Demos
Movate Interaction
Analytics
Movate Interaction Analytics is an AI-based platform for Quality Assurance covering voice, chat, and email channels. It automates monitoring agents’ performance and delivers insights on coaching in real-time, increasing agent productivity and engagement. Secure, scalable, event driven execution with acceleration patterns easily into enterprise behaviors. Improved insights into CSAT, enhanced operational efficiency, and improved customer experiences. Constant learning allows for increased accuracy and supports large scale implementation.
Customer ChurnPrediction
Movate’s Customer Subscriber Churn Prediction uses machine learning to identify subscribers at risk of cancellation and the factors driving it. By combining data from demographics, transactions, campaigns, and support interactions, it delivers a unified customer view. Advanced techniques like survival analysis and engagement modeling ensure accurate churn predictions. These insights help businesses personalize engagement and improve retention. The result is higher revenue, efficiency, and a stronger competitive edge.
Customer Lifetime Value
Movate uses Customer Lifetime Value Solution to offer non-subscription retailers the ability to predict future customer behavior using transaction, demographic and interaction data. Using advanced machine learning models, such as BetaGeo and GammaGamma, Movate analyzes purchases to estimate future levels of customer engagement and monetary value. This method allows retailers to identify their high-value customers and simultaneously make informed predictions for future spending. This propels a better retention and campaign decisions along with long-term commitments from customers.
Smart Infra Analytics
Movate’s Smart Infra Analytics helps tackle cloud log challenges like manual effort, limited visibility, difficulty correlating distributed logs, undetected security anomalies, and scalability issues. It captures key infrastructure metrics, automates scaling, applies predictive analytics for failure anticipation and capacity planning, and uses forecasting models to project cloud spend. Leveraging datasets such as application logs, firewall and monitoring data, and inventory records, it delivers faster, more accurate insights, improved efficiency, enhanced security, and optimized cloud costs, with demo access still WIP.