日本ソーシャルデータサイエンス論文誌
第 4 巻 第 1 号（2020 年 3 月）

原著論文

Proposal of Loyal Customer Discriminant Model Based on RFM
Concept—Empirical Analysis using Golf EC Site Data—
Hiroyuki OIKAWA†1

Kohei OTAKE†2

Takashi NAMATAME†3a

Abstract: Customer loyalty measurements attract more attention from a viewpoint of customer relationship management; hence,
there are some studies that have explored this field. However, none of these studies have considered the potential customer features.
In this study, the customer behaviors out of and in the site were considered. This helped determine a wide variety of customer
behaviors by adding the behavior data out of the site and has also helped identify a loyal customer more appropriately. We designed
a model that can determine if a customer has high loyalty to the electronic commerce (EC) site. To achieve this design, we used
the behavioral data out of the EC site in addition to recency, frequency, and monetary (RFM) data from the purchasing data.
Furthermore, we adopted a logistic regression from previous studies to accomplish this model. Moreover, we analyzed the purchasehistory of the loyal customer identified in the discriminant model in order to understand the purchasing tendency. Three customer
classifications were used in this study: Customer A, B, and C, and the precision of the RFM and our RFMO models was compared.
The RFMO model proved to be more precise in classifying loyal customers and the other customers with high precision comparedwith the ordinal RFM model about Customer A and Customer B. In addition, it was shown that purchasing tendencies are different
between the loyal customers.
Keyword: Marketing, EC Sites, Loyal Customer, Logistic Regression

1. Introduction

Owing to a reduction in semiconductor prices, companies

Recently, the size of the electronic commerce (EC) market has

accumulate a large amount of various data; this is called the Big

increased in Japan. According to the Ministry of Economy, Trade

Data era, which plays an important role in businesses.

and Industry in Japan, the EC market size in Japan has increased

The customer loyalty on the EC site is influenced by various

by 3 times in eight years from 2010 to 2018 [1]. On the EC site,

factors and is measured using purchasing data (ID-POS data) or

consumers can purchase conveniently at any time without anybrowsing data (access log). Above all, characterization by three

temporal or spatial constraints.

indices of RFM is commonly used to classify a customer.

Owing to the recent prevalence of big data, the quantity andCompanies predominantly use recency (recent purchasing date),

types of data that can be archived at a company unit have alsofrequency (purchasing frequency), and monetary (purchasing

increased remarkably. As a result, various analyses are conductedamount of money), in short RFM, to measure customer loyalty.

using big data on an EC site run by a company. Noteworthy is

However, these indices are based on data obtained fromthat customer loyalty measurements attract more attention from acustomers at a certain point in time. Therefore, not all individual

viewpoint of customer relationship management (CRM).

characteristics of customers are captured [2].
On the other hand, due to the big data era mentioned above, we

2,200.00
5.79%
5.43%

1,800.00
4.37%
1,400.00(Billion JPY)

6.00%

site (the real world) from new services and social media. For

1,798.45

4.75%

1,600.00
3.85%

1,200.00

3.17%

3.40%

2.84%

5.00%

1,650.54

778.801,377.46

4.00%

1,279.70
1,116.60

the EC site), which could not be known in the past. Thus, they

2.00%

can be useful when planning marketing promotions.

1.00%

This paper comprises 9 sections. In Section 2, we explain the

0.00%

purpose of this study; Section 3 describes previous studies and200.00
0.00
2011

2012

2013

EC market size

2014

2015

2016

2017

2018

Ratio of EC

These data sets show customer’s real behavior (behavior outside

3.00%

845.90400.002010

example, on a golf EC site, there are a few new services such asgolf play data (game score) and reservation data of the golf course.

