Early indicators are a popular tool when it comes to planning and forecasting in business. “Prediction is very difficult, especially if it’s about the future” , as Nobel laureate Nils Bohr once said. Hence, managers and planners desperately search for some early signs that help to improve the quality of the guess about the future.
Early indicators actually can serve for this purpose. However, they should be used with care, since there are some fallacies associated with them. This article gives advice on how to make sense of early indicators and how to avoid the nonsense.
Why early indicators are popular
Early indicators are very popular in the business world. People normally like them because they see a lot of advantages, such as
- Early indicators are perceived to provide some objectivity to otherwise fuzzy planning and forecasting processes.
- They are most often based on external data, ideally from an institution with a very high reputation. That adds trustworthiness to the own predictions and forecasts based on the indicator.
- The external source also serves as an excellent justification, in case something should go wrong: My forecast was excellent, just the external input data failed.
- Last but not least – there actually are statistically relevant correlations between sets of data which occur with a time delay. If you are able to figure out the right pair of data, the correct interpretation of the earlier events may indeed improve predictions for the later events.
Indicators in forecasting
Distinction by perspective
Nouriel Roubini provides a simple framework for distinguishing indictors in the context of business cycles. Such economic variables are used for forecasting the economy. These forecasts, in turn, are used for forecasting the business development. Hence, these characteristics are also applicable for business planning purposes:
- Procyclical variables – they move in the same direction as the business cycle
- Countercyclical variables – they move in the opposite direction as the business cycle
- Acyclical variables – no direct correlation with the business cycle
- Leading variables – the variable changes before the change in business cycle – they indicate what might happen in the future
- Coincident variables – the variable changes at the same time as the business cycle – they indicate what is happening at the moment
- Lagging variables – the variable changes after the business cycle had changed – they indicate what has happened in the past
Early indicators in the context of business decisions
This article discusses the use of early indicators as a basis for various business decisions. In this context, early indicators are various …
- internal and external variables
- direct and indirect variables
- that the business systematically compiles, monitors and analyzes in order to
- get an early indication for trends and developments
- that are relevant for various business decisions
By their nature, early indicators in business are leading variables. Lagging variables, such as accounting figures, only show what has happened in the past. They too may provide signals for action. However, that would be a reaction compared to proactive measures that would be derived from leading indicators.
Use of early indicators in business
Not all indicators are suitable
Many businesses want to use early indicators to support their planning and their strategic decision making. It is essential for them to fully understand how to use such indicators and to know about their shortcomings. It all starts with the identification of suitable and relevant indicators. Only then they will be able to get some value out of this effort.
This article provices more information on how to choose the most appropriate measurements and indicators.
Characteristics of suitable indicators
Many variables may have some impact on the business development. However, not all of them are really suitable for regular use as an early indicator. The ideal indicator should have the following characteristics:
- It is measurable
- Accurate data is available without much time delay.
- Ideally, there is a time series of historic data readily available.
- There is a stable and well understood cause-and-effect-relationship between the variable and the intended result.
- The data can be gathered with reasonable efforts
- They are easy to understand for everyone who is concerned with major business decisions
Note that these characteristics shall apply to both, internal and external variables. Data availability is a special issue. External statistical data is often available only with a considerable time delay or at too large intervals. Internal variables may be difficult to extract from the companies systems or may be difficult to measure at all.
How to identify suitable indicators
Tim OConnor gives some advice on How to use leading indicators to drive business execution. Tim suggests starting with the key outcomes or results your business is working toward.
- What is it you want to achieve?
- What developments you want to have indicated as early as possible?
- How do these developments influence your overall goals and objectives?
These organizational goals should be deconstructed backwards. That means to deconstruct the organization’s core processes and to identify cause-and-effect-chains. As Tim states,
this exercise alone can generate important discussions about how core processes occur in any business, and it can uncover inaccurate assumptions that some leaders or team members may have about how things get done in the organization.
This deconstruction process should lead to some variables that have a direct or indirect impact …
- on the overall goal or
- on major decision situations for the business.
Problems with the use of early indicators for business purposes
Despite all the benefits, it is not advisable to rely on early indicators for business decisions too much. There are some problems that are not easily solved:
- There are few direct cause-and-effect-relationships in business. Organizations operate in a complex, networked world. Business outcomes normally depend on a whole range of variables that have complex interdependencies. The change in one variable may weaken or enhance the effect of the change from another variable.
Users of early indicators have a natural tendency to implicitly assume simple and direct relationships. Interdependencies are easily ignored. They have, however, a significant impact on the quality of the forecast.
- Known interrelations between two variables may change over time. This is even more the case in today’s dynamic business world. Consequently, early indicators may not be relevant forever. Thus, the indicator should be checked regularly and – if necessary – adjusted. I guess that companies don’t do that all too often.
- It is very difficult to identify a suitable indicator for a particular forecasting issue. You not only have to find a matching indicator that has some correlation with your businesses performance (or with whatever you want to have indicated). You also have to understand how exactly those two sets of data are interrelated. Does a 50 % increase in the indicator lead to a 50 % increase in your performance? Or will the increase be 20 % / 85 %? Or will your performance actually decrease? Are there any other factors that have an impact on your performance?
