Value Creation for Customers by Segmentation Approach: An Online Game and Entertainment Website Case Study Sahar Sohrabi, Bahman Nikkhahan
Value Creation for Customers by Segmentation Approach: An Online Game and Entertainment Website Case Study 1
Sahar Sohrabi, 2Bahman Nikkhahan Lecturer in Department of Computer Engineering Ardabil branch, Islamic Azad University, Ardabil, Iran,
[email protected] 2 Lecturer in Department of Computer Engineering Ardabil branch, Islamic Azad University, Ardabil, Iran,
[email protected] *1,Corresponding Author
Abstract: Value creation for customers is an important constituent of any business model especially the online ones. There are various factors in the literature regarding the perceived value of customers like efficiency, novelty, or lock-in. These factors are so general and may be different in different countries, times, or even cultures. In this research, customers’ perceived value is analyzed based on his or her behavior in the website. This is done by using data mining techniques. A case study of 3tark that is a pioneer website in online game and entertainment in Iran is evaluated in this paper. By using data mining techniques, some of the valuable items for customers of this website are specified. These valuable items could also be a guideline for those that are active in the area of online games and entertainment, especially the Iranian websites.
Keywords: Data mining, Value creation, Segmentation, Clustering. 1. Introduction Facing with more complexity and competition in today’s business, firms need to develop innovation activities to capture customer needs and improve customer satisfaction and retention [5]. Value creation for customers or other stakeholders of a business is a vital element of any business models [1]. Porter (1985) defines value as “what buyers are willing to pay”(p.3) [8]. Value creation may be studied for a particular business; for example Simpson, Siguaw, and Baker (2001) focused on value creation in marketing channels[9], while Amit and Zott (2001) studied value creation in e-business[2]. They suggested four sources of value creation in e-business: novelty, efficiency, complementarities and lockin. Afuah and Tucci (2003) believe that customer value is an important component of any e-business model[1]. In their opinion, customer would buy a product from a firm if the product offers something that products of competitors don’t. This value could be differentiated or low cost products/services. A firm can differentiate its products in eight different ways: product features, timing, location, service, product mix, linkage between functions, linkage with other firms, and reputation. In Applegate’s (2001) business model framework, value of a business model is measured by its return to all stakeholders, return to the organization, market share, brand and reputation, and financial performance[3]. Steinfield et al. (1999) found out that E-commerce business value may be achieved from the click-and-mortar synergies. Synergy benefits include potential costs savings, differentiation through value-added services, improved trust, and market extension[10]. Various methods of value creation are existed in the literature and most of them are focused on identifying some building blocks to create value for businesses’ stakeholders like customers. They achieved some value constituents that are based on analyzing the customers’ view points. For example as said above Afuah and Tucci believe that a valuable product must be differentiated or low cost. These factors may be valuable for many of the customers but they may be variable in different countries or even different time periods [1]. Zhu et al. (2004) found that e-commerce value is significantly influenced by technological, organizational, and environmental factors (e.g., technology readiness, firm size and scope, competition, and government regulation) [15]. Culture and laws of a country influenced a Customer’s choice to buy a product or service. For example some companies restrict customers of a few countries to buy products or use services. On the other side, customer needs are changing continuously. For example suppose a less developed country in 5 years ago that network bandwidth
International Journal of Digital Content Technology and its Applications(JDCTA) Volume5,Number12,December 2011 doi:10.4156/jdcta.vol5.issue12.16
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Value Creation for Customers by Segmentation Approach: An Online Game and Entertainment Website Case Study Sahar Sohrabi, Bahman Nikkhahan
were a problem for internet users of that country. At that time a customer would choose a service provider if he or she could reach to the service completely, but now this problem resolved and they prefer to select a service provider that offer more features, and bandwidth is not a problem anymore. Another example could be economic situation of a country or region. For example under an economic recession, people prefer to buy low price products but after then they may prefer to buy a product with high quality rather than cheap one. This research, proposes to study the actual behavior of customers in interaction with the company. We must analyze customers’ historical data to analyzethe value creation for various customer segments. This could be done by using data mining techniques like clustering. In this way we can monitor changes in customers’ needs and preferences. For example we can analyze different customer clusters to find what is valuable for them (figure1). Data mining Larose (2005) defines data mining as "the process of discovering meaningful new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques" (p. 2) [7]. Different authors used these techniques to generate meaningful results [11, 14]. In this paper we use clustering as a fundamental tool to segment customers. Larose (2005) believes that “clustering refers to the grouping of records, observations, or cases into classes of similar objects. A cluster is a collection of records that are similar to one another, and dissimilar to records in other clusters. The clustering task does not try to classify, estimate, or predict the value of a target variable. Instead, clustering algorithms seek to segment the entire data set into relatively homogeneous subgroups or clusters, where the similarity of the records within the cluster is maximized and the similarity to records outside the cluster is minimized” (p. 16) [7].
