What is a what-if analysis? Unlocking the secret to better decision-making

With the continuously developing nature of the SaaS industry, companies need to stay agile to keep up. A what-if analysis is an excellent way to do that.

A what-if analysis is a process used in financial planning and risk management to explore various outcomes by ‘playing around’ or manipulating the levers for key business variables. It works on top of a baseline model and poses a what-if question to see how the future might be impacted given a certain change in a variable. 

It aims to predict the possible outcomes based on different conditions or circumstances, both positive and negative. The main goal of a what-if analysis is to gain valuable insights that can help inform important business decisions.

It’s all about questions like:

‍"what-if we altered our pricing?"

‍"what-if our new customer acquisition rate fell by 50%?"

Since the SaaS landscape is highly dynamic, answering these questions with as much certainty as possible is crucial. Understanding and using what-if analysis is vital for SaaS companies looking to thrive in a competitive market.

So, let's explore this topic to learn more about the power behind the what-if analysis. And while we're at it, we'll show you how you can use it to build more resiliency in your business and help you create a more effective, data-driven strategy.

What is a what-if analysis?

At any given time in a company, the CFO or other CXOs can take one variable (at a time) and test its stress level. The point of the exercise, other than the obvious decision-making part, is to answer questions such as, “How easily or at what point does my model break? If I flex these things down, how long will it take for my business to fail?”

Typically, the process is pretty straightforward, helping CXOs find a quick answer to a specific question. If the question requires a deeper analysis, then the finance team (and sometimes data analysts) will do a deeper scenario analysis. As a note of caution, though, it is best to experiment with a single variable at a time to understand and analyze its impact on your business.

Some of the best-case and worst-case scenarios include:  

• What if sales decline by 10%?
• What if a major customer churns, or X% customers churn? 
• What if there are supply chain disruptions? 
• What if I increase my pricing by X%?
• What if I enter a market?

Are there different types of what-if analyses?

There aren’t really any different “types” of what-if analysis, just different questions. There are, however, different techniques you can use to do a what-if analysis.  

These are sensitivity analysis and scenario analysis, both of which can be used separately or together to help understand the possible impacts of different potential realities or actions you’re considering in your business. 

Both methods test the impact of independent variables on a dependent variable. The dependent variable is the “what” in your what-if analysis (i.e. the outcome you’re looking at). The independent variable is the “if” part of the analysis (the action or situation you want to test). Independent variables can also be thought of as inputs in the analysis.  

Sensitivity analysis

Sensitivity analysis is a method that involves varying or “tweaking” one independent (input) variable at a time to see the effect it will have on the dependent variable. 

By isolating the impact of each input, you can determine how sensitive the dependent variable is to that specific input. You can test any number of independent variables (as long as you’re just testing one at a time) to determine which one will have the biggest impact. 

For example, if you want to know what the impact that a price increase will have on churn, testing different prices over a range of values (maybe +/- 10%) will probably tell you how high is too high in terms of the expected churn. This information can then help you more accurately weigh the expected ARR resulting from the price increase against that which you’ll lose from churn. 

Scenario analysis

Scenario analysis is a more comprehensive technique in which you’re creating multiple hypothetical scenarios by varying different inputs simultaneously to see their combined impact. 

For example, let’s say you’re looking at a recession scenario. You can test two independent variables, such as an expected decrease in sales coupled with higher cloud hosting costs to determine what their combined impact might be on your gross margin.  

Scenario analysis requires a much more sophisticated model because there are so many different variables involved, and often requires the involvement of analysts in addition to finance teams. However, some financial modeling software tools have these scenario analysis capabilities built-in and can significantly streamline the process.

Advantages of using what-if analysis in SaaS

SaaS businesses exist in an environment of constant change—from evolving technologies and shifting customer preferences to the continuous influx of competition. In such an environment, a what-if analysis becomes a necessity that companies can leverage to overcome challenges they might encounter. Here’s how…

Financial planning and forecasting in a complex market

Static modeling and best guesses aren't enough in the industry with a multi-tenant nature, subscription-based models and a relentless drive for innovation.

But such an environment is exactly where a what-if analysis shines. SaaS companies can use it to recognize shifts in the market and adapt more quickly. For example, running a what-if analysis on a question like, “what-if a new technology disrupts our primary service offering?” allows you to anticipate and prepare for a shift before it even occurs.

What-if analysis also allows you to test the impact of different variables on your business, such as failing to meet or exceed revenue targets. Or, you might use a what-if analysis to figure out how sudden fluctuations in your operating expenses or an increase in customer acquisition costs might affect your gross margin.

You can model various scenarios involving faster or slower growth rates, potential pricing changes, or expansion into new markets. By performing a what-if analysis, you can identify your SaaS business’ key drivers and the best levers to pull in different market scenarios, if needed, to achieve your company’s financial goals. 

