The Covid-19 pandemic has already changed – maybe permanently—the way we all live, shop, work, learn, socialize…and much more. We are having to rethink and adapt in almost every aspect of our lives.
And our SaaS operations are no exception. The global shutdown has forced many SaaS operations to shift their focus from generating a constant stream of new business to maintaining their existing revenue base. During times like these, churn—or rather, churn mitigation—is the most potent lever for short-term survival, medium-term recovery, and long-term success.
As SaaS businesses the world round shift their attention to maintaining their existing customer bases, they are having to think about churn in a way they haven’t before. And because of this, we think that a new skill set – a new competency – will emerge.
Like for real. If they are paying attention, many SaaS businesses will come out of this crisis with a much more robust churn forecasting practice.
Why do we need a churn forecasting framework?
Raise your hand if your company forecasts monthly sales.
Ha. A bit of a rhetorical one there. 😁
OF COURSE your company forecasts monthly sales. In fact, I bet you spend hours on it each and every month. You likely have forecasting models built into your CRMs, weekly meetings across the sales organization to project the likelihoods of converting current deals, weekly updates at the exec level, real-time model adjustments, and more. You have probably built an entire infrastructure to help you accurately forecast monthly sales.
So, why don’t we apply a similar rigor to monthly churn forecasting? Let’s be honest…up until this point, the way you forecasted churn was to drag a simple, consistent churn rate across your spreadsheet like so:
Maybe you go one better and have a churn model that applies a different churn rate against account types (small, mid, large) given their different churn profiles. If so…bravo. You’re ahead of most.
But the reality is that not many teams put much more rigor than this in forecasting their monthly churn. And truth-be-told, that might have been fine for a lot of companies up until this point. Under normal circumstances, you may not have had a lot of variability in your churn rate.
But these ain’t normal times, Toto.
This pandemic and unprecedented global shutdown is will most definitely affect your churn rates. And in ways that are far from normal. It is essential to take a truly hard look at the way you project churn – in a way that can not only help provide some visibility over the next few months, but in a way that can extend beyond this shock and provide much more clarity into your business going forward.
And the basis for any good churn forecasting model is product engagement.
Four steps to designing churn forecasting model
A solid churn forecasting model should be designed in the same way you build a sales forecasting model. Traditional sales forecasting models are based on a simple formula:
Deal size (potential $$) X Likelihood to close = FORECASTED REVENUE
Add a close date to this model and you’ve got the building blocks of a forward-looking sales model:
The same model can be applied to churn forecasting:
Account size (current MRR) X Likelihood to churn
Easy, right? 😉
Of course, whether you are forecasting sales or churn, the biggest challenge is figuring out a good way to calculate the “Likelihood” factor.
In traditional Sales forecasting, “Likelihood” is typically based on the “stage” of a deal. For example:
Of course there are more complex and nuanced versions of this model, but this is the basic framework (of course it doesn’t work well for product-led businesses – here is an alternative methodology for forecasting sales in a product-led business).
That leaves us with the question – how do you create a “Likelihood” factor for churn? How do you assign a risk factor to each of your existing accounts so that you can accurately forecast a monthly churn number?
This is the crux of designing a churn forecasting model. The four steps to building this model include:
Understand churn indicators;
Track these indicators closely;
Create top “risk” cohorts and assign a churn likelihood factor to each cohort;
Plug likelihood factors into revenue forecasting model.
Step One: Understand churn indicators
When it comes to churn, there is no greater indicator than product engagement (or, more accurately, lack thereof). Some of the most common reasons SaaS accounts churn inc
Never got to first value before buying
Never was able to figure out how to integrate the product into business process
Bad fit – didn’t have a high-value use case
Key user left the company
Found a competitor that better solved their problem
Problem issues (bugs, performance, etc)
Went out of business
Maybe with the exception of Budget cuts (which sometimes do come seemingly out-of-the-blue), every one of these reasons will be expressed through a product engagement metric or indicator.
For example, if the account was never able to get to first value, they will have a low Activation rate. in their first few months with the product.
If a key user leaves a company, the account’s overall engagement will drop significantly. If the users found a competitive product, their engagement will drop over a period of time (when they are assessing the other solution) and they may eventually trigger some key event that would indicate they are ready to leave (like export data, or disconnect integrations, etc).
