Abstract: A system and method for automated recommendations of competitors for sales opportunities. The system includes a customer relationship management database, a calls log and email database, an enterprise resource planning database, an external public server, a system server, and a sales representative device. The system server includes server processing unit, and a server memory. The server processing unit executes computer-readable instructions to receive direct signal and indirect signal of competitors related to particular sales opportunities from customer relationship management database and the enterprise resource planning database. Further the server processing unit uses the trained machine learning model to receive indirect signals of competitors related to particular deals from the calls log and email database. The server processing unit uses the trained machine learning model to extract data of competitors related to particular deals from the external public server.

Abstract: A system (100) and method for automated generation of optimum thresholds for post processing of machine learning models in case of imbalanced classification. The system (100) includes a server computer (104) and an user device (112). The server computer (104) includes a system processing unit (106), and an system server memory (120). The system processing unit (106) executes computer-readable instructions to automatically calculate the optimum thresholds for post processing of machine learning models. The machine learning model predicts a probability of class, and that probability is used to decide a crisp class label and for deciding a crisp class label a threshold is set, thus based on amount of variation of probability from threshold the crisp class label is decided. Thus optimum threshold needs to be generated to accurately decide a crisp class label in case of imbalance classification.

Abstract: A system (100) for sales forecasting and optimal path to opportunity closure. The system (100) including an enterprise internal database (110), external database (108), a server computer (104), and a sales-representative device (112). The enterprise internal database (110) further includes a customer relationship management database (102). The external database (108) stores all data related to buyers social profile and buyer professional profile. The server computer (104) includes a system processor (106), and a system server memory (120). The system processor (106) extracts data from the customer relationship management database (102), the external database (108), the enterprise internal database (110), to automatically calculate engagement score, buyer segmentation, and further the system processor (106) uses engagement score, buyer segmentation to recommends the best buyer to contact.

Abstract: A system (100) and method for automated discount recommendations. The system (100) includes a customer relationship management database (102), a server computer (104), and a user device (112). A system processing unit (106) extracts data from the customer relationship management database (102), and further uses the trained artificial intelligence based classification model to identify the open deals that are on risk. Then the system processing unit (106) uses the trained machine learning scoring model, to recommends best optimize sales quote to sales representative for winning the deal. A system server memory (120) stores computer-readable instructions, the trained artificial intelligence based. classification model and the trained machine learning scoring model. The user device (112) is connected to the server computer (104). A sales representative receives optimize sales quote, on a user device (116), for winning the deal.