webinar: increase customer acquisition and cross-sell with predictive analytics

Beyond the Arc, Inc. | Management Consulting & Strategic Communications © 2012 Beyond the Arc, Inc.

Increase customer acquisition and cross-sell with Predictive Analytics

Steven J. Ramirez, CEOBruce Johnson, Technical Consultant

Beyond the Arc, Inc.

Beyond the ArcMonthly Webinar series

November 15, 2012

Beyond the Arc, Inc. | Management Consulting & Strategic Communications

Imagine If you could…

Contact Center banker Loan Officer Banker in the

branch

…apply inferred social relationships of customers to prevent churn?

…adjust credit lines as transactions are occurring to account for risk fluctuations?

…determine who is most likely to buy if offered a discount at time of sale?

…optimize every transaction, process and decision at the point of impact, based on the current situation,

without requiring that everyone be an analytics expert

2


Discussion agenda

• Introductions

• The business analytics imperative

• Live demo: predictive analytics with IBM SPSS

• Next steps

3


Discussion agenda

• Introductions



• Next steps

4


Introductions – tell us about yourself

What are you hoping to get out of today’s session?

5

Please use the webinar chat function to answer

Beyond the Arc, Inc. | Management Consulting & Strategic Communications6

Beyond the Arc helps businesses improve their customer’s experience through analytics and insight, strategic consulting, and innovative software solutions.

A bit about us

6


Discussion agenda

• Introductions



• Next steps

7


What is Predictive Analytics?

Predictive Analytics helps connect data to effective action by drawing reliable conclusions about current conditions and future events

–Gareth Herschel, Research Director, Gartner Group

8

Beyond the Arc, Inc. | Management Consulting & Strategic Communications 9

How can Predictive Analytics help?

• Predictive Analytics is a transformational technology that enables more proactive decision making, driving new forms of competitive advantage

• Analyzes patterns found in historical and current transaction data as well as attitudinal survey data to predict potential future outcomes

• Unique insights that help answer key business questions:• How do I reduce churn and retain the most loyal customers to maximize profitability?

• How can I detect and prevent fraudulent activityto reduce risk?

• Which factors are most likely to drive customers to choose my product vs. the competitor’s?


Variety of Information80% of new data growth is unstructured contentEmails, images, audio, video.

Volume of Digital Data57% CAGR for enterprise data through 2010Machine generated data: Sensors, RFID, GPS.

Velocity of Decision MakingRapidly changing business climate Need to get ahead of the curve: predict issues and fix them.

Business Imperative:Accelerate innovation, business optimization, and sustained competitive differentiation

An information explosion … and faster decisions are required

10


Challenges facing decision makers

1 in 3frequently make critical decisions without the information they need

1 in 2don’t have access to info across their org. needed to do their jobs

19+ hoursSpent each week just searching for and understanding information

Beyond the Arc, Inc. | Management Consulting & Strategic Communications12

Analytics critical for driving competitive advantage

“At a time when companies in many industries offer similar products and use comparable technology, highperformance business processes are among the last remaining points of differentiation.” Tom Davenport, “Competing on Analytics”

57%

55%

61%

62%

63%

64%

66%

70%

76%

80%

63%

68%

70%

67%

71%

73%

71%

73%

77%

86%

Unif ied Communication

SOA/Web Services

Business Process Management

Application Harmonization

Self-Service Portals

Customer and Partner Collaboration

Mobility Solutions

Risk Management and Compliance

Virtualization

Business Intelligence and Analytics

Ten Most Important Visionary Plan ElementsInterviewed CIOs could select as many as they wanted

Source: IBM Global CIO Study 2009n = 2345

Highgrowth

Low growth

BI/Analytics #1investment to improve competitiveness

IBM Global CIO Study 2009


Predictive analytics: questions you can address

Strategic Questions

Sales & Marketing

• What products sell well together and what products are my customers most likely to purchase?

• What campaigns and offers have the highest chance of success and what types of customers should we be targeting?

• Which CRM activities are more effective in leading to higher sales opportunities?

Finance

• How can we lower marketing costs without sacrificing effectiveness or hampering sales?

• How accurate and reliable is our current forecast (and targets) based on historical trends?

Operations

• What defects occur in our credit card fulfillment process and can we prevent the most frequent issues?

• Can we forecast our call center staffing levels based on the expected response from marketing campaigns?

13


Predictive Analytics can use ALL available data

Behavioral data- Orders- Transactions- Payment history- Usage history

Descriptive data- Attributes- Characteristics- Self-declared info- (Geo)demographics

Attitudinal data- Opinions- Preferences- Needs & desires- Survey results- Social media

Interaction data- Email / chat transcripts- Call center notes - Web click-streams- In person dialogues

14


Where is your

company?

