Art and Science of Revenue Automation: Practical Lessons for Automating Revenue under ASC 606 – Part 2

ASC 606 is the new revenue recognition guidance that is scheduled to go into effect at various points depending on the entity. Many companies are focusing significant attention on the adoption process.

Companies are often surprised by the variety of data necessary to automate revenue. Revenue systems are powerful rules-engines and therefore require varying data inputs to make revenue decisions and determinations. This requires breaking down a judgement-based decision-making process into pieces and defining the data used for each portion of these decisions.

 

For example, a software company may group orders that relate to the same customer and sale opportunity (i.e. deemed to be one contract under ASC 606 for contract price allocation and recognition purposes) and start revenue recognition on the later of software fulfillment or go-live. This revenue treatment has at least 4 decision points:

• Are orders from the same customer?
• Are orders coming from the same sales opportunity?
• When was the software fulfilled?
• When did the client go-live?

Companies often derive this information from various sources including the financial ERP, CRM system, emails, phone calls, contracts, etc. However, to support automation, data needs to be available and consistent at all times.

So, where does data go wrong? We think of it in 3 tiers:

 

Tier 1, data does not exist: Often times, data needed does not exist in any system. A common example may be acceptance criteria or terms that are only defined and recorded in the physical contract and manually monitored by members of the accounting or revenue team.

Tier 2, data is not structured: In this case, the data is captured in the system but not in a structured and defined fashion or not structured appropriately. A common example here includes using free-form text fields to capture information (e.g. opportunity ID, contract ID, etc.). This unstructured data is often inconsistent or in a form that is not useable by the revenue systems.

Tier 3, data is inaccurate: Lastly, the data exists and is structured (i.e. format and content defined), but is often inaccurate. A simple example here may be dates – these are structured data fields, but may be captured incorrectly due to user-error.

ASC 606 introduces additional data requirements, which companies will need to contemplate, for both adoption and automation. Example areas where additional data may be required, include:

• Standalone selling prices
• Material rights
• Modifications vs. new contracts
• Variable consideration
• Significant financing
• Costs to obtain and fulfill the contract

All these areas include data critical for both performing revenue recognition going forward and to support the transition to the new standard (whether full retrospective or modified retrospective).

Yet understanding the critical data requirements for revenue automation under ASC 606 is only the first step. There is a more “scientific” approach to understanding your data gaps. Below are a few key steps to help you determine what your data needs are:

Step 1: As we already discussed, start with defining your use- cases.

Step 2: From the use-cases, determine the specific rules being applied for revenue recognition. This is often a large and complex portion of defining your revenue automation business requirements.

Step 3: Define the exact master data and transactional data you need to support the accounting and rules you have defined.

Step 4: Perform a gap analysis between the data you need and data that exists. The 3-tiered framework (in the third post of this series) can also help determine what you need to do to remediate the gaps.

Step 5: Fix your data gaps!

The good news – the approach above is mostly “science” – clearly defined steps with a known and specific outcome. However, fixing the gaps is not always as straight forward. Some of the changes may require process changes outside of the direct control of Finance and Accounting; while others may require an investment in “upstream” systems, for which there is not budget or you are competing with other initiatives.

To support revenue automation, some artful and creative thinking might be necessary to balance some near-term tactical solutions with longer term, large-scale changes. Some practical examples we have seen include:

• Using data proxies (i.e. other pieces of data that although not exactly matching what is needed, closely approximates the need). For example, existing financial systems may not capture the implementation go-live date (and in this example, this is deemed to be the trigger to start revenue recognition). However, business practice at the company is to invoice for professional services shortly after the go-live, therefore, the invoice date for the professional service could be used as a proxy for the go-live date.
• Adding new Finance and Accounting procedures to fix data quality issues or capture additional data.
• Our experience has shown that in all revenue automation initiatives there are data gaps. Overlay this with a new revenue standard, and we expect data gaps will be prevalent. However, this presents companies with an opportunity to solve many common data challenges that hamper businesses (e.g. duplicate data, data inconsistencies, limited reporting) while supporting revenue automation – don’t let this opportunity pass you by.
• In Part 3, we will explore timelines and a few key drivers that impact the revenue automation timeframes.

Interested in reading the full Art & Science of Revenue Automation white paper? Download here: Art & Science or Revenue Automation

 

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Jason Pikoos is a Partner at Connor Group and leads the Financial Operations practice. Jason brings over 15 years of accounting and operational experience, working with high-growth and technology companies. As the Financial Operations leader at Connor Group, Jason has helped and guide over 30 companies in preparing for being public, planning and managing their IPO plan and/or improving operations and controls shortly after the IPO. Jason is an expert in helping clients drive business transformation through improved processes, systems, controls and organizational changes. Jason works across all finance and accounting functions, helping clients define their business strategy/goals and aligning resources and stakeholders to achieve desired business objectives. Jason’s focus is on high-growth and technology companies.

Connor Group has assisted 30+ companies with adoption of the ASC 606 standard and delivered 20,000+ hours on ASC 606 projects. Connor Group has helped 100’s of companies navigate the complexities of revenue recognition. We are in the forefront of ASC 606 guidance and adoption, participating in the drafting process of the standard through comment letters, roundtables and direct FASB discussions.