Across the globe, CFOs and senior finance executives have been relying on analytics and reporting to comprehend the health of their business. For instance, from an accounts receivable perspective, it is important to closely monitor metrics such as Days Sales Outstanding(DSO), Average Days Delinquent(ADD), Bad Debt to initiate course corrections. Today, any CFO will be able to identify the possible red flags in their O2C process by clicking a few buttons on their analytics and reporting dashboards, however, this was not the norm a few years back.
Analytics has traversed a long way from spreadsheet-based reporting to the latest version of predictive analytics. This blog will walk you through the evolution of order to cash analytics and how an ideal analytics-driven accounts receivable tracking software should look like.
Before digging into how analytics has evolved over the years, let us first clarify the difference between analytics and reporting. These two terms are sometimes used interchangeably, however, they serve different purposes:
Now, let us understand the evolution of analytics in various phases:
The first generation of accounts receivables reporting was paper-based. The A/R teams had to manually aggregate data across the various order to cash processes, and calculate the value of the metrics. For eg, in collections, everything starting from aging analysis to worklist prioritization had to be performed manually. However, the senior management was unable to make accurate data-driven decisions as there was no scope of further drill down for more insights and the data wasn’t real-time data.
In the next phase, A/R teams started to rely on excel-based automation capabilities such as macros. To simplify the reporting process, they used built-in formulae for simpler calculations, ready-to-use templates and various charts. However, as the businesses evolved, advanced reporting requirements were highlighted. Also, the analysts still had to spend a substantial amount of time gathering data to prepare excel-based reports. In a nutshell, the A/R department had to go through the same inefficient process.
In this phase, ERP systems such as SAP, Oracle started to dominate the finance ecosystem. These ERPs acted as the single source of truth with in-built reporting capabilities, therefore, it was easier to pull data, remove duplicate values and create accurate reports. But the major drawback was that the IT analysts would conduct operations on behalf of the A/R team who lacked the necessary technical knowledge. Also, with built in reporting templates, there were minimal options of customization. To make it worse, it had high maintenance costs and because of it’s on-premise nature, it was not feasible for the global enterprises to access the ERP from everywhere in the world.
Software-as-a-Service(SaaS) is a cloud-based technology that redefines analytics and reporting in order to cash with it’s real-time data insights and easy accessibility from anywhere across the world. In this phase, CFOs and senior finance leaders are able to access intuitive analytics dashboards and reports to identify critical trends related to their O2C process. These real-time dashboards also help in accounts receivable benchmarking by populating industry benchmarks and suggesting course-corrections based on them.
Let us understand the common challenges encountered by organizations in their reporting process:
The legacy reporting in accounts receivable involved a lot of manual intervention, calculations across thousands of payments which made reporting a time-consuming and error-prone process. Apart from this, the analysts had to rework and revise the metrics based on additional insights demanded by the senior management which led to a loss of their productivity.
For a CFO, it is important to have a centralized view of their order to cash process health and performance metrics. For example, they would want to track the various credit limits for customer portfolios, bad debt to write off ratio globally/regionally, number of productive vs unproductive collection calls. However, in most of the cases, the CFOs aren’t able to get this holistic view across their business units while using spreadsheet-driven reporting or on-premise solutions.
Excel-based manual reporting or on-premise solutions don’t offer real-time data insights or additional drill down capabilities. For instance, if the DSO increases rapidly, there isn’t a way to dig deeper to analyze the root cause behind this anomaly. It becomes difficult for the senior A/R leaders to make data-driven decisions.
With manual reporting or in-built reporting templates, it became extremely difficult for the A/R teams to add customization in the reports. In case, the senior management wanted to review a metric which wasn’t in the reporting template, the analysts needed to create a report from scratch which took a lot of their time.
All these shortcomings highlighted the need for user-friendly analytics-driven reporting and dashboards for accounts receivable which would be able to share real-time data insights to aid executive decision-making. Let us understand how that dashboard would look like.
