The Segmentation of Garuda Frequent Flyer
THE SEGMENTATION OF GARUDA FREQUENT FLYER
DEVI FITRI YANI
DEPARTMENT OF STATISTICS
FACULTY OF MATHEMATICS AND NATURAL SCIENCES
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2013
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Pertanian Bogor.
Bogor, Juli 2013
Devi Fitri Yani
NIM G14090107
ABSTRACT
DEVI FITRI YANI. The Segmentation of Garuda Frequent Flyer. Advised by
ASEP SAEFUDDIN dan ENNY KRISTIANI.
Airline loyalty program such as frequent flyer program, is a marketing
strategy used by airline industries to maximize their profit and to satisfy their
loyal customers. Garuda Frequent Flyer is a program presented by Garuda
Indonesia as an appreciation to their loyal customers. The model is created to
segment customers based on key airline industry drivers such as yield, frequency,
product, distance and activity pattern. RFM analysis is used to score individuals
based on Recency, Frequency and Monetary using Principal Component Analysis.
This score is used to segment customers using k-means algorithm to interpret
Garuda Frequent Flyer member purchasing behavior which is important for
marketing strategy.
Keywords: frequent flyer program, k-means clustering, marketing strategy,
principal component analysis, RFM analysis
THE SEGMENTATION OF GARUDA FREQUENT FLYER
DEVI FITRI YANI
Scientific Paper
to complete the requirement for graduation of
Bachelor Degree in Statistics
at
Department of Statistics
DEPARTMENT OF STATISTICS
FACULTY OF MATHEMATICS AND NATURAL SCIENCES
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2013
Title
Name
NIM
: The Segmentation of Garuda Frequent Flyer
: Devi Fitri Yani
: G14090107
Approved by
Prof Dr Ir Asep Saefuddin, MSc
Advisor I
Ir Enny Kristiani, MSc
Advisor II
Acknowledged by
Dr Ir Hari Wijayanto, MS
Head of Department
Graduation Date:
ACKNOWLEDGEMENTS
Alhamdulillah wa Syukurillah, many thanks to Allah subhanahu wa ta’ala
for the strength opportunity that I can finish my research with the title The
Segmentation of Garuda Frequent Flyer.
I realize the completion of this research will not happen without the support
from many people. I would like to express my sincere gratitude to my advisors,
Prof Asep Saefuddin for his advices and helpful guidance and Ir Enny Kristiani,
MSc for her warm suggestions and comments from airline industry point of view.
Thank you for all my friends who have continuously supported me during
this research and paper writing. I can not mention one by one and also for both of
my parents who always give support that I keep on fighting to finish research.
Finally I hope this research can be useful for many people.
Bogor, July 2013
Devi Fitri Yani
CONTENT
LIST OF TABLE
vi
LIST OF FIGURE
vi
LIST OF APPENDIX
vi
INTRODUCTION
1
Background
1
Objectives
2
METHODOLOGY
3
Data Sources
3
Methods
3
RESULT AND DISCUSSION
5
Data Exploration
5
RFM Analysis
6
Garuda Frequent Flyer Segmentation
CONCLUSION AND RECOMMENDATION
10
13
Conclusion
13
Recommendation
13
REFERENCES
14
APPENDIX
13
BIOGRAPHY
15
LIST OF TABLE
1 Data type and source
3
2 Average transaction amount of Garuda Frequent Flyer
6
3 Indicator variables for RFM
7
4 Recency score
8
5 Pearson correlation among Recency, Frequency and Monetary
8
6 Garuda Frequent Flyer RFM score
9
LIST OF FIGURE
1 The percentage of Garuda Frequent Flyer based on tier
6
2 The age distribution of Garuda Frequent Flyer
7
3 The RS value
9
4 The member percentage in each cluster
11
LIST OF APPENDIX
1 Principal component analysis results for RFM score
15
2 Weighted Variable for RFM Score
16
3 Cluster classification using PROC CANDISC
17
4 Cluster centroids
18
5 Distances between cluster
15
INTRODUCTION
Background
Since geographical boundaries are no longer considered as restriction for
many people to do business or pleasure, the profit from airline industry nowadays
has been massively increased. The growth in air traffic is accelerated by the
falling price of air transport and an increase in economic activities. Falling airfares
and rising personal incomes have also led to an increase in the demand for leisure
trips. Globalization, accelerated economic growth, liberalization of trade and the
natural growth of population have had positive impacts on the demand for
business travel (Chin 2002).
This situation leads every airline industry to compete each other to attract
most passengers in order to use services they are offering. Because most
competitors operate more or less technology platform and offer almost similar
services, players within airline industries have to battle to present the best services
and offerings to their passengers. To win this competition airline needs to work
harder to gain their customer‟s loyalty by offering them the best deals to stay with
single airline rather than travel with many airlines. Bolton (2000) stated that
naturally companies that offer loyalty reward programs believe that their
programs have long-run positive effect on customer evaluations and behavior.
In appreciation of its valued loyal customers, Garuda Indonesia undertakes a
frequent flyer program, known as the Garuda Frequent Flyer (GFF). Frequent
flyer programs are initially devised by airlines to encourage customers who travel
frequently – mainly as a result of increasing switching costs to the competitors.
Such strategy is based on rewarding schemes by offering their members upgrades
from tourist class to business or first class, free flight tickets or other advantages
in check-ins, priority on the waiting lists of overbooked flights, extra baggage
allowances or business lounges. These benefits are obtained by converting a
certain number of „air miles‟ which are accrued each time the member flies
(Martin et al. 2011).
FFPs are designed to ensure customer loyalty by offering various forms of
bonuses to customers for whom the customers save up each time they fly. It is not
something new that airlines industry gains significant profit from FFPs. Dreze and
Nunes (2004) stated that the frequent flyer miles represent one of the world‟s most
popular currencies, with 100 million people collecting the 500 billion miles that
130 airlines distribute annually. The US loyalty marketing industry, a $6 billion
market, contains more than 2,000 loyalty programs, and 90% of Americans in at
least one loyalty program (Berman, 2006).
Even if FFPs gives many benefit to airlines industry both to maintain
customers loyalty and also to gain more financial benefit from members, but the
segmentation is essentially needed. Market segmentation is developed through
different marketing activities. It can be said that the process of segmentation
requires the knowledge of the characteristics of its own customers in order to
check if they are coherent with the corporate objectives (Montinaro & Sciascia
2011).
2
Customer segmentation gives quantifiable way to analyze the customer data
and distinguish the customers based on their purchase behavior. In this way the
customers can be segmented into different groups for which the company can
employ targeted marketing and thus retain the customers. Once the customers are
segmented, rules can be generated to describe the customers in each group based
on their purchase behavior.
RFM method is very effective for customer segmentation. R means recency
which indicates the time interval between the present and previous transaction
date of a customer. F means frequency which indicates the number of transactions
that the customer has done in a particular interval of time. M means monetary
which indicates the total value of the customer‟s transaction amount. It has been
proven that the value of R, F and M decides the characteristics of the customer
behavior.
Segmentation based on RFM has been used for over 50 years by direct
marketers to target a subset of their customers. RFM analysis is commonly
performed using the Arthur Hughes method, which bins each of the three RFM
attributes independently into five equal frequency bins. The resulting 125 cells are
depicted in a tabular format or as bar graphs and analyzed by marketers, who
determine the best cells (customer segments) to target. Hughes also stated that
RFM analysis is easy to perform and results in a good segmentation.
Hughes (1994) mentioned that the three variables in the RFM model are
equal in the importance; therefore, the weights of the three variables are identical.
On the other hand, some literatures (Liu et al. 2005, Shen et al. 2009, Deng et al.
2008) indicates that the three variables are different in the importance due to the
characteristic of industry. Thus, the weights of the three variables are not equal.
In this study it is assumed that the importance of the three variables are not
equal so that weighing method using PCA is used to combine RFM score. After
that the data mining techniques is used to analyze historical data of Garuda
Frequent Flyer Members. These techniques including K-mean algorithm are used
to segment customers into groups according to their personal profiles and mileage.
As a partitioning method, the k-means algorithm is one of popular heuristic by
most of applications (Han et al. 2011).
The idea behind the model is to identify and understand separate customer
into several groups. Each customer segment has different behaviors and needs
from the airline, and also needs different marketing approach. By creating
segmentation, Garuda Indonesia is able to send relevant offers and messages to
each of their Garuda Frequent Flyer Members.
