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An important component of performance measurement is represented by the data collection capability. However, when applied in the organizational context, this process is neither easy, nor lacking obstacles, as practitioners often discover.
“We can’t ignore facts. We can’t ignore data.” – Barack Obama, former President of the United States
In most cases, in small businesses within the service sector, the lack of managerial education of entrepreneurs negatively impacts the quality of the entire managerial process.
This is one of the reasons for employing modern methods of management in small companies from the third sector, in order to obtain qualitative management, even though in some specialty researches, these methods are recommended only for managers of large enterprises or businesses.
Whether a business is considered a success or not is largely determined by its financial performance which is measured via a number of financial KPIs.
So knowing that, how can we detect which are the truly “Key” indicators, i.e. those Critical Factors or Key Value Drivers that, if they change (for whatever reasons) they produce a chain reaction that leads to a material change in earnings, returns and ultimately the value of the company.
Let me suggest the two ways in which you should look at KPIs, and if you’re still confused, you can look at how Visa measures its key value drivers.
What are the EBITDA margins, operating profit (or EBIT) margin or net profit margin and the top line growth? Cash Conversion Cycle, Working Capital ratio, Current Ratio, etc. As long as the benchmarks are met, management is usually content; this is mostly looking at historical data aka in the “review mirror”.
Which potential triggers, aka KPIs or Key Value Drivers – if they change, could make an impact on the stock price?
If we look at Table III – through scenarios and sensitivity analysis, one can detect those key factors that are critical to the overall value of the company, i.e. if they change, they produce a material effect on the value.
In Table I, besides the data shown in historical, we can see the current and forecast form. As when driving a car, one does not look in the review mirror but seldom, similarly when analyzing data and trends, one looks in the past to better grasp the capability of the company and be able to forecast. As the road ahead is never as the one behind, other parameters have to be considered for a more accurate forecast.
In financial terminology, Business Intelligence (BI) is using and looking at historical data while Business Analytics (BA) is looking forward. BI is important to improve one’s understanding of business based on past results, while BA tools assist one better understand what might be going to happen.
Through trial and error, using sensitivity analysis (as shown in Table III), one detects the critical factors/key value drivers for a company. Table IIIa, or even IIIb, IIIc and so forth, is shown empty since it could be filled with data that results from the variation of any two other parameters.
Identifying materiality is often a complicated and time-consuming process, but it is imperative for an analyst to narrow down the list of factors to those that may be critical and which are worth monitoring. I suggest that materiality can be identified by common sense and quantified by performing various scenarios and sensitivity analyses.
The analyst should develop an accurate financial model, with a correct interrelation of the elements in worksheets, i.e. inputs, balance sheet items, income statement components, depreciation and investment, debt, working capital assumptions and cash flow. Only by having implemented a reliable financial model can one correctly capture when a slight variation in KPIs produces an impact on company value.
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Great (equity research & financial) analysts spend 80% of their allotted research time on analyzing and forecasting only a few of the factors which are likely to impact the value of a company/or “move” their stocks.
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