đ§ Deep dive â ditch aimlessness and set clear goals for 2024
Make goals foolproof with the GSM framework.
Happy New Year! đ
As the first Deep Dive of 2024, what better topic than goal-setting? Letâs get right to it:
Startups often wander aimlessly.
The problem isnât effort, grit, or guile. And it isnât because theyâre not setting goals.
Itâs that theyâre setting the wrong goals.
Thatâs where the Goals Signals Metrics (GSM) framework comes in. Itâs not just a tool; itâs a beacon, guiding startups to identify the right metrics at the right time that align with their goals.
Today, I'm talking about how the GSM framework can be your guide in establishing a focused, data-driven path for your startup.
Letâs dive deep đ
Letâs start with an obvious truth:
One of the greatest predictors of startup performance is knowing what to measure.
It provides a clear plan of action of what to do today, and it provides data to show if that action is successful, which, in turn, helps us figure out what weâll do tomorrow.
Or, to resort to cliché: what we measure is what we improve.
Unfortunately, startup measurement is really hard, and, though Iâm not sure why, measurement is not something we teach founders.
The result?
Most startups have crappy goals.
Often, theyâre not measurable. Many startups espouse broad, overly-inclusive goals like âfinding customersâ.
But abstract goals arenât actionable.
Other times, startups measure the wrong things. Commonly, early-stage startups will eye revenue before their value proposition is tested, which will almost guarantee missing the mark and wasting precious time and money.
This is a common theme in my advisory work.
While I wouldnât deign to tell you what your startupâs goals should be, I can give you frameworks that you can use to create targets that drive results.
The best framework? Goals, Signals, Metrics.
Academically, the Goals Signals Metrics (GSM) framework is a strategic management tool designed to align your objectives with measurable outcomes.
Itâs a 3-tiered hierarchy:
Goals represent your high-level objectives, reflecting your startupâs mission and strategic priorities.
Signals are indicators that suggest youâre on track â qualitative predictors of success or failure.
Metrics are quantitative measures used to assess specific performance against your signals and goals.
The GSM framework facilitates a purpose-driven, goal-oriented focus at the high level, while promoting organisational learning and being highly adaptable to change â which is something startups experience a lot of. đ
Goal-setting isnât not about knowing exactly where youâll be in three years and setting a metric to track it.
GSM is about being directionally accurate:
Success is⊠over there somewhere.
Success is not⊠all other directions.
But by balancing qualitative and quantitative analysis, and through chaining leading indicators together, it tells you if youâre on the right track, and provides a system for changing it when youâre wrong.
When youâre wrong. đŹ
This is the basis of innovation accounting.
GSM gives you all this, while also providing the benefits expected from any goal-setting framework (e.g. BSC, OKR, ToC, etc.):
improved decision-making;
increased accountability;
early detection of problems;
more alignment within the team;
enhanced communication and clarity;
and more.
But enough theoryâŠ
Hereâs how to use it.
Setting up Goals Signals Metrics within your startup is actually a fairly straightforward enterprise, if you start with a good hypothesis.
In fact, itâs only three steps:
What is the big goal?
What are the signals (leading indicators) that youâre the right track?
How can you measure those signals?
Thatâs too high-level to be actionable, so letâs break it down:
1/ What is the big goal?
It may be the best way to get somewhere youâve never been, but the worst way to try to get where you want to go is to not know where it is you want to go.
Duh.
At the highest level, your goal is a scalable startup. That means you need what I call a âcredible theory of hugenessâ:
It has to have the potential to get really huge;
Youâre not huge yet, so need a theory of how youâre going to get huge;
That theory is only worth anything if itâs a credible one.
At the highest level, your goal is that hugeness. In other words:
What is your startup trying to do?
Where are you heading?
Why?
It should be big; it should be bold; it should be audacious. But itâs not be immediately measurable â if itâs measurable at all.
Within that big, hairy, audacious goal (BHAG), are smaller, more meaningful goals that are part of our theory or credibility:
Getting in front of early adopters efficiently;
Achieving rapid user growth;
Maximising retention and LTV;
etc.
These may look more specific for your startup, but it should still big something and immeasurable â and audacious.
As a thought experiment, letâs go with ârapid user growthâ for a seed-stage SaaS company with a freemium offering. We need to demonstrate market potential and viability (credibility), which is crucial for attracting further investment and scaling the company.
In the early days, we canât just measure our customer acquisition rate. At one point, itâs zero. Once itâs no longer zero, it spends quite a while functionally zero.
âWe tripled our user base last month, and doubled it the month before! (*whispers* we have 12 customers)â
Since we canât measure it directlyâŠ
2/ What are the signals youâre on the right track?
The problem with a big goal is that itâs big â we canât just âachieveâ rapid user growth.
Honestly, thatâs not a thing. The chasm is too wide to cross.
Think of it like an explorer in the wilderness. Thereâs a far-off mountain youâre trying to get to, but you canât just leap there. Itâs not a walk; itâs an expedition. Itâs dangerous. It takes days or weeks or months.
Often, you canât even see the mountain through the dense forest foliage.
So you have to look for signals that youâre on the right track:
Is the river bank still to our right?
Are the stars oriented to our destination?
When we can see it, is the mountain getting larger?
etc.
