A key indicator of great finance teams is their ability to tell a complete story about the underlying performance of the business and make timely recommendations for course correction where appropriate. To build a compelling story, such as through variance analysis, Finance relies on four essential ingredients:
- Timely data
- Analytical skill
- Knowledge of the business
- The right tools
Finance teams employ a number of different methods in assessing business performance and identifying threats and opportunities. The monthly process of reviewing variances to the prior year (trend analysis) and against management expectations (variance analysis), acts as a detection mechanism in highlighting diverging trends or dramatic shifts in income and expenditure.
Once a threat has been detected, the business will rely on finance to quantify the impact, establish the root cause, and suggest recommendations for corrective action.
While accounting ensures that the results reported each month-end are materially accurate and compliant with GAAP, FP&A is focused on the road ahead. Despite the enormous effort involved in extrapolating historical financial performance to predict the future, we know that financial forecasts are all but guaranteed to be wrong.
Nonetheless, comparing actual performance against planned performance often highlights areas where actual behaviors within the business deviate from planned behaviors. Insights from the monthly variance report can lead to more accurate forecasting over time and also highlight business practices and decisions that may not be in line with corporate goals.
Variance analysis is, essentially, the handoff in the relay race played by accounting and FP&A teams across the globe—the only point where actuals meet the forecast. Great finance teams will closely collaborate and share knowledge at this critical juncture to ensure a smooth handoff—enabling FP&A to face forward, recalibrate as needed, and focus on the business achieving its performance goals.
Variance Analysis: Understanding What Happened
Preparing a variance analysis report involves extracting historical financial performance data from the ERP system (typically the last fiscal year, and the current year-to-date numbers), and sourcing forecast financials for the current year from the Annual Operating Plan (this could be in Excel, or in a dedicated budgeting system).
Variances to prior year highlight areas where the business is accelerating, or decelerating, year over year. Variances to forecast highlight areas where the business is deviating from planned expectations. The role of variance analysis is therefore to highlight areas of under, or over, performance in the business in an effort to understand what happened and inform FP&A of changing dynamics in the business model that help them build a robust outlook for the months ahead.
With so much data to review, and many teams involved in the variance analysis process, there are some key factors to consider when reviewing performance:
- Reliability: We need to be certain that the numbers are accurate. A variance analysis built on inaccurate or incomplete data could compromise the decision-making process, putting the business at risk.
- Materiality: Not all variances are equal. It is important to focus on high-risk areas for the business, as well as significant variances. Many companies choose to apply tolerance limits to variance reporting whereby only variances exceeding +/-10%, or +/- $100K in absolute dollar terms, require an explanation.
- Inherent Variability: Some costs are by nature quite volatile. Other costs such as office rent are far more stable, and even a minor variance may indicate an underlying issue.
- Adverse or Favorable: Adverse variances tend to attract the most attention as they indicate poor performance or overspend. However, favorable variances can also uncover learnings that inform the business of what’s working or identify external forces impacting the business, such as the recent shift to remote working and the resultant impact on lower facilities costs.
- Trends: A single adverse variance may be caused by a random event or a timing difference. However, a series of increasingly adverse variances may indicate shifting dynamics in the underlying business model. Trends are easier to visualize when presented in graphs and charts over a 12-month period, rather than in tables with endless columns of numbers.
- Causality: Variances should not be interpreted in isolation from each other. Understanding the inter-relationships across finance and operational data enables finance to connect cause and effect. For example, if a favorable variance is reported in revenue, it is likely that a corresponding increase will show up in sales commissions.
To keep reading, click over to part 2. We’ll move beyond reporting what has happened in the business and towards why the business is behaving differently than expected.
Want a sneak peak on how we’re rethinking variance analysis?