The Financial Data Goldmine Of Order-To-Cash
October 13, 2017
Order-to-cash is an area of cash flow management that has the potential to significantly enhance a business’s financial performance. But ongoing reliance on paper invoices, continuing acceptance of paper checks and evolving business models are preventing companies – especially large multinationals – from grasping the power of data analytics to strategically take control of order-to-cash.
Veena Gundavelli, founder and CEO of order-to-cash management company Emagia Corporation, said this focus of financial management is a goldmine for data that can be used to analyze key metrics, like accounts receivable and days sales outstanding.
“For very large companies, when you have thousands of invoices and thousands of customers, there is a big complexity to even understand whether there are any patterns going on in order-to-cash,” Gundavelli told PYMNTS in a recent interview. “Is there a product line that customers are not liking, or disputing? Is there some customer segment that isn’t paying as quickly, or that is constantly delaying payments? All of those aspects impact cash flow for the company.”
This information is critical for a variety of reasons, the CEO explained. Key metrics are important for monthly and yearly reporting, for example, while overall, accurate visibility into cash flow is key to understanding what working capital a company has at its disposal. Data analytics can go deeper, not only to analyze historical data to understand customer payment behavior and other patterns, but – using technologies like artificial intelligence and machine learning – to also predict order-to-cash metrics. (Emagia recently announced a partnership with Solix Technologies, a big data company, with these capabilities in mind.)
But large multinationals have a lot of roadblocks – some old, some new – to gaining that order-to-cash visibility.
One of the more modern challenges to managing the order-to-cash cycle is the emergence of subscription and recurring billing business models.
“More and more companies are moving toward digital business models,” Gundavelli said. “One of the key characteristics of the digital business model is the Subscription-as-a-Service scenario.”
That business model means companies that would have previously invoiced a customer once a year are now splitting that bill into, for instance, 12 monthly bills. It drastically increases the number of bills going out and the volume of payments coming in. This is a space, Gundavelli noted, that can greatly benefit from data analytics capabilities.
“It puts more pressure in order-to-cash,” the executive said, “but at the same time it offers opportunities to the companies. There is more downstream working capital and financial supply chain enablement. There is a lot of activity happening in the financial supply chain.”
Of course, ongoing reliance on paper invoicing and the receipt of non-electronic payments like paper checks remains a challenge, said Gundavelli. Luckily, she said, many organizations are shifting away from these burdens.
“Right now, many companies are moving toward, first and foremost, digitizing transactions – invoicing and payment, as well as credit application forms,” the executive noted, then added that even when companies receive paper checks, today’s technology, like OCR, can digitize payment information for order-to-cash analytics purposes.
Still, electronic payments are an important part of supporting businesses’ ability to gain visibility not only into cash positions, but into trends and patterns stemming from their order-to-cash cycles – especially when e-payments and e-invoicing is coupled with more sophisticated technologies, like artificial intelligence and machine learning.
“Automated, digital invoicing and payments means cash application can be done almost in real time,” Gundavelli stated. “It eliminates about 70 percent of manual labor-intensive tasks, and brings a lot of efficiency. Usually, manual cash application is very error-prone, whereas in automated, AI-based application, accuracy rates reach more than 95, 96 percent. With machine learning, that accuracy keeps increasing.
“What this means is your cash posting can be done almost instantaneously,” she continued. “Your cash is available a lot faster, and helps the company to be able to grow revenues and become stronger. Electronic invoices and electronic payments are fundamental blocks upon which you can build.”