Objectives
The objectives of this study are to analyze the potential customers from
Garuda Frequent Flyer using RFM analysis and to segment those members into
several groups based on their characteristic and purchasing behavior.
3
METHODOLOGY
Data Sources
This study uses data from Garuda Frequent Flyer Membership in 2012 with
the total population 669,330 members. The explanatory variables are selected
from five aspects, which include age, current tier type, frequency, last transaction
date and transaction amount.
Table 1 Data type and source
Data Type
- Customer‟s Profile Data: gender,
age, location, country, company,
tier type
- Transaction Data: transaction date,
frequency balance, flight number,
ticket price
- Marketing data
- Garuda Indonesia Profile
- Garuda Frequent Flyer Profile
Source
- Garuda Indonesia
- Garuda Indonesia Official Website
- Magazines: Tempo, Info Airlines,
CAPA, Yahoo! Finance
- Internet
Methods
The methods used in this research are:
1. Choose individuals by proportional systematic sampling. The sampling method
following the amount of each tier which is classified to Blue, Silver, Platinum,
Kids and Gold. After choosing the tier proportionally, then the individuals are
chosen based on systematic random sampling.
2. Explore the data
3. Estimate the RFM Score
The goal of RFM is to segment customers based on buying behavior.
The chosen customers are ranked based on each individual RFM factor. Two
common scoring methods on RFM are: (1) Customer Quintiles and (2)
Behavior Quintiles (Miglautsch 2000). This research implements the second
method with the following steps:
a. Select last transaction date as recency variable, transaction frequency as
frequency variable and transaction amount as monetary variable for each
customers
b. Combine the Recency, Frequency and Monetary score altogether using
Principal Component Analysis.
Principal component analysis is a statistical approach that can be used
to analyze interrelationships among a large number of variables and to
explain these variables in terms of their common underlying dimensions
(Anderson et al. 2010).
Suppose that the random variables
of interest have a certain
multivariate distribution with mean vector µ and covariance matrix ∑. We
4
assume, of course, that the elements of µ and ∑ are finite. The rank of ∑ is r
≤ p, and the q largest characteristic roots
of ∑ are all distinct.
In principal components, the total eigenvalues of the correlation matrix is
equal to the total number of variables being analyzed. So that it can be
summarized the contribution from the i-th principal component is:
∑
and for q principal component is:
∑
∑
If the result of eigenvector which is going to be picked is more than one
component, because some researchers believe that the optimum amount of
components to be picked is when the cumulative variance of those variables
is bigger or equal to 70% of the total variance, then weighted value is
needed calculated from this formula:
√
The output of this method is a single RFM Score combination.
4. Check the multicolinearity among the explanatory variables by using Pearson‟s
correlation.
5. Check the existence of outliers among the explanatory variables.
6. Allocate customers into clusters based on similarity with the following steps:
a. Clustering Evaluation.
The good clustering is the one which attain high similarity within
cluster and low similarity among clusters. In order to achieve the optimum
clustering method, we may use some method to evaluate the number of
clusters such as R-square value
RS is used to measure the dissimilarity of clusters. Formally it
measures the degree of homogeneity between groups. The values of RS
range for 0 to 1 where 0 means there is no difference among the clusters and
1 indicates that there are significant difference among the clusters.
, where
∑ ∑ (
∑
̅) ,
(
̅)
where nc is the number of clusters, d the number of variables (data
dimensionality), nj is the number of data value of j dimension while nij
corresponds to the number of data value of j dimension that belong to
cluster i. also ̅ is the mean of data values of j dimension.
b. Nonhierarchical method using k-means.
K-means algorithm is the simplest clustering and widely used. Kmeans requires an input which is a predefined number of clusters. This input
is named k. The steps of the k-means algorithm are given below.
1. Select randomly k points to be seeds for the centroids of k clusters.
2. Assign each point to the centroids closest to the point.
5
3. After all points have been assigned, recalculate new centroids of each
cluster.
4. Repeat step 2 and step 3 until the centroids no longer change.
7. Interpret the clustering result
RESULT AND DISCUSSION
Data Exploration
From a total of 26,513 transaction data, there are 9,468 unique members
from five different tier types which are Blue, Silver, Gold, Kids and Platinum.
Figure 1 shows that 67% of the total population is Blue Tier, 24% is Silver Tier,
6% is Gold Tier, 3% is Kids Tier and the last 1% is Platinum Tier.
Platinum
1%
Silver
24%
KIDS
3%
Blue
66%
Gold
6%
Figure 1 The percentage of Garuda Frequent
Flyer based on tier
However, based on transaction average point of view I analyze that
customers from Platinum Tier reach the highest amount of transactions, in average
there are 95 transactions made by each member. This number is high enough
compared to the average of transactions which comes from Blue Tier, even though
it consists of 67% of the GFF population, there is only three transactions in
average per member. The rest of the transaction average is shown in the table
below.
Table 2 Average transaction amount of Garuda Frequent Flyer
Tier Type
Blue
Gold
Kids
Platinum
Silver
Grand Total
Member Quantity
445626
40528
17899
6267
158987
669307
Transactions
1324665
1033887
49974
598492
1187282
4194300
Average
3
26
3
95
7
6
From the data it can be concluded that customers who come from Platinum
Tier give the most beneficial to the company as they travel so frequent with
significant number of transactions compared to the rest of the tiers. On the other
6
hand, it needs to be analyzed further to prove the hypothesis using RFM analysis
and k-means algorithm where the consideration does not merely come from
transaction numbers, but also from the amount of revenue they gave, the record of
the time when they did their last transaction, their age and the mileage they kept.
The age distribution of the population is ranged from 3 years old to 115
years old. In term of age, figure 2 shows that most frequent flyers of Garuda
Indonesia are around 40 years old ranged from 31 to 45 years old. This actually
makes sense as it is the productive age ones can have when they still actively do
business and travel frequently.
300000
250000
200000
150000
100000
50000
0
0-15
16-30
31-45
46-60
61-75
76-90
91-115
Figure 2 The age distribution of Garuda
Frequent Flyer
The second highest age comes from the range of 46 to 60 years old, this is
also considered as the period when the customers are still active to travel and this
age is assumed that they are mature enough to make their decision.
RFM Analysis
RFM Model Definition
RFM analysis depends on Recency, Frequency, and Monetary measures
which are three important purchase-related variables that influence the future
purchase possibilities of the customers. According to the literature (Wu et al.
2005), the RFM model measures when people buy, how often they buy and how
much they buy. Because past purchases of customers can effectively predict their
future purchase behavior. Firms can identify the customers, who are worthy to be
contacted based on their past purchase behavior via RFM model. This analysis is
widely applied in database marketing and is a common tool to develop marketing
strategies.
In another occasion Hughes (1994) also believes that RFM is the most
accurate method to predict customer future behavior compared to any possible
combination of demographic combination because RFM exactly measures what
people do: when they buy, how often they buy, how much they buy.
Although RFM model is a good method that differentiates important
customers from a large data by three variables, there are two studies, (Hughes
1994; Stone 1995), having some different opinions with respect to the three
variables of RFM model. Hughes (1994) considered that the three variables are
7
equal in the importance; therefore, the weights of the three variables are identical.
On the other hand, Stone (1995) indicated that the three variables are different in
the importance due to the characteristic of industry. Thus, the weights of the three
variables are not equal.
The detail of variable concept along with its scale is shown in the table
below:
Table 3 Indicator variables for RFM
Variable
Recency
Frequency
Monetary
Indicator
Data Type
Time period since the last purchase, the more Date
recent the value is, the higher the probability
of giving better response to company‟s
message
Number of transactions, higher frquency Ratio
indicates higher loyalty
Amount of money spent during a certain time Ratio
period
In this research it is assumed that the three variables are different in the
importance due to the existence of correlation among the three variables. In order
to see the importance of each variable, we need to apply certain method where the
weight of Recency, Frequency and Monetary can be seen clearly. Here we choose
Principal Component Analysis to do so. Since the data type of Recency is not
supported the using of Principal Component Analysis so we need to transform the
value first into other type of data.
