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Your Objective in CAS: Simplify the Complex

Mike Triantos
Posted by Mike Triantos on Oct 3, 2023 8:46:14 AM

Accountants are experts at working with facts and data. Your deliverables, such as financial statements and tax filing, all deal with clear information about what has happened. 

With client advisory services (CAS), you’re providing forecasts, budgets, and financial planning, all of which deal with predictions. Predictions aren’t rooted in just historical information. 

This prediction aspect can open up a can of complexity worms. In trying to help clients make their murky future more predictable, it’s easy to get hung up on the what-ifs and whys in creating the perfect model that takes into account every possibility. 

But it doesn’t need to be this way. In most cases, CAS is better served by simplifying the complexities for ease of use and understanding.

What CAS is and (more importantly) isn’t 

Let’s roll things back a bit and talk about what CAS actually is.  

Providing CAS doesn’t mean you need to get so deep in the details that you lose sight of the goal. The goal of CAS is to help businesses understand, iterate, and make informed decisions based on their data.  

Complexity isn’t necessary in this practice. In fact, complexity can actually hold your services back.  

In CAS, by pairing financial reporting and modeling, you answer questions and provide insights that might be missed. Often, you’ll make assumptions to do this, in particular when building out forecasts. The trade-off of an assumption is accuracy over precision.  

Delivering accurate vs. precise services 

A client comes to you asking you to make a forecast based on their growth projections. They want to sell 100,000 more units this year than the amount they sold last year. But first, they want to know how that growth would affect their operating costs, workforce, and margins. 

Precise forecasting would involve digging into every single expense and getting exact numbers on how they would increase with each unit sold. Then, you’d build a model where they set the increase in units sold to simulate every possible situation. 

Sounds like a lot, right? But is it necessary? 

CAS isn’t about providing precise numbers but accurate conclusions. Accurate conclusions can come from simplified models that are usable, easy to update, and intuitive to understand.   

In the example earlier, is the client really asking for the exact financial outcome of the goal they set for themselves? Likely not. They’re more interested in answering what it will take to get there. That question can still be answered on a simple but accurate model that incorporates assumptions and focuses on drivers.  

Think about it like giving directions for a destination. You could go deep into the details of the distance traveled and precise turns to the exact degree, but you could get them to their destination with something more general and easy to understand. 

The best reporting is buildable and usable 

Building out a complex, precise model means a lot of moving parts. Once this model is built and you show it to your client, what are they going to do with it?  

Letting precision get in the way of usability is a trap that will make your work go underutilized. If your client needs to call you up to understand how to use it and how it works, they aren’t getting the value of what you’re providing.  

If you think of CAS as something complex, you’re actually creating a potential gap between the work you provide and your client’s understanding. Your number one guiding factor should always be usability.  

By keeping things simple at the start, you don’t just make CAS more approachable for yourselves but for your clients. CAS is about the continued engagement and relationship, not the deliverables.  

You can continue to build out your models and add in some complexity as you get deeper into CAS engagements. However, it should come as you learn more about your client’s wants and needs. 

Start with the past and build with small steps 

How should you shape your CAS engagement? It’s easier than you think:  

  1. Start with historical data: You have a wealth of information to kick off your engagement. Map it all out and you’ll have the beginnings of what you need for a rolling forecast. 
  1. Build out simple models: Don’t get too hung up on the details, focus on building something that functions, focuses on drivers, and is easy to work with.  
  1. Meet with your client regularly: Take meetings with your client as an opportunity to review your work. This is your opportunity to ask questions and, more importantly, help them connect the dots on any of the information you’re presenting. 
  1. Adjust your model for new information: After each meeting, you’ll likely have new information and results that can be incorporated into the model. Make iterative changes, and slowly, you’ll model will become more robust.  

If you think you need to build out a complex model right out of the gates, you’re skipping essential steps. It’s like taking out the putter while teeing off your first swing of the day. Have confidence that the more you work with the client, the more your work becomes tailored over time. 

Simplify further with technology 

For the simplest, most usable experience, leverage technology to minimize your manual work. With Jirav, you can leave the pains of spreadsheets and calculators behind with an all-in-one financial planning and analysis tool.  

Build out budgets, forecasts, and scenario plans with an intuitive tool built by finance professionals for finance professionals.  


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