Automating a quoting process
A lease company’s quoting process is manual & time-consuming: incoming requests for quotes are unstructured and need to be manually mapped to an internal quote configurator which generates a quote for the customer.
The challenge of this case is two-fold. Most importantly, the unstructured data needs to be translated into a known structure – this is done through an algorithm that “trains” the data.
Yet more upstream, the data firstly needs to be transformed in a way it can be trained – in a similar way that a brain cannot work without a proper body to reside in. In this case, the body is provided by the ZAZA.rocks data access & content sharing platform, onto which a specific machine learning algorithm is integrated.
The ultimate goal: creating a user experience where an incoming unstructured request is automatically generated into a ready-to-send quote.
Our predefined “Classifier” DataOps pattern provides rapid framework to use machine learning algorithms.
Eliminate 80%+ of manual effort of admin agents related to quoting (5+ FTE in this case).
The simplest of workflows: drop a mail request into a folder, and retrieve a calculated quote that can be sent to consumer.
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