The way of assigning value is adopted based on the quintiles concept where
the data will be divided into five equal-sized groups with different score (started
from 1 to 5) with the same allocated number of member for each score. The more
recent the transaction date is, the higher the score will be given. Below is the score
allocation for the Recency score:
Table 4 Recency score detail
Score
Year
Month
Member Qty
1
2012
January
February
March
April
May
984
2
2012
June
July
3
2012
August
4
5
2012
2012
September October
November
December
992
974
993
1000
Because the type of data for Frequency and Monetary has been identified as
a ratio, it is not necessary to assign those values into something new. The next
step is to combine all these three variables into one single value named RFM
Score. It is because we believe that among the variables, there is still overlapping
information, for instance, there is a probability that certain information which is
explained by Recency is being re-explained as it is shown in the Frequency. Due
8
to the different importance among Recency, Frequency and Monetary, the
weighting method is needed to combine the values into one single value; in this
case we implement the using of Principal Component Analysis.
RFM Scoring: Weighted PCA
First thing to do before generating the analysis is to calculate the correlation
among variables. The calculation of Pearson‟s Correlation is used to quantify the
amount of dependence which exists among variables, in the concept of Pearson,
the data is not necessarily ranked first before conducting the calculation. Another
reason to use correlation matrix is because it can prevent the effect of different
measurement unit relies on the data, which can not be handled by the variancecovariance matrix. The effect of pushing the use of variance-covariance matrix
when the measurement unit is different is that the result will not be optimum
because it will be dominated by the variance which comes from the biggest value
of data.
In table 5, it is shown that the strongest correlation is 0.70077 which lies
between Frequency and Monetary; it means that the higher number of flying is
always followed by the higher amount of transaction in financial form, this fact is
definitely making sense because the more customers do transaction means the
more customers spend money for purchasing activity. On the other hand, the
correlation between Recency and Frequency is weak so is the correlation between
Recency and Monetary. So that we can assume there is no such a strong
relationship between the amounts of transaction activity with the frequencies of
this customer doing transactions.
Table 5 Pearson correlation among Recency,
Frequency and Monetary
Recency
Frequency
Monetary
Recency
1.00000
0.35538
0.25284
Frequency
0.35538
1.00000
0.70077
Monetary
0.25284
0.70077
1.00000
The next step is determining the number of principal components which are
going to be used in this research. Basically, the first principal component
extracted from the principal component analysis contributes the maximum amount
of total variance in the observed variables. It is usually correlated with many
variables or at least some variables from the observed variables under typical
conditions. While for the second principal component, it will contribute the
maximum amount of variance which is not accounted for by the first principal
component. The second principal component will be also correlated with some of
the observed variables that did not appear in the first principal component.
The thing that we need to underline is that the second principal component
is not correlated with the first principal component, so that if we literally calculate
the correlation between these two components the result will be zero. The rest of
the components will have the same characteristic that they will explain the
maximum amount of variance which is not accounted for by the previous
components and there is no correlation among each component.
9
According to the result of Principal Component Analysis written on
Appendix 1, from the proportion column in the eigenvalue table it shows that the
first principal component alone accounts for 64% of the total variance, the second
principal component alone accounts for 27% of the total variance and the third
component accounts for 7% of the total variance.
When researchers use the “cumulative percent of variance accounted for” as
the criterion to solve the number of components problems, they will pick enough
components so that the cumulative percentage of variance accounted for at least
70% (sometime 80%). If we are using 70% as the “critical value” for determining
the number of components to retain, we would retain component 1 and component
2 in this analysis. By adding these percentages together, we will get the sum of
93% of the total variance. This number has passed the criteria and can be
classified as optimum amount of components already. So that we can write the
combination from the first and second principal component as shown below:
here, the first and second component will be combined to find the equation for
RFM Score using weighted method for principal component analysis.
Sumertajaya (2005) used the general formula for weighted principal component
variable, in this occasion is defined as w:
√
assuming
and
refers to Recency Score (R), Frequency Score (F) and
Monetary Score respectively, so that we find the equation for RFM Score can be
written as:
In the case of combining the RFM Score, we need to make sure that the
three variables have been standardized so that they are additive. The variable
which needs to be standardized is the monetary variable whose form is still
financial revenue. The standardization used in this study is the z-score with the
formula written as below:
̅
A standardized z-score shows both the relative position of an individual
score in a distribution as compared to the mean and the variation of scores in the
distribution. After standardizing the monetary value now that we will have a new
score range from negative to positive. A negative z-score means indicates the
score is below the distribution mean while a positive z-score indicates otherwise.
After that we can directly proceed to calculate the RFM Score with the
above formula. For Example if one passenger did the last transaction on June 28
2012, means that the Recency Score is 2, and the transactions made on that period
is five transactions with the budget of Rp 35,012,274 or after standardized
becomes 2.4894, we can calculate the RFM Score for this GFF Member is
. The RFM Score for each
customer is referred on table 6, which explains customer‟s purchasing behavior
and their profitability value for Garuda Indonesia.
10
Table 6 Garuda Frequent Flyer RFM score
Member No.
100007714
100016442
…
832237700
Recency
3
5
…
2
Frequency
13
11
…
2
Monetary
0.7378
0.4972
…
0.1008
RFM
6.458348
6.976716
…
2.07552
Garuda Frequent Flyer Segmentation
Determining the optimum number of clusters
There are no completely satisfactory methods that can be used for
determining the number of population clusters for any type of cluster analysis
(Everitt 2011; Hartigan 1985; Bock 1985). If the purpose in clustering is
dissection, that is, to summarize the data without trying to uncover the real
clusters, it is enough to look at R square for each variable.
We wish to decide the range of the clusters is between 2 to 9 clusters, and
then we can evaluate the optimum cluster by calculating R-square value. RS
measures the dissimilarity of cluster. This RS value is interpreted as the
proportion of variation explained by a particular clustering from the observations.
As we know that RS is written as:
̅
̅
∑ ∑ ∑
∑ ∑ ∑
where
,
. ESS refers
to Error Sum Square, here we are summing over a; variables, and all of the units
within each cluster. In this case, it is comparing the individual observations for
each variable against the cluster means for that variable. Note that when the ESS
is small, then this suggests that our data are close to their cluster means, implying
that we have a cluster with high homogeneity units. TSS refers to Total Sum of
Squares which is comparing the individual observations for each variable against
the grand mean for that variable.
The graph on Figure 3 shows that the higher the RS is, the more difference
the groups are. As it‟s been stated earlier that RS value indicates the percentage of
variance explained by particular cluster which is chosen as the model. We see the
RS value keeps on increasing in line with the increase of number of clusters. But
we may assume that the peak starts to get steady after the seven clusters, on the
other hand it‟s assumed that the significance of variance percentage explained by
cluster is decreased among the seven, eight and nine clusters respectively.
11
1.00
0.95
ERSQ
0.90
0.85
0.80
0.75
2
3
4
5
6
Number of Cluster
7
8
9
Figure 3 The RS value
K-mean Clustering
The result of RS concludes that seven clusters is good enough to be the
number of clusters. The classification of the cluster can be seen on Appendix 3.
Thus, we can directly proceed to the second phase in clustering analysis, which
uses the application of k-mean algorithm to segment Garuda Frequent Flyer.
This research adopts k-means algorithm for segmenting customers due to
several advantages which is listed by Ye et al. (2013) such as it is scalable and
efficient in processing big data, the algorithm is not sensitive to the input order of
data and the result is easier to understand.
There are seven clusters in this data set with the classification of members
as follow:
Average Member per Cluster
0%
10%
5%
2%
Cluster 1
1%
Cluster 2
24%
58%
Cluster 3
Cluster 4
Cluster 5
Cluster 6
Cluster 7
Figure 4 The member percentage in each
cluster
Cluster 1 has average members, 12.8% of the total members. Members
from this cluster do average transactions which are shown by the average amount
of transactions frequency, seven transactions throughout the year and contributes
about IDR 11,804,900 which is double of the monetary value from Cluster 1.
Most members come from Silver and Blue Tiers with the average age of 43 years
old.
12
Cluster 2 has a lot of members, 53% of the total members. Members on this
cluster contribute the least benefit for the company, which is IDR 903,838 per
year and can be classified as inactive members because not only the monetary but
the recency and frequency values are also very low. They only do transaction
range from one transaction to no transaction at all. Most members come from
Blue Tier with the average age of 39 years old which is relatively still very young
and can be assumed to be price sensitive compared to those with older age.
Cluster 3 has a few members, only 0.2% of the total members. Members
come from this cluster lead as the first top member the company has. They usually
take the international route and consistently using this airline throughout the year.
Almost all of the members come from Platinum Tier with the average of age 55
years old. When we see the mileage balance owned by members from this cluster,
we can conclude that company has the biggest liability towards these members. So
that it‟s suggested to give such more rewards or bonuses to them.
