Episode 179: Account progression framework with Steve Armenti

This is the Full Funnel bTB Marketing podcast, brought to you by full Funnel dot io. Let's starve well join us of course. Yeah, thank you so much for having me. I'm psyched to dive into anything account based.

Absolutely. One thing that I would love to kick off with is the fact that I have seen you mentioned on your linked and profile. So when you are leading the Global Enterprise Demand GAN at Google, you were able to increase SCL quality score from such a five percent to age of five percent in under one year by partner with the SDR team right developing the shared okayrs and also create this cross functional alignment what I feel and I feel this is kind of the universe problem for most bit of B companies, right, but simply don't have this cross functional alignment.

Not just to say that we often fight for the credit, right, because this is kind of the typical bit of the setup. Looks like marketing needs to prove the marketing so are traving, the sales needs to prove the sales aurce aving to kind of measure everything by the last click attribution, and that's it. So well, I would love to dive deeper and understand The first thing is how do you define SQL quality score? Yeah, good question.

So SQL quality score, think about your your typical score is going to be probably driven off of a band like budget, authority, need timing, and it's going to be driven by your SDR team. When I was at Google, you know, granted, this was you know, probably four or five years ago now, and we were doing this, so we didn't have all of the fancy AI stuff. That we have today. But quality was a combination of marketing data and sales data.

And one of the particular metrics we've looked at obsessively was the what was called a rejection reason, so other companies might call it like disqualification reason, but it's a basically a sub field in salesforce. When an SDR was on the phone talking to a marketing qualified lead, they had to market as you know, for six different stages to either progress it forward or you know, disqualify it. And so one of those was disqualification, and then we had a whole sub field of reasons that they had to select for why they disqualified it.

And that data alone became the most important part of this SQL quality score because we could see, you know, if marketing registered a lead as a market and qualified lead, meaning they met the marketing scoring criteria of you know, ICP fit or right job title or really right persona, and then they hit some kind of behavioral engagement score. Marketing thought it was good, right, And so this is where you get humbled because marketing said we created the MQL. Here you go sales.

But then when you get a feedback loop from sales that says, we just qualified this because it's the wrong persona. Now, all of a sudden, that data got us thinking, well, what happened because on our side it was the right persona. And so, you know, small example, but you know, let's say in this case, they were selling to a purely it right, nothing engineering, nothing infrastructure, nothing data, just pure information technology. When