AN EFFICIENT METHOD TO IDENTIFY CUSTOMER VALUE
IN TOURIST HOTEL MANAGEMENT
Changqiu Li
Tourism Management Department, Zhengzhou Tourism College, Zhengzhou, China
Keywords: Customer value identification, RFM analysis, CRM.
Abstract: Fierce market competition forces tourist enterprise to put more and more attention on the demands of the
customer. Customer relationship management (CRM) becomes increasingly important in tourist industry.
Identify valuable customers and cultivate these valuable customers is the two basic tasks of CRM. So it has
significant meaning for manager to indentify the value of customers. In this paper, we introduce a more
convenient statistical method based on RFM (Recency, Frequency, Monetary value) analysis (Xiaoyu Zhao,
2005) to the customer value identification in tourist hotel. We study how to use this method in the
management of a tourist hotel. The convenience and importance of this method are demonstrated through
comprehensive analysis.
1 INTRODUCTION
Customer relationship management (CRM)
(Qingliang Meng, 2005) is the key for tourist
enterprise to gain winning in the age of
E-Commerce. Judging customer value and
keeping the relationship with valuable customers
are the core activities of CRM. Through research
on customer value (Xuhui Yan, 2009), enterprise
managers are able to judge it by the contribution
of customer and invest in customers who make
the largest contribution with effective resources
selectively. In this way, it will bring to more
profits for enterprise.
The existing methods to judge customer value
mainly base on data mining (Xiaoyu Zhao, 2006)
and fuzzy comprehensive evaluation (Xin Zan,
2008). The method based on data mining is a little
complicated for enterprise managers, especially
when we do not need the result to be very
accurate. The method based on fuzzy
comprehensive evaluation is also not simple in
practical application. Generally it has to use
Delphi method to determine the weighted value of
evaluation indicators at first.
In this paper, we introduce a statistical method
based on RFM (recency, frequency and monetary
value) analysis theory to judge customer value in
tourist hotels. RFM analysis is a very important
analysis method for customer response (Xiaoyu
Zhao, 2005). RFM model is used to represent
customer behavior characteristics. This approach
records three dimensions of customer
transactional data, namely recency, frequency and
monetary value, to classify customer behavior
(Jinyao Luo, 2009). Combining RFM analysis
with statistical method to judge customer value is
more convenient than the two methods mentioned
above. It does not need to know about data mining
knowledge, and also not need to use Delphi
method to determine the weighted value of
evaluation indicators like fuzzy comprehensive
evaluation method. Especially when we do not
need the results are extremely accurate, the
convenience of statistical method based on RFM
analysis are more obvious. In this paper, we
introduce how to use this statistical method based
on RFM analysis in tourist enterprises through a
case study of tourist hotel.
Rest parts of this paper are organized as
follows. The second part introduces briefly the
customer value and RFM theory. The third part is
a case study. It specifically introduces the method
indicators and its application process. The forth
part is an effect analysis to demonstrate the
convenience of this method in practical
165
Li C..
AN EFFICIENT METHOD TO IDENTIFY CUSTOMER VALUE IN TOURIST HOTEL MANAGEMENT.
DOI: 10.5220/0003441301650169
In Proceedings of the 13th International Conference on Enterprise Information Systems (ICEIS-2011), pages 165-169
ISBN: 978-989-8425-55-3
Copyright
c
 2011 SCITEPRESS (Science and Technology Publications, Lda.)

application. The last part is a conclusion about the
text.
2 RELATED THEORIES
2.1 Customer Value
“Marketing Management” (Philip Kotler, 2002),
authored by Kotler, says that customer value
generally refers to the total value which customers
get from products or services. This includes the
product value, service value, personal values, image
value, etc. The concept is to measure the value
customers received, but for customers, they must
spend a considerable cost in order to obtain the value
generated by these products and services. It can be
named customer cost. The value customers really
obtain is the value customers get minus that they
spend. But for ordinary enterprises, the so-called
customer value refers to the customers’ importance
for enterprise. It can be simply understood as a kind
of ability that customers bring profits for enterprise.
Now customer cost refers to what enterprise spent to
obtain some clients. If the customer cost is higher
than the customer value, it means that what
enterprise spends for the customer is more than what
the customer brings. Then the client will be
abandoned gradually.
Judging valuable customers and cultivating
valuable customer is the two basic tasks of
enterprise CRM and two basic methods to keep
customers’ loyalty (
Mingliang Chen, 2006). Fierce
tourist market competition forces enterprise to put
more and more attention to the demands of the
customer. More and more enterprises realize the
importance of customer relationship management.
