Table of ContentsSummaryIntroductionResearch and SurveyMethodologyInterpretation of ResultsConclusionSummaryIn this article, the process of forecasting sales opportunities using mining technique data is presented. It is very important for any customer relationship management (CRM) based organization to analyze the behavior of customers towards the product, which creates a sales opportunity for them. The sales opportunity status of the target variable is predicted using the C5.0 algorithm. There are many independent variables used as a condition rule defined at each segment of the decision tree. The accuracy of this prediction helps the salesperson plan their strategy on sales opportunities based on customer behavior. Say no to plagiarism. Get a tailor-made essay on “Why violent video games should not be banned”?Get the original essayIntroductionIn real life, business organizations are more conscious about their customer relationship management (CRM) to seize the opportunity on the markets concerned to direct their organization. against their competitors. Data mining techniques are used to predict the outcome, using the prediction the organization can plan its strategy for a particular product. The main thing in sales is to present the product to the customer to convert the contact into a prospect and offering the right product to the customer constitutes a sales opportunity. Multiple sellers work together with different products, with the seller needing to have knowledge and opportunities on the particular product. Analyze the strength of competitors, wisely plan marketing and offers for products that give us the edge over competitors. Through marketplace campaigns, marketers are used to attracting customers to purchase the different products based on their customer account history and predicting whether the customer is ready to allocate a budget for a particular product. After analyzing customer behavior based on the budget allocation to the product, the organization can think about cross-selling and upselling the product. Selling the products based on customer needs, where the organization gathers the customer needs with the help of market campaign. Paying attention to customer behavior helps the organization plan its business marketing strategy more effectively. The performance and coordination of work performed within the sales team determines the status of the opportunity, whether it is won or lost. There are many important factors that can influence the opportunity status, such as customer, competitors, cross-sells, upsells, product, seller, and competitors. Finally, the status of the targeted opportunity can be predicted whether it is won or lost.Research and InvestigationThe research work is based on four predictive machine learning models such as Random Forest, Decision Tree, Naive Bayes, Support Vector Machine (SVM) and Artificial Neural Network (ANN) are implemented to predict the accuracy of new sales opportunity status and error rate. The performance accuracy of each model is compared by the classification accuracy (CA) and area under the curve (AUC). Research has shown that how model performance is affected by the quality and quantity of data. However, the accuracy of Random forest (77.6%) is higher than that of other models. But the accuracy of C5.0 is not evaluated. K-means is used to group the data, Random forest is used to reduce thedimensions and select important attributes. Finally, the C5.0 algorithm is used as the main classifier to predict customer churn over two to three months. Decision tree is an important classification scheme, where classification is a supervised data mining operation, similar data elements are grouped together. and they divide the dataset into segments. The C5.0 algorithm is used for low memory usage, higher accuracy and increased speed with small decision trees. The accuracy performance of the improved C5.0 is much better than that of the traditional C5.0 [5].Here, the machine learning algorithm is used to classify and predict the accuracy of inventory handling. The accuracy percentage of C5.0 is still very close compared to other models [6]. The performance of CART and C5.0 is measured using sampling techniques. CART uses the Gini index measurement to construct trees, while C5.0 uses information gain to generate trees. The accuracy of C5.0 is higher than that of CART.MethodologyMethodology clearly explains how the dataset was extracted from the source. This is the procedure to clean and transform the dataset. Techniques that have been used to predict accuracy.A. Data acquisition The dataset is downloaded from the Salvirt website as raw data in Comma Separated Value (.csv) format. The dataset contains 448 cases with 23 attributes included, with 51 percent won and 49 percent lost.B. Data PreprocessingData preprocessing is one of the important methods mainly known as cleaning and transformation phase, raw data is collected from the source and processed according to the implementation. Using the R programming language, duplicate values, unwanted special characters and noisy data are cleaned. Missing values ​​are generated based on other attributes of the dataset. The attributes are coded for easy understanding.C. DatasetAttributes contain many independent variables and one dependent variable. Target variable: The target variable is "Status", it contains the values ​​indicating whether the opportunity status is won or lost. Attribute DescriptionStatus Opportunity Result salePredictors: There are 22 predictor variables that are placed in the dataset. These predictor variables influence the dependent variable, using these predictor variables the outcome of the dependent variable can be predicted either it will win or it will lose.D. Technical: The dataset is based on the classification model. The classification model consists of various techniques, but the decision tree using C5.0 implementation is carried out to predict the accuracy of the dependent variable “Status”. The decision tree consists of a sequence of decision conditions, where each part of the tree consists of a condition for classification. The decision-making variable is placed as the root node in the tree. C5.0 algorithm becomes the important implementation method for classification problems in industry. Tools Used: Rapid MinerR Studio In the decision tree described below, red color indicates the chances of winning the opportunity and blue indicates the possibility of a lost opportunity using different customer segments. , where the customer is an important factor that is placed at the root node of decision making. Focusing on current customers helps the organization be more likely to increase its won opportunities than on new and.