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What forecasting techniques can help your growing company succeed?

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Forecasting is a vital skill for any company that wants to grow and succeed in the competitive and dynamic market. Forecasting helps you plan your budget, manage your cash flow, set your goals, and evaluate your performance. But how do you choose the best forecasting techniques for your company? In this article, we will explore some of the most common and effective forecasting techniques that can help you improve your decision-making and financial management.

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Lisa David
Hanan Hasbullah

Senior Accountant @ Coreo Real Estate | Head of Finance

1 Qualitative forecasting

Qualitative forecasting is based on expert opinions, market research, surveys, or intuition. It is useful when you have limited or no historical data, or when you want to capture the impact of new trends, events, or scenarios on your demand or sales. Qualitative forecasting can help you identify opportunities, threats, and customer preferences, as well as generate new ideas and insights. However, qualitative forecasting can also be subjective, biased, or inaccurate, so you should always validate your assumptions and test your results.

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Hanan Hasbullah

Senior Accountant @ Coreo Real Estate | Head of Finance
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Qualitative forecasting relies on expert judgment and subjective assessments. For example, a growing tech company might use qualitative input from industry experts to anticipate market trends and potential disruptions, helping shape strategic decisions in the absence of extensive historical data.

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2 Quantitative forecasting

Quantitative forecasting is based on numerical data, statistical methods, or mathematical models. It is useful when you have sufficient and reliable historical data, or when you want to apply a consistent and objective approach to your projections. Quantitative forecasting can help you measure your past performance, identify patterns and trends, and estimate future outcomes and uncertainties. However, quantitative forecasting can also be complex, costly, or misleading, so you should always select the appropriate methods and parameters and evaluate your assumptions and limitations.

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Hanan Hasbullah

Senior Accountant @ Coreo Real Estate | Head of Finance
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Quantitative forecasting involves the use of numerical data and statistical models to predict future trends. It relies on historical data and mathematical calculations to make predictions. For instance, a growing company might use quantitative forecasting to analyze past sales data, identify patterns, and project future sales figures. This approach provides a data-driven perspective, offering a more objective basis for decision-making in a dynamic business environment.

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3 Time series forecasting

Time series forecasting is a type of quantitative forecasting that uses historical data to predict future values based on the assumption that the past behavior will continue in the future. Time series forecasting can be divided into two categories: univariate and multivariate. Univariate time series forecasting uses only one variable, such as sales or revenue, to generate forecasts. Multivariate time series forecasting uses multiple variables, such as price, cost, or seasonality, to account for the relationships and interactions among them. Time series forecasting can help you analyze your historical performance, detect trends and cycles, and forecast short-term or long-term outcomes. However, time series forecasting can also be affected by outliers, noise, or structural changes, so you should always monitor and update your data and models.

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Mohamed Aly

Head of Internal Audit, Member of Strategic Committee, Risk and Compliance Committee.
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Time series forecasting involves analyzing historical data to make predictions about future trends. It uses past observations and patterns to forecast future values, making it valuable for businesses, especially in finance consulting. Time series methods include moving averages, exponential smoothing, and advanced techniques like ARIMA models. This approach helps in anticipating financial trends and making informed decisions for your growing company.

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4 Causal forecasting

Causal forecasting is another type of quantitative forecasting that uses historical data to predict future values based on the assumption that there is a causal relationship between two or more variables. Causal forecasting can be divided into two categories: regression and simulation. Regression forecasting uses statistical techniques, such as linear or multiple regression, to estimate the relationship between a dependent variable, such as sales or revenue, and one or more independent variables, such as price, advertising, or income. Simulation forecasting uses mathematical techniques, such as Monte Carlo or discrete event simulation, to model the behavior of a system or a process under different scenarios or conditions. Causal forecasting can help you understand the factors that affect your performance, test the impact of different strategies or policies, and forecast complex or uncertain outcomes. However, causal forecasting can also be challenging, time-consuming, or inaccurate, so you should always verify the validity and significance of your relationship and scenarios.

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Hanan Hasbullah

Senior Accountant @ Coreo Real Estate | Head of Finance
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Causal forecasting explores cause-and-effect relationships between variables for predictions. For example, a growing business might assess how changes in marketing spending impact sales. This method enhances decision-making by understanding the factors influencing outcomes.

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5 Scenario planning

Scenario planning is a type of qualitative forecasting that uses a combination of analysis, creativity, and intuition to create multiple plausible scenarios for the future based on the uncertainties and drivers of change in your environment. Scenario planning can help you explore the possible outcomes of your decisions, assess the risks and opportunities of each scenario, and prepare for the contingencies and implications of each scenario. However, scenario planning can also be subjective, speculative, or overwhelming, so you should always focus on the most relevant and critical scenarios and update them regularly.

Forecasting is not an exact science, but a skill that requires judgment, experience, and flexibility. By using a variety of forecasting techniques, you can improve your accuracy, reliability, and confidence in your projections, and ultimately, your success in your growing company.

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Hanan Hasbullah

Senior Accountant @ Coreo Real Estate | Head of Finance
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Scenario planning involves creating alternative future scenarios to prepare for various possibilities. For instance, a growing company might envision scenarios like market fluctuations or unexpected disruptions. This proactive approach helps in developing flexible strategies, ensuring adaptability in a dynamic business landscape.

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Lisa David
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In my experience, you always need to test that the models are not pulling tricks on you. A great place to start is to create an obvious data set i.e. you're in charge of recommending products for an e-commerce site. You can create a database that's as clear as day, showing which products users buy together and how they rate them. This data is a treasure map for testing, guiding you on what to expect. Then "Back-Test" by using historical data to see if your model can accurately predict past events. If it can't predict the past, it might struggle with the future too!

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Lisa David
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If you haven't done so already, start bringing AI and large language models that can gather data points to help predict what's worth testing. It's like having a magic wand that points you in the right direction. While not everyone has access to these tools, even small steps toward using AI for testing can improve your testing game. You might uncover new ideas and better data or become a better tester.

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What forecasting techniques can help your growing company succeed?

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1 Qualitative forecasting

2 Quantitative forecasting

3 Time series forecasting

4 Causal forecasting

5 Scenario planning

6 Here’s what else to consider

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