On-line personalized sales promotion in electronic commerce

Expert Systems with Applications 27 (2004) 35–52 www.elsevier.com/locate/eswa

On-line personalized sales promotion in electronic commerce S. Wesley Changchiena,*, Chin-Feng Leeb, Yu-Jung Hsub b

a Institute of Electronic Commerce, National Chung-Hsing University, 250 Kuo Kuang Road, Taichung 403, Taiwan, ROC Department of Information Management, Chaoyang University of Technology, 168 Gifeng E. Rd, Wufeng, Taichung County, Taiwan, ROC

Abstract Electronic Commerce encompasses all electronically conducted business activities, operations, and transaction processing. With the development of electronic commerce in the Internet, companies have changed the way they connect to and deal with their customers and partners. Businesses now could overcome the space and time barriers and are capable of serving customers electronically and intelligently. However, it is quite a great challenge to attract and retain the customers over Internet due to the low barrier of entrance and severe competition. Personalization, a special form of differentiation, when applied in market fragmentation can transform a standard product or service into a specialized solution for an individual. In this research, an on-line personalized sales promotion decision support system is proposed. The proposed system consists of three modules: (1) marketing strategies, (2) promotion patterns model, and (3) personalized promotion products. The marketing strategies contain sales promotion strategies and pricing strategies. Promotion patterns are generated according to various sales promotion strategies, and the promoted prices for the promotion products are generated by considering both the current stages of business life cycle and product life cycle. In the promotion patterns model, by segmenting the market, customer behaviors of three categories can be analyzed by utilizing data mining techniques and statistical analysis to generate personalized candidate promotion products. Finally, multiple evaluation indicators are used and adjusted to rank and obtain the final personalized promotion products. With the promotion products based on customers’ past frequent purchase patterns, it has the potential to increase the success rate of promotion, customer satisfaction, and loyalty. In this paper, a prototype system was developed to illustrate how the proposed on-line personalized promotion decision support system works in electronic commerce and a simplified case of performance analysis was conducted for evaluation. q 2003 Elsevier Ltd. All rights reserved. Keywords: Promotion; Electronic commerce; Data mining; Decision support system

1. Introduction In addition to providing a new channel, Electronic commerce (EC) has the potential of serving customers better if it can take good advantage of information technology and develop EC-specific marketing strategy. Marketing is a social and managerial process by which individuals and groups obtain what they need and want through creating, offering, and exchanging products of value with others (Kotler, 1997). EC, not just the purchase of goods and services over the Internet, is a broad term. It encompasses all electronically conducted business activities, operations, and transaction processing. With the development of the Internet and EC, companies have changed the way * Corresponding author. Tel.: þ 886-4-22859465; fax: þ 886-422859497. E-mail address: [email protected] (S.W. Changchien). 0957-4174/$ - see front matter q 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2003.12.017

they connect to and deal with their customers and partners. Businesses hence could overcome the space and time barriers and are now capable of serving customers electronically and intelligently. Internet marketing and commerce has shown many cases of uncertainties, potentials, and impact. It expands the opportunities for branding, innovation, pricing, and selling. However, the exponentially increasing amount of data and information along with the rapid expansion of the business web sites and information systems makes a business hard to manage and leverage the potential power of EC. Therefore, emerging data analysis techniques such as data mining are capturing researchers’ and businesses’ great attention. One main purpose of utilizing data mining technology in EC is to attract and retain customers. While there are diverse approaches in Internet marketing, one solution is to provide personalized information services (Schafer, Konstan, & Riedl, 2001).

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Personalization, a special form of differentiation, when applied in market fragmentation can transform a standard product or service into a specialized solution for an individual. Through personalization, businesses can get to know customers’ buying behaviors and accordingly develop more appropriate marketing strategies to attract each customer of a specific type and efficiently deliver the suitable information and products/services to him/her. The customer’s satisfaction and loyalty can thus be enhanced, and the increase in each customer’s visiting frequency can further create more transaction opportunities and benefit the Internet businesses (Lee, Liu, & Lu, 2002). Surprenant and Solomon (1987) proposed three types of personalization: option, programmed, and customized personalization, while Dean (1998) classified web site personalization into three categories: rule-based, collaborative, and learning-agent personalization. A number of web-based personalized systems have been proposed recently (Borchers, Herlocker, Konstan, & Reidl, 1998). Personalization usually works by filtering a candidate set of items (such as products) through some representation of a personal profile. The technology challenges to supporting personalization include the need to perform clustering and searching in a very large dimensional data space with huge amount of data. In general, there are two major approaches to provide personalized information: content-based and collaborative filtering (Yu, 1999; Aggarwal, Wolf, Wu, & Yu, 1999). In the content-based approach, it provides items that are similar to what the user has favored in the past. Some recommendation systems operate based on this approach, such as NewsWeeder (Lang, 1995) and Infofinder (Krulwich & Burkey, 1996). However, there are some shortcomings: implement difficultly to several non-text multimedia resources, like movies, music, etc.; moreover, a user’s preferences localize one specified domain, unable to make other classified recommendations. As a result, collaborative filtering approach is presented. In the collaborative filtering approach, it identifies other users that have showed similar preference to the given users and provides what they would like. A lot of recommendation systems are developed based on this approach, such as Tapestry (Goldberg, Nichols, Oki, & Terry, 1992), GroupLens (Konstan et al., 1997), Ringo (Shardanand & Maes, 1995), PHOAKS (Terveen, Hill, Armento, McDonald, & Creter, 1997), and Siteseer (Rucker & Polenco, 1997). But, some drawbacks come up, e.g. unable to provide new items to a user, and unsuited to a user whose liking is different from other users. After analyzing the existing approaches, in this paper, we integrate the two approaches and present an on-line personalized promotion decision support system (DSS), which uses the data mining techniques to help the business discover suitable promotion products for each individual customer. This research aims to propose a personalized promotion DSS, which can provide personalized promotion products at customized prices for each specific individual customer.

