Customer Segmentation In The Full Service Restaurant Business Marketing Essay

guest Segmentation In The Full forward motion Restaurant Business Marketing EssayABSTRACT. The purpose of this acquire was to stripping factors that affect nodes conception to invert eating places using selective information minelaying. A number of 390 usable disbeliefnaires were design of goods and servicesd in the entropy analysis. AnswerTree, a selective information exploit softw be, was employ as a major analytical system. AnswerTree changes a police detective to discoer and cigarette desirable client classs and thus is fit to use in nominateing differences between virtuoso group with a recidivate aspiration and one group with no return design. turn over results indicated that different subdivision factors affected clients return designing between the cardinal groups. Three factors ( preachation, nutrition bea, and number of dining do per hebdomad) nearly affected a groups revisit intention. On the opposite hand, one-third factors ( p reachation, occupation, and about frequent dining destination) or so affected the group with no revisit intention. Study results provide meaningful culture for marketing strategies that batch be successfully employ by full-service eatery operators.KEYWORDS. node division, full-service eating place, selective information excavation, AnswerTreeINTRODUCTIONWith increasing competition in the restaurant business, marketing strategies posit to be much than significant to tally node satisfaction (Kara et al., 1997 Kivela, 1997 Murphy et al., 1996). Since node satisfaction affects businesses ability to hold back their status in a competitive restaurant market, many researchers behave studied customer satisfaction (Almanza et al., 1994 Andaleeb Con commission, 2006 Barsky Labagh, 1992 Domingo, 2002 James, 1995 Johns Tyas, 1996 Oh, 1999, 2000 Oliver, 1980, 1981). Previous researchers have queried restaurant customers to tell the agreements for their decisions to revis it and recommend restaurants to different potential visitors, thitherby suggesting impelling marketing strategies. Although there is no assurance that customers provide make a return visit (Dube et al., 1994), restaurateurs assume that satisfied customers will return fleck customers who had a poor experience will not come again. Thus, understanding some(prenominal) satisfied and unsatisfied customers is an grievous process in making sub due marketing strategies. In order to segment customers according to their attributes, knowledge of those attributes is an effective tool in finding appropriate marketing strategies (Bowen, 1998 Gregoire et al., 1995 Klosgen Zytkow, 2002 Reid, 1983 Richard Sundaram, 1994 Swinyard Struman, 1986 Woo, 1998 Yksel Yksel, 2002). Customer segmentation can be determined by customer attributes such as demographic and behavioral characteristics buy patterns, billet and use or response to a product (Kotler et al., 2005). In this study, the term cu stomer attributes was defined as customers demographic and behavior patterns, attitude and use or response to a product via recommendation intention.To identify customer segmentation, entropy mining was use in this study. Data mining is the process of determination or classifying trends and patterns in data to analyze customers past tense behaviors (Adriaans Zantinge, 1997 Chat knowledge domain, 1995 Fadairo Onyekelu-Eze, 2008 Kudyba Hoptroff, 2001 Lovell, 1983 Pyle, 1999 Thuraisingham, 1999). If restaurant managers all the delegacy know their target customer attributes, they can develop a relegate service strategy or make up for their weak points.The ram of this research was to identify factors affecting customers intention to revisit a restaurant. Study results provide meaningful information to the process of developing marketing strategies that can be successfully used by full-service restaurant operators. Further, study results will inform restaurateurs nigh important and non-important factors in return intention and enable them to select those factors on which to boil down on and improve.REVIEW OF THE LITERATURECustomer SegmentationCustomer segmentation is a crucial position of todays besides competitive restaurant business. In the mid-1950s, Wendell R. Smith, an American marketer, commencement ceremony introduced the concept of customer segmentation (Nairn Berthon, 2003). Russell Haley then developed segmentation theory in 1968. Customer segmentation can be defined as a stylus to separate customers into groups for decision making purposes or to support effective wariness in acquiring or tutelage customers (Bowen, 1998 Bahn Granzin, 1985 Chen et al., 2007 Yksel Yksel, 2002). Since customer segmentation can help restaurants increase revenue, a marketing strategy found on customer segmentation can be more powerful and effective (Auty, 1992 Bojanic Shea, 1997 Chen et al., 2006).Customer segmentation has foster researchers to take a c loser look at customer segmentation as part of an effective marketing strategy. Lewis (1981) used discriminant analysis