Marketers and sales leaders can get lost in hundreds of KPIs, dozens of attribution models, OKRs, etc.
Mohamed Zahid, Hootsuite‘s first analytics leader and current VP of Self Serve Revenue and Demand Generation, shares a simple, elegant, and timeless approach to GTM strategy and measurement learned after over a decade at Hootsuite.
In this session, you will learn:
- How to design and iterate toward a profitable and sustainable GTM strategy
- What metrics matter (and which ones don’t as much)
- How to effectively communicate with senior leaders and the board
[Transcript] The One Equation Underpinning GTM Success
Mo, very excited to have you host!
Thank you, it’s a pleasure!
So, your session is particularly interesting. Go-to-market teams heavily overcomplicate metrics. You really only need one! Take it away.
Awesome! Really looking forward to the discussion and the questions. It’s a pretty short deck, actually. But I’m also lying; there are actually two metrics, but it’s fine. It’s the spirit of one metric.
So, a little bit about myself. I work at Hootsuite. I’m the VP of Self-Serve. I’ve been at Hootsuite for about 11 years and was asked to have some interesting facts. My best friend helped me out, so I speak French & Arabic. My favorite show isn’t actually Emily in Paris. He wrote this, but it is a show that I’m watching right now. I do appreciate a good pair of corduroy pants. So all accurate facts!
I’m going to get straight to it. The metric, in my opinion, that every go-to-market team needs to be looking at is LTV to CAC.
Why is that? The goal, if you go back a second and think about the goal of a go-to-market function or go-to-market leader, it’s to create a high-performance revenue engine. That’s why the one metric you need to think about non-stop is LTV to CAC.
Why LTV to CAC?
In short, if you have a good LTV to CAC, you’ve created an engine where you put in a dollar and get back three dollars. That’s incredible! That’s essentially “Financial Alchemy.” That means you have a high-performance engine. Your company is operating really well. These are the kind of benchmarks you have on the right:
Under 1.0 is essentially unacceptable. That’s not a real business, in my opinion. You can be there in the short term when you’re figuring things out, but long term, you can’t be below 1.
What it means is, every customer you’re bringing in is only going to pay itself off after a few years. That’s not a real, sustainable business. Typically, you see a lot of businesses between 1.1 to 3, and then 3+ is “Best in Class.”
As with all benchmarks, this is very contextual. There’s the lifecycle of the business. Are you in early venture mode? Are you in scale mode? Are you in a more mature company?
So there are a bunch of factors that play into this, but as a general rule of thumb, that’s a great return. Something I often talk to people about is the S&P 500. If you can’t beat the S&P 500 with the returns your company is generating, that’s something you need to really look into.
Here’s where I kind of lied. It’s not really 1 metric; it’s 2 metrics. I’m a big fan of pairing metrics. This is something that a lot of people do, including Keith Rabois, who’s ex-PayPal and a venture capitalist. He talks about the concept, that I love, of pairing metrics because if you don’t pair metrics, sometimes you can get adverse outcomes or perverse incentives. In math, this is called the constrained maximization problem. Essentially that’s what we’re solving–for the math geeks in the audience.
Basically, you want to pair your LTV/CAC with the rule of 40, and I’ll explain what the rule of 40 is in a second, but I’ll tell you why it’s so important to pair your metrics.
You can really decrease the scale of your go-to-market significantly to improve your LTV/CAC. So you can say, “Okay, I’m acquiring a thousand customers. Not very profitable today. What if I just acquired one customer next year, but in LTV/CAC?”
Again, you don’t really have a business there. It needs to be done at a certain scale and a certain growth.
A lot of the people, I assume, in the audience are going to be venture-backed. They’re companies that are trying to scale, and they’re companies that are trying to grow, so that’s where the rule of 40 comes in. This is kind of a classic Venture Capital metric that a lot of SaaS companies are judged on.
Rule of 40 is super dumb, super simple. It’s basically your growth rate plus your profitability. The idea here is that you can trade those off at different stages of your company.
So, I’ll give you a few examples of what the rule of 40 looks like because then it makes more sense. Let’s say you’re growing at 100% a year, but your profitability is negative 60%. You would still be rule of 40 because it’s a 100 minus 60.