They spend a lot of resources in this field. However,
enterprises’ resources are limited. How to use
limited resources to maximize benefits becomes the
focus of enterprises’ attention. This is just why
customer value identification is so important.
2.2 RFM Analysis
RFM analysis (Xiaoyu Zhao, 2005) is the
comprehensive analysis about recency, frequency
and monetary value. It is an important method used
in judging customer value. The use of RFM analysis
method can enable enterprises to pay more attention
to high-value customers, and thus get the most profit
through the best use of limited resources.
The basis of RFM analysis is three key indicators
about customer behavior. Bult and Wansbeek give a
definition of these three indicators as follows (
Bult
J R, 1995
).
(1) R (recency) refers to the interval from the last
purchase to the current time. Customers who
patronize your hotel recently are more likely to
come again than those who came a few months or
even a few years ago.
(2) F (frequency) refers to the number of
purchase times in a certain period. Customers who
patronize your hotel frequently are more likely to
come again than those who come rarely.
(3) M (monetary value) refers to the total
consumption of customer in a certain period.
Customers who spend plenty of money in your hotel
are more likely to come again than those who spend
a little.
These three indicators can be nearly applied to
all products, no matter whether the product is
tangible or intangible (such as hotel services). Also
they can be applied to different business models:
B2B model (such as hotel products supply) and B2C
models (such as retail trade and service industry).
Just because of these three principle’s universal
applicability and their ability to respond customer
behavior, they can be used in enterprise marketing
and management. And it will be more convenient to
judge the customer value than other methods.
3 TOURIST HOTEL CUSTOMER
VALUE JUDGMENT
As is mentioned above, RFM analysis is a very
important analysis method for customer response.
Combining RFM analysis with statistical method to
judge customer value is more convenient than
existing methods in practical application especially
when we do not need the results are fairly accurate.
Now we introduce how managers conduct customer
value judgment using statistical method based on
RFM analysis through a case study of tourist hotel.
3.1 Indicator of Judging Customer
Value by RFM Analysis
Recency indicator: The list of customers will be
arranged according to the time of patronage. The
customer who has patronized the tourist hotel most
nearly is ranked first; on the contrary, the one who
ICEIS 2011 - 13th International Conference on Enterprise Information Systems
166

patronized the tourist hotel long ago is ranked last.
Then we divided the arranged list into five equal
portions. If the tourist hotel has 500,000 customers,
each portion includes 100,000 customers. Label each
portion, that is, the first portion (the group which has
patronized the hotel most nearly) is numbered one
and the last portion (the group who patronized the
hotel long ago) is numbered five.
Frequency indicator: The list of customers will
be arranged according to the frequency of patronage.
The customer who has patronized the tourist hotel
frequently is ranked first; on the contrary, the one
who patronized the hotel rarely is ranked last. Then
we divided the arranged list into five equal portions.
If the tourist hotel has 500,000 customers, each
portion includes 100,000 customers. Label each
portion, that is, the first portion (the group who has
patronized the hotel frequently) is numbered one
and the last portion (the group who come rarely) is
numbered five.
Monetary value indicator: The list of customers
will be arranged according to the amount of
consumption. The customer who has the largest
amount is ranked first; on the contrary, the one who
has the smallest amount is ranked last. Then we
divided the arranged list into five equal portions. If
the tourist hotel has 500,000 customers, each portion
includes 100,000 customers. Label each portion, that
is, the first portion (the group who has the largest
amount) is numbered one and the last portion (the
group who has the smallest amount) is numbered
five.
3.2 Application of Judging Customer
Value by RFM Analysis
Based on the method mentioned above (judging
customer value by RFM analysis), the specific
applications are as follows.
(1) Only considering the recency indicator:
If the tourist hotel managers carry out
marketing activities for these customers, no matter
it is hotel room discount or free hotel service
upgrade, the situation of response only
considering the recency indicator is like Figure 1.
(2) Only considering the frequency indicator
If the tourist hotel managers carry out
marketing activities for these customers, the
situation of response only considering the
frequency indicator is like Figure 2.
0
0.5
1
1.5
2
2.5
3
3.5
r
esponsivity
(%)
12345
groups by recency indicator
Figure 1: Situation of response base on recency indicator.
0
0.5
1
1.5
2
responsivity
(%)
12345
groups by frequency indicator
Figure 2: Situation of response base on frequency
principle.
(3) Only considering the monetary value
indicator
If the tourist hotel managers carry out
marketing activities for these customers, the
situation of response only considering the
monetary value indicator is like Figure 3.
