On-Demand Workshop: The $100B Lifecycle Playbook - Session 1

Everything from the July 8th session is here. The slides and the full transcript.

The premise: I spent months auditing how Slack, Notion, Clay, Apollo, Lovable, Canva, Calendly, and Monday.com move a user from signup to revenue. It started selfishly. I wanted to see what companies with effectively unlimited resources were doing so we could use some of it in our own work. I posted the first audits on LinkedIn, they reached about 40,000 people and pulled close to a thousand comments, and after enough of them it was obvious these brands were all running the same handful of plays underneath. The playbook came out of that.

The Perfect Handshake

Most teams treat product UX, product data, and lifecycle marketing as three separate motions. The best ones don't.

The Perfect Handshake is the symbiotic loop between them. Product creates a signal the moment a user does something meaningful, and lifecycle reads that signal and acts on it: which campaign to enroll them in, which message to send, and how to personalize it. Without good product data landing in the lifecycle platform, behavior-based programs aren't hard. They're impossible.

Our own research found that nearly half of PLG SaaS companies don't track whether users reach activation at all. The ones that do see those users convert at 5x the rate. So the difference between companies that get real revenue out of their signups and companies that don't usually isn't strategy. The ideas are there. It's the gap between the product data and the lifecycle program.

Every revenue event these companies generate maps to one of five stages. Signup, activation, conversion, expansion, and churn or winback. No user journey is truly linear, but that's the frame the whole playbook hangs on.

What's actually in the playbook

I walked through the Figma file itself in this session, and it's worth knowing how it's organized before you open it.

It opens with a best practices section, which is the checklist every lifecycle program should be able to answer for. Global holdout groups, so you can prove what the program is actually worth holistically and by segment. PQL definition, so that in a hybrid go-to-market you can pull the enterprise opportunities out of the self-serve pool and route them to sales rather than converting everyone the same way. You would be surprised how few big programs have these in place.

From there it runs left to right by lifecycle stage, with the campaign initiatives that belong to each one and a note on which brand we learned it from. Activation and onboarding, for example, holds linear onboarding, which is the standardized content every user needs, alongside trial activation and behavior-based activation. Late-stage holds churn prevention and re-engagement. Churned holds winback. Underneath all of it is a global layer for the always-on work: feature updates, webinar invites, customer spotlights.

Play 1: Fix the Day One Cliff

The Day One Cliff is the users who disappear inside 24 hours, before they ever hit an aha moment.

Slack pushes activation up into onboarding. You name a workspace and invite at least one collaborator before setup finishes, so you've experienced the collaboration value before you've had a chance to drop off. Calendly does the same thing, making you link your calendar and email during onboarding and then pushing you to book your first meeting. Canva skips the blank canvas by asking what you're there to do and dropping you into editable templates rather than an empty page.

Notion runs it the other way around. It leads with education on everything the platform can do, watches what you actually build, and then comes back with event-based follow-up. Add a database to a page and Notion returns with fifty database templates.

That last one is the distinction worth internalizing. Linear onboarding is the content every new user needs. Behavior-based activation is lifecycle reading what a specific user just did and responding to it.

Play 2: Stop the clock, start the trigger

Plenty of programs still drop every new signup into the same timed sequence. On day three, ask them to convert. It ignores reality, so users ignore it back.

These brands run on triggers instead. A free Slack user visits the pricing page and Slackbot engages in the moment, with an email carrying the same message concurrently. Apollo caps trial usage by feature category and deliberately drives users toward those caps, so the upgrade prompt lands at the friction point, when they're most motivated to keep something they're already using. Canva takes what you said you were there for during onboarding and reshapes the whole activation sequence around it, so a salesperson gets "selling made easy" rather than a generic feature tour. Calendly runs the same behavioral signals across email, in-app, and push, so the channel is chosen by the moment rather than by habit.

Play 3: Defense wins championships

You can convert a lot of power users to paid, but if they don't stay, that investment disappears.

