Talent Market Fit: How to Measure It
Is Talent Market Fit a gut feeling or is it something that can be objectively measured?
The same question has been asked for as long as I can remember by revenue teams regarding Product Market Fit. Throughout this article we'll draw measurement parallels between Talent Market Fit & Product Market Fit, and then propose a better framework for how we think Talent Market Fit could -- and should -- be measured.
The Rise of the Scientific Revenue System
It's been fascinating to watch over the past two decades how revenue teams have become increasingly methodical when decoding Product Market Fit. From becoming incredibly prescriptive with defining an ICP (persona development, market segmentation, buyer journeys, current-to-future state messaging, etc) to tangibly measuring what is pulling through to topline numbers (ACVs, single- vs multi-year contracts, NRR, etc), revenue teams have shifted from once-being go-to-market artists to now-being go-to-market scientists.
With this shift, we have also seen a rise in new measurement systems across the go-to-market funnel, from buyer intent tools at top-of-funnel to NPS surveys after customer onboarding.
As such, the revenue world is drowning in data. Now the questions are: does one need all of that data? And how does one best use that data?
The Challenge with Lagging Indicators
I've recently been obsessing over The Science of Scaling by Mark Roberge. Mark is a co-founder of Stage 2 Capital & a lecturer at Harvard Business School. He was also the founding Chief Revenue Officer of HubSpot. Throughout his book, Mark provides a roadmap for how to use data to decide when & how fast to scale revenue, focusing more heavily on leaning into signals that indicate consistent customer value creation as opposed to chasing topline revenue signals that may lead to premature scaling.
It's a great read for anyone who touches revenue, because it replaces guesswork with framework for when & how quickly to scale. It's also a great read for anyone who doesn't touch revenue, because it teaches how to apply a framework to tangibly measure what was once considered intangible. Oh, and all proceeds are donated McLean Hospital to advance mental health research & care.
In the first chapter of his book, Mark teases out a case study that tees up a significant problem: companies use Product Market Fit to make critical "how to scale" decisions but often lack a scientifically-based & data-driven definition of what Product Market Fit is. He then poses an incredibly important question.
How can we formulate a more data-driven, scientific approach to product-market fit?
He states, very simply, that Product Market Fit occurs when customers continuously realize the value they were promised when they purchased a product, and that this is best quantified by customer retention. Why customer retention? Because when someone renews a contract, they have purchased a product, used it, and chosen to continue using it upon renewal.
The challenge with customer retention, though, is that it's a lagging indicator because it often takes quarters or years for companies to understand the retention rates of their customers.
So how does Mark fix this?
In the second chapter of his book, Mark proposes that we should define a leading indicator of retention (which will vary from business to business) & that doing such can be accomplished with the following simple equation:
[Product-market fit] is "True" if P% of customers achieve E event every T time
The chapter does a great job of diving into key considerations when applying the above formula. Mark also walks through the formula nicely via this LinkedIn post. Some of those key considerations for the formula include:
An optimal P value is usually somewhere between 60% and 80%. Choosing a value that is too low (e.g. 10%) indicates a "leaky bucket," since too few customers are meeting the leading indicator of retention. Therefore, a company should not move forward with scaling. Conversely, a P value that is too high (e.g. 95%) may mean that a company is overly cautious & therefore may be delaying scaling for too long, which can result in losing ground to competitors or missing the window for capturing maximum customer demand.
A defining E event should be objective (what Mark describes as factual & binary --> it either happened or it didn't), instrumentable (this should be an automated measurement, not vague), and quantified to customer value creation (think "a % increase in operational efficiency" as opposed to a vanity metric such as "met w/ customer 1x per month"). Mark also talks about how the E event can evolve over time as a product matures in a market, starting with a "setup" event and moving to an "engagement" event before finally maturing into an "ROI" event. Setup events are easy to measure, engagement events indicate volume or usage, & ROI events are difficult to measure but usually tie back to a lever that drives enterprise value.
T time should be at a frequency that aligns with a customer's needs to use a product and spread out over a long enough period that normalizes volatility of usage.
Ok, enough about the Science of Scaling. It's a great book that I recommend to any & all to read; and if you do read it, please let me know your thoughts about it!
