Müşteri analitiği ve öneri sistemleri uygulaması
Customer analytics and recommender systems application
Yazar: ÖZGE ELMAS
Danışman: PROF. DR. ALP ÜSTÜNDAĞ
İstanbul Teknik Üniversitesi / Fen Bilimleri Enstitüsü / Endüstri Mühendisliği Ana Bilim Dalı / Mühendislik Yönetimi Bilim Dalı
Derece: Yüksek Lisans, 2018
Konu: Endüstri ve Endüstri Mühendisliği = Industrial and Industrial Engineering ; Mühendislik Bilimleri = Engineering Sciences

Özet:
Modern dünyadaki rekabetçi pazar ve dalgalı ekonomiler, müşterileri uzun vadede karlı olarak yönetmenin zaruri bir ihtiyaç olduğunu ortaya çıkarmıştır. Müşteri İlişkileri Yönetimi, bir firmanın stratejik olarak en karlı şekilde hizmet edebileceği müşterileri seçmesi ve söz konusu müşteriler ile firma arasındaki etkileşimi şekillendirmesidir. Burada asıl amaç, müşterilerin mevcut ve gelecek değerini firma için optimize etmektir. Günümüzde Müşteri İlişkileri Yönetimi, modern ekonomi için vazgeçilemez bir strateji, taktikler kümesi ve teknolojiyi ifade eder. 
Çalışmanın başında değişik kaynakların müşterilere farklı olarak tahsis edilmesini temel alan Müşteri Değer Yönetimi incelenmiştir. Farklı kaynak aktarımının temeli, müşterinin firma için ekonomik değeridir. Bundan dolayı, bir firma müşteri yönetimine geçmeden önce her bir müşterinin firmaya ne kadar değer kattığı hesaplanmalıdır. Bunun için kullanılan yöntemlerin bazıları basit hesaplamaları temel alır, bazıları ise matematiksel teknikleri gerektirir. Çalışmada pazar payı, satış büyümesi gibi metriklerin yanı sıra cüzdan büyüklüğü, müşteri yaşam boyu değeri, RFM metodu gibi metrikler örneklerle incelenmiştir. 
Daha sonra Müşteri Değer Yönetiminin en önemli hedeflerinden biri olan, mevcut müşterileri değerlerine göre profilleyip, bu veriler doğrultusunda benzer müşteriler bularak elde etmek amacıyla yapılan Pazarlama Kampanyalarının nasıl yapılması gerektiği adım adım irdelenmiştir. 
Kampanya Yönetimi ardından, firmaların müşterileri ve satın alma davranışlarını daha iyi anlamasını sağlayarak, birbiriyle ilişkili ürünleri keşfetmesine, birbiriyle ilişkili ürünlere dayalı müşteri yanıtının olasılığını tahmin etmesine ve pazarlama ve satış işlemlerini optimize etmesine olanak tanıyan Öneri Sistemleri incelenmiştir. Öneri Sistemleri çalışmaları sonucu yapılabilecek pazarlama ve satış işlemleri arasında; 
•İlişkili ürünlerden satın almayan müşterilere ürün önerisi yapmak ya da indirimli set halinde ilişkili ürünleri sunarak satış hacmi artırmak, 
•Önerilen ürünleri yüksek doğruluk oranıyla yaparak müşteri memnuniyeti sağlamak,
•Perakende mağaza yöneticisinin ilişkili ürünler bazında stok kontrolü yapmasını sağlamak,
•Mağaza ürün yerleşiminde ilişkili ürünler göz önünde bulundurularak tasarım yapmak vb. sayılabilir. 
Bu amaçla çalışmada, UCI Machine Learning Repository'den alınan, İngiltere merkezli bir e-ticaret şirketinin 01/12/2010 ve 09/12/2011 tarihleri arasında gerçekleşen 541.910 adet işlemi içeren veri seti ile açık kaynaklı Knime Platformu'nda Ortaklık Kuralları oluşturulmuştur.  
