Advanced Analytics in Order to Cash Process
In a previous article, author Sibanjan Das focused on advanced analytics in procurement. In this article, I will pick up where Sibanjan left off, taking a look at the cases where advanced analytics can be implemented in an Order to Cash (O2C) process. Similar to the Procure to Pay process (P2P), O2C is also a business process which mostly concentrates on receiving, fulfilling, and managing a customer’s request for goods and services. It’s an inverse of the P2P process which was procuring goods and availing services from suppliers.
An order to cash cycle typically consists of multiple business offices working with each other to give customers a great experience. The series of functions and stages of a standard Order to Cash cycle is depicted in the diagram below.
The standard process almost appears to be a six step process. But, the complexity involved in each of these stages is enormous. The order management team, warehouse personnel, accounts receivable team, and customer relationship crew must work hand in hand to keep each and every order tracked from its inception until it reaches the intended consumer. There are tons of Key Performance Indicators (KPIs) used by organizations to track this complex process. I’ll list a few important metrics that are necessary for each one of us managing the O2C process.
These metrics are important to all teams involved in the O2C process.
This subprocess mostly includes the Account Receivables KPIs such as:
Order to Cash (OTC) is often challenged by siloed operations and inefficient processes. Advanced analytics can support to break these silos by utilizing the most important data available from the enterprise business systems, social media, and IoT. The analytical application and intelligent systems can help reduce consumer fraud, identify anomalies in the processes, predict risk, and much more. We have outlined some possible use cases below to improve and optimize your order to cash process using advanced analytics.
Today most of the financial transactions are done on credit and almost every time for B2B customers. Accounts receivable is the most critical function for an organization to use well. Their competence and judgment will help the organization to receive money for the sold good and services. This requires the AR group to be pro-active in knowing whom to give credit and when to start the credit recovery process. Advanced analytics can help go beyond the standard AR aging report. Using advanced analytics, AR personnel can predict payments at risk, judge the likelihood of recovery of long overdue payments, and identify customers who are at risk of potentially not paying.
Advanced analytics can build models that identify attributes or patterns that can identify fraud. For example, anomaly detection can detect a sudden drift in historical customer payment pattern. Customer profiling can assist in determining potential fraudsters by matching attributes with known fraudsters attributes. Text mining can help discover the customer sentiment’s towards the product/service.
A product recommendation system provides suggestions for products to a user. The recommendations relate to various decision-making processes such as what items to buy or what service to avail. A recommendation system provides useful and practical suggestions for the particular type of product that is of benefit to a user. The goal of this system is to provide relevant product recommendations for customers so that the probability of a sales conversion is high. Product recommendation systems are widely used in e-commerce applications. However, they can also be leveraged for physical stores. There are various types of recommendation systems. Content-based and collaborative filtering are two widely used recommendation system designs in the e-commerce space.
