Obviously, it is important for businesses to keep track of who owes them money and to manage cash flow. Somewhat less obvious (especially to those outside of AR) is that actually getting the money from those that owe you can be incredibly complex. Not only must businesses effectively collect from their customers, but they must also be strategic about how they go about collecting from their customers as it can heavily impact business relationships. As a result, revenue management is incredibly nuanced and extremely challenging.
Goals and Objectives
Recently, companies have turned to structured machine learning to speedup/streamline revenue management processes, including AR automation, invoice remittance, reconciliation, recognition, and credit details. In addition, early adopters of machine learning have been able to eliminate a large amount of time spent on manual tasking while also decreasing the error rate.
Enterprise hardware, personal devices, cloud, Big Data and analytics, mobile, applications, business consulting, cognitive technologies, and IoT
Use Case Summary
Technology is used to develop, implement, and measure revenue strategies, forecast revenue, and receivable collections.