How can you use forecasting to stay ahead of emerging trends in your industry?

1 What is forecasting?

Forecasting is the process of using historical and current data, assumptions, and models to predict future outcomes. This can help you answer questions such as how much revenue you will generate in the next quarter or year, what products or services to offer to your target segments, how competitors will react to pricing, promotions, or new features, and what risks or opportunities may affect your sales performance. Forecasting can be done at different levels of granularity - such as by region, channel, account, or product - and for different time horizons - such as short-term, medium-term, or long-term.

3 How to use forecasting?

To use forecasting effectively, you need to follow a systematic and consistent process that involves four steps. Firstly, you should collect and analyze data from various sources such as your CRM, market research, industry reports, and customer feedback. This data should be used to identify patterns, trends, correlations, and outliers that can influence your forecasts. Secondly, you should build and test models such as linear regression, exponential smoothing, or machine learning algorithms. These models should be tested for accuracy, validity, and reliability by comparing them with historical data and actual results. Thirdly, you should generate and evaluate scenarios that reflect possible future outcomes based on various assumptions and inputs. These scenarios should be assessed for their likelihood, impact, and implications for your sales operations. Lastly, you should communicate and update forecasts to your sales team and other stakeholders in a clear way. You should explain the assumptions, methods, and limitations of your forecasts as well as how they support your sales strategy. Additionally, forecasts should be updated regularly by incorporating new data, feedback, and changes in the market.

5 What are some common challenges for forecasting?

Forecasting is a difficult and imprecise science, often requiring complex assumptions, uncertainty, and data availability. Common challenges include data quality and availability, model selection and validation, scenario generation and evaluation, and communication and alignment. Data may be missing or outdated, models can be biased or overfitted, scenarios can be unrealistic or unreliable, and expectations can change or conflict. It is important to consider these challenges when forecasting in order to ensure accurate results.

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?