4 Financial Models for Decision Support

These financial models can be derived from various components, including the company's past financial reports and accounting statements, such as balance sheets and cash flow statements. These can be combined with assumptions about other variables, like interest rates, expected expenses, employment rates, market conditions, or even new government regulations. 

Business executives can draw from these models during the decision-making process. They can help company leaders set budgets, invest, allocate resources, assess potential risks, and test the impact of different scenarios. 

Companies can utilize financial modeling in many different ways, such as during the launch of a new product. In this example of financial modeling, companies can use such models to predict the potential revenue, demand, and profit of a new product. 

Return on investment, or ROI, estimates how much a company will earn from investing in a certain asset or project. You can calculate ROI using the following formula:

If this ratio exceeds 1, it shows that your venture is profitable. Some people may present the ROI in percentage form by multiplying the above formula by 100. The result in percentage form also depicts the profitability of the investment. 

Payback is often confused with ROI because the two are closely related. However, payback calculates the amount of time in months or years that your company will need to earn back the initial investment. 

The formula you can use is as follows: 

The result shows when the company is expected to recover its initial investment. From the payback point onwards, it will start to earn profits. 

Businesses can use ROI and payback models to calculate the effectiveness of their investments and tailor their strategies to increase growth and profit. 

DCF models help you estimate the present value of an investment based on its projected future cash flows. These future cash flows are discounted to the present value to give you a more accurate analysis by accounting for the time value of money. 

DCF calculations involve a few key steps. You'll calculate the forecasted future cash flows, which is usually the revenue from selling a product or service. Then, you'll pick the discount rate. This might be the company's cost of capital or other opportunity costs from a different investment. You'll then use this rate to discount the projected cash flows back to the present day.

These models consider the revenue derived from a customer and the expected duration of the business relationship. The higher the value, the more valuable the customer is to the business. 

You might have also heard of Customer Lifetime Value (CLTV or LTV). These terms share similar meanings with CLV. The biggest difference is that CLTV or LTV measures the average profit from your entire customer base, while CLV measures it for an individual customer. 

There are many different benefits of using CLV models. Let's make it simpler to understand by using an example. Let's say you're managing a café, and you notice that returning customers typically have a higher CLV. In this case, you might offer freebies or special discounts for repeat customers. Perhaps you realize that posting organically on your café's Instagram attracts higher-CLV customers than paid Google ads. In this case, you might invest more time and resources into organic social media marketing. 

These examples show how using CLV models can help businesses make informed decisions in different areas, such as marketing or strategic planning. 

Essentially, you can manipulate and run "what-if" simulations to see how changing a specific variable impacts the break-even point. Examples of these variables include the product's sales price, material costs, and even marketing spend. 

Let's explore a simple example. Say a company has decided to work with a supplier who will be increasing its cost prices. The company calculates the break-even point and realizes it needs to sell many more products to prevent a loss. This figure of sales seems unrealistic, so the company has to make a strategic adjustment, like raising the sales price of its product or switching to another supplier with lower costs. 

By using sensitivity and break-even analysis models, businesses can make informed decisions to prevent loss and increase their profits.

If incorrect historical data or unrealistic assumptions are included in the model, this can reduce its accuracy and reliability. Because of this, you'd need to be a little more thoughtful with the assumptions you feed your models, which, in turn, makes it a time-consuming process. 

External factors often out of our control, such as market volatility, new rules and regulations, tax rates, and employment rates, can also affect the accuracy of your results. Of course, no one has a magic ball to predict how and when these factors will change the numbers, especially if you're looking very far into the future. Thus, it's always best to take the results with a pinch of salt. 

But the good news is that these models don't have to provide 100% precision. Financial modeling is still an incredible decision-making tool to point you in the right direction and plan for varying scenarios — rather than to predict an exact outcome. 

Want to know exactly how you can do this? The CPE Flow Financial Modeling For Decision Support is designed especially for finance professionals who want to learn how to build and validate key models that can drive business success through strategic planning. 

You'll gain hands-on experience through immersive exercises and real-life applications throughout the course. And did I mention you'd get to do all this without even leaving your couch? The course is entirely online and can be completed at your own pace. This makes it easier for busy professionals like yourself to optimize their decision-making and risk-assessment skills!