Part 3: Put Your Money Where your Mouth is 👁👅👁

Hot take time. 🔥 🕐

In many ways, the first two parts of this series are easy.

They don’t really require much more than putting some concrete form around esoteric, cerebral conversations and ideas. Come up with a framework to measure performance, then have conversations about how you apply that framework. Sure, with some creativity, alignment, and good organisation you should be able to employ principles of product management to take that idea and turn it into a reality.

This bit, the last bit, that’s where the rubber hits the road. It’s haaard. It’s hard for a few reasons:

1. It requires you to actually do something that has external, often irreversible outcomes,
2. It’s logic behind the numbers is important, and will be scrutinised by every eye that sees it,
3. Everyone in your Executive Team needs to buy into it, everyone in your Management Team needs to understand it, and
4. It will touch your company’s bottom line in a meaningful way.

Forecasting, budgeting, and applying performance to actual compensation is challenging. But it’s not so challenging that great People Operations and Operations leaders should avoid facing it. In fact, it’s all the more the kind of challenge you should rise to. This kind of problem is what your role is all about. The reason so many companies shy away from having both an effectively repeatable and transparent approach to performance based pay is because of the above four challenges. However, the reasons to do it are overwhelming:

1. You can forecast remuneration much more effectively year on year (Note: nothing is perfect, a forecast is a forecast). Your P&L will love you.
2. You will significantly improve both employee satisfaction with compensation, but also attraction and retention during challenging talent-markets.
3. Your company will be much more robust when facing geographic expansion, remote working, and unexpected economic conditions.

So you’re all convinced, appropriately terrified, and suitably galvanised to get this done for the betterment of everyone you work with. Nice. 💪

Forecasting performance based compensation

In a previous company I worked with, everyone in the team received both annual performance assessments and any salary changes on the date of their anniversary within their role. Salary changes were sometimes percentage based, but usually pegged to a “human friendly” round number.

A wonky process for several reasons, but one of the most pressing for me was observed when I looked into the data: Those who had an anniversary in Q1 received 13% higher pay changes than those who had an anniversary in Q4. A terrible stat on its own, but even more awful when you considered the fact that gap compounded year on year on year.

The hypothesis we ended up proving was that those who had anniversaries in Q1 were being discussed during a fresh budget, managers were flush wish new cash and ready to deploy on all of their strategic initiatives. By Q4 things had changed, successes and failures had materialised and budgets were dwindling as we approached end of year. Managers were less bullish on changes, and executive teams were less likely to approve them. Despite a collective mental commitment to doing things as fairly as possible, the effects were still visible.

This is the kind of thing that feels like a pure process problem, but it is actually a forecasting problem.

Process wise, a solution is to implement a single company-wide change annually. The problem with this, of course, is that dynamic changes happen, and people don’t always conform willingly to your progression timelines. That ‘solution’ on its own causes new problems. People progress at unique and dazzling rates, and being able to move dynamically is really important for retention of your strongest performers, who are least likely to conform to (or accept) fixed annual changes. This whole blog series is about addressing this exact issue and looking at performance as it exists in reality: messy, irregular, chronic.

What this is, is a forecasting problem. If you are able to forecast for more dynamic, ad-hoc changes, and then deploy the cash fairly and in a repeatable way — the problem is more or less addressed. Issue is forecasting budgets is incredibly hard, and start-ups and scale-ups are notoriously bad at it (and for good reason) beyond a 3 to 6 month horizon. That said, our lovely friends in Finance often expect an annual look-ahead into compensation and headcount overheads. So how do you bridge this gap?

At Whereby we have an approach I’ll talk you through, but there are tweaks and changes you can make to make this more applicable to your company and ways of rolling out budgets. It’s worth stating that this forecasting model is susceptible to mid-year changes or large external market shifts, and should be considered with that in mind, no annual forecasting is immune to that. This also gives you the tools to respond to those changes more effectively instead of the dreaded “promotions are on hold” message of yesteryear.

Every year in Q4 at Whereby we begin our annual budget forecasting process (note, we also constantly review and revise our budgets on a quarterly basis, ensuring we’re both sensible and strategic with deploying funds). Within this process we also run a full-company calibration. This calibration usually happens in October, and forms step one of the forecasting process. We call this a “Dry-Run” calibration, and set the tone with managers that this calibration is not a guaranteed time to change company-wide salaries, but is a place to highlight strong performance or due salary changes should they be necessary. The primary focus for this calibration is actually collecting forecast data in terms of the distribution of performance data across our headcount.