So how does this translate into measuring Talent Market Fit?
Measuring Talent Market Fit
To start, let's draw some parallels between Product Market Fit & Talent Market Fit. Like Product Market Fit, Talent Market Fit has been measured via gut decisions. Also like Product Market Fit, Talent Market Fit is a critical determinant for answering when and how fast to scale build archetypes. Finally, like Product Market Fit, Talent Market Fit lacks a scientifically-based and data-driven definition of what it is.
I've said it elsewhere, but I'll say it here, too. If Product Market Fit fuels the why for growth, then Talent Market Fit fuels the how.
Given the similarities between the two, I'm going to alter Mark's definition of Product Market Fit and apply it to Talent Market Fit. Put very simply:
Talent market fit occurs when companies continuously realize the value they were promised when they built a business function.
And that value is best measured by annual performance reviews of business units. Why annual performance reviews? Because when a company reviews year-end performance data, it then uses those insights to bake into annual planning for the upcoming year.
The challenge with an annual review, though, is that it's a lagging indicator. So how do we fix this?
I propose that each business function should define a leading indicator of performance. By doing such, we can then alter Mark's Product Market Fit equation and apply it to Talent Market Fit:
Talent Market Fit is true if P percent of a business unit's actions achieve E event every T time.
The P should increase as a business unit matures in its build journey. A P value that is too low indicates the business unit is not effective.
E should be factual, binary, measurable, and directly quantified to company value creation. Like with Product Market Fit, the E event can evolve over time as a business function matures within that company, starting with something simple such as a "setup event," moving to something volumetric such as an "engagement event," and finally maturing into an "ROI event."
Finally, T should happen on intervals that normalize to the operating cadence of the event, such as weeks, months, or quarters.
So what does this look like in practice?
The Maturation of Accounts Receivable
Lets examine what this could look like for accounts receivable across the stages of corporate build at a startup.
For the "E" Event across each stage in the table below, we'll use "Customers who pay their invoices in-full within two weeks of revenue being earned," as collecting revenue for services or value rendered is the leading indicator of value provided to the business by accounts receivable.
Stage of Corporate Build | Function Owner | P Value | T Time |
Build | Chief of Staff | 70% | Quarterly |
Optimize | Head of Finance | 85% | Quarterly |
Operate | Accounts Receivable Specialist | 95% | Monthly |
Build
During the Build phase, lets say that a Chief of Staff sets up the infrastructure & manages the accounts receivable function for the startup. In doing such, the business sets a goal that it would like at least 70% of its customers to pay their invoices in-full within two weeks of that revenue being earned.
70% feels like a reasonable number because anything lower than that could result in cash flow problems if enough customers aren't paying within a reasonable time window of when services or value were rendered. And although having 100% of invoices paid on-time would be great, setting a P Value higher than 70% may not make a lot of sense, either. This is because to raise the bar from 70% to 100% would likely require an amount of effort that would be substantially greater than the value derived from that effort. In other words, the juice may not be worth the squeeze, as the Chief of Staff would be pulling away from other value-adding activities across the business in pursuit of accounts receivable perfection.
Optimize
As the startup moves from its Build phase to its Optimize phase, it begins to better understand what the archetype of its build should look like. In moving into this phase, it hires a Head of Finance, and the responsibilities of owning accounts receivable shifts from the Chief of Staff to the Head of Finance. With this shift also comes the expectation that the P Value increases, too, from 70% to 85%. In other words, the company will know it has Talent Market Fit for accounts receivable at this stage if 85% of its customers pay their invoices in-full within two weeks of revenue being earned.
The jump from 70% to 85% feels realistic because the Head of Finance starts to optimize on what is working and deprioritize what is not. For example, perhaps under the Chief of Staff, customer accounts were tracked in spreadsheets, in which the spreadsheets had a series of triggers imbedded in them that would flag when specific invoices would have to be sent.
These flags would be triggered based on data uploads or changes for when revenue was earned, and the uploads/changes would be driven by the person earning that revenue for the company. In many cases, the person earning that revenue may be the founder, and the founder may not update the spreadsheet immediately after revenue was earned. For example, perhaps the founder is in back-to-back meetings every day, so she has to block time on Fridays to update the spreadsheet with her earned revenue for the week. If revenue were earned on a Monday but not reported until Friday, then that starts to already eat a few days into the two-week window of when invoices should be paid in-full from when revenue was earned. Then perhaps the Chief of Staff doesn't get around to sending those invoices until after the weekend, which starts to eat further into that two-week window... the reliance on manual updates begins to compound the time delays.
