Abstract: In one embodiment, a method may access, from one or more data sources associated with a user, customer data associated with a customer of the user, wherein the customer data includes qualitative and quantitative data. The method can generate a plurality of normalized key performance indicators (KPIs) using the customer data and a normalization algorithm. The method can access a customer-health score model, wherein the customer-health score model is trained to determine a customer-health score using the plurality of normalized KPIs. The method can determine, using the customer-health score model, a customer-health score for the customer using the plurality of normalized KPIs. The method can send instructions for presenting a user interface to the user, the user interface comprising information associated with the plurality of normalized KPIs and the customer-health score for the customer.

Abstract: In one embodiment, a method includes accessing customer data of a business entity at multiple data sources. The customer data includes product-usage signals, support signals, and communication signals associated with each of multiple customers and each of one or more products of the business entity. At least some of the customer data is quantitative, and at least some of the customer data is qualitative. The method includes analyzing the customer data by applying one or more AI models to the customer data and, based on the analysis of the customer data, determining, for each of the customers and each of the products a score for each of multiple KPIs for the business entity that include one or more of the following interaction frequency; product usag; NPS or CSAT score; number of support tickets; severity of support tickets; customer sentiment; customer-owner pulse; up-sells or down-sells; or customer maturity.

Abstract: A named entity recognition system performs analysis on textual information and identifies intents, sentiments, or actionable items. The system develops a training dataset for training a machine learning model. The data preparation process includes gathering textual data, cleaning the dataset, masking sensitive information, and mapping each textual content item to a set of predefined named entities. The system further generates a template that includes guidelines and criteria defining each label. The system provides instructions to human classifiers for labeling training data and identifying keywords that encapsulate meaning of the label. The resulted training dataset includes value pairs, each value pair including a label and corresponding keywords. The system trains a machine learning model pipeline using the training dataset.