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 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.

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 conversational system and a method for personalized query and interaction set generation. The conversational system includes a system server, a business database server, a user device. The system server further includes a system processing unit. The data points are extracted by a system processing unit from a business database server. The system processing unit creates improved multiple datasets that include the grammatically correct query, corresponding responses of the grammatically correct query, and corresponding data points related to the grammatically correct query. The multiple datasets are being fed into the conversational module to train the conversational module. The user sends queries to the system server through the user device. The system processing unit sends a query to the conversational module. The conversation module sends the query to a search engine that searches data and sends data to an answer generating module to send the answer to the user.

Abstract: The present invention is related to a system and method for automatic updates of customer relationship management and enterprise resource planning fields with the next best actions using machine learning. A system processing unit (106) of a server computer (104), executes computer-readable instructions to retrieve data from a customer relationship management database (102), a calls log and email database (108), an enterprise resource planning database (110), and data from external sources. The system processing unit (106) executes computer-readable instruction to integrate all data into the datasets and feed the datasets into a machine learning analytical module to train the machine learning analytical module. The trained machine learning analytical module analyses various information that suggests the next best actions to be taken.

Abstract: The present invention relates to a method and system for automated sales forecast on a deal level during the black swan scenario. A list of features is being generated that influence the sales forecast on the deal level. The data related to a list of features are processed and transformed into an appropriate form through feature engineering. The artificial intelligence-based model is being selected and trained by the feeding data. The artificial intelligence-based model is optimized with the help of hyper parameter values. The artificial intelligence-based model uses previous data and generates probability scores, forecast close date postponement, and forecast amount on which sale deal would close. Thus, based on the above forecast, overall sales on the deal level are being forecasted. The artificial intelligence-based model is trained and deployed for the sales forecast on the deal level with the help of a computational unit.

Abstract: The present invention relates to a system (100) and method for automated sales forecast on an aggregate level during the black swan scenario. The present invention includes a computational unit (102), and a display unit (108). In an embodiment, the computational unit (102) including, but limited to, a desktop computer, a laptop, a tablet, a smartphone, a mobile phone. The computational unit (102) includes a database unit (104) and a system processing unit (106). The system processing unit (106) executes computer-readable instructions to collect the company data through the company servers and the system processing unit (106) further executes computer-readable instruction to forecast sales of the company on an aggregate level during the black swan scenario. The system processing unit (106) executes computer-readable instructions to provide a comprehensive analysis of forecasts from the top-down level that serves the basis for the Finance Team/CSO/CFO to set modified targets.