The first step in creating a churn forecasting model is understanding engagement-based churn indicators. As mentioned above, there are many reasons why accounts cancel their subscription, but they will all be expressed by some engagement metric. These include:
Low Activation: Activation is a measure of how far an account has gone down the path of becoming “Activated.” A paying account with low Activation rates is an indication of an account that hasn’t been properly onboarded and therefore may be missing some key value of the platform – which could lead to early cancellation.
Low Engagement: An account with low engagement levels is certainly more likely to churn than an account with higher engagement. But if that low level of engagement is relatively consistent, it may not be a major risk.
Significant drop in Engagement: An account that shows a significant drop in engagement over time is a bigger red flag than consistent levels of low engagement. There can be many reasons why an account’s engagement would drop overtime (including some of the reasons mentioned above) – none of them very good.
Inactivity: Low engagement is one thing. NO engagement is another – and one that carries a much higher churn likelihood. An account that has been inactive for 7 days it not good. An account that hasn’t been active for more than 30 days is a real problem.
Triggered farewell event: In some products, there are certain activities that users do when they are preparing to close down the account (like exporting data, or removing a key integration, or deleting templates, etc). We call these “farewell” events.
Step Two: Track these indicators
Admittedly, this might be the hardest step in this exercise. To be honest, it’s precisely why we built Sherlock. If you are a Sherlock user, tracking these metrics will be easy and second nature. If not, then you will need to have an internal system to track and calculate these important metrics for all of your paid accounts. You can learn more about building an engagement scoring system in this post if this is something you want to build internally to support this effort. If you don’t, you can try to build some kind of proxy for engagement. The important part is to be able to measure both engagement and activation (and they are different) separately.
Step Three: Create your top “risk” cohorts
With these engagement indicators in mind, the next step is to put together a churn likelihood model that creates different churn threat segments and assigns a respective likelihood factor to each. Generally, we recommend creating these segments with account tenure in mind. Accounts engage with your product differently based on how long they’ve been using the product and therefore should be treated differently in any kind of churn forecast model. Your churn likelihood model will look something like this:
Step Four: Combine likelihood factors with MRR for each account
Once you have likelihood estimates in place per segment, you can plug them into your larger forecasting model (shown earlier) – accounting for the MRR of each account:
Et voila! You’ve got a churn forecast model based on account engagement!
Looks awesome – but how do I set this up??
I know, I know. This sounds like a lot. But don’t worry. So long as you have a solid handle on your key engagement metrics for each account, you can absolutely build a solid model without a PhD in machine learning (or any machine learning at all – trust me!). Start by looking at the last X number of churned accounts. Let’s call it the last 3 months to start. Some of this historical data will be difficult to get (even with Sherlock), so alternatively you could just commit to tracking churned accounts over the next 1-3 months. These accounts will serve as the basis of your model.
For each churned account, look at the engagement indicators from your model. You will start to understand which indicators are more consistently correlated with an account cancelling. This is a very iterative process so spend time setting up an initial model – knowing that it will evolve overtime. Play with it and use it to forecast churn in your third month. See how accurate you are. I think you will surprise yourself.
What about timeframe? Should I only forecast one month out?
This is a really good question and it’s really up to you how far out you want to look. Like with any forecasting, it’s often harder to be accurate with a forecast in a very small window – like one month out – and easier if you use a slightly wider one – like 3 months out. Anything beyond that is really planning (versus forecasting).
We recommend a forecasting model that covers both short-term churn (1 month) and a mid-term window (3 months). Simply append your likelihood model with columns for “likelihood of cancelation in next 1 month, next 3-months” and adjust your “likelihood” rates to each time frame. For example, an account that is inactive for 7 days might not be likely to cancel this month, but might have a 10% likelihood of cancelling the following month.
WHAT IF I SELL ANNUAL CONTRACTS?
This type of model works the same with annual or monthly contracts. If you sell only annual contracts, simply apply the model against those accounts whose contract is scheduled to renew within the next three months. This will give you a very good indication which contracts are at risk.
With that said, with an annual contract model, it is likely someone from your team will be in touch with account as contract renewal approaches. The direct feedback from the account (ie – “We’re not going to renew this contract”) may override the “likelihood factor” in this kind of engagement model.
NOT AN EXACT SCIENCE – BUT INCREDIBLY ACCURATE
No forecasting is an exact science. Churn forecasting is no exception. But when you have the proper, detailed visibility into how your accounts are engaging (or not engaging) with the product, you can build a model that is incredibly accurate and gives you essential transparency into this key metric.
