Companies often want to know how they compare to their peers, especially when they suspect they are leading or lagging.  For example, perhaps you want to know how your performance metrics compare to others’ in the industry — if yours are better than the industry norm, then you might infer you are doing things better; if worse, you might use that as internal motivation for improvement.

As an example, consider margins.  Margins are a function of price paid by the customer (after all adjustments, rebates, discounts, etc. are taken into account), and the cost to produce (including overheads and cost of capital / depreciation).  There are some challenges to building a set of margin data to compare to, beginning with estimating margins in the first place:

  • Actual price paid is surprisingly difficult to estimate, even for many companies themselves (especially those which rely on “back end money” for sales incentives) — and estimating realized prices for other companies is virtually impossible.
  • Actual cost to produce is also difficult to estimate for other companies (although relative costs can be estimated, with enough effort).
  • Margin estimates are further complicated by who pays shipping and duties — and this often varies by customer and market.

Consequently, most industry margin estimates delivered by analysts, academics or consultants are developed from aggregate financial data (as reported by public companies, at least) for companies “in the same industry.”  That way, one needn’t know anything about companies’ pricing cascades, just how it all nets out.  Yet SIC classes are known to be notoriously unreliable for grouping like-with-like, casting into doubt whether most groupings are actually representative of the industry in question; and there are considerable challenges in using aggregate financial data:

  • If you use total operating margins (for example), you have abstracted away from needing to understand competitors’ price cascades; but you don’t know what is “in” the reported operating margin.  Companies report very differently (even in compliance with GAAP), and they often have good reasons to bias their reported metrics in one way or another (bonus metrics, for example).
  • Not only must different companies’ reporting be harmonized (usually by the reporting service, and in ways you may not agree with or even discover), but that aggregate number rolls up:
    • Customer types and sizes — yet customer mix varies between companies, even those in the same industry.
    • Product types — different types of products carry different margins, and product mix generally varies across companies in the same space.
    • Business models — some companies price the hardware for less, then make their margins on services and/or parts; if you look at operating margins for products only, you may be misled; yet if you look at margins in total, you may be including unrelated businesses, because…
    • Lines of business — firms (especially larger ones) rarely operate in the same set of businesses as all their peers — and many companies do not break out detailed metrics by line of business.

Controlling for all these factors is very difficult (if not impossible) across the broad range of companies which would be required to establish benchmarks for an “industry norm.”  And the more narrowly one defines the industry, the less likely metrics are available.

So, what can you do?  Pick a small number of key competitors and develop estimates for them based on a combination of their reported financials and targeted research into their particular margin drivers (product mix, customer mix, business models, etc.).  This won’t be an industry benchmark (because you haven’t surveyed the industry) but it will tell you what you really need to know — which is how you might differ from key competitors.  Developing this information will require some work and informed business judgment; it won’t be available in someone’s database.

Most important, if you are comparing your performance metrics to others’, you need to understand — and adjust for — how the drivers of those metrics differ between your business and theirs.  Only then can you infer (for example) that your operations or pricing are better or worse than others’.

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