Slack uses behavioral data to find users showing negative signals and goes aggressive before they churn. Not 10% or 15%, but 50%. The targeting is what makes that survivable. A time-based program sends the discount on day 20 whether or not the user was going to activate on their own, which is margin you didn't need to give away. Monday.com offers a self-serve trial extension to users who haven't activated, buying them more time with the right touch points around it. Canva builds a real user profile over the whole relationship, use case at the start and survey and feedback data at the end, so a winback attempt months later has something specific to say.

The three-layer stack underneath

None of this works without the infrastructure, and the failure is almost never strategy. It's an orchestration gap, a black hole between the product data and the marketing platforms.

Three layers. A data warehouse as the single source of truth. A bridge, whether that's pipelines or a CDP, moving data out of the warehouse into the tools that act on it. Then a marketing automation platform as the engine, Braze or Customer.io or Iterable, running email, SMS, in-app, and push. Don't pass go without it.

The bonus section was the AI decisioning and testing model we're rolling out with a few clients. It replaces rule-based triggers with a decisioning engine that ingests enrichment, profile, attribute, and behavioral data and holds context on the whole campaign library, then decides campaign enrollment, channel, and the copy and visual for that specific person. That's the piece I'm most excited about right now.

Where to start

Define your activation event. What happens inside your product that best predicts a user will convert? That's the exchange of value, not the payment.

Map the enrollment moment. What has to happen before that activation event, and are you responding to it?

Then audit the lifecycle content you already have and ask one question of every send: is this driving activation, or is it pitching a feature? There's a difference between a feature and the experience of value.

The Q&A covered which of these tactics is genuinely new rather than just behavior-triggered, which came down to how Slack handles a hybrid go-to-market and uses enrichment data to route enterprise opportunities to sales instead of self-serve. The second question was the one everybody has: how do you get the product team to prioritize piping new feature data into the CDP before launch rather than after? Both are in the transcript below.

The slides

The recording (from session 2, the recording failed for this session):

The transcript

00:07:23

Jon Farah: So, I scheduled this workshop for 45 minutes. And the way that we're going to approach it is we've got about 30 to 35 minutes of content and then we'll carve out plenty of time for Q&A at the end as well. So, let me share my screen really quickly. I'll pull that up. Okay, great. And I also want to pull up the chat on my end to make sure that I have that.

00:08:33

Jon Farah: So, as you all know, and I'm sure you can see my shared screen here, we'll be walking through some really exciting learnings from the hundred billion dollar lifecycle playbook that we built. Now why is it a hundred billion dollar lifecycle playbook? And ultimately is because it's developed from learnings across companies like Slack, Notion, Clay, Apollo, Lovable, Canva, even Calendly and Monday.com. And what we did is we audited and analyzed exactly how they turn signups into revenue and how they use lifecycle to do it. And ultimately the workshop today is focused on how you can recreate it in your own lifecycle programs. So there are a lot of familiar faces on the call today, some folks I know personally and have even chatted with recently. And then there's some new faces on as well. So before I jump into sharing the content, sharing the playbook, I want to share exactly who I am and what LifecycleX is. It's the agency that I founded where we are a full stack lifecycle marketing partner for largely PLG SaaS companies and we're focused on bridging the gap between marketing and engineering.

00:09:42

Jon Farah: I've had the opportunity to work in SaaS for about a decade now. Our clients largely range from three to 100 million in annual revenue. And we've had the opportunity to drive some really exciting results for our clients, notably increasing transaction volume for our client VectorCare by 176%. So I wanted to give a little bit of context on who I am before we jump into the content for today. Now how we'll approach the session is we'll start by looking at the macro, right? What are the overarching themes that we've seen across these brands? Why did we even build this playbook in the first place? We'll take a look at the playbook itself. And then on the latter stages of the workshop, we're actually going to get into the individual tactics, literal screenshots of the emails, literal screenshots of the product experience, how ultimately all of this intertwines to convert subscriptions and signups into revenue, in the overarching common playbook that we saw amongst a lot of the companies that we audited.

