A CPG Company Avoided Risk Of Late Monthly Invoice Collections Worth $5.2M, Through Smart Predictions & Resource Optimization




North America & Europe


End-to-end Collections Engine Framework

Avoided the risk of late payment of invoices worth $5.2M per month on an average

Optimized the use of collection resources and improved collection time by 1 week

The Order-to-Cash (O2C) division of a leading CPG company wanted to improve cash inflow by transforming the invoice-to-cash collections process. Traditionally, as invoice-to-cash collections is a reactive process, the collection agents follow-up with customers only when an invoice is delayed beyond payment terms, or a dispute is raised. This approach is slow, manual and expensive.The O2C division noticed that the delinquent invoice payments were directly affecting the EBITDA and cash inflow, and in turn leading to inconsistencies in financial stability.

TheMathCompany teamed up with the CPG company to take an intelligent and proactive approach in transforming the invoice-to-cash collection process and improving cash inflows, by using predictive analytics. The predictions were used to optimize collection resources and prioritize follow-ups of delinquent invoices, to avoid late collections and improve overall EBITDA.

We developed an end-to-end collections engine framework that utilised invoice, SKU and customer data to predict invoice delays, thereby enabling collections agents to take proactive action before an invoice is delayed beyond payment terms.

An intuitive and forward-looking dashboard was developed to ensure prediction and performance tracking information was available in an easily consumable format.

The dashboard was built in a modular manner and was scaled to 5 new geographies along with geography specific customizations.

The modularity of solution led to low development time and reduced time to value for each new geography.

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