Hybrid approaches to salary structures

“One does not simply build a salary structure”

Following up on my previous posts on the differences in globally graded vs traditional salary structures and job-based approaches vs salary structures, I’d like to add one more piece to make it a trilogy. But hopefully a good trilogy like the Before trilogy or Lord of the Rings, and not Look Who’s Talking or the Star Wars prequels.

In my sheltered tech-client-heavy existence, it is easy for me to forget that for many industries and many countries, a globally graded salary structure doesn’t work all that well and even within tech, it has its flaws. That said, companies will often look for the elegance of career levels that tie to salary and other components of pay such as bonus and equity, without the need for a completely separate set of grades for their salary structures, or a job-based approach.

Enter the Hybrid, or “Cluster” approach…

I like to think of this as creating a mini traditional structure within each career level. The process begins as it would with a globally graded approach where each job aligns to a career level, then we need to decide what to do with the assortment of salaries that fall within each career level.

In the example above, in our hypothetical Grade 7, we see a collection of roles, at the Individual Contributor 4 and Manager 3 career levels. Naturally, in most companies, this data will be varied depending on the value of the role. The first step is to organize this data from high to low (as is the first step with a traditional salary structure):

Once we have the data organized, we then look for natural groupings, or “clusters” and average out the data to create a midpoint intended to accommodate the jobs in said cluster. Once all the grades and roles are accounted for, the result is something that looks like this:

The illustration above is very basic and intended to demonstrate the mechanics, but it isn’t difficult to imagine things getting more complicated. The flaws with this approach are similar to the flaws of a traditional salary structure. Clusters that make sense from a market data standpoint, may not make sense from an internal equity or job architecture standpoint meaning that some override and adjustment is required. A lack of job-specific market data, may also be an issue with this approach placing more emphasis on data cleaning and “patching up”. Finally, the resulting midpoints may not give a reasonable progression between clusters meaning that some smoothing is required (as is the case with all approaches).

In theory though, once constructed, this allows for a global grading approach, with a closer alignment to market data, without some of the issues that arise from a globally grade salary structure (in the way I described it here with sets of ranges per functional grouping).

This also, isn’t the only “hybrid” approach, and there are other ways to include multiple bands within grades. One approach is to create a pre-defined set of tiers within each grade and then slot roles in based on the market data which creates a consistent number of tiers per grade. Another approach is to create functional groups of roles within the grades themselves, and use average differentials to maintain consistent relationships between role types. This last suggestion may end up as a set of midpoints that are very close to, or the same as, the globally graded approach and the difference ends up predominantly one of look and feel.

As always, there is still no “right” approach. A hybrid approach such as this, would be my personal preference instead of a completely independent set of traditional salary grades in most scenarios but may come across as convoluted and difficult to understand in a complex organization. It also only adds value if career levels are used and understood for things like career pathing and internal seniority which isn’t always the case.

That concludes the trilogy. Probably more Jar Jar Binks than Frodo and Sam in the end but let me know your thoughts on the hybrid approach in the comments below:

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