Literature Review
Business Understanding
Data preparation
Data analysis
Data table preparation
Customer Attraction
Data cleaning
Users’personal information
Discussion
Handling missing data Churned customers Identifying misclassification Figure1. Paper overview Identifying outliers Data transformation
Activity basedclusters
Correlations
2. Business Understanding In this paper,3tark, an Iranian entertainment website, considered as a case study. This website consisted of different items like online games, online matches, and auctions. Every user has its own features in the website consisting of points, activity, risk, chance, and value. Participating in every itemneeds point. When a user wins a match or game his or her features will be increased. Active users can receive prize for breaking the games’ record, having too much activity in a day, winning auctions and matches and so on. As a result users compete with each other’s to win the prizes. Online games are creating huge business and millions of users are engaged in this environment. Enjoyment is one of the important factors of gaming behavior [13]. People play online games to remove challenges, make friends and spend time but the basic reason is to enjoy [4][6]. The behavioral intentions of the online game players were measured by two variables: trust and enjoyment. Wu and
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Value Creation for Customers by Segmentation Approach: An Online Game and Entertainment Website Case Study Sahar Sohrabi, Bahman Nikkhahan
Liu (2007) developed a model called Theory of Reasoned Action (TRA). This model is used to measure attitude, enjoyment and subjective norms of players. The games are generating a huge amount of revenue. It is estimated that by 2011, the gross revenue will be 13 billion dollar. This powerful potential of revenue is relatively unknown in Iranian websites. Therefore as an important step we must know the customers of these websites by segmenting them. Then according to their profile in each segment, suitable strategies to attract new customers and retain the existed ones must be formulated.
3. Data preparation Figure1 shows various steps of data preparation. First step is data table preparation. Data base of the website includes 157 data tables. Therefore we must summarize these tables to a fewsimplerdata tables. Customers’ data table is a summarized activityhistory of 2953 customers. For each customer we have these information: age, gender, marriage, activities, user value, active days, customer life days, items used count, number of distinct items used, poll usage count, auction usage count, brain games usage count, skill games usage count, action games usage count, sport games usage count, and match usage count. These fields describe the user’s history in using different items of the website. Activitiesnumberdemonstrates the number of activities of a user in the website. User value is a measure of user importance based on his earned points, risk, chance, and other features. Active days show the number of days that user was active. Customer life days demonstrate the distance between register date and last login date of a user.Among different items of the website, some of them were chosen here: poll, auction, matches, and games including brain, skill, action, and sport games. In poll section users must answer the questionnaires to earn points. Auctions were held on some useful products that were provided by sponsors. Matches were online games that two or more players played with each others. Other online games like brain, skill, action and sport games were just simple single player games. Usagenumber of these items for every user is available in our data table. Adding these numbers forms overall itemsthat were used by each user. Number of distinct items used is also an indicator to find out how many distinct items were used by each user. Second step is data cleaning that meaningless data must be treated. For example it is not possible to have a user with negative activities. If there is, it is because of some errors in the website’s application and it must be treated. After cleaning data, next step is handling missing data. Some of the fields have missing value especially those fields that user entered himself or herself like gender, age, or marriage situation.To handle these missed values we can omit the entire record or just replace the missed value. There are some methods to replace the missed value but here we replaced these values by mean of nearby points. Next step is identifying misclassification. In this step, values that caused creating unreal categories must be treated. For example in gender field, 0 represents female, 1 represents male and 9 shows that user didn’t want to mention his or her gender. Any value other than these three values is unreal and must be treated. Identifying outliers is an important step in data preparation. Larose (2005) defines outliers as “values that are near the limits of the data range” (p.34).He believes that identifying these values is important because “they may represent some data entry errors. Also, even if an outlier is a valid data point and not in error, certain statistical methods are sensitive to the presence of outliers and may deliver unstable results.” (p.34) [7]. Final step in data preparation is data transformation. Because some of the fields may have different ranges, to compare them with each other we must normalize and standardize data. Some of the methods that will discuss later in this paper need excluding outliers, normalization and standardization of variables.
