The Effect of Customer
Relationship Management
Systems on Firm
Performance
JACOB HAISLIP AND VERN RICHARDSON
Research Question
Does implementing a Customer Relationship
Management (CRM) system improve firm
performance?
CRM Systems
Payne and Frow (2005) define CRM as follows:
“CRM is a strategic approach that is concerned with creating improved shareholder
value through the development of appropriate relationships with key customers and
customer segments.”
Wikipedia (from Shaw 1991) defines it as:
“Customer relationship management (CRM) is an approach to managing a company's
interactions with current and future customers. It often involves using technology to
organize, automate, and synchronize sales, marketing, customer service, and technical
support.”
Prior Literature – ERP and SCM Systems
Gartner (2011) notes that worldwide spending on enterprise systems exceeded
$250 billion in 2011.
Initial research papers in this area were unable to find a positive relation
between IT spending and profitability measures (Weill 1992; Landauer 1995).
More recent papers, find positive payoffs from investments in IT, and are
specifically documented for ERP systems and Supply Chain Management
Systems (Hitt 2002; Nicolaou 2004; Dehning et al. 2007; Hendricks et al. 2007; Brazel and Dang 2008; Dorantes et al. 2013).
Prior Literature – CRM Systems
Customer satisfaction and knowledge improves following CRM system
implementation (Sutton and Klein 2003; Boulding et al. 2005; Mithas et al. 2005).
Other papers argue that customers do not necessarily desire or value strong
relationships (Dowling 2002; Danaher et al. 2008).
Hendricks et al. (2007) do not find improved stock returns or profitability
following CRM system implementation.
Spending on CRM systems alone was approximately $24 billion in 2014.
Figure 1
Framework for the Benefits of IT Investments (adapted from Dehning and Richardson
2002)
Impact of IT on the Firm
Information
Technology
Direct Effects
Indirect Effects
Business
Processes
Firm
Performance
As Measured by Researchers
1
Information
Technology Measures:
1. Spending
2. Strategy
3. Management
or Capability
Process Measures:
2
1. Sales
2. Operating
Margin
3. SGA Expenses
4. AR Turnover
Firm Performance
Measures:
3
A. Market
B. Accounting
a. ROA
b. Cash Flows
Vendor Claims
Vendors boast improvements for the following after CRM system
implementation:
Profitability
Customer Satisfaction
Sales Productivity
Sales Predictability
Hypotheses
Hypothesis 1: Firms that implement CRM systems improve their business
process measures to a greater degree than firms that do not implement CRM
systems.
This includes sales, sales efficiency, and accounts receivable collectability.
Hypotheses
Hypothesis 2: Firms that implement CRM systems improve operational
performance to a greater degree than firms that do not implement CRM
systems.
Hypothesis 3: Firms that implement CRM systems improve their sales
predictability (as evidenced by management earnings forecasts to a greater
degree than firms that do not implement CRM systems.
Sample
•Identify announcements of CRM system implementations using Lexis-Nexis.
•138 public firms that adopt a CRM system between 2001-2011.
•After eliminating observations with missing data we arrive at 87 treatment firms.
•We then match each treatment firm with control firms based on year, industry, and ROA.
Following Hendricks et al. (2007) we include all control firms within the same year and industry,
and with a ROA within 10%. This gives us 1,169 control observations.
Table 1
Sample Statistics
Panel A: Year Distributions
Year
CRM Implementations
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Total
13
22
11
13
10
11
3
1
2
0
1
87
Control
Firms
108
304
152
288
174
89
12
13
15
0
14
1,169
All Firms
121
326
163
301
184
100
15
14
17
0
15
1,256
Panel B: Industry Distributions
Industry
Chemicals
Electrical
Equipment
Retail Sales
Services
All Others
Total
2-Digit SIC
Code
28-29
36, 38
35
50-59
70-79
All Others
CRM Implementations
Control
Firms
All Firms
6
13
5
9
16
38
87
101
178
39
37
375
439
1,169
107
191
44
46
391
477
1,256
Research Design
