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No, net promoter score can't predict churn. We cover the alternatives in todays episode
***
- (00:00) - Introduction
- (04:14) - What is NPS and why does it suck?
- (10:22) - Predict & prevent churn
- (11:02) - Signals
- (16:13) - Customer vintages
- (21:32) - The process
***
This episode is brought to you by Growblocks. Finding and fixing problems in your GTM shouldn't take weeks. It should happen instantly.
That's why Growblocks built the first RevOps platform that shows you your entire funnel, split by motions, segments and more - so you can find problems, the root-cause and identify solutions fast, all in the same platform.
***
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What is The Revenue Formula?
This podcast is about scaling tech startups.
Hosted by Toni Hohlbein & Mikkel Plaehn, together they look at the full funnel.
With a combined 20 years of experience in B2B SaaS and 3 exits, they discuss growing pains, challenges and opportunities they’ve faced. Whether you're working in RevOps, sales, operations, finance or marketing - if you care about revenue, you'll care about this podcast.
If there’s one thing they hate, it’s talk. We know, it’s a bit of an oxymoron. But execution and focus is the key - that’s why each episode is designed to give 1-2 very concrete takeaways.
[00:00:00] Toni: Hey everyone, this is Toni Hohlbein from Growblocks. You are listening to the Revenue Formula with Mikkel and Toni. In today's episode, we are talking about why NPS has a hard time predicting your churn and we share three different ways instead, how you could do it. And much more importantly, how you can maybe even prevent it.
[00:00:19] Enjoy.
[00:00:23] Mikkel: So what were you up to this weekend?
[00:00:26] Toni: I was at a wedding.
[00:00:28] Mikkel: Wow. That's why you have like the black spots underneath your eyes.
[00:00:34] Toni: Is it
[00:00:34] Mikkel: you're all red in your face.
[00:00:35] No, I'm joking.
[00:00:36] Toni: it was actually, it was really cool venue. It was like in a, in one of those Danish, countryside castles.
[00:00:43] That they turned into a venue for wedding stuff. and it was fantastic weather.
[00:00:48] And, uh, obviously, and this is, I'm not sure if this is like a global rule of it's just German or only Danish, but you have to keep on your jacket until the groom takes off his jacket. And you know what he didn't do that. So we were basically like on the patio. Um, there were no, no umbrellas.
[00:01:08] umbrellas. And it was also really nicely protected from wind, so it was just the
[00:01:14] Mikkel: the
[00:01:14] Toni: Um, and, um, I was okay, kind of, you know, I managed, a lot of other people gave up, uh, I, you know, a couple of us managed through this. The thing is, you know, then the groom at some point, we were like, yes! And he had a three piece on, because obviously he wanted to be fancy. So what did he take off? His vest.
[00:01:33] Mikkel: No, but that's, you know, um, it was,
[00:01:40] Toni: but that's, you know, um, it was, it was actually pretty cool. Really cool. Really good wedding. And then there's, there's one thing that I was extremely crazy. Uh, we started to have dinner at 6. 30 and, uh, as, as per the plan, this was not even going over anything, as per the plan, um, the, the dance kind of between bride and groom was scheduled for 11. 30. So what happened between 6. 30 and 11. 30, it was a four course dinner. Which doesn't take, what is it, 5 hours? What took the time was, and I kid you not, 19 speeches. Jesus. 19. It's like
[00:02:22] Mikkel: a conference right there,
[00:02:24] Toni: Um, yeah, exactly, exactly, um, and that was, um, you know, uh, also all in Danish, I only got like 80, 80 percent or something like
[00:02:34] Mikkel: you listening to the podcast meanwhile?
[00:02:36] Toni: yeah, I was taking notes, um, and then in Denmark, there's also, they have so many funky little kind of games, right?
[00:02:43] So one is, When one or the other, so bride or groom leaves to go to the restroom, um, let's just say the, the, the bride goes to the restroom, every single lady in the room then needs to stand up, go to the groom and kiss him on the cheek,
[00:02:59] Mikkel: Yeah, or mouth, whichever they are.