Cluster 4 has average members, 9.78% of the total members. Members
from this cluster do a slightly above average transactions which are shown by the
average amount of transactions frequency, seven transactions throughout the year
and contributes about IDR 11,804,900. Most members come from Silver and Blue
Tiers with the average age of 45 years old. This cluster can be classified as one of
the next top member who needs to be targeted intensively by the company due to
their consistent engagement of transaction behavior with the company.
Cluster 5 has a few to average members, 5.4% of the total members.
Members come from this group can be classified as an above average member,
but still can not be called as the best one. With the average recency score, the
members have a high frequency amount, about fifteen transactions per year. They
in average contribute about IDR 22,911,600 to the company profit. Most members
come from Gold and Silver Tier which are two top tiers in Garuda Frequent Flyer
right below Platinum. This cluster is also classified as one of the next top member
to be targeted.
Cluster 6 has a few members, 2.27% of the total members. Members do
frequent transaction, 13 transactions throughout the year also followed by good
recency score. This cluster contributes about IDR 39,304,500 per year. Most
members come from Silver and Gold with the average age of 48 years old.
Cluster 7 has a very few members, 1.05% of the total members. Members
on this cluster are classified as one of the top members the company has as they
do high frequent of transactions throughout the year, it reaches the number on 16
transactions per year and the high amount of recency also indicates their
engagement with the company is very good. They also contribute big profit from
financial side, which is about IDR 65,614,500 per year. Most of the members
come from Gold and Platinum Tier with the average age of 53 years old. Here we
are able to see that customers with older year engage better to company and less
price-sensitive. So no matter how much the fare policy happened throughout the
year, it will impact nothing toward their airline‟s preference.
13
CONCLUSION AND RECOMMENDATION
Conclusion
This study has introduced the step by step approach of marketing
segmentation including: (1) weighing the RFM using principal component
analysis and (2) segmenting customers into certain clusters using k-means
algorithm. K-means clustering divides Garuda Frequent Flyer into seven clusters.
They are mainly grouped as top members which are the best customers the
company has with high RFM and consistent purchasing behavior, above average
members which are the members with high recency and frequency but lesser
monetary score compared to the top members, next top members which are
indicated by low frequency but high recency and monetary score, average
members which have average transaction record and inactive members which are
rarely doing transaction and have been missing from the company. From this
segmentation it is expected that company can make a better marketing approach
toward each segment thus it can minimize the cost and maximize the profit.
Another interesting conclusion found from this research is the fact that member
with older age usually engages better toward offerings made by Garuda Indonesia
compared to those whose age is younger.
Recommendation
Because it is impossible to implement single marketing strategy to the
whole members the company has, and in order to evaluate the membership, it is
highly recommended for the company to do segmentation gradually. Because only
by understanding customer‟s behavior company can minimize marketing cost and
maximize the profit.
This research has several limitations that still can be improved for further
research such as the more fundamental concept of sampling method, the future
prediction of customers from customer lifetime value and marketing cost spent by
company which still hasn‟t been analyzed in this research. Other than that, it
would be interesting to implement other methods for clustering beside k-means
algorithm (i.e.: Kohonen Self Organizing Maps) in order to enrich the concept of
marketing segmentation.
14
REFERENCES
Aviliani. 2011. Analisis Segmentasi Nasabah Tabungan Bank berdasarkan
Customer Value [disertasi]. Bogor (ID): Institut Pertanian Bogor.
Berman B. 2006. Developing an effective customer loyalty program. California
Management Review. 49(1):123-148.
Bolton RN, Kannan PK, Bramlett MD. Implications of Loyalty Program
Membership and Service Experiences for Customer Retention and Value. J of
the Academy of Marketing Science. 28(1):95-108.
Chin ATH. 2002. Impact of Frequent Flyer Programs on the Demand for Air
Travel. J of Air Transportation. 6(2):53-86.
Dreze X, Nunes JC. 2004. Using Combined-Currency Prices to Lower Consumer‟
Perceived Cost. J of Marketing Research. 41(1):59-72.
Everitt BS, Landau S, Leese M, Stahl D. 2011. Cluster Analysis, Fifth Edition.
London: John Wiley & Sons, Ltd.
Hair JF Jr, Black WC, Babin BJ, Anderson RE. 2010. Multivariate Data Analysis
A Global Perspective, Seventh Edition. New York: Pearson.
Han J, Kamber M, Pei J. 2011. Data Mining: Concepts and Techniques, Third
Edition. San Francisco: Morgan Kaufmann Publishers.
Hartigan JA. 1972. Direct Clustering of a Data Matrix. J of the American
Statistical Association. 67(337):123-129.
Hartigan J A. 1978. Asymptotic Distributions for Clustering Criteria. Annals of
Statistics. 6(1):117–131.
Hughes AM. 1994. Boosting Response with RFM. Marketing Tool. 3(3):4-5.
Johnson RA, Wichern DW. 2002. Applied Multivariate Statistical Analysis, Fifth
Edition. New Jersey: Prentice Hall.
Lin SY, Wei JT, Wu HH. 2010. A Review of the application of RFM model. Journal
of Business Management. 4(19):4199-4206.
Liu DR, Shih YY. 2005. Integrating AHP and data mining for product
recommendation based on customer lifetime value. Information & Management.
42(1):387-400.
Martin JC, Roman C, Espino R. 2011. Evaluating Frequent Flyer Programs from the
Air Passenger’ Perspective. J of Air Transport Management. 17(1):364-368.
Miglautsch JR. 2000. Thoughts on RFM Scoring. J of Database Marketing. 8(1):3-4.
Montinaro M, Sciascia I. 2011. Market Segmentation to Obtain Different Kinds of
Customer Loyalty. J of Applied Sciences. 11(4):655-662.
Morrison DF. 1967. Multivariate Statistical Methods. USA: McGraw-Hill Book
Company.
Niyagas W, Srivihok A, Kristin S. 2006. Clustering e-Banking Customer Using
Data Mining & Marketing Segmentation. ECTI Transaction on Computer &
Information Technology. 2(1): 63-69.
Sarle WS. 1983. Cubic Clustering Criterion. SAS Technical Report A-108. Cary.
North Carolina: SAS Institute Inc.
Shen CC, Chuang HM. 2009. A study on the applications of data mining
techniques to enhance customer lifetime value. WSEAS Transactions on
Information Science and Applications. 6(2):319-328.
15
Sumertajaya IM. 2005. Kajian Pengaruh Inter Blok dan Interaksi pada Uji Lokasi
Ganda dan Respon Ganda [disertasi]. Bogor (ID): Institut Pertanian Bogor.
Ye L, Qiuru C, Haixu X, Yijun L, Guangping Z. 2013. Customer Segmentation
for Telecom with the k-means Clustering Method. Information Technology
Journal. 12(3):409-413.
16
Appendix 1 Principal Component Analysis Results
17
Appendix 2 Weighted Variable for RFM Score
√
√
√
√
√
√
18
Appendix 3 Cluster Classification using PROC CANDISC
19
Appendix 4 Cluster Centroids
20
Appendix 5 Distances between Cluster
21
BIOGRAPHY
Devi Fitri Yani was born in Banda Aceh as the eldest daughter of Abri Yani
and Nurlela on April 10 1992. She took high school academic on the same city
and was graduated from SMAN 10 Fajar Harapan Banda Aceh and SMPN 19
Percontohan Banda Aceh before continuing her study to IPB in 2009.
During her college life, besides taking Statistics in formal academic form,
she also joined a debating club and represented IPB in some debate competitions
both in national and international level. Several achievements she ever hit are
being a delegation for World University Debating Championship 2013 in Berlin,
Germany, the second runner up of United Asian Debating Competition 2011 in
Macau, China, the first winner novice breaking category of Indonesian Varsity
English Debating 2011 in Universitas Hassanuddin, Makassar and the grand
finalist of National University Debating Championship 2013 held by DIKTI.
When she has some free times she loves reading, writing on her websites or
traveling. In 2008 she also became one of Indonesia youth delegation to have a
cultural exchange to United Stated of America and visited ten cities on that
country. In her organization experiences she ever sat as the vice president of IPB
Debating Community and became the head of Beta Club in Gamma Sigma Beta.
