Pazar bölümlendirmede GSP analizine dayalı bir modelleme çalışması
A modeling study based on RFM analysis for market segmentation
Yazar: NERGİS KİRİŞÇİOĞLU
Danışman: PROF. DR. HURİYE ŞEBNEM BURNAZ
İstanbul Teknik Üniversitesi / Fen Bilimleri Enstitüsü / İşletme Mühendisliği Ana Bilim Dalı / İşletme Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2020
Konu: Mühendislik Bilimleri = Engineering Sciences

Özet:
Hızlı değişen pazarlarda ve kar marjlarının küçüldüğü sektörlerde; özellikle yüksek sayıda ve farklı profillerde müşterisi olan işletmeler için mevcut müşterileri uzun vadede memnun etmek ve elde tutmak, en az yeni müşteri elde etmek kadar önemli bir konudur. Bu nedenle işletmeler yaptıkları çalışmalarda, müşteri odaklı olmaya büyük öncelik vermektedir. Sürekli değişen ve gelişen dünyada müşterilerin talepleri, günlük yaşantıları ve öncelikleri de hızlı bir şekilde değişmektedir. Bu nedenle artık işletmelerin de müşteri odaklılık anlayışında yeni nesil yaklaşımlara yönelmesi gerekmektedir. Bu noktada gelişen teknoloji ile birlikte aynı hızda ilerleyen analitik yöntemlerin yeri şüphesiz ki oldukça önemlidir.

Literatür incelendiğinde, müşteri verilerinin toplanması, işlenmesi, bu yolla müşteri davranışlarının modellenmesi sonucunda işletme kararlarında fayda sağlayacak öngörülerin elde edilmesini amaçlayan birçok çalışmanın mevcut olduğu görülmektedir. Müşteri yaşam boyu değeri ve müşteri sermayesi kavramları, işletme açısından müşterileriyle ilişkilerine bütüncül bir bakış açısı sağlanması bakımından önemlidir. Öncelikle, bu tez çalışmasında, bankacılık sektöründe, perakende bankacılık müşterileri için müşteri davranış ve ihtiyaçları temel alınarak yeni bir pazar bölümlendirme yapısının oluşturulması ve her bir pazarın özelliklerine uygun kampanya stratejilerinin geliştirilmesi amaçlanmıştır. Yöntem olarak, daha çok davranış modellemeye dayalı pazar bölümlendirme amacıyla kullanılan bir model olan GSP (Güncellik, Para, Sıklık) tekniği esas alınmıştır. Perakende bankacılık müşterileri, veri madenciliğinde kümeleme tekniklerinden biri olarak kullanılan K-ortalamalar algoritması aracılığıyla müşterinin en yakın zamandaki işlemi, işlem sıklığı ve işlem miktarı boyutlarına göre bölümlendirilmiş, sonrasında ise müşteri yaşam döngüsü içerisindeki aşamaya uygun olacak kampanya stratejileri önerilmiştir. 

Kurgulanan yöntem ve önerilen stratejiler ile yaşam döngüsü içerisinde müşterinin döngünün hangi kısmında olduğunun proaktif olarak belirlenmesi, böylelikle müşteri ihtiyacına uygun ve bu ihtiyaca doğru zamanda cevap verecek bir yapı oluşturulması hedeflenmektedir. Bu hedefe hizmet etmek üzere, pazarlama yönetimine tutundurma süreçlerinin planlanması ve organizasyonunda karar desteği sunacak bir modelin ortaya konulduğu düşünülmektedir.

Abstract:
In fast-changing markets and sectors where profit margin squeeze; especially for enterprises with a high number of customers with different profiles, keeping existing customers in the long run is as important as acquiring new customers. For this reason, compainies give a great priority to being a customer oriented and focus on customers' needs, wants, attitudes, behaviors, preferences and perceptions in their works. In a constantly changing and developing world, customers' demands, daily lives and priorities are changing rapidly. For this reason, it is necessary for businesses to turn to new generation approaches in their customer-oriented understanding. At this point, the place of the analytical methods advancing at the same speed with the developing technology is undoubtly important. 

