A Beginner’s Guide to Variance Analysis – Part 3
Welcome to the final chapter in the Beginner’s Guide to Variance Analysis!
In part 1, I described what variance analysis is and why it is a crucial activity for your business. The important lesson to take away from this was that variance analysis gives you the information you need to make more accurate forecasts, reducing risk and letting you plan better for the future.
In part 2, I looked at the practical side of what information you need to perform variance analysis, using actual data from your business’ operations alongside a financial forecast for the business.
In this article, I’ll cover how to make the best use of variance analysis to help your business. You’ll learn about these topics:
- Interpreting variance analysis
- Budgeting, course correction and re-forecasting
- Balancing analysis and growing the business
Recap! How to perform variance analysis
Variance analysis uses two sets of data.
- The financial forecast, a future prediction of how the business will perform financially in a specific period (usually a month, but sometimes a quarter or year).
- The actual data – information on the business’ performance in the same period as the forecast.
Both actual and forecast must be the same kind of financial data. They must be matchable. For most businesses, this comes down to a choice of comparing Cash Flow data or Profit and Loss data.
If this is all sounding a bit much, check out the last article, where I go through this in detail and touch on the big three financial reports.
So, the data being compared should be for the same period of time, and should be matchable. The easiest way to do this is to look at a financial statement of the business’ last month and compare this to the same month in the business’ financial forecast.
Ensure each line in the forecast data is matched to the same line in the actual data. If you are doing this manually, the quickest way to compare the two data sets is to create a spreadsheet that includes 3 rows for each line you want to compare – the forecast value, the actual value and a row for displaying the variance.
You will need an additional row if you wish to display the variance as a percentage and again as a numeric difference.
If you are doing variance analysis in Brixx this will already be formatted for you, with the variance percentage being calculated automatically from the forecast and actual values.
You’ll still need to enter your actual data but will be prompted for the lines you need to fill to match your forecast.
What information does variance analysis give you?
Variance analysis shows the % (or numeric) difference between the financial forecast and what actually happened.
Typically, you’ll perform this calculation for each row of a financial report that you are interested in – showing the variance for each line of the report.
As discussed in the last article, it’s very useful to “zoom in” as much as possible in your variance analysis.
It may be plain to see from your variance analysis that sales are higher or lower than expected – but unless you can dig deeper into your sales figures, you won’t know which product, service, initiative or project is the cause of the rise or fall in sales.
It is in providing this kind of information that detailed variance analysis really shines.
To do detailed variance analysis you will need to have the same levels of detail present in both actual and forecast data.
This can be a challenge – it is a worthwhile one!
There is no need to go into detail for the report lines that can easily be predicted – recurring bills for example. In these cases, there will be little variation between your forecast and the actual values.
But for cases where forecast and actuals may differ greatly (sales!) breaking down single rows in a report into their component parts will give you useful information.
Interpreting variance analysis
Performing variance analysis for a single report line for a single month will just give you a single % difference between two values. What can you do with this?
Let’s take an example. Let’s say I forecast sales for apples and oranges:
Sales for both apples and oranges were lower than the predictions I made in my forecast. Variance analysis shows that oranges have performed worse than apples against my expectations in the forecast, even though sales of oranges still outperform sales of apples.
It’s a simple example, but already it’s homed in on a problem. Something is amiss with oranges. It could be the marketing, the product itself, the delivery, or the kind of customer my grocery store is attracting. This is where I would need to take a step back from the numbers and start asking more nuanced questions about the business and its operations.
If I persist with this variance analysis, comparing actual data with my original forecast, I can try and spot trends.
Month: September October November
Forecast: £1,000 1,000 1,200
Actual: £900 1,000 1,300
Variance: -10% 0% +8%
Month: September October November
Forecast: £3,000 2,500 2,500
Actual: £2,200 2,000 1,500
Variance: -27% -20% -40%
Apple sales are increasing, even beating my forecast’s expectations by November.
Orange sales continue to worsen, particularly in November.
Clearly, something is very wrong, either with the assumptions I based my orange sale predictions on or with the oranges themselves, their delivery, marketing, pricing, etc.
