Benchmark figures, on their own, have modest value, such as the average number of patent lawyers among a group of pharmaceutical law departments. Of more value to general counsel are ratios of such figures, normalized figures such as the median of the number of patent lawyers for each company divided by the company’s revenue. But the most value to come from benchmarking is to learn correlations.

For example, there is a correlation between R&D spending and the number of patent lawyers employed by a company. To illustrate with hypothetical numbers, for every additional $200 million of R&D spending, a typical law department might add a patent lawyer. That would be a positive correlation, where one metric rises a certain amount as a second metric rises by a certain amount. (A negative correlation means that one number falls when the other rises.) Generally speaking, there is an intuitive explanation for a strong positive or negative correlation. On the illustration, research investment inevitably leads to patentable inventions which in due course justifies more patent lawyers.

Another possible correlation based on benchmark metrics might be that the higher the worldwide annuity payments for a patent, the higher the legal expenses associated with that patent. One possible explanation for the correlation is that older and more valuable patents–as suggested by the investment a company makes in maintaining the patent broadly over time–are subject to more commercial opportunities and legal attack.

Simply to know that there is a linear relationship between two metrics gives a general counsel useful insight. And you can calculate correlations between ratios, such as between R&D spending as a percentage of revenue and patent lawyers as a percentage of all the lawyers in a department. To know how much a change in one metric leads to a change in a second metric multiplies the value of the two metrics on their own.

If analysis reveals no correlation between two metrics, even that can help managers of law departments. For example, what if it turns out that there is no statistically meaningful correlation between the average number of lawyers per office location and total legal spending? That determination would suggest that it doesn’t matter about the number of in-house lawyers at various locations.

Everyone who becomes comfortable with correlations must bear in mind that correlation does not prove causation. Just because the degree of regulation of an industry correlates with higher legal spend per billion of revenue does not mean that regulation is the sole cause of those legal expenses. There may be other factors at work such as political influence, case-law development, maturity of the industry, or specific legislation. Sometimes a third factor accounts for the close relationship between one metric and another, such as concentration in an industry.

More advanced than correlations that show the associated rise and fall of two metrics is a statistical technique called multiple regression. Multiple regression looks at several metrics and determines their relationships among each other and to a particular result. For example, with enough data about employment discrimination lawsuits, multiple regression could tease out the relative influence on the total cost of resolution of such factors as the venue of the case, the size of the plaintiff’s law firm, the particular claims at issue, and so forth.