So you’re investing a ton in your business to grow in order to scale. So in the short term, that’s okay; you are burning a lot of cash. That’s totally fine because you only get the lifetime value of a customer over three, four, or five years. So when you bring in a new customer, you’re effectively losing money. You’re going to be profitable on that customer down the line, and so that’s where the rule of 40 can come in.
On the flip side, a more mature business may be growing at 10%, but they have a 30% profitability in their operations. They would also hit the rule of 40 because they’re very profitable. They’re growing less fast than the first example, but they are profitable.
Examples of companies that don’t strike the rule of 40: if you’re running at a deficit and growing at 0%, that’s problematic because you’re going to be at 0 for the rule of 40 or even in the red.
So very simple, very dumb way to think about the concept of growth, and so that’s what I mean by pairing metrics. LTV/CAC is your “efficiency metric,” and Rule of 40 is your “scale metric” because you can cheat at the LTV/CAC if you don’t have the rule of 40.
Vice versa, you can cheat at the rule of 40 if you don’t have LTV/CAC. A lot of companies make the mistake of growing really fast. They’ll look good from a rule of 40 perspective, but their LTV/CAC will be under one. That’s just not sustainable long-term. It looks good for one or two years, but then you very quickly realize that this isn’t a sustainable business in the long run.
So this is just a very simple framework of how to think about it over a time period. A lot of companies will be either in the orange or in the red.
Your goal is to get to the top right quadrant, and strategically there are two ways to do it. Again, I think it’s really contextual. It depends on the problems you’re trying to solve. You can either try to fix your efficiency before scaling, there are times in places where that’s the best course of action, or first, you just want to get into a place where you’re scaling and you’re growing quickly, so your rule of 40 is good. Then you can make your acquisition engine more effective so that your LTV/CAC gets better.
There honestly isn’t a hard and fast rule of which of those two quadrants you want to go to first, but the ultimate goal is to always end up in the top right quadrant. Those are the healthiest companies; they have the best valuations, they have the best long-term prospects, and they’re the most in control of their own destiny. So that’s what you’re always going for.
This is the other point. It doesn’t mean that you shouldn’t look at other metrics. In a given week, I look at hundreds of different metrics, but in my head, I have a mental model–a mutually exclusive, completely exhaustive model–and I am a big fan of the MECE framework.
I have a mental model where I can stagger all of those from most to least important, and they all ladder up to each other, and this is a big mistake. That’s why I picked this topic. It’s a topic that’s very near and dear. A lot of marketers, just because they’re not as metric savvy, over time, do what’s called “the tail starts wagging the dog.”
So are attribution models helpful? They can be. Are benchmarks helpful? They can be. Are OKRs and KPIs helpful? They can be.
But when you start to run your business to hit a specific metric or to fit a particular attribution model, the order of operations is flipped, and your logic is flipped.
The whole point of having numbers and data is to guide your strategy. It’s never a means to an end, and I see so many businesses, companies, and teams that will do the flip. They will design their business in service of their metrics, their attribution model, or their OKRs, and that is just not a good strategy.
A great example is having an attribution model that only gives you credit if you do X. Okay, now we have to change our go-to-market to do X. However, X is actually hurting your conversion rates, and it’s not good for customers. That is the wrong thing.
Either you need to change your attribution model, or you need to come up with a clever solution where you say, “Hey, we know we’re not going to get full credit, so what’s a good proxy for this metric? What’s an indirect way that we can measure the effectiveness?” Rather than what most teams will do, they’ll actually change the customer experience to better fit the attribution model. I can’t discourage this enough.
This is a big mistake that a lot of companies make. Same with benchmarks; they just get obsessed with a benchmark. They’re the best-in-class companies and have X number for this particular final metric.
Your funnel is unique to your business and the stuff that you’re trying to accomplish. You need to look at benchmarks to know if you are in the right order of magnitude and what do other companies look like.
Never chase a benchmark for a particular metric, especially when your funnel has eight different metrics. Focusing on just one metric will never serve you well, and that goes for OKRs and KPIs too.
So, in short, attribution models are often a distraction, to be honest, and I wish I could give a whole separate talk about attribution models. People often say, “A little bit of data is better than no data.”
I actually have the opposite assessment. I think a little bit of data often hurts way more than no data, and the short version of that is that it’s often used to stuff out or circumvent discussion. Whereas, when you have no data, you’re forced to think logically about a problem, and you often have to get to a better outcome than having a little bit of data.
