Framing with Variance Analysis
1. Introduction
Last week, I covered the basics of framing—the first component of Seahorse’s decision-support framework: Frame → Decide → Act → Learn.
The primary goals of framing are to:
Ensure all stakeholders are addressing the same question (and ideally the right one)
Provide the right amount of information to support a decision.
Present data in a way that is meaningful to each stakeholder.
This week, I want to share a practical approach to framing called Diagnostic Variance Analysis. DVA allows us to take a simple variance, break it down into its key drivers, and identify opportunities for improvement.
Here’s a quick overview.
2. Basic Variance Analysis
Most monthly or quarterly expense analyses have four columns:
Expense type
Budget
Actual
Variance
Costs above budget are typically shown as a negative (unfavorable). Those below budget are shown as a positive (favorable).
But it’s easy to imagine reasons this simple heuristic breaks down.
3. Multidimensionality
Imagine a human services company with a negative variance (spend above budget), except that:
Volume was well ahead of plan,
The increased demand was for more complex services,
Productivity increased, and
Wage rates for frontline staff went up.
Is the overall negative variance bad—or is it good?
4. Diagnostic Variance Analysis
Diagnostic variance analysis lets us examine the effect of each component. By isolating the impact of each driver in turn, we gain far more insight—as analysts and as storytellers.
This Google Sheet shows a completed Diagnostic Variance Analysis.
As you’ll see from the chart, July’s costs were higher than June’s. The biggest contributor to this increase was volume (which will also show up in revenue and gross margin), followed by wage rates (which will depress gross margin) and a more complex, costlier service mix. Offsetting these was a significant productivity gain.
It was this productivity gain which prompted further investigation. Working with operations leaders, we determined that the change in service mix enabled much better utilization and throughput. These insights allowed the client to sharpen pricing and—through revised marketing—consciously push the mix toward higher-margin services. By right-sizing staffing, we avoided the overtime premiums that had driven the wage variance and ultimately lifted revenue by 4.3% and margin by 2.4% on a sustained basis.
5. Bottom line
(Re)framing a change in costs using diagnostic variance analysis produced richer insight, surfaced new options, and delivered sustained improvements in key financial results.
If you would like to develop richer insights into the drivers of your Key Performance Indicators (KPIs), don’t hesitate the reach out. Seahorse offers Fractional FP&A and Business Analytics services to a range of companies, and supports leadership in making data-driven decisions that accelerate growth and profitability.