From there, I build a budget forecasting sheet (of which I’ve added a handy, but basic, template below). This sheet takes all of the roles in our team, anonymises them, and applies their performance rating in a “Forecasting Input” tab. This tab should include the outputs (in Column N of the linked template) of a simple ‘performance number’, as I mentioned in Part 1. That number corresponds somewhere to the performance grid (seen below).

A quick reminder of the performance grid discussed in Part 1.

From here, a column should show you an “Grid Outcome Change” as seen in Column O. These outcomes are based on a percentage which can be set in the “Forecast Changes” tab of the sheet, related to each position on the performance framework. From there, their salary, a pro-rata number (on tenure or last salary change as “Start Date”) and a “Forecast Outcome in USD” is given.

So many ugly screenshots. I’m sorry. Ok, here is an overview of the breakdown of this forecasting exercise.

And an outcome that shows the total salary output amount to around $114k annually.

From here the “Forecast Changes” tab can have the percentage figure shifted up and down to get a more sensible understanding of the overall budget you may to assign or deploy in the next round of performance reviews. You can set the levels to match an external number, such as “all reviews this year must be under $100k”, or reach a number and agree as an exec, “if we offer 10% to our highest performers, it’ll be approx $120k annually.” The sheet can even be formed to allow for a distribution of those who may have two changes within the year (I generally duplicate some for the junior folks who move quicker, and a random 5% of our highest performers).

This forecasting allows you to work closely with your finance team to have an evidence based, repeatable budget methodology by which to set your salary changes for any forthcoming year. Of course, there will be exceptions — sudden promotions, regrettable exits, etc — but in most years all of these changes should fall within a range of tolerance for any reasonable budget forecast.

Tip! If you have not run a calibration in the past and want to do this without a “dry-run”, I suggest you use bell-curve data, of which I’ve added two links into the forecasting sheet. I am not a fan of forced calibration against a bell-curve, as I see it as being meaninglessly unfair and also inappropriate for companies who have mid headcount (say, under 1000) or have an organisational design methodology centred around a high employee LTV payback. Bell curve data is great for forecasting and general sense-checking at a high level, but keep it there. We also use the bell-curve data in the final tab of the sheet to do some top-level calibration of the performance data as an executive team, which you can see in the final tab of the template below (and shared in the previous installment of this blog series).

Applying performance based compensation

Great, so you’ve set your forecast for the upcoming year and you and your team have finally completed your first calibration. The next step I take is to use this same template to apply these changes within the period of change. I suggest at least one or two review periods outside of the “dry-run” so that you can move quickly on folks who are ready to be reviewed. At Whereby we have three windows. One in Q1: January to March. One May through September, and then the dry-run in Q4 where some exceptional changes may happen.

In Q1 (but you can do whenever works) everyone in the company is reviewed on the last six to 12 months of performance. We take all of their performance data, calibrate it in the same budgeting document (using the “Actuals” tabs), and then produce the actual changes in a sheet against the budgeted number. Sometimes we have higher performance than budget and we need to move our percentages overall down. Sometimes we have lower performance, and we move it up. Sometimes we need to slightly adjust our process if we see large market changes, such as in 2021. We also take this time to apply all compensation calculator changes and ensure our team are still paid according to inflating market rates.

A note on inflation

At Whereby we do not offer generic inflation increases across all roles. The reason for this is because inflation is not a direct salary problem as I see it. Some wages see wage inflation, some don’t. Marketing, Engineering, Leadership are services like any other which move within a market, like the price of eggs or gas. To offer a standard 2% raise across all roles may move some above market rates, but may leave others lagging. For this reason we offer:

• One-time cost of living adjustments for those who may see financial distress in periods of higher inflation (£400 in a single pay period, for example)
• Consistent market and exchange rate reviews, of which we do a formal all-company review in Q1.

Once all of the team have been lifted to market rates, we then apply the performance changes on top of that figure. For Whereby we have no “upper band” on salaries, but we do flag if someone is paid significantly above the market rates and have a discussion about viability for promotion to ensure we are being consistent and fair.