to identify the differences between goers and non-goers with forage quality, menu variety, outlay, atmosphere, and convenience. Bahan and Granzin (1985) investigated intravenous feeding customer segments health, gourmet, value, and unconcerned. They reported that to each one group had different preferences for service quality. Auty (1992) divided up respondents into ternion customer groups (student, well-to-do middle-aged people, and older people) and examined restaurant image and atmosphere. Oh and Jeong (1996) revealed the characteristics of four customer segments massive service go throughker, convenience seeker, classic dinner seeker, and indifferent dinner seeker. Bojanic and Shea (1997) desire differences between downtown eatrs and suburban eatrs. Yksel and Yksel (2002) identified the attributes of fin customer segments (value seekers, service s eekers, adventurous feed seekers, atmosphere seekers, and healthy food seekers) based on nine factors (service quality, product quality and hygiene, adventurous menu, price and value, atmosphere, healthy food, location and appearance, availability of nonsmoking ara, and visibility of food preparation area). In this study, the researcher divided customers into two groups based on revisit intention in order to identify which factors most affect these two groups. The heavyset of the literature review is presented in duck 1.Table 1 about hereData MiningTo divide respondents into two groups based on revisit intention, unlike previous studies, data mining was used to identify revisit intention in the restaurant business. Data mining was introduced in the late 1980s and developed in the 1990s. Its origin is in the field of statistics and a specialized area at bottom artificial intelligence (AI) that is part of computer science (Ogut et al., 2008 Roiger Geatz, 2003). According to Fad airo and Onyekelu-Eze (2008), data mining is a way to find information in a huge database. Such information can be used to identify relationships between variants. That is, data mining may be utilized in analyzing a specific data set with the intention of identifying patterns and establishing relationships using data mining, it is possible to sort by dint of massive volumes of data and discover new information or an analytic rule for reasoning useful knowledge and bespeaking future trends (Bolshakova et al., 2005 Chatfield, 1995 Chen et al., 1998 Groth, 2000 extend to et al., 2001 Koyuncugil, 2004 Lovell, 1983 Westphal Blaxton, 1998). Data mining can perform two basic operations predicting customer behaviors and identifying segmentation (Lampe Garcia, 2004 Wang et al., 2008). For that reason, many researchers have attempted to apply data mining in the business industry (Keating, 2008 Liu et al., 2008 Rygielski et al., 2002 Zambochov, 2008). Previous studies have stress that companies could use data mining to identify customer dispositions or trends regarding the patronise of trustworthy companies. With this information, companies can focus their efforts on good customers from whom they would make the most profit. Further, all industries can take advantage of data mining in seeking to understand inconsistent segmentation of their target customers. In summary, data mining is a powerful technology that may be used in support of companies engaging in decision-making on issues such as customer attrition, customer retention, customer segmentation and sales forecast (Ogut et al., 2008).CHAID in AnswerTreeTo apply data mining, the AnswerTree program was used in this study. AnswerTree, a data mining software, is a foreseeable model that shows results in a manoeuver model (SPSS, 2009). Variables may be analyzed in the AnswerTree program in three ways CHAID (chi-squared automatic interaction detector), CART (classification and regression trees), and necessita te ( loyal, unbiased, efficient, statistical tree). Basically, the CHAID method acting is a more comprehensive method and generates more accurate results when using categorical variables, while CART and QUEST are suitable when using continuous variables. Since categorical variables were used in this study, the CHAID method was apply here.The original CHAID method grew from a 1975 doctoral dissertation by Kass, who published a more botherible article four old age later (Kass, 1980). Since the CHAID method allows marketers to identify segments in relation to a myrmecophilous variable having two or more categories based on the compounding of in mutualist variables (Chen, 2003), the CHAID method has popularly been applied in the consumer research field (Haughton Oulabi, 1997 Levin Zahavi, 2001). In the CHAID procedure, a pendant variable and key in mutualist variables are initially chosen. According to chi-squared, the dependent variable can be divided by the levels of a real i ndependent variable that has the strongest association with the dependent variable. That is, the most important and related independent variable with a dependent variable bring to passs the first node. This analysis process occurs when one of three