Satış adedini etkileyen değişkenlerin keşfi ve duyarlılık analizi uygulaması: E-ticaret örneği
Discovery of variables affecting the number of sales and application of sensitivity analysis: E-commerce example
Yazar: RABİA AYDIN
Danışman: PROF. DR. FETHİ ÇALIŞIR
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / Endüstri Mühendisliği Ana Bilim Dalı / Endüstri Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2023
Konu: Endüstri ve Endüstri Mühendisliği = Industrial and Industrial Engineering

Özet:
Şirketler geçmiş verilerini analiz ederek gelecekteki durumları hakkında fikir sahibi olmayı ve daha etkili bir yol haritası oluşturmayı mümkün kılabilir. Bir şirketin gelirlerini belirleyen temel faktör olan satış adetleri, gelecekteki durumlarını değerlendirebilmek adına öngörüler oluşturmak için kullanılan geçmiş verilere dayalı bir metriktir. Bu çalışma, bir şirketin gelecekteki durumunu tahmin etmek için geçmiş verilere dayalı olarak satış adedi tahminleri gerçekleştirme amacını taşımaktadır.
Çalışmanın ilk aşaması, veri setinde yer alan bilgilerin keşfedilmesiyle başlamıştır. Veri keşfi, veri setindeki değişkenlerin özelliklerini, dağılımlarını ve ilişkilerini anlamak için gerçekleştirilmiştir. 
Veri seti, çalışmanın gerekliliklerine uygun hale getirilmek üzere işlenmiş ve düzenlenmiştir. Daha sonra satış adedi tahminlerini gerçekleştirmek için makine öğrenmesi algoritmaları kullanılmıştır. Bu çalışmada, Karar Ağaçları (Decision Tree, CART), Rastgele Orman (Random Forest, RF), Kategori Artırma (Category Boosting, CatBoost), Gradyan Artırma (Gradient Boosting, GBM) ve Ekstrem Gradyan Artırma (Extreme Gradient Boosting, XGBoost) olmak üzere beş farklı makine öğrenmesi algoritması kullanılmıştır.
Modellerin performansları karşılaştırılarak değerlendirilmiştir. Performans karşılaştırmasından sonra CatBoost, Gradient Boosting ve Random Forest algoritmalarının en iyi performansı gösterdiği belirlenmiştir. Bu algoritmalara dayalı olarak seçilen değişkenler incelenmiş ve bu algoritmalarda en önemli değişkenlerin benzer olduğu tespit edilmiştir.
Seçilen en önemli değişkenler, gelecekteki durumların daha iyi anlaşılabilmesi amacıyla kullanılmak üzere belirlenmiş ve bu değişkenler üzerinden duyarlılık analizi çalışması gerçekleştirilmiştir. Bu çalışma için Random Forest algoritması kullanılmış ve her bir değişkenin hedef değişken üzerindeki etkisi incelenmiştir.
Değişkenlerin bir birim artması durumunda tahmin edilen hedef değişkeniyle gerçekleştirilen karşılaştırmalar, değişkenlerin hedef değişken üzerindeki etkilerini ortaya çıkarmıştır.
Bu çalışmada, bir e-ticaret uygulaması üzerinden taze sebze ve meyve satışı yapan bir şirkete ait olan 2022 Ocak-Kasım dönemine ait veri seti kullanılmıştır. Veri seti, sipariş bilgilerini ve sipariş detaylarını içermektedir. Çalışmanın kapsamını genişletmek adına, veri setinde bulunmayan ancak anlamlı olabileceği düşünülen değişkenler veri setine eklenmiştir.
Bu çalışma, şirketlere geçmiş verileri kullanarak gelecekteki durumlarına yönelik fikirler sağlama ve daha etkili bir yol haritası oluşturma imkanı sunmaktadır. Satış adedi tahminleri ve duyarlılık analizi sayesinde, şirketler satışlarını etkileyen faktörleri anlayabilir ve gelecekteki durumları hakkında daha sağlam bir temel üzerinde planlama yapabilirler.

Abstract:
Analyzing historical data empowers companies to gain insights into their future circumstances and develop a more effective strategic roadmap. Sales volume, a pivotal determinant of a company's revenue, serves as a metric to glean knowledge about their future prospects by leveraging past data. This study endeavors to forecast sales volume through the utilization of machine learning algorithms, utilizing historical data sourced from an e-commerce platform specializing in the distribution of fresh fruits and vegetables.
The dataset encompassed the period from January to November 2022, comprising comprehensive information pertaining to customer orders and associated details. Furthermore, additional relevant variables were integrated into the dataset to encompass potential influential factors that were initially absent.