According to a recent study, conducted by ACCA and CAANZ members, an analytics-driven solution in accounts receivable should be built on the following parameters:
Analytics in order to cash should be simple yet informative. The reports and dashboards should be easy to access, and dynamic in nature to ensure effective decision-making. Let us understand the must-have features of a receivables analytics solution.
The senior finance leaders should be able to review critical A/R metrics such as DSO, Bad debt, ADD, Percentage Current, DDO at a global scale. They would also be able to click on a particular geography to review regional metrics or even dig deeper to analyze the analyst’s productivity through their KPIs.
Determining your accounts receivable benchmarks with respect to your industry peers is a critical step to set the right expectations for your O2C operations. However, organizations should also track their progress with respect to these accounts receivable benchmarks and suggest ways to improve continuously.
The order to cash analytics dashboard should enable real-time visibility into critical order to cash metrics such as bad debt. It should also notify in case there is a major red flag such as a bankruptcy alert or an acquisition taking place with the customer. These real-time notifications would help receivables teams to stay on top of upcoming risks.
The accounts receivable tracking software should provide user-friendly reports and interactive dashboards. The managers and senior leaders of finance should be able to set various geographical filters or double click into the report to review critical A/R metrics and benchmarks. Apart from this, the O2C analytics system would also have the capability to automatically schedule emails to various teams.
To get more insights about interactive dashboards, explore our complimentary ebook- “ The Perfect A/R Dashboard”
The intuitive reports and dashboards should be available in multiple languages and currency formats. The users should be able to change the language or convert to a different currency with a few clicks.
Managers should be able to set business goals for their accounts receivable teams. For instance, in collections, DSO, ADD, percent current, bad debt to sales would be certain common goals. But these lagging indicators would have multiple leading indicators(such as # of outbound collection calls, #of promise to pays honored) mapped to them in order to build a proper roadmap towards achieving those business goals or lagging indicators.
With the accounts receivable tracking software, managers would be able to track their team’s performance as well as an analyst’s performance to understand their progress and scope of improvements.
With the help of the accounts receivable reporting and analytics software, various A/R teams should be able to collaborate within themselves or with sales or other financial teams. For instance, in case of interdepartmental collaboration, a collector would be able to get real-time updates whenever a customer’s credit risk changes. In case of an interdepartmental collaboration, a credit analyst would be able to notify the sales rep about the upcoming blocked orders to ensure a smooth sales cycle.
The biggest advantage of an advanced analytics solution is that it is highly configurable in nature, and can be built according to your business’ reporting requirements. In a similar way GE had configured their analytics-driven dashboards to enable dynamic reporting and effective decisions in A/R.
Learn how you can build a customized dashboard for your order to cash process. Check out the Highradius dotONE Performance and achieve real-time visibility to global receivables health so as to deliver real business results.
Clive Webb,” Analytics in Finance and Accountancy”, ACCA, Dated September 16,2020.
0 likes
Automate invoicing, collections, deduction, and credit risk management with our AI-powered AR suite and experience enhanced cash flow and lower DSO & bad debt
Talk to our expertsHighRadius Integrated Receivables Software Platform is the world’s only end-to-end accounts receivable software platform to lower DSO and bad-debt, automate cash posting, speed-up collections, and dispute resolution, and improve team productivity. It leverages RivanaTM Artificial Intelligence for Accounts Receivable to convert receivables faster and more effectively by using machine learning for accurate decision making across both credit and receivable processes and also enables suppliers to digitally connect with buyers via the radiusOneTM network, closing the loop from the supplier accounts receivable process to the buyer accounts payable process. Integrated Receivables have been divided into 6 distinct applications: Credit Software, EIPP Software, Cash Application Software, Deductions Software, Collections Software, and ERP Payment Gateway – covering the entire gamut of credit-to-cash.
HighRadius Corporation
Westlake 4 Building (BP Campus)
200 Westlake Park Blvd.
8th floor
Houston, TX 77079
