How to use compensation analytics to build high-performing teams

Compensation analytics is the practice of using payroll, market benchmark and performance data together to decide what people are paid. It answers four questions: whether pay is equitable inside the business, where your rates sit against the market, what the next pay cycle will cost, and which valuable people are underpaid relative to their contribution. Done properly it replaces "what the last person got, plus a bit" with a position you can defend in a board paper or a pay dispute.

Last reviewed September 2026

This guide covers what the discipline includes, the reports that answer most compensation questions, how the same data flags flight risk in key talent, how forecasting works for a people analytics team, where the market data comes from, and how to choose a system to run it in.

What compensation analytics covers

Four bodies of data feed it. Internal pay data from payroll and your HR records, which gives you salary, band, tenure, level and location. Market data from salary surveys and published benchmarks, which tells you what the same role pays elsewhere. Performance and capability data, which connects pay to contribution. Movement data such as resignations, offer acceptances and internal promotions, which tells you whether the current settings are working.

On its own, payroll only reports history. Compensation analytics is the layer that turns that history into a decision: who gets an adjustment this cycle, what the adjustment should be, and what the total bill looks like before you commit to it.

Most HR leaders can feel a pay problem well before they can prove one. It arrives as a spike in resignations in a single team, or a run of declined offers at the same level. What the data adds is the diagnosis. Base salary, bonus design, band structure and equity between departments are different problems with different fixes, and guessing which one you have is an expensive way to spend a remuneration budget.

Compensation analytics and reporting: the core reports

A compensation reporting pack does not need to be large. These are the views that earn their place, and most of them come straight out of payroll once roles are mapped to bands.

• Compa-ratio. An individual's salary divided by the midpoint of their pay band. Below 1.0 means they sit under the middle of the range, above 1.0 means over. Grouped by team or manager, it shows where pay decisions have drifted.
• Range penetration. Where a salary falls between the floor and ceiling of its band, expressed as a percentage. It is more readable than compa-ratio when your bands are wide.
• Pay equity by group. Pay compared across gender, ethnicity, tenure and location for people doing like work. Run it on like-for-like comparisons rather than a raw organisation-wide average, which mostly measures who holds the senior roles.
• Pay compression. The gap between new hires and long-serving staff at the same level. When it narrows to nothing, your loyal people find out, usually from a job ad.
• Spend against budget. Total remuneration by division, tracked through the cycle rather than reconciled at the end of it.
• Turnover by pay quartile. Resignation rates for the bottom, middle and upper quartiles of each band. A concentration of exits in the bottom quartile is a pay signal. Exits spread evenly across quartiles is usually a manager or workload signal instead.

Keep the definitions written down and stable. Half the arguments in a remuneration review are two people using the same word for different calculations. Our pay equity glossary entry sets out the standard definition if you need a shared starting point before the first review meeting.

How compensation analytics helps identify flight risk in key talent

Flight risk shows up in compensation data as a mismatch between what someone contributes and where they sit in the range. The people at highest risk are not the lowest paid. They are the ones whose pay has fallen behind their own performance and behind the current market rate for their skills, because those are the people other employers are actively calling.

The practical method is to cross three fields you already hold. Take performance or capability rating, compa-ratio, and time since the last increase. Anyone rated highly, sitting well below the midpoint, and untouched for more than a year is on the list. Add market movement for their job family and the list gets sharper, because a role where external rates have jumped needs attention even when internal equity looks fine.

Here is a worked example with illustrative numbers. An engineer earns $120,000 against a band midpoint of $140,000, giving a compa-ratio of about 0.86. Her last increase was 16 months ago, her performance rating is in the top band, and the market rate for her specialisation has moved up over the year. A $12,000 correction lifts her to $132,000 and a compa-ratio of about 0.94. Replacing her instead means recruitment fees, an offer likely at or above the new market rate, and months before a replacement reaches full output. Our cost of employee turnover calculator puts your own figures against that comparison.

Two cautions. Pay is not the only reason good people leave, and a correction made after someone has already resigned rarely holds them for long. There is more on the non-pay half of this in our talent retention strategy guide.

Compensation forecasting for people analytics teams

Forecasting is where compensation analytics stops being a report and starts being a planning tool. A people analytics team is usually asked to model four things before a budget is signed.