It may look like this:

A software engineer level 2 is on $100,000 per annum. The new market data we have says the minimum range is $103,000 and above (this is also where we would move them up if they were due a promotion). We would apply a $3k adjustment to bring them into the market range, and then we would apply the performance change. In this instance, let’s say the person is meeting all expectations and gets a 5% increase (if they are being promoted their increase is generally based on their previous salary). Their increase would then be $5,150. This brings their total new salary proposed to $108,150.

Here, the figures for each team are given to the appropriate budget owners within the teams (VPs, Directors, C-Suite) and any exceptions are applied. Some folks may have not had a change for a longer period, or was promised a different change based on some external factors, or maybe the figure is so uneven that we round it up for ease. Generally though, managers simply approve the changes and we issue them to the team.

At Whereby we run no negotiation. Salaries are determined solely by this process. It works for us because we do have several checks and balances where teams are asked to share their expectations as the compensation model evolves. Every time we run a review like this, I publish the methodology to the whole company so the team can understand how we went about the changes, including which percentages we offered at each “level” of performance.

What if something goes wrong?

One year we had a spanner in the works. We’d forecasted say $300k for annual changes. What we didn’t predict was a huge market change across product and engineering. If we were to offer their changes to the compensation, and then layer performance on top, we would have seen some 25% changes for the same level of performance as in Sales where they only saw 9%. This seemed inappropriate to us, so we decided to institute a cap of 20% total change, and no changes for Engineering & Product if they “met expectations” as they were all receiving market changes. This methodology meant our budget was met and the entire team had more evenly distributed access to our compensation changes based on performance.

What about the second round?

So once we’ve completed these changes in Q1 what we have is a rolling open window. Anyone from May through September is welcome to either request a performance calibration individually, or their manager can request it. When this happens there is a mini-calibration for that one individual and the same methodology we used earlier in the year is applied. This is often used for those who did not receive a performance change in Q1 but later deserved one, or was not quite ready for promotion, but later becomes eligible. This way we are able to offer consistent change “windows” without blowing budget or operating on different methodologies.

This also means I remain acutely aware throughout the year of ensuring we have budget which may be applicable until the Q4 dry-run so that exceptional changes can be applied. We have, at times, gone marginally over budget (but a salary change in Q4 has much less impact on the bottom line in that year), and sometimes that we “gave money back”.

What don’t I like about this method

It’s administratively complex and requires a good head for numbers, you really have to spend a lot of time with your team explaining the methodology and how it’s being applied. Managers have to have a deep understanding because they are ultimately responsible for communicating the changes to their teams.

It is vulnerable to external counter-offers, but we simply do not tolerate them. We only ever offer changes within the scope of our compensation framework. Over the years we’ve discovered this is simply such a valuable retention mechanism for us, that deviating from the trust and transparency would result in much lower retention of our top-performers who care deeply about this way of working. Once that trust is broken it’s incredibly difficult to build it back up. If, however, you do include counter offers, I’d find a way to bake those into your forecast based on some historical averages.

I want to do it, what first?

First, I suggest you build a compensation methodology that includes elements of: calibration, transparency, and forecasting. Once you have these in place you should ensure you do an audit to bring your team to levels where further changes will not compound inadequacies. Acknowledge it may cause some frustration initially as folks are given transparency around being paid higher than market, or that you may need to give some increases to folks who may not be strong performers but should be paid fairly with their peers. Ultimately, fair and transparent compensation will wash these problems out in time.

You will need to convince your executive team and finance team of these changes, so come prepared with the numbers: what will it cost this year, what about next year, how can you do it in a cost-conscious way initially and roll further into the future. Perhaps my next blogs can be about how to build some kind of ROI demonstration… hmm.

I’ve built a very simple little budgeting & actuals template here which should serve you well until around 500 people. Beyond that the complexity starts to break down a little, and you may need to explore building something more robust. I’d consider adding in more variables such as multiple changes, performance based one-off bonuses etc. If you have a highly variable compensation methodology this may not be a suitable template at all, but it should give you some food for thought.

As always, I love hearing your feedback. There is no perfect way to do this work and at the very least I hope I’ve helped you on your path to more transparent & repeatable performance compensation.

Phew, that’s a lot of words from me.

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