[00:03:01] Toni: wherever, wherever,
[00:03:02] Mikkel: It's a bit
[00:03:03] Toni: yeah, I know it's pretty, pretty weird, and the same also the other way around, when the groom goes to the restroom, then every single man, not every single man, Every man, not every single man, but every, every, every man needs to get up and go and kiss, um, kiss the bride.
[00:03:18] Mikkel: That, but those parties, they're the best, like the whole vibe. It's everyone is happy.
[00:03:23] It's like, yeah. Really more people should get married in like our family at least, so we can go to a party.
[00:03:29] So I'll say the last wedding I was at, there was also a real problem. Party Pooper there because she was like, we were standing after the dinner and everything having drinks.
[00:03:36] She was like, you know, statistically X percent they get divorced which one of us here is gonna get divorced and you just stand there's like That's what you want to talk about is, you know, so, you know, it's hard to predict
[00:03:47] Toni: statistics are changing a lot. So I actually had like a peak, um, after getting divorced. Now, after getting divorced was not like a massive, uh, like a big social no, no, anymore. Suddenly it was okay ish to get divorced. And I think it peaked in the eighties or nineties or something like this.
[00:04:04] And since then it actually came down, it was like up to 50%. and I think now we're like 20
[00:04:09] Mikkel: But the point I was trying to make
[00:04:12] Toni: But you can't predict. Yeah,
[00:04:14] Mikkel: The point I was trying to make is you can't predict it. Right. And we're going to talk a bit about something, uh, you also cannot predict at least with NPS and that's churn.
[00:04:23] Toni: That's right. So
[00:04:24] Mikkel: we had an episode where we basically just pooped all over NPS and we're going to do it a little bit again today.
[00:04:30] But we have a bit of a structure to also give some, you know, practical advice. Someone in the room said we also can't just sit and trash talk NPS. We actually have to share advice. So I guess we're doing that also. I guess we're doing that also today.
[00:04:44] Toni: So what is NPS? Net Promoter Score. This was invented by I think Gartner at some point. Really cool idea. Basically, on a scale from one to 10, you ask your customers And you can also do with employees, by the way, you ask your customers to rate you how much they basically like you,
[00:05:05] Mikkel: Yeah. How likely you are. How
[00:05:07] Toni: are you to mention us to
[00:05:09] Mikkel: To recommend. Yeah.
[00:05:11] Toni: kind of something like that. Um, and, uh, then the, the story goes that one to six, I think is detractors. Um, seven and eight is, uh, is neutral basically. And then nine and 10 is attractors. So the promoters, those are the people that basically kind of will help you.
[00:05:31] Right. And, and then, you know, you do some math.
[00:05:34] Mikkel: uh, and
[00:05:35] Toni: The whole scale is obviously slated towards the negative. Only the nines and the tens give you a plus one. Everything else gives you either zero or minus one. Um, and you try and get to the, I don't know, thirties. You can also be in the minus. You can be minus 100.
[00:05:48] You can be plus 100. You can be all the way in between. Um, but basically you try and get into the thirties and above for B2B SaaS basically. So B2C apparently is in the eighties sometimes, but like B2B SaaS is, I don't know, thirties plus.
[00:06:00] Mikkel: know, So,
[00:06:02] Toni: that sounds pretty cool, right? For a, oh wow, you know, that, that probably is going to help me to figure out who likes us and who doesn't.
[00:06:09] Um, but there are all kinds of issues around the NPS thing. Lots of people trying to cheat it. I've seen people color coding it. I've seen, so then, then the whole idea goes away, right? When you suddenly know that only nine and 10 is green and everything is kind of red. Kind of the person, ah, okay. I was about to give a six because you're kind of good above average, but now I know that that's actually not what it means to you.
[00:06:31] Right? So that doesn't work out. I think another thing that I've seen is to just exclude people from the survey that have given you a bad,
[00:06:39] review,
[00:06:40] bad review.
[00:06:41] you That's a way how you get it up
[00:06:44] Mikkel: it's when, that's when it's on an investor metric, by the
[00:06:47] Toni: yeah, yeah. So exactly. Um, and, um, then thinking about, oh, you know, my MPS is good. Don't need to worry about churn.