DEVI FITRI YANI
DEPARTMENT OF STATISTICS
FACULTY OF MATHEMATICS AND NATURAL SCIENCES
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2013
PERNYATAAN MENGENAI SKRIPSI DAN
SUMBER INFORMASI SERTA PELIMPAHAN HAK CIPTA*
Dengan ini saya menyatakan bahwa skripsi berjudul The Segmentation of
Garuda Frequent Flyer adalah benar karya saya dengan arahan dari komisi
pembimbing dan belum diajukan dalam bentuk apa pun kepada perguruan tinggi
mana pun. Sumber informasi yang berasal atau dikutip dari karya yang diterbitkan
maupun tidak diterbitkan dari penulis lain telah disebutkan dalam teks dan
dicantumkan dalam Daftar Pustaka di bagian akhir disertasi ini.
Dengan ini saya melimpahkan hak cipta dari karya tulis saya kepada Institut
Pertanian Bogor.
Bogor, Juli 2013
Devi Fitri Yani
NIM G14090107
ABSTRACT
DEVI FITRI YANI. The Segmentation of Garuda Frequent Flyer. Advised by
ASEP SAEFUDDIN dan ENNY KRISTIANI.
Airline loyalty program such as frequent flyer program, is a marketing
strategy used by airline industries to maximize their profit and to satisfy their
loyal customers. Garuda Frequent Flyer is a program presented by Garuda
Indonesia as an appreciation to their loyal customers. The model is created to
segment customers based on key airline industry drivers such as yield, frequency,
product, distance and activity pattern. RFM analysis is used to score individuals
based on Recency, Frequency and Monetary using Principal Component Analysis.
This score is used to segment customers using k-means algorithm to interpret
Garuda Frequent Flyer member purchasing behavior which is important for
marketing strategy.
Keywords: frequent flyer program, k-means clustering, marketing strategy,
principal component analysis, RFM analysis
THE SEGMENTATION OF GARUDA FREQUENT FLYER
DEVI FITRI YANI
Scientific Paper
to complete the requirement for graduation of
Bachelor Degree in Statistics
at
Department of Statistics
DEPARTMENT OF STATISTICS
FACULTY OF MATHEMATICS AND NATURAL SCIENCES
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2013
Title
Name
NIM
: The Segmentation of Garuda Frequent Flyer
: Devi Fitri Yani
: G14090107
Approved by
Prof Dr Ir Asep Saefuddin, MSc
Advisor I
Ir Enny Kristiani, MSc
Advisor II
Acknowledged by
Dr Ir Hari Wijayanto, MS
Head of Department
Graduation Date:
ACKNOWLEDGEMENTS
Alhamdulillah wa Syukurillah, many thanks to Allah subhanahu wa ta’ala
for the strength opportunity that I can finish my research with the title The
Segmentation of Garuda Frequent Flyer.
I realize the completion of this research will not happen without the support
from many people. I would like to express my sincere gratitude to my advisors,
Prof Asep Saefuddin for his advices and helpful guidance and Ir Enny Kristiani,
MSc for her warm suggestions and comments from airline industry point of view.
Thank you for all my friends who have continuously supported me during
this research and paper writing. I can not mention one by one and also for both of
my parents who always give support that I keep on fighting to finish research.
Finally I hope this research can be useful for many people.
Bogor, July 2013
Devi Fitri Yani
CONTENT
LIST OF TABLE
vi
LIST OF FIGURE
vi
LIST OF APPENDIX
vi
INTRODUCTION
1
Background
1
Objectives
2
METHODOLOGY
3
Data Sources
3
Methods
3
RESULT AND DISCUSSION
5
Data Exploration
5
RFM Analysis
6
Garuda Frequent Flyer Segmentation
CONCLUSION AND RECOMMENDATION
10
13
Conclusion
13
Recommendation
13
REFERENCES
14
APPENDIX
13
BIOGRAPHY
15
LIST OF TABLE
1 Data type and source
3
2 Average transaction amount of Garuda Frequent Flyer
6
3 Indicator variables for RFM
7
4 Recency score
8
5 Pearson correlation among Recency, Frequency and Monetary
8
6 Garuda Frequent Flyer RFM score
9
LIST OF FIGURE
1 The percentage of Garuda Frequent Flyer based on tier
6
2 The age distribution of Garuda Frequent Flyer
7
3 The RS value
9
4 The member percentage in each cluster
11
LIST OF APPENDIX
1 Principal component analysis results for RFM score
15
2 Weighted Variable for RFM Score
16
3 Cluster classification using PROC CANDISC
17
4 Cluster centroids
18
5 Distances between cluster
15
INTRODUCTION
Background
Since geographical boundaries are no longer considered as restriction for
many people to do business or pleasure, the profit from airline industry nowadays
has been massively increased. The growth in air traffic is accelerated by the
falling price of air transport and an increase in economic activities. Falling airfares
and rising personal incomes have also led to an increase in the demand for leisure
trips. Globalization, accelerated economic growth, liberalization of trade and the
natural growth of population have had positive impacts on the demand for
business travel (Chin 2002).
This situation leads every airline industry to compete each other to attract
most passengers in order to use services they are offering. Because most
competitors operate more or less technology platform and offer almost similar
services, players within airline industries have to battle to present the best services
and offerings to their passengers. To win this competition airline needs to work
harder to gain their customer‟s loyalty by offering them the best deals to stay with
single airline rather than travel with many airlines. Bolton (2000) stated that
naturally companies that offer loyalty reward programs believe that their
programs have long-run positive effect on customer evaluations and behavior.
In appreciation of its valued loyal customers, Garuda Indonesia undertakes a
frequent flyer program, known as the Garuda Frequent Flyer (GFF). Frequent
flyer programs are initially devised by airlines to encourage customers who travel
frequently – mainly as a result of increasing switching costs to the competitors.
Such strategy is based on rewarding schemes by offering their members upgrades
from tourist class to business or first class, free flight tickets or other advantages
in check-ins, priority on the waiting lists of overbooked flights, extra baggage
allowances or business lounges. These benefits are obtained by converting a
certain number of „air miles‟ which are accrued each time the member flies
(Martin et al. 2011).
FFPs are designed to ensure customer loyalty by offering various forms of
bonuses to customers for whom the customers save up each time they fly. It is not
something new that airlines industry gains significant profit from FFPs. Dreze and
Nunes (2004) stated that the frequent flyer miles represent one of the world‟s most
popular currencies, with 100 million people collecting the 500 billion miles that
130 airlines distribute annually. The US loyalty marketing industry, a $6 billion
market, contains more than 2,000 loyalty programs, and 90% of Americans in at
least one loyalty program (Berman, 2006).
Even if FFPs gives many benefit to airlines industry both to maintain
customers loyalty and also to gain more financial benefit from members, but the
segmentation is essentially needed. Market segmentation is developed through
different marketing activities. It can be said that the process of segmentation
requires the knowledge of the characteristics of its own customers in order to
check if they are coherent with the corporate objectives (Montinaro & Sciascia
2011).
2
Customer segmentation gives quantifiable way to analyze the customer data
and distinguish the customers based on their purchase behavior. In this way the
customers can be segmented into different groups for which the company can
employ targeted marketing and thus retain the customers. Once the customers are
segmented, rules can be generated to describe the customers in each group based
on their purchase behavior.
RFM method is very effective for customer segmentation. R means recency
which indicates the time interval between the present and previous transaction
date of a customer. F means frequency which indicates the number of transactions
that the customer has done in a particular interval of time. M means monetary
which indicates the total value of the customer‟s transaction amount. It has been
proven that the value of R, F and M decides the characteristics of the customer
behavior.
Segmentation based on RFM has been used for over 50 years by direct
marketers to target a subset of their customers. RFM analysis is commonly
performed using the Arthur Hughes method, which bins each of the three RFM
attributes independently into five equal frequency bins. The resulting 125 cells are
depicted in a tabular format or as bar graphs and analyzed by marketers, who
determine the best cells (customer segments) to target. Hughes also stated that
RFM analysis is easy to perform and results in a good segmentation.
Hughes (1994) mentioned that the three variables in the RFM model are
equal in the importance; therefore, the weights of the three variables are identical.
On the other hand, some literatures (Liu et al. 2005, Shen et al. 2009, Deng et al.
2008) indicates that the three variables are different in the importance due to the
characteristic of industry. Thus, the weights of the three variables are not equal.
In this study it is assumed that the importance of the three variables are not
equal so that weighing method using PCA is used to combine RFM score. After
that the data mining techniques is used to analyze historical data of Garuda
Frequent Flyer Members. These techniques including K-mean algorithm are used
to segment customers into groups according to their personal profiles and mileage.
As a partitioning method, the k-means algorithm is one of popular heuristic by
most of applications (Han et al. 2011).
