%, 5%) MATURED DEFAULTERS (12%, 16%)
IMPROVERS (10%, 10%)
Desired movement
Segment (Size% , SOR%)
Holistic Lifecycle Segmentation Solution Was Used To Develop Engagement Plan For Each Segment
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A Complete lifecycle of customer was created which was further used to lay out the engagement plan for each segment
Engagement
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Production Output
Segment of each
customer
Automated Offer
Identification
Self Execution
Implementation Helped Client Take Daily Actions As Per Segmentation Scheme
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Segmentation Algorithm
Business Rules
Implementation tools such as
Alteryx
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Thank You