[00:06:55] That, that, you know, in that particular case doesn't work out, but it doesn't work out in general, right? There might be so many other reasons. Why someone says like, no, I actually don't want to recommend you to anyone. It doesn't mean that I don't like your service. It just means that I don't want to recommend you
[00:07:10] Mikkel: you to
[00:07:11] Toni: like, is that okay? Can I really like you and kind of really love this thing on maybe upsell, but not talk about you, please. Um, and, uh, and, and the other way around. Right. Um, uh, just because someone wants to talk about you with other folks doesn't mean that they're actually kind of getting the value out of this thing.
[00:07:27] It might be by the way, but it's not really the same
[00:07:30] Mikkel: Yeah, and I think one of my favorites were the whole, well, in some cases, I'm going to promote it. In some cases, I'm going to do the opposite and say, no, you don't want to buy this solution. It's not for you, my friend. And I think it's just, again, it's a tool to measure potential word of mouth.
[00:07:46] It's not got anything to do with retention or expansion or churn. That's not what it was designed for, yet there's some kind of insistence to connect the dots. And even still today. When it's proven, I would say a couple of times that there's no relationship between NPS and retention. People still go, no, no, actually, you know what, actually, we used it for that and it works out.
[00:08:11] It's like, great, congrats,
[00:08:12] Toni: So to kind of underline this, we recently found, um, some insight partners, uh, research and they didn't kind of, you know, they didn't intend to show it like this, but, but they're basically plotted, uh, on a, on a, on a graph that plotted
[00:08:27] net revenue retention versus NPS.
[00:08:31] Mikkel: Yeah.
[00:08:32] Toni: And, you know, if, if that, if NPS was actually good for churn prediction, what you would actually then want to see is if you have a higher NPS, let's just say you have an NPS that's kind of going towards the 80.
[00:08:44] then you want to see a higher net retention rate because there's a correlation between those two things. It may be even a causation because it's like, you know, it's obvious, right? If they're promoting you, then they must be like happy and they want to upsell. And then the, the, um, the opposite also would need to be true that if you're going, you know, into a, a bad lower or negative net promoter score, you should have a lower net revenue retention.
[00:09:08] Turns out. that, you know, the data, and this is, you know, only a sample of 66. So it's only kind of, um, you know, from folks from the insight portfolio that, that answered that specific survey. it's basically completely flat. There's nothing, those two things have like nothing to do with one another. There, there's, there's a range of people in terms of, you know, the NPS that they're having.
[00:09:29] And then there's a range of people in terms of the, um, you know, uh, net dollar retention that they're having. Those two things have nothing Nothing in common with one another. and that's, uh, it's completely after.
[00:09:40] Mikkel: But also just imagine this, you've gone and bought Salesforce, the CRM. You have hundreds and hundreds of reps using it.
[00:09:48] You've built a ton of reports, a lot of, you know, operating cadences around the tool, all these integrations, you could be deeply dissatisfied, yet still renew. Yeah. Right. You could literally give a zero or, you can’t do minus but you know, you could give the worst score on nps and everyone on your team could probably do it, but the likelihood of you ripping that out?
[00:10:09] Toni: No, but also every person you meet on the street, you could tell them, don't buy Salesforce, like, just like everyone you meet, don't buy Salesforce, you also don't buy Salesforce. It still doesn't mean that you're going to churn Salesforce.
[00:10:22] Mikkel: So, but ultimately what people want to achieve is they, they want to be able to predict and ideally prevent churn, right?
[00:10:30] So let's get into what are folks doing? What can they do? How should they look at this
[00:10:34] Toni: So, I think the best way to look at this is to try and You know, break it down into three different buckets.
[00:10:43] And this is, you know, what we've learned running our own businesses, you know, uh, working with our customers to figure this thing out. Um, and it's really, for me, it's, it's three main things. One is signals. Next one is cohorts. And the third one is process. So let's unpack each of them as we go through this.
[00:11:02] So let's start with signals. a signal could be, um, that, someone is checking out a competitor of yours, right? So in Falcon and social media management, we could see, um, the posts that were, that were published on, uh, on Facebook. And we could actually see the fingerprints. Of a potential competitor, right?
[00:11:25] And if one of our customers suddenly started publishing content on those networks, using one of our competitive apps, we're like, okay, danger, that's, that's a problem, right? That's an intent signal, uh, or this is signal based kind of people would call it intent. Um, I think there are other examples.
