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8 min read

How To Use Historical Data in Your Financial Model: 5 Important Tips

The world of startups and SMBs is a thrilling arena, ripe with raw potential. On the other side of that coin, however, is uncertainty and challenge. In this setting, a reliable financial model is invaluable because it lets you accurately forecast your financial performance.

Effective forecasting lets you anticipate future outcomes, make strategic decisions, and prepare for various potential scenarios.

While there are many elements that contribute to a reliable forecast, this post will focus on the role of historical financial data. We’ll cover the things you should do if you have lots of good data to work with. We’ll also cover what you should do if you don’t have historical data, or if the data you have is questionable.

Illustration: Stylized number 5

Table of Contents

The Importance of Historical Financial Data in Forecasting

Your historical financial data is your most reliable record of past events and performance. We’re talking about data like sales figures, growth rates, expense receipts, and any other financial metrics you have recorded and tracked.

Your data serves as a cornerstone for forecasting because it offers insights into patterns, trends, and relationships that will likely persist into the future. It gives you a baseline you can use to make educated assumptions about what is to come.

Startups and SMBs can use this data to analyze trends, understand customer behavior, and identify the most important levers you can pull to optimize your business and improve your performance.

As an example, if you experienced a 20% increase in sales every time you ran a particular marketing campaign, you might reasonably project a similar boost when you run that campaign in the future. You can then look at your anticipated cash flow for the coming period and run the campaign at the most opportune time.

Illustration: A Delorean, as in Back to the Future

How to Use Historical Financial Data Effectively

Tip #1: Understand the Context of Your Historical Data

When you’re analyzing historical data, you need to be mindful of the specific circumstances under which the data was generated. Conditions can significantly impact your data and the insights you derive from it.

Here are some specific factors to consider:

Market Conditions
The state of the market can have a substantial impact on your performance. Economic booms can spur spending, leading to increased sales, while economic downturns may have the opposite effect. When you’re looking at past revenue data, it's crucial to account for the overall economic climate during that period. If it’s much different than the current climate, adjust your forecast accordingly.

Competitive Landscape
The number of competitors in your niche, their strategies, and their performance can have a big impact on startups and SMBs. If a competitor has entered or exited the market, this could affect your customer base, your pricing, your conversion rates, and more.

Internal Business Decisions
The decisions you’ve made within your business since your historical data was gathered, can affect the accuracy of your forecasts. These could include changes to your marketing campaigns, pricing strategies, product roadmap, or operational improvements. Any of these can cause fluctuations in your data that you need to understand when you’re creating your forecast for the future.

Seasonality and Trends
Most businesses experience some form of seasonality and long-term trends. These could be tied to the calendar (like holiday shopping seasons), or they could be industry-specific (like tech product cycles). Recognizing these patterns in your historical data is crucial. Understand what causes them, and understand how they will likely play out in the future.

Regulatory Environment
Changes in laws and regulations can have dramatic impacts on your performance. New privacy laws might have impacted your marketing efforts. Changes in trade regulations could have affected your supply chain. If any legal or regulatory changes have impacted your business (or are likely to impact you in the future), be sure to adjust your forecast to account for these changes.

Context provides you with the ‘why’ behind your data, adding depth and richness to your understanding. It ensures that you’re not just basing your forecasts on what happened, but also factoring in why it happened that way, opening the door to more nuanced and informed decision-making.

Illustration: Plot graph showing trends

Tip #2: Look for Patterns and Trends in Your Data

Identifying patterns and trends in your data is critical for making well-informed forecasts. They provide insights into recurring themes and directional shifts in your performance, giving you the ability to predict future outcomes more accurately.

Here’s how to approach it:

Seasonal Patterns
Many businesses experience strong seasonal variations in their data. You might see major swings around specific dates like holidays or financial year-end. Seasonal patterns are extremely important for some businesses and less important for others, but almost all businesses see some recurring seasonal patterns that should be built into their financial model.

Growth Trends
Trends in your growth rates can provide valuable insights into the trajectory of your business. These trends could be linear, exponential, or cyclical. Identifying them allows you to build them into your projections and then make strategic changes to leverage or mitigate them.

Correlation with Events
Some patterns are triggered by specific events and can be seen in your data every time that event occurs. Such an event might be a marketing campaign that you run periodically, an annual industry exhibition, or an internal sales competition. Identify recurring events that impact your metrics, and include them in your forecast.

Anomalies and Outliers
When you’re identifying patterns and trends, be mindful of anomalies and outliers - data points that significantly deviate from the norm. These might be one-off events that will not repeat or errors in your data collection. Just like it’s important to identify patterns that will recur, it’s also important to identify patterns that will not recur. When you find an anomaly or an outlier, make sure it doesn’t impact your projections.

Trend Breaks
Trends can change abruptly. Unforeseen events like regulatory changes and geopolitical changes can create big changes in your metrics. It’s important to recognize trend breaks so you can build your forecasts to reflect your current reality, not your previous reality.

Identifying patterns and trends requires a blend of quantitative analysis and qualitative understanding. Visual tools like charts and graphs can be super helpful for spotting trends and patterns easily. However, your own personal understanding of your business is usually the best tool you have to identify and interpret trends and predict how they will impact your business.

Tip #3: Use Historical Data to Inform Your Growth Assumptions

Building a financial model requires you to make assumptions about future events. These assumptions, while inherently uncertain, should be as informed and realistic as possible.

Here’s how you can use your historical financial data to set strong assumptions:

Projecting Sales
Use your historical sales data to estimate future sales. If your business has shown consistent growth over the past few years, you can probably assume that similar growth will continue. However, be aware of factors that could change this, such as entering a new market, launching a new product, or a new competitor entering the market.