Attribution models often fall into that fallacy where they can actually get you to make the wrong decisions. They can be powerful. I use them at Hootsuite, but they are something of a double-edged sword. So, it’s great to have supporting metrics, but if you remember two things from the talk, it’s that:
LTV/CAC is King. That is how you know you have developed a high-performance revenue engine that takes a dollar, gives you back three, or gives you back four, gives you back five. That depends on the business, but now you know you have an engine that you can scale, and you have a sustainable engine.
You need to pair it with the rule of 40 in order to push for growth and scale because having a highly effective business that’s doing a thousand dollars of ARR is not the same thing as having an effective business that’s doing 100 million dollars of ARR. So the rule of 40 forces you to also think about growth and scale.
Short bonus slide! How to calculate LTV/CAC? This could be a whole other talk, but I’ll leave you with two points. You can also Google and do the research, and feel free to reach out to me on LinkedIn if you ever want some advice.
I actually would argue, this is a little bit controversial to invest in a slightly more accurate LTV model because basic segments and cohorted retention really improve the accuracy. You can actually make a lot of mistakes on your LTV model if you don’t have that.
The other mistake I see people make is their LTV/CAC has to be fully loaded. That means your LTV is your gross profit, and your CAC includes all your variable costs. A lot of times, people think they have a good LTV/CAC, but they actually don’t because they’re only looking at part of the picture.
So that was my talk! I don’t know if I’m over Armando.
Hot Takes Live
Catch the replay of Hot Takes Live, where 30 of the top SaaS leaders across Marketing, Sales, and RevOps revealed some of their most unpopular opinions about their niche.
These leaders shared what lessons they learned and how they disrupted their industry by going against the grain (and achieved better results in the process).
A little bit, but it was a good one! So first and foremost, if there are questions from the audience, write them in the chat. We’ll pick them up from there—a couple of reactions. First and foremost, thank you for this! Second, you lied through and through. It was not a short talk, it was a long one. It was not an equation, it was two, and Emily in Paris is not your favorite show.
Essentially the rule of 40 rules everything around you. Why 40 and not 30 or 50?
That is such a great question! I think, as with all benchmarks, it’s just a general order of magnitude. There are so many dynamics to think about. I don’t think there’s anything particularly valuable about 40 other than the fact that capital markets keep looking for, and that’s the comparable that a lot of companies are getting.
I love it! I found something quite interesting, and that’s that one of the other sections that we had was the CFO’s point of view. Funny enough, we were talking about LTV versus CAC as well. One thing that they mentioned was also payback time, but you didn’t. Do you think about that, or is that not as important? What’s your point of view?
100%! I think that’s in the sub-metrics. So that is exactly a great example of “you can only look at so many metrics.” Payback time is important. You basically capture the whole picture with the two metrics that I talk about; although payback time is a really important metric we look at, I would consider it as a sub-metric.
Okay, awesome! So Antonio is asking isn’t the main attribution model required to be important in order to establish consistency between all the different revenue channels?
So, my hot take on that is, it’s essentially impossible to have that consistency, and I think that’s where a lot of companies make huge mistakes and get hung up. Some tactics are just very easy to measure. Typically, these are click-type tactics that are at the bottom of the funnel.
Other types of tactics are really hard to measure. So any attribution model is going to prioritize and de-prioritize certain tactics and channels. Everyone wants a simple, clear-cut answer, and there are so many people who want to sell you a solution, software, or service, and it’s never that easy, unfortunately. That’s why I lied through the whole talk.
Another thing that I was thinking about, and if we can click super quickly on this: is the idea of the tail wag and the dog. How do you prevent that?
You prevent it by keeping people really focused on the North Star. I also spend a lot of time on the concept of MECE to explain to people how metrics ladder up and how to think about the problem. I think you just have to be vigilant, and it’s because people are so stressed they want to do a good job. They don’t want to get in trouble. They can easily drift back to what I call the bad habits of the tail wag. It’s so much easier to want to improve this one metric instead of thinking about the big picture.
Then it’s like the seven steps down the funnel. Things are going astray.
Last comment from Tory: “Great, now we have to delete the whole session because you lied….”
All right, thank you so much, Mo! This was awesome, and thanks, everyone for watching.