By utilizing powerful data mining techniques, more suitable promotion products can be selected even better than experienced professional sales persons. For achieving the purpose of personalization, we must cluster all the customers firstly. By using ART (Adaptive Resonance Theory network) (Carpenter & Grossberg, 1988), we cluster the customers according to two types of attributes, one is the factual attributes which include demographic information such as gender, age, income; and the other is transactional data, which consist of the customers’ purchase records, for instance, the purchase date, amount paid, etc. Secondly, according to the traditional marketing techniques, we embed three sales promotion strategies into the promotion decision support system: general promotion, cross-selling and up-selling, to find personalized promotion products by analyzing customers’ purchasing behavior using data mining, where customers are divided into three categories: all customers, customer cluster, and an individual customer. That is to say, we not only analyze the all customers’ purchasing behaviors, but also extract the purchasing behaviors of different customer clusters and individual customers. The first strategy, general promotion strategy aims at finding the best-selling and worst-selling products. Cross-selling strategy is to sell additional crossrelated products to the customers. In this paper, we will discover the association and sequential products by utilizing association rule mining and sequential patterns mining, respectively. The last one, up-selling strategy is the closely related case of getting existing customers to trade up to more profitable products. Here, up-selling is applied to the best-selling, association, and sequential products restricted within the same ‘brand’. In short, we integrate data mining techniques and cross analysis to carry out the three strategies and propose a new pricing strategy to provide personalized promotion products for each customer. Accordingly, an on-line personalized promotion DSS for EC is developed and proposed in this paper. In Section 2, related data mining techniques are introduced. Section 3 proposes an on-line personalized promotion DSS and Section 4 describes the developed DSS. A performance evaluation of the proposed DSS is discussed in Section 5. Finally, Section 6 concludes the paper and presents some future research directions towards the further development and enhancement of the system.

2. Data mining Data mining refers to extracting knowledge from a large amount of data (Han & Kamber, 2001). Data mining by automatic or semi-automatic exploration and analysis on a large amount of data items set in a database can discover potentially significant patterns inherent in the database. Kleissner (1998) defined that data mining is a new decision support analysis process to find buried knowledge in corporate data and deliver understanding to business

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professionals. Hence with data mining analysis, decision makers can make better decisions with more information and knowledge which other tools may not be able to provide. In this paper, we utilize three approaches of data mining techniques: cluster analysis, association rules mining, and sequential patterns mining. 2.1. Cluster analysis Cluster analysis identifies groups within data that maximize intra-class similarity and minimize interclass similarity. Nowadays, there are various approaches to cluster analysis, including multivariate statistical method, artificial neural network, and other algorithms. The traditional methods can be divided into two types, hierarchical and partitional clustering (Jain & Dubes, 1988), and the primary methods are minimum spanning tree and k-mean, respectively (Kaufman & Rousseeuw, 1990). However, some of the methods like self-organizing map algorithm (Kohonen, 1995) need to specify the expected number of clusters in advance, which may affect the clustering results due to subjective paramer setting. For this reason, we employ ART, one of the clustering methods using neural network, for cluster analysis. ART was developed by Carpenter and Grossberg in 1976. It is capable of determining the number of clusters through progressive adaptation. ART allows a training example to modify an existing cluster only if the cluster is sufficiently close to the example; otherwise a new cluster is formed to handle the example. Using a ‘Vigilance parameter’ as a threshold of similarity, ART can determine when to form a new cluster. This algorithm uses an unsupervised learning and feedback network. It accepts an input vector and classifies it into one of a number of clusters depending upon which it best resembles. The single recognition layer that fires indicates its classification decision. If the input vector does not match any stored pattern, it creates a pattern that is like the input vector as a new category. Once a stored pattern is found that matches the input vector within a specified threshold (the vigilance), that pattern is adjusted to make it accommodate the new input vector. 2.2. Association rules mining Association rules mining that was first proposed by Agrawal and Srikant can discover correlation’s of events which can be represented as probabilistic rules (Agrawal & Srikant, 1994). ‘Correlation of events’ means those events are frequently observed together. Discovering association rules in large databases can be a good step of knowledge discovery in databases (KDD). Association rules can be formally defined as follows. Let I ¼ {i1 ; i2 ; …; im } be a set of literals, called items. Let D be a set of transactions, where each transaction T is a set of

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items such that T # I: Let X be a set of items in I: A transaction T is said to contain X; if X # T: An association rule is an implication of the form X ) Y; where X , I; Y , I and X > Y ¼ Y: The rule X ) Y holds with confidence c in transaction set D; if and only if c% of transactions in D that contain X also contain Y: The rule X ) Y has support s in transaction set D; if and only if s% of transactions in D contain X < Y: There are two major steps in discovering all association rules: Firstly, find all sets of items (itemsets) that have transaction support larger than or equal to the minimum support. These itemsets are called large itemsets. Secondly, for each large itemset, generate the corresponding rule. Besides the association of items, the sequential patterns of items can be explored. 2.3. Sequential patterns mining Agrawal and Srikant developed sequential patterns mining (Agrawal & Srikant, 1995), whose purpose is to find the hidden information regarding the sequences among items. For example, a customer may have many transactions at different times, and the orders of the items in these transactions form the sequential patterns. A sequential pattern P is defined as an ordered list of itemsets (Agrawal & Srikant, 1995). Thus P ¼ ðX1 ; …; Xn Þ; where each Xi itemset is called an element of P: Ordering is considered between items of different itemsets, but not between items of the same itemset (Nanopoulos, Zakrzewicz, Morzy, & Manolopoulos, 2003). The method to mine sequential patterns, same as the association rules mining has two steps as follows: First, find all sets of items (itemsets) that have transaction support larger than or equal to the minimum support. Then generate sequential rules containing items from the large itemsets.

3. An on-line personalized promotion decision support system In this section, an on-line personalized promotion decision support system (PPDSS), which uses data mining techniques in accordance with the proposed marketing strategies to help the business prepare the highly potential and suitable promotion products for each individual customer, is presented. Fig. 1 shows the architecture of the proposed PPDSS, which consists of three modules: (1) marketing strategies, (2) promotion patterns model, and (3) personalized promotion products. Each module of the proposed PPDSS is described in detail as follows. 3.1. Marketing strategies The marketing strategies contain sales promotion strategies and pricing strategies. Promotion patterns are generated according to various sales promotion strategies,

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and the promoted prices for these promotion products are generated by considering both the current stages of business life cycle and product life cycle. 3.1.1. Sales promotion strategies Most scholars believe that sales promotion can give the promoted objects immediate incentives and excite customers’ interest. Davis (1981) considered that sales promotion strengthens the marketed effect and increases the sales volume in short-term. Some other scholars subsumed the objects of sales promotion under the definition of promoted activities. In general, promoted activities include customer promotion, trade promotion, and sales-force promotion. In this paper, we focus on customer promotion in Internet. According to the classification of on-line sales promotion proposed by Hsu (1998), on-line sales promotion models consist of cash discount, commodity presentation, and rewards drawing activities as listed in Table 1. In our proposed PPDSS, three sales promotion strategies are considered in implementation: general promotion, crossselling, and up-selling strategies as shown in Table 2. Through associated techniques, the three strategies carry out two sales promotion models, cash discount and commodity presentation. For general promotion, cross-selling, and upselling, statistical analysis and cross analysis, association and sequential mining, and cross analysis and association and sequential mining will be used, respectively.

Fig. 1. The architecture of proposed PPDSS.

