Jason Burke
Solution Specialist - SPSS
What is Predictive Customer
Analytics?
March 2014
A conversation �.
… with millions of people
Personal Offers, Continuous Learning
Customer Analytics are�
• Capabilities that improve your efforts to:
– Acquire new customers
• Identify prospects who will yield better return
– Grow the contribution of existing customers
• Identify customers unique needs
– Retain existing customers
• Identify customers likely to leave before they do
Today’s Objectives
• The Crisis: The Age of the Empowered Consumer
• The Course of Action: Use of analytics to better
understand, anticipate and focus on customers
• The Conclusion: How organisations are using customer
analytics to gain advantage
Consumer experience framework – 10 years ago
Marketing
Sales
Support/Services
UseProduct
Purchase (More)
Products
Get Customer Service
The consumer has taken charge
Technology is changing how customers interact
• Social media changed purchaser influence; opinions viewable instantly
• Mass customisation and personalisation of products and services
Customers have lost confidence in institutions
• 76% of customers believe companies lie in advertisements
• Growing trust gap in many consumer focused industries
Expectations have changed
• Focus is on value, transparency and accountability
• Customers want to be seen holistically across the enterprise
Institutions need to rediscover their customers
• Consumers are experiencing brands in new ways though new channels
• Micro-targeting: the move beyond 1 on 1 is accelerating
Sources: http//www.nae.edu/cms/Publications/The Brodge/Archives/7356/7596.aspx; Internetworldstats.com; Strategy Analytics; Informa
Customer experience framework today
Research
Product
Marketing
Sales
Support/Services
Feedback Management
Social Intelligence ResearchProduct
PurchaseProduct
UseProduct
AdvocateProduct
Get Customer Service
Up/CrossSell
The course of action�
IBM C-Suite studies
Getting closer to customer
People skills
Insight and intelligence
Enterprise model changes
Risk management
Industry model changes
Revenue model changes
88%
81%
76%
57%
55%
54%
51%
CEO Focus Over Next 5 Years
Enhance customer loyalty/advocacy 67%
Design experiences for tablet/ mobile
Use social media as a key channel
Use integrated software to manage
customersMonitor the brand via social media
57%
56%
56%
51%
Measure ROI of digital technologies
Analyse online / offline transactions
47%
45%
CMO 5 Year Focus Toward Digital
Sources: IBM’s 2011 Global CMO Study: From Stretched to Strengthened (2011) & IBM’s 2010 Global CEO Study – Capitalising on Complexity
Business
Partners
Prospective
Employees
Suppliers
Current &
Prospective
Customers
Increase conversion, marginReduce sales cycle time
Increase brand preference, especially for customers with the greatest lifetime value
Improve customer satisfaction & loyalty
Enable flexible payment options based on need
Enable employee access to usable analytics
Manage inventory and optimize supply chain
Understand the met and unmet needs
Hire capable employees and enable with appropriate skills
Who owns customer experience?
New business challenges create
a need for (a new look at) analytics
Sense and respond
Instinct and intuition
Automated
Skilled analytics experts
Back office
Traditional Approach
Predict and act
Real-time, fact-driven
Optimized
Everyone
Point of impact
New Approach
Lack of Insight
Inability
to Predict
Inefficient Access
Variety
Volume
Velocity
Data remains at the heart of
customer analytics
Behavioral data•Orders
•Transactions
•Payment history
•Usage history
Descriptive data•Attributes•Characteristics•Self-declared info•(Geo)demographics
Traditional approach
WHAT?WHO?
Behavioral data•Orders
•Transactions
•Payment history
•Usage history
Descriptive data•Attributes•Characteristics•Self-declared info•(Geo)demographics
Attitudinal data•Market Research•Social Media
Interaction data•E-Mail / chat transcripts
•Call center notes
•Web Click-streams
•In person dialogues
Traditional approach
High-value, dynamic approach
- source of competitive differentiation
WHY?
WHAT?
HOW?
WHO?
