Order books, sales pipeline analysis, and market data


RR BWRachel Russell, Head of Client Service, writes on industry

The finance industry has a curious relationship with the quarterly GDP figure – on the one hand, quick to point out the measure’s many flaws, on the other, never quite brave enough to ignore it.

The monthly Purchasing Managers’ Index (PMI) figures do not receive quite the same level of attention but, in many ways, they are a good deal more informative as a source of market data. That’s not just because they afford a more granular insight at an industry level, but because of the type of data that they include. From an analytical perspective, GDP is backward looking – although PMI figures also tell us about the month just gone, they also include data that can be used for forward looking analysis, and that is considerably more valuable.

The last couple of days have seen PMI data from the UK construction and manufacturing industries, as well as the comparable ISM figure on US manufacturing. Notable in coverage of all of these was the focus on the state of order books across the industries in question. That is a very powerful piece of predictive data (and incidentally, in this case, all three reports had positive implications in this respect).

In a company context, the order book and sales pipeline is one of the most powerful sources of data for forward looking and predictive analysis. It is also one of the simplest but, in spite of that, many large organisations simply do not exploit it to its full potential. Particularly in the construction and manufacturing industries, where altering capacity in response to market changes is a relatively cumbersome process, the ability to spot risk and opportunity as it develops is a potent competitive weapon.

This starts with a very basic financial integration – that of actuals, budget and forecast over a series of months. Many organisations stop at that, and trust that the total estimates made by divisional managers will be accurate enough to offer a credible total. The problem is, often those divisional managers have an interest in inflating forecasts to make up for any shortfall against the yearly budget. As long as the forecast still tallies with the year-end target, the discrepancy may not be noticed for some time.

However, if we integrate data from the order book (perhaps from contracts management software) and on confirmed prospects (say, from Salesforce.com), then the gap between that total and the forecast (i.e. work to win) offers a very clear indication of how credible the forecast is. Market data, such as the PMI figure, can then be used to give an industry benchmark, and indicate the probable direction of new orders. That gives senior management the opportunity to query a forecast early, and to take action to correct any shortfall against the budget.

That is much more useful than a backward-looking analysis, and much more effective than reviewing forecasts at the end of a given period. Armed with this insight, a company is much better able to manage its capacity in the response to market fluctuations, and maintain margin whatever the performance of the industry as a whole.

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