Oluşturulan Ortaklık Kuralları sonucunda, firmanın hangi ürünlerden kimlere öneri yapabileceği, promosyonlu veya promosyonsuz ürün seti tasarlanabileceği, ilişkili ürün gamında yeni ürün tasarım fırsatlarının neler olabileceği ve stok kontrolünde hangi ürünleri göz önünde bulundurması gerektiği gibi sonuçlara ulaşılmıştır. 
Çalışmanın ikinci kısmında söz konusu veri setine, gelecek müşteri davranışlarını daha iyi tahmin etmek ve hedef müşterileri daha iyi belirlemek için RFM analizi yapılmıştır. RFM analizi yapılan 36 farklı segmentteki müşteri için Ortaklık Kuralları hesaplanmış ve incelenmiştir. Böylece segment bazında daha yüksek müşteri memnuniyeti ve daha çok çapraz satış ile sonuçlanacak, ürün öneri kural setleri oluşturulmuştur. 
RFM analizi sonrası oluşturulan müşteri segmentlerinde hesaplanan Ortaklık Kuralları'nın firma hedefleri doğrultusunda daha odaklı kampanya performansı sağlayacağı düşünülmektedir. Daha doğru sonuçlara ulaşabilmek için, müşterilerin demografik bilgilerinin de eklenerek Ortaklık Kuralları oluşturulması tavsiye edilmektedir. Böylece yöneticiler, daha doğru ve daha güçlü kurallar kullanarak, doğru müşteri segmentlerine daha hedefli ve etkili kampanyalar hazırlayabilir.

Abstract:
Today, understanding and meeting individual customer needs have become the key dimension on which firms forge their competitive advantage. With this shift, it is important to state the importance of the customer concept. The customer concept is the conduct of all marketing activities with the belief that the individual customer is the central unit of analysis and action. This definition emphasizes the analysis and measurement of marketing activities and consequences at the individual customer level. 
The notion of CRM with customer value at its core, enables us to define CRM from a customer value perspective: CRM is the practice of analyzing and using marketing databases and leveraging communication technologies to determine corporate practices and methods that maximize the lifetime value of each customer to the firm. 
At the beginning of the study, Customer Value Management, which is based on the allocation of different resources to customers differently, has been examined. The basis of different resource allocation is the economic value of the customer for the firm. Therefore, before transitioning to customer value management, it is necessary to calculate how much each customer adds value to the firm. Some of the methods used for this are based on simple calculations, others require mathematical techniques. Market share, sales growth, wallet size, customer life time value and RFM method are metrics and methods used in customer analytics which are some of the ones examined in the study with examples. 
In the absence of individual customer data, companies have relied on traditional marketing metrics such as market share and sales growth. The availability of customer-level data helps firms utilize a new set of metrics which enables the assignment of value to each individual customer. These so-called primary customer-based metrics can be subdivided into customer acquisition metrics and customer activity metrics. Customer acquisition metrics measure the customer level success of marketing efforts to acquire new customers.
The most important customer analytics topic for this study is RFM method. Because it is used to segment the customers before calculating Association Rules in the application. RFM stands for recency, frequency, and monetary value. This technique utilizes these three metrics to evaluate customer behavior and customer value and is often used in practice. Recency is a measure of how long it has been since a customer last placed an order with the company. Frequency is a measure of how often a customer orders from the company in a certain defined period. Monetary value is the amount that a customer spends on an average transaction. The general idea of RFM is to classify customers based on their RFM measure. The resulting groups of customers are associated with purchase behavior, e.g., likelihood to respond to a marketing campaign. RFM also tracks customer behavior over time in what is called a state-space. That is, customers move over time through space with certain defined activity states.