Table 1 On-line sales promotion models (Hsu, 1998) Promotion model Cash discount

Promotion manner Universal price discount Differential price discount

Commodity presentation

Same commodity presentation Different commodity presentation

Price-offs Price discount Quality discount Total discount Bonus packs Premiums Bundling

Rewards drawing activities

Trading stamps and sweepstakes

3.1.2. Pricing strategies with business life cycle and product life cycle In general, pricing strategies can be divided into general pricing, new product pricing, and life cycle pricing (Nagle & Holden, 1995; Philip, 2000). 3.1.2.1. General pricing. Cost-oriented pricing. Generally speaking, cost-oriented pricing aims to be a method that uses cost plus a standard or fixed profit to decide a product price. There are five methods used to set prices: markup, keystoning, profit maximization, break-even, and target-return pricings.

Table 2 Three sales promotion strategies implemented in the proposed PPDSS Strategy

Product categories

Promotion condition

Price manner

Applied techniques

General promotion

Best-selling Worst-selling Seasonal product Festival product

Purchased quantity Purchased total amount

Price discount

Cross analysis Statistical analysis

Cross-selling

Association product Sequential product

Bundling Purchased total amount

Association mining Sequential mining

Up-selling

Upgrading product

Purchased total amount

Cross analysis Association mining Sequential mining

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Demand-oriented pricing. In this method, prices are based on how much customers will pay. One is demandbackward pricing, and the other is discriminatory pricing. Demand-backward pricing. Decide the suitable pricing firstly. Then, calculate the cost of the most expensive product. Discriminatory pricing. A product will be provided at dissimilar prices for different customers or segmented markets to increase sales volume and profit. There are several kinds of discriminatory pricing as follows (Dolan & Simon, 1996; Iyer, Miyazaki, Grewal, & Giordano, 2002; Philip, 2000). Market based: Offering different prices based on different segmented markets or geographical regions. Product based: Deciding dissimilar prices according to product type or color. Time based: Getting distinct prices by purchase time, such as milk, whose price in summer is different from that in winter. Purchase based: Observing the characteristics as the base to provide discriminatory pricing. Usage based: Due to the different degree of using product, the business charging prices on the basis of usage. Competition-oriented pricing. It is a pricing method which decides prices based on the competition. There are two main methods, going-rating pricing and sealed-bid pricing. Going-rating pricing. The product prices set are similar to the main competitors or general market prices, and the pricing does not consider either cost or market demand. Sealed-bid pricing. Those businesses striving to get the contracts will adopt the pricing. The business must consider the competitors’ prices and beat them with a lower price. 3.1.2.2. New product pricing. Skimming pricing. Charge a high price for a new product during the introductory stage, and lower the price later. Penetration pricing. Introduce a new product at a low price in hopes of building sales volume quickly. 3.1.2.3. Life cycle pricing. At different stages of product life cycle, products’ related costs, buyers’ sensitivity for price, and competitors’ behaviors vary over time. Hence, the pricing strategy should be timely adjusted to ensure the effectiveness. Life cycle pricing differs at four stages, introduction, growth, maturity, and decline stages. Pricing for introduction stage. There are usually two methods, skimming pricing and penetration pricing. When the product is unknown to customers, the main goal of the business is making the potential customers attracted to the new product.

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Pricing for growth stage. At growth stage, the customer takes care of not only product’s effectiveness, but also cost and characteristic between different brands. At this time, the business can adopt differentiated product strategy or cost leading strategy to set up the price. Pricing for maturity stage. The business needs to distribute sales over product mix and services, improve cost control, and extend product lines to create competitive advantage at this stage. Pricing for decline stage. There are three strategies to select. The first one is contraction, business giving up all or partial market and refocusing on the market which it has more advantage over. The second one is harvest, business withdrawing from a competition market. The third one is stable, business trying to strengthen competitive advantage in the decline stage to reap benefit. This strategy is only suitable for the business whose financial capability is abundant. This paper firstly combines cost-oriented pricing and life cycle pricing, then utilizes the concept of discriminatory pricing in the Internet and, as a consequence, the promoted price will be adjusted dynamically according to each customer’s personal characteristics. For the pricing strategy, we define the promoted price based on two factors. One is the pricing strategy for each stage in business life cycle, and the other is the pricing strategy for each stage in product life cycle. In addition, the promoted price will be adjusted dynamically with time; that is to say, when the business situation or the product life cycle stage changes, the system will automatically, on the basis of the two factors, update all products’ promoted prices. Firstly, according to the methods presented by Chow (Chow, 1998) and Smith et al. (Smith, Mitchell, & Summer, 1985), an approach is proposed that assesses business life cycle and product life cycle based on the capital growth ratio, employee growth ratio, and sales volume growth ratio to set up the pricing strategies. The pricing strategy for each stage of business life cycle and product life cycle is shown Table 3 Pricing strategy for each stage of business life cycle and product life cycle Characteristic

Life cycle stage Introduction Growth

Business life cycle Growth ratio (capital, Rbi $ 2 employee and sales volume) Pricing strategy for Low each business stage Product life cycle Growth ratio (sales volume) Pricing strategy for each product stage

Maturity

Decline

2 . Rbi $ 1 1 . Rbi $ 0 Rbi , 0

Highest

High

Medium

Rpi $ 2

2 . Rpi $ 1 1 . Rpi $ 0 Rpi , 0

Highest

High

Low

Medium

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in Table 3. It divides the business life cycle and product life cycle into four stages including introduction, growth, maturity, and decline stages. The value of growth ratio differentiates at each stage. The growth ratio defined by Chow is described as the following formula (Chow, 1998): Rbi ¼

Gbi ; lRbi21 l

Gbi ¼

Sbi 2 Sbi21 £ 100%; Sbi21

where Rbi is the ith period growth ratio, Gbi is the ith period growth rate, and Sbi is the ith period value of capital, employee and sales volume. In the same way, Rpi represents the ith period growth ratio for product life cycle and is defined as follows: Rpi ¼

Gpi lRpi21 l

;

Gpi ¼

Spi

Spi21

2 Spi21

Fig. 2. Membership function for various stages of business and product life cycles.

and both profits according to current business life cycle and product life cycle stages are considered.