00:10:48

Jon Farah: So, let me start by contextualizing, you know, why many of you may be on this call before we get into like the tactics and everything that we've seen from the audits. Largely, a lot of companies that we talked to, and maybe why you signed up for this session, is you're watching users sign up, they're ultimately clicking around and they're likely disappearing and they don't convert and we don't know why. Right? This is super common for a lot of these software companies that we speak with and what we hear a lot is they have the tools and they've figured out top of funnel acquisition. So they have the users but there's a massive gap between the product data and lifecycle revenue. Now this is ultimately the problem that all of these companies have solved and what we'll ultimately share in the playbook. Now this gap is very costly, right, and in the research that we've done we found that nearly half of PLG SaaS companies don't actually track whether users reach activation in their platform or not. Right? So it's incredibly difficult to identify how exactly to push users to activation and then conversion if we're not tracking it in the first place. And this is common knowledge to be expected, users who do reach activation, who do experience the value in which they signed up for in the first place,

00:12:16

Jon Farah: they convert at a 5x rate. Naturally, someone who experienced the value is going to convert and pay at a much higher clip than those who don't. And the difference between the companies that succeed at lifecycle and succeed at driving long-term revenue from all of the signups that they're acquiring in the first place, the difference really isn't strategy. Like for most companies the strategy and ideation is there, but ultimately what these companies have done an amazing job of, and what we focus on in our work, is closing the gap between the product data and lifecycle marketing so that lifecycle touch points very much so action upon the behavioral data, upon the behavior that's taking place in platform. Now, as we look at the one shared playbook, to contextualize what we built in the playbook and why, we were auditing each of these companies because we wanted to learn what they're doing that we could potentially apply in our own work as well. And along the way, realized that, hey, they have one shared common playbook.

00:13:26

Jon Farah: And that's what we'll take a look at today. So, to contextualize, we spent months going through their product flows, documenting their product flows. Many of you may have seen the audits that I posted and published where we evaluate each individual step of the user's journey and the tactics that each brand uses to convert signups to revenue. And along the way, something kind of surprising happened and this is where the playbook kind of came from. I started auditing these brands selfishly. I was saying to myself, hey, how can I look at what some major players who have virtually unlimited resources are doing and use some of those tactics in our work as well. And that ultimately led to the creation of the playbook. I posted about it on LinkedIn. The post went viral. We reached like 40,000 people and had like a thousand commented looking for the audits and the playbooks, that ultimately led to building what I'm sharing today. So before we get into the tactics, we categorized the tactics that we observed into a few key categories and that's what we'll talk through as we get into the individual details that we're sharing today.

00:14:36

Jon Farah: So first we'll look at the Perfect Handshake framework and how these companies use it. We'll look at the three highest leverage plays and specific examples, like I mentioned, screenshots and explanations and ultimately how to map it to your own platform, like how to replicate this. And as a bonus, we're implementing AI decisioning and testing models for a lot of our clients. So I'll share that framework because it's very much so like the next thing in lifecycle marketing and how teams are approaching it. So let's take a look at the overarching strategies before we get into the individualized tactics and literal screenshots of emails and things like that. So each of these teams, I mentioned it earlier, they do an amazing job of creating the Perfect Handshake between the product experience and lifecycle marketing. Now the best teams that do this, that comes to life as the product is very much so creating the signals in which lifecycle then ingests and actions upon. So for example, user did X in platform, therefore lifecycle does Y. User did X, therefore lifecycle uses this channel or that channel.

00:15:45

Jon Farah: So creating the Perfect Handshake between the data that's available to us and the lifecycle touch points. One to reason the campaign in which we enroll those users in, two to identify the message, and then three to create the personalization that actually drives users forward to full activation. Now, as we think about driving revenue in this model, right? Okay, we've created the Perfect Handshake. We have behavioral data syncing from our data warehouse through to our marketing automation platforms. Well, how do we drive revenue based on that data? Well, each of these companies uses five core journey stages in which lifecycle then applies tactics to progress users forward. So naturally in a product-led SaaS motion we have sign up that then drives users through to activation. There's a big gap between a user signing up and them actually experiencing the value in which they signed up for in the first place. That's where lifecycle and the Perfect Handshake comes into play. From there we're driving users to conversion.

