When the Bank Comes to You: Branch Network and
Customer Multi-Channel Banking Behavior
Geng Dan
Singapore Management University
Omni-channel Circle
Customers
Branch
Call
Centre
Mobile
Internet
ATM
Omni-channel Circle
Customers
$4.00
$4.04
$0.19
$0.09
$0.61
Source: PwC
Average transaction costs
for a typical US bank
Bank’s Opportunity
Servi
ci
ng
Cos
t
Customer
Management
•
Migrate routine transactions
to digital channels
•
Increase use of online bill
pay and statements
•
Thin the branch network and
introduce new branch formats
Cross-selling
Retention
Cos
t
Oppor
tuni
ty
R
ev
enu
e
Oppor
tuni
ty
Reduce efficiency ratio by up to 7 percentage points
(Source: Mckinsey)
Related Literature
•
Retail Banking:
•
Online banking adoption: Campbell and Frei 2009, Xue
et al. 2007 and Xue et al. 2011
•
Physical branches: ?
•
Retailing:
•
Online channel: Ansari et al. 2008, Biyalogorsky and
Naik 2003, Danaher et al. 2003, Deleersnyder et al.
2002, Geykens et al. 2002
•
Physical store entries: Foreman et al. 2009, Avery et al.
2012, Bell et al. 2013
Research Questions
•
What are the effects of branch opening and branch
closure on customer multi-channel usage?
•
What are the short-term vs. long-term impacts on
customer multi-channel usage after the branch
network change?
•
Does the physical presence of a bank improve
cross-selling performance?
Data
•
Anonymized individual-level transaction data from
a retail bank in the United States
•
Constructed panel data: 1.2M
customer-location-month observations
Branch Network Change Overview
BranchOpen 39.15% BranchClose 11.03% Both 21.99% Neither 27.84%Customers and Branch Network Change
BranchOpen 16.73% BranchClose 3.59% Both 5.82% Neither 73.86%
Longitudinal Propensity Score
Matching
•
Resolve customer endogeneity with branch network change
Pr
(
BranchOpen
it
/ BranchClose
it
= 1 |
)
= f
(
Age
i
, State
i
,
LowIncome
i
, Tenure
i
, LogTransaction$
it
, #Channels
it
, BRH
it
, ATM
it
,
VRU
it
, CallCentre
it
, Online
it
, #DepositAccounts
it
, #LoanAccounts
it
,
#InvestmentAccounts
it
, #OtherAccounts
it
, $DepositAccounts
it
,
$LoanAccounts
it
, $InvestmentAccounts
it
, $OtherAccounts
it
, ε
it
)
•
A random sample of
15,000 customers
with
branch network change
A random sample of
30,000 customers
with
no branch network change
1-to-1 match
Difference-in-Differences Model
•
E
(
y
ijt
)
= exp
(
C
ij
+ β
1
#BranchOpened
jt
+ β
2
#BranchClosed
jt
+ β
3
#FirstBranch
jt
+ β
4
#LastBranch
jt
+
ϕX
it
+ ε
ijt
)
•
y
ijt
: Number of transactions through estimated channel(s)
•
C
ij
: Individual-location fixed effects
•
#BranchOpened
jt
: Number of branches opened
•
#BranchClosed
jt
: Number of branches closed
•
#FirstBranch
jt
: Number of first branches opened
Branch Offline Online Branch Within Outside Branch #BranchOpened -0.030*** -0.078*** 0.024*** 0.116*** -0.231*** (0.002) (0.001) (0.000) (0.004) (0.005) #BranchClosed -0.032*** -0.028*** 0.098*** -0.002 -0.106*** (0.003) (0.001) (0.001) (0.006) (0.008) #FirstBranch 0.023*** -0.002 0.069*** 0.400*** -0.046*** (0.005) (0.002) (0.001) (0.013) (0.010) #LastBranch -0.062*** -0.017*** -0.068*** -2.038*** 0.493*** (0.009) (0.004) (0.002) (0.045) (0.018) Observations 1,178,586 1,178,586 1,178,586 694,256 ª 694,256 ª
ATM VRU CallCenter #Channels #Non-branch
Channels #BranchOpened -0.029*** -0.211*** -0.110*** -0.011*** -0.009*** (0.001) (0.002) (0.003) (0.001) (0.001) #BranchClosed -0.001 0.023*** -0.182*** -0.007*** -0.002 (0.002) (0.003) (0.005) (0.002) (0.002) #FirstBranch -0.017*** 0.045*** -0.030*** 0.003 0.004 (0.003) (0.005) (0.007) (0.003) (0.003) #LastBranch 0.043*** -0.014 -0.063*** 0.015*** 0.024*** (0.006) (0.009) (0.013) (0.005) (0.006) Observations 1,178,586 1,178,586 1,178,586 1,178,586 1,178,586 Controls —LogTransaction$ —#AccountTypes —#Transactions (on available transaction types)
Notes. Standard errors are in parentheses. Regressions include individual-location fixed effects. ª Customers whose branch transaction location cannot be detected are excluded.