Estimating Costs
If your historical data shows that costs rise in tandem with sales, you can use this relationship to project future costs. Similarly, you can use historical data to anticipate fixed costs that will not vary with your sales volume.

Pricing Decisions
Historical data can provide insights into how changes in your pricing strategy have impacted your sales volumes and revenues in the past. This data can inform your assumptions about how future pricing decisions will affect your financial outcomes.

Customer Behavior
Data on customer acquisition, retention, and churn can help you make assumptions about these factors in the future. For instance, if you have a high churn rate, your forecasts should reflect the need to continually acquire new customers to maintain growth.

Impact of Marketing and Sales Efforts
You can use past data to understand the impact your marketing and sales efforts have had on revenue. You can use this data to make informed decisions about the likely ROI of future marketing campaigns.

Risk Assessment
Historical data can help identify potential risks and inform your assumptions about them. For example, if you've faced significant supply chain disruptions in the past, it's prudent to factor in the possibility of such disruptions occurring again in your forecasts for the future.

Remember, while historical data is invaluable in informing your assumptions, it should not be the only factor considered. Future conditions may not mirror the past, especially in rapidly changing markets or industries. Therefore, your assumptions also need to incorporate your personal understanding of the market, industry trends, and your strategic plans.

Illustration: Update financial model

Tip #4: Regularly Update Your Model with Your Most Recent Data

A financial model is a living, breathing document, and updating your projections is a vital part of the process. As your business evolves, more and more data becomes available, and your forecasts need to reflect this to remain accurate and relevant.

Here’s why and how you should regularly update your forecasts:

Incorporate New Data
As your startup operates, it will generate more data. This new data can provide updated insights into trends, growth rates, customer behavior, and other critical factors. Incorporating this data into your forecast will help enhance its accuracy and relevance.

Adjust for Strategic Changes
When your business makes a significant change - such as launching a new product, entering a new market, or altering its pricing strategy - it’s important to account for these changes in your forecasts.

Enhance Decision-Making
Regularly updated forecasts provide more accurate, timely information. This allows you to make better-informed decisions and respond quickly to opportunities and challenges. An up-to-date forecast enhances your agility and competitiveness.

Reflect Changes in Market Conditions
Markets are dynamic and conditions can change rapidly. Economic fluctuations, shifts in consumer behavior, changes in your competitive landscape, and new regulations can all significantly impact your performance. A regularly updated forecast lets you see how these changes are impacting your business and adjust your strategies to respond quickly.

Improve Accuracy Over Time
Regular updates allow for iterative improvements to your financial model. Every time you update your data and compare your forecast against your actual performance, you can identify areas where your projections are high or low. Adjusting your assumptions to reflect your reality keeps your long-term projections as accurate and useful as possible.

Updating your forecasts is a continuous process, not a one-time event. Here at Forecastr, we update our Actuals every month, and we work with our customers to make sure their models are up-to-date as well.

While the frequency of updates might vary depending on your industry and the nature of your business, a good rule of thumb is to update your model quarterly at a minimum. Your model can get stale as your data ages, and this damages your ability to understand your business’s current performance and make informed decisions about how to proceed.

Illustration: Puzzle with one piece missing

Tip #5: How to Handle Missing or Unreliable Data

Not all companies will have a wealth of historical financial data. You might be too early to have accumulated significant data, or you might have data that is marred by inaccuracies or inconsistencies. Don’t worry, this is perfectly normal for early companies, and there are ways to work around it.

Here’s how you can handle missing or inaccurate data:

Use a Bottom-Up Forecasting Approach
When you don’t have data, it’s tempting to be overly optimistic about your forecast. You might think, “Well, the total market is X, and we can probably capture Y% of it.” Never do this. Top-down forecasts like this are a big red flag for investors.

Instead, gather up every bit of data that you do have, and start building your projections from the bottom up. You might have to get creative to fill in some of the blanks. Just be transparent about where your data came from and most investors will appreciate your attempt to use a bottom-up approach.

Lean on Industry Data When Necessary
If you lack historical data for a particular metric, try to find industry averages and benchmarks from reputable sources. Use your gut, and the data you do have, to determine whether your current performance is higher or lower than the average, and do your best to create an accurate and objective assessment. Of course, this is basically an educated guess on your part, and you should be transparent about that.

Test Different Scenarios
Modeling alternate scenarios is another way to work around missing metrics. Create different forecasts with different assumptions (like high, moderate, and low performance) for a missing metric. You’ll have a realistic range of outcomes, and over time you’ll begin to see where your actual performance falls within that range.

Evaluate and Improve Your Existing Data
This might not sound helpful at first, but the best way to fill in missing data is to build a financial model and start tracking your performance as soon as possible. You only need a few months of reliable data to build a realistic forecast.

If you don’t have all the metrics on day one, put processes in place to start capturing that data right away, and accept that it’s going to take a few months to accumulate the data you need. This approach will create the best outcomes for you in fundraising and in terms of making informed decisions for your business.

Making the Most of Your Data

Every startup and SMB, by definition, is a venture into the unknown. But that doesn’t mean that you have to navigate your business blindly. Your historical financial data can shed light on your most likely future, providing valuable insights you can use to guide your decisions.

Remember that your historical data is just one piece of the puzzle. A financial model gives you the ability to consolidate your data and factor in other elements like market trends, the competitive landscape, and your personal understanding of your business model. If historical data is the missing piece in this puzzle for you, start collecting it today to create realistic forecasts as soon as possible.

At the end of the day, financial modeling isn’t about predicting the future with 100% accuracy. It’s about reducing uncertainty, preparing for the future, and making data-driven decisions to steer your business toward success.

If you want someone to help make this happen, reach out to Forecastr today. We built the best financial modeling platform on the market, and every customer works with a dedicated financial analyst to make sure their model is accurate, useful, and reliable.


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