Revealing potential risks and challenges

Using a what-if analysis tool lets you simulate potential issues to better understand and prepare for the downside risks should they arise. These could include economic downturns, increased customer churn, or possible regulatory implications. 

For example, imagine a SaaS company specializing in data analytics. By asking, "what-if a global privacy regulation restricts data collection methods?" you can foresee a possible decrease in user base or increase in expenses. As a result, the company can change its strategy in time to avoid significant losses.

Although it's tempting to only envision positive outcomes, looking into potential challenges is important, too. Considering a variety of what-if scenarios, including both best-case and worst-case scenarios, will help you become better prepared for every possibility. 

Adjusting prices and exploring different pricing models

Considering raising your prices but are afraid of losing customers? what-if analysis can help you predict how different prices and pricing models might impact your profit and customer satisfaction. 

Resource allocation and optimization

What-if analysis is an excellent tool for determining the ideal headcount levels and hiring plans based on your company’s growth forecasts. 

You can model marketing campaigns to determine their relative ROI, giving you a clearer picture of how to allocate your resources. And in the process, you’ll also discover which strategy fits best with your target audience.

It can also help you better manage the variable costs in your business. For example, cloud services can vary significantly based on growth (more customers means more cloud storage and compute). This makes it inherently difficult to plan for. Using a what-if analysis, you can better predict how those costs will change under different growth scenarios so you can more effectively plan your infrastructure needs.  

Decision-making based on data-driven insights

Fighting for your market share in the SaaS industry means making a lot of tough decisions. So why guess when you can do a what-if analysis and make a more informed, strategic decision?

Specifically, it helps you assess opportunity costs and financial impacts of strategic business moves, including launching new products, exploring new market expansion initiatives, or pursuing other growth avenues. 

With the right “what-ifs”, you can test the sensitivities and (potential) impact of variables, such as pricing, adoption rates, and market size estimates. Additionally, what-if analyses can help understand the trade-offs involved in build vs. buy, hiring vs. outsourcing, and other such operational decisions.

How to perform a what-if analysis with an example

At the outset, try not to capture all your business needs in the model because you’ll never finish building it. You can choose to make the analysis simple or complex based on your business requirements. 

The trick is to keep your financial forecasting model simple though — doing a deep dive doesn’t necessarily make the analysis more impactful. It’s really more art than science. 

Next, we’ll explain the step-by-step process of performing a what-if analysis. Then, we’ll illustrate the main steps with a practical example.

Step 1. Identify the question and the key input variables

Every what-if analysis starts with a question. What do you want to know? 

Once you formulae the question you want to answer, you then need to identify all the variables that might affect the outcome.

A what-if analysis can be as simple or complex as you want to make it. The key is knowing what variables to consider. 

Step 2. Choose a what-if analysis technique

Recall that the two main types of what-if analysis are:

• Sensitivity Analysis: This method tweaks one key variable at a time to determine the effect.
• Scenario Analysis: This is a more comprehensive technique that modifies multiple inputs simultaneously and lets you visualize the combined repercussions.

The technique you’ll choose will depend on the question you’re asking. If you wish to look at the impact a single variable can make, a sensitivity analysis will suit your needs better.

On the other hand, if your goal is to map out a broader landscape with interrelated variables, a scenario analysis will help you do that. For best results, combine both.

Step 3. Gather relevant data and information to establish your baseline

For accurate predictions, you need a solid base of data to build on. The what-if analysis can only work if you have a baseline against which you can compare the potential impacts of the variables you’re testing. 

To establish your baseline, you’ll need to aggregate all the relevant data that provides insight into the variables you determine to be important for the analysis. 

You can use assumptions if you need to. But, the more data you have to work with, the more reliable your result will be, so you’ll always want to prioritize thoroughness and relevancy when choosing what data to bring into the analysis. 

Step 4. Vary input values to see their impact

Start by flexing each value up or down by 10% to see their relative impacts on other metrics. You can do this for one value at a time if you’re performing a sensitivity analysis, or in combination if you’re doing a scenario analysis. 

Then, verify the impact of each ‘flexing’ by analyzing how the change to the input variable(s) flows through the model output. Note that if you’re doing a scenario analysis, you can do this with any variable because they’re all interconnected. 

Scenario planning tools like Drivetrain makes it easy to play with the different variables in your model to see their relative impacts. Whatever tool you use, the goal is to pinpoint the drivers and quantify their individual and/or combined effect. 

Step 5. Perform a stress test on your model

In addition to testing out different actions or scenarios, it’s important to stress test your model by flexing inputs to the very extremes to understand worst-case downside scenarios and tipping points where the business model breaks.

