Strategy Dynamics Breifing 6: An important distinction: time-periods versus instants in time

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There is an important distinction between time-periods and instants in time. A previous briefing emphasised that ‘performance depends on resources’, but if performance is reported over a period and resources are reported at points in time, we risk comparing apples with oranges!

Most of what companies report about their performance concerns how well they have done during a certain period, the latest month or a financial year for example – how high their sales rate has been, how much profit they have generated and so on. Many companies, however, also track and report how much of certain things they have got at a point in time, such as the number of customers they have. Some even declare such numbers publicly at the end of each reporting period, such as subscribers for cable TV or cellphone companies. We need to be careful about this distinction between total activity over the period as a whole, and the opening and closing numbers of customers who give rise to those totals or averages. This should be a familiar distinction because it has always been necessary when looking at financial numbers – it is exactly the same distinction as exists between the cash flows that have come in during the year versus the cash amount at the end of the year.

This is important to get right when setting out how we hope our strategy will develop. A previous briefing emphasised that ‘performance depends on resources’, but if performance is reported over a period and resources are reported at points in time, we risk comparing apples with oranges.

The figure below shows a scenario in which revenue is driven by customers, and the number of customers changes during the year. The number of customers at the start of the year certainly does not ‘explain’ the year’s revenue on the right, nor the number at year-end. Not even the average of opening and closing numbers will work either. We have to break down the periods of time into shorter chunks – quarters in this figure – to get a more accurate relationship between customer numbers and sales revenue.

tracking sales revenue as customer numbers shange

Even magnifying the timescale by this amount may not be enough, depending on how rapidly things are changing, and how accurate our understanding of revenues needs to be. Just as cash levels may move up and down within a quarter—indeed, from day to day—so may numbers of customers. The more quickly things are changing, and the more precise the analysis needs to be, the shorter the time periods for which data must be assessed. The time periods must be short enough that the change in the resource level during each period is small, relative to the quantity of the resource at the start of the period.

Until next time…

 

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People are sometimes surprised at the importance I put on tracking the numbers of key resources – especially customers and staff – on a regular basis, and struggle to find the data. But we certainly expect our head of Finance to be on top of how much cash we have from period to period, so why would we not want to be equally in command of the resources that drive that cash.

Strategic Management Dynamics book cover If pushed, most organizations can identify numbers of staff at a point it time, but it may require them to go back to their raw payroll data to find how that number has changed over time. Customers pose much bigger problems. First, many companies are not in direct contact with the ultimate consumer of their product, perhaps because sales are made through stores or dealers, so have to rely on research to get an estimate. Even when we do deal directly with them it can be surprisingly hard to determine how many real customers we have. You’ have thought that a bank would know how many customers it has, because everyone has an account, but lots of those will be inactive. For other cases, a customer may remain on database long after they last gave the firm any sales, and now have no intention at all of buying again.

Early on in many projects, then, we have to identify exactly when a customer has actually ‘left’ and look back over time to understand how the number of real customers has changed.

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