5 key questions to ask when considering salary benchmarking

Failing to benchmark your terms and conditions against the market can put your organisation at risk of:

• Struggling to attract candidates with the right skills
• Losing your best employees to competitors, or
• Paying more than necessary.

Salary benchmarking can help address these recruitment and retention challenges – if approached strategically. Before investing in salary data, there are a number of critical decisions to make that will directly influence the accuracy and impact of your benchmarking outcomes.

The main questions to ask are:

3. How do we choose which salary benchmarking data sources to use?

There is a lot of benchmarking data available and it breaks down into two main categories:

1. Primary sources: These are sources where the publisher collects salary and benefits data from employers (often on an employee-by-employee basis). They generally have a job matching approach based on defined jobs and/or classes of job and may be supported by an underlying job sizing methodology.
2. Secondary sources: These data sources may contain recruitment data, details of salary ranges (as opposed to actual pay) and surveys that rely on employees providing data about their jobs and their pay.

When selecting your benchmarking data sources, start off by asking the publisher to provide you with a list of jobs covered, some details about the job-matching approach, a participant list and a sample report.  Ask yourself the following questions:

1. Does the publisher provide data on jobs that are relevant to my organisation?
2. Is the job matching approach robust?
3. How easy will it be for me to match my jobs to the data?
4. Are the other participants relevant to my organisation?
5. Is the salary benchmarking report easy to use and does it provide the data I am looking for?

It’s very likely that only a small number of the data sources that are available will tick all the boxes.  You may have to use several sources to cover all your roles, dependent on industry, function and/or location requirements.

4. How do we interpret the results?

Having selected the right data sources, you need to review the information provided. Most sources analyse the cash elements of the package individually e.g. base pay, bonus payments, etc., some combine all the cash elements into a total compensation figure. Many examine the key benefits e.g. holidays, private medical, pension scheme, company car etc.

To maximise the value of the data, you need to be clear about your desired market position and what you want to achieve. Analysis will then inform you as to where your reward policies and practice sits in comparison to your preferred position and the levels paid by your comparator groups. From this you can then review the approach to your cash and non-cash elements to create a well-balanced overall package.

5. How do we communicate the outcomes?

It is important to communicate the process that you have gone through to the organisation during pay review conversations. By assuring individuals that their pay is fair and competitive because it is benchmarked against the wider market, can put pay into the wider conversation of the full value that individuals enjoy from working at an organisation – whether this is culture and their working environment, their benefits, or the purpose of their work.

By communicating that benchmarking informs the organisation’s approach to setting pay, the whole process becomes more transparent and objective for individuals. It is important as an employer to underline how the whole process is a delicate balancing act of affordability against rewarding individuals as much as they can. If the organisation has stretched to offer the market average this year, being candid about this is often appreciated by employees.

Taking an evidence-based approach to design

In summary salary benchmarking is neither an art or a science, it’s both! Considering the information provided requires both interpretation and decision-making skills to enable you to use the results appropriately. The data will be telling you a story; it’s then all about how you translate this into your reward approach to gain competitive advantage.