The economy has forced a lot of companies to freeze raises for the time being, and understandably so. Employees are most likely accepting this situation because 1) they want their company to succeed and 2) they have few other options but to stay put.
But what about when profits start climbing, jobless rates start dropping and recruiters begin to come out of the woodwork searching for the best of the best? Raises will have to return at some point, and high performers (HiPos) - defined as "those where there is an expectation of outstanding performance and an aspiration for significant advancement" - will be getting the itch sooner than the rest. These future leaders and managers are what makes things tick, but many companies don't treat them like the assets they are.
When looking at all companies who participated in i4cp's Pay-for-Performance Pulse Survey
, we see that high-performing employees do benefit greatly from their hard work, with salary raises ranging from 4 - 10 %. Over 30% of companies, however, do not create as much distinction between high-performers and the rest. But look at the difference when responses are filtered by an organizations market performance rating:
Using i4cp's recently released Interactive Data functionality, we're able to break this data down by high-performing and low-performing companies
(not to be confused with employees). As we can see, almost 48% of high-performing companies award their best employees with 7-10% raises; whereas only 15% of low-performing companies do the same.
In other words, high-performing companies are, at least in terms of salary compensation, giving more to keep their HiPos on staff. After all, HiPos are presumably the employees with the most profit potential.
The biggest questions are: Are you rewarding HiPos with better salary increases than LoPo employees? And has anyone coined the term LoPos?
A fully functional Pay-for-Performance Interactive Data file is available to i4cp members, but you can download a preview
of the study here (Tableau Reader
, which is free, is required to view and filter the data).