Türkiye pazarında tüketicilerin elektrikli süpürge satın alırken hangi özelliklere daha fazla önem verdiğinin tümleşik hiyerarşik konjoint analizi ile belirlenmesi
In Turkey market determination of which features are more important for consumers when buying vacuum cleaner with integrated hierarchical conjoint analysis
Yazar: RECEP SATICI
Danışman: PROF. DR. NİZAMETTİN BAYYURT
İstanbul Teknik Üniversitesi / Fen Bilimleri Enstitüsü / Endüstri Mühendisliği Ana Bilim Dalı / Endüstri Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2019
Konu: Endüstri ve Endüstri Mühendisliği = Industrial and Industrial Engineering

Özet:
Konjoint analizi tüketcilerin ürün veya hizmet satın alırken hangi özelliklere daha çok önem verdiğini belirlemek için yaygın olarak kullanılan bir pazarlama metodudur. Yeni ürün tasarımı ve özelliklerinin belirlenmesi aşamasında müşteri talep ve isteklerinin ürüne yansıtılabilmesi için çok sık başvurulan bir yöntemdir. Ayrıca pazar analizi çalışmalarında, pazarlama stratejisi belirleme çalışmlarında, ürün geliştirme aşamlarında, fiyat analizi araştırmalarında yaygın olarak kullanılan çok değişkenli istatistiksel bir yöntemdir. 
Konjoint analizinin diğer istatistiksel yöntemlere göre avatajları arasında, varsayımlarının az olması, kolay uygulanabilir olması, aynı anda birçok özellik hakkında bilgi vermesi, küçük örneklem sayılarında da güçlü sonuçlar vermesi ve maliyet olarak ucuz olması sayılabilir. 
Bu çalışmada Türkiye pazarında tüketicilerin eletrikli süpürge satın alırken hangi özelliklere önem verdiğinin belirlenebilmesi için tümleşik hiyerarşik konjoint analizi çalışması yapılmıştır. Tümleşik hiyerarşik konjoint analizinin geleneksek konjoint analizine göre avantajı, özellik sayısının fazla olduğu çalışmalarda daha iyi sonuçlar verebilmesidir. 
Tümleşik hiyerarşik konjoint analizinde çok fazla olan özellikler belirli bir mantık çerçevesinde gruplanır. Daha sonra her grup için anket kartları oluşturmuştur. Oluşturulan kartlara giğer grupları yansıtacak özet bir özellik eklenir. Ayrı olarak analiz edilen bu gruplar daha sonra birleştirilerek, tüm özelliklerin göreceli önem seviyeleri belirlenir.
Bu çalışmada öncelikli olarak Türkiye'de beyaz eşya sektöründe faaliyet göstermekte olan bir şirketin elektrikli süpürge ürün yönetimi takımı ile analiz edilecek özellik ve düzeyleri belirlenmiştir. Daha sonra özellikler tümleşik hiyararşik konjoint analizine uygun olarak gruplanmıştır. SPSS22 paket programı kullanılarak orthogonal düzen ile anket kartları oluşturulmuştur. E-mail ve yüz yüze mülakat yöntemleri kullanılarak 73 katılımcıdan veri toplanmıştır. Daha sonra toplanan veriler SPSS22 paket programında syntax kodu yazılarak analiz edilmiştir. Öncelikli olarak oluşturulan her bir grup tek başına analiz edilmiştir. Daha sonra gruplar birleştirilerek bir bütün olarak analiz edilmiştir. Yapılan kolerasyon analizlerine göre kurulan modelin anlamlılık düzeyi yüksek çıkmıştır. Ayrıca Pearsons'r ve Kendall's tau sayılarının yüksek çıkması modelin temsi gücünün yüksek olduğunu göstermektedir.
Yapılan analiz sonucunda tüketicilerin elektrikli süpürge satın alırken daha çok fiyat, halı toz toplama sınıfı, hepa filtre sayısı ve motor gücüne önem verdiği görülmüştür. Garanti süresi ise tüketicilerin en az önem verdiği özellik olmuştur.

Abstract:
Conjoint analysis is a widely used marketing method to determine which features consumers give more importance when purchasing products or services. It is a frequently used method for reflecting customer demands and requests to the product during the determination of new product design and features. In addition, it is a multivariate statistical method commonly used in market analysis studies, marketing strategy determination studies, product development stages, and price analysis studies. Other methods for reflecting consumer wishes and demands in product design process include quality function deployment, user experience studies.
Conjoint analysis has many advantages when it is compared with other statistical methods. These advantages include that the method has low assumptions, easy applicability. Moreover it provides information on many features at the same time and gives strong results with small sample sizes. 
The most important stage in conjoint analysis is to determine the properties and levels in a correct way. The features and levels identified must be accurately understood by everyone. The chosen conjoint analysis method must be in accordance with the research design and number of properties.