criteria are met, according to Berson et al. (2000)1. The segment contains only one record. (There is no other question that you can ask to further refine a segment of in force(p) one.)2. every the records in the segment have identical characteristics. (There is no reason to continue asking further questions because all the remaining records are the same.)3. The improvement is not substantial enough to warrant asking the question (p. 162).All variables used in this study were categorical measurements with two or more categorical levels. The stopping rules for AnswerTree analyses were a maximum tree understanding of 3, tokenish number of pillow campaigns of 25 for a given node, and significance level for ripping of 0.05.RESEARCH METH ODOLOGYData Collection and QuestionnaireThe data used for this study were collected in Miami via face-to-face hearings. The response rate for the face-to-face interview has revealed it to be the best method among various survey methods (The Monkey Team, 2008). Surveys were administered from whitethorn 1 to May 31, 2007. To increase result reliability, we selected respondents who had visited a full-service restaurant within the last one month. The selected full-service restaurants offered full table service and the average guest expenditure was at least $25 per person. Of the 414 questionnaires collected, 24 were incomplete and were eliminated. As a result, a total of 390 questionnaires were used in the data analysis.Since AnswerTree enables a researcher to identify and target desirable customer groups (SPSS, 2009), it is a suitable analysis method for identifying differences between groups. Further, AnswerTree is a more robust method than existing statistical methods in identifying segment characteristics (Byrd Gustke, 2006). There were two groups in the dependent variable one group with the intent to revisit and one group with no intent to revisit. Questionnaire items for revisit intention were rated on seven-point Likert crustal plate ranging from strongly disagree to strongly agree. To apply CHAID (Chi-square Automatic Interaction Detection) analysis, researchers born-again the seven-point scale into categorical variable (agree, so-so, disagree). Even though respondents answers were a volt or six-spot on the seven-point scale, the information sufficiently ensured positive responses. Finally, those with five, six and seven points were converted into a positive group (agree). On the other hand, those with one, two and three points were converted into a negative group (disagree). Finally, four on the seven-point scale was converted into a so-so group.Among respondents (n=390), 83.33% (n=325) indicated that they were unbidden to revisit the restaurant on the other hand, 11.54% (n=45) thought that they would not revisit the restaurant and 5.13% (n=20) replied so-so, which heart I dont know. Customer segmentation can be sort by demographic and behavioral characteristics such as buying patterns, attitude and use or response to a product (Johns Pine, 2002 Kotler et al., 2005). Independent variables were cool of demographic profiles (gender, age, marital status, occupation, income, reinforcement area) and customer attributes in relation to buying behavior, a pattern of full-service restaurant use (how many dining occasions per workweek, how much spent at a restaurant, frequency of restaurant visits, with whom did they dine there). Finally, to ascertain a response to a product, recommendation intention was used in this study as an independent variable. The summary of data definition is presented in Table 2.Table 2 about hereRESULTS course 1 shows the general model for revisit intention. The general model makes it lightsome for r eaders to figure out how many nodes are included in the results and shows the sinless model design. In this study, 13 nodes were used to explain the factors affecting a group who intended to revisit and a group with no intention to revisit. For the dependent variable, originally there were three groups yes, so-so, and no. However, because of small attempt size, the so-so group was not divided by descriptor. The dependent variable was divided by five descriptors recommend intention, living area, how many times to dine per week, occupation, and when a good deal dine.Figure 1 about hereIn terms of yes segmentation on revisit intention, in Figure 2, the first disconnected was recommend yes (2=376.4356, d.f.=4 p=.000). In thickening 3, 98.10% (n=309) of respondents thought that they had a revisit intention. client 3 was divided into two groups knob 7 and leaf node 8. The second split was based on the variable of Living area South Florida, other Florida, and other U.S states (2= 68.5075, d.f.=2 p=.000). Node 7 was divided into two groups Node 11 and Node 12. 98.69% (n=302) of respondents (Node 7) who lived in South Florida, other Florida, and other U.S states showed that they were willing to return to the restaurant. The last split was how many times dine per week over 3 times (2=19.7972, d.f.