Create a simple salary structure for each role in your organisation

## 📣 Intro

Any organisation benefits from having a structured approach to determining salary ranges for their roles and job families. Bringing this structure and process to pay decisions, ultimately ensures that pay is **fair** and there is **pay equity** across the organisation. It will also demonstrate to employees what their earning potential will look like as the grow within your organisation and help motivate them.

**A typical salary structure will have the following components**

**Levels**- How senior / experienced the role is. As seniority increases, so does the level of responsibility and consequently the salary. Levels are sometimes referred to as ‘grades’.**Salary band**- this represents the minimum, middle and maximum pay rate for a given level and how salaries grow within that level. This may also be referred to as a ‘salary range’.

## 5️⃣ simple models to build your salary structure

There are a few ways to build a salary range for any role. Which model you choose will depend on the amount of market data you have for the particular role and the challenges that you need to solve.

**These models will…**

✅ **Help you fill in missing data for levels**

If you don’t have market data for every level, the models will fill in the gaps.

✅ **Solve for when pay doesn’t progress**

Sometimes market-based compensation data can be counterintuitive, where market data suggests that a lower level job is paid more than a higher level job. These models help fix this by using regression and simple progression formulas.

✅ **Allow you to create salary bands and then determine the width of them**

Help you create a pay band and then define how wide your band needs to be for each level.

✅ **Allow you to create overlapping salary bands**

You have the ability to determine how much over lap there is between levels, or simply have not overlap at all. Your choice.

**Toggle below to learn more about each model including explainer videos**

**Model 1 - Raw market data + Spread %**

**Model 2 - Exponential Regression using the Growth Function + Spread %**

This model takes your existing market data and applies **exponential regression** to it which helps:

- Fill in the gaps where data doesn’t exist
- Adjusting for when pay doesn’t progress

- Where data is higher for level 5 rather than level 6, the model takes the highest amount as the max value.
- Where data doesn’t exist for level 1, it will forecast data for level 1 using data from the levels where it exists.
- Where data doesn’t exist at all, the model will fill that hole.
- The model needs a minimum of two data points.
- The model uses the
`=GROWTH`

function to carry this operation out. - In simple terms, the growth function calculates Y values on the basis of an exponential growth rate of given X values. In our case salary is our response variable (y) since it is our target predicted value, and levels are our explanatory variable (X).
- You can learn more about how the Growth function works here.

**Model 3 - Linear regression using Forecast Function + Spread %**

- Like exponential regression in model 2, linear regression analysis shows the relationship between an independent variable, such as the Level (x-axis), and a dependent variable, such as Salary Range Midpoint (y-axis).
- In linear regression, the function is a linear (straight-line) equation.
- Linear regression has been a traditional approach used in the past to predict salaries, however this infers that salaries and levels increase in a linear format which is typically not always the case.

**Model 4 - Lowest Midpoint + Midpoint Differential % + Spread %**

- Unlike the other models, this one only requires one data point for level 1. This is very useful when you have no robust data at all.
- You create you range by modifying the midpoint progression from one level to another.
- See the FAQs for more information on the typical midpoint progression %’s.

**Model 5 - Salary Minimums + Salary Maximums**

- Got a min and max for a level but not sure what the mid point is or how this connect with the other levels? This model will help you figure all of this out for you.

**Bonus - Exec level salary ranges**

## ❓ FAQs

**Why is a salary structure useful?**

**Showing future earning potential and growth within your organisation**

- Salary bands are great at giving an employee an idea of their earning potential within a level and also combatting pay disparities. By having a minimum and maximum rate for every job, and then placing employees into this range by selecting a sub-level, you can easily explain to employees why one is earning way more than the other despite having a comparable role and level.
- Transparency around minimum-maximum earnings promotes compensation clarity, motivates employees to set development goals, and even prevents regrettable turnover.