In this study, an integrated hierarchical conjoint analysis study was conducted to determine the features that consumers give more importance about when buying vacuum cleaners. The scope of the study has been limited by the market of Turkey. 
The advantage of integrated hierarchical conjoint analysis over conventional conjoint analysis is that it can give better results in studies which their number of properties is higher. As the number of features investigated in the traditional conjoint analysis increase, it is very difficult for participants to respond to the questionnaire. In the integrated hierarchical conjugate analysis, the properties are subdivided then the design cards which include a feature that is a summary of other groups are created. The questionnaire for each group is answered by the participants separately. The data collected for each group are first analyzed separately, then all groups are analyzed together. Therefore, integrated hierarchical conjoint analysis gives good results in studies with high number of properties. 
In this study, firstly all properties of the vacuum cleaners were analyzed. The situation of the vacuum cleaner has been investigated in Turkey market by comparing the vacuum cleaner models of different brands.  After all this, based on past experience, features and levels were determined with a white goods company's product management team. As a result of the studies conducted with the product management team, 14 features and 37 levels of the vacuum cleaner were determined.
The integrated hierarchical conjoint analysis was chosen as the method because the number of levels were too high for the traditional conjugate analysis. After the features and levels were determined, subgroups were determined.  Three groups were determined as perceptual quality ease of use, quality perception and price. Then design cards were created for each group using the orthogonal layout on SPSS. A questionnaire was created with the cards and demographic questions. Textual method was determined as survey presentation technique. The reason not using the prototype to receive the answers of the consumers and choosing this method is that the cost of the vacuum cleaner is very high. The data were collected from 73 participants by e-mail and one to one interviews. The preference function of each feature was determined. The data collected was analyzed by writing syntax code in SPSS package program. For each group design, a separate syntax code is written and each group is analyzed separately.
When each group was analyzed separately, the significance levels of the group characteristics were found separately. When the characteristics of the cleaning performance group were examined, the feature with the highest importance was "carpet cleaning performance". The second high-priority feature was "motor power". The "motor power" feature and the "accessories" feature were close to each other. The least significant feature is the "hard floor cleaning class". Then, the value and standard deviation values of the levels were calculated. When the accessory feature is examined, the most beneficial level was the "turbo brush". The reason for this that 61 percent of the participants can have carpet in every room of the house. Then, using the benefit coefficients of the properties, the benefit values of the cards were found. The most useful card was the 11th card. It was found that the least benefit card was the 2nd card.  Then, Pearson's R analysis and Kendall's tau analysis were performed. High values of these tests show that our model is strong. The significant value indicates that the significance level of our model is very high.
When the characteristics of the ease of use group were examined, the feature with the highest importance was " type of vacuum cleaner". The second high-priority feature was "capacity". The " suction adjustment" feature and the " weight" feature are close to each other. The least significant feature is the "sound level". Then, the value and standard deviation values of the levels were calculated. Then, using the benefit coefficients of the properties, the benefit values of the cards were found. The most useful card was the 11th card.  It was found that the least benefit card was the 2nd card. Then, Pearson's R analysis and Kendall's tau analysis were performed. High values of these tests show that our model is strong. Since the significant value is zero, we can say that the significance level of our model is very high.
When the characteristics of the perceptual quality and price group were examined, the feature with the highest importance was " price". The second high-priority feature was "color". he least significant feature is the " warranty period". Then, the value and standard deviation values of the levels were calculated. When the color feature is examined, the most beneficial level has been the "red color". Then, using the benefit coefficients of the properties, the benefit values of the cards were found. The most useful card was the 2nd  card.  It was found that the least benefit card was the 12th card. Then, Pearson's R analysis and Kendall's tau analysis were performed. High values of these tests show that our model is strong. Since the significant value is zero, we can say that the significance level of our model is very high. In addition, the Kendall's tau value of all cards is measured and the Kendall's tau values of all cards are high.
Finally, the groups were combined and analyzed. The percentages of each sub-design within the other designs and the percentages of the characteristics in its design are collected and divided by the total number of designs. When the relative importance values of the sub-designs are examined, it is understood that the sub-group design which has the highest importance is the cleaning performance. It is seen that the importance of cleaning performance is 42.26 percent. Quality perception and price bottom design have the least importance. 
According to each of the features included in the sub-design groups, the method which is valid in the traditional conjoint analysis was used to determine the relative importance levels. According to this method, firstly the difference between the benefit values of all properties is calculated. Then, in order to find the relative significance of each property, the difference between the benefit values of each property is divided by the difference of the benefit values of all properties. When the relative significance values of all features are examined, it is seen that the most important feature is the price. It is seen that the importance of price characteristic is 17.69 percent. Another important feature is the carpet dust collection class. The importance of the carpet dust collection class is 13,68 percent. The reason for the high value of carpet dust collection is that 61% of the participants can have carpet in every room of their house. The lowest level of importance is the guarantee period. The value of the guarantee period is 1.19%.