The idea behind the model is to identify and understand separate customer
into several groups. Each customer segment has different behaviors and needs
from the airline, and also needs different marketing approach. By creating
segmentation, Garuda Indonesia is able to send relevant offers and messages to
each of their Garuda Frequent Flyer Members.
Objectives
The objectives of this study are to analyze the potential customers from
Garuda Frequent Flyer using RFM analysis and to segment those members into
several groups based on their characteristic and purchasing behavior.
3
METHODOLOGY
Data Sources
This study uses data from Garuda Frequent Flyer Membership in 2012 with
the total population 669,330 members. The explanatory variables are selected
from five aspects, which include age, current tier type, frequency, last transaction
date and transaction amount.
Table 1 Data type and source
Data Type
- Customer‟s Profile Data: gender,
age, location, country, company,
tier type
- Transaction Data: transaction date,
frequency balance, flight number,
ticket price
- Marketing data
- Garuda Indonesia Profile
- Garuda Frequent Flyer Profile
Source
- Garuda Indonesia
- Garuda Indonesia Official Website
- Magazines: Tempo, Info Airlines,
CAPA, Yahoo! Finance
- Internet
Methods
The methods used in this research are:
1. Choose individuals by proportional systematic sampling. The sampling method
following the amount of each tier which is classified to Blue, Silver, Platinum,
Kids and Gold. After choosing the tier proportionally, then the individuals are
chosen based on systematic random sampling.
2. Explore the data
3. Estimate the RFM Score
The goal of RFM is to segment customers based on buying behavior.
The chosen customers are ranked based on each individual RFM factor. Two
common scoring methods on RFM are: (1) Customer Quintiles and (2)
Behavior Quintiles (Miglautsch 2000). This research implements the second
method with the following steps:
a. Select last transaction date as recency variable, transaction frequency as
frequency variable and transaction amount as monetary variable for each
customers
b. Combine the Recency, Frequency and Monetary score altogether using
Principal Component Analysis.
Principal component analysis is a statistical approach that can be used
to analyze interrelationships among a large number of variables and to
explain these variables in terms of their common underlying dimensions
(Anderson et al. 2010).
Suppose that the random variables
of interest have a certain
multivariate distribution with mean vector µ and covariance matrix ∑. We
4
assume, of course, that the elements of µ and ∑ are finite. The rank of ∑ is r
≤ p, and the q largest characteristic roots
of ∑ are all distinct.
In principal components, the total eigenvalues of the correlation matrix is
equal to the total number of variables being analyzed. So that it can be
summarized the contribution from the i-th principal component is:
∑
and for q principal component is:
∑
∑
If the result of eigenvector which is going to be picked is more than one
component, because some researchers believe that the optimum amount of
components to be picked is when the cumulative variance of those variables
is bigger or equal to 70% of the total variance, then weighted value is
needed calculated from this formula:
√
The output of this method is a single RFM Score combination.
4. Check the multicolinearity among the explanatory variables by using Pearson‟s
correlation.
5. Check the existence of outliers among the explanatory variables.
6. Allocate customers into clusters based on similarity with the following steps:
a. Clustering Evaluation.
The good clustering is the one which attain high similarity within
cluster and low similarity among clusters. In order to achieve the optimum
clustering method, we may use some method to evaluate the number of
clusters such as R-square value
RS is used to measure the dissimilarity of clusters. Formally it
measures the degree of homogeneity between groups. The values of RS
range for 0 to 1 where 0 means there is no difference among the clusters and
1 indicates that there are significant difference among the clusters.
, where
∑ ∑ (
∑
̅) ,
(
̅)
where nc is the number of clusters, d the number of variables (data
dimensionality), nj is the number of data value of j dimension while nij
corresponds to the number of data value of j dimension that belong to
cluster i. also ̅ is the mean of data values of j dimension.
b. Nonhierarchical method using k-means.
K-means algorithm is the simplest clustering and widely used. Kmeans requires an input which is a predefined number of clusters. This input
is named k. The steps of the k-means algorithm are given below.
1. Select randomly k points to be seeds for the centroids of k clusters.
2. Assign each point to the centroids closest to the point.
5
3. After all points have been assigned, recalculate new centroids of each
cluster.
4. Repeat step 2 and step 3 until the centroids no longer change.
7. Interpret the clustering result
RESULT AND DISCUSSION
Data Exploration
From a total of 26,513 transaction data, there are 9,468 unique members
from five different tier types which are Blue, Silver, Gold, Kids and Platinum.
Figure 1 shows that 67% of the total population is Blue Tier, 24% is Silver Tier,
6% is Gold Tier, 3% is Kids Tier and the last 1% is Platinum Tier.
Platinum
1%
Silver
24%
KIDS
3%
Blue
66%
Gold
6%
Figure 1 The percentage of Garuda Frequent
Flyer based on tier
However, based on transaction average point of view I analyze that
customers from Platinum Tier reach the highest amount of transactions, in average
there are 95 transactions made by each member. This number is high enough
compared to the average of transactions which comes from Blue Tier, even though
it consists of 67% of the GFF population, there is only three transactions in
average per member. The rest of the transaction average is shown in the table
below.
Table 2 Average transaction amount of Garuda Frequent Flyer
Tier Type
Blue
Gold
Kids
Platinum
Silver
Grand Total
Member Quantity
445626
40528
17899
6267
158987
669307
Transactions
1324665
1033887
49974
598492
1187282
4194300
Average
3
26
3
95
7
6
From the data it can be concluded that customers who come from Platinum
Tier give the most beneficial to the company as they travel so frequent with
significant number of transactions compared to the rest of the tiers. On the other
6
hand, it needs to be analyzed further to prove the hypothesis using RFM analysis
and k-means algorithm where the consideration does not merely come from
transaction numbers, but also from the amount of revenue they gave, the record of
the time when they did their last transaction, their age and the mileage they kept.
The age distribution of the population is ranged from 3 years old to 115
years old. In term of age, figure 2 shows that most frequent flyers of Garuda
Indonesia are around 40 years old ranged from 31 to 45 years old. This actually
makes sense as it is the productive age ones can have when they still actively do
business and travel frequently.
300000
250000
200000
150000
100000
50000
0
0-15
16-30
31-45
46-60
61-75
76-90
91-115
Figure 2 The age distribution of Garuda
Frequent Flyer
The second highest age comes from the range of 46 to 60 years old, this is
also considered as the period when the customers are still active to travel and this
age is assumed that they are mature enough to make their decision.
RFM Analysis
RFM Model Definition
RFM analysis depends on Recency, Frequency, and Monetary measures
which are three important purchase-related variables that influence the future
purchase possibilities of the customers. According to the literature (Wu et al.
2005), the RFM model measures when people buy, how often they buy and how
much they buy. Because past purchases of customers can effectively predict their
future purchase behavior. Firms can identify the customers, who are worthy to be
contacted based on their past purchase behavior via RFM model. This analysis is
widely applied in database marketing and is a common tool to develop marketing
strategies.
In another occasion Hughes (1994) also believes that RFM is the most
accurate method to predict customer future behavior compared to any possible
combination of demographic combination because RFM exactly measures what
people do: when they buy, how often they buy, how much they buy.
Although RFM model is a good method that differentiates important
customers from a large data by three variables, there are two studies, (Hughes
1994; Stone 1995), having some different opinions with respect to the three
variables of RFM model. Hughes (1994) considered that the three variables are
7
equal in the importance; therefore, the weights of the three variables are identical.
On the other hand, Stone (1995) indicated that the three variables are different in
the importance due to the characteristic of industry. Thus, the weights of the three
variables are not equal.
The detail of variable concept along with its scale is shown in the table
below:
Table 3 Indicator variables for RFM
Variable
Recency
Frequency
Monetary
Indicator
Data Type
Time period since the last purchase, the more Date
recent the value is, the higher the probability
of giving better response to company‟s
message
Number of transactions, higher frquency Ratio
indicates higher loyalty
Amount of money spent during a certain time Ratio
period
In this research it is assumed that the three variables are different in the
importance due to the existence of correlation among the three variables. In order
to see the importance of each variable, we need to apply certain method where the
weight of Recency, Frequency and Monetary can be seen clearly. Here we choose
Principal Component Analysis to do so. Since the data type of Recency is not
supported the using of Principal Component Analysis so we need to transform the
value first into other type of data.