£ 100%;

where Rpi represents the ith period growth ratio, Gpi is the ith period growth rate, and Spi is the ith period value of sales volume. Secondly, the two factors are considered to help the business decide the final prices for promotion products. Generally, the product price is set depending on the desired business profit. Let the final profit be P; the cost be C; and the final promoted price be PP, then PP ¼ Cð1 þ PÞ: As shown in Table 3, the terms ‘Low’, ‘Medium’, and ‘High’ are semantic words which cannot be directly processed in quantifying the final profit, and the overlapping period between every two neighboring life cycle stages are not reflected. In this paper, fuzzy theory is used to decide the final profit. Fuzzy set theory, was first introduced in Zadeh’s paper in 1965 (Zadeh, 1965). This theory, which deals with uncertain values and is suitable to process human’s expression, is primarily concerned with quantifying and reasoning words that have ambiguous meanings, such as small, medium, high, etc. The theory has been widely extended and applied to many fields, including control, engineering, economics, literature, etc. In fuzzy sets, an object may belong partially to a set. The degree of membership in a fuzzy set is measured by a generalization of the characteristic function called the membership function defined as:

3.1.3. Define membership functions In fuzzy logic, a fuzzy set A on universe X is defined by the ordered pair ðx; mA ðxÞÞ; where x is the object on X; and mA ðxÞ is called the membership function of A: The membership function can be any value in the range of ½0:0; 1:0: Here, we define the membership function uL ðxÞ ! ½0; 1 for various stages of business and product life cycles and uP ðxÞ ! ½0; 1 for levels of profit as shown in Figs. 2 and 3, respectively. 3.1.4. Compute the promotion price Profit based on current business life cycle stage. Suppose that the value R for business life cycle is 2 0.3 (as shown in Fig. 4) presently, and it belongs to decline stage and maturity stage at the degrees of 0.8 and 0.2, respectively. Since the corresponding pricing strategies of decline stage and maturity stage are medium and high (Table 3), the range of fuzzy number for profit based on current business life cycle stage is 0.8(the range of fuzzy number for ‘Medium’) þ 0.2(the range of fuzzy number for ‘High’) ¼ 0.8(210,10,30) þ 0.2(10,30,50) ¼ (28,8,24) þ (2,6,10) ¼ (2 6,4,4). To derive a discrete value, we apply one of the defuzzication methods, center of gravity method (Han & Kamber, 2001), to transform the fuzzy number to an unfuzzy number. The formula is as follows. DFi ¼

½ðURi 2 LRi Þ þ ðMRi 2 LRi Þ þ LRi ; ;i ; 3

uA ðxÞ ! ½0; 1; where x [ X; and uA ðxÞ is the membership degree for x in uA : The characteristic function maps all elements of X into one of exact two elements: 0 or 1. In contrast the membership function maps X into the real numbers defined in the interval from 0 to 1 inclusive and symbolized by [0,1], where 0 means no membership and 1 means full membership in the set. There are several kinds of membership functions like Triangular, Trapezoidal, and Bell-shaped membership functions. In obtaining the final promotion price for each promotion product, fuzzy membership functions need to be defined first

Fig. 3. Membership function for levels of profit.

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Fig. 4. Fuzzy degree of business life cycle.

where DFi is an unfuzzy value, URi is the maximum of trigonometric fuzzy number, MRi is the medium of trigonometric fuzzy number, and LRi is the minimum of trigonometric fuzzy number. Accordingly, the fuzzy number (2 2,18,38) can be transformed into an unfuzzy value 14; that is 14% business profit. Profit based on current product life cycle stage. Assume that now the original expected profit of product A based on the list price is 20%, and the value R for present life cycle stage of product A is 1.5, and its degree conforming to growth stage is 1.0. The corresponding pricing strategy for growth stage is high (Table 3). The range of fuzzy number for product’ profit is then computed as: 1.0(the range of fuzzy number for ‘High’) þ 0.3(the range of fuzzy number for ‘High’) ¼ 1.0(10,30,50) ¼ (10,30,50). After performing defuzzication, the unfuzzy value for required profit based on current life cycle stage of product A is 30%. By increasing the profit of product A by 30%, the required profit based on present product life cycle stage turns into 50%. Final promotion price. The final profit of product A is defined as the average of the expected business profit and product profit, i.e. FP ¼ (ProdProf þ BusiProf)/2, where FP is the final profit in percentage, ProdProf is profit based on product life cycle stage, and BusiProf is the profit based on business life cycle stage. The average of 14% from business profit plus 50% from product profit equals 32%. Hence, in this example, the final promotion price for product A is PP ¼ Cð1 þ 32%Þ; where PP is the promotion price and C is the cost of the product. Pricing strategies in accordance with present business life cycle stage and product life cycle stage described above suit the three sales promotion strategies in Table 2. In addition, we provide extra price discount with the characteristics of each customer. The opportunities conforming to the conditions are described as follows: Purchase total amount discount. The customer will obtain the extra price discount when the purchased total amount is greater than or equal to a prespecified purchase total amount threshold.

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Accumulated total amount discount. A customer is subject to a different extra price discount if his grand accumulated total amount in the pass is greater than or equal to a pre-specified accumulated total threshold. Purchase patterns discount. Comparing the purchased products in the past of the customers with the personalized promotion products to decide if the system will apply an extra discount to the promotion price. There are two purposes in applying this discount: Attract new customers: Promotion product belonging to purchase patterns of all customers: In PPDSS, promotion products are discovered from purchase records of all customers, cluster customers, and each individual customer. When the promotion product is included in the purchase patterns of all customers, whose correlation with the individual customer’s patterns is low, for the purpose of attracting new customers, the system will apply a high price discount in addition to the original promotion price. Promotion product belonging to purchase patterns of cluster customers: When the promotion product is one of the purchase patterns of his corresponding cluster customers, whose correlation with the individual customer’s patterns is higher, for the purpose of attracting new customers, the system will provide a medium price discount besides the original promotion price. Promotion product belonging to purchase patterns of the specific customer: When the promotion product conforms to the purchase patterns of the specific individual customer, since it does not tally with the purpose of attracting new scustomers, the system will not apply any extra price discount to the original promotion price. If the promotion product belongs to more than one of the above three cases, for example, conforming to both all customers’ and cluster customers’ purchase patterns, the highest discount will be applied. Retain existing customers: Promotion product belonging to purchase patterns of all customers: When the promotion product is included in the purchase patterns of all customers, whose correlation with the individual customer’s patterns is low, since it does not tally with the purpose of retaining existing customers, the system will not provide any extra price discount. Promotion product belonging to purchase patterns of cluster customers: When the promotion product is one of the purchase patterns of his corresponding cluster customers, whose corre-

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Fig. 5. Promotion pattern model.

lation with the individual customer’s patterns is higher, for the purpose of retaining existing customers, the system will provide medium price discount besides the original promotion price. Promotion products belonging to purchase patterns of the specific customer: When the promotion product conforms to the purchase patterns of the specific individual customer, for achieving the purpose of retaining the specific existing customer, the system will not only provide the original promotion price but also apply a high extra price discount. Similarly, if the promotion product belongs to more than one of the above three cases, the highest discount will be applied.