Now say the Head of Finance comes in and implements QuickBooks. In setting up QuickBooks, he syncs it to the CRM system so that when there is an earned revenue event in the CRM system, QuickBooks automatically generates and emails an invoice to the customer for that earned revenue event. When QuickBooks does not receive a paid invoice after one week of the invoice being sent, it then sends an auto-reminder to the customer that says, "Please remember to pay your invoice by Date X, as you have one week left to pay it." By automating parts of the process, the startup is deprioritizing what isn't working (e.g. living in spreadsheets) and therefore should see a reasonable increase in number of invoices paid on-time.
Operate
As the startup moves from its Optimize phase to its Operate phase, then codification of its build really starts to happen. Through this maturation, finance (as well as other BUs) starts to expand its function to keep up with the pace of the business. No longer does it make sense for the Head of Finance to manage accounts receivable, as doing such pulls away from other critical value-adding activities that the Head of Finance needs to focus. As such, Finance adds an Accounts Receivable Specialist to its team. With this addition to the team, the organization also increases its P Value from 85% to 95%.
This jump from 85% to 95% of expecting customers to pay in-full and on-time feels realistic, as it is the core responsibility of the AR specialist to ensure accurate & timely receivables. Not only are pieces of the process automated, but there is also a trained employee who can work through special cases & nuances to the standard process for customers who need some customization. So, too, can this trained employee escalate issues to legal for customers who are delinquent on payments. Additionally, there may be a significant number of customers who pay in-full ahead of revenue being earned, as finance & sales may incentivize customers with a discount off of total pricing for those who pay for a full year instead of paying monthly.
The combination of decreasing the risk of missing realized revenue (automated invoicing, escalation to legal, etc) while increasing the flow of cash into the organization before revenue is earned increases the overall cash flow of that organization, which then can be tied to ROI metrics that Mark talks about when moving E events from setup to engagement to ROI. For example, by having increased cash on hand, the business may be able to fund product enhancements or market expansions, which will therefore impact total growth of the business and translate back to ROI.
Sequencing Hires is the Result of Finitude
So why not just have the startup hire an Accounts Receivable Specialist during its Build phase if AR can help drive enterprise value? The simple answer is finitude. Startups only have a finite amount of capital during Build phases to make a select number of hires, and startups will extract more value from hiring a Chief of Staff who can build across multiple aspects of their businesses as opposed to an AR specialist who may only be capable of doing AR.
As such, the types of hires that help establish Talent Market Fit tend to be those who are enigmatic to explain: Chiefs of Staff, Operating Partners, Strategy & Ops folks, etc. Each role may be defined differently at different companies, but the underlying value of each is the same: they help codify a company's build journey, and through doing such, they increase the predictability of value provided by the hires a company makes in its more-mature stages so that one has a clear idea of what one is getting when one hires someone into a given role.
Final Thoughts
Measuring Talent Market Fit will never be an exact science, but companies could be more rigorous with how they measure such than just chalking it up to a "gut feeling." Further, for companies who are undergoing build or rebuild journeys, the traditional mechanisms (e.g. annual performance reviews) for measuring Talent Market Fit are out of sync with the pace at which a build journey or transformation needs to move. As such, if we can apply a more formulaic approach to Talent Market Fit that adapts something like Mark Roberge's Product Market Fit equation, then we end up with a more-prescriptive outline for understanding how Talent Market Fit is established as a company matures in its (re)build journey.
And to be clear, as a company becomes more mature in its (re)build journey, that doesn't mean that it won't make mis-hires. However, as predictability of value increases in correlation to company maturation, then the cost of making a mis-hire in terms of value detraction from the business decreases significantly. For example, hiring the wrong AR specialist when a company is in its Operate stage is less detrimental than hiring the wrong Chief of Staff when a company is in its Build stage. The former may slow a function's operating rhythm, whereas the latter may derail a company's trajectory altogether.
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