When the literature is analyzed, it is seen that there are many studies aiming to obtain predictions that will benefit business decisions as a result of collecting, processing customer data and modeling customer behaviors in this way.  For enterprises, the concepts of customer life-time value and customer capital are important in terms of providing a holistic view of their relationship with their customers. First of all, in this thesis, it is aimed to create a new segmentation structure based on customer behavior and needs for one of private bank, retail banking segment and to develop campaign strategies in accordance with each segment's characteristics. The method is based on RFM technique which is a model used for customer segmentation based on behavior modeling. RFM technique, one of the leading parts of behaviour modeling techniques, has been used over 50 years in order to segment customers.  It has been used by many scholars to accomplish customer segmentation. Since RFM analyzes the behavior of the customers, it can be possible to encounter behavior-based models in the literature.

RFM based on Recency, Frequency and Monetary value of purchases is simple-in-use and powerful for producing knowledge from customer data. This technique models three dimensions of customer transactional data, namely recency, frequency and monetary, to classify customer behavior. The first dimension is recency, which indicates the length of time since the start of a transaction. Meanwhile, the second dimension is frequency, which indicates how frequently a customer purchases products during a particular period. Finally, monetary value measures the amount of money that customer spending during a period. 
After using the RFM model to represent customer behavior, bank customers were segmented according to RFM dimensions by using K-means algorithm, which is used as one of the clustering techniques in data mining. Integration of RFM analysis and data mining techniques provides useful information for current and new customers. Data mining technologies and techniques for recognizing and tracking patterns within data - helps businesses sift through layers of seemingly unrelated data  for meaningful relationships, where they can anticipate, rather than simply react to, customer needs. Clustering based on RFM attributes provides more behavioral knowledge of customers' actual marketing levels than other cluster analyses.

Clustering is the process of dividing the entire data into groups, also known as clusters, based on the patterns in the data. K-means is the simplest clustering algorithm. K-means algorithm is an iterative algorithm that tries to partition the dataset into Kpre-defined distinct non-overlapping clusters where each data point belongs to only one cluster. It tries to make the intra-cluster data points as similar as possible while also keeping the clusters as different as possible. It assigns data points to a cluster such that the sum of the squared distance between the data points and the cluster's centroid (arithmetic mean of all the data points that belong to that cluster) is at the minimum. The less variation we have within clusters, the more homogeneous the data points are within the same cluster.
Last stage in this study, campaign strategies were developed in accordance with the segmentation determined within the customer life cycle. The cycle of customer lifetime comprises customer acquisition, customer development and customer retention. In the stage of customer acquisition, most sellers consider three issues: the identity of their profitable customers, their needs, and how to attract them. The second step is customer cultivation. Marketers have to consider issues in this step: matching customer wants and, delivery method. The final step is customer retention. The key issue is ''How to establish and sustain customer loyalty?''.

Most marketers have difficulty in identifying the right customers to engage in successful campaigns. This causes unsuccessful loyalty programs and promotions conjunction with waste of marketing resources. So far, customer segmentation is a popular method that is used for selecting appropriate customers for a launch campaign. Unfortunately, the link between customer segmentation and marketing campaign is missing. Another problem is that database marketers generally use different models to conduct customer segmentation and customer targeting. This study presents a novel approach that combines customer targeting and customer segmentation for campaign strategies. With the methods and strategies developed; proactively determining which part of the customer is in the life cycle to create a structure that meets the customer needs and responds to this need at the right time. In order to serve this goal, a model has been put forward to provide decision support to the marketing management in planning and organization of retention processes.

A key role of marketing is to identify the customers or segments with the greatest value-creating potential and target them successfully with corresponding marketing strategies to reduce the risk of these high lifetime value customers defecting to competitors. In this construction mode, segmenting customer is the basic work of data mining according to known historic segmentation information.
Because customer behavior is uncertain and inconsistent, researchers and managers should construct dynamic customer segmentation model in order to objectively reflect the characteristic. In general the marketing follows the yearly plan set around different campaigns, but the customers´ needs do not follow a pre-set timetable. This according to Pöllänen (1999) may lead to difficulties for the companies to react and adapt the marketing plan to suite a more personalized marketing offering. With the digital environment it is easier than ever for the customer to compare the offerings. It is therefore more important to personalize marketing content. 

In customer-centric era, customer segmentation result is concern with the establishment of enterprise's stratagy and tactics. Best practice demands that marketers develop their understanding of customer segmentation based on data mining techniques and use the output to develop marketing strategies creatively to maximize shareholder value.