The research methodology employed encompassed a systematic approach involving data exploration, data preparation, model development, and performance evaluation. Initially, a comprehensive exploration of the dataset was conducted to acquire a profound understanding of its structure, characteristics, and patterns. This step facilitated the identification of potential variables that could impact sales volumes, including delivery-related factors, customer membership duration, pricing, discounts, temporal factors (e.g., month, weekends), and specific product IDs.
Subsequently, meticulous preparation of the dataset was conducted to ensure compatibility with the employed machine learning algorithms. This entailed addressing missing data, transforming variables when necessary, encoding categorical variables, and splitting the dataset into training and testing sets.
Five machine learning algorithms were selected for the task of sales volume prediction, namely Decision Trees (CART), Random Forest (RF), Category Boosting, (CatBoost), Gradient Boosting (GBM), and Extreme Gradient Boosting (XGBoost). These algorithms were chosen due to their effectiveness in handling intricate non-linear relationships and their capacity to effectively process large datasets.
To assess model performance and determine the most suitable algorithm, a comprehensive comparison utilizing appropriate evaluation metrics such as mean squared error, mean absolute error, and R-squared was conducted. Model performance was evaluated using both the training and testing datasets to ensure generalizability and mitigate overfitting.
Based on the performance evaluation, the CatBoost, Gradient Boosting, and Random Forest algorithms exhibited superior results compared to others. Consequently, these  
algorithms were selected for further analysis owing to their ability to capture complex interactions, effectively handle categorical variables, and provide insights into feature importance.
To gain deeper comprehension regarding the impact of individual variables on sales volume, a sensitivity analysis was conducted employing the selected Random Forest model. This analysis involved examining changes in predicted sales volume as each variable was incremented by one unit while keeping other variables constant. By comparing predicted values before and after the increment, the influence of each variable on sales volume was quantified. This analysis provided valuable insights into variables that exerted the most significant effects on sales volume and highlighted how manipulating these variables could potentially drive business growth.
The findings of this study contribute to companies' ability to make informed decisions, develop effective marketing strategies, optimize pricing and discounts, enhance supply chain management, and improve overall operational efficiency. By comprehending the factors influencing sales volume and their respective magnitudes, businesses can align their resources, investments, and marketing efforts accordingly, positioning themselves for sustainable growth and gaining a competitive edge in the market.
Overall, this research serves as a practical and valuable framework for businesses operating in the e-commerce sector, specifically those involved in the sale of fresh fruits and vegetables. The employed methodology, encompassing data exploration, machine learning modeling, and sensitivity analysis, establishes a robust foundation for sales volume prediction and facilitates the acquisition of actionable insights into the underlying drivers of business performance.
Upon conducting a thorough analysis and interpreting the results, several key findings emerged from this research. The predictive models utilizing machine learning algorithms, particularly CatBoost, Gradient Boosting, and Random Forest, demonstrated robust capabilities in accurately forecasting sales volume. The insights provided by the sensitivity analysis shed light on the variables that have the most substantial impact on sales volume. For instance, it was observed that discounts offered on specific products during weekends had a significant positive effect on sales volume, indicating the potential benefits of strategic  promotions.
Furthermore, customer membership duration was found to play a crucial role in driving sales. Longer-term memberships exhibited a positive correlation with higher sales volume, indicating the importance of customer loyalty and retention programs. By understanding the factors affecting sales, businesses can tailor their marketing initiatives and customer engagement strategies to boost revenue and customer satisfaction.
The findings of this research offer practical implications for companies operating in the fresh produce e-commerce sector. By leveraging historical data and predictive models, businesses can make data-driven decisions to optimize inventory management, streamline supply chain operations, and reduce wastage. Accurate sales volume forecasting enables companies to plan their procurement and distribution processes efficiently, minimizing stockouts and maximizing revenue.
Additionally, the insights gained from the sensitivity analysis can guide companies in fine-tuning their pricing and discount strategies to attract more customers while maintaining profitability. Aligning these strategies with customer preferences and behaviors can strengthen brand loyalty and foster repeat business.
In conclusion, this study demonstrates the value of analyzing historical data and employing machine learning algorithms to forecast sales volume in the fresh fruits and vegetables e-commerce domain. The research methodology, encompassing data exploration, modeling, and sensitivity analysis, provides a robust framework for sales prediction and actionable insights into the factors influencing business performance.
By embracing data-driven approaches, companies can adapt to the dynamic nature of the market, make informed decisions, and stay ahead of the competition. The knowledge gained from this study empowers businesses to optimize their operations, develop targeted marketing initiatives, and cultivate long-term growth in the fast-evolving landscape of e-commerce.