The use of predictive planning is increasing fast, in large part because of the great demand of easy-to-use modern financial planning tools. By working with predictions, the work with budgets and forecasts will be made more efficient, and the quality of the work will improve. In this article I give you 5 tips of how to successfully implement predictive planning in your forecasting process.
A financial budget or forecast process is often a mix of making a plan (what do we want to happen) and an actual forecast (what can happen). Predictive algorithms can help make both these scenarios more accurate by leveraging historical data. Two examples of this are the periodisation of a forecast down to months, weeks or days and deriving more accurate trend curves adjusted for past external effects unlikely to be repeated in the future. It can also be very helpful in clearly showing potential faults in a forecast that are based on incorrect assumptions.
For any business which wants to create a more automated and value-creating planning process. With predictive models, the work with budgets and forecasts will be made more efficient, the quality of work will improve, and the outlook can be increased. You will gain better insights, reduce the risk of manual errors and it will also enable you to work more frequently with rolling forecasts.
The use of predictive planning is increasing fast, not least because of the emergence of easy-to-use modern financial planning tools. According to an international survey* 11% of organisations use predictive functions in their financial planning, and a staggering 57% plan to implement it in the next few years. The early adopters have proved the worth of the technology, 83% of those organisations perceive their financial forecasting to be great or good compared to 53% of average organisations. With such clear advantages, the question then becomes how you can leverage these predictive functions in your financial planning?
When implementing predictive planning it’s usually a great opportunity to also reimagine the planning process. The immediate effect will often be time saved for key persons involved in the process, especially as the predictions are often tied to various levels of automation. Look at the bigger picture early in the process and decide what this saved time should be spent on. That can in turn lead to valuable insights when designing the solution.
Predictions tied to automation can enable both more frequent forecasts as well as a higher level of detail. Take these opportunities in to account but stay focused on what the business actually needs and avoid the trap of implementing very frequent highly detailed forecasts just because it can be done.
All predictive models approximate the reality more or less accurately, but it’s important to remember that the goal is not to develop the model that delivers the highest possible accuracy. Instead, the goal is to develop the most useful model that will add most value to the financial planning process.
This might seem like a contradiction, but forecasters will typically value and use a predictive model more if they understand it. A model that strives to maximise accuracy often becomes a “black box” only understandable to data scientists. For an FP&A expert tasked not only with creating a forecast but also to be able to answer the question “Why?” such a model is less than helpful.
On the other hand, basic predictive models, while easy to understand, might lack accuracy. The challenge is to find the correct balance between accuracy and usefulness, while making sure that the users fully understand how the model works.
It´s easy to use prediction and automation to centralise the process, but this is in most cases a mistake. Instead design clear and easy to use predictive models and involve key stakeholders in the organisation.
This has two clear advantages. First it creates accountability and a sense of ownership over the data which in turn drives a commitment to achieve or beat the forecast. Second these key stakeholders with their intimate knowledge of their respective fields can often give valuable input to the predictive model, such as if a store will be closed for renovation, or even point out when the prediction is misleading and might need tweaking.
Naturally, qualitative data is the prerequisite for a successful predictive planning implementation. Generally speaking, a minimum of 4 years of historical data is required to accurately detect trends and seasonal patterns.
As is often the case this amount of historical data is not available for every key KPI, at least not with sufficient data quality. In these situations, an incremental approach is recommended, start with implementing your predictive models where the data is available while at the same time expanding your data capture process to enable prediction of additional metrics in the future.
Don’t try to implement predictive functionality for every part of the forecast at the same time, this will only risk delaying any added value for years while waiting for all the necessary data to be collected and verified. Instead, find a clear use case within the organisation where predictive planning could add real value and where the necessary data is already at hand, for example a significant KPI, an income stream or a cost measurement. Start with implementing predictive algorithms for this case. This way you will quickly add some value to your process while at the same time learning valuable lessons. This approach will also let you evaluate the potential of predictive planning and decide how you want to apply it while striving to achieve your optimal planning process.
We are still in the early days of the journey towards automated predictive financial planning, but it’s becoming clear that this approach represents a paradigm shift in how to plan a business. At this stage my recommendation is clear, it’s time to start experimenting. Try out different approaches to predictive planning and gain first hand experiences in how they work and which value they can add to your organisation. Aim for limited implementations and proof-of-concepts at first and go onwards from there with more comprehensive solutions. It´s an exciting journey which we look forward to continuing together with our customers.
*FP&A Trends 2021 survey “Planning and forecasting in times of high uncertainty”
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