00:16:49

Jon Farah: Right? A user has activated, they've experienced the value in which they signed up for in the first place. So then it's lifecycle's job and product experience's job to ultimately convert those free users to paid users. From there, we're looking for expansion opportunities. How do we use the behavioral data at hand to do things like drive upsells and drive seat expansion to create more lifetime revenue per user for the long term? And then lastly, we're looking at later stages of the user's journey. So how are we looking at churn and winbacks to ultimately identify at risk users, prevent them from churning, and re-engage and win back users who have already churned. So what we'll do from here is we'll screen share the whole playbook that you will get a link to after this workshop session. And then we'll take a look at individual tactics and screenshots to give examples of how you can replicate this in your own strategy. However, before I do, I want to take a moment to pause and ask you all to jump into the chat so I can get a better understanding of where folks are in their lifecycle marketing journey.

00:17:57

Jon Farah: Simply just type and send into the chat A, if you're building the foundation for lifecycle or starting to work on it, B, if you're fixing the leaks, or C, if you're scaling the engine. And it's also a great opportunity for me to have a sip of water as well. Perfect. So, I'm seeing a lot of C's. I'm seeing some B's and some A's. Yeah, this is great. This is great. I'm going to make sure to grab this really quick as well. Okay, perfect. Well, I appreciate you guys jumping in. And naturally the playbook is applicable no matter the stage. I think what I find is if you're starting from day one, it's a little bit overwhelming to look at the whole playbook and attempt to implement it, right? So, we need to identify, based on the behavioral and revenue data that we have, a few quick wins that we can implement initially. And then as we're looking at like B and C, right? That's then looking at the playbook and saying, okay, what sort of gaps exist that we need to fill with programs that are relevant.

00:19:18

Jon Farah: So, I appreciate you guys jumping in and giving me context. So, at this stage, what we'll do is I'll switch the screen I'm sharing and I'll share the actual playbook itself. So remember, and I know I already walked through this, playbook is very much so a consolidation of the one common playbook that we observed both in the work that we do with our clients and then also what we observed from the audits that we did with these brands as well. So let me pull up this window and this is going to be a Figma design file that you'll get a link to after the session as well. Now, I know on first glance it's a little overwhelming. So, I'll zoom in and walk through. And that's ultimately why I want to take a moment before we dive into the tactics to share the playbook so I can contextualize it and focus really actioning upon it once you have it in hand. So as we move from the left to the right here, we have a best practices section.

00:20:13

Jon Farah: So this is really outlining key tactics that every single lifecycle program needs to consider. These are things like, and I know I saw a comment earlier like, oh my gosh, you know, product data not actually syncing to lifecycle. You'd be surprised, right, at how clean it really is out there. And you'd be surprised as far as how few of these best practices are actually followed by some pretty big lifecycle programs. So these are going to detail things like how exactly to approach global holdout groups so we can identify the actual impact of the lifecycle program holistically or based on segment. It'll also outline things like how to approach PQLs, so product qualified leads. Let's say that we have a hybrid go to market motion and we have free trials signing up and we're aiming to convert them. However, we also need to identify the best enterprise opportunities within that pool of users so the sales team can reach out. So this best practices section is kind of like a checklist. Everything that you want to apply to your lifecycle program to make sure that you can, you know, once you've set up the Perfect Handshake, implement campaigns and an overarching program that really succeeds.

00:21:19

Jon Farah: Now, as we're looking down into the key here, what we'll see is these bars across the top of each section. These are lifecycle stages. And then the boxes within these are the lifecycle campaign initiatives that apply to each of those stages that users are in the journey. We also have some notes on which brand we kind of learned these from and which brand we pulled them from. So I'll just give a quick example as to how the playbook comes to life and then I'll switch back and start looking at tactics because I know we're about halfway on time here. So in this second section you'll see it's focused on activation and onboarding and each of these individual campaigns are lifecycle strategies that we need to implement based on this user's journey stage. So naturally we have something like linear onboarding. Now we call linear onboarding standardized onboarding that's relevant for every user. So information that our users need to know no matter what, no matter the behavior that's taking place inside their platform experience.