4. Data analysis In this paper data mining techniques of SPSS software are used to analyze the customers’ historical data in 3tark website.
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Value Creation for Customers by Segmentation Approach: An Online Game and Entertainment Website Case Study Sahar Sohrabi, Bahman Nikkhahan
4.1. Customer attraction The existing customers’ data belong to 154working days of the website. In this 5 months average of 18 users joined the website each day. As shown in figure2 registration number varied from 0 to 109 users per day. Advertisement caused the increasing number of registration in figure2. Some of advertising methods of this website were: - Advertising in search engines. - Advertising in famous and related Iranian websites. - Registering the website in Iranian advertisement websites. - Registering in directories and search engines. - Link and banner exchange. - Sending email to Iranian users. Advertising in famous and related Iranian websites caused major increasing of registration number. After finishing this type of advertisement, the registration number remained approximatelyconstant because the rank of website’s pages was increased in search engines. Therefore some of the mentioned ways like advertising in famous and related websites had short term effect and others like exchanging link and banner or registering in directories and search engines had long term effect on attracting new users. Figure2 shows users’ unique login number in each day. This number represents how many unique users legged in per day and it doesn’t consider the returning users in that day.It is obvious that this number must have similar trends with register number. Another interesting conclusion in figure2 can be made by analyzing the holidays. In holidays both users’ login number and registration number dropped. Therefore in holidays, our target customers are unwilling to visit the website. 300 250 200 150 100 50 1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139 145 151
0
Registration Number
Holidays
Users Unique Login
Holidays
Figure2. Registrations and users’ unique login in the website per day
4.2. Users’ personal information Customer segmentation can be done based on personal information of users like age, gender, and marriage, customers’ activities in the website or combination of them. At first, customers will be segmented based on their personal information. To do so,Two step cluster -a useful tool of SPSS software- is used. Table1 shows various clusters based on age of customers. We call cluster1 as teenager, cluster2 as young, and cluster3 as middle aged. Most of the customers are between 11 and 30 years old. About 90 percents of the customers are teenager and young and average age of customers is 22.
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Value Creation for Customers by Segmentation Approach: An Online Game and Entertainment Website Case Study Sahar Sohrabi, Bahman Nikkhahan
Table1. Cluster distribution based on customers’ age N Cluster
% of Combined
Mean
% of Total
1
1129
38.2%
38.2%
15.71
2
1516
51.3%
51.3%
23.58
3
308
10.4%
10.4%
39.39
2953
100.0%
100.0%
21.90
100.0%
21.90
Combined Total
2953
79 percent of the customers are single and 21 percent of them are married. 20 percent of customers are female and 80 percent of them are male. By using the Crosstabs tool of SPSS we can combine multiple fields. For example table2 shows cross tabulation of gender and marriage. This table shows that 56% of females are single and 85% of males are single. As a result, married women are more active than married men in the website. By cross tabulation the gender and age we can find out that 77% of female users are categorized as young and middle aged but 58% of male users are in young and middle aged group. Table2. Gender * Marriage Crosstabulation Marriage Single Gender
Female
Male
Total
Count
Married
Total
314
243
557
% within Gender
56.4%
43.6%
100.0%
% within Marriage
14.3%
41.8%
20.0%
% of Total
11.3%
8.7%
20.0%
1886
339
2225
% within Gender
84.8%
15.2%
100.0%
% within Marriage
85.7%
58.2%
80.0%
% of Total
67.8%
12.2%
80.0%
2200
582
2782
79.1%
20.9%
100.0%
100.0%
100.0%
100.0%
79.1%
20.9%
100.0%
Count
Count % within Gender % within Marriage % of Total
4.3. Churned customers As said before, 3tark is an online entertainment website and most of the users visit the website and use the services continuously. So if a user doesn’t visit the website in a specified time period, we can consider him as a churned customer. To calculate this time period Customer’s Life Days defined as distance between his or her registration date and last login date. Average of this measure for customers is 20 days. But because most of the users (58 percent) visit the website just one time and on the other side some of the users may leave the website for a while and come back after some days, here average of Customer’s Life Days is defined 30 days. As a result a churned user is one that didn’t come back after 30 days. After calculating this item for every customer, we could add it to our data table. This field will help us to predict the users’ behavior based on his or her profile. Figure3 shows active customers versus churned customer. Along With increasing the number of users, active customers and churned customers also increased. At the end of the 5 months these two numbers are approximately equal. It means that churn rate of customers is equal to attraction rate of them. Churn rate of customers in the website is 52 percent that is relatively high.