The way of assigning value is adopted based on the quintiles concept where
the data will be divided into five equal-sized groups with different score (started
from 1 to 5) with the same allocated number of member for each score. The more
recent the transaction date is, the higher the score will be given. Below is the score
allocation for the Recency score:
Table 4 Recency score detail
Score
Year
Month
Member Qty
1
2012
January
February
March
April
May
984
2
2012
June
July
3
2012
August
4
5
2012
2012
September October
November
December
992
974
993
1000
Because the type of data for Frequency and Monetary has been identified as
a ratio, it is not necessary to assign those values into something new. The next
step is to combine all these three variables into one single value named RFM
Score. It is because we believe that among the variables, there is still overlapping
information, for instance, there is a probability that certain information which is
explained by Recency is being re-explained as it is shown in the Frequency. Due
8
to the different importance among Recency, Frequency and Monetary, the
weighting method is needed to combine the values into one single value; in this
case we implement the using of Principal Component Analysis.
RFM Scoring: Weighted PCA
First thing to do before generating the analysis is to calculate the correlation
among variables. The calculation of Pearson‟s Correlation is used to quantify the
amount of dependence which exists among variables, in the concept of Pearson,
the data is not necessarily ranked first before conducting the calculation. Another
reason to use correlation matrix is because it can prevent the effect of different
measurement unit relies on the data, which can not be handled by the variancecovariance matrix. The effect of pushing the use of variance-covariance matrix
when the measurement unit is different is that the result will not be optimum
because it will be dominated by the variance which comes from the biggest value
of data.
In table 5, it is shown that the strongest correlation is 0.70077 which lies
between Frequency and Monetary; it means that the higher number of flying is
always followed by the higher amount of transaction in financial form, this fact is
definitely making sense because the more customers do transaction means the
more customers spend money for purchasing activity. On the other hand, the
correlation between Recency and Frequency is weak so is the correlation between
Recency and Monetary. So that we can assume there is no such a strong
relationship between the amounts of transaction activity with the frequencies of
this customer doing transactions.
Table 5 Pearson correlation among Recency,
Frequency and Monetary
Recency
Frequency
Monetary
Recency
1.00000
0.35538
0.25284
Frequency
0.35538
1.00000
0.70077
Monetary
0.25284
0.70077
1.00000
The next step is determining the number of principal components which are
going to be used in this research. Basically, the first principal component
extracted from the principal component analysis contributes the maximum amount
of total variance in the observed variables. It is usually correlated with many
variables or at least some variables from the observed variables under typical
conditions. While for the second principal component, it will contribute the
maximum amount of variance which is not accounted for by the first principal
component. The second principal component will be also correlated with some of
the observed variables that did not appear in the first principal component.
The thing that we need to underline is that the second principal component
is not correlated with the first principal component, so that if we literally calculate
the correlation between these two components the result will be zero. The rest of
the components will have the same characteristic that they will explain the
maximum amount of variance which is not accounted for by the previous
components and there is no correlation among each component.
9
According to the result of Principal Component Analysis written on
Appendix 1, from the proportion column in the eigenvalue table it shows that the
first principal component alone accounts for 64% of the total variance, the second
principal component alone accounts for 27% of the total variance and the third
component accounts for 7% of the total variance.
When researchers use the “cumulative percent of variance accounted for” as
the criterion to solve the number of components problems, they will pick enough
components so that the cumulative percentage of variance accounted for at least
70% (sometime 80%). If we are using 70% as the “critical value” for determining
the number of components to retain, we would retain component 1 and component
2 in this analysis. By adding these percentages together, we will get the sum of
93% of the total variance. This number has passed the criteria and can be
classified as optimum amount of components already. So that we can write the
combination from the first and second principal component as shown below:
here, the first and second component will be combined to find the equation for
RFM Score using weighted method for principal component analysis.
Sumertajaya (2005) used the general formula for weighted principal component
variable, in this occasion is defined as w:
√
assuming
and
refers to Recency Score (R), Frequency Score (F) and
Monetary Score respectively, so that we find the equation for RFM Score can be
written as:
In the case of combining the RFM Score, we need to make sure that the
three variables have been standardized so that they are additive. The variable
which needs to be standardized is the monetary variable whose form is still
financial revenue. The standardization used in this study is the z-score with the
formula written as below:
̅
A standardized z-score shows both the relative position of an individual
score in a distribution as compared to the mean and the variation of scores in the
distribution. After standardizing the monetary value now that we will have a new
score range from negative to positive. A negative z-score means indicates the
score is below the distribution mean while a positive z-score indicates otherwise.
After that we can directly proceed to calculate the RFM Score with the
above formula. For Example if one passenger did the last transaction on June 28
2012, means that the Recency Score is 2, and the transactions made on that period
is five transactions with the budget of Rp 35,012,274 or after standardized
becomes 2.4894, we can calculate the RFM Score for this GFF Member is
. The RFM Score for each
customer is referred on table 6, which explains customer‟s purchasing behavior
and their profitability value for Garuda Indonesia.
10
Table 6 Garuda Frequent Flyer RFM score
Member No.
100007714
100016442
…
832237700
Recency
3
5
…
2
Frequency
13
11
…
2
Monetary
0.7378
0.4972
…
0.1008
RFM
6.458348
6.976716
…
2.07552
Garuda Frequent Flyer Segmentation
Determining the optimum number of clusters
There are no completely satisfactory methods that can be used for
determining the number of population clusters for any type of cluster analysis
(Everitt 2011; Hartigan 1985; Bock 1985). If the purpose in clustering is
dissection, that is, to summarize the data without trying to uncover the real
clusters, it is enough to look at R square for each variable.
We wish to decide the range of the clusters is between 2 to 9 clusters, and
then we can evaluate the optimum cluster by calculating R-square value. RS
measures the dissimilarity of cluster. This RS value is interpreted as the
proportion of variation explained by a particular clustering from the observations.
As we know that RS is written as:
̅
̅
∑ ∑ ∑
∑ ∑ ∑
where
,
. ESS refers
to Error Sum Square, here we are summing over a; variables, and all of the units
within each cluster. In this case, it is comparing the individual observations for
each variable against the cluster means for that variable. Note that when the ESS
is small, then this suggests that our data are close to their cluster means, implying
that we have a cluster with high homogeneity units. TSS refers to Total Sum of
Squares which is comparing the individual observations for each variable against
the grand mean for that variable.
The graph on Figure 3 shows that the higher the RS is, the more difference
the groups are. As it‟s been stated earlier that RS value indicates the percentage of
variance explained by particular cluster which is chosen as the model. We see the
RS value keeps on increasing in line with the increase of number of clusters. But
we may assume that the peak starts to get steady after the seven clusters, on the
other hand it‟s assumed that the significance of variance percentage explained by
cluster is decreased among the seven, eight and nine clusters respectively.
11
1.00
0.95
ERSQ
0.90
0.85
0.80
0.75
2
3
4
5
6
Number of Cluster
7
8
9
Figure 3 The RS value
K-mean Clustering
The result of RS concludes that seven clusters is good enough to be the
number of clusters. The classification of the cluster can be seen on Appendix 3.
Thus, we can directly proceed to the second phase in clustering analysis, which
uses the application of k-mean algorithm to segment Garuda Frequent Flyer.
This research adopts k-means algorithm for segmenting customers due to
several advantages which is listed by Ye et al. (2013) such as it is scalable and
efficient in processing big data, the algorithm is not sensitive to the input order of
data and the result is easier to understand.
There are seven clusters in this data set with the classification of members
as follow:
Average Member per Cluster
0%
10%
5%
2%
Cluster 1
1%
Cluster 2
24%
58%
Cluster 3
Cluster 4
Cluster 5
Cluster 6
Cluster 7
Figure 4 The member percentage in each
cluster
Cluster 1 has average members, 12.8% of the total members. Members
from this cluster do average transactions which are shown by the average amount
of transactions frequency, seven transactions throughout the year and contributes
about IDR 11,804,900 which is double of the monetary value from Cluster 1.
Most members come from Silver and Blue Tiers with the average age of 43 years
old.
12
Cluster 2 has a lot of members, 53% of the total members. Members on this
cluster contribute the least benefit for the company, which is IDR 903,838 per
year and can be classified as inactive members because not only the monetary but
the recency and frequency values are also very low. They only do transaction
range from one transaction to no transaction at all. Most members come from
Blue Tier with the average age of 39 years old which is relatively still very young
and can be assumed to be price sensitive compared to those with older age.
Cluster 3 has a few members, only 0.2% of the total members. Members
come from this cluster lead as the first top member the company has. They usually
take the international route and consistently using this airline throughout the year.