00:22:17

Jon Farah: Now we need to apply personalization, ideally use case personalization here as well, which is essential. But then we get into more complex strategies like how are we approaching trial activation and then how are we using behavior-based activation to identify when users qualify for campaigns that are going to drive them to the next best action in the platform. And then I'll show kind of a later stage example here. Like as we progress from left to right, we're getting later and later in the user's journey. So, as we look at late-stage users, we're doing things like implementing churn prevention and re-engagement campaigns. And then for churned users, we're implementing winback strategies and ultimately identifying how we're going to reactivate them. Down at the bottom here, and this is the last section that we'll look at before I flip back to the tactics, is a global section. So, this is where we're looking at naturally all the lifecycle priorities that we need to deploy on an ongoing basis. So these are things like feature updates when we update the platform.

00:23:18

Jon Farah: These are things like webinar invites depending on different content strategies that we have for long-term user engagement, customer spotlights, and so on. So this is very much so focused on consolidating our learnings across all of the audits and the work that we do and then documenting that against the user stage and how they progress through their journey with the platform. Great. And like I mentioned, you all will get a link to this Figma file afterwards as well. So I'll share that over. Now, let me switch my screen back and we'll take a look at a few of the tactics and screenshots that we observed from auditing each of these companies. So the first thing that we'll take a look at, I'm just going to take a quick sip of my water here before, is we'll look at, and we have three categories for tactics, and the first one that we'll look at is how these companies go about fixing the day one cliff. Now the day one cliff is when new users sign up and in the first 24 hours they disappear.

00:24:19

Jon Farah: They ultimately don't see an aha moment or full activation that keeps them coming back to ultimately down the road convert to paid. Now we'll take a look at exactly how Slack, Notion, Calendly, and Canva approach it. So let's start with Slack. So Slack has a really interesting onboarding strategy, where they drive users to full activation, or at least aha, before they've completed onboarding and setup. So for Slack, their aha moment is when primary workspace owners collaborate with their team, right? They're a team collaboration platform at their core. So what Slack does is they require users to name their workspace and invite collaborators before they've even completed onboarding and activation. So what Slack does is they push that forced activation really far up into the onboarding steps to drive users through it before they have the opportunity to drop off. Now Notion on the other hand, if anyone here has started with a new Notion account before, you know that there are a million and one different things that you can do.

00:25:27

Jon Farah: So, Notion takes like a cat and mouse approach where they first educate users on all of the possibilities in the platform, right? Driving users into the platform to start tracking behavioral data, see what they're doing. Then based on that behavioral data, they're using event-based activation to create personalization focused triggers that drive users to the next best action based on what they've already done. So they're not doing time-based generalized sequences, right? They're really focused on educating users so that they start to follow a use case and then using event-based and behavior-based strategies to drive users to the next best action. For example, you started a database. Well, here are 50 database templates that will help you make that database exactly what you want it to be. Now, Calendly, similar to Slack, they use onboarding to drive users to full activation. So, they're using onboarding steps where they require users to do things like link their email and link their calendar during onboarding, so that they experience that aha moment before they've even completed signing up.

00:26:40

Jon Farah: What they'll also do is drive users to book their first meeting. So they start to experience the platform versus just dropping them in an open platform and hoping that they get inspired by in-app notifications or guides to ultimately activate and adopt. Canva does an amazing job at skipping the blank canvas. Now, it's a combo of their in-app experience along with their lifecycle touch points do an amazing job of guiding users through templates that are editable versus landing them in a blank slate. Right? So, within their in-app experience, they're getting users to identify exactly what they're aiming to do. This is amazing behavioral data, right? Are you aiming to create your first flyer? Are you a salesperson trying to create a sales deck? Are you a marketer trying to build social content? Right? So, this behavioral data will come in. We'll talk about it later. But they do an amazing job, in setup in those initial 24 hours, in that initial touch point, of driving users to experience value using templates, editable templates inside that onboarding.

00:27:52

Jon Farah: Now, as we look at the next tactic that we really learned from all of these companies, we look at how they approached stopping the clock and starting the trigger. Now, the lifecycle marketers in here, or the stage C or B folks, are hopefully wholly familiar and hopefully aren't doing anymore, you know, simple time-based drip strategies, right? Hopefully they've all moved to a behavior-based lifecycle strategy and ultimately that's what we're sharing today and talking about today as well, and ultimately this is like the secret sauce of a lifecycle. So we'll share how Slack, Apollo, Canva and Calendly ultimately approach using behavior to inform their in-app experience and their lifecycle marketing touch points. And Slack specifically does an amazing job of asking for the upsell, asking for the conversion at the right time based on behavioral data. Right? So let's say that a trial user visits the pricing page. Immediately Slackbot is engaging with them to nudge them toward a conversion or ask them, you know, what they need to ultimately make that decision.