- I have seen top managers who demanded “an early indicator for our business” – meaning one indicator. The problem was that this was a billion-Euro business with a lot of product lines, markets and customers. It would have been difficult enough to find one indicator for each business unit.
- Early indicators are easier to find for business activities in later stages of an industries value chain, such as products for end-users (cars, home appliances, services). If you happen to work in an earlier stage of the value chain (e.g. supplier of raw materials or components) you may find it very hard to find something that happens even before you see an impact in your business figures. On the contrary, your results (i.e. orders, earnings, stock quotes) may serve as an early indicator for your customers and for your customers’ customers.
- Early indicators in use for corporate purposes were developed either by scientists with a mathematical / statistical background or by other business people. In the first case, the indicator has more credibility, since it has a scientific basis. However, this scientific basis often is a complex mathematical model that few non-mathematicians will understand. Thus the indicator will always be a bit of a ‘black box’. If needed, this fact may serve as a good reason to question the results.
In the other case, the indicator lacks that scientific basis. Hence, the indicator could be questioned as ‘just a best guess’ which is not verified.
- Time delay is another issue. Many internal and external data are available only with a significant time delay. The moment they are published you may already see the change that the variable was actually intended to indicate.
We can see that early indicators should be used with care. As Nouriel Roubini states:
The best advice I can give you is to realize that there is an unavoidable amount of uncertainty in the economy. … Perhaps the best lesson you can take from this is that the future is, to a large extent, unpredictable. It’s misleading, and probably dangerous, to assume otherwise, no matter what you pay your economists.
Practical advice for the use of early indicators in businesses
I have worked in the strategic planning and research departments of two different organizations. One is a global player with different products and markets. The other is a specialized financial services provider that serves a particular national market segment. Both companies had the idea of finding early indicators to support their planning and decision processes.
Use a set of variables
I am not a fan of the ‘One-and-only-Indicator-Approach’. I think that such an approach is not even necessary in most cases. If managers ask for an early indicator (in the singular form), they actually require some sort of consistent data which helps them with their business decisions. Most managers really don’t need something as simple as ‘If A happens, B follows and we have to do C’. The managers I met know their industry’s mechanisms and drivers perfectly well.
I developed an information system based on external indicators for one of my former companies. This system has the following features:
- There are four groups of indicators: general economic data, our industries data, our costumers’ industries data, and competitor data
- For each group, three to five relevant indicators were selected. We preferred indicators that are available on a monthly basis, at least quarterly.
- For each indicator, the system provides a short data history (one year back maximum), a forecast (if available) and a brief comment (as appropriate).
- The basic information for all indicators (in form of graphs) is provided on one single page so that users can get a comprehensive view without scrolling of flipping pages.
- For reference, there is the option for a little drill down to more information. This is where the comments and, of course, the historical data is available.
Thus, the company had some sort of management dashboard, showing most relevant impact variables for the business.
Forecasting the change of variables
My information system for early indicates also features forecasts on how the indicators may develop in the next future.
To add such a forecast, there are two options:
- Forecasting specific values (e.g. 2 % increase)
- Forecasting general trends (e.g. will increase slightly)
For many external indicators there are good forecasts available. There are plenty of forecasts for economic indicators such as GDP growth or inflation. If you come down to a particular industry, things might become more difficult. You may be lucky if you find some general assessment such as ‘growing’, ‘constant’ or ‘falling’. I don’t like the idea of having two types of forecasts (figures and trends) in one system. Taken into account the fact that my company already prepares their own trend assessments for many of the relevant indicators on a regular basis, we decided to use just such trend statements in the indicator dashboard. They are easy to visualize with little arrows going up or down. On top, you avoid the discussion if something will go up by 2.5% or 2.7% – which is of no relevance at all in many cases.
Basically, that’s it. I am sure that every good manager who knows his business will be able to make sense of this limited set of data and its development over time.
The idea behind this is not to have one or two early indicators which directly influence business decisions, but to constantly monitor the development of those drivers that are most relevant for our business – provided they are measurable.
Our book recommendations on early indicators
- How to Measure Anything: Finding the Value of Intangibles in Business
Douglas W. Hubbard
This insightful and eloquent book will show you how to measure those things in your own organization that, until now, you may have considered “immeasurable,” including customer satisfaction, organizational flexibility, technology risk, and technology ROI.
- Key Performance Indicators: The 75 measures every manager needs to know
By identifying and describing the most powerful financial and non-financial KPIs, this book will make life easier for you by defining them, explaining how and when they should be used and providing a rich library of KPIs that have been proven to significantly improve performance.
- The WSJ Guide to the 50 Economic Indicators That Really Matter: From Big Macs to “Zombie Banks”
Simon Constable and Robert E. Wright
Unlike other investment handbooks, Constable and Wright’s guide explores the not widely known economic indicators that the smartest investors watch closely in order to beat the stock market–from “Big Macs” to “Zombie Banks.”