Don’t consider this post an exact formula for how you should be using product engagement to forecast your churn. Our goal is to encourage you to start thinking about churn forecasting more seriously – and to put product engagement data at the heart of it. The specifics of how you do it aren’t as important as the process here. Give it a shot. I promise you won’t be disappointed. In fact, you’ll wonder why you didn’t do it a long time ago.
I recently had a conversation with a top CS leader who was struggling to build and get their “Playbooks” to work.
I heard her struggles and they felt very familiar. My suggestion to her was — forget the playbooks. I knew she wasn’t going to get them to work because they just don’t fit the way a SaaS business works. They don’t fit the desired journey of a modern SaaS customer. She was obviously surprised at my take on this because the “Playbook” has been a pretty standard part of the Customer Success function for a long time. And I understand why.
Playbooks — when used in the context of a Sales or CS team — represent lists of “plays” (or actions) that a rep is required to apply against any new lead or customer. They are borne from the belief (hope?) that there is some consistent, linear set of steps that can be designed and followed to ensure success for every one your accounts.
I know this dream. I’ve held it. I’ve designed plenty of playbooks (on both the Sales and CS side) — all of them some flavor of this:
We use this type of playbook with the stated goals of:
Ensuring success for our customers (follow these steps and success is yours! follow them not and…well); and
Making things easier for our reps (just follow the playbook for each account and you’ll never have to think about anything!).
But let’s be honest — these playbooks are really just management hacks. We mostly use them to hold our reps accountable for working their accounts. This account is only on step two of the playbook — what’s the deal??
And while this kind of approach made sense in a traditional software sale (or even more so in a service-type offering) — it falls apart really quickly in a modern SaaS business…especially in a product-led operation.
Why is that?
Well…because SaaS — especially product-led SaaS — is different than a traditional software offering. It is a different business model. It’s a model that is built on the premise of reduced human intervention. In fact:
These are products — and business models — designed to support a self-serve customer journey.
Of course, this isn’t the reality. Even though some companies don’t like to admit it, we know that most of the companies in this space have strong Sales and CS teams. These teams are still important/essential parts of the customer experience. The reality is that very few companies can drive strong growth without these teams in place and operating at a high-level.
But that doesn’t change the fact that the product-led model was built to work without them. This is important to remember. Users of these software products don’t care about following some linear adoption process. With a try-before-you-buy offering (free-trial and especially freemium), users expect to sign-up and get started without talking to anyone. And they expect to upgrade without talking to anyone. And they expect to use the product without intervention. In short,
Customers don’t care about your playbook.
This means that the effectiveness of these playbooks collapse shortly after you build them.
The playbook failure
In most cases, playbooks require reps to follow some rigid, linear process that, as I just mentioned, is not in-line with how customers experience (or want to experience) your product. These playbooks basically force a rigid process onto a fluid experience. Square peg, round hole.
The typical playbook is designed to create a bunch of tasks for reps at different points of the customer journey (which, generally, will be defined by the age of the account).
Do this on day one; then two days later, do that; then five days later, do that; etc.
And these tasks are generally created without respect to a previous task being completed. Inevitably, after a few weeks with this new process, your reps will wake up to an insurmountable mountain of overdue tasks in their CRM.
This leads to stress and frustration for everyone involved. Reps start to feel overwhelmed, management starts to question their work ethic, and customers start receiving a bunch of desperate emails — not because they need help, but because reps are trying to check off the steps in their playbook.
Whispers of “screw these playbooks” will be start easing into water cooler conversations (ie — private Slack channels). The seeds of resentment will be planted.
Wide-scale mutiny is not far off.
Is there a better way? What is the solution?
But we need to do everything possible to ensure our accounts are successful!
I appreciate that instinct, but you have to remember: the product-led model is not designed for manual intervention. It’s designed to open your software up to larger audiences and drive larger numbers of accounts. This means you simply can’t touch every customer — as much as you may want to. And, not for nothing, many of your customers don’t want that either.
This means that the playbook is the wrong framework for a product-led CS (or Sales operation). What you need instead is a proper signaling system. A system that highlights which accounts on which you should (and can afford to) be spending time. These signals can generate/trigger specific actions (like task creation, or emails, for example), but they will be targeted to specific accounts that deserve/require your attention.