**Salary bands and hiring**

- Salary bands give you flexibility on where to pay a new hire. You don’t have to hire people at the bottom of the salary band, and in fact we don’t always recommend hiring at the dead-bottom of the range.
- You may encounter a candidate who negotiates aggressively, pressing you to break out of the top of the new-hire salary targets or maybe you’re unsure which level to hire them in at. In both cases you have
*flexibility*and there’s some headroom to decide if this candidate is being offered the correct role, and how high you really want to go. - Remember though, that going to the top of the actual salary band can put you in a bad spot down the line!

**What are levels and do I really need them?**

- Yes, you need them. Like it or not, they play a critical role in salary benchmarking.
- An organisation can have any number of levels. There is no magic number, however most of the salary benchmarking providers have between 8-12 levels, and these will depend on the size of your org.
- Below is a typical organisation structure consisting of:
*Two tracks**8 levels*(IC1 - C-Level)*Sub-levels*- This is just another way to talk about the min, mid and max of a range. I personally find it as an easy way to objectively place an employee into either the min, mid or max point of a range. To determine a person’s sub-level we ask ourselves:*“How established are they in the role within your organisation?”*

I regularly meet orgs who feel that they are too small for levels or that they’ve managed to date without them and introducing them would damage the culture. I’m not going to dismiss these challenges, however if you want to carry out a salary benchmarking exercise you will need levels in some shape or form. If you don’t want to introduce levels or some form of seniority/hierarchy into the the organisation, then you won’t be able to carry out salary benchmarking. There is no halfway house.

**Where do I get the data for the models?**

- Before using any of these models, you will need your own market data. I have populated the models with sample data in order to demonstrate how they work.
- If you don’t have any market data, then you are welcome to schedule a call with me to discuss how to choose a salary benchmarking provider using the link here. There are lots out there.
- Here are a few that I have worked with:
- Option Impact (now part of Pave)
- Pave
- Ravio
- Figures
- Radford
- Willis Tower Watson
- Salary.com
- Mercer
- TalentUp

**How do I choose which model to use?**

- There are no right or wrong decisions to make when choosing which model to use. The choice you make will depend on the data you have and also the pay strategy you wish to implement. Feel free to book in time with me to chat about the models here.
**Key decisions:**- Which model best suits your needs? (FWIW, I personally favour Model 2 😃)
- Do you have plenty of market data to feed into the model you’ve chosen?
- Do you want overlapping bands at some levels and not others?
- How wide do you want your bands? i.e. the distance from the min to the max point at each level?

**How do I add / remove levels to the models?**

**Can you help with exec levels?**

- There is a separate tab on this, which focuses on creating a salary structure for exec levels using Models 4 & 5.

**How do I place employees in the salary band?**

Employees maybe placed within a range using the following descriptions.

- Learning
- Partially meets job responsibilities | - Fully competent
- Has mastered all job responsibilities and performs them independently | - Fully competent
- Has mastered all job responsibilities and performs them independently |

**What is the minimum?**

- The lowest point in the range.
**Formula:**`Minimum = Midpoint / (1 + (range spread/2))`

**Example:**£21,500 / 1 + (30%/2) = £18,700

**What is the midpoint?**

- This is the exact middle of the salary range.
- The midpoint is set to provide a market competitive and fair salary for the organisation. This is sometimes referred to as the ‘market rate’ and will differ from business to business based on their compensation philosophy and the strategic importance of that role.
- The data you input here, is used as the ‘anchor’ from which to create the min and max salaries in the range.

**What is the maximum?**

- The max salary in the range.
**Formula:**`Minimum x (1+ Desired range spread %)`

**Example:**£18,700 x (1 + 30%) = £24,300

**What is the midpoint differential?**

- This is the difference between the midpoints of each adjacent level.
**Formula:**`(Midpoint of level above - Midpoint of level below) / Midpoint of level below`

- Typically, lower-level jobs tend to have a smaller midpoint differential, and the differential increases for higher-level positions, but this does depend on the market data being used.
- It is common to see salary range midpoint progressions (the percent difference between midpoints) within a salary structure as follows:
- As you’re using pure market data for the midpoint, it may well result in inconsistent midpoint differentials for some of the models. These uneven midpoint differences happen unintentionally over time and very common when you use data submitted and aggregated from 1,000’s of companies.