Customer analytics maturity model
Insight for Decision Makers
The Next Best Action
Information Cost Reduction
Foundational
0.2% - 2.9%
Information Sharing
Competitive
6.2% - 18.7%
Information Responsiveness
Differentiating
16.9% - 38.2%
Information on Demand
Breakaway
24.1% - 64.3%
The conclusion
Customer experience framework – From the
enterprise viewpoint
Prescription/
Services
Recommendation
Up/Cross Sell
Analysis
Price
OptimizationCustomer
Feedback
Analysis
Customer
Loyalty/
Advocacy
Customer
Satisfaction
Market
Segmentation
Lead
Optimization
Customer
Lifetime Value
Influencer
Network
Optimization
Customer Analytics
Customer acquisition & customer lifetime value
Personalize up-sell & cross-sell
offers with social media data
Target customers with the
correct offer, channel, & time
Measure marketing
performance & customer
profitability
Optimize marketing
budgets aligned with goals
Techniques & capabilities
RFM Analysis
• Cheap (low overhead) way to segment a
database of customers
• Example: determine the groups of people for a
coupon marketing offer
Predictive Modeling
• Ability to find hidden clusters / groups of people
• Example: identifying the people that are likely
to buy
Reporting & Analysis
• Provide historical and current views of business
operations
• Example: provide insight into how a current
sales campaign is performing
Planning & Forecasting
• What-if analysis to drive timely decision making
• Example: determine the optimal marketing plan
based on a set budget
Customer Segmentation
Cross-Channel Campaign Optimization
Optimal Trigger Marketing
Market Basket Analysis
Budget Optimization
Analytics journey
Customer Data• Demographics
• Account Activity
• Product Holdings
• Channel Activity
• Information Requests
• Complaints
• ?
Campaign Data• Contact history
• Response/purchases
• Test campaigns
• ?
Align Anticipate Act
Analyses
Predict who is likely to
respond, based on their
customer profile when
receiving the campaign
Scoring
Marketing
campaign process
Key Performance
Predictors and
Campaign Results
Rank best 3 offers
Attitudinal Data• Customer Surveys
• Discussion Forums
• Blogs
• ?
Website
recommendation engine
Sales
campaigns
What if predictive analytics could uncover customer behaviour and enhance customer relationships?
“�. we have improved customer retention by 7 percent,
with 54 percent of Fiat customers now replacing their
existing cars with another Fiat brand.””
Giovanni Lux, Customer Database & Business Intelligence Manager.
Customer Experience Management, Fiat Group Automobiles
• Fiat Group Automobiles designs, produces and sells vehicles under the Fiat, Alfa Romeo, Lancia,
Fiat Professional, Abarth and Jeep brands. Auto sales fuel the company’s growth, daily operations,
research and development and expanding global presence–in short, they underpin Fiat’s success in
today’s highly competitive and volatile automotive marketplace.
The Opportunity
Fiat Group Automobiles
What Makes It Smarter
• Leveraging predictive analytics Fiat is better able to determine the likelihood that a customer will
purchase a specific brand and model, and the related timing of the purchase – and it can efficiently
analyze and report on customer service and warranty issues. “The ability to predict customer
behavior and enhance customer relationships is absolutely critical to the success of Fiat Group
Automobiles,” concludes Lux. “At the end of the day, IBM SPSS Statistics and IBM SPSS Modeler
help us sell cars – and that’s what keeps the wheels turning at Fiat.”
• Fiat Group Automobiles needed to determine the likelihood that future and returning customers would buy specific brands and models of Fiat cars, so that individual dealers could optimize the use of available marketing funds. The company also needed to better understand customer experience with dealerships and repair facilities.
Real Business
Results
� Improved customer response rate to
marketing initiatives by 15-20 percent.
� Improved customer loyalty by 7 percent.
� Supports continuous improvement of
dealerships and repair facilities.
� Centralized reporting and predictive
analytics system enhances productivity
and lowers costs.
� Efficiently works with large Oracle
database containing history on 64 million
customers.
What if predictive analytics could bring science to Optimising scarce marketing resources?
“Our aim was to shift from the ‘marketing-as-an expense’
mindset to the idea that marketing is a true profit driver.”
-- Dan Marks
Chief Marketing Officer
• The banking market is defined by increasing competitive intensity and strategic challenges, not the
least of which is how to optimally focus marketing resources. Banks offer a more diverse portfolio of
services than before, and they do so over a wider range of channels. While this trend has given
banks more latitude to compete, it has made formulating and modulating marketing strategies,
tactics and programs considerably more complex. To optimise how they invest in them, banks need
to continually measure their effectiveness, learn what works and adapt over time.