And this is mainly a question of smart segmentation — based on more than the age of the account.
Creating a Signaling system to replace playbooks
A good signaling system is much more in line with how a product-led model should operate. It replaces the rigid, linear nature of the playbook with a framework that “bubbles” up the right accounts and truly makes your team more efficient (and, dare I say, more proactive).
To build a good signaling system, you need to (1) define your signals; and (2) create a plan for actions against those signals.
When creating signals, you should focus on two major vectors:
There are 3 stages of Tenure we’ll consider in this discussion (you can define more if it makes sense for your business):
Brand New: accounts less than 30 days old
Young: accounts between 1 and 3 months old
Mature: accounts 4+ months old
When we talk about Engagement/Activation, we talk about them in the context of metrics created and tracked in Sherlock (but again, you can have your own versions of these metrics if you want to build them):
Engagement: An over-time measurement of how much a user or account is using your product.
Activation: A 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.
Of course, every product/business is different, but this is an example of a signaling framework you could create for you SaaS product based on Tenure and Engagement/Activation:
For some businesses, it might make sense to add a third vector — account size (or potential account size). You may want to create signals for accounts of a certain size or you would plan different actions for accounts of different sizes. That might look something like this:
Defining Signal actions
Once you have your signals established and you are tracking them well, you should build a set of suggested actions for each. The actions that you design need to be unique and tailored for your business and operation. But as an example, your program could look something like this:
As you can see, by assigning specific actions to your signals, your signals will start to act as triggers. While at first glance this may resemble “a playbook”, there are a couple of big differences between the playbook and this signaling approach.
First of all, Playbooks are traditionally applied in a more global fashion — we must apply this playbook to every account (or the majority). Signals, on the other hand, are designed under the assumption your team simply can’t deliver high-touch attention to every account (or even most of them) and need to be able to focus their work on those with the highest priority. They don’t need a system to ensure every account is getting attention — they need a system to help them focus on the right accounts.
Secondly, playbooks are generally designed as linear processes — they call for several steps that the rep and user need to pass through to complete the playbook. Whereas signals assume a non-linear engagement pattern for accounts. They trigger single actions when certain engagement conditions are met.
For these two reasons, a good signal program is much more in-line with the needs and flow of a product-led business — and the journey that these customers take.
Other non-behavioral, scheduled signals
After reading a draft of this post, another friend in the CS space reminded me of a few “signals” (he referred to them as “touchpoints”) that he considered “scheduled.” These included things like QBRs (when relevant), NPS surveys, and renewal conversations that happen at specific times and are independent of any behavioral criteria.
These made a lot of sense and if applicable, should be added to your program appropriately.
While much of this discussion will seem obvious to many in the space, my hope is that it will help break others from an approach that was borne at a different time. A different era of the software business. By moving your approach to a better framework — one more in-line with how modern software is sold and serviced — you can liberate you and your teams from the oppressive weight of the traditional playbook!
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?
TL;DR: Here’s a video version of this post
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.
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:
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)
Total for month
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.
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.
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.
Likelihood to close (based on Activation)
Total for month
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!
Top 10 Product Engagement Segments for Customer Success Teams
Segmentation: it’s all the rage. And rightfully so.
Marketers know that bucketing people and sending more targeted messages based on traits leads to increased ROI. Product knows segmentation helps them find feature adjustments that specific users will find more useful. Execs know looking at segmented data helps them find holes in the business. In short, modern SaaS leaders know: if you want to get the most out of your data, you need to segment it.
Another way to segment your audience — product engagement. First, let’s clarify: These segments aren’t based on firmographics or website visits. The segments we’re talking about here are based on engagement with your specific product. And while every SaaS business has its own singular nuances, there are some standard product engagement segments that all modern SaaS business should be tracking (and acting on).
That’s how you win the game of SaaS.
Some questions to ask when setting up your product engagement segments
What actions are these people taking? What are they using?
As you are well aware (surely), there are several things that one might do in any given SaaS product. Some events are more important than others. Login, for example, is not that important. Sure, everyone does it, but how much value does someone get from logging in? An obvious fact, that — not a lot.
Creating a report, however, is more valuable. A user would certainly derive more value from creating a report than logging in. (This is, of course, assuming you have the sort of SaaS app where creating a report is part of the value.) Some events are likely even more valuable than creating a report and similarly some events are likely less valuable than creating a report.
