Predicting Maintenance Contract Service Renewals
using the Internet of Things and Customer Behaviors:
A Supplier Perspective
Yogi Tri Prasetyo*
International Bachelor Program
in Engineering
Yuan Ze University
Chung-Li, Taoyuan, Taiwan
yogi.tri.prasetyo@saturn.yzu.edu.
tw
Paul Dominic Ilagan Completado
School of Industrial Engineering
and Engineering Management
Mapúa University
Manila, Philippines
pdicompletado@mymail.mapua.e
du.ph
Krisna Chandra Susanto
Department of Industrial
Engineering and Management
Yuan Ze University
Chung-Li, Taoyuan, Taiwan
s1115458@mail.yzu.edu.tw
Satria Fadil Persada
Entrepreneurship Department,
BINUS Business School
Undergraduate Program
Bina Nusantara University
Jakarta, Indonesia
satria.fadil@binus.ac.id
Irene Dyah Ayuwati
Department of Information
System
Institut Teknologi Telkom
Surabaya
Surabaya, Indonesia
irene@ittelkom-sby.ac.id
Abstract—The contract terminations for the provision of
maintenance services to the tenants of the managed mall affected
our organization’s planning, strategy, operations, and revenue.
Being able to predict the contract renewals, prevention and
readiness plans can be made. Using the data from the computeraided facilities management (CAFM) software and the available
research papers about customer behaviors; the contract renewal
outcomes were predicted. In this paper, the operations data of 47
mall tenants (customers) that operates different types of retails
were utilized. The data by performance, satisfaction, asset
condition and relationship based on the review of the previous
studies were categorized. Binary logistic regression was utilized to
test the data. The results showed that communication was the
strongest influencer to the customers’ behaviors in service
contract renewals more than price, satisfaction, and experience.
When we understood how to interpret and use the data from the
internet of things (IoT), it helped suppliers not only to predict the
renewal outcomes, but also build a strategic customer retention
program by using the significant variables identified.
Keywords— Maintenance Service, Contract Renewal, Internet of
Things (IOT), Facilities Management
I. INTRODUCTION
This template, modified in MS Word 2007 and saved as a “
Provision of third-party maintenance service has been a big
industry especially in the Middle East. Depending on the type of
business, it is one of the common functions that is outsourced by
most organizations. The maintenance activities can be fully
entrusted to a third-party agent, or sometimes partial [1].
Outsourcing enables the organization to focus on their core
business and share the operational risks and liabilities.
XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE
Michael Nayat Young
School of Industrial Engineering
and Engineering Management
Mapúa University
Manila, Philippines
mnyoung@mapua.edu.ph
To control the risk, manage and measure the maintenance
activities; the internet of things (IoT) has been one of the
advancements that is used to control, measure, record and
monitor the performance. This technology is used both by the
provider and the customer. Large scale companies often invest
in having their own software while smaller businesses utilize the
system application that contractors offer. IoT’s capability to
monitor and store the operational data enabled businesses to
develop their corporate strategies [2]. Even though that data is
the main component of IoT [3], there is no study that explored
how this information can be utilized in facilities maintenance
(FM) service contract renewals. The closest reference found is
the study made related to Information Technology (IT) service
contracts [4].
Predicting and preventing customer contract termination
have been the main challenge in the service provision industry
[4]. For a company to continuously grow, it is very important to
keep its existing customers [5]. From previous business
research, it was understood that obtaining new customers is
more expensive than retaining old ones [6]. Therefore, it is
beneficial to foresee the service contract renewals. Having this
information empowers the company and its operations to
manage budget, resources, and plan strategically. This is one of
the reasons why businesses exerted a lot of efforts in trying to
under-stand customer behaviors [7].
Customers’ decision on agreeing to a service contract
usually depends on the value that they see [7]. It is their
perceived benefit weighed against the money that they paid for.
Since contract proposals requires negotiation skills, it is often
left with the sales representatives. This is the case for the current
retail contracts managed by the company in one of the shopping
malls in Dubai, United Arab Emirates.
This paper is different from the previous research in two
ways. Our study used the IoT data from various tenant categories
with different contract values, value added ser-vices (VAS) and
service level agreements. This involves multiple different
approaches and, no 2 contracts are the same. Next, the data used
are all in the same mall, who deals with the same team, thus
eliminating factors of inconsistency when it comes to skill set of
the servicing team and long travel time.
B. Information from the CAFM Software
1) Response time Service Level Agreement (SLA)
Set as part of the contract; it contains the attendance and
resolution time of the service provider when responding to a
complaint received. Figure 2 shows that it influences both cost
and the scope compliance. Failure to attend or to complete a
complaint as per the agreed service time in the contract is a noncompliance. That’s why adequate number of staffing is required
which directly affects the price of the contract.
