Forecasting is a critical element in business strategy and planning. Accurate forecasts allow appropriate investment decisions to be made, whilst poor forecasting can cost you dearly, and in some circumstances even send you out of business.
By its very nature, forecasting is about describing something that does not yet exist; projecting about something that will happen in the future. There is no ‘right’ or ‘wrong’ in forecasting, only being as thoughtful and accurate as possible. No matter what information we use, forecasting involves uncertainty, and some judgements and guesswork. We simply cannot know everything. However, creating quality forecasts is something that every business needs to do.
How do you forecast?
To shift your forecasting from crystal ball to crystal clear, it is important to select the right approach, use it in the right way, and truly understand the value of the outputs.
Selecting a forecasting approach
There are many ways to forecast, but you need to ask yourself some critical questions before you choose your approach:
- What level of data do I have at my disposal? What volume, granularity and accuracy does it demonstrate?
- Who and what am I forecasting for, and what level of accuracy is required?
- What is the cost/return for the level of accuracy that I need?
- What may change significantly in the market?
- What is our strategy?
Based upon the answer to these questions, you can employ a number of different methods:
Qualitative, judgemental or naive methods:
When there is little data, or a low need for accuracy, you can use qualitative methods by simply making a reasoned judgement (best guess).
Simple estimation, especially time series methods:
The next step is to use historical data as the basis for predicting the future (time series) with a judgement call of things that may change moving forward.
Causal methods:
By determining the key underlying factors that drive the market, and breaking the analysis down into the actions and responses on each of these factors, we can begin to predict cause and effect. By using time series, insights and some judgement, we can model the impact of these actions and amalgamate the result into the forecast.
Algorithmic methods:
Uses predictive modelling of Big Data to take causal methods to more precise application. Sophisticated programming algorithms are applied to data mined from a range of sources and used to predict behaviour, which can be used to model a range of possible scenarios and amalgamated over time to provide a forecast.
Probabilistic methods:
By estimating the likelihood that an outcome will be achieved, the value of that outcome can be ‘tempered’ by that probability, which provides a result for forecasting which includes a risk amelioration approach.
Case model method:
Any of the approaches above can be used to model different scenarios (cases), which can then be compared for strategic selection or preparation for external contingencies occurring.
Using your forecast:
The forecast is incomplete without the methodology, models, insights and strategy choices that led to its creation. It is more than just a number. It is a critical input into leadership decision making and needs to be understood in its full context.
To further enhance forecasting in your business:
- Keep politics and ‘fit for result’ forecasting out of your organisation. Creative forecasting may mean that certain pet projects are approved, but they bring false information into leadership decisions. (I have seen $300m in sales ‘magically’ appear in a forecast to allow a project to suddenly deliver a positive net present value).
- Review forecasts versus actual. Hold people accountable for their forecasts, and use them as a learning and review process of both the forecast and the market.
- Train your people in forecasting.
Focus on the logic, assumptions and insights behind the numbers to help make quality business decisions (not just the number).