Gartner discusses stages of analytics maturity

15


Customer acquisition starts with mapping the stages of the relationship

1616


Deeper insight about how customers interact with you is key

Awareness Presales Quote, Sales Customer Service Advocacy

InsuranceRep

Agent

Carrier

Customer

Online

Quote, SalesCustomerProcesses

Life Cycle

EngagedParty

17


We often analyze specific customer actions by lifecycle phase

Awareness Presales Quote, Sales Customer Service Advocacy

Capture customer needs, to ensure proper quote

Customer is quoted possible options, and selects one

Customer purchases home insurance

Customer is sent survey requesting feedback on Quote/Sales process

Insurance implements insights, conducts surveys measures impact, repeats

18

Key customer actions during the “Quote, Sales” phase of the lifecycle

18


The journey map helps ensure your acquisition and cross-sell analytics lead to action

Capture Predict Act

Customer Data• Demographics• Channel Activity• Information Requests• …

AnalysesPredict who is likely to

respond, based on clusters of similar customers that have

purchased in the past

Scoring

Marketing campaign process

Key Performance Predictors and Campaign Results

Rank best 3 offers

Social Media Data• Twitter• Discussion Forums• Blogs• …

Marketing list recommendation

Sales campaigns

19


Discussion agenda

• Introductions



• Next steps

20


Overview: cross-sell demo using IBM SPSS

• We analyzed simulated banking data* to see which “customers” would be likely to accept an offer for a Home Equity Line of Credit (HELOC).

• “Customers” are people who have a mortgage with our bank.

• Analysis was based on data about customers who had accepted similar offers in the past.

• We talked to our client about what theories they had concerning offer acceptance, and captured as much data measuring these issues as we could. (Journey mapping)

• We used algorithms to look for patterns in the data.

21

*This data is representative. Your data and findings will be different and more detailed.


Building a predictive model

• This process shows how a model might be built using data about customers who responded to a similar offer in the past.

• The model is “predictive” in the sense that we can pick out which new (or future) customers are more likely to respond favorably to the new offer.

22


Three types of customers: data-driven segments

1. Married people who do not have our credit card

2. Single people who do not have our credit card and who have paid greater than 20% of their mortgage loan

3. People who have $2000 or less on our credit card, who also come to the branch (rather than banking primarily online)

The following slides show how we discovered these segments in the data.

23

Single person in front of condo

Well-dressed person in a bank branch


Married people who do not have our credit card

Single people who do not have our credit card and who have paid greater than 20% of their loan

People who have $2000 or less on our credit card, who also come to the branch (rather than banking primarily online)


Campaign results: acting on the insights

• A campaign manager might select likelihood values from 80 to 100, thus taking the top 20% of prospects for the campaign.

• Or a campaign manager might target by segment. The RuleID shows the segment.

◦ It is the node number from the decision tree.

• RuleID 7 is

• Custom campaigns using the segment information are sent.• Cross‐sell modeling typically performs at least twice as good as mailing without using any targeting

Married people who do not have our credit card

28


Discussion agenda

• Introductions



• Next steps

29


Collect

Define

Report

Analyze

Context is key to generating actionable insights

Improve

1. Define your objectives

2. Collect data sources and “listen”

3. Analyze, translating to business processes

4. Report on the findings

5. Improve based on insights revealed

30


Analytics won’t create value, unless you act on the insights… a few ideas to get you started

• Personalize offers based on model variables

• Send regular emails to most likely to purchase

• Re‐mail to people who didn’t respond but should have

• Re‐mail based on web/mobile click‐through

• Email those least likely to accept the offer less frequently, to minimize opt‐outs and keep them in the campaign pool

• Consider incentives for long‐tenured customers acknowledging their ‘loyalty’

31


Thank you

Steven J. Ramirez, CEOBeyond the Arc, Inc.

32

Office 1

Email sramirez@bey

.877.676.3743

ondthearc.com

Digital b ondthearey c.com

beyondthearc.com/blog

@beyondthearc

Facebook.com/beyondthearc

Slideshare.net/beyondthearc


Disclaimer and copyright

This document was prepared by Beyond the Arc, Inc.

This document provides our commentary and analysis. The information contained herein is of a general nature and is not intended to address the circumstances of any particular individual or entity.

Although we endeavor to provide accurate and timely information, there can be no guarantee that such information is accurate as of the date it is received or that it will continue to be accurate in the future. No one should act on such information without appropriate professional advice after a thorough examination of the particular situation. No warranty expressed or implied.

This material may not be distributed.

IBM, the IBM logo, ibm.com, SPSS are trademarks or registered trademarks of International Business Machines Corporation in the United States, other countries, or both. Other company, product, and service names may be trademarks or service marks of others.

33

webinar: increase customer acquisition and cross-sell with predictive analytics

Technology

enterprise data

predictive analytics

new data growth

velocity of decision

predictive analyticsstevenj

decision make

analytics expertbeyondthearc

variety of information