According to the Harvard Business Review, the right forecasting technique for a business depends on several factors. These range from the forecast’s context to the availability of relevant historical data.

Most importantly, the degree of accuracy is the most critical factor for producing accurate forecasting models. Often, excessive optimism leads to inaccurate forecasts, resulting in disappointment down the line. Therefore, it’s important to understand the complexity of generating accurate forecasting models.

The following is an overview of how to produce accurate forecasting models.

What Is a Forecasting Model?

Every startup wants to predict the future of their organization accurately. A forecasting model is a tool that helps businesses analyze and interpret data to make predictions about the future.

It includes analyzing past data, considering changing markets, and determining how these changes will affect future outcomes. The forecasting model uses these data points to create a reliable prediction. It can predict:

• Sales and revenue
• Expenditure and profits
• Trends in customer behavior
• Resource utilization
• Market share

Besides predicting future outcomes, the forecasting model also identifies potential problems. It helps businesses devise strategies to address upcoming issues and opportunities.

Types of Forecasting Models

Since there’s no one-fits-all solution for businesses, numerous forecasting models exist. Here are some common ones.

Seasonal Forecasting Model

Some businesses are seasonal. For instance, many shoppers wait for the holiday season, like Christmas, before making their purchases.

A seasonal forecasting model helps companies predict their products’ demand for specific periods. It helps businesses understand the type of resources they need to meet the demand.

Time-Series Forecasting Model

A time-series forecasting model is best used when businesses want to compare present performance with past performance. It helps them analyze the trends in their sales, revenue or customer data over time.

It’s ideal for businesses that want to make long-term predictions, such as predicting sales a few quarters down the line.

Econometric Forecasting Model

An econometric forecasting model identifies and measures the relationship between economic variables. It’s often used to predict economic trends and outcomes, such as GDP. The variables this model considers include:

• Interest rates
• Gross Domestic Product (GDP)
• Employment figures
• Inflation rate
• Producer and consumer sentiment

Demand Forecasting Model

A demand forecasting model identifies customer needs and predicts future demand. It analyzes past customer data, understands customer preferences, and studies industry trends.

Length of Sales Cycle Forecasting Model

The length of the sales cycle forecasting model predicts when a customer will place an order or convert from a lead. It analyzes factors like:

• The number of touchpoints
• Buying behavior and decision-making process
• Knowledge of the company’s offering
• Complexity and duration of the sales cycle

How to Perfect Forecasting Models?

When businesses use the right forecasting models, they can improve their decision-making and focus on achieving more profitable outcomes. Here are some tips to help you achieve this.

Keep Your Records Clean

It’s not possible to make accurate assumptions based on dirty data. Before you start forecasting, make sure to clean up all of your records and inputs so that they are clear and reliable.

Use data cleansing tools to ensure that all numbers are accurate and up-to-date. Also, consider the accuracy of your data sources to make sure you’re getting reliable information.

Choose the Right Forecasting Model

The forecasting model you choose should be determined by your specific needs. Start by looking at the accuracy and complexity of the model, plus its ability to adapt to changing data.

For instance, if you’re a small startup, you may want to look at simpler models such as exponential smoothing or moving average. A SaaS startup will need different types of models, such as the length of the sales cycle or time-series models.

Take Seasonality Into Account

Some businesses are more affected by seasonality than others, so make sure to take this into account when choosing a forecasting model. Look at the data you’re using to ensure it includes seasonal data points.

For example, if you have a fashion business, you’ll want to consider the impact of weather and holidays on consumer buying habits. Likewise, a toy or retail startup might see excessive Christmas or Black Friday sales.

Ensure Inter-Departmental Collaboration

Forecasting yields the best results when everyone in the organization is on board. All departments, including sales, marketing, and finance, should be involved in the process to ensure accuracy and collaboration.

Involvement from every department ensures that forecasts are based on up-to-date, accurate data and incorporate market trends, customer expectations, and sales cycles.

Test & Validate Your Model

It’s important to regularly test and validate your forecasting model to ensure accuracy. Look for discrepancies between your forecasts and actual results, then adjust accordingly.

The best way to do this is to use statistical tests such as the Root Mean Squared Error and the Mean Absolute Percent Error to measure how well your forecasts are performing.

Use External Data When Required

The most common route companies take is to use internal historical data. While that’s essential, you might need external data to finish the puzzle sometimes.

For example, has your competitor introduced a new product or changed an existing one? Has there been a recent expansion in your industry?

Did a new regulation change the market dynamics? Analyzing external data will help you factor in all of these changes and create more accurate forecasts.

Hope for the Best, But Be Prepared For the Worst Too

Every startup wants to stay optimistic, but you should also prepare for the worst. With forecasting models, you get a glimpse into the future and can make informed decisions to reduce risk and uncertainty.

Make sure you factor in a wide range of scenarios, including the worst-case one, when constructing your model to ensure you’re protected in all situations. Here are some examples:

• An unprecedented increase in consumer demand that affects your supply chain
• A sudden drop in customer spending due to changes in the economy
• An unexpected disruption from a competitor entering the market

It Might Be Time to Involve Experts

In conclusion, forecasting for startups is not easy, but if done correctly, it can be a great asset. If you’re feeling overwhelmed or need help getting started, it might be best to work with a professional financial modeling service, such as Numberly.

Our customized approach to each startup ensures that your model is tailored to meet your unique needs. With our expertise, you’ll be able to create accurate forecasts and make data-driven decisions. Contact us today to get started.