Level | Typical midpoint progression |

Support position | 10%-15% |

Professional, Managerial | 15%-25% |

Executive | 20%-35% |

**What is the pay range spread?**

- Displayed as a percentage to show the range or width from the minimum to maximum within each salary band.
- We use the range spread to calculate the min and max points in the range.
- You do not need to calculate the spread, as this is something that you determine yourself for each level.
- The width of your range i.e. the difference between the min and max salaries in a level is very important as it determines the growth opportunities for your employees within that level. Too narrow and the growth opportunities are limited. Too large and they’ll start stagnate within that level as it takes too long to progress through it.
- We do recommend avoiding large ranges, especially if you have budgetary limitations as this can lead to too much band overlap.

**Step 1:**Enter your market data into the ‘Raw data’ column. The template will then populate this into the range as the mid point.**Step 2:**You now need to set the pay range spread. This is the width from the min to the max point. The % spread for each level is entirely up to you.**Step 3:**The template the calculates the min and max points by using the following**formulas**:- Minimum =
`Midpoint / (1 + (range spread/2))`

- Maximum =
`Minimum x (1+ Desired range spread %)`

Level | Recommendation |

Lower levels | We recommend smaller range spreads. These can benefit lower level positions where the market rates are closer.
If the range spread is too large it becomes harder to ensure the internal progression of entry hires to the market rate/ midpoint. Typically these vary between 15%-30% for lower levels. |

Higher levels | Having a wider spread is appropriate, where there is a lot of variance in incumbent pay.
A range could be up to 50% for VPs and C-Levels. |

**What is band overlap and how much should I have?**

- Displayed as both a % and £$€, it shows the degree of overlap of bands across different levels.
**Formula:**`(Max of lower level - Min of higher level) / Max of higher level - Min of higher level)`

- The degree of overlap is entirely up to you.
**No overlap or a small amount**- You would need to promote someone to the next level in order to increase their salary even if they are not ready. Risky!
**Too much overlap**- You promote someone to the next level but their pay doesn’t actually go up that much, which isn’t particularly motivating for the employee.
- Too much overlap may occur when there is too little difference in market rates between levels or if you set the widths too wide.
- Typically there will be a higher degree of overlap in the higher levels, where there is a lot of variance in incumbent pay.

Overlapping bands | |

Pros | When your salary bands overlap, your company has more flexibility. Employees can receive merit raises without taking on promotions they’re not ready to tackle.
Employees have some runway to grow in their current level without a constant focus on promotions.
Overlapping bands recognise the greater value of the input from a highly experienced/skilled individual at the top of their level compared to a newly-appointed employee on a learning curve at the lower end of the level above. |

Cons | It’s not as ‘neat’. Some people prefer the fact that there is a clear step in salary from one level to the next.
Recently promoted employees who move up to the next level don’t always see an increase in salary due to the overlap and especially if they were already being over paid at their previous level. |

**How should I sense check the output?**

- I always recommend checking the data with the hiring managers and any in-house recruiters who will know the role and market best. For many job families you may decide that the raw data behaves in the way it should. If this is the case, you’ll find that the raw data and regressed data will track pretty much exactly the same and there is no need to use anything but model 1 to create your salary structure.

**What should I do with this data?**

- Now that you’ve defined your salary bands, you need to understand where your employees sit within them. If you need help with this, then check out Option 1 here What we do

**Why don’t you use polynomial regression?**

- The main disadvantage of the polynomial models is their sensitivity to outliers in the data set. Even a single outlier can significantly impact the results and render analysis useless.

**I’d like help with customising this template**

If you’ve purchased a template and would like help customising it, then you can book in a 1 hour session with me as a jumpstart to using it and making it work for you particular needs.

`60mins - £150+VAT`