The product-led model has changed the way the SaaS world operates. Unlike previous sales-led models, it relies on making a great product and letting people try all (or part) of it before paying. But when most of your conversions happen without a Sales touch, how do you forecast monthly revenue?
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Certainly, monthly revenue forecasting in a product-led growth business doesn’t work quite the same way that it does in a traditional sales-led world.
Before we get into the differences, let’s get clear about what we’re talking about here. This isn’t a years-out projection for your investor deck or some high-level estimate of what you’re hoping your killer product will bring in if everything goes as planned.
This is a monthly sales forecast. We are answering questions like What are we going to bring in for net new MRR? To figure out how to answer this question in a product-led growth world, let’s first look at how we answered it a sales-led world.
Revenue forecasting in a sales-led world
In a traditional sales-led approach, deals went into a CRM and salespeople worked in the CRM. They chased the deals, they updated the deal stage and they used that deal pipeline to determine which deals were going to close and which weren’t. Sure, your Sales Manager had to take into account how optimistic each salesperson was, but after doing that, they had a pretty good idea of the potential revenue for the month. In fact, they probably used a simple equation that looked a little something like this:
deal size x likelihood to close
Easy, right? Based on that, the Sales manager was able to forecast the potential revenue for the month. But things work a little differently in a product-led growth world.
How the PLG model is different when it comes to revenue forecasting
In a sales-led model, there’s a salesperson (read: human) interacting with the client every step of the way. They are gauging the clients’ interest. They are tracking the deals. Someone is sitting there and knows what’s going on.
In a product-led growth model, there are all these trial signups with very little sales interaction. There’s no human to hold the client’s hand and figure out how interested they are. Most of the conversions are self-conversions, which is great when it comes to overall growth, but it definitely makes it harder to forecast revenue.
So how do you go from trial signups to forecasting how much revenue you’ll get from those signups every month? How do you get a net new MRR forecast for the month? There are two options.
Two approaches to get to a net new MRR forecast for the month
The top-down approach
Disclaimer: This is not the preferred method.
If you’re like most SaaS businesses that have been around for a while, you probably have some historical data that tells you on average how many of your trial leads convert to paid every month. All you have to do is take that average and apply it going forward. For example, let’s say your monthly conversions for the past few months have looked a little like this:
|Month||Trial Sign-ups||Converted to Paid|
That means, on average, you have a 10% conversion rate.
If you take that average and do a little math magic, it’s easy to get a rough estimate of what your net new MRR forecast for the month will be. Watch:
Conversion rate x Number of trials x Average revenue per customer = Net New MRR
It’s definitely easy, but it’s not an ideal option because it doesn’t give an accurate forecast. Just look at the variation in conversion rate over your past few months. It’s probably pretty high. If you just lazily take the average conversion rate and apply it across the number of trials you have, you’re not factoring in the quality of those trials. That means your forecast is inconsistent and has no room to take into account that big marketing campaign your team did. It was great for exposure, but you got a lot of not-great sign-ups from it.
Lucky for you, there’s a way to modify this equation so it takes into account both the volume and quality of your trial signups.
Start from the bottom when trying to get a net new MRR forecast for the month
This method of sales forecasting is more in line with the revenue forecasting you’d do in a traditional sales-led model. You look at each deal, you evaluate how likely it is to convert and then you factor in the potential size of the deal based on how big the account is.
The sticking point here is the “how likely the deal is to convert” — it’s not based on the deal stage in your CRM anymore. In a traditional sales-led model, you would apply a different likelihood to close factor based on how far along the deal was.
A demo request, for example, would have been around 20% likely to close. Someone who had already requested pricing might be at a 50% likelihood to close. Proposals sent? Those guys are 70% or 80% likely to close. Let’s make another revenue forecasting table for the traditional sales-led model:
|Deals for May||Likelihood to close (based on deal stage)||Deal size||Estimated Revenue|
|Total for month||28k|
As you can see, when you multiply likelihood to close by deal size, you can get a good idea of projected revenue for the particular deal. And when you add up all those estimates, you get a good idea of what your net new MRR forecast for the month is going to be. Pipeline to monthly forecast. Check.
But (and we’ve said this before) in a product-led growth world, there’s no salesperson on the ground bucketing people into different deal stages.
So the big question: How do you get to that likelihood to close number in a product-led growth model? It’s elementary — Activation Rate
n. Static measurement of how far along a user or account is in their journey toward “first value” or the “aha” moment where they realize how great your product is.
n. An indicator of how far along someone is to becoming a product qualified lead
There are a few things a user would need to do to get set up in your product and get value out of it. These things vary based on the product. Let’s say you’re a modern SaaS company with a GSuite plugin for email collaboration. Your Activation steps might look like this:
Here’s an obvious fact that follows: Activation rate is just the percentage of steps completed. That means if an account or user has done 2 out of the 5 steps above, they are 20% Activated.
They are 20% of the way to hitting first value with your product. Excellent!
The Activation steps are going to be different for every product, but what is not different is that Activation measures how much value someone is getting from your product and therefore how likely they are to convert. It makes sense, right? The more value someone has gotten from your product during their trial period, the more likely they are to pay for the product after the trial is over.
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One thing to keep in mind is that Activation isn’t binary. There are degrees of Activation for every account. And that nuanced Activation rate is super important when it comes to forecasting sales.
Activation as a proxy for the likelihood that they’ll convert
To figure out likelihood to convert from Activation rate, take a look at the historical relationship between the two numbers. For example, in our business, we know when a trial account gets to about 50% activation or above, they convert to paid 70% to 80% of the time. Similarly, we know that, if someone gets to an Activation rate of 25% to 50%, they will close about 20% of the time. Anyone less than that is not going to close. (Even our most optimistic salesperson agrees with that statement!)
Here’s what a product-led growth revenue forecast for our business might look like when taking into account Activation rate.
|Deal||Activation Rate||Likelihood to close |
(based on Activation)
|Deal Size||Estimated Revenue|
|Total for month||$2075|
And the best part is, we’ve found Activation works even better than the guesstimates of an overzealous (or underwhelmed) salesperson. That’s because it’s based on an objective measurement of how much value users are getting from the product, not a person’s estimation of how much they can make an account believe in a future promise of value.
Forecasting net new MRR in a product-led growth world is actually more accurate than in a traditional sales-led model
Product-led growth is still a pretty new concept, but it opens up avenues to understand users in a previously unthinkable way. When you base your monthly sales forecast on actual user data, it’s going to be more accurate. It’s just a matter of figuring out a system that works for you.
We’ve found Activation is a good proxy for likelihood to convert. Do you have any others? Email us at email@example.com and we’ll feature them in our next post!