I recently read a piece in a well-known HR publication that made me wince.
To summarise, it had published a tall story claiming that shorter men earn £1,500 less than their non-cuban heel wearing counterparts. I stand at 5'9" and an all-important ½, so obviously, as a man mountain, I could have simply flicked straight past the article without a second thought, however it made me stop and wonder – where, and how on earth have they managed to established this fact?
Well it turns out the tabloid-esqe headline had been derived from a study conducted by a reputable university. The article (no larger than a snippet) explained that upon examining weight, height and earnings data, researchers had found that men shorter than the national average (5'9" for those wondering – I told you the half was important) earn around £1,500 less than those above it. But that was it. The article failed to explain where and how the data was collected by the researchers.
This never sits comfortably with me, as anyone who’s ever had to carry out the laborious task of backing up their theories and research via citations for an essay or research paper will know – quality of source is everything. Now I know I’m being over cynical – the headline was there to grab readers’ attention - but for a statement based on ‘research’ to hold any kind of credence, I want and need to know how a study has been carried out. I wonder what methodology shampoo manufacturers follow for their satisfaction polls - '32 out of 57 people agree their hair was fuller bodied and shinier than before.' A sample size of 57 - really?
So how do potentially misleading studies on height and disingenuous promises of nice looking hair relate to reward? Well, they all share a common factor, the use of quantitative data. In reward, in particular, one of our most common applications of quantitative data comes when looking at external market comparisons of salaries and bonuses (pay benchmarking).
All too frequently for businesses large and small, salaries are set based on anecdotal evidence, user populated internet surveys or recruitment websites and publications – in an age where robust data is easily accessible this just shouldn’t be happening. There are so many factors that can distort the accuracy of these sources of information – more often than not there is no distinct explanation of where the data has been collected, the methodology used to arrive at the figures, and no indication of sample size. All incredibly important factors in determining the validity of data for use in reward.
There are so many questions begging to be asked. Which and how many companies contributed to this data? Where are they based geographically? Is the data up to date? Is it based on a 35-hour week or 42-hour week? Is it sharing an average salary figure, of a number of employees?
So what’s the answer to avoiding these pitfalls? Well in a shameless display of self-advertisement, hire a reward consultancy, with the tools and knowledge to accurately identify what salaries, bonuses and benefits are currently being paid in your sector, your region or even among some of your competitors. We use one, or blend a number of data sources, that we know and trust and, when handled correctly, the salary surveys we use provide the most precise marker of reward for most roles imaginable. If a survey doesn’t exist for a role or sector, then we can create a bespoke one. And it pays for itself. Good benchmarking provides huge savings from recruitment savings, improved productivity and ineffective and unfocussed in-year pay increases.
You may not end up using our services, but if you set off like a pioneer into the benchmarking wild, just promise me you’ll use the most reliable data as a comparator when it comes to setting your levels of reward; you’ll give yourself the edge in the war on talent and save time and money by the ledger load. To put a slight twist on the old adage, accurate knowledge is power, 98% of 273 people agree.