1,513.58

951.30800.00600.00were able to extract the behavioral data of the customer out of the

6.22%

2,000.00

1,000.00

7.00%

the positioning of this study; Section 4 presents the data summary
used in this study; Section 5 is an analysis of current conditions

Figure 1 Transition in Japan EC market size

based on real onsite problems; in Section 6, we explain the†1 NTT Communications Corp.
†2 Tokai University†3 Chuo University (Correspondence Author: nama@indsys.chuo-u.ac.jp)ⓒ2020 Japan Social Data Science Society

25日本ソーシャルデータサイエンス論文誌
第 4 巻 第 1 号（2020 年 3 月）
development of the model to use for this analysis; in Section 7,

data, and accesses logs when measuring the loyalty of thewe explain purchasing trends of loyal customers’ products;

customers. Behavioral data from out of the site accumulate in the

Section 8 presents the discussion of this study; and lastly, inenterprise unit in the current big data era. However, there are very

Section 9, we draw our conclusion and mention future

few applications wherein these data were utilized.

applications.

Recently, data acquisition from various services has become
possible thanks to the widespread portal site. In addition to the

2. Purpose of this study

purchasing data that have been used to measure customer's
loyalty in the past, reservation data in a golf course and play score

In this study, we propose a model that can classify a loyal

data of EC site members for a course were employed as thecustomer with a high loyalty to the EC site. We therefore usedbehavioral data from out of the site.

behavioral data out of the EC site in addition to RFM from thepurchasing data. The behavioral data capture a wide variety ofcustomer behavior hence we can identify a loyal customer more

4. Data summary

appropriately. To develop the model, we used a logistic regression

In this study, we focus on one of the largest golf portal sites in

adopted from previous studies.

Japan. We used the purchasing, reservation, and the play score

In addition, we analyzed the purchase history of the loyal

data of the golf course from A.

customer identified in the discriminant model. This helps us

4.1 Purchasing data

understand the purchasing tendency of the loyal customer.

The purchasing data refer to the data that record the purchasing

Finally, we proposed a marketing tool by using the analysis ofbehavior history in the golf EC site. Information such as thethe purchasing tendency and the discriminant model. In this study,

purchase date, product information, price, and the amount is

we examine golf EC sites to identify loyal customers.

considered.
4.2 Golf course reservation data

3. Previous studies and relevance of this study

The reservation data refer to the data that record the reservation
history of the golf course. Information such as the location for thePreviously, the evaluation index of RFM from the purchasing

golf course, reservation date, and the play date is recorded.

data was used to measure the customer's loyalty. The index has

4.3 Golf score data

proved to be effective.

The golf score data refer to the data used to record the score at

In addition to the RFM index, Mahboubeh et al. [3] used an

the time of the play and the score after every 1 round of each user.

index to evaluate the customer's loyalty based on the number of

The players registered the score themselves when they played.

items purchased; however, it showed no significance. As

Only the data that satisfy the following conditions and areobserved in the result, the RFM index had a certain

common to a subscriber ID will be analyzed. The periods

interpretability.

considered are as follows.

On the other hand, Abe [2] reported that the RFM index is
widely used to determine excellent customers at a site in CRM.

Purchase Data

Golf course reservation data

However, these indices are significantly influenced by the time ofobservation; thus, it is not enough to use only the index of RFM

when measuring the customer's loyalty.
As a result, various behavioral data are used in addition to
RFM. Doi et al. [4] proposed a model that determines excellentPeriod: September 1, 2013 to August 31, 2015
Period: September 1, 2001 to August 31, 2015

Golf score data

Period: September 1, 1970 to August 31, 2015

customers in a store using the history of the system that registers
the check-in to stores by smartphone applications. The proposedmodel and the discriminant model are highly accurate. The results

5. Analysis of the current condition

show that other types of data in addition to the purchasing data

We first analyzed the loyal customers in Company A after

can be acquired.

acquiring data following our discussion with the marketing

As Abe [3] points out, the RFM index depends on how it isobserved; thus, it is an inadequate tool to classify customers.

manager. Company A runs the operation of a golf portal site. The
following points were noted from our discussion:

Instead, we need an index that shows the customer's behavior outof site, uses behavioral data in the site similar to the purchasingⓒ2020 Japan Social Data Science Society

26日本ソーシャルデータサイエンス論文誌
第 4 巻 第 1 号（2020 年 3 月）
Table 1 Data summary.
Purchase Data

two periods, as shown in Table 2.