Almost all of the members come from Platinum Tier with the average of age 55
years old. When we see the mileage balance owned by members from this cluster,
we can conclude that company has the biggest liability towards these members. So
that it‟s suggested to give such more rewards or bonuses to them.
Cluster 4 has average members, 9.78% of the total members. Members
from this cluster do a slightly above average transactions which are shown by the
average amount of transactions frequency, seven transactions throughout the year
and contributes about IDR 11,804,900. Most members come from Silver and Blue
Tiers with the average age of 45 years old. This cluster can be classified as one of
the next top member who needs to be targeted intensively by the company due to
their consistent engagement of transaction behavior with the company.
Cluster 5 has a few to average members, 5.4% of the total members.
Members come from this group can be classified as an above average member,
but still can not be called as the best one. With the average recency score, the
members have a high frequency amount, about fifteen transactions per year. They
in average contribute about IDR 22,911,600 to the company profit. Most members
come from Gold and Silver Tier which are two top tiers in Garuda Frequent Flyer
right below Platinum. This cluster is also classified as one of the next top member
to be targeted.
Cluster 6 has a few members, 2.27% of the total members. Members do
frequent transaction, 13 transactions throughout the year also followed by good
recency score. This cluster contributes about IDR 39,304,500 per year. Most
members come from Silver and Gold with the average age of 48 years old.
Cluster 7 has a very few members, 1.05% of the total members. Members
on this cluster are classified as one of the top members the company has as they
do high frequent of transactions throughout the year, it reaches the number on 16
transactions per year and the high amount of recency also indicates their
engagement with the company is very good. They also contribute big profit from
financial side, which is about IDR 65,614,500 per year. Most of the members
come from Gold and Platinum Tier with the average age of 53 years old. Here we
are able to see that customers with older year engage better to company and less
price-sensitive. So no matter how much the fare policy happened throughout the
year, it will impact nothing toward their airline‟s preference.
13
CONCLUSION AND RECOMMENDATION
Conclusion
This study has introduced the step by step approach of marketing
segmentation including: (1) weighing the RFM using principal component
analysis and (2) segmenting customers into certain clusters using k-means
algorithm. K-means clustering divides Garuda Frequent Flyer into seven clusters.
They are mainly grouped as top members which are the best customers the
company has with high RFM and consistent purchasing behavior, above average
members which are the members with high recency and frequency but lesser
monetary score compared to the top members, next top members which are
indicated by low frequency but high recency and monetary score, average
members which have average transaction record and inactive members which are
rarely doing transaction and have been missing from the company. From this
segmentation it is expected that company can make a better marketing approach
toward each segment thus it can minimize the cost and maximize the profit.
Another interesting conclusion found from this research is the fact that member
with older age usually engages better toward offerings made by Garuda Indonesia
compared to those whose age is younger.
Recommendation
Because it is impossible to implement single marketing strategy to the
whole members the company has, and in order to evaluate the membership, it is
highly recommended for the company to do segmentation gradually. Because only
by understanding customer‟s behavior company can minimize marketing cost and
maximize the profit.
This research has several limitations that still can be improved for further
research such as the more fundamental concept of sampling method, the future
prediction of customers from customer lifetime value and marketing cost spent by
company which still hasn‟t been analyzed in this research. Other than that, it
would be interesting to implement other methods for clustering beside k-means
algorithm (i.e.: Kohonen Self Organizing Maps) in order to enrich the concept of
marketing segmentation.
14
REFERENCES
Aviliani. 2011. Analisis Segmentasi Nasabah Tabungan Bank berdasarkan
Customer Value [disertasi]. Bogor (ID): Institut Pertanian Bogor.
Berman B. 2006. Developing an effective customer loyalty program. California
Management Review. 49(1):123-148.
Bolton RN, Kannan PK, Bramlett MD. Implications of Loyalty Program
Membership and Service Experiences for Customer Retention and Value. J of
the Academy of Marketing Science. 28(1):95-108.
Chin ATH. 2002. Impact of Frequent Flyer Programs on the Demand for Air
Travel. J of Air Transportation. 6(2):53-86.
Dreze X, Nunes JC. 2004. Using Combined-Currency Prices to Lower Consumer‟
Perceived Cost. J of Marketing Research. 41(1):59-72.
Everitt BS, Landau S, Leese M, Stahl D. 2011. Cluster Analysis, Fifth Edition.
London: John Wiley & Sons, Ltd.
Hair JF Jr, Black WC, Babin BJ, Anderson RE. 2010. Multivariate Data Analysis
A Global Perspective, Seventh Edition. New York: Pearson.
Han J, Kamber M, Pei J. 2011. Data Mining: Concepts and Techniques, Third
Edition. San Francisco: Morgan Kaufmann Publishers.
Hartigan JA. 1972. Direct Clustering of a Data Matrix. J of the American
Statistical Association. 67(337):123-129.
Hartigan J A. 1978. Asymptotic Distributions for Clustering Criteria. Annals of
Statistics. 6(1):117–131.
Hughes AM. 1994. Boosting Response with RFM. Marketing Tool. 3(3):4-5.
Johnson RA, Wichern DW. 2002. Applied Multivariate Statistical Analysis, Fifth
Edition. New Jersey: Prentice Hall.
Lin SY, Wei JT, Wu HH. 2010. A Review of the application of RFM model. Journal
of Business Management. 4(19):4199-4206.
Liu DR, Shih YY. 2005. Integrating AHP and data mining for product
recommendation based on customer lifetime value. Information & Management.
42(1):387-400.
Martin JC, Roman C, Espino R. 2011. Evaluating Frequent Flyer Programs from the
Air Passenger’ Perspective. J of Air Transport Management. 17(1):364-368.
Miglautsch JR. 2000. Thoughts on RFM Scoring. J of Database Marketing. 8(1):3-4.
Montinaro M, Sciascia I. 2011. Market Segmentation to Obtain Different Kinds of
Customer Loyalty. J of Applied Sciences. 11(4):655-662.
Morrison DF. 1967. Multivariate Statistical Methods. USA: McGraw-Hill Book
Company.
Niyagas W, Srivihok A, Kristin S. 2006. Clustering e-Banking Customer Using
Data Mining & Marketing Segmentation. ECTI Transaction on Computer &
Information Technology. 2(1): 63-69.
Sarle WS. 1983. Cubic Clustering Criterion. SAS Technical Report A-108. Cary.
North Carolina: SAS Institute Inc.
Shen CC, Chuang HM. 2009. A study on the applications of data mining
techniques to enhance customer lifetime value. WSEAS Transactions on
Information Science and Applications. 6(2):319-328.
15
Sumertajaya IM. 2005. Kajian Pengaruh Inter Blok dan Interaksi pada Uji Lokasi
Ganda dan Respon Ganda [disertasi]. Bogor (ID): Institut Pertanian Bogor.
Ye L, Qiuru C, Haixu X, Yijun L, Guangping Z. 2013. Customer Segmentation
for Telecom with the k-means Clustering Method. Information Technology
Journal. 12(3):409-413.
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Appendix 1 Principal Component Analysis Results
17
Appendix 2 Weighted Variable for RFM Score
√
√
√
√
√
√
18
Appendix 3 Cluster Classification using PROC CANDISC
19
Appendix 4 Cluster Centroids
20
Appendix 5 Distances between Cluster
21
BIOGRAPHY
Devi Fitri Yani was born in Banda Aceh as the eldest daughter of Abri Yani
and Nurlela on April 10 1992. She took high school academic on the same city
and was graduated from SMAN 10 Fajar Harapan Banda Aceh and SMPN 19
Percontohan Banda Aceh before continuing her study to IPB in 2009.
During her college life, besides taking Statistics in formal academic form,
she also joined a debating club and represented IPB in some debate competitions
both in national and international level. Several achievements she ever hit are
being a delegation for World University Debating Championship 2013 in Berlin,
Germany, the second runner up of United Asian Debating Competition 2011 in
Macau, China, the first winner novice breaking category of Indonesian Varsity
English Debating 2011 in Universitas Hassanuddin, Makassar and the grand
finalist of National University Debating Championship 2013 held by DIKTI.
When she has some free times she loves reading, writing on her websites or
traveling. In 2008 she also became one of Indonesia youth delegation to have a
cultural exchange to United Stated of America and visited ten cities on that
country. In her organization experiences she ever sat as the vice president of IPB
Debating Community and became the head of Beta Club in Gamma Sigma Beta.