We reviewed both the system data from IoT and the written
customer behavior journals. In the process, we analyzed the
interactions of these variables and found out how they influence
customers renewal decisions [4]. By using an illustrative model,
we managed to link the data to the perceived behaviors.
II. MATERIALS AND METHODS
We studied and analyzed the data gathered from the retail
contracts managed by the company. All the operational
information is extracted from the CAFM software which is
offered as part of the service contract. There are varieties of
available software solutions. ServiceMax [8] is the company’s
CAFM software, this is where the data was extracted. Re-search
made on customer’s behavior on service contracts were gathered
and reviewed. The next sections will discuss on how the studied
behavior and IoT data were linked and correlated.
A. Customer Behavior in Contractual Service Settings
Research on consumers and their behaviors in a service
contract, displayed that renewal decisions are mostly based on
rational evaluation [4]. In business to business (B2B) set-tings,
it was perceived that price and experience on the service period
are the key drivers of contract renewals [9]. Earlier research
linked contract retentions positively with service quality [10].
Figure 1 shows the usual cycle of a customer’s decision process
in a maintenance service contract.
The perceived decision process is not absolute. Changes in
the service quality during the contract period is also believed to
reduce the renewal chances [11]. Depending on the terms and
condition of the contract; terminations which lead to tender, can
start at any point of time during the service contract period.
Contract termination and replacement of service providers were
commonly caused by a service-failure [12].
Fig. 1. Perceived customer’s decision process.
Identify applicable funding agency here. If none, delete this text box.
Fig. 2. Conceptual model of predictors on maintenance service contracts
interaction between customer behavior and IoT data
2) Equipment Health/ Asset Information
Quantity, make, location, criticality, uptime / downtime,
history of repairs, life cycle plan, asset condition, and any
information related to the equipment are captured and stored in
the CAFM. Referring to figure 2, it is perceived to influence the
customer’s behavior when it comes to price and scope
compliance. For example, the criticality of the equipment sets
the response time (SLA) that it requires, this factor should be
considered in building the manpower staff which is directly
impacting the cost. Similarly, repeated breakdowns on the
equipment are costing the customer not only in the repair parts
but also on the downtime, revenue losses and worst, reputation.
3) Satisfaction/ Customer Feedback
Results of surveys and feedbacks of the customers for the
received service is also captured in the system. This is recorded
for every service report that is signed-off by the client. It is part
of the completion report. The variable affects the cost and scope
compliance. For example, if you have highly qualified staff or
invested on regular trainings, the staff would be able to manage
the customer relationship better. Both costs money as investment, that’s why it affects the price. On the other hand, these
measures are expected to increase the customer experience.
4) Relationship
This data is the period that the customer is in contract with
the company. It also includes the number of additional works
awarded to the company. In figure 2, it is perceived to influence
the price. As in every business, longer customers are having
slight benefits and negotiation power when it comes to contract
renewal pricing. It sounded one-way; however, the same goes
for the service provider. The longer the contract is, mobilization
charges and familiarity becomes the benefit of the contractor.
This enables them to cut costs on special equipment arrangement
and manpower because, the team is already familiar and
developed already the skills to speed up work method on repair/
maintenance activities. More additional works means, the
customers trusting the company. It also directly increases the
experience as these variations adds knowledge and site
familiarization.
To understand the influence of different variables correlation
and binary logistic regression was used. Minitab was the
software used analyze the information. Table 1 shows how the
grouping of the data was made based on the created model. The
categorized data made it easier to understand the relationship of
the IoT data to the perceived customer behavior influencers.
C. Methodology
TABLE I.
III. PREPARE YOUR PAPER BEFORE STYLING
CATEGORIZED DATA FROM IOT
Attendance
Time
Equipment
Health
Satisfaction
Response
No. of Failures
Surveys
Completion
Re-occurrence
Reporting
From the results, we found out the significant drivers of the
service contract termination or renewal and use a binary value
to predict the outcome [13].
Relationship
Years of
service
Additional
works
Communication
Health and
Safety
The ServiceMax CAFM software of the company which is
used by the 47 customers is the main source of the information.
The customers are from different mall tenant categories; food
and beverage, jewelry, clothing, furniture, pharmacy,
entertainment, salons, barber shops, groceries are the most
common categories. Using the conceptual model as the
reference, the data gathered were classified into four categories.
The model, which was based on customer behavior literatures,
helped us identify the significant data.
TABLE II.