Step 6. Summarize and communicate your results

Summarize the results of your analysis to make them easy for stakeholders to understand and act on if needed. 

Your summary should provide the context for the analysis (i.e., the question it was meant to answer) along with potential outcomes, risks/opportunities, and recommendations.  

It’s also important to present the findings of your what-if analysis visually. Using charts, graphs, and data visualizations can help communicate the key insights and potential impacts more effectively to your stakeholders. 

Step 7. Monitor regularly

Remember that a what-if analysis cannot predict the future with absolute certainty. It's all about making informed decisions amidst the myriad of choices in the SaaS environment.

This is why you should have a process in place for updating your what-if analyses regularly. 

As the business evolves, it’s important to continually pressure test your assumption to see if they are still holding true. With each iteration, your what-if analyses will become sharper. 

An example of how to use a what-if analysis in SaaS

Let’s say you have a target ARR (top-line) of $100M next year, and your current ACV is $10M. Based on these numbers, you would need 10 new contracts to reach the target. However, given the effects of recent inflation, you might not need 10 deals — nine contracts should help you achieve your target. 

Now, you would need a certain number of people to execute the deal end-to-end, from lead generation to landing the account. This number comes from your capacity model. So, let’s assume that you need 10 people to work for six months to close one deal. And, they are expected to work at 70% utilization.

So, our dependent variable is our top-line ARR, and our independent variables are ACV, inflation, capacity, and utilization.  

The next step would be to identify the variables and use what-if to effectively decide which factor(s) to tweak to achieve your goal, topline ARR in the above example. In this example, you could use a sensitivity analysis to test each variable, one at a time, to see which one will have the biggest impact. 

But, if you want to see what changing two variables or maybe all three at once would do, you’d want to opt for a scenario analysis. 

Note that in either case, you need a connected model. All the variables in the model must be linked so that when you change one or more inputs, that change flows through all the other models your analysis is built on to show the potential impact.

Challenges and limitations of what-if analysis

Truth be told, building the financial model is not a significant challenge. However, using Excel to build your model can present challenges—once interconnected in a common workspace, any sort of edits, e.g., link or formula changes, can break the entire model. 

Data accuracy and availability

The finance team must have sharp control over numbers, and using a financial planning and analysis (FP&A) tool can prove invaluable here.  Accurate and up-to-date data inputs are essential for what-if analyses to provide reliable insights. 

Complexity of models and interpretation

Since SaaS financial models come with detailed metrics, drivers, and interdependencies, interpreting what-if analysis outputs becomes increasingly difficult due to the ripple effects. Tracing impacts across an intricate model requires a deep understanding of the model and the business in general. 

Addressing uncertainty and bias

Given the nature of the future, any analysis has inherent uncertainties. The results must be interpreted cautiously with as little cognitive bias as possible. In this situation, it is prudent to involve multiple stakeholders to reduce individual bias. 

Involving key stakeholders from different departments ensures a holistic perspective. In other words, by pooling collective wisdom, you can refine the accuracy of your analysis.

However, simply playing out a scenario doesn’t mean the business will evolve as projected. For CXOs, this exercise must not lead to overconfidence or bias towards certain scenarios over others. It becomes all the more important to combine what-if modeling with pragmatic considerations for the best business outcome.

How technology makes what-if analysis easier  

Using a what-if analysis in your business is an act of shifting from "best guesses" to data-driven decisions, an approach from which every SaaS company can benefit. 

What-if analysis helps SaaS and B2B finance teams identify risks and opportunities, and make better-informed decisions. The goal of what-if analysis is to help you predict the future so you can proactively manage uncertainty and optimize resources today.

You can use Excel to become familiar with the what-if analysis concept. It can be quite useful as a sort of "beginner's guide” allowing you to perform some fairly simple analyses.  However, performing a robust what-if analysis with many different variables in Excel spreadsheets comes with some significant challenges. 

Complex models built on interconnected spreadsheets can easily break when changing assumptions, formulas, or references. And maintaining separate model versions for each what-if case can quickly become cumbersome. 

‍FP&A software with built-in capabilities for what-if analysis will eliminate these issues, allowing you to easily run advanced business models with as many scenarios as you want with as many values as you want. 

With 200+ integrations, Drivetrain makes pulling together all the data you need for a robust what-if analysis easy no matter how many different source systems you have. As Drivetrain is a system-based model, you can change variables on the fly to see the impact in real time, which supports proactive, agile decision-making. 

Leveraging a dedicated FP&A solution like Drivetrain can help SaaS and B2B companies unlock key insights they can use to more easily navigate uncertainties in the market and take advantage of new opportunities hidden in their data. Contact us to book your demo today!