Some of these important events are required for a user or account to reach first value, which you might bucket into an Activated segment for your Sales team (more on that later).
Where are they in the customer journey?
(PSA: This post is going to talk about customers in the trial phase, i.e. the ones that your Sales team cares about)
Trial? Onboarding? Growth? The SaaS Sales cycle is not a one-and-done operation. If you’re going to keep your business alive, you’re going to need to both acquire and retain customers.
There are three major phases of the SaaS journey and the actions your teams will want to take are different in each:
The Trial phase (this is the one to consider when making segments for Sales teams)
The Onboarding phase (for Customer Success Teams, which we’ll get to in a later post)
The Retention and Growth phase (for Customer Success Teams, which we’ll get to in a later post)
Keep your Sales team focused with product engagement segments during the trial phase
Too many leads is not a bad problem to have, but it is still a problem. Product engagement segments during the trial phase need to help your Sales team weed through the trial accounts to find the ones that will convert.
When thinking about engagement segments for your Sales team, we find it helpful to think about both engagement with the product (Activation) and where customers are time-wise (because trials are a finite length).
You’ll want to segment your trial audience by:
Product Engagement Segments for Sales Teams
New trial accounts
Trial accounts that have just signed up. You want to focus on getting these guys to first value.
Product qualified leads
Activated trials. These ones are ripe for conversion.
“Red Hot” Trial accounts
These are hottest of the hot product qualified leads. Not only have they hit first value, but they are actively engaging with your features.
Soon to expire trials
The trials that are almost over. If they’re not Activated, you might have one last shot to push them to first value before they fall into the “need to reengage in a year” bucket
Activated trials not converted
Expired trials that hit first value, but didn’t convert.
1. New Trial Accounts
For most modern SaaS businesses, this New Trial Accounts segment is where it all begins. This segment will be the focus of your inside Sales team (if you even have one of those).
Who goes in this product engagement segment for Sales teams? Any account that has signed up in the last X days. Generally, the “X days” should be a fraction of your trial period (whatever you decide qualifies as “new”).
What do to with this product engagement segment for Sales teams?Send it to your Sales team. Send it via Slack, a CRM, email — wherever your Sales team is managing their leads because this engagement segment is their new lead list.
Keep in mind, it’s important that your Sales team not get overly excited and smother these new users before they have a chance to try the product (you have a free trial for a reason — you do, don’t you?).
Let these accounts work in the product and monitor their Activation progress closely. Once they get to a certain point in Activation, they will make their way to your next segment:
2. Activated Trials, i.e. Product Qualified Leads (PQLs)
This is an essential segment for every SaaS business with a try-before-you-buy model (whether that is a trial or freemium option). Tracking new accounts is great, but understanding when they have become Activated is even better. This Activation rate is how modern SaaS businesses qualify leads. This qualification is based on how engaged your accounts are with your product during the trial and, more specifically, how far they’ve gotten toward Activation.
Why is this product engagement segment for Sales teams important?Measuring Product Qualified Leads is key to building a high-performing, efficient sales process for a modern SaaS business. In fact, when salespeople call on Product Qualified Leads, they convert at 25-30%!
If you aren’t qualifying leads based on product engagement, you need to start today. And this is the segment where you will track them.
Who goes in this product engagement segment for Sales teams? At its most basic level, a Product Qualified Lead is an account that has become engaged enough with your product during a trial to have reached “first value.” This usually means that an account has completed some specific pre-defined actions, which you defined in an Activation checklist.
If you choose, you can also include some firmographic data in the definition of this segment for Sales teams. For example, you may want your Sales team only chasing larger, or more targeted opportunities. In this case, your definition of a PQL might be an account located in North America with more than 1,000 employees, which also becomes Activated in the product.
How do you take action on this product engagement segment for Sales teams? First of all, let’s get this straight — you just do! This is the list of trial leads that justify personalized, higher-touch outreach. These are the accounts that your Sales team should focus on. Make sure:
This segment is tagged in your CRM. Salespeople should be able to look at a list of their existing leads and quickly sort them by whether or not they are in a Product Qualified Lead;
You have smart alerts set up for this segment so that your Sales team is alerted whenever any account becomes activated and enters this segment.
Product Qualified Leads are the leads that are most likely to convert to a paid account. They are the leads that have shown strong engagement and achieved initial value with your product. These are the accounts on which your Sales team should be focused.
