## 6 Conclusions  
In the e-commerce sector, which has a dynamic and variable structure, managers have to make and implement decisions at the same speed. Therefore, there is a need for solutions that will contribute to the decision-making processes of managers in this field. Demand forecasting has been one of the tools widely used by managers in making decisions in finance, industry, services, energy, and other sectors. Using the forecast outputs, inventory decisions, purchasing policies, marketing activities, and production activities are also directed. On the other hand, as far as it is examined, demand forecasting studies in the field of e-commerce have also been found in the literature. In the studies in the literature, time series, analytical methods, artificial neural networks, and regression-based methods have been applied for demand forecasting. While some studies focused on combining method estimations, some studies used individual estimation methods on a single-product basis. However, it is thought that most models proposed in the literature will be difficult to implement in the e-commerce sector, and low success will be achieved due to the huge multiproducts. Undoubtedly, the characteristics of the sector mentioned in the original contributions section are also effective in this. This study was built on the proposal of creating a hybrid forecasting model, which is suitable for the structure of the sector and an approach that has become widespread in demand forecasting in recent years. By using the proposed model in this study, which uses genetic algorithm and cluster analysis as one of the data mining techniques, test results with real e-commerce sales data showed that the hybrid model CBGA both produces faster predictions and increases the prediction performance for a large number of products. In testing the proposed cluster-based GA forecasting model, 15,525 sales data from 53 weeks for 50 selected products sold by the selected B2C company in Turkey were used. In this model in which a large number of categorical and numerical variables are used, the prediction performance has increased in 40 of the 50 selected products. Moreover, the estimations made with the GA model have gained more success than other methods. The forecast horizons are selected as weekly. According to the results obtained:  
1. In the cluster analysis in which the k-means method was used, it was concluded that this method also increased the estimation success of GA for MSE values. Therefore, when the non-clustered and clustered conditions of 50 products were compared, the proposed CBGA performance increased in 40 out of 47 frequently sold products after cluster analysis. Especially in almost all products with high-order volumes, GA did very well and recovered. 2. At the same time, the grid search method was preferred in the study to obtain the correct values of the GA tuning parameters to increase the solution performance. 3. In the study, demand-based values and e-commerce-based characteristic values were taken into account to obtain a better solution for the demand forecasting problem. It was also seen in the tests that this suggestion was successful. 4. The fact that the coefficients created by the proposed CBGA method can be updated in each horizon improves the demand forecasting performance and prevents the repetition of past errors. Today, electronic commerce, which has replaced traditional commerce websites, should prefer models that are suitable for their dynamic structures. Also, software has been developed in this study, and working by establishing a connection between the developedsoftware and e-commerce sites will help site managers make more dynamic and successful decisions. Despite the success achieved in this study, it must be said that there are some limitations. The first of these is the number of products selected. It is estimated that there are at least 10,000 different products on an average e-commerce site. Therefore, the number of 50 products represents a very small audience to evaluate the success of the model.