2,179,348(No. of transaction)
Member• Determination of analysis period• Selection of customers to beanalyzed• Determining the definition of good
customers

49,068(No. of ID)
Golf score data

• Creation of data set
to create discriminant model

17,340,121(No. of transaction)
Golf course reservation data

9,308,890(No. of transaction)1.

seasonality, the target data for the analysis were separated into

• Creation of dataset
to learn created discriminantmodel

The repeaters had previously contributed immensely to the• Creation discriminant modelusing previous method (RFM)

rate of sales but have decreased in recent years.
2.

• Creation discriminant modelusing proposal method (RFMO)

Other companies have intensified the competition of
attracting customers.

3.

• Creation of data set
to test created discriminant
model

Specificcustomers

areaffordedcustom• Determination of evaluationindex of accuracy

services(incentives).
4.

It is difficult to detect loyal customers.

• 10 fold cross validation
using to learn discriminantmodel (RFM)• 10 fold cross validation
using to learn discriminantmodel (RFMO)• Testing accuracy of createddiscriminant model using test
data (RFM)• Testing accuracy of createddiscriminant model using test
data (RFMO)

Points 1 and 2 above show how increasingly difficult it is toattract loyal customers in Company A. Regarding point 3,
periodic magazines were mailed; nonetheless, they proved
ineffective. With regard to point 4, although Company A has

Figure 2 Flowchart of analysis

acquired the data pertaining to customer attributes, it has notrealized any concrete marketing campaign. Generally, gathering

Table 2 Analysis period

new customers requires more advertising costs than retaining

Period No.

loyal customers. Thus, the marketing manager of Company A said
that the specification of a loyal customer is very important for

Period 1

them to save advertisement expenses and make profits.
After the discussion, we realized that a customer might become

Period 2

loyal in two ways:
I.

Through years of continuous purchases

II.

After a sudden change in purchasing behavior

Analysis period
September 1, 2013
~ August 31, 2014
September 1, 2014
~ August 31, 2015

First, the number of purchase categories and the accumulated
number of purchases were used as the linear measurement forcustomer loyalty. The kind of clubs (driver), clothes (shirt, pants,

We define customer [I] as a “continuous loyal customer” andand skirt), a ball, and a glove fall in purchase categories. The

customer [II] as a “hidden loyal customer.” We have designed aaccumulated number of purchases indicates the total number ofmodel to identify each loyal customer ([I] and [II]) to understandpurchases in 1 year. Customers were segmented in each period

their purchasing tendency.

following the conditions outlined in Table 3.

6. Proposed model
Figure 2 below presents the outline of the analysis of this study.

Table 3 Ranking conditions of customer
Rank

Condition

upper

5 or more number of purchases andMoreover, it describes the specific creation summary of themodel.
6.1 Statistical model
To control the fluctuations in purchasing behavior due toⓒ2020 Japan Social Data Science Society

5 or more in the number of product categories
middleExcept for upper and lower

lower

2or less number of purchases and2 or less in the number of product categories

27日本ソーシャルデータサイエンス論文誌
第 4 巻 第 1 号（2020 年 3 月）
In this study, we analyzed customers who switch like A, B, andusing each explanatory variable of RFM and the other is a modelC, as shown in Figure 3.

that includes an index of O, which shows customers outside the

Period 1

site behavior. Here, the O index refers to a general term for the

Period 2

behavior variables of each customer; it was made using somereservation data and play score data. In particular, the play scoreupper

data contain detailed information on the golfer's play per round.
Specifically, there are data on the hole score, which indicate thenumber of occurrences of birdies, pars, bogies, and triple bogies,

middleand data on the play content such as the number of OBs and thenumber of fairways. We created variables to use in the O index;
these variables are listed in Table 4.

lower
Table 4 Explanatory Variables for index O
Figure 3 State of the customer segments switch

Type of data

We regarded customers who switch like A as continuous loyal

Explanatory Variable

Purchase data

RFM score

Golf score data

No. of Playedcustomers, and B and C as hidden loyal customers.