0
0.5
1
1.5
2
responsivity
(%)
12345
groups by monetary value indicator
Figure 3: Situation of response base on expenditure
principle.
(4) Considering three indicator
Combining the three indicator, we can get 111
... 545,551,552,553,554,555, a total of 125 (5*5
*5 =125) sub-groups. Select about 30,000 clients
and conduct test marketing activities for the 125
AN EFFICIENT METHOD TO IDENTIFY CUSTOMER VALUE IN TOURIST HOTEL MANAGEMENT
167

groups. It means each group includes about 240
people (240 × 125 = 29600). When we conduct
marketing activities to these test samples, it is
necessary to record the feedback from these
customers. Then we can calculate the cost and
return of these marketing activities. A figure can
also be made according to these data. It is similar
with the Figure 4.
Figure 4: Situation of response base on the three
principles.
Zero-scale line is a break-even one. 111 is the
group which has the highest rate of return, 555 is
the group which has the lowest rate of return.
Among all 125 groups, some groups’ return are
greater than their costs (for example: 111,112,
etc.), and some groups’ return are less than their
costs (for example: 555,554, etc.). As is
calculated, in the 125 groups, only 34 groups are
profitable.
4 RESULT ANALYSIS
4.1 Results of RFM Analysis
According to the results of application, if the tourist
hotel managers conduct marketing activities for the
whole 500,000 customers, forecasting results are as
follows:
In the all 125 groups, the average response rate is
about 1.14%, so a total of 500,000*1.14%=5,700
people respond to it.
Per capita marketing cost is 0.55 Yuan, so there
will be totally 500,000*0.55=275,000 Yuan.
The net profit per respondent is 40 Yuan, so the
total is 5,700*40=228,000 Yuan.
Net profit is amounted to
228,000-275000=-47000 Yuan. (Deficit)
If the tourist hotel managers conduct marketing
activities only for the 34 profitable groups,
forecasting results are as follows:
In these 34 groups, the average response rate is
about 2.61%, so a total of 136000*2.61%=3549
people respond to it.
Per capita marketing cost is 0.55 Yuan, so there
will be totally 136,000*0.55=74,800 Yuan.
The net profit per respondent is 41 Yuan, so the
total is 3,549*41=145,509 Yuan.
Net profit is amounted to
145,509-74,800=70,709 Yuan. (Profit)
Return on investment (ROI):
70,709/74,800=94.53%.
Table 1: Test results.
All groups Profitable
groups
Number of
groups
125 34
Total number
of people
500,000 136,000
Total costs 275,000 74,800
Response rate 1.14% 2.61%
Number of
respondent
5,700 3,549
Net profit per
respondent
40 41
Total profits 228,000 145,509
Net profit -47,000(deficit) 70,709(profit)
Return on
investment
17.09% 94.53%
Based on the above application test, if the tourist
hotel managers conduct marketing activities for the
whole 500,000 customers, there will be a loss of
47,000 Yuan. In another way, if the tourist hotel
conducts marketing activities only for the profitable
customers, then 70,709 Yuan can be made. The
return on investment is 94.53%. Obviously, it is
impossible for the tourist hotel to carry out
marketing activities for all the customers. Therefore,
those who bring profits greater than costs to tourist
hotel are the marketing object.
4.2 Method Comparison
From what we have discussed above, we can see the
identification method of customer value based on
RFM analysis only need simple statistical
knowledge and some uncomplicated calculation.
Through them it is convenient for enterprise
managers to judge the customer value. Not like the
method based on data mining, managers do not have
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168

to know any professional data mining knowledge.
Also not like the method based on fuzzy
comprehensive evaluation, it does not have to use
Delphi method to determine the weighted value of
evaluation indicators at first. So when we do not
need the identification results to be fairly accurate,
using the statistical method based on RFM analysis
is more convenience.
5 CONCLUSIONS
Customer value judgment is the core activity of
CRM, especially in the service industries such as
tourist hotels. It is just like the famous idea that 20%
of the customers will create 80% of the profit, the
valuable customer can bring most of interest to an
enterprise. So finding a method to judge customer
value in tourist enterprises becomes very important.
In this paper, we introduce the statistical method of
judging customer value by RFM analysis, and show
how to use this method in tourist enterprise. The
convenience and correctness of this method in
practice is demonstrated through an effect analysis.
After the study on customer, the enterprise manager
can more clearly determine who the most valuable
customers are. And they can accordingly invest in
them with effective resources selectively in order to
gain more profit and to maintain good customer
relationship.
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