How can you use market forecasting to select the most promising geographic markets?

1 Why forecast markets?

Market forecasting can be a valuable tool for making informed decisions about where to invest, expand, or enter new markets. It can also help you to avoid costly mistakes, such as entering a saturated or declining market, or missing out on a growing or emerging market. Market forecasting can help you answer questions such as which markets have the highest potential for your products or services, how the market size and growth rate will change over time, what the key drivers and barriers of market demand and supply are, how the competitive landscape and customer preferences will evolve, and what the risks and opportunities of different market scenarios are.

2 How to forecast markets?

Market forecasting involves four main steps: defining the market, collecting data, building models, and analyzing scenarios. To begin, you must specify the scope and boundaries of the market you want to forecast, such as the geographic region, product category, customer segment, and time horizon. Data must be gathered from reliable sources like market reports, industry publications, surveys, interviews, and online databases. To create mathematical models that describe the relationships between market variables such as demand, supply, price, cost, and profit, statistical methods and techniques must be used. The models must be tested and validated with historical data and expert opinions. Finally, the models should be used to generate and compare different market scenarios like best-case, worst-case, and most-likely scenarios. The implications and impacts of each scenario on business goals and strategies should then be assessed.

3 What are the challenges of market forecasting?

Market forecasting is far from an exact science, and it presents various challenges that can affect the accuracy and reliability of your forecasts. These include data availability and quality, model complexity and validity, as well as scenario uncertainty and variability. You may not have access to enough or relevant data, or the data may be outdated, incomplete, or inaccurate. Additionally, you may not be able to capture all the factors and interactions that influence the market, or your models may not reflect the current or future market conditions. Furthermore, you may not be able to anticipate all the possible events and outcomes that can affect the market, or your scenarios may not account for the changes and variations in the market environment.

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?