3.2. Promotion patterns model In the second module, the promotion patterns model as shown in Fig. 5, will be described in this section. It utilizes data mining techniques, statistical analysis, and cross analysis to generate various promotion products according to the three sales promotion strategies and pricing strategies. For illustration, suppose there are 15 products, and all products are classified into five classes A, B, C, D, and E in advance as listed in Table 4.

3.2.1. Mining the customer database Market segmentation divides a larger market into submarkets based upon different customer profiles and product preferences. Clustering analysis is one of the frequently used methods for segmenting a market (Changchien & Lu, 2001). Clustering analysis is to identify clusters embedded in the data where a cluster is a collection of data objects that are ‘similar’ to one another. Similarity can be expressed by distance functions, specified by users or experts. As Table 4 A sample product table Item_ID

Class

Brand

Price

Cost

Status

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

A A B B B C C C D D D E E E E

TWINHEAD IBM IBM ACER ACER EPSON HP HP EPSON HP HP LEMEL PLEXTOR PLEXTOR RITEX

$2293 $1829 $740 $1250 $1567 $183 $110 $170 $83 $153 $223 $87 $100 $200 $0.33

$1700 $1460 $643 $937 $1160 $153 $100 $143 $67 $127 $157 $70 $83 $153 $0.30

– – – – New – – – – – New – – New –

S.W. Changchien et al. / Expert Systems with Applications 27 (2004) 35–52 Table 5 A sample customer table Customer_ID

Name

Age

Gender

Income

Total purchase amount

111 113 122 123 133

Bob Alex Annie Linda Peter

35 52 27 22 55

M M F F M

1800 1900 1300 800 1400

400 500 1200 2000 1500

introduced in Section 2, we employ ART to cluster customers. Selected customer demographics and behavior are used to segment customers. In other words, all the customers in the same cluster have the similar selected demographics and behavior, based on which some of the promotion products will be derived. For example, there are five customers shown in Table 5. Since the inputs of ART should be binary values, we must perform transformation of the input data. Here, some discretization technique is used to divide the ranges of customer attributes (Han & Kamber, 2001), e.g. Income, Age, and Total purchase amount, into intervals labeled as Low, Medium, and High. Take attribute Income as an example. The Low interval defines values below 1000, and can be coded as ‘00.’ The Medium interval defines values between 1000 and 1700, and can be coded as ‘01.’ The High interval defines values above 1700, and can be coded as ‘11’. Similarly, attributes Age and Total purchase amount are discretized and encoded in the same way as in Table 6. According to the discretization and encoding method, five vectors in the format of {Income, Age, Sex, Total amount} corresponding to the five customers Bob, Alex, Annie, Linda, and Peter shown in Table 5 can be encoded as {01,1,11,01}, {11,1,11,01}, {00,0,01,11}, {00,0,00,11}, and {11,1,01,11}, respectively. Set the Vigilance parameter as 0.5, then three clusters can be generated by ART: Bob and Alex (i.e. IDs 111 and 113) belong to the first cluster A, Annie and Linda (i.e. IDs 122 and 123) belong to the second cluster B, and the third cluster C contains Peter (i.e. ID 133).

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data mining and cross analysis in order to locate suitable promotion products, out of a great number of products, for the different cluster customers. 3.2.2.1. Data mining for cross-selling. To promote products by cross-selling, the correlation among products should be analyzed. The correlation among products could be either product association within a transaction or product sequence from sequential transactions over a period of time. The proposed PPDSS will discover the cross-selling opportunities by mining product correlation from the customers’ past purchase records in the transaction database. Association mining and sequential patterns mining will be conducted to discover product associations and product sequences, respectively, for promotion by cross-selling. Association mining. It is intuitive that what products are purchased together can be extracted through association mining. In the paper, an association mining method (Agrawal & Srikant, 1994) is used to retrieve the affinity of products purchased together from the transaction data of all customers. Furthermore, it can also be applied to each customer cluster and individual customer to discover the purchasing patterns for each customer cluster and each individual customer, respectively. Consider a sample transaction table in Table 7. Assume that the minimum support count is 4. The product association patterns {4,10} and {13,15} are obtained for all customers. It means that all the customers tend to buy items 4 and 10 together or items 13 and 15 together. Since customer 111 belongs to customer cluster A, we further perform data mining on customer cluster A. By association mining on transaction data of customer cluster A, it is discovered that the customers of cluster A tend to buy items 13 and 15 at the same time. Similarly, by applying association mining to each individual customer (say customer 111), it is found that the customer loved to buy items 13 and 15 together as well. All the product association patterns for the three customer categories (all customers, Table 7 A sample transaction table

3.2.2. Mining the transaction database How the business divides customers into several different clusters based on a clustering technique has been discussed in Section 3.2.1. We can take that as a basis to provide different promotion products for different customer clusters. In this section, we will progress towards the data analysis of transaction database by Table 6 Discretized and encoded attributes as input data to ART Attribute

Range (Code)

Age Sex Income Total purchase amount

,30 (00) M (1) ,1000 (00) ,300 (00)

30–50 (01) F (0) 1000– 1700 (01) 300– 1000 (01)

.50 (11) .1700 (11) .1000 (11)

TID

Itemset

Customer ID

Cluster

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

{10,13,15} {1,7,13,15} {4,10,11} {6,7,12} {2,4,10} {12} {4,10} {1,4,6} {7,13} {4,8,10,14} {5,13} {4,13,15} {15} {2,4,10} {3,11,12,13}

111 111 112 112 113 131 131 132 132 133 121 122 122 123 123

A A A A A B B B B B C C C C C

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Table 8 Product association patterns for customer ID 111 in three customer categories Customer category

Product association patterns

All customers Customer cluster A Individual customer

{4,10}, {13,15} {13,15} {13,15}

Table 9 Product sequence patterns for customer ID 111 in three customer categories Customer category

Product sequence patterns

All customers Customer cluster A Individual customer (Customer 111)

{4,12}, {10,12}, {10,7} {10,7} –

customer cluster and individual customer) in this example are shown in Table 8. Sequential pattern mining. The sequence of products purchased over a period of time reveals another important correlation of products, which can be explored through sequential patterns mining. In the paper, a sequential patterns mining method (Agrawal & Srikant, 1995) is used to uncover the products which were frequently purchased in sequence over time from the transaction data by three different data sets: all customers, each customer cluster, and each individual customer. Consider the transaction database in Table 7. Assume that the minimum support count is 2. The product sequence patterns {4,12}, {10,12} and {10,7} are obtained for all customers’ transaction data. That means that all the customers have a similar trend to buy item 12 after buying item 4, item 12 after item 10, and item 7 after item 10. Since customer 111 belongs to customer cluster A, by mining sequence patterns on transaction data of customer cluster A, it is found that the customers in the customer cluster to which customer 111 belongs tend to buy item 7 after buying item 10. No product sequence pattern is found for transaction data of customer 111 given the minimum support count of 2. In summary, all the product sequence patterns are shown in Table 9.

