Eight forecasting best practice tips

A good forecasting process should allow organisations to have a better understanding of where they are headed based on current actions and plans.

With this visibility, finance and business leaders can make and enable better decisions such as identifying and acting on opportunities for growth or risks to strategic plans, the reallocation of resources throughout the year, as well as how to ensure cash needs are met. There is no single best practice to achieve this – a good process depends on the characteristics of the organisation and sector as well as how the forecast is being used.

However, there are a few principles that can improve the accuracy and efficiency of the forecasting process, such as:

1. Integrate

Integrated forecasting, which includes both operational and financial data, drives cross-functional collaboration and business ownership of the forecast. A clear, standardised process for updating the forecast, alignment on cycles assumptions and metrics allows business leaders to make more informed, agile decisions. Where relevant, changes in the external environment should be monitored and included.

2. Driver-based metrics

Driver-based metrics are quicker, easier and more consistent, and they enable leaders to focus on areas where insight can actually improve business performance and also facilitate scenario analysis.

3. Automate

According to the CFO1, FP&A teams typically spend 46% of their time manually collecting, combining, cleaning and manipulating data. Finance teams should aim to eliminate this task, so that actual data is readily available. Automated forecasts allow stakeholders greater time for collaboration and value-added discussion, with adjustments to the forecast being captured along the way. It is effective in reducing bias in the forecast, and allows issues to be uncovered earlier, giving the organisation time to act.

4. Iterate group discussions

Budgets are often put together at the very last minute to meet artificial deadlines. However, the best budgeting process narrows broad goals into specific business plans and metrics through iterative group discussion, but only after initial metrics and numbers are generated by the groups responsible for each area. Best practice processes promote constructive communication and provide a narrative to support the numbers.

5. What-if scenarios

Best-in-class forecasts allow multiple models (including machine learning-based) to be evaluated for best fit and automatically improved over time. This enables teams to consider multiple, simultaneous options, reforecast in minutes (not days), and enable better decisions.

6. Consider the budgeting cycle

Appropriate time horizons vary by sector, but 12 – 18 month detailed (feeding into a 3 – 5 year long range) forecast is typical. Traditionally forecasts would only extend to year-end, but many organisations have adopted rolling forecasts, which extend the time horizon by 1 period with each forecasting cycle. When combined with a driver-based approach, organisations are able to achieve earlier visibility of future impacts of current actions and events (as well as early view of budgets).

7. Adopt analytics tool

Using analytics technology can greatly improve the budgeting, forecasting and performance management capabilities of an organisation. Analytics speed up the decision-making process by making data more accessible. It also highlights trends and exceptions, fosters a common understanding of the various drivers of performance and their relationship to future outcomes, and helps develop new insights based on the extensive use of data, predictive modelling, and statistical and quantitative analysis.

8. Include non-financial data

When preparing the forecast, it’s worth considering the impact of non-financial data such as customer conversion and retention rates, customer support tickets etc. CFOs and senior executives who make good use of non-financial data are able to forecast within 90% to 95% accuracy. They’re also twice as likely to be able to forecast beyond a 12-month window compared with execs not using nonfinancial data.

For further information, including the differing transformation options, download our guide