[Performance Measures]i,t = λ0 + λ1CRMi,t + λ2Afteri,t + λ3CRM*Afteri,t + λ4Sizei,t + λ5MTBi,t + λ6RDi,t-1
+ λ7ADVi,t-1 + λ8ROAi,t-1 + λ8CapInti,t-1 + зi,t
Abs_Errori,t = β0 + β1CRMi,t + β2Afteri,t + β3CRM*Afteri,t +β4Sizei,t + β5ROAi,t + β6Lossi,t + β7Leveragei,t
+ β8EarnVoli,t + β9CFOVoli,t + β10Growthi,t + β11IndConi,t + β12Big4i,t + β13LnAnalystsi,t + β14Std_AFi,t +
β15Surprisei,t + β16Horizonj,i,t + β17Litigationi,t + β18High Techi,t + β19Weaki,t + εi,t
Table 4
Univariate Analysis
CRMBefore
ControlBefore
CRM &
Control
Before
CRMAfter
CRM
Before and
After
ControlAfter
CRM &
Control
After
N=87
N=1169
Difference
P-Value
N=87
Difference
P-Value
N=1169
Difference
P-Value
Abs_Error
0.021
0.009
-0.012
<0.001***
0.006
-0.015
<0.001***
0.014
0.008
0.002***
ADV
0.016
0.017
0.000
0.995
0.015
-0.001
0.765
0.013
-0.002
0.502
ARTurn
11.428
13.791
2.363
0.623
15.995
4.567
0.386
11.195
-4.799
0.196
CapInt
2.781
9.191
6.411
0.439
4.628
1.848
0.472
6.224
1.596
0.294
CFO
1565.625
358.033
-1207.592
<0.001***
1985.184
419.559
0.503
469.150
-1516.034
<0.001***
Doubtful
129.121
19.013
-110.107
<0.001***
83.18
-45.941
0.255
25.833
-57.347
0.002***
MTB
3.393
1.880
-1.512
0.761
1.910
-1.483
0.292
1.072
-0.838
0.776
Oper Margin
-0.084
-0.572
-0.488
0.604
-1.224
-1.140
0.431
-0.380
0.844
0.309
RD
0.221
0.473
0.252
0.753
1.198
0.977
0.412
0.248
-0.950
0.017**
ROA
-0.310
-0.940
-0.630
0.254
0.020
0.331
0.075*
-0.057
-0.077
0.135
Sales
10492.81
1787.851
-8704.959
<0.001***
12494.68
2001.87
0.610
2433.191
-10061.489
<0.001***
SGA
0.319
0.524
0.205
0.275
0.294
-0.025
0.595
0.757
0.463
0.531
Size
7.693
5.933
-1.760
<0.001***
7.896
0.203
0.567
6.226
-1.67
0.002***
All p-values are two-tailed. *, **, and *** represent significance levels of 0.10, 0.05, and 0.01 respectively.
Table 5
Business Process Improvements -- Sales
Column 1
Column 2
Variables
Sales
Sales Scaled
CRM
2,808.67
0.138**
(0.128)
(0.041)
After
-992.544**
0.100***
(0.019)
(0.005)
CRM*After
2,103.457***
0.062*
(0.008)
(0.078)
Control Variables
Included
Included
Year and Industry
Included
Included
Indicators
Number of observations
2,512
2,512
Adjusted R2
0.338
0.357
F-Statistic
24.130***
140.590***
Variables
CRM
After
CRM*After
Table 6
Business Process Improvements – Sales Efficiency
Column 1
Column 2
Column 3
Oper
SGA
SGA Scaled
Margin
-0.378**
0.199
0.108***
(0.039)
(0.140)
(0.000)
0.209
0.012
0.063***
(0.414)
(0.879)
(0.001)
0.443**
-0.273*
-0.025*
(0.049)
(0.069)
(0.090)
Control Variables
Included
Included
Included
Year and Industry
Indicators
Included
Included
Included
Number of observations
2,512
2,512
2,512
Adjusted R2
0.647
0.040
0.419
25.360***
20.460***
80.550***
F-Statistic
Table 7
Business Process Improvements -- Receivables Collectability
Column 1
Column 2
Column 3
Doubtful
Variables
ARTurn
Doubtful
Scaled
CRM
-2.176
66.951***
0.010*
(0.372)
(0.000)
(0.067)
After
-4.718*
0.357
0.006**
(0.098)
(0.969)
(0.035)
CRM*After
Control Variables
Year and Industry
Indicators
Number of observations
Adjusted R2
F-Statistic
0.971
-50.857***
-0.010**
(0.279)
Included
(0.010)
Included
(0.041)
Included
Included
Included
Included
1,558
1,558
1,558
0.121
3.690***
0.202
15.070***
0.176
5.560***
Table 8
Operational Performance
Column 1
Column 2
Variables
CRM
After
CRM*After
Control Variables
Year and Industry
Indicators
Number of observations
Adjusted R2
F-Statistic
Column 3
ROA
CFO
CFO Scaled
-0.064**
(0.026)
-0.032*
(0.075)
0.037
(0.111)
Included
297.755
(0.385)
-227.699*
(0.061)
320.988**
(0.019)
Included
-0.023
(0.198)
-0.031**
(0.011)
0.035**
(0.018)
Included
Included
Included
Included
2,512
2,512
2,512
0.301
8.510***
0.169
42.020***
0.293
14.430***
Table 9
Management Earnings Forecast Error
Column 1
Variables
CRM
After
CRM*After
Absolute Forecast Error
0.005
(0.243)
-0.004
(0.136)
-0.008**
(0.012)
Year Indicators
Included
Industry Controls
Included
Number of
observations
342
Adjusted R2
F-Statistic
0.560
14.330***
Conclusions
CRM systems do provide benefits for firms especially in specific
business processes.
◦ Increased Sales
◦ Decreased SGA Expense
◦ Reduced Allowance
◦ Improved Operational Performance
◦ More Accurate Earnings Forecasts