VOZIQ AI can work with any structured and unstructured customer data. While structured data will help in segmentation, unstructured data will improve the accuracy of predictions in multiples by infusing very early-stage risk signals from customer conversations.
We carefully select mission-critical data sources based on needs and goals assessment. Typical data sources we use include:
1. Direct feedback and sentiment (email/SMS/phone surveys),
2. Indirect feedback and sentiment (contact center/web/mobile interactions), and
3. Inferred feedback from other time series and IoT data like usage/trouble histories.
Integrating this engagement and interaction data with billing, transactions, demographic, and cancel history allows us to develop the most accurate customer profiles. This enables machine learning models to assign not only an accurate churn score but also an NPS score, cancel reason, likely contract extension, likely intervention through either rate change or add-on equipment for 100% of customers for micro-segmentation, and targeted campaigns through contact center and marketing channels.
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