00:29:05

Jon Farah: And then they're also using lifecycle touch points off platform like an email to concurrently send the same messaging. Right? So they're using that behavioral data versus just like a time-based, oh my gosh, when I take a look at existing lifecycle programs and it's like on day three ask the user to convert to paid, right, like kind of throw off my mind a little bit. So Slack is a great example of how they use behavioral data to inform when they actually CTA someone to convert versus just scheduling it over time, which is horrible. Now Apollo on the other hand, their trial is very much so usage based. So they categorize a few different features or use case categories and cap the usage for each of those. Now what Apollo does is they drive users to complete those activities. They drive them toward maxing out their trials and hitting their usage caps so that when they do, they prompt them to upgrade at that friction point.

00:30:08

Jon Farah: So a user has already experienced and used the feature that they want, and at that moment they're then called to actioning them to convert. So teams again that are just saying, hey, convert on day three, totally missing the whole point of driving the user through their experience to activation and experiencing value and then call to actioning them in the moment, because ultimately they're the most motivated when they want to continue to get something that they're already enjoying in the first place. Now, Canva uses behavioral triggers and behavioral context to create personalization in their messaging. So, for example, Canva, right on onboarding, I know we talked about it before, I mentioned we talk about it later. Well, this is later, where we're looking at exactly how Canva uses some of the initial onboarding touch points to build context around users and their use case to then create personalization. Let's say that during the onboarding process, you identify that you're a salesperson that wants to create sales materials using Canva.

00:31:18

Jon Farah: Well, then the entirety of the personalization inside the content activation content that you get from there is selling made easy, right? So Canva has done an amazing job of creating personalization around the use cases of individual users to compel them to actually activate in the platform based on their individualized use case. So these are all examples of how exactly you use behavioral data to create the personalization and create the one-to-one experiences that drive users to activation. And I know we're running short on time so I'm going to kind of look through here. Calendly does an amazing job at using behavioral signals and creating unified journeys across channels. So, they're using email, in-app, and push alike to ultimately measure those behavioral events that are taking place on platform and send the right message across the right channel at the right time. So, they tap behavioral data really well as well. The last tactic that we'll take a look at is defense wins championships. Like I preach this all the time in the work that we do.

00:32:26

Jon Farah: What do I mean when I say defense in lifecycle? Well, it's looking at those later stage user journey steps where users are potentially looking at churning. So we're implementing churn prevention strategies. We're also implementing winback mechanisms for users that do churn. So we'll take a look first at how Slack approaches churn prevention. So they're using behavioral data to identify some of those negative signals. So when is a user doing actions or not doing actions that ultimately inform a signal that they may churn, and in that moment they're not waiting for the user to churn or fall off. They're getting out ahead of it and they're not offering a 10 or 15% discount. They're offering a 50% discount which is really strong, right? And again, when you mature from time-based strategies to behavior-based strategies, you go from sending a discount on day 20, even if a user may activate on their own, to sending a discount when the user is showing behavior that justifies the need for a discount to re-engage them.

00:33:32

Jon Farah: So, from a revenue perspective, not only are we driving more revenue for the long term, we're also avoiding, you know, giving offers when we don't need to. So, tapping that behavioral data is essential. Monday.com approaches this a little bit differently. They identify when users aren't activating and they offer them a trial extension, right? A let's start again focus, like how can we look at reactivating this user and how can we create an environment where the right touch points are in place based on the use case data that we have to create more time, right, more time for them to activate. So Monday approaches it a little bit differently and Canva does as well. As we look at behavioral data, as we look at signals, as we look at context for each individual user, Canva through all these examples I've shared, they do an amazing job at building a user profile. So they understand what's the use case when you're getting started. How do you create personalization around that use case? And then on the tail end of your experience, how do we collect data, survey data, feedback data that we can then use to add to your user profile so that when we look to re-engage or win back for the long term, we have a ton of content, a ton of information in that user's profile to personalize on.