On
time
Term
C_Valid
C_Value
C_Value
On time
Late
On time_1
Late_1
-0.026
-0.053
-0.169
-0.049
-0.153
0.665
0.708
0.710
0.618
0.679
0.946
0.874
Total
-0.146
0.087
-0.118
Reoccurrence
-0.082
0.043
-0.040
Satisfaction
0.089
-0.127
0.096
Comm
0.222
0.233
0.152
Report
0.355
0.152
-0.028
Clean
0.151
-0.261
-0.542
0.266
0.000
-0.015
0.230
0.248
0.190
Years of
History
Additional
works
Late
0.810
0.677
0.048
0.019
0.123
0.096
0.077
0.372
On
time_1
A. Correlation
Table 2 shows how the variables are interacting and affecting
each other. Focusing only on significant values. Contract Value
(C_Value) is affected by the response and completion time. Reoccurring problems adds up obviously to the total complaints
received. This directly increases the number of variables related
to the performance. Response and completion time and the
number of complaints.
Additional works and the history also showed a good
correlation value. The more variation jobs performed, the more
experience on the site and it means knowledge is increased.
CORRELATION MATRIX
Late_1
Total
Reoccur
Satisfaction
Comm
Report
Clean
Yrs of
History
0.744
-0.089
-0.123
0.010
-0.126
0.791
0.009
0.103
-0.005
0.053
0.119
0.179
-0.041
-0.145
-0.139
-0.102
0.087
-0.010
0.039
-0.087
0.412
-0.503
-0.498
0.214
0.179
-0.039
0.039
0.370
0.049
-0.014
0.034
-0.141
-0.176
-0.052
-0.108
0.030
0.017
0.166
0.159
0.237
-0.016
-0.093
-0.019
-0.104
-0.028
-0.161
0.649
B. Binary Logistics Regression
TABLE III.
COEFFICIENTS
Term
Coef
SE Coef
Constant
On time
-27.5
0.327
0.949
3.34
5.08
1.909
11.3
0.144
ZValue
-2.43
2.27
0.389
1.67
2.08
0.766
Late_1
Satisfaction
Communication
Yrs. of History
P-Value
VIF
0.015
0.023
14.33
-2.44
0.015
19.26
2.00
2.44
2.49
0.046
0.015
0.013
2.54
4.63
4.03
The results of the regression strongly supported the
conceptual model that we used. The significant variables’ Pvalue were all below 0.05. Satisfaction, communication, on time
attendance and years of history’s all reflected positive
coefficient values. This means that the higher these predictors’
values increase, the more likely the customers will renew.
TABLE IV.
ODDS RATIOS FOR CONTINUOUS PREDICTORS
Odds Ratio
95% CI
1.3872
(1.0454, 1.8407)
On time
Late_1
0.3872
(0.1807, 0.8300)
Satisfaction
28.2237
(1.0626, 750.3663)
Communication
160.7694
(2.7214, 9497.5658)
Yrs. of History
6.7490
(1.5045, 30.2740)
TABLE V.
MODEL SUMMARY
Deviance
R-Sq
Deviance
R-Sq
(adj)
AIC
AICc
BIC
50.79%
39.13%
33.11
35.21
44.21
Area
Under
ROC
Curve
0.9432
Out of the 5 significant predictors, 3 of them are related to
customer service/ relationship while the other 2 are directly from
performance. Table 4 shows how high the ratio increases
whenever the communication line between the customers is
increased.
The R2 of 50.79% is a good indication on how well the data
that we extracted from the system fits our model. This is what is
shown in the model summary in Table 5.
Finally, the regression equation was developed. Y’ is the
contract renewal decision. In this paper its value is 1 to renew
and 0 for termination.
P (1) = EXP(Y’)/ (1 + EXP(Y’))
Y’ = -27.5 + 0.327 On time – 0.949 Late_1 + 3.34
Satisfaction + 5.08 Comm + 1.909 Yrs. of History
IV. DISCUSSION
Ruth N. Bolton’s study, The Effect of Service Experiences
over Time on a Supplier’s Retention of Business Customers
(2006) [7], explained how experience and contract dura-tion
influences renewals. There was a similarity on our approach
since this study is also from a service industry. The difference
was how the operations data from the CAFM was used.
Although both samples were offering service contracts to
multiple customers, ours offered different types of contracts to
different customers. This created a wider range of clients. In
addition, only a single team dealt with all the customer
categories in our study. This eliminated other variables which
were significantly contributing to the performance like,
manpower skills and response/attendance time. With this, the
research focused on identifying the significant variable that
affects the customer behavior in our site operations.
Available literatures on IoT also differs from our study.
Compared to Wangenheim’s, Renew, or cancel? Drivers of
customer renewal decisions for IT-based service contracts
(2017) [4]. Our study differs in the analysis and data gathering.