[00:11:43] There are
[00:11:44] Mikkel: had Slack, right? So it's got kind of the inverse though. But, uh, Mark Roberge talked about, well, within 30 days, if they post 2, 000 messages, that's a leading indicator of retention, right?
[00:11:54] And opposite, if they don't reach that, then it's like, uh, oh, uh, that's, that's not a great signal. Yes.
[00:11:59] Toni: and that kind of correlates a little bit with process also, we're going to talk about this later. But then there are things like, um, you know, MailChimp had this, When someone hits the, uh, download all my emails,
[00:12:12] Mikkel: basically export your list,
[00:12:13] Toni: export my list button.
[00:12:15] That was, that only happened for like a really good reason, you know, and there might be a couple of other things. And then those, those are signals and those signals you can use. in order to kick off specific actions, you can, you can look at the account and it's, it's good for account based churn prevention, right?
[00:12:34] It's kind of reactive, but it's still more leading than getting a cancellation. You know what I mean? you could say getting a cancellation is also a signal,
[00:12:42] Mikkel: Yeah. Well, yeah, yeah. It's, uh,
[00:12:45] Toni: And you usually have 60 to 90 days time still to turn this around. Yeah. But it's, that's also a signal that you get that, Oh, there's something not right with this account.
[00:12:53] We need to do something about it. And then there might be other signals that are leading to a degree, right? And those signals could be a specific usage pattern on your platform. Um, it could be. Um, it could be one of your main stakeholders leaving. That could also be a signal, right? Um, and, and all of these different signals, they're then, they're leading indicators and they should kick off a specific sequence of action that comes out of it.
[00:13:18] So you can, you can take, um, you can try and win back the account or secure the account or double check that everything is fine, right? That is signal based churn prediction.
[00:13:28] Mikkel: So how would you go about this? Like, would you do, I'm just guessing here, do some ad hoc analysis to figure out what are possible signals and then start instrumenting it to, to have them in, uh, you know, available and use them or?
[00:13:40] Toni: that's exactly how I would do it. Uh, I would basically kind of use, um, it's almost like very specific, um, I would say listening for what, what's going on. It's like, Hey, you know, I would almost build a Slack integration
[00:13:54] for someone hit this button, boom, this kind of goes into the Slack, you know, churn prediction channel and says like, Hey, here's, here's an issue, right.
[00:14:03] Um, I think that's pretty straightforward. And I think the, the difficulty actually is.
[00:14:09] Mikkel: um,
[00:14:09] Toni: You should only have those signals if you have an idea what you want to do when they come. So just, you know, looking for all kinds of signals and then you have like a very busy Slack channel with, with these things coming in and no one is taking action, that's not going to work out for you.
[00:14:26] But really having a playbook in the draw. Okay. If, if this happens, then, you know, those sequence of events kicks off and we need to kind of do something about it.
[00:14:33] Mikkel: But I think it also triggers the question, like, for some, this is at least some very SMB ish PLG vendors will do this. They will try and just hide anything to do with cancellation, not even have it in the product, like talk with a person. So I don't like that play, by the way. But maybe there's an argument to be made that in Whatever product you have, you make available stuff like the contract, when it's up for renewal, some of the terms, even cancellation.
[00:15:00] So when people view something like that, you kind of have a, Oh, they're looking at their terms right now. We should probably get on the horn and talk with these folks and do something,
[00:15:10] Toni: I think that's a good idea.
[00:15:11] It's very similar to, let's just say you don't want to publish your pricing on your website. Because you're B2B, you're trying to hide this, you're not, you know, it's not so easy basically. Um, You should still have a pricing page, but it doesn't show any numbers potentially, but it's a fantastic signal for you to see when someone hits the pricing page, like, okay, so this is, this is, you know, a very strong intent signal, basically, and, and the same in the reverse, right?
[00:15:39] Kind of give them options where they can, where they can express, um, that, you know, maybe something isn't going so well. You know, it could be in an SMB when also the, the, the account, uh, page, and then they kind of go into contracts and kind of, that's, that should be a signal for you, right? That someone is looking for the cancellation button, for example.