Statistical values of total
score

A: Continuous loyal customer

Statistical values of hole

The customer who purchases as an upper customer together inscore ratio (e.g. birdie, par,sites during both periods. Thereafter, we call a Continuous loyal

bogey)

customer as Customer A.

Statistical values of play

B: Hidden loyal customer-1

content

The customer who was a middle customer in the first period but

fairway, bunker)

became an upper customer in the second period. Hereafter, we

Golf course reservation data

ratio

(e.g.

OB,

No. of reserved

call Hidden loyal customer-1 as Customer B.

No. of types which have been

C: Hidden loyal customer-2

reserved

The customer who was a lower customer in the first period, but

No. of cancel after reserved

became an upper customer or a middle customer in the second
period. Hereafter, we call Hidden loyal customer-2 as CustomerC.

Here, we calculated the average, maximum, minimum, andmedian values for the three statistical values of the "total score,”

Logistic regression was widely and effectively used whencreating a discriminant model for customers in previous studies[5, 6, 7, 8]. Therefore, in this study, we propose a logistic
regression model to distinguish between a continuous loyal
customer and a hidden loyal customer. The continuous loyal
customer probability 𝑝𝑖 is expressed by the following equation(1):"hole score ratio", and "play content ratio" using the play history
for each user. These statistical variables are treated as different
explanatory variables in this study.
From the variables shown in the table, we choose explanatory
variables, which are to be used in the model as the index of O.
We have assigned loyal customers as the objective variable andhave conducted a logistic regression using all the explanatory
variables. This was achieved by choosing the combination of
explanatory variables with the lowest Akaike’s Information𝑝𝑖 =

exp{𝑏0 + ∑𝑗 𝑏𝑗 𝑥𝑖𝑗 }1 + exp{𝑏0 + ∑𝑗 𝑏𝑗 𝑥𝑖𝑗 }(1)

where 𝑥𝑖𝑗 refers to the factors that affect loyalties and 𝛽𝑗 refers
to parameters for each explanatory variable (𝛽0 is intercept).
Next, we will report on the explanatory variables used in
identification. We created two kinds of models: one is a modelⓒ2020 Japan Social Data Science Society

Criterion (AIC). Table 5 lists the explanatory variables of theindex of O that were applied to each model.
6.2 Model evaluation
In this section, we conduct a comparative verification using themodel created to identify all three kinds of loyal customers.
RFM and RFMO models are used as explanatory variables.

28

日本ソーシャルデータサイエンス論文誌
第 4 巻 第 1 号（2020 年 3 月）
Table 5 Explanatory Variables
Customer-A

Customer-B

Recall: The ratio of predicted true result in the whole
Customer-C
TP
TP + FN

total-score-Median

total-pat-Max

total-score-Min

rate-birdie-Max

rate-par-Mean

total-pat-Mean

rate-par-Min

rate-double-bogey-Mean

total-pat-Min

rate-bogey-Mean

rate-double-bogey-Min

total-pat-Medianrate-bogey-Max

rate-double-bogey-Max

rate-par-Medianrate-bogey-Medianrate-triple-bogey-Min

rate-double-bogey-Max

rate-double-bogey-Mean

rate-fairway-Mean

rate-triple-bogey-Medianrate-triple-bogey-Min

rate-fairway-Max

rate-penalty-Max

rate-fairway-Min

rate-penalty-Min

rate-bunker-Mean

Moreover, we inspected these models using period 2. In

rate-ob-Mean

rate-penalty-Max

rate-bunker-Min

addition, we adopted the model with the highest F-value from therate-ob-Min

rate-bunker-Min

rate-sandsave-Min

result of the 10-fold cross validation for the inspection. We show

rate-ob-Medianrate-bunker-Medianrate-par-on-Max

the mean of all the evaluation indexes in Table 7 as the precisionrate-penalty-Mean

rate-bogey-on-Max

rate-bogey-on-Max

value of each model.

