By mapping out the social networks of our customers, we can acquire data that can prove valuable across the entire field of CRM. Our company possesses a proprietary algorithm suitable for exploring various contact groups within customer networks. The structure of a social network assumes the availability of data on interactions taking place between customers (calls, text messages, e-mails, wire transfers etc.).
With the help of networking analysis and indicators relevant to networking information, the output of CRM processes and analytical processes can be enhanced, such as:
- churn forecasts can be made more accurate when supplemented with networking information (attrition by peers etc.)
- by holding onto core customers, retention can be made more efficient and more cost effective as well,
- by leveraging the nature of a social network, more efficient sales campaigns can be constructed (seeking out opinion leaders and core customers),
- when made aware of economic dependencies, the collections management at financial institutions can greatly improve default forecasting and fraud detection.
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