Create Focus in Advisory Conversations With Budget to Actuals

Up to this point, we’ve talked about everything that comes before the data starts rolling in. Now that the actuals are coming in, it’s time to start talking about the results. 

Advisory conversations are not a one-way street, they’re a dialogue. You’ll have points to present and clarifying questions to ask that will help you get to the bottom of why there was variance from the prediction. 

These conversations also should also find a balance between being about the past and the future. Yes, you’re talking about historical data, but the true value lies in learning from these differences and iterating on them. 

Let’s get into how you can create value in your advisory services by comparing the budget to actuals. 

Forecasts are a starting point 

Now that time has passed and it’s time to review the forecasted performance versus the actual, where do you start? 

Your forecast acts as an anchor for conversations with your client. Every point that you make should stem from something observed in the forecast and the variance report. Before you meet with your client, comb through the results to see where the biggest differences in predicted and actual outcomes occurred. 

Don’t look to explain every difference as you come across them. Compile the areas where the biggest differences occurred first. Once you have the biggest factors singled out, then start the analysis. 

Just as a carpenter measures twice and cuts once, you want to compile before analyzing. There could be more reasoning to paint a bigger picture of what happened when you see everything together. Don’t waste time making an analysis that might be contradicted by another finding. 

Diagnosing the differences 

A crucial thing to remember with forecasts is this: differences aren’t failures. 

Many things can happen that create a gap between the real world and the prediction, and some of them are even external to the operations of your clients. 

Work with the data and try to paint a picture as clearly as you can to explain where these differences came from. Tie in operational data where applicable, there will be key information about things like headcount that will fill in some of the gaps. 

As you go through the differences, write down notes on what may have happened. Structure your notes as a cause and effect. Single out the factors that contributed to the variance first and then the resulting outcome. 

An unexplainable difference is a problem—for now. There’s still one last place you can go to for information to explain the gap: your client. 

Presenting information to clients 

You have the variances and now you have some proposed explanations. How do you maximize the value of presenting this to your client? You ask questions. 

It sounds strange. You’re supposed to be the expert, right? But you’re an accounting expert and your client is the expert on their operations. So, before you go into your big song and dance about what happened, get more insight from your client. 

Ask your client about any challenges they faced or any stressors that infringed on their operations. You might get some new information that’s the final piece of the puzzle.  

Don’t hesitate to ask clarifying questions about variances you can’t explain, as well. You’re not going to have every answer as some details will only be known by your client. 

Treat it like you’re collaborating to find the answer. Your responsibilities are compiling the information, highlighting the biggest differences, proposing hypotheses based on prior experience, asking the right questions to your clients, and then adjusting the model when the review is done. 

Adjusting the model 

The final step in the process is adjusting the model. 

You’ve now identified the variance, explained the differences, and discussed the results with your client. There’s going to be tons of new information to incorporate into your model to make it better reflect reality. 

Some common variances that you’ll need to adjust for are: 

• Price variance: Their prices or the prices of their suppliers were different than expected. Maybe a discount was offered or prices increased to reflect increased costs. 
• Usage variance: The business became more or less efficient in how they use their capital and raw materials. 
• Labor variance: Headcount estimates were off or salary and wages ended up being different than expected.  
• Overhead variance: This can be a catch-all for factors other unexpected factors that affect the bottom line. Examples include repairing broken equipment, higher usage of utilities, and any penalties or fees. 

The first three points are what you should focus on adjusting the model to reflect. These are factors that are likely to continue to affect the business beyond the period you’re looking at. Other random overhead variance factors should be used to explain, but not assumed to continue. 

Work More Efficient With Tech 

The work that comes after a meeting with a client is a lot of adjusting and iterating on your model. Doing this work in spreadsheets can result in errors, broken formulas, endless troubleshooting, and visually unappealing results. 

Leverage tech to make modeling a breeze. With automated financial modeling and reporting apps, you get an all-in-one financial planning tool that’s built by financial experts for financial experts. It’s built for quick adjustments, in-depth breakdowns, and intuitive customization.