3.2.2.2. Cross analysis for general promotion and up-selling. Cross analysis is applied for implementing best-selling, worst-selling and up-selling strategy. The best-selling and worst-selling products can be found directly based on statistical analysis of the sales records. Assume we carry out up-selling strategy according to the same product class and brand, then product upgrades can be found in conjunction with product associations, sequences, and best-selling products. General promotion. For each product class, cross analysis is firstly carried out on product data (as shown in Table 4) and transaction data (as shown in Table 7) of each kind of customer category for acquiring the best-selling (e.g. the percentage of transactions containing the item is greater than or equal to 50%) and worst-selling (e.g. the percentage of transactions containing the item is less than 3%) products. The best-selling and worst-selling items in this example are shown in Table 10. Here, cross analysis will not be carried out on the products with the status labeled as ‘new’ because no sufficient transaction data can be analyzed for those new products. Up-selling. The resultant associated, sequential, and bestselling products from mining and analysis establish the candidates for up-selling. For example, an item set {4,10} is a product association pattern mined for the category of all customers. For item 4, item 5 becomes the up-selling product because items 4 and 5 belong to the same product brand and have the similar product characteristic but item 5 has a higher price. Here for illustration, up-selling products are restricted within the same product brand. Besides, combined items can also be obtained as up-selling products by bundling an upselling item to other items belonging to an extracted product association or sequence pattern. For instance, {5,10} is also an up-selling product since item 5 is an upgrade for item 4. All up-selling products in the preceding example are listed in Table 11 for each customer category. 3.3. Personalized promotion products In the proposed PPDSS many candidate promotion products (including those generated for all customers, the corresponding customer cluster, and the specific customer) will be generated for a customer. However, it is not practical to offer all of them for promotion. Proper evaluation should be conducted for providing the most

Table 10 Best-selling and worst-selling items of all product classes for all customers, customer cluster A, and customer 111 Product class

A B C D E

All customers

Customer cluster A

Individual customer (Customer 111)

Best selling

Worst selling

Best selling

Worst selling

Best selling

Worst selling

{1}, {2} {4} {7} {10} –

– – – {9} –

{1}, {2} {4} {7} {10} –

– {3}, {5} – {9} –

{1} – {7} {10} {13}, {15}

– – – – –

S.W. Changchien et al. / Expert Systems with Applications 27 (2004) 35–52 Table 11 Up-selling products for all customers, customer cluster A, and customer 111 Kind of customers

Base product Product pattern Up-selling products patterns for up-selling

All customers

Association patterns Sequence patterns Best-selling

Customer cluster A

Association patterns Sequence patterns Best-selling

Individual Association customer patterns ID 111 Sequence patterns Best-selling

{4,10}{13,15} {4,12}{10,12} {10,17} {1} {2} {4} {7} {10}

{5}{11}{5,11}{4,11} {5,10}{14}{14,15} {5}{5,12}{11}{11,12} {8} {8,11}{8,10}{7,11} – – – {8} {11}

{13,15}

{14}{14,15}

{10,7}

{8}{11}{8,11}{7,11}{8,10}

in terms of the satisfaction rate of the promoted manners, including the product bundling, product price, etc. This evaluation can be implemented on EC every time when or after a promotion product is purchased. The third indicator is used to estimate the acceptance ratio of all promotion attempts. The success ratio is defined as follows. Success ratio ¼ number of promotions accepted=number of promotions proposed: We then calculate the score of each candidate promotion product by utilizing the WSM method. If there are m candidate promotion products and n indicators, then the WSM score, the highest promotion potential PP, can be calculated as follows: PP ¼ max i

{1} {2} {4} {7} {10}

– – – {8} {11}

{13,15}

{14}{14,15}

–

–

{1} {7} {10} {13} {15}

– {8} {11} {14} –

suitable personalized promotion products. In this paper, a multiple criteria decision making method WSM (Weighted Sum Model) (Triantaphyllou, 2001) is used for ranking. Fig. 6 shows the generation of personalized promotion products. To rate the candidate promotion products, three evaluation indicators are proposed for implementation, which are profit, customer satisfaction, and success ratio. More indicators can be employed according to the marketing concerns of the business. Before applying the WSM method, all the values of the three indicators need to be normalized. Making money is the primary goal for a business. Therefore, the promotion products with high profit should have a high priority in ranking the candidate promotion products. Additionally, customer satisfaction must be subsumed too

Fig. 6. Generation of personalized promotion products.

45

n X

aij wj ;

for i ¼ 1; 2; …; m;

j¼1

where aij is the jth normalized indicator value for the ith candidate promotion product and wj is the importance of the jth indicator. We assume the weights of the three indicators profit, customer satisfaction, and success ratio are 0.2, 0.5, and 0.3, Table 12 Promotion potential for each candidate promotion product Candidate promotion products

Profit indicator

Customer satis. indicator

Success ratio indicator

Promotion potential

0.7 0.7 0.3 0.8 0.8 0.7 0.5 0.7 0.4 0.5 0.7 0.1 0.2 0.9 0.6 0.7 0.4 0.5

0.51 0.49 0.41 0.42 0.55 0.53 0.43 0.67 0.41 0.40 0.40 0.19 0.22 0.76 0.36 0.66 0.41 0.26

Purchased quantity discount promotion products {1} 0.35 0.4 0.3 {2} 0.25 0.7 0.6 {4} 0.33 0.5 0.7 {7} 0.1 0.8 0.8 {10} 0.2 0.5 0.7 {3} 0.15 0.5 0.3 {5} 0.35 0.5 0.3 {9} 0.2 0.2 0.8 {13} 0.2 0.5 0.3 {15} 0.1 0.5 0.7

0.36 0.58 0.53 0.66 0.50 0.37 0.41 0.38 0.38 0.48

General discount promotion products {4,10} 0.27 0.5 {13,15} 0.15 0.5 {5} 0.35 0.5 {11} 0.4 0.2 {5,10} 0.28 0.5 {4,11} 0.37 0.5 {5,11} 0.38 0.4 {14} 0.3 0.8 {14,15} 0.2 0.5 {4,12} 0.27 0.4 {10,12} 0.2 0.3 {5,12} 0.28 0.2 {11,12} 0.3 0.2 {8} 0.18 0.9 {7,10} 0.15 0.3 {7,11} 0.25 0.8 {8,10} 0.19 0.5 {8,11} 0.29 0.1

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respectively. After applying the WSM method, the promotion potentials are listed in Table 12. A filtering process then proceeds. Given 0.5 as the minimum threshold, candidate promotion products with good promotion potentials will remain and be ranked. The final ranking of general discount promotion products is (price discount will be applied when customer 111 purchase the following promoted products): {8} . {14} . {7,11} . {5,10} . {4,11} . {4,10} and that for purchased quantity discount promotion products is (the purchased total quantity is greater than the pre-defined threshold): {7} . {2} . {4}. More constraints can be applied to obtain the final expected number of best personalized promotion products. 4. Development of the PPDSS In order to illustrate how the proposed PPDSS can function and work well with EC virtual stores, a prototype is developed using programming languages JAVA and PHP. In the prototype system, the number of products is 50, the number of customers is 1500, and there are 10,000 transaction records in the experimental database. Sections 4.1 and 4.2 describe all the interfaces for the backend decision maker, and Section 4.3 introduces the interface for online personalized promotion.