rate-penalty-Medianreservation-countreservation-count

Recall =(4)

F-value: Harmonic mean of Precision and Recall
2 × Recall × Precision
Recall + Precision

F − Value =(5)

The numbers for the RFMO model are larger than in the RFM

rate-sandsave-Minmodel, particularly in Accuracy and Precision out of the 4

rate-bogey-on-Meanevaluation indexes. However, the numbers for the RFM model

rate-bogey-on-Max

became larger than in the RFMO model for the Recall and F-value.

rate-bogey-on-Median

We distinguished each loyal customer by the respective models

reservation-countand conducted a comparative inspection using the inspection data.

number-of-types-of-golf

The obtained result is shown in Table 8 to Table 10.

6.2.1 Evaluation method

Table 7 Accuracy results of 10-fold cross validation

The proposed model is evaluated in this section. In addition, we

Modelcompare the precision, evaluated by 10-fold cross validation

RFM

using period 1 to assess the validity of the analysis for each model.

model

The evaluation results are shown using the confusion matrix in

Customer-A

model

value as indicators to evaluate precision. These indicators are

RFM

shown in equations (2) to (5).

model

Table 6 Confusion matrix of the classification results
False

Prediction

Positive

TP

FP

ResultNegative

FN

TN

RecallF-value

0.654

0.594

0.920

0.736

0.861

0.841

0.900

0.879

0.553

0.530

0.951

0.680

0.590

0.589

0.659

0.608

0.590

0.668

0.359

0.486

0.697

0.683

0.737

0.709

RFMO
model

Actual Result
True

Precision

RFMO

Table 6. In addition, we used accuracy, precision, recall, and F-

Customer-B

Accuracy

RFM
Customer-C

model
RFMO
model

Table 8 Comparison results of the four values of the

Accuracy: The ratio of the predicted real result in the wholediscriminant model in Customer-A
Accuracy =TP + TNTP + FP + FN + TN

(2)

Model
RFM
model

Precision: The ratio of the predicted true results in the whole

RFMO
model

Precision =TP
TP + FPⓒ2020 Japan Social Data Science Society

Accuracy

Precision

RecallF-value

0.450

0.235

0.974

0.378

0.838

0.517

0.901

0.657

(3)

29

日本ソーシャルデータサイエンス論文誌
第 4 巻 第 1 号（2020 年 3 月）
Table 9 Comparison results of the four values of thediscriminant model in Customer-B
Model
RFM
model
RFMO
model

Accuracy

Precision

RecallF-value

0.283

0.077

0.996

0.142

0.612

0.127

0.938

0.224

Table 10 Comparison results of the four values of thediscriminant model in Customer-C
Model
RFM
model
RFMO
model

Accuracy

Precision

RecallF-value

0.674

0.069

0.095

0.080

0.470

0.026

0.071

0.038

From the results obtained, we performed the comparison,
which is the result of the obtained confusion matrix at the RFM
and RFMO models for each loyal customer.
Customer A: As Table 8 shows, the Accuracy of the RFMO

Figure 4 Boxplot of each loyalty variable in Customer-C ofmodel is higher than that of the RFM model. The value of Recall

teacher data

is similar for both models, and the precision of the RFMO model
is higher than that of the RFM model. Therefore, the F-value ofAs shown in Figure 4, the data set in each explanatory variable

the RFMO model became high.

yielded no change. Therefore, regarding Customer C, the variable

Customer B: As Table 9 shows, the Accuracy of the RFMO

used for this model is not that significant to differentiate between

model is higher than that of the RFM model. The value of Recall

it.

is similar for both models, and the precision of the RFMO model
is higher than that of the RFM model. Therefore, the F-value of
the RFMO model became high.
Customer C: As Table 10 shows, the Accuracy of the RFM
model is higher than that of the RFMO model. The Precision of
the RFM model is higher than that of the RFMO model. Similarly,
the Recall value of the RFM model is higher than that of the
RFMO model. Therefore, the F-value of the RFM model became