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Value Creation for Customers by Segmentation Approach: An Online Game and Entertainment Website Case Study Sahar Sohrabi, Bahman Nikkhahan
1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139 145 151
1600 1400 1200 1000 800 600 400 200 0
Count of Active Customers
Count of Churned Customers
Figure3. Active customers versus churned customers Table3 shows cross tabulation of Age clusters and churned. The aim of this table is to find out how different age clusters churned. In teenager cluster customers’ churn rate is 47% that is lower than the overall churn rate (52%). But in two other clusters, churn rate is higher than overall churn rate. Customers in young cluster have churn rate of 54% and customers in middle aged cluster have churn rate of 59%. So we can conclude that younger customers are more willing to stay in the website and use its’ services. Table3.AgeClusterNo * Churned Cross tabulation Churned 0 AgeClusterNo
Teenager
Young
MiddleAged
Count
Total 536
1129
% within AgeClusterNo
52.5%
47.5%
100.0%
% of Total
20.1%
18.2%
38.2%
691
825
1516
% within AgeClusterNo
45.6%
54.4%
100.0%
% of Total
23.4%
27.9%
51.3%
126
182
308
40.9%
59.1%
100.0%
% of Total
4.3%
6.2%
10.4%
Count
1410
1543
2953
% within AgeClusterNo
47.7%
52.3%
100.0%
% of Total
47.7%
52.3%
100.0%
Count
Count % within AgeClusterNo
Total
1 593
4.4.Activity based clusters Most of the fields in the data table are related to user’s activity in the website. Here, another clustering is done based on these activity related fields. The cluster distribution of Two step cluster of SPSS software showed in table4. We call cluster1 as low activity, cluster2 as medium activity and cluster3 as high activity.Low activity cluster is the most crowded cluster (84%) and high activity cluster is the least crowded cluster (3%). Customers of high activity cluster are the most loyal customers and they spend most of their times in the website (table5 and table6).
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Value Creation for Customers by Segmentation Approach: An Online Game and Entertainment Website Case Study Sahar Sohrabi, Bahman Nikkhahan
Table4. Activity based cluster distribution N Cluster
2485
84.2%
2
365
12.4%
3 Combined Total
Cluster
Active Days
1 2 3 Combined
2 17 54 6
Cluster 1 2 3 Combined
Auction usage count 0 1 12 1
% of Total
1
103
3.5%
2953
100.0%
2953
100.0%
Table5. Activity based clusters’ profile (part1) User Customer Items Distinct Activities value Life Days Usage Items Count used 24 259 5 26 4 566 269 41 584 44 2164 328 77 2298 55 166 263 12 173 10 Table6. Activity based clusters’ profile (part 2) Brain Skill games Action Sport games games usage count games usage count usage usage count count 8 15 0 0 129 347 17 35 549 1348 69 200 42 103 5 12
Poll usage count 1 48 79 10
Match usage count 0 8 47 3
Table7 shows cross tabulation of churned field and activity based cluster number field of the data table. This table gives us some interesting results. Approximately 97% of churned customers are in low activity cluster and just 3% of them are in medium activity cluster. 60% of low activity and 14% of the medium activity customers decided to not use the website’s services anymore.
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Value Creation for Customers by Segmentation Approach: An Online Game and Entertainment Website Case Study Sahar Sohrabi, Bahman Nikkhahan
Table7. Churned * ActivityBasedClusterNo Cross tabulation ActivityBasedClusterNo Low Churned
0
1
Total
Count
Medium
High
Total
994
313
103
1410
% within Churned
70.5%
22.2%
7.3%
100.0%
% within ActivityBasedClusterNo
40.0%
85.8%
100.0%
47.7%
% of Total
33.7%
10.6%
3.5%
47.7%
Count
1491
52
0
1543
% within Churned
96.6%
3.4%
.0%
100.0%
% within ActivityBasedClusterNo
60.0%
14.2%
.0%
52.3%
% of Total
50.5%
1.8%
.0%
52.3%
2485
365
103
2953
Count % within Churned % within ActivityBasedClusterNo % of Total
84.2%
12.4%
3.5%
100.0%
100.0%
100.0%
100.0%
100.0%
84.2%
12.4%
3.5%
100.0%
4.5. Correlations A useful way for finding hidden relation between data table fields is using Bivariate Correlation in SPSS software.Square of values in the table8 show how much variances do variables share. For example value of correlation between Activity Based Cluster No and Poll usage count is .718. It means that these two fields share 51 percent (.718×.718×100) of their variances. Activity based fields like games usage count are tightly related to each other and they are also related to Activity Based Cluster No. Gender has its higher correlation with brain games usage count. This correlation is negative,and means they have inverted correlation. It means that females used brain games more than men (gender 0 means female). Age Cluster No also shows the same. Those that are older are willing to use brain games among other items of the website.