Similarly, your job as an entrepreneur is to find the signals that you should be seeing if youâre getting closer to your mountain â to your goal.
(It's not necessarily one signal btw)
If youâre looking to rapid user growth, but you have yet to create demand in the market, then growth itself is a lagging indicator of progress.
To oversimplify:
You canât get revenue without customers signing up;
Customers wonât sign up if they donât hit your landing page;
They wonât hit your landing page if they donât click on your ad;
They wonât click on your ad if they donât find it compelling;
They canât find it compelling if theyâre not the right customer;
etc.
These are a chain of signals that lead toward the ultimate goal. We canât get there without them, and measuring them lets us know if weâre still oriented toward the mountain.
Based on your current stage, you might be one step removed from measuring revenue directly â or you might be 5 or 10.
Regardless, your goal is lagging indicator of what youâre accomplishing today. Focusing on measuring the goal itself will cause you to learn far too late (and far too expensively) that youâre on the wrong track.
Thatâs like picking a direction and taking a blindfolded march for a week, just hoping that youâre a lot closer to the mountain.
If you picked wrong, you lost 5 days â or more!
Instead, chain indicators together, starting at the revenue target and working backward, until you find a signal that you can see and meaningfully measure right now â even if thatâs just âcustomers are seeing our messageâ.
For our example seed-stage startup with the rapid user growth goal, letâs say the right thing to measure right now is freemium conversions on a landing page.
We already know:
Who our early adopters are;
That we can reach them on social media; and
They resonate with our value proposition (clicks).
But these are freemium conversions. We donât know:
To what degree theyâll convert to premium;
What the right premium pricing is;
How long theyâll stay;
etc.
We need them to reach our rapid user growth goal, but theyâre functionally zero right now.
If we try to measure a lagging indicator, like revenue in this example, we might completely and dramatically miss the mark while still making incredible forward progress!
In fact, this is a really common problem for founders with investors. Founders communicate the wrong metric, they miss it, and they're stuck trying to justify the great progress they made anyway. But it just seems like backpedaling to the investors.
But once we have a meaningful signalâŠ
3/ How do you measure those signals?
Honestly, most of the hard goal-setting work is already done.
What is a harder question than how.
By the time you get to metrics, you know what to look for. Now you just have to figure out how to measure it.
This is where specificity comes into play â where you make the metric discrete, precise, and targeted.
Metrics should be discrete.
Whatâs one thing you can measure that would indicate youâre seeing the signal?
If your signal is âcustomers are out thereâ, then it could be ad impressions or LinkedIn messages sent.
If itâs exposure to your value prop, it could be landing page visits, time spent of the site, video watch duration, etc.
What can you measure right now that would show the signal?
In our example, the discreteness of our signal works itself out. Weâre looking for the number of people who didnât have an account before, who sign up for a free one, subsequent to and immediately following a visit the landing page.
That said, if our signal was value prop resonance on LinkedIn, weâd still have more definitional work to do (ad vs organic; type of engagement; etc) to get the discreteness we need.
Metrics should be precise.
With one thing identified, get specific about it.
Document the who, what, when, and how clearly. Measurement is not the time for ambiguity.
If your metric is ad impressions:
What is the ad/copy?
Who will see the ad?
Where is it placed?
When will it run?
etc.
In our example, weâre tracking the number of those people who click the big blue âStart for freeâ button and log in with their email address. Weâll measure over the period of one week, and weâll drive the traffic to the landing page through a particular set of planned organic posts on LinkedIn.
Boom.
Metrics should be targeted.
Without a specific target, data is just noise.
With a measurement clearly defined, you need to specify what success looks like. What is a good value? Whatâs a bad value?
You should never just âsee what happensâ, though itâs a mistake we all make â even me.
You want to pick a target that will unambiguously be a success or a failure â black and white, because grey sucks.
There is a science and art to picking these targets, but the general idea is that you want to base it on the theory of your startup. When youâre wrong, you want to force yourself to ask, âwhat does this mean?â
For most standard startup signals, you can look to pirate metrics.
When you put together the model of your startup (the âtheoryâ in your credible theory of hugeness), you should be putting some back-of-napkin guesses to each pirate metric to ensure the math of your startup works.
These numbers are wrong. Thatâs not the point.
The point is that when we measure them and miss, we can revise our model and see if it still works â and to what degree it works. Is it still worth pursuing?
Then we revise the model.
And thatâs the full cycle!
The goal is to get to a metric you can measure TODAY, which gives data on the next most important question to ask.
To recap:
Pick a big, immeasurable, audacious goal.
Find signals that will let you know youâre on the right track.
Run experiments on (i.e. measure) those signals.
Ask, âwhat does this meanâ?
Revise the model; rinse and repeat.
Go forth and do likewise.
And thatâs it for this week. Leave a comment and let me know what your most important goal, signal, and metric is right now in 2024.
Iâll see you next week.
âjdm
I just listened to your entire article. I was completely jazzed in the beginning - thinking âthis is just what I need, Iâm ready, Iâm workingâ but as I listened more I got lost in the weeds. Everything you said sounded true, but my brain kept saying, âwhere is the model to accomplish this? Where is the canvas to fill out?â. So now Iâll look through the article for clues of what I have missed to implement this. Iâll keep going. I need to accomplish this. (So thatâs my comment)