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

• Twitter

• 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