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Value Creation for Customers by Segmentation Approach: An Online Game and Entertainment Website Case Study Sahar Sohrabi, Bahman Nikkhahan
Table8. Correlations of data fields Poll Usage Count Poll Usage Count Auction Usage Count Brain games usage count Skill games usage count Action games usage count Sport games usage count Match usage count Gender Activity Based Cluster No Age Cluster No
Auction Usage Count
Brain games usage count
Skill games usage count
Action games usage count
Sport games usage count
Match usage count
Gender
Activity Based Cluster No
Age Cluste r No
1
.275
1
.501
.457
1
.607
.392
.601
1
.439
.437
.394
.558
1
.414
.428
.413
.638
.466
1
.528
.354
.603
.634
.478
.505
1
.033
-.016
-.105
-.009
-.002
.025
.012
1
.718
.403
.668
.760
.575
.583
.645
-.017
1
-.017
.045
.102
.025
.009
.045
.020
-1.45
.025
1
5. Discussion As said before, most of the researchers consider customer value as some general items from the customer’s point of view like novelty and efficiency. These items usually are so general and consideredsimilarly in all business models. But businesses have different products or services and they create value for their customers in a different way.Therefore customer perceptions of value in different websites are not the same. For example customers of an online game website think differently about value rather than customers of an electronic shop.So at first we must know the customers of our business, then find out what is valuable for them, and finally try to develop and extendtheir perceived value. To do so data mining tools are used to analyze customers’ behavior. In the previous section, customers’ data of 3tark website are analyzed. Here in this section, resultswould be discussed and we could find out what are the valuable issues for the customers. In spite of this fact that this website usually fill leisure time of the users, results show that users are unwilling to stay in the website on holidays. Maybe this is because of users’ willingness to use the website’s services on their work time or maybe they want to do other amusements in holidays except of playing in the Internet. So being the website workable in working days of the week is very important. If it needs updating it could be done in holidays.
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Value Creation for Customers by Segmentation Approach: An Online Game and Entertainment Website Case Study Sahar Sohrabi, Bahman Nikkhahan
Most of the users are teenager and young (90%). Older usershavehigher possibility to leave than younger users.Sosome items that fit to the interest of younger users must be added to create more value for them. 79% of the users are single that is consistent with the previous fact. There are some interesting facts about the females. Females form 20% of all users but they have different behavior than males. There are older than males. Furthermore %44 of females are married compared to 15% of males. Many of the Iranian married women are householder. Probably this could be the cause of relatively high ratio of married female users than married male users. Another interesting issue about the females is their willing to play brain games. In their mind this type of game is the most valuable game. On the other side, males consider skill games the most valuable item in the website. Customer’s churn rate is an important factor in evaluating his or her loyalty. As fast as customers are attracted to this website they are leaving it. 84% of the customers have average life time of 5 days or less. This group of users was attracted to the website by some advertisements but when they came in, the website couldn’t retain them anymore. It could have many reasons and needs further researches. Users of medium and high activity cluster in table4 are relatively active in the website and have high loyalty (16% of all users). They know all of the items very well and use them regularly. They used all of the items more than average. In their viewpoint, the provided services are valuable and interesting. These customers are valuable for the website too; they must be served well and special loyalty program must exist for them.
6. Conclusion Value creation for e-business customers is analyzed by many researchers in the literature and they presented valuable and interesting models and ideas about this important matter. But most of them presented some general building blocks for value that could have different meaning in different times, locations, and cultures. To apply these models to a website, more research and investigation is needed. But in this paper instead of asking the customer about value to build some factors, we would analyze his or her behavior in the website by some data mining techniques to evaluate what is valuable for him or her. As a case study, 3tark that is a pioneer website in online games and entertainments in Iran is considered. Valuable issues for different customer segments are described in this paper. For example women and those users that are older think brain games are the most valuable item in the website. But for men, skill games are valuable. The services of this website are more valuable for younger users than older ones. Most of the users tend to stay in the website on working days rather than holidays. Users will be attracted to the website easily and will leave it easily. The results of this case study can also be used as a value creation guideline for businesses that work in the area of online games or entertainments especially in Iran.
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