Fig. 8. Interface for cross-selling.

Customer clustering precedes generation of candidate promotion products. The clustering of customers is done using MATLAB, which provides the required ART algorithm in a user-friendly environment. After importing the customer data file, the ART algorithm divides all customers into many clusters; the number of clusters can be adjusted by the vigilance parameter. In the prototype system, there are 1500 customers and with the vigilance parameter set to 0.55, the customers are separated into 10 clusters.

In the PPDSS, three sales promotion strategies are implemented on all customers, each customer cluster and each customer: general promotion, cross-selling, and up-selling strategies. Fig. 7 is the interface for general promotion, where best-selling products are set as those with percentages of transactions containing the item greater than or equal to 50% and worst-selling as those with percentages of transactions containing the item less than or equal to 3%. Cross-selling analysis is shown in Fig. 8 with minimum support set equal to 0.01. Based on the best-selling products and extracted product association and sequence patterns, upselling products will be generated. In Fig. 9, the products 2 and 37 are the up-selling products for best-selling products 5 and 38 (Fig. 7), respectively. Remember that an up-selling product is qualified only for that with the same brand. After all the promotion products are generated via the three sales promotion strategies implemented on all

Fig. 7. Interface for general promotion.

Fig. 9. Interface for up-selling.

4.1. Generating personalized promotion products

S.W. Changchien et al. / Expert Systems with Applications 27 (2004) 35–52

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Fig. 10. Interface for generating personalized promotion products. Fig. 11. Pricing strategies with business life cycle and product life cycle.

customers, each customer cluster and each customer, they will be ranked to recommend the most suitable personalized promotion products for each customer. For instance, for a customer ID a; which belongs to customer cluster A; all the promotion products (including two categories, general discount products and purchased quantity discount products) generated for all customers, customer cluster A; and customer ID a are candidate promotion products for customer ID a: They will be ranked by the WSM method and three evaluation indicators (as presented in Section 3.3). Fig. 10 shows the interface that decision makers can give different weights for the three evaluation indicators according to business’ current goals. That is to say; the developed PPDSS can dynamically modify the personalized promotion products on the basis of the business’ present situation and the primary purpose of promotion by adjusting the weight for each evaluation indicator. Fig. 10 shows the final personalized promotion products after setting up the indicator weights and pressing the ‘GO’ button. Take customer 1, for example; the top ranked general discount promotion products include {43}, {37,41,43}, {37,41}, {37,43}, and {43}, {29}, {23}, {42}, {48} will also be offered at promoted prices if the purchased total quantity is greater than the pre-defined threshold.

cycle stage and its corresponding pricing strategy. Once upon the determination of the pricing strategy, the system calculates the promotion price for each promotion product as shown in Fig. 11. The second one, purchased quantity discount, the marketing manager ought to fill in the blank with the minimum quantity threshold eligible for discount at the lower left corner in Fig. 11. If a customer buys a product that belongs to the best-selling or worst-selling products and the quantity is greater than the given threshold, a price discount will be applied to the purchased product. Similarly, for the third situation, purchased total amount discount, the marketing manager should fill in the threshold at the lower right corner of the interface. When a customer’s total amount of a purchase is greater than the threshold, the products will be promoted at the calculated discount price.

4.2. Pricing of the promotion products Fig. 11 is the interface for pricing strategies with business life cycle and product life cycle. In our system, there are three opportunities to offer the customers price discount: general discount, purchased quantity discount, and purchased total amount discount. For general discount, the marketing manager should select the period for calculation to get current business life

Fig. 12. On-line pushing of personalized promotion products.

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Table 13 Costs and prices for the 10 promotion products Product

Cost Price

1

2

3

4

5

6

7

8

9

10

60 90

30 60

150 263

70 210

80 160

200 332

250 333

75 169

180 450

130 234

Table 14 Three different allocations of the 10 promotion products with three different price discounts Allocation

Product 1

2

3

4

5

6

7

8

9

10

(30,40,30%) Discount Promoted price Gross profit

30% off 63 3

42 12

184 34

20% off 168 98

128 48

266 66

266 16

15% off 144 69

383 203

199 69

(60,20,20%) Discount Promoted price Gross profit

30% off 63 3

42 12

184 34

147 77

112 32

232 32

20% off 266 16

135 60

15% off 383 203

199 69

(20,40,40%) Discount Promoted price Gross profit

30% off 63 3

42 12

20% off 210 60

168 98

128 48

266 66

15% off 283 33

144 69

383 203

199 69

4.3. Online pushing of personalized promotion In promoting the sales products to customers, the PPDSS adopts the push technology. When a member customer logs in to the EC store, the PPDSS will select the personalized promotion products from the database, which were generated by the backend system in advance. As the example in Fig. 12, the customer ‘Sam’ is provided with the personalized promotion products that are shown in the two tables to the right of the screen. The promotion products were prepared especially for Sam according to the three marketing strategies and pricing strategy.

5. Performance evaluation In this section, the proposed PPDSS is evaluated in terms of profit gain. In the simplified simulation scenario, the costs, within the range between 30 and 250, of 10 promotion products are randomly generated and their list prices are also generated accordingly with various amounts of gross profit per unit as shown in Table 13. Since different personalized promotion products will be provided for different customers, the PPDSS will actively provide some of them as promotion products when each customer visits the EC store. The 10 products may be at different stages of product life cycle, and hence different price discounts may be applied in promotion. Assume there are three price discounts applied

Table 15 Comparisons of sales with versus without PPDSS in terms of the improvement of revenue and profit for different promotion sales quantities Allocations

Quantity 100

200

300

400

500

207,090 96,840

207,090 96,840

207,090 96,840

207,090 96,840

With PPDSS (30% off, 20% off, 15% off) (30,40,30%) Total revenue 184,200 368,400 IRr 211% 78% Gross profit 61,700 123,400 IRp 236% 27%