-40.00% -30.00% -20.00% -10.00% 0.00% 10.00% 20.00%
Branch Offline Online Branch Within
Branch Outside
ATM VRU CallCenter #Channels #Non-branch Channels Av er ag e Tr ea tmen t Ef fec ts
Multi-channel Usage by User Segment (Branch Open Effects)
HeavyBranchUser LightBranchUser NonBranchUser
-20.00% -10.00% 0.00% 10.00% 20.00% 30.00% Av er ag e Tr ea tmen t Ef fec ts
Multi-channel Usage by User Segment (Branch Close Effects)
Post-treatment Trends
•
E
(
y
ijt
)
= exp
(
C
ij
+ ∑
[
β PostOpenT
jt
+ γ
PostOpenT
jt
× D_FirstBranch
jt
]
+ ϕX
it
+ ε
ijt
)
, where
T = 3, 6, 9, 12 and 12+
•
E
(
y
ijt
)
= exp
(
C
ij
+ ∑
[
β PostCloseT
jt
+ γ
PostCloseT
jt
× D_LastBranch
jt
]
+ ϕX
it
+ ε
ijt
)
, where
-80.00% -60.00% -40.00% -20.00% 0.00% 20.00% 40.00%
Branch Offline Online Branch
Within OutsideBranch ATM VRU CallCenter #Channels #Non-branchChannels
Av er ag e Tr ea tmen t Ef fec ts
Post-opening Trends of Multi-channel Usage
PostOpen3 PostOpen6 PostOpen9 PostOpen12 PostOpen12+
-40.00% -30.00% -20.00% -10.00% 0.00% 10.00% 20.00% 30.00% Av er ag e Tr ea tmen t Ef fec ts
Post-closure Trends of Multi-channel Usage
Cross-sectional Logit Model
Pr
(
D_OpenAccounts
i
= 1 |
)
= β
0
+ β
1
D_BranchOpen
i
+ β
2
D_FirstBranch
i
+ β
3
Age
i
+ β
4
Tenure
i
+ β
5
LowIncome
i
+ ε
i
Pr
(
D_CloseAccounts
i
= 1 |
)
= β
0
+ β
1
D_BranchClose
i
+ β
2
D_LastBranch
i
+ β
3
Age
i
+ β
4
D_OpenAccounts
Post-3 months Post-6 months Post-9 months Post-12 months D_BranchOpen 0.248*** 0.190*** 0.134*** 0.090** (0.045) (0.041) (0.039) (0.037) D_FirstBranch 0.227*** 0.201*** 0.165*** 0.145*** (0.046) (0.042) (0.040) (0.038) Observations 14,200 14,200 14,200 14,200 D_CloseAccounts
Post-3 months Post-6 months Post-9 months Post-12 months
D_BranchClose 0.189* 0.095 0.004 0.036
(0.102) (0.079) (0.068) (0.063)
D_LastBranch -0.062 0.076 -0.007 -0.004
(0.113) (0.085) (0.075) (0.069)
Observations 6,783 6,783 6,783 6,783
Controls —Age —Tenure —LowIncome