The Opportunity
First Tennessee Bank
What Makes It Smarter
• Leveraging predictive analytics, First Tennessee Bank is combining a granular understanding of
the needs of customer segments with real P&L data to optimise its marketing spend, focusing on
programs that deliver the highest ROI. First Tennessee’s model relies on constantly updated
customer account information, enabling it to detect changes in service consumption patterns and
preferences. Blending customer segment profiles with profitability data enables First Tennessee to
identify and target the most attractive segments. Its predictive analytics provides the basis to shift
marketing resources from lower performing programs to those with the highest ROI.
• Banks today have more ways to communicate with customers, but this also made it harder for them to figure out where and how to most profitably commit their marketing resources. Real Business
Results
� 600% overall return on its investment
through more efficiently allocated
marketing resources
� 3.1% increase in marketing response rate
through more accurate targeting of offers
to high-value customer segments
� 20% reduction in mailing costs and 17%
reduction in printing costs due to the
ability to target the most attractive
segment for specific offers
What if predictive analytics could reduce customer churn?
“By enabling our client services managers to prioritise their proactive outbound calls – basically, a ‘health check’ on the customer – we can cover more risk with our existing Client Services team . It’s been a very successful business model for us and has helped us organise our resources better.”
-- Trent Taylor, Director
Customer Intelligence, XO Comm.
• XO Communications needed to identify which of its small business customers were at the highest risk of switching to a competitor.
The Opportunity
XO Communications
What Makes it Smarter
• Understanding critical data is key to identifying risk factors. XO Communications
deployed an IBM SPSS predictive analytics solution that evaluates more than 500
variables for predicting customer defections within 90 days. That allowed the
Customer Intelligence team at XO to build an accurate regression model keying on
the 25 most relevant variables. Using this information, the client service managers
can then proactively prioritise outbound calls to at-risk accounts.
Real Business
Results
• 60 percent improvement in revenue
retention rates
• Realising millions of dollars in
annualised revenue protection
• Fewer client services managers are
needed for the same level of risk
coverage
• To improve its small business retention rate, a U.S. telecommunications company is
using predictive analytics to anticipate voluntary customer defections.
Customer Analytics delivers key business benefits
� Efficiency; do more with less
� Improve Return on Investment
� Improved Customer Satisfaction
� Improved customer loyalty and more
profitable customers
� More effective utilization and satisfaction
amongst employees
30%Reduction in
heating bills
The Opportunities from Big Data &Analytics Are Infinite
15 minResponse time
to requests 150%Revenue
growth rate
95%Accuracy of 18+ month
sales forecasts
80%Less time required to open an account
98.5%On-time deliverytarget achieved
70%Counterparty
measurements changed
80%Reduction in
serious accidents
• Channel and interaction
consistency
• Real-time and personalized
insights
• Actionable descriptive, predictive,
and prescriptive information
• Rapid return on investment
New Capabilities
Companies Need…
• Multiple disruptive forces
� Economy
� New media
� Competition
� Speed of information
• More empowered customers,
subscribers, citizens, etc.
• New sources of competition and
connection
• A need for revolutionary
strategies
New Concerns
Companies Face…
• Application of science to the art of customer analytics
• It’s about action, not just algorithms
• Software expertise
• Deep, global industry experience
• Demonstrable return via a proven approach
New Solutions
IBM Provides…
Benefits for IBM customer analytics solutions
CustomerAnalytics
Unparalleled Consumer Experience• Drives personalized engagement across multiple touch points
• High customer satisfaction and loyalty, advocates, results in increased revenues
Optimizing Customer Interactions• Generates the right offer at the right time, in the right place
• Attract the ideal customer and maximizes customer lifetime value
360⁰⁰⁰⁰ View of the Consumer
• Aligns the capacity to deliver with the propensity to buy
• Goes beyond traditional 1:1 interactions
Social / Sentiment Analysis
Reporting & Analysis
Scorecarding & Dashboarding
Forecasting & Planning
Predictive Analytics
DecisionManagement