[00:15:58] those kinds of things. Okay. Yeah. So
[00:16:00] Mikkel: So this is you know quite frequently to let's say trigger process those signals Let's move to the other because this is very much on the prevention side. I would say then you mentioned cohorts
[00:16:13] Toni: Yes. So cohorts. This can be cohorts based on time. So think about it as like vintages, um, vintage from Q1 two years ago, vintage from Q3 five years ago, whatever.
[00:16:27] and you know, this is how most people think about it. Uh, basically a time based cohort. What is much more helpful, though, to think about it as a, as predictive cohorts using other ways of segmenting this. So time based can be a segment, but another segment can be, um, you know, a segment in the, in the, in the company size way.
[00:16:49] So is it mid market, is it enterprises, is it SMB? What product did we sell them? Through which channel? Inbound, outbound? Um, those are all segments. and you can use them in order to predict how that cohort Is going to behave over time, right? So let's just say you are, you're outbound mid market EMEA cohort will have one specific behavior over time, uh, that might be different from, you know, another segment, right?
[00:17:17] Uh, another cohort. so what, what would you want to use this for? So number one, you want to use this in order to. uh, predict out your, your churn, uh, over a year's time, so to speak. Right. And that, that is now important for a couple of things. So for example, if you want to increase the exposure in the US, if you want to add more outbound, if you want to do all kinds of different things, these things, uh, these customers that you acquire that there might be similar in terms of how they churn and upsell, there might also not be.
[00:17:49] So as you push around the weights on kind of where you want to have more versus less. you then want on the flip side, understand how that is probably going to affect your churn, right? And, and this can be pretty expensive if you get it wrong. Um, so basic kind of using this is for planning purposes, kind of a good thing.
[00:18:07] And then for monitoring observability purpose really is. Okay, this cohort, we expected it to behave like such, but now that we're getting, you know, all the updated actuals and we're seeing that it diverts from what it actually should have been, what's going on here? And this is important for two reasons.
[00:18:25] Number one, you won't be able to spot that. And the overall, like it's, you, you, you overall gross retention ticks from 91. 3 to 91 flat. It's like, you won't, you won't be able to spot that, but if you go down to the cohort level, you will maybe see that it went from a hundred to 80 or something like that.
[00:18:49] Right. And that actually, that can give you, you know, tons of insights to then dig into this.
[00:18:54] Mikkel: Mm.
[00:18:54] Toni: Um, to basically take action. Right. Um, so that's, that's kind of how you can think about it. And, and sometimes people, use like, um, or many times they use the like churn reason, for example, out of which they're like a gazillion different ones. And every time it's like, ah, yeah, I understand. I understand. And I think this is less helpful. to, um, you know, predict and take action than it is, for example, to do that through this potential cohort, you know, approach.
[00:19:21] Right. And, and then you basically see. Out of this cohort of, I don't know, 80 accounts, you see the ones that actually churn and you kind of really dig into this. And instead of going at it with a, ah, we know that those 30 accounts churn because of A, B and C reason, which is kind of the wrong way to kind of go about it, actually can actually truly investigate and say like, okay, What was the actual story that happened here and what really happened?
[00:19:46] And those, those reasons might map or not map to the churn reasons you have. And that kind of enables you in a much better way to actually take action based on this, right?
[00:19:56] Mikkel: I think you also, uh, we talked about another point of error, which was basically imagine buying gong.
[00:20:02] four, five years ago, um, and then coming up for renewal today, the product has fundamentally changed, right? Um, and I think that's also something, you know, that, that's important to reflect over and why the cohort actually matters. You might be able to realize a couple of things you wouldn't otherwise, actually.
[00:20:20] Um, and I think that's, that's a big factor. Another could be if you've, you know, increased the prices year over year and someone were locked in a multi year contract, all of a sudden they're looking at a massive cliff, they now need to climb up and they were like, well, we just got it for this so I could send an invoice or whatever it was.
