Analyze historical customer churn, contracts, customers, policies, and sales to build models that allow you to identify key churn signals, predict churn propensity, and take prescriptive actions.

Detect early churn signals and help sales take preventive actions quickly. AI-discovered features provide a deep understanding of churn reasons and allow you to develop longer-term tactical actions to increase customer satisfaction, decrease churn, and grow revenue.

Increase revenue by reducing churn, and improve long-term customer satisfaction by addressing key dissatisfaction factors.

Develop models for predicting the risk of accidents – like cargo transportation and storage – by utilizing historical data on past incidents and damage. Tailor risk-based policy, provide sales with identified risk factors to enable better risk consultation suited to client needs.

dotData identifies key risk factors/features that influence accidents. Understanding such risk factors allows you to determine appropriate actions, like transportation methods, transportation sections, packaging methods, etc. to consult your clients to reduce the risk of accidents.

Reduce loss ratio and create more well-tailored proposals for clients to reduce accident risk and increase customer satisfaction.

Analyze high-performing employees based on historical data such as attendance, performance evaluations, training history, and daily reports. Build a predictive model for each performance indicator, and design educational measures and KPIs according to common criteria associated with high-performing employees.

dotData allows you to efficiently develop models for employee performance in different segments and understand the commonnalities of high-performing employees. Similarly, with dotData, you can predict employee resignation factors and implement prescriptive actions to reduce employee attrition.

Boost employee performance, morale, and satisfaction by identifying key factors of high-performing employees.

Forecasting cross-selling, upselling and customer behaviors at the heart of calculating lifetime value. Until now they have been time-consuming and difficult to build.

Leverage historical data on customer behavior, cancellation patterns, usage, and upsell patterns to predict customer lifetime value.

Analyze and optimize customer lifetime value by leveraging the power of predictive analytics to build models that help you forecast consumer behavior in multiple scenarios.

Modeling claims predictions and processing is time consuming, complicated and at the core of how insurance companies operate. Accelerate the process and optimize results with the power of AI.

Leverage historical claims data to understand and predict factors that result in new claims as well as the factors that influence claim processing timelines to build better products and to optimize claim processing.

Forecast upcoming claims to build more competitive pricing models that provide value to your consumers while minimizing costs and maximizing profitability.