Joint time-series learning framework for maximizing purchase order renewals

Abstract

When Information Technology (IT) service providers cater services to their customers, there’s a common practice to document the intent of buyer to purchase them through a Purchase Order (PO). The details of a PO document are complex in nature, as they include hierarchical structure of sub-services, price points over the duration of PO, base setup cost, billing frequency, renewal terms etc. In large enterprises with high volume PO’s, the traditional approach of managing PO’s with their invoices involved a great deal of inefficiency and labor-intensive manual work due to lack of automation and disconnected processing. As a result, they often suffered tedious manual monitoring and failed PO renewals, resulting in delays and added costs.In this paper, we propose a joint time-series learning framework that tackles the high dimensionality PO data. It identifies the metrics to monitor, while using time-series prediction coupled with tone analysis to reach out to customers, in order to maximise PO renewals. We demonstrate utility of our approach by implementing our method on a dataset from a global IT service provider that indicates encouraging results.

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