[00:20:36] And now it's 10X the price. It's like, thanks, no thanks. Right. So I think there might be some important distinctions there. And it is like unblending the funnel, by the way. We've talked, we've talked a lot about it on, let's say the acquisition side. And it's super critical for you to understand where the efficiencies truly are in the same turn its, its you know where potentially are we gonna get hit by churn and can we do something about it
[00:21:00] Toni: and I think the, you know, in terms of predicting churn, preventing churn, um, this is not going to help you pinpoint which account needs attention, you know, it's like, that just be, that's not what this is trying to achieve.
[00:21:13] What this is trying to achieve is really the, You know, number one, the prediction side in general, and then how does my prediction live up to the reality and then digging into understand why. Right. And that can help you update your prediction going forward. Uh, but it can also help you learn from a different lens, from a more naive perspective, what went actually wrong here.
[00:21:31] Right.
[00:21:32] Mikkel: So let's get into process.
[00:21:33] Toni: So the third one is process. And, um, And this is also something that, for example, Winning by Design is using.
[00:21:42] They, whenever they go into a client, I kind of talked with them about this, they obviously have the bowtie and then, you know, defining the different conversion rates. And when you, when you really look at the right hand side of the bowtie, and you go through those conversion rates, you have gross retention rate as one of the conversion rates, you have net retention rate as one of the conversion rates, but you also have something that's called onboarding or implementation.
[00:22:04] I forgot, right? Basically how many of the customers we sold to, uh, how many of them actually failed onboarding and therefore churning or churned or it's like, there's a bit like gray area stuff that I don't fully understand. but this is another. Um, this is for, in the bowtie, that's another conversion rate, right?
[00:22:25] So why do they look at this? Well, it's basically from a process perspective, they can help you predict churn. Because they can say, if, and this is a known fact, by the way, this is not winning by the way, this is a known thing, folks that don't complete their onboarding in the first 90 days, High likelihood to churn.
[00:22:45] And it's different for all kinds of different products. There are products that take like a year to roll out. Don't get me wrong. but it can basically, you know, set the expectations around this, be like, Hey, you know, if,
[00:22:55] If the process doesn't work out or isn't, you know, hitting the timeline or something is kind of at fault there, that will actually lead to you being able to predict churn in a way, right?
[00:23:06] It's almost like its own little segment. Uh, to think about, the good thing about it, it's very clear what you can do about it. It's not a, Oh, you know, what do we need to change the product? No, you need to fix the process. Kind of, it's, it's pretty, it's pretty straightforward. Actually. You need to kind of get these guys from A to B faster and you kind of focus on that.
[00:23:23] Right. Another process example can be that.
[00:23:28] Mikkel: and
[00:23:28] Toni: this doesn't need to be PLG, um, but think about a customer only using, commoditized parts of your product, areas that every other competitor can also do, and maybe even cheaper competitors can do the same thing. Um, folks that never actually ended up adopting the, the second or third level stuff, right?
[00:23:52] that can be, that can be a process signal where they're basically in their adoption. You always want to start with the main problem. Don't get me wrong, that's always the start. But basically kind of they stopped there and weren't able, uh, or didn't achieve to go deeper and deeper and deeper and really are able to unlock the full value from this thing.
[00:24:07] And therefore, You have a moat to your competition, right? Because you're getting them to use the sophisticated stuff. and that, that kind of helps you to, to prevent the churn if you will. Right. Another thing is, and this is very much PLG and it depends a little bit on where do you, where, where do you have a customer or not, but really, um, you know, going through the funnel in terms of the aha moment, building your rituals around it and so forth, right?
[00:24:31] This is also a process, right? They go, they go from putting in a credit card or not to getting what this thing is about, to then making this part of their life. Um, if they're stumbling there, that's, and I mean, this is basically the drop off and they're immediately out, right? In this PLG world, but that's also a way of, you know, process thinking about it.
[00:24:53] And there are kind of many other ways to. Um, uh, to, to, to eventually see that and then understand, okay, here's something wrong here. that, you know, is what we are doing wrong or what the product is doing wrong in terms of onboarding. where we probably kind of have something that we can improve, right?
[00:25:11] And again, why is this useful? Well, number one, it's great because, you know, the, the percentage of folks that have failed onboarding or dragged out onboarding, high likelihood that they will churn kind of that's, that's a little bit of a, you know, account level prediction or segment level prediction already there.
