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