IN THOSE MOMENTS

IDENTIFYING AND UNDERSTANDING THE KEY INFLECTION POINTS FOR WHEN AND HOW TO INFLUENCE LOYAL CUSTOMER BEHAVIOUR

MICHAEL O’SULLIVAN President, Proprietary Loyalty, Canada AIMIA

Name: Michael O’Sullivan President, Proprietary Loyalty, Canada AIMIA

BIG DATA – BIO DATA

2013

iTunes MONTHLY BILL

$60 $46

$22 2012 2011

CAGR 65%

2013 ► 1.6 2012 ► 1.4 2011 ► 1.0

FAMILY WIRELESS

DEVICES PER

PERSON

FREQUENT FLIER MILES

2010 2011 2012

CAGR -37%

160K 140K 350K

MILES ACCUMULATION

95 % SUNNY DAYS EXPERIENCED

84 236

-64%*

HOCKEY TV CONSUMPTION*

TIME ON

ICE 7 0

*Subject to downward revision driven by Leafs performance

HOURS WATCHED/PERSON

75

Hypothesis: There are many interactions each day that drive

or destroy loyalty. When identified and managed, we can

provide greater value to customers and brands

800 12 different sub-verticals

800 Represents over 2,700 products

500 18 common product categories

Loyalty Trust in Brand 53% 59%

Source: Accenture Global Consumer Behavior Study, 2009

Price Sensitivity

63%

Shopping Around

63%

CHALLENGES FOR FINANCIAL INSTITUTIONS

Customers of financial institutions do not feel their expectations are consistently being met.

Managing switching by building refined analyses

Customer “Stickiness” in a transparent world

Differentiating products, services and relationships

Source: AIMIA 2012 Retail Banking Survey (Why did you switch your main provider?)

Source: Canadian Bankers Association Total 6 banks includes BMO, CIBC, National Bank, RBC, Scotiabank, TD Bank

$0

$10,000

$20,000

$30,000

Total Interest Income 6 Banks

$ M

illio

ns

$6.6BN

“Drifting” Top-line NPV

2007

2011

Demographics

Product Difference

Customer Experience

Life Events & Milestones

Loyalty Program

Membership

SOCIAL RESIDUAL IMPACT

SOCIAL/DIGITAL INFLUENCE

SWITCHING VARIABLES

Con

trol

labl

e by

cu

stom

ers

Not monitored by financial institutions

Unc

ontr

olla

ble

by

cust

omer

s

Currently Predictable by financial institutions

31%

Less than ten occurrences High incidence of switch Low incidence of switch

Examples

Got job with new organization

Changed job in same organization

Quit smoking

Examples

Bought leased vehicle

Started your own business

Bought your first home

26%

39% Examples

Automobile accident

Wallet purse lost stolen

Victim of identity theft

Examples

Bankruptcy

Child left home

Child graduated college/ university

3%

0.00

0.50

1.00

1.50

2.00

2.50

3.00

No switches 1 switch 2 switches 3+ switches

Not monitored/ Uncontrollable

Not monitored/ Controllable

Predictable/ Uncontrollable

Predictable/ Controllable

2.72

2.04

+30% Life Events per person in the category Which of these events occurred within the past 12 months? (N=800)

0

10

20

30

40

50

0 20 40 60 80 100 120 140

Tota

l pro

duct

s sw

itche

d

Total occurrence of life event

39

Not currently monitored by institutions / Controlled by customers

97 Changed jobs in same organization

Outstanding personal achievement

Quit smoking Got a job with a new organization

Went on vacation outside North America

Renovated your home

Changes in your circle of friends

Got a pet Started new exercise/ diet habits 40%

2.6

2.1

1.8

1.5 1.3

0.0

1.0

2.0

3.0

Loan/line of credit

Mortgage Savings account

Credit card Chequing account

Loan/line of credit Mortgage Savings account

Credit card Chequing account

Num

ber o

f add

ition

al p

rodu

cts

switc

hed

Number of additional products switched from main provider with initial product switch and estimated top-line NPV

Top-line NPV impact of product switches

Estim

ated top-line NP

V of additional

products switched

6.8 11.5

36.0

16.4

8.1

$2,342

$1,050 $1,434

$1,108 $1,148

0

500

1000

1500

2000

2500

0

10

20

30

40

50

Loan/line of credit

Mortgage Savings account Credit card Chequing account

Top-line NPV per product

Estimated top-line NPV of additional products switched P

er-p

rodu

ct to

p lin

e N

PV

val

ue

(incl

udin

g or

igin

al p

rodu

ct s

witc

hed)