3. “Red Hot” Trials
When looking at the engagement of your trials, having a static Activated or not-Activated state is great, but more important is knowing how engagement with the product changes over time.
This Ret Hot segment highlights those users and/or accounts that have a significant increase in their engagement. (In Sherlock, we define that as an increase of more than 10 points.)
Basically, these are Product Qualified Leads on steroids. These accounts have reached first value and continued to engage with your product in a meaningful way after that. They’re really into your core features and are either completing Activation steps multiple times or delving deeper into what your product has to offer.
How do you take action on this product engagement segment for Sales teams? If there’s one segment that’s even more likely to convert than a straight-up Product Qualified Lead, it’s this one. Make sure your Sales team has these trial accounts tagged in their CRM of choice and they’re regularly checking in with them. As they say, get ‘em while they’re hot!
4. Soon to Expire Trials
This segment will help your Sales team focus on those accounts whose trial is coming to an end. Let’s face it: your trial period is an arbitrary time frame, but it does create some sense of urgency. As your accounts near this arbitrary decision point, your Sales team should be in contact with the main decision makers to do what they can to “encourage” a conversion, whether that means making one final push toward PQL status or making a push from PQL status to paid.
How do you take action on this product engagement segment for Sales teams? You should obviously tag accounts in this segment in your CRM of choice, but you should also set up alerts for your team for when an account enters this segment. You don’t want your Sales team to allow strong potential trials to expire unnoticed.
Sort this engagement segment for Sales teams by Activation rate so that your Sales team can be sure to focus their energy on those trials that are most likely to convert. (We’re looking at you, PQLs).
5. Activated Trials, Not Converted 30/60/90 Days Ago
These accounts are no longer in a trial, but that doesn’t mean they’re gone for good. Too often, once a trial is completed without a conversion, we write the account off, stop tracking them, stop communicating with them and just generally kind of forget about them.
But we should not.
“When we ran an analysis of 10 companies and looked at how long it took people to convert, 50% of conversions happened after the end of the trial period. That shows that there’s a lot of value in nurturing people. Even if people have already gone through your trial [without converting]”
Why is this product engagement segment for Sales teams important?Because these accounts may just be your most important future prospects. They tried your product and found enough value to become Activated (or a significant way toward becoming Activated). They just didn’t convert.
Instead of crying about it (and for God’s sake don’t let your Sales team send some snarky “breakup email”), keep this segment handy. Many times, a lack of conversion is due more to “bad timing”’ than not seeing value in your product. A change of internal priorities, organizational shifts, short-term budget issues, and any variety of “emergencies” could lead an account to not convert. So this is a segment for Sales teams you should have handy and readily available for your team.
How do you take action on this product engagement segment for Sales teams? In general, anyone connected to an account that doesn’t convert during a trial should be sent back to marketing and included in nurturing campaigns. They should receive all your product update emails (in case there was a feature that would reignite them), and your Sales team should revisit them regularly.
As a general rule, your sales reps should send a personal follow-up to these accounts every 6-12 weeks. They can mention new features, new relevant reference accounts, etc. They should treat these accounts as higher-touch, warmish leads. Don’t give up on them. You know what fixes bad timing better than anything?
Meet Pete. Pete is a salesman at a modern SaaS business.
Pete is amazed! Marketing has brought him many, many leads. Pete can’t wait to close all these deals. So many deals. “All these leads signed up for our trial,” Marketing said. “They’re definitely qualified.”
“Ok.” Pete is on-board with the idea of qualified leads. “Go get ‘em, Pete,” Marketing said. “Close those deals!” “Ok!”
Pete emails one of the trials. They don’t remember signing up. He tries another: “What do you do again?” They didn’t even know what his modern SaaS business did! Doesn’t sound like they love us.Pete is unimpressed.
Pete has spent too many days trying to chase all these trial leads. So many trial leads, so little success. Pete is a SaaS salesman, but he is not a happy SaaS salesman. Pete is a frustrated SaaS salesman. He doesn’t know what to do. None of these trials he are working out.
Finally, Pete connects with an account that has used the product. They have gotten themselves set up and added three other users. They are happy. They convert to paid. Pete is happy. Pete wants to find more trials like this. Pete wants to find all the trials like this.
He did a Google search to find a solution. There must a way to find our best trials. Pete believes.