Here’s what I could do with this information:
1. Look outside the business. Look again at the assumptions I based my orange sale forecast on – do they still seem valid? Are my competitors selling healthy volumes of oranges, while my orange sales plummet? Have orange sales declined across the region? Or was I just hugely over-optimistic in my forecast?
2. Look inside the business. Investigate how the business goes about selling oranges – are there problems than can be addressed?
These activities feed into the next big step…
3. Look to the future – re-forecasting. Based on the investigation of the variance disparity, adjust the future forecast to improve its accuracy.
Budgeting, course correction and re-forecasting
Step 3 mentioned above, re-forecasting, is one of the major benefits of variance analysis. To re-forecast doesn’t mean throwing away your previous predictions – these are still useful to keep hold of to understand the evolving accuracy of your forecasts.
Reforecasting could be done after only 1 month of actual data has been accumulated and in a lean startup it might be. However, a re-forecast will generally be most accurate if it is based on several months of variance data that show a steady trend that can be extrapolated into a new forecast.
Think of re-forecasting as a means of honing your forecast to greater accuracy. The aim of a re-forecast should be two-fold:
1. To reduce the variance between your forecast and actual data, resulting in a more accurate, and therefore more useful prediction of the future.
2. To incorporate a new market, or business information into your existing forecast. Once again, the result of this should be increased forecast accuracy.
From a re-forecast that takes into account the trends you have identified in your variance analysis, you will be able to create realistic budgets, plan for realistic future scenarios, and correct the course of the business before it heads too far down a bad path. In the case of my example, this may mean reducing our stock of oranges, and moving to an apple-centric business model…
How to optimise your planning cycle and avoid wasting time
Many businesses, especially young startups, feel the need to pour all of their time and energy into growing the business. The same attitude can be found in more established companies that have a large number of responsibilities, or a huge demand for their products or services. Conducting business intelligence activities of any kind, including variance analysis, can take a back seat to going out there (digitally or physically) and getting new customers.
But if you have made it this far through this series on variance analysis, I hope that you are part of a group who understands the value in measuring business performance in order to drive growth.
The predictions that we make in financial forecasts are what guides our budgets, the targets we set for ourselves and for the business as a whole.
I do not see “Planning” as sitting in an ivory tower ignoring the day-to-day pressures of business. Rather, it should be an active attempt to identify problems, identify opportunities and improve the business.
But getting the balance between planning and “doing” right can be hard. I wrote about this at length in this article on Organising Your Business Planning Cycle.
For variance analysis, it may be too much to do a re-forecast every single month – and nor may it be appropriate for the business. As I have suggested above, re-forecasting is most effective when it uses existing trends in actual data to extrapolate future forecasts AND takes into account new market research information and internal changes and projects within the business. That’s a lot to include in a full re-forecast!
A quarterly re-forecast based on trends in the business’ actuals may be suitable for many businesses, while a full round of market research may not be possible more than once or twice a year.
One of the most important things to keep on top of in variance analysis is the detail of the data you are dealing with. Business operations change all the time, and you may find yourself needing more or less information on certain parts of the business as time goes forward.
This makes a monthly or quarterly review of variance an excellent timeframe to take stock of the information you are gathering and make sure it is still useful to you, and that you are not missing any levels of detail or hidden trends that you need to be aware of.
At the end of the day, all businesses need to be honest with themselves about things that aren’t working out, and either fix them or focus on the things that are working out.
These are the kind of decisions that you can only make when you spend some time each month looking at the strategic trajectory of the business.
The future of data comparison in Brixx
Comparing two sets of data lets you see more than just those individual data points – it generates a third data set – the variance between the two – and the trend of this variance over time. The importance of this data set in decision making can’t be overstated.
With the actuals vs forecast feature we’re taking the first step towards a system that allows direct comparisons between different sets of data in Brixx. Our intention is that over time these comparisons will expand to include actual data from accounting platforms, like Xero, QuickBooks and Sage, as well as other Brixx plans, prior years in the same plan, and more.