00:34:49

Jon Farah: So, as we look at a little reality check here, and I know we've talked about it throughout, but as we look at teams that struggle to replicate this playbook, this strategy, what we've learned from these companies, we see like two different worlds. On the left hand side, what these companies do incredibly well that then informs their ability to implement this strategy is they're using behavior-triggered messaging, right? So they're actually looking at the user's journey and identifying the right message for them at the right moment. They're using real-time product signals that are landing in their marketing automation platforms. And then they're using event schemas that are feeding into their bridge. We call it, you know, a CDP, a customer data platform, but they're using the right bridge to then sync and create the Perfect Handshake. So they then can do things like behavior-triggered messaging. Now teams that really struggle with it, they're living in a world where they're using time-based drips. They're manually managing lists and segments and they have disconnected tools and data pipelines.

00:35:51

Jon Farah: So like I said before, the difference between being able to implement this playbook and not is not strategy, right? It's really the infrastructure. It's really an orchestration gap where there's like a black hole that exists between product data and marketing platforms. Now, for teams that want to, if you're on here and you want to implement this playbook when I share over the Figma file, but you don't have the behavioral data to do it, there's a simple starting point, and this is kind of what we're looking at here, right? Where we're looking at a three-layered stack where we have our single source of truth on the left side. This is our data warehouse, which we then bridge, whether we're using pipelines or we're using CDPs. We then bridge to our marketing automation platforms like Braze, like Customer.io, like Iterable, and that's the engine. That's where we actually implement multi-channel strategies across email, SMS, in-app notifications and push to drive users to full activation and conversion. So step one, phase one, don't pass go unless this is implemented.

00:36:58

Jon Farah: But if you are ready to pass go, I'm really excited to share the AI decisioning and testing model that we've built and that we're implementing for a few of our clients. And what the model really does, and I know this is a complex visual, you'll also get a link to it as well, but what we're looking at from left to right here is on the left side, we're looking at organizing signals and data. So, we're looking at all of the enrichment data, all of the user profile data, all of the attribute data, all of the behavioral and event data, organizing and orchestrating that data so that in the middle, our AI decisioning engine can ingest all of that data, all of that information, while having context around the lifecycle program to know exactly which journey, exactly which campaign, exactly which touch point is relevant for that user based on the behavioral data that's been ingested. Now, it goes from campaign enrollment, like which campaign is relevant, to which touch point and which channel and then all the way into the content within, like what's the right copy to say to that person, what's the right visual to show them.

00:38:06

Jon Farah: So these AI decisioning models are really interesting because we're moving from rule-based lifecycle campaigns, lifecycle strategies where we have strict parameters as far as when someone does and doesn't receive a message, into a more dynamic environment where we've equipped AI to decision around exactly what message is right. So what does this look like in practice? Like if you were to look at implementing this playbook, how should you initially approach it? Where I always like to start is defining the activation event. So, what event inside of your product is the best indicator that a user will convert? What is the ultimate pre-conversion event? What's the aha moment? What's the full activation? What's the exchange of value? Then you need to map the enrollment moment. So, what sort of activity takes place before that activation moment? What are some of the precursors that we need to drive users through so that they ultimately experience value and convert for the long term?

00:39:08

Jon Farah: Then audit your activation strategy and ask yourself, are journeys actually driving users to activation or are we just pitching features? Like are we just making callouts as far as what you could do versus what you should do? Are we just telling users about features instead of informing them based on their use case and providing the guidance that they need? So the playbook is yours. I'll share these slides. You'll have all the content for the day one cliff, how to stop the clock and start the trigger, building defense in churn and late stage user experiences, and then also our AI decisioning and testing model. Now, as you know, we built the playbook at LifecycleX from all of our learnings, but we also implement it. And what we continually find is that what stands in front of teams that struggle to implement this playbook is it's not strategy, right? It's not ideation. It's ultimately the infrastructure. So, at LifecycleX, we're the only lifecycle partner that brings data engineering as a core discipline to our team.

