In our last post, we talked about variance analysis being the only point where actuals meet the forecast, and how it’s critical for understanding what happened in the business. This post talks about how variance analysis can explain why the business is behaving differently than expected.
Moving from What to Why
To put some context around the variances, we need to look at what has happened in the month as well as the results for the quarter-to-date. Looking at quarter-to-date numbers allows for seasonal differences in revenue and expenditures, as well as minor timing differences in spending patterns. An analysis of year-to-date numbers versus last year, and also compared to forecast, helps formulate a picture of business performance on a macro level.
Throughout the month-end process, speed and agility are critical to achieving a clean, and timely close. Once the books are closed, management is eager to be updated on business performance and ready to dive deeper into the numbers. However, with the number of platforms used across finance increasing, the explosion of disparate datasets introduces additional complexities in creating a holistic view of business performance. While the volume of financial data continues to rise at an unprecedented rate, there is also growing urgency within the business and an expectation of real-time financial information.
Achieving real-time visibility across the organization has been at the center of finance transformation initiatives for the past decade, however, many finance teams are still spending up to five working days after the month-end, reconciling balances and preparing reports before they even begin to analyze performance.
To avoid losing your way down a rabbit hole of analysis, it helps to keep some context in mind:
- Assumptions: Circumstances may have changed since the Annual Operating Plan (AOP) was finalized, or incorrect assumptions may have been used to generate the forecast. A solid understanding of the key assumptions used in the AOP will help to identify areas where an updated forecast may be required.
- Fraud: In 2019, Evaldus Rimasauskas scammed more than $100 million from Facebook and Google by impersonating an approved supplier, sending fake invoices, and having the funds wired to bank accounts he controlled. A thorough analysis of actual vs planned expenditure, coupled with a trend analysis by supplier and by expense category may have highlighted this fraud before any cash was despatched.
- Accounting Errors: Accounting errors differ from deliberate fraudulent activity and can commonly be classed into errors of omission, input errors, calculation errors, and errors in accounting treatment. Immaterial accounting errors detected during the variance analysis process can typically be corrected in the next accounting period. Material errors, however, may warrant an immediate adjustment to the current period under review.
- Shifting Economic Realities: External forces such as changing market conditions may cause large deviations from expectations. For example, the recent pandemic will have impacted revenue projections for many companies, and businesses will record a lower spend on travel and conferences. In these situations, a revised forecast should be prepared, adjusted for the shift in market conditions.
- Timing: An expense may be accounted for in a different period than originally forecast. Timing variances can arise where accounting guidance requires an expense to be recognized over a defined period, rather than all at once. For example, Julie may plan to spend $50K on running an advertising campaign in January 2021. She signs the contract in August 2020 and approves the invoice in September, knowing she has enough left in her Q4 budget to cover the spend. However, when the invoice is reviewed by accounting, the spend is deferred to December 2020 when the service will be delivered by the supplier. A favorable variance to the budget will be reported in September. Timing differences typically will not warrant an adjustment to actuals or forecasts as they will correct themselves throughout the year.
- Overspend: Adverse variances may also be caused by budget overruns. Many companies will have approval mechanisms and controls in place to prevent significant overspend, but in reality, unexpected costs do materialize from time to time.
The Best Tool for Variance Analysis
With an increasing array of tools available to support finance through the reporting process, finding the right tool for the job is not always straightforward. In many ways, excel is the ideal prototyping tool for finance users – with just the right amount of flexibility and functionality to accommodate evolving business requirements and ad hoc analyses. And that aligns with what we recently reported: that Excel remains the tool of choice for business users.
Because finance is rooted in integrity and strong governance—and is increasingly decentralized—software tools need to enable distributed teams to collaborate seamlessly, without compromising the security and integrity of reporting and analysis.
In those areas, Excel has historically fallen a bit short. Without granular role-based access controls, Excel doesn’t allow the rigorous change control processes that finance should leverage. Collaborating in excel is also fraught with risk – even the slightest unintended change to a formula, or a careless keystroke, can compromise the integrity of even the most robust financial model.
At Kloudio, we view the above shortcomings as opportunities, as features and functions that we’re building into our tools.
What a sneak peak of how we’re rethinking variance analysis?