He was measuring the customer’s interaction with the system
that they offered and developed. His data is more specific and
detailed because the customers’ interaction is stored directly in
the IoT. Un-like our research, more human interaction is
occurring in our sample. This interaction is converted into
figures through surveys, which is affected by reports, mails,
calls, and site visits.
Another comparison to our research is the study made using
predictive approaches in maintenance. L. Deprez’ Pricing
service maintenance contracts using predictive analytics [17],
was directed to link the contract pricing based on the predictive
analysis of the equipment. Our paper in contrast, used the
predictive model to foresee renewal outcomes that helped us to
improve our operations and focus on customer retention plans.
Our study does not only intend to achieve cost reductions but to
win the renewals.
A. Theoretical Contributions
This paper’s theoretical contribution is how it identified the
significant data from the CAFM and developed a regression
formula that the organization can use to predict the maintenance
contract renewal outcomes. We believe that this is the first
attempt to create a model that interprets the IoT data that is
linked to the decisions of retail owner customers when it comes
to maintenance service contract renewals. In doing so, it became
evident that the main driver of the contract renewals are not price
nor performance satisfaction.
B. Practical Implications
VAS are the drivers of customer satisfaction that our
company believed in. This is why the company aggressively
invested on it. It’s not completely wrong especially for trying to
reach out to new customers. It supports the marketing made by
the company as it promises that the customers will get more than
the value that they pay for. This paper identified which variables
actually have a significant impact on contract renewals from the
company’s CAFM system.
The result of this research can be used to direct some of the
company’s investment and efforts to ensure customer retention.
The study pointed out that it is not heavily driven by satisfaction
or the value added services. It is more likely influenced directly
by the past experiences and not the contract scope of works [7].
Understanding the results, this is where the company can shift
the gear. Instead of using the IoT to only gather operational
information, evaluate performance and store data; the
technology advancements can be made to open further the
communication line with the customers. Creating mobile apps,
platforms, dashboards, and staff trainings on communication are
just few examples where transparency can be encouraged and
communication gaps can be shorten.
Managers can utilize the newly created model not only to
predict, but to improve the communication line particularly for
those clients whose score will go below 0.5 after applying the
regression formula. An evaluation can be made on each
customer on a quarterly basis to be able to create an
improvement and a prevention plan before the end of the
contract terms which is normally 1 year. It only makes sense to
strengthen the company’s strategy on it. The estimation/
contracts department might be the ones in charge for formulating
contracts but, the operations team are the key people who can
strengthen the line being the ones interacting with customers
more often. From the helpdesk operators, to the customers’
interaction with the system, the technical staff’s attendance to
the complaint, the necessary updates and closure of it, these are
all part of the communication line. This is the experience that
the customers remembers.
C. Limitations, and Future Research Limitations
In this research we only examined the customers available in
the same shopping mall where service contracts are mainly for
mechanical, electrical and plumbing systems that are all
delivered by our in-house team. In other service contracts,
subcontracting other services like cleaning, pest control, fire
fighting and alarm, other specialized systems, etc., are part of
the facilities management (FM) agreement. It would be ideal if
a similar operational data on the deliverables of the
subcontracted services can be captured. This would enable
further research to conduct a similar analysis and cover a total
FM delivery evaluation especially for large scale contracts.
Next, the contracts are all in the same mall. This means that there
is no difficulty in reaching to the customers as the travel time is
almost neglible. It is both an advantage and disadvantage for this
paper. It enabled us to focus our study to the main factors
affecting the customers’ decisions. The draw back is that the
contract service data and sample that was tested is less. We
suggest that this is factored considerably in future study as it is
obviously not the arrangement for all customers and contracts.
This definitely can influence the data in the CAFM system
linked to the communication experience that would require a
different arrangement.
always consider that despite the technology advancements,
human interactions and communications are still what
influences the customers’ experience. This eventually gives the
“yes” in maintenance service contract renewals.
ACKNOWLEDGMENT
The authors would like to thank Mapúa University Directed
Research for Innovation and Value Enhancement (DRIVE) for
funding this study.
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V. CONCLUSIONS
Companies invested a lot on IoT to be able to get close to
their customers, understand them better and measure their
operations’ performance. Our study highlights the im-portance
of how these data can be linked and make use to understand,
strategize and predict the maintenance service renewal
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of maintenance service contract renewals. Part of the service
delivery is making the customers understand their problems in a
less technical language. By doing so, we gain their trust and
confidence. Therefore, only focusing on price, scope of work
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AUTHORS’ BACKGROUND
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Yogi
Tri
Praset
yo
Assis
tant
Profe
ssor
Industri
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Engine
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w2NMgSLWLRcVt8XEEt9Or8ExAW6MSw9rL2ymnWkLV0
Paul
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Susanto
Mast
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Engine
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Michae
l Nayat
Young
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Industri
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