=1 p=.000). In Node 12, 100.00% (n=254) of respondents who visited the restaurant over 3 times per week indicated that they had a revisit intention. In summary, there were three descriptors as follows recommend yes, living South Florida, other Florida, and other U.S states, and how many times to dine per week over 3 times.Figure 2 about hereConsidering no segmentation on revisit intention, in Figure 3, the first split was recommend (no) (2=376.4356, d.f.=4 p=.000). In Node 1, 81.13% (n=43) of respondents indicated that they would not visit the restaurant again. Node 1 then was divided into three groups Node 4, Node 5, and Node 6. The second pruning tree was based on t he occupation variable position role player (professional, salesman, and self-employed) (2=20.1046, d.f.=4 p=.000). In Node 4, 94.87% agreed that they were not willing to return to a restaurant. Node 4 was divided into two groups Node 9 and Node 10. The last split was when often dine luncheon (2=25.2973, d.f.=1 p=.000). In Node 9, all office workers (100.00%, n=36) who often visited the restaurant at lunchtime did not have a recommendation as well as revisit intention. In picture, three descriptors split the node recommend no, occupation office worker (professional, salesman, and self-employed), and when often dine lunch.Figure 3 about hereFigure 4 presents summary statistics. The bar graph makes it easy for readers to understand which node most costs the dependent variable and provides the variation for each dependent variable. In this study, the dependent variable, revisit intention, was classified by a maximum tree depth of 3, minimum number of cases of 25 for a given node, and significance level for splitting of 0.05. The bar graph for AnswerTree showed that the particular nodes most often represent groups intention or non-intention to revisit, respectively (Node 12 agree group, Node 9 disagree group).Figure 4 about hereTable 3 presents a gain chart of yes segments. A gain chart is a table that summarizes the entire model descriptively. In the gain chart, we can see the circumstances representation of each node for the dependent variable. In the case of the yes segment, the root node was 83.33% (n=325). Node 12 was computed by taking 100.00% (Gain % computed from Node N divided by Resp N) and then dividing it by 83.33% (root node). The result was 120.00%, the magnate score for Node 12. That is, Node 12 (recommend yes, living area entropy Florida, other Florida, and other U.S states, how many times dine per week over 3 times) represents a root node about 1.2 times. Thus, in the case of yes segmentation, three variables (recommend, living, and how m any times to dine per week) are important factors in dividing respondents into groups that answered yes regarding revisit intention.Table 3 about hereTable 4 presents a gain chart for no segment. The root node was 11.54% (n=45).Node 9 was computed by taking 100.00% (Gain %) and then dividing it by 11.54% (root node). The result was 866.66%, the powerfulness score for Node 9. That is, Node 9 (recommend no, occupation professional, salesman, and self-employed, when often dine lunch) represents a root node about 8.6 times. Thus, in the case of no segment, three variables (recommend, occupation, when often dine) are important factors in dividing a group that answered no with respect to revisit intention.Table 4 about hereTable 5 offers a risk chart indicating the preciseness of the classification. It resembles the percentage of classified respondents in the discriminant analysis. The risk estimate predicted the risk incurred due to misclassification of the respondents in the AnswerTr ee analysis. A lower risk estimate indicates a more just now classified model. According to the results of the assessment of revisit intention, the risk estimate was 0.0615385. This means that the precision of classifying respondents in the AnswerTree analysis was 99.9384615%. That is, about 99.93% of the respondents were classified accurately on split nodes.Table 5 about hereDISCUSSION AND importeeIn the restaurant business, customer segmentation enables restaurant managers or marketers to develop effective marketing strategies. The purpose of this study was to identify factors that affect intention to revisit a full-service restaurant. To ascertain differences between group intent to revisit and group non-intent to revisit, data mining was used. There has been little use of data mining in the hospitality field. Because data mining is one way to take a leak decision-making models that predict future behavior based on analyses of past activity, using collected data from segment targeting is the best way to create suitable marketing strategies (Lampe Garcia, 2004 Wang et al., 2008). Among the respondents (n=390), 83.33% (n=325) indicated that they were willing to visit the restaurant again while 11.54% (n=45) thought that they would not visit the restaurant again and 5.13% (n=20) were so-so. As mentioned earlier, due to the small sample size, the so-so group was not divided.The AnswerTree results revealed different voice factors between the two groups. In the case of the yes segment, there were three descriptors recommend yes (2=376.4356, d.f.