Sherlock is a tool for modern SaaS businesses. Do you work at a modern SaaS business, Pete?
You do!? Sherlock is for you. Find trial leads that are actually product qualified. Have them delivered to Slack. Do you have Slack, Pete? You can have your product qualified leads delivered other places, too! HubSpot? Slack? A CRM? You like that, huh Pete?
Pete is excited. He has finally found a smart way to prioritize his SaaS trial leads. Now, Pete will close deals. Many, many deals.
In case you haven’t heard, PQLs are leads that are more likely to convert because they have found value in your product. If you’re a modern SaaS business, you want to find these leads. So the only question left — how? It’s elementary: you need a Product Qualified Lead process.
Ask yourself how many of your last 10 signups self-served their way to first value. If the answer is less than 4, read on!
A new user is almost always going to require manual support to get to the promised land. Complex products often require technical implementation, access to data or tools from other departments, or some deeper domain knowledge for a user to get value.
A complex product might have a free trial period, but is more likely to only sell to customers who go through a sales demo with a more hands-on approach.
Now that you’ve decided you have a complex product, you should think of the other main factor that will determine how quickly your Sales team gets involved. Namely, the size of a Sales opportunity. As with simple products, if a new trial holds the potential for more revenue, you’ll want to get your Sales team involved earlier.
At the very least, you should be able to categorize your new trial signups into Small, Mid-Size and Large revenue opportunities.
Oftentimes, companies will use the company size of a new trial as a proxy for opportunity size. This is certainly one way to go about it, but you define opportunity size based on whatever criteria makes sense for your business.
The more complex a product, the earlier your Sales team should intervene. Similarly, the bigger the size of the opportunity, the sooner you want your Sales team on it. Sounds simple, right? (That’s good, because it is).
If you have a complex product, you’ll need to get involved sooner regardless of the deal size. That being said, you’ll probably still want to wait for accounts to hit a certain level of Activation and keep your Sales and CS team focused on bigger deals.
Here are good rule-of-thumb Activation rate guidelines for a complex product:
Reach out when:
50% – 75% Activated
25% – 50% Activated
0% – 25% Activated
As you go through the next section, keep in mind the goal of a Product Qualified Lead process for complex products is to balance the time of your Sales team. That way, they are focusing on larger opportunities but aren’t abandoning smaller opportunities with a high likelihood to close.
These pose quite the conundrum for Complex products. You know your product needs manual support for most users to get value, but how much time can you afford to give such a small opportunity?
In these cases, the complexity of your product may just work in your favor. Wait, what?!
That’s right. Small accounts that are really interested in your value proposition — in the promise of your product — will make the effort to get set up and experience some of the value.
When their Activation is high, you’ll know they’re committed and it’ll be worth it for your Sales team to give them some attention. How high is high enough? We recommend waiting till they’re at least 50% Activated.
How to engage these Product Qualified Leads
Because it is harder for users to recognize the eventual value of a Complex product on their own, trial leads on this product will likely need some kind of demo of the product from your Sales team. They will need to see the finished cake (complete with frosting) if they are going to move forward and convert.
In these cases, it’s important for your Sales team to engage these users and invite them for an in-person demo of the platform, perhaps with an email.
These mid-sized opportunities are exactly what they sound like. Bigger accounts than a small opportunity, but smaller than a large one. The bigger the account, the more users involved, the more coordination necessary and the more complex the path to conversion.
So let’s be honest: Not many opportunities will self-serve their way to conversion with a Complex product, regardless of the size. But because these are bigger than small opportunities, you’ll want to get your Sales team involved soonish, probably at around a 25-50% Activation level.
How to engage these Product Qualified Leads
The challenge here is still the complexity of the product. It’s hard for users to identify the full value of the platform on their own.
Focus your manual intervention on booking a time to talk to your Sales team instead of pushing the account toward deeper Activation
When you get large opportunities to sign-up for you Complex product, you don’t need a great deal of product usage to justify, at least, an initial touch point from your team. In fact, you should feel comfortable reaching out to these accounts soon after they sign-up or when they reach just about 25% Activation.
From there, it’ll be somewhat similar to a traditional Sales process. Or at least as you’re going to get with a modern SaaS product. Start by identifying the key decision makers and make sure that they see the complete, fully-baked value of your product, which can be hard to do during a trial so get them on a demo ASAP.
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