In times of uncertainty, many different moods can prevail in both the boardroom and the chief executive officer’s (CEO) office. Some businesses batten down the hatches, cutting investment and slashing costs; others withdraw to their core strengths, divesting assets and focusing on cash. Some simply continue as normal and hope that competitors are more badly affected.
The point is, however, that all of these reactions are based on gut feeling and boardroom groupthink, rather than actual business reasoning. However, in challenging circumstances there is no substitute for objective analysis – anything else is about as useful as voodoo or black magic.
The foundation of effective analysis is a clear understanding of what drives value in the business. Most organisations have this – the problem is that the understanding tends to vary according to who is asking the question. It is not uncommon for senior management and finance to have differing ideas about what contributes to good performance; it is very common indeed for sales and operations to give answers that are at odds with their senior colleagues.
When this discordance exists, it can be very difficult to define what success actually entails – let alone how to achieve it – and the usefulness of analysis is always limited. What is needed instead is a clear picture of how the economy; the market; customers; operations; and financing each contribute to the business’s strategic goals. If contributions are sought from across the business, then a business driver diagram, such as the one in Figure 1 below, is a powerful means of achieving consensus and understanding; not just at the top of the organisation, but throughout its structure.
Appropriate data transformations
Even the most sudden and dramatic of economic changes is unlikely to make itself felt fully within a single month. Most external factors affecting a business will do so subtly, with a gradual impact over a longer period of time so that the effects are only felt long after the external environment has changed. Most businesses monitor external influences, but their effects can be hard to divine from the typical monthly reporting cycle. A simple change is to calculate not only the difference between budget and actuals, but to compute a cumulative variance as well. As can be seen in Figure 2 below, a developing shortfall in revenue becomes much clearer than it would appear from the more conventional comparison only, as is the work needed to make good the difference.
A forecast that can be relied on is extremely reassuring. However, forecasts generated by conventional means – that is, by soliciting predictions from the sales team – do not have this quality. They are particularly unreliable during times of uncertainty in the market, since the optimistic are likely to stick their heads in the sand while the pessimistic predict a doomsday scenario.
Creating a better forecasting process is not an effective emergency reaction to anything, but organisations with an objective forecasting process in place are considerably more resilient when threatened with market disruption or economic change. A forecast is the basis of much financial analysis, and a flawed forecast will lead to flawed decision-making.
While a better process cannot be created overnight, it can be developed within a matter of months. Using the historical differences between the sales pipeline and results as a basis, it is a relatively simple task to create a basic predictive algorithm that takes into account the seasonality of the business. It is then possible to create factors that account for other key influences, such as changes in competitor activity.
The visualisation in Figure 3 below shows how the difference between pipeline and actuals translates to a short term forecast, and the second bar chart in Figure 4 shows the accuracy of this over a long-term test period. Because this is an objective measure, it is not subject to the same inaccuracies as the conventional method.
Insight is useful, and often reassuring – but it can only go so far. Rare is the business that can come through adversity without making some significant changes, but such action must be based on fact lest it do more harm than good. This calls for analysis that is focused on identifying specific operational changes that will have a positive financial impact.
While a business driver diagram is an indispensable tool for promoting understanding in the business, working out which metrics and key performance indicators (KPIs) are having the most influence takes further work. At the operational level, there is rarely any sort of mathematical constant at work, so data analysis that looks at correlations across different dimensions of the business is hugely important.
For example, Figure 5 below shows a strong correlation between staff attrition and reduced productivity. Productivity can be key to competitiveness in a challenging market, and staff attrition is something that can be addressed with a range of management actions.
In difficult times, it can be hard to maintain a clear-headed and rational outlook. Yet this is when a data-driven, objective approach to management is more important than ever. Choosing the right analysis can make the goal easier to achieve and help businesses continue to thrive, even in more challenging circumstances.
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