552,600 167% 185,100 91%

736,800 256% 246,800 155%

921,000 345% 308,500 219%

(60,20,20%) Total revenue IRr Gross profit IRp

176,400 215% 53,900 244%

352,800 70% 107,800 11%

529,200 156% 161,700 67%

705,600 241% 215,600 123%

882,000 326% 269,500 178%

(20,40,40%) Total revenue IRr Gross profit IRp

188,500 29% 66,000 232%

377,000 82% 132,000 36%

565,500 173% 198,000 104%

754,000 264% 264,000 173%

942,500 355% 330,000 241%

Without PPDSS (10% off) (100%) Total revenue 207,090 Gross profit 96,840

S.W. Changchien et al. / Expert Systems with Applications 27 (2004) 35–52

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Fig. 13. Comparisons of promotions with and without PPDSS in terms of total sales revenue.

in the personalized promotion products: 30, 20, and 15% off. The three different price discounts are allocated to the 10 promotion products in three different ways as shown in Table 14. Take the first allocation (30,40,30%) as an example. It means that with the first 30% of the 10 promotion products (i.e. products 1 – 3) are applied with 30% off price discount; the next 40% of the 10 promotion products (i.e. products 4 –7) are applied with 20% off price discount; and the rest 30% of the 10 promotion products (i.e. products 8– 10) are given 15% price discount. The total profit of sales of promotion products with price discount is calculated as follows: Total profit of sales ¼ Total revenue 2 Total cost ¼

n X i¼1

pi ð1 2 di Þqi 2

n X i¼1

c i qi ¼

n X i¼1

ðpi ð1 2 di Þ 2 ci Þ £ qi ;

where pi is the original price, di is the discount (e.g. di ¼ 0:2 if discount is 20% off), qi is the quantity sold, and ci is the cost of the ith product. The comparisons of sales with versus without PPDSS for different promotion sales quantities are shown in Table 15. In the traditional promotion approach (without personalized promotion), a 10% off price discount is applied to each of the 10 products, and in our approach (with PPDSS), 30% off, 20% off, and 15% off price discounts are applied to three portions of the 10 products (three different combinations). The sales volume is experimented from 100 to 500 every day. For example, when the sales volume is 500, with the first discount allocation (30,40,30%), indicating that 30% of products promoted (i.e. 150) with 30% off price discount, 40% of the products promoted (i.e. 200) with 20% off price discount, and 30% of the products promoted (i.e. 150) with

Fig. 14. Comparisons of promotions with and without PPDSS in terms of total gross profit.

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Fig. 15. Comparisons of promotions with and without PPDSS in terms of IRr.

15% off price discount, the total revenue of sales and the total gross profit for this case are 921,000 and 308,500 dollars, respectively. Similarly, the total revenues for the other cases are calculated. The comparisons of sales promotions with and without PPDSS in terms of total sales revenue and total gross profit are plotted in Figs. 13 and 14, respectively. To evaluate the performance of the proposed PPDSS, the improvement ratios of total revenue (IRr) and gross profit (IRp) are defined as follows: IRr ¼ ðRPPDSS 2 R0 Þ=R0 ; IRp ¼ ðPPPDSS 2 P0 Þ=P0 ; where RPPDSS is the total revenue with PPDSS, R0 is total revenue without PPDSS, PPPDSS is the gross profit with PPDSS, and P0 is the gross profit without PPDSS.

Take the first combination as an example. For the case when sales volume is 500, the total revenue is 921,000 dollars. Compared with the all 10% off promotion case (all products are subject to 10% off without PPDSS), its IRr and IRp are 345% and 219%, respectively. To contrast promotions with versus without PPDSS in terms of total revenue and gross profit, the results are plotted in Figs. 13 and 14 separately, and IRr and IRp are compared in Figs. 15 and 16, respectively. Through the simplified analysis, it is found that even though the personalized promotion products are provided with greater price discount, promotion with PPDSS makes more gross profit and larger sales revenue in all cases except the case with quantity equal to 100. In addition, the improvements for both IRr and IRp show a linear trend as the quantity increases. However in reality, some of the promotion products are promoted as bundles of multiple products or larger quantity is required to be

Fig. 16. Comparisons of promotions with and without PPDSS in terms of IRp.

S.W. Changchien et al. / Expert Systems with Applications 27 (2004) 35–52

eligible for the low discount price. This may increase the total payment, which to some certain extent will affect the buyer’s decision. Moreover, since this is only a simplified simulated case study for evaluating the expected performance of the proposed PPDSS, more detailed analysis and larger scale data may be necessary for further verification.

6. Conclusions It is quite a challenge that a business will face more competitors in Internet than in traditional market, and the customers’ loyalty in the Internet is low compared with traditional market so that it is a difficult problem for a business to attract and retain customers in EC. Traditional mass marketing is no longer effective for EC in the Internet, and thus more precise on-line one-to-one marketing for better suiting each customer becomes more and more important for competing in the Internet, along with the use of highly advanced data analysis techniques and the development of new marketing strategies for EC. Hence, in this paper, promotion products are carefully selected based on the experiences analyzed and retrieved from the historical transactions and proposed for each customer. An on-line personalized promotion decision support system is developed to assist a business in intelligently developing the on-line promotion products. The system consists of three modules: (1) marketing strategies, (2) promotion patterns model, and (3) personalized promotion products. The main concept of the system is that business can utilize data mining techniques to find out effective promotion products based on customers’ purchasing behaviors, in accordance with business’ marketing strategies and pricing strategies. To avoid missing potential patterns, behaviors of customers from three categories, all customers, customer clusters, and each individual customer, are all extracted from the past transactions. Then the best promotion products are selected after ranking all candidate promotion products in terms of multiple criteria, which may be dynamically changed according to the business’ current marketing goals and strategies. In the simulated case study of performance evaluation, it is found that promotion with PPDSS makes more gross profit and achieves larger sales revenue and has a linear trend in improvement for both total revenue and gross profit. With promotion products based on significant past customers’ purchase patters, it has the potential to increase the success rate of promotion, and customer satisfaction and loyalty as well. Although we have proposed a personalized promotion decision support system, pricing strategy, methods for clustering customers, and dynamic mining can be further enhanced in future studies. For pricing strategy, with the pricing strategies of the other competitors considered in

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deciding promotion prices, decision analysis such as game theory can be employed in generating more effective and competitive prices for EC. As for customer clustering, clustering directly based on customers’ profiles may not lead to good clustering results. Due to the diversity in individual consumer behavior, cognitive needs, and personality, further research of methods on clustering customers may be quite interesting and helpful. For example, some professional products, such as digital camera, the professional knowledge and specific needs of the customers need to be included as factors for recommendation. Last, since customers change over time, the use of dynamic data mining methods can efficiently analyze and adjust consumer behaviors dynamically.

Acknowledgements This research was supported by the National Science Council, Taiwan, R.O.C., under contract no.: NSC 92-2416H-005-005.

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