The merit cycle. Current salary bill, plus the proposed increase percentage, applied across the population and split by division so leaders can see their own number. Model at least two scenarios, because the first one is never the one that gets approved.

The equity correction. The cost of closing the gaps your equity audit found, shown separately from merit. Keeping them separate matters. A correction fixes a defect and a merit increase rewards contribution, and blending them hides both.

Headcount plan multiplied by pay. The hiring plan costed at current market rates rather than at last year's band midpoints, including on-costs such as superannuation, payroll tax and leave loading where they apply.

Attrition and replacement. Expected departures, the cost of replacing them, and the effect of hiring replacements at a higher market rate than the person who left. This is the line most forecasts miss, and it is often larger than the merit pool.

Build the model so a leader can change one assumption and see the result. A forecast that only the analyst can operate gets overridden in the meeting by whoever speaks with most confidence.

Compensation intelligence and where the market data comes from

Compensation intelligence is the market-facing half of the discipline: knowing what a role pays outside your walls and how fast that number is moving. Internal data cannot tell you this, because your own history only reflects your own past decisions.

Sources vary in quality. Paid salary surveys built on submitted payroll data from participating employers are the most reliable, and the most expensive. Aggregated job ad data is fast and cheap but reflects what employers advertise rather than what they pay. Crowd-sourced salary sites are useful for direction and unreliable for precision. In Australia, modern award minimums set a legal floor for many roles and are updated through the Fair Work Commission's annual wage review, so award-covered roles have a published baseline that sits underneath any benchmark you buy. Award coverage and classification are technical questions, so take professional advice rather than reading the pay guide yourself.

Whichever source you use, the discipline is matching by job content rather than job title. Titles inflate, and benchmarking a Senior Analyst against everyone else's Senior Analyst compares two very different jobs. Match on what the person actually does and at what level of accountability.

Choosing a compensation analytics system

There is no single right answer here, and the honest one usually depends on headcount and how often pay decisions get made.

Before you buy anything, check whether your job architecture is sound. Bands, levels and job families have to exist and be applied consistently, or a compensation platform will simply report your inconsistencies faster. The boundary between systems is worth understanding as well, and our guide comparing an applicant tracking system with an HRIS covers which employee records tend to live where.

What pay data alone will not tell you

Compensation analytics answers what you are paying and how that compares. It does not answer why someone is disengaging, whether a manager is the reason a team keeps resigning, or whether the work itself has stopped matching what a person is good at. Those show up in engagement and culture data, and they routinely get misread as pay problems because pay is the variable that is easiest to measure.

That is the boundary to be honest about. Compono does not set salaries and does not hold your payroll. Compono Engage measures the culture and engagement side, so when your compensation data shows turnover concentrated in one division you can check whether the money is actually the cause before you spend the correction budget there. Used together, pay data and people data answer a question neither answers alone.

Frequently asked questions

What data do I need to start with compensation analytics?

Internal payroll data, a job architecture that maps people to levels and job families, and performance ratings if you have them. External salary benchmark data comes next, and it is the piece most organisations buy rather than build. Without the job architecture, the rest of the analysis compares jobs that are not comparable.

How does compensation analytics help with pay equity?

It lets you compare pay for people doing like work across gender, ethnicity, tenure and location, so gaps become visible and correctable rather than anecdotal. The important discipline is comparing like with like, because an organisation-wide average mostly reflects who holds the senior roles.

What is the difference between compensation analytics and compensation intelligence?

Compensation analytics is the broader practice, covering internal equity, reporting, forecasting and the link to performance. Compensation intelligence usually refers to the market-facing part: knowing external pay rates for each role and how quickly they are moving. Most organisations need both, and the second is normally purchased.

How often should we review compensation data?

A detailed review once a year, aligned to the remuneration cycle, is the common pattern. Lighter quarterly checks on new hire rates, offer acceptance and turnover by band catch market movement between cycles, which is when compression usually creeps in.

Can compensation analytics predict who is about to resign?

It can flag higher risk, not individual intent. Strong performers sitting well below their band midpoint, with no recent increase and a job family where market rates have moved, are statistically more exposed. Treat the output as a prompt for a conversation rather than a prediction about a named person.