[00:25:29] But then number two, this is, those are things where you from a revenue engineering side can actually. Ledge onto very easily because it's not a big conversation that you have for the product. It's, you know, it's, it's, it's not that it's just like, well, we need to get our shit together on the, on the right hand side of the bowtie.
[00:25:46] Mikkel: Yeah.
[00:25:46] Toni: And that's where kind of that is extremely helpful.
[00:25:48] Mikkel: But I think it's also the interesting point of this is when you build a process, a outcome of the process, it's usually also data points when you think about it, right? And if you don't have all the processes in place for, hey, are they achieving the impact they bought us for?
[00:26:04] I'm boarding completed, implementation done, all that stuff. If you don't run the process and produce data on the back of it, you just won't know. And what you're left with is, okay, these are the accounts that are up for renewal. This is, you know, our retention rate. So ipso facto, we're going to end up here, right?
[00:26:19] So all of a sudden you do have even some more predictive measures of some of those accounts versus ending up with a, well, the CSM put this account in green, so it's not going to churn,
[00:26:29] Toni: Yeah. And, and I think kind of what really sits behind these three buckets for me personally, is you, you don't feel like your hands are tied anymore. You feel like you can actually do something about it.
[00:26:41] Um, you get some signals in that helps you and you can kind of progress from there. And I think that's, that's the power with, you know, thinking those three buckets. Um, and, um, uh, and that's why it's. You know, that's why you can use it predictively because it tells you something about the future, but at the same time, you can also use it preventively, right?
[00:27:02] Kind of to do something actually about it. So
[00:27:04] Mikkel: really what it means if we kind of summarize the episode is that there's, Let's say almost two kind of data points you want to build up. There's the signals part and then outcomes of the process. You can use that to predict churn differently than you could before, really. Right. And also with the process, you have something you can then go and improve at the end of the day.
[00:27:24] and then you talked a bit about the cohort level, which is the different vintages you have. basically a unblending the funnel exercise, uh, for you to see, yeah, maybe on the top line, things look good, but there's a cohort from, I don't know, four years ago on a certain market. That's just churning like crazy.
[00:27:41] What's, what's going on there. Right. And that's ultimately what you want to achieve. That's, you know, and you can't use NPS for that, by the way, you just, you just, there's no way, right. These, these are better measures for you to use, uh, operationally speaking. Yeah. That's it. Okay. So how long, uh, you mentioned, uh, implementation time, if it's like, uh, above 90 days and high likelihood to churn, how long does it take to, uh, implement Growblocks
[00:28:05] Toni: implement people are always surprised about this. It takes between two weeks and 30 days.
[00:28:10] Mikkel: days. Boom.
[00:28:11] Toni: But the thing is actually the other side, you know, usually invest like four to five hours only, so it's kind of pretty I had a couple of conversations the other week. where people were like, ah, you know, we're not ready, you know, blah, all of that, all of those.
[00:28:23] And, you know, the objection is like, actually Growblocks gets you bowtie ready as part of the implementation. That's basically, you know, we never talk
[00:28:32] Mikkel: about it. Bowtie ready.
[00:28:33] Toni: but we basically kind of get your bowtie ready. Um, so you have all the fields that you need, you know, all the timestamps and stuff, and then obviously you can use it in Growblocks.
[00:28:41] Mikkel: Roblox. Yeah, and I mean, reference point BI is like, we're talking year plus. Right? That's like the
[00:28:48] Toni: if at all. But, uh,
[00:28:50] Mikkel: If you succeed,
[00:28:51] Toni: Who cares about bi? Who cares about bi? Honestly.
[00:28:54] Mikkel: good stuff. I hope this was helpful, uh, for you, wonderful listener. If you enjoyed it, please hit subscribe, follow, like, comment, whatever.
[00:29:02] Toni: To help us build, you know, this mission of educating or giving the MBA to SaaS go to market to everyone who needs it.
[00:29:09] Right? To have fewer people fucking up.
[00:29:12] Mikkel: Yeah,
[00:29:14] Toni: Actually, isn't that the mission?
[00:29:16] Mikkel: yeah, fewer people fucking up. Let's do that. Anyway,
[00:29:18] Toni: good one. Bye
[00:29:19] Mikkel: Bye.