RECENT ENTRANTS

PLANNING CANADIAN OPENING

NEW BUSINESS MODELS

Population growth

Gen Z segment growing

up

Don’t discount the baby boomers

7.02 7.29

5.72 6.52

5.96 6.33

5.63

7.09

5.83 6.1 6.01 Average level

of loyalty stated

Average loyalty stated vs. # of (other) retailers shopped by category

3.7

2.6

3.1 3.1 2.9 2.9 3.3

2.9 3.4

4.2

3.5

Grocery Pharmacy / drug

Gasoline / fuel Department stores

Books Home improvement

Electronics Sporting goods & clothing

Furniture / home

furnishings

Women’s clothing

Men’s clothing

Average level of loyalty

stated

% Share at Main

Grocery Pharmacy / drug

Gasoline / fuel Department stores

Books Home improvement

Electronics Sporting goods & clothing

Furniture / home

furnishings

Women’s clothing

Men’s clothing 0%

25%

50%

75%

100%

76% 74%

72% 64% 60% 60%

50% 46%

40% 34% 32%

Loyalty level stated vs. Share of shopping with main

7.02 7.29

5.72 6.52

5.96 6.33

5.63

7.09

5.83 6.1 6.01

q5c1. Why did you change your main (retail category) store?

51%

55%

36%

50%

25%

42%

41%

43%

40%

45%

38%

46%

26%

32%

21%

14%

8%

60%

41%

37%

30%

45%

8%

11%

13%

14%

18%

20%

5%

4%

12%

11%

25%

11%

12%

17%

26%

26%

23%

3%

10%

13%

12%

2%

10%

12%

18%

11%

14%

11%

15%

14%

17%

9%

8%

Furniture / Home Furnishings

Electronics

Home Improvement

Grocery

Pharmacy/drug

Gasoline/fuel

Women's Clothing

Men's Clothing

Sporting good/clothing

Department store

Books

Reason for switching main retailer

Price Related Product Related Service Related Convenience Related Other or don’t know

7.89 7.88 7.88 7.92

Total Loyalists Low switchers

High switchers

Location

8.75 8.72 8.79 8.78

Total Loyalists Low switchers

High switchers

Price

6.25 5.88 6.32 7.54

Total Loyalists Low switchers

High switchers

Staff quality

6.25 5.88 6.32 7.54

Total Loyalists Low switchers

High switchers

Loyalty/reward program

Store design

5.79 5.41 6 6.94

Total Loyalists Low switchers

High switchers

Product Quality

8.54 8.5 8.62 8.6

Total Loyalists Low switchers

High switchers

Not monitored Currently Predictable

Con

trol

labl

e by

cu

stom

ers

Unc

ontr

olla

ble

by

cust

omer

s

33%

High incidence of switch Low incidence of switch

Renovated your home

Got a pet

Quit smoking

Started your own business

Got married

Bought your first home

26%

34% Victim of identity theft

Child Spouse health issues

Personal injury/ illness

Grandchild born

Child graduated high school

Child graduated college/ university

7%

0

5,000,000

10,000,000

15,000,000

20,000,000

0

50,000,000

100,000,000

150,000,000

200,000,000

Retail Facebook fans Retail Twitter followers

CPG and Retail Brand Engagement on Social Media (Facebook and Twitter), by fans and followers

Source: Fanpagelist.com as of December 17, 2012

0

5,000,000

10,000,000

15,000,000

20,000,000

0

50,000,000

100,000,000

150,000,000

200,000,000

CPG Facebook fans CPG Twitter followers

Num

ber o

f fac

eboo

k fa

ns

Num

ber o

f fac

eboo

k fa

ns N

umber of Tw

itter followers

Consumers engage more than 10 times more with CPG brands on social media than they do with retail brands

Num

ber of Twitter follow

ers

190

241

69

136

36

138

0

100

200

300

400

500

No, will not get one

Yes, have one now

Plan to get one

No, not interested

Already have one

Yes, interested in app

2011

36% 54% available market

Smartphone penetration in Canada

Opportunity

Opportunity 28%

Mobile device adoption Do you own a device (like a smartphone) with mobile internet access?

Interest in interacting with CPG company Would you be interested in downloading an App that signals in-store deals for program members? 2012

46%

Name: Ryan Age: 24

IMAGINE IF YOU COULD… Current Income: $45,000 Status: Single

Motivate Ryan to actively share his personal information with you: Design special incentives for sharing information

Know before he does the life events that will influence his purchase needs: He graduates, gets a job, moves to a bigger apartment with a yard… He meets someone, they go out for dinner and shows regularly, they get a dog… He gets promoted, they become common-law and move to a bigger place a little further from downtown What comes next?

Control switching better than everyone else Drive customers from competitors to you and have them stick

Give Ryan personalized rewards for doing what he does best: Sharing your value offers with his friends Tell you about people he knows Even comparing rates and fees easily

Engage with Ryan – listen, understand and suggest: Be “super-relevant” Choose the channels and frequency of contact that he prefers Proactively suggest solutions when non predictable events happen

A TRUE STORY…

Data will provide the

answers, but the value is in

execution

Situational and life cycle

applications can drive

“structured loyalty”

Adopt a test and learn

mindset, but tackle the big

potentials first

Solutions closest to the moment, plus simple and practical, will drive most

value

If customer decisions are

not data driven then

you are already behind

THANK YOU