=4 p=.000), living area south Florida, other Florida, and other U.S states (2=68.5075, d.f.=2 p=.000), and how many times dine per week over 3 times (2=19.7972, d.f.=1 p=.000). Analysis results revealed that the more customers like to dine out, the more they intend to revisit a restaurant. In other words, people who have a great interest in dining are more likely to be truehearted customers. Moreover, as they intend to recommend the restaurant to other people, saveing such a customer means other potential customers could be positively affected. According to Sderlund (1998), Word-of-mouth is defined here as the extent to which a customer informs friends, relatives and colleagues about an event that has created a certain level of satisfaction (p. 172). As word-of-mouth can be a significant determinant of behavioral intentions, recommendation intention greatly affects a restaurant businesss sales (Babin et al., 2005 Edwards Meisleman, 2005 Mangold et al., 1999 Mattila, 2001 Sderlund, 1998). This studys results supported the finding that faithful customers are more likely to encourage other people to in addition have their exceptional experience, which is consistent with previous studies (e.g., Bowen, 1998). It is less costly to keep an existing loyal customer than to attract a new customer. Also, loyal customers return to make more repeat purchases at the restaurant. Therefore, identifying lo yal customers is an important part of the restaurant businesses (Bowen Shoemaker, 1998 Fierman, 1994 Jang Mattila 2005 OBrien Jones, 1995 Orr, 1995 Schneider et al., 1998). From a managerial standpoint, the results of this study based on data mining help restaurateurs in identifying the characteristics of a loyal customer segment. In the case of the no segment, three descriptors split the node recommend no ( 2=376.4356, d.f.=4 p=.000), occupation office worker (professional, salesman, and self-employed) (2=20.1046, d.f.=4 p=.000), and when often dine lunch (2=25.2973, d.f.=1 p=.000). The results of this analysis revealed that office workers who often visited a restaurant at lunch did not intend to revisit. In other words, office workers, salesmen, and the self-employed were less satisfied with lunch at the full-service restaurant. In general, some full-service restaurants focus on lunch for office workers, who usually eat lunch away from the office. Even though some lunch menus do focus on the office worker, offering this meal is usually expensive and time-consuming. However, as office workers tend to eat lunch in a brief span of time, they prefer to eat lunch at fast food restaurants rather than at a full-service restaurant. From a managerial viewpoint, if full-service restaurateurs could provide a lunch menu with low prices as well as quick service, they could obtain part of the office worker market currently dismission to fast food restaurants. The summary of segments is presented in Table 6.Insert Table 6The purpose of customer segmentation is to target a certain type of customer when developing a marketing strategy. If a restaurant cannot develop correct and appropriate marketing strategies, they may not generate their existence in this highly competitive business. As attributes have changed and become more complicated, customer segmentation is becoming more important in providing basic source material for marketing strategies. In order to respond apace to changing customer attributes, restaurant marketers require rapid access to information on various customer attributes. In this context, restaurant marketers need to be able to identify customers past behaviors in order to predict future tendencies. This ability can be provided and maintained in the restaurant business by using data mining technologies. Data mining enables restaurant marketers to draw information more effectively from databases (Bolshakova et al., 2005 Chatfield, 1995 Chen et al., 1998 Groth, 2000 put across et al., 2001 Koyuncugil, 2004 Lovell, 1983 Westphal Blaxton, 1998). Through the effective use of data mining, managers can more quickly analyze customer attribute in the restaurant business.This study of customer revisit intention occurred in the Miami area only. Thus, findings might not be generalized to other areas. Another limitation is that study results cannot be applied to all restaurant services, because our focus was on full-service restaurants only. Therefore, findings must be applied to other restaurants with due caution. Lastly, this study did not use a oversize enough sample size for data mining. Although we used 390 samples to identify and target customer groups, data mining is typically used with a bighearted database. Thus, future research may use data mining with a larger sample size. Data mining was a useful method in predicting restaurant customers intentions to revisit. Unfortunately, very few studies have used this method. Thus, further research in other hospitality fields would benefit from data mining.