Financial forecasting models pave the way for companies to estimate future revenue and expenses. Moreover, startups use models in order to anticipate needs for inventory and personnel. These models aim to provide decision-makers with a clear picture of the organization’s financial health so they can make well-informed strategic decisions.

One of the key components of an effective financial forecasting model is operational data. It is defined as data pertaining to a business’s day-to-day operations. The data can help inform several aspects of the financial forecast, including sales, expenses, and production.

So how do you incorporate it into your financial models? Let’s explore.

What Is Operational Data?

Operational data constitutes the data your company generates through its normal business activities. It can include information about:

• Inventory
• Sales
• Revenue
• Expenses
• Employees
• Customers

The data is usually stored in your organization’s enterprise resource planning (ERP) or customer relationship management (CRM) system.

Why Operational Data Matters for Financial Forecasting

In the past, financial forecasting was done primarily with historical financial data stored in accounting systems. However, this approach has some limitations:

• It can take weeks or even months to close the books and generate accurate financial reports.
• The data may be outdated by the time it’s available.
• It doesn’t reflect real-time changes in the business, such as a sudden increase in customer demand.

Operational data can help you overcome these challenges by providing timely, accurate information about what’s happening in your business. Thus, you can use it to make more informed decisions about where to allocate resources and how to respond to changes in the market.

How to Incorporate Operational Data Into Financial Models

Including operational data in financial forecasting models can be challenging. Here are some tips to help you get started:

Identify the Key Operational Data Sets

The first step is to identify the operational data sets that are most relevant to your business. It will vary depending on your industry and business model.

For example, if you’re a manufacturing company, you’ll want to track production levels, inventory, and sales data. Meanwhile, a SaaS company will focus more on customer churn, subscription revenue, and new sign-ups.

Once you’ve identified the key data sets, you need to determine how to access them. It may require working with IT or the data team to set up automatic data feeds from your ERP or CRM system.

Clean and Prepare the Data

Now, you’ve got the data. You can go ahead and plug it into your financial model, right? Wrong.

Before you can use the data, you need to clean and prepare it. Which of the operational data sets go into your financial forecasting models?

Let’s explain this with an example. You’re a SaaS company creating a forecasting model for the next six months. Which operation data will you take? Since you want to forecast growth potential in the remaining half of the year, you can take the following data sets:

• Custom churn
• Average new sign-ups per month
• Revenue from new sign-ups
• Recurring revenue churn
• Total recurring revenue

The key to selecting the right data sets is to focus on the drivers of growth. In the case of a SaaS company, it’s new sign-ups and recurring revenue.

Transform the Data Into Financial Metrics

After selecting the data sets, you need to transform them into financial metrics because operational data is usually non-financial. To do this, you must establish a link between the operational and financial metrics.

For example, let’s say you want to forecast the number of new customers in the next six months. You should take the number of new sign-ups per month and multiply it by six.

This is called a bottom-up approach because you start with the operational data and derive the financial metric from it. But keep in mind this is only the base scenario.

You also have to calculate the worst and best-case scenarios. How do you do that? Look at the month with the least number of sign-ups and the most number of sign-ups in the past six months. Multiply both by six. One is your best-case scenario, and the other is your worst-case scenario.

Where Startups Go Wrong

A common mistake startups make when using operational data for financial forecasting is that they overcomplicate things. Financial models shouldn’t be saturated with too many data sets.

The goal is to find the key drivers and include only those in your financial forecast model. Otherwise, you run the risk of introducing too much uncertainty and making it difficult to interpret the results.

Another mistake is using data that’s not actionable. For example, if an e-commerce company is trying to forecast the next six months, including data sets like website traffic and the number of social media followers won’t help.

While they impact the company’s growth, they’re not directly associated with sales. Instead, the number of new customers, repeat customers, revenue, and gross margin are more actionable data sets.

Additionally, startups should avoid using vanity metrics, which are data sets that don’t provide any valuable insights. A good rule of thumb is to ask yourself if the metric can help you make better business decisions. If the answer is no, then it’s a vanity metric.

An example of a vanity metric is social media followers. Unless you’re using social media to generate leads, it’s not going to have a direct impact on your sales.

Make Operational Data-Based Financial Forecasting Models

Since startups are new to financial modeling, it’s easy to make mistakes. But when trying to stay afloat, it’s best to steer clear of any uncertainty in terms of a financial model’s quality and accuracy.

That’s why working with a team of financial modeling experts like Numberly can be helpful for early-stage founders. Numberly creates tailored olibespoke financial forecasting models that include operational data, historical performance, and dynamic assumptions.

Besides helping forecast the company’s growth, our financial models are also investor-ready. Thus, if you’re looking to raise capital, our models will give you the insights you need to make a strong pitch to investors. Schedule a call to reach our representatives for more information.