Marketing Campaigns which is one of the most important goals of Customer Value Management is done by profiling customers according to the profiles of existing customers and finding similar new customers by the help of these data. A successful campaign management process comprises of planning, development, execution and analysis. At the campaign planning stage, marketers make strategic decisions that define the overall objectives of the campaign, the best communication message and the best target audience. The objectives often are market penetration, market extension, product development or diversification. When pursuing a customer retention strategy, ideally the company should target its most profitable customers via LTV (lifetime value) and RFM (recency, frequency, and monetary) analyses. The company can choose to pursue market penetration or extension, market diversification or new product development. Communication strategy involves choosing the most effective message and media (for retention and acquisition strategies) to efficiently reach its target segments.
Identification of the customer segments that the campaign will target can be done using lifetime segmentation and profiling based on purchase behavior and profile data. The CRM database plays a central role in the segmentation process by providing information on customer behaviors and profiles, channel preferences and brand awareness.
Following Campaign Management, Recommender Systems are reviewed, which enable companies to better understand customers and their buying behaviors, explore associated products, predict the likelihood of customer response based on associated products, and optimize marketing and sales processes. Some of the applications of Recommender Systems are;
•Entertainment - recommendations for movies, music, games, and IPTV.
•Content - personalized newspapers, recommendation for documents, recommendations of webpages, e-learning applications, and e-mail filters.
•E-commerce - recommendations of products to buy such as books, cameras, PCs etc. for consumers.
•Services - recommendations of travel services, recommendation of experts for consultation, recommendation of houses to rent, or matchmaking services.
•Social - recommendation of people in social networks, and recommendations of content social media content such as tweets, Facebook feeds, LinkedIn updates, and others.
Among the marketing and sales operations that can be done using recommender systems are;
•Increase sales volume by making product recommendations to customers who do not buy associated products or by offering associated products in a discounted bundle,
•Providing customer satisfaction by recommending products with high accuracy,
•Ensuring that the retail store manager controls the inventory for the associated products,
•Designing store product placement by considering associated products. 
To this end, the Association Rules have been calculated using the Apriori Algorithm via Knime Platform with the dataset containing transactions (between 01.12.2010 and 09.12.2011) retrieved from UCI Machine Learning Repository. The dataset belongs to an operating UK based e-commerce company. 
There are 21 Association Rules calculated with a minimum confidence level of %60.   Examining the Association Rules, the e-commerce company's Marketing-Sales Group and Management can design a Regency tea cup and saucer sets with different colors and create cross-selling opportunities. Also new products can be designed in the same product lines to increase sales. Similarly, there are cross selling and new product design opportunities for Jumbo Bags, Alarm Clocks and Lunch Boxes. 
In the second part of the study, the data set was subjected to RFM analysis to better predict future customer behavior and better target customers before calculating Association Rules. The customers were analyzed in 4 main and 9 sub segments adding up to 36 segments to identify which customers are valuable for the firm and which are not. Depending on the market it is operating, the firm can change the weight of RFM values. 
Knime's advantages include being a free and an open source data analysis platform that can easily be installed and used by anyone. But it also has shortcomings. For example, it does not offer a node that can automatically calculate RFM method. The data set containing 541.910 operations can be processed with the Pivot function in the Excel file. Since larger data sets can not be processed in this way, it is recommended to use a platform such as IBM SPSS Modeller with RFM analysis function.
After the RFM analysis, Association Rules were calculated separately for each of 36 segments. By this way, any firm can have more information about the customer segments and run more customer-focused and resource-efficient marketing campaigns.  
For an example of how the e-commerce company can use such data, the Association Rules have been calculated for 1,336 customers in W3 segment with high frequency and an RFM value of 6. There are 13 Association Rules calculated with a minimum confidence level of %60. The Association Rules are slightly different from The Association Rules calculated for the mass customers. There are cross-selling, new product design and inventory control and management opportunities for the products that are associated. 
In order to achieve more accurate results, it is recommended that the demographic information of the customers be added to form the Association Rules. Thus, marketing executives can use more accurate and stronger rules to create more targeted and effective campaigns based on the right customer segments.