00:40:21

Jon Farah: So, we're not just building campaigns, we're also implementing the data layer that equips the ability to implement behavior-based marketing strategies. And we've seen some amazing results from taking a few of our clients through that maturity stage into behavior-based lifecycle strategies. A lot of the teams that we work with, as we look at post-implementation and scaling, we're also doing things like proving organizational value, like what's the impact of the lifecycle program, you know, using holdout groups and attribution for an organization and how can we drive more revenue using it.

00:41:29

Jon Farah: I also want to take a pause here since we've got a couple minutes before we wrap, and open it up for questions. Feel free to come off mute and ask any questions that you have. I'm happy to chat. All right.

Attendee: Quick question. You shared a lot of great examples there of what all these companies are doing and very impressive journeys. I think it's definitely something to strive for for a lot of us. What is something in those examples that really stands out as something new beyond just behavior driven triggers, like something you're like, wow, I haven't really seen this before, and this really like now you're excited to test it elsewhere.

Jon Farah: Yeah. No, I appreciate the question. One thing that's really interesting that we saw specifically from Slack's approach was how you handle hybrid go to market motions. So how you handle enterprise opportunities, how you use lifecycle to drive the right users to engaging with the sales teams, have those human touch points, but before you even get to those human touch points, how you use enrichment data to appropriately path those enterprise opportunities, right?

00:42:57

Jon Farah: Because some PQLs are more SMBs and can self-service into a team license, and others you have the enrichment data to drive them to a salesperson or have the sales team reach out to them. So I think the approach for multi-motion and using enrichment data to handle those touch points appropriately, like there's a lot of, I think it's very underrated, like how much complexity is really there and how to create that multi-motion in the user's experience, whether they're a self-service user, whether they're potentially an enterprise opportunity, and then how you approach those enterprise opportunities appropriately.

Attendee: Yeah, we're trying to untangle that currently on the B2B side. How do we do this multi-motion? It's complicated for sure.

Jon Farah: Yeah. Yeah.

Attendee: Another question. As you've been talking a lot about connecting CDP and connecting product, comms, marketing, like all that data together, how do you find bridging the gap between like lifecycle marketing comms and working with product teams, like that relationship and getting that data into systems?

00:44:11

Attendee: Like I ask the question because right now I'm trying to get our product team on board. We're doing the full journey mapping exercise which is great, and I'm also trying to think of ways where, like an example of, okay we have a new feature coming out. It always feels like an afterthought to pipe that data into the CDP, which really slows down our ability to support that launch, right? It comes so much later. So trying to bake that into the product process of when we're launching something new, that is just now part of the process to launch anything, is to get that data piped in. But I'm just curious, since you work with so many of those kind of teams, how that looks.

Jon Farah: Yeah. So, how do we essentially work across teams, right? And that's what I love about lifecycle, is that we have the opportunity to work with marketing, product, engineering, sometimes the sales teams depending on the go to market motion.

00:45:06

Jon Farah: And what's really interesting, we were talking about this the other day, is how you create SLAs, like service level agreements, with the other departments so that they provide the resources that are needed. And ultimately struggling to bring this to life is why I built out a lifecycle marketing pod within LifecycleX where we have data engineering along with our lifecycle and our creative resources as well, because it's don't pass go without it. Like lifecycle doesn't work unless we have that behavioral data. So some recommendations and things that I've seen work in larger organizations is when you sit down with the leadership teams that own those resources and say, hey, we need to put together an agreement where we get 10 hours a week of an engineer to do this so that we have really fast turn times when it's needed and we don't need to do things like sit on our hands for a feature launch because we don't have the behavioral data around that feature yet, you know, to send the right message to the right person. And so I ran into that issue so much that I built the solve within my agency at LifecycleX. But creating service level agreements and pre-existing agreements with the department leads, I've seen work at larger orgs.

Attendee: Brilliant. Thank you.

Jon Farah: Great. Well, I know we're running on time. So I think most of us are connected on LinkedIn. So feel free to send me a LinkedIn DM if I can help with anything. Happy to answer questions, and then I'll follow up with an email with all of the assets, all of the stuff that we looked at today, the slides, the playbook, the AI decisioning and testing model. And yeah, I appreciate you all carving out the time and chatting through this with me. Thank you all.