7. Item trend of loyal customer
In this study, we know the purchase tendency of each loyal
customer besides that of Customer C that has a small F-value. The
tendency of the total purchase price for every category in eachperiod of Customer A classified with the RFM and RFMO modelis indicated using Figure 5.

higher than that of the RFMO model. However, the F-value of thetwo models is relatively low because both the Precision and
Recall values were also low.
From the comparative inspection discussed above, theprecisions of classification of the RFMO model were higher thanin the RFM model for Customer A and Customer B. On the other
hand, the F-value was lower in Customer C. Therefore, we findout that both models did not grasp Customer C. As a result, we
checked the data set that was divided into loyal customers andother customers. Figure 4 shows the boxplot of explanatory
variables for two data sets.

ⓒ2020 Japan Social Data Science Society

Figure 5 The purchase total amount ratio of Customer-A30日本ソーシャルデータサイエンス論文誌
第 4 巻 第 1 号（2020 年 3 月）
As shown in Figure 5, the total purchase price of each category

customers. Customers who do not use the EC site in the portal

for the RFM model and RFMO model has a similar componentsite can also be classified as excellent customers in the future.

ratio. For the above reasons, the loyal customers identified by

However, no model could specify Customer C in our analysis.

each model find out that they could be customers with a similar

We thought that it is not possible to make a model withoutpurchase tendency. The percentage occupied by the dominating 5

customers who play golf have high level of proficiency. Golf

categories in the total purchasing total amount of the top 10 items

course reservation and play score data were used when making

is approximately 60%, and the purchase tendency of the goods

the index that shows customer loyalty on the site. The customer

does not change in each period. In addition, club kinds are asegments from these data were extracted have a high level ofdominant category. Therefore, this model shows that the

proficiency with golf.

identified Customer A is the customer who purchases high pricerange goods.
Figure 6 summarizes the tendency of the accumulated purchaseprice for every category in each period of Customer B.

Moreover, the amount of the purchasing data and play scoredata for Customer C in this study was insufficient, but the data
were discriminated properly. Therefore, we thought that the
precision of the model improves using data, which much acquire
access log data and the data about social login.
When we grasped each loyal customer and looked for thepurchase tendency of the goods, Customer A and Customer B
showed different purchase tendencies.
From our comparison results, the total purchase amount of theball in Period 2 of Customer B decreased from Period 1. The
purchase tendency in the above categories changed from balls toother categories such as clubs and clothes.
We can deduce that Customer B focuses on playing golf or isinterested in other categories. In addition, we assumed that
Customer A is strongly committed to a club because it waspreviously devoted to golf as part of its lifestyle. We also assumed

Figure 6 The purchase total amount ratio of Customer-B

that not all loyal customers have the same loyalty to golf; hence,
we developed a marketing policy on a small scale for each

Figure 6 compares the RFM and RFMO models; the purchasecustomer.

tendency calls period 2 from period 1, and there is no difference.

Therefore, when the customers who are in the upper segment

For this reason, each model identified the same Customer-B. On

of Customer B in Period 2, measures which treat the club as tothe other hand, when comparing the purchase tendency of theacquire customers is effective. The following measures to attract

goods in each period by the respective models, the names of thecustomers were considered:

5 dominating categories do not change. However, the ratio of theaccumulating purchase price of the ball decreases and the

Discount for a club

percentage that the 5 dominating categories occupy decreases. AsIncreasing point giving ratea result, Customer B could be shifting purchases to otherconsumables such as a ball.

The customer will therefore react to a marketing policy of aclub and purchase goods. As described above, in order to increase

8. Discussion

a customer's loyalty to a store, a strong promotion and marketing
policy are needed.

As seen in the result, the RFMO model can classify a loyal
customer and the other customer with a higher precision than the
RFM model regarding Customer A and Customer B. We assumed

9. Conclusion and future applications

that the loyalty variable could be useful when discriminating a

In this study, we designed a model that can classify and discovercustomer with high loyalty.

a customer with high loyalty by using customer behavioral data

The knowledge imparted by this study can be applied inand purchase data. It was possible to use customer's loyalty

business. We used novice data techniques never used before.

variable for a customer with accumulated golf course reservation

Owing to new indices, we were able to discriminate excellent

data and playing score data and the utility was also admitted. Byⓒ2020 Japan Social Data Science Society

31日本ソーシャルデータサイエンス論文誌
第 4 巻 第 1 号（2020 年 3 月）
discriminating a loyal customer and analyzing the purchase data,
the purchase tendency of a customer with high loyalty to an EC
site can be known. Furthermore, it was possible to discern thepurchase tendency for goods disregarded by loyal customers.
The data set used for a customer of a lower segment in period
1 was small and so could not be used as a loyalty variable.
Therefore, a good model for application could not be extended toeach model.
Recently, the portal site dealing with golf items focused on
customer development using various contents such as lessons,news, and event information. In addition, it was possible to record
the customer's reading history in a portal site.
Given the literature established in this study, a combination ofdata obtained through different contents and before purchasing isneeded for effective analysis, in addition to the data used in thisstudy. Another model should also be proposed for othercommodities. This model will be more effective in analyzing thedata obtained from the customers outside behavior.

Acknowledgement
We thank a golf portal site company for permission to use
valuable datasets and for useful comments. This work was
supported by JSPS KAKENHI Grant Number 19K01945 and
17K13809.

References

Improvement of Information Infrastructure and Improvement ofService Infrastructure in Japan's Economic Society, (Market
Research on E-commerce) (2016). (in Japanese)[2] Abe M.: “RFM Measures and Customer Lifetime Value:
Investigating the Behavioral Relationship in a Non-Contractual
Setting using a Hierarchical Bayes Model,” Journal of the Japan
Statistical Society, Vol. 41, pp. 52-54, (2011), (in Japanese)[3] Manboubeh, K., Kiyana, Z., Sarah, A. and Somayeh, A.: “Estimating
Customer Lifetime Value Based on RFM Analysis of Customer
Purchase Behavior: Case study,” Procedia Computer Science, Vol.
3, pp. 57-63 (2010).
[4] Doi, C., Katagiri, M., Ishii A., Araki, T., Inamura, H. and Ohta, K.:“Estimating Value of Customer Through Store Check-in Histories
and its Application for Visitor Promotion,” Journal of Multimedia,
Distributed, Cooperative and Mobile Symposium 2016, pp.735-741(2016). (in Japanese)[5] Suzuki, H., Mizuno, M., Sumita, U. and Saji, A.: “Characteristic
Evaluation and the Financial Effect of the Excellent Customer
Discrimination Technique for CRM,” Department of Social Systems
and Management Discussion Paper Series, University of Tsukuba,
No. 1123 (2005) (in Japanese)[6] Nakahara, T., Uno, T. and Yada, K.: “Extracting Promising Sequential
Patterns from RFID Data Using the LCM Sequence,” 14th
International Conference on Knowledge-Based and Intelligent
Information & Engineering Systems, Part III, pp. 244-253 (2010).
[7] Hara, A., Takano, M. Shtykh, R. and Kawabata, T.: “Extracting
Segments of Users with Conversion Probabilities for Internet
Advertising,” Proceedings of Annual Conference of Artificial
Intelligence Society, Vol. 29, pp.1-4 (2015).
[8] Rosales, R., Chang, H. and Manavoglu, E.: “Post-click Conversion
Modeling and Analysis for Non-Guaranteed Delivery Display
Advertising,” Proceedings of the Fifth ACM International
Conference on Web Search and Data Mining, pp.293-302 (2012).

[1] Examination of Ministry of Economy, Trade and Industry,ⓒ2020 Japan Social Data Science Society

32