The client is a leading manufacturer and supplier of personal care products in the USA with operations across other regions like Latin America, Europe and APAC.
Data Scientists from Tenzai followed a three-step approach to design a solution for the client.
The team first integrated the data from multiple sources like invoice, customer and product data required for analysis.
Then an in-depth exploratory analysis was conducted to extract key patterns and influencing factors about customer payment behaviour especially concerning delayed payments.
To identify the delayed accounts and timelines, a stacked ensemble model was built.
The first model employed classification algorithms to predict the propensity of payment delays for any given customer.
The second model built using regression techniques predicts the estimated timelines for the payment. The predicted results are displayed in an intuitive dashboard and made available to the collection team.
The collection team could identify accounts at risk, estimate delays and assess the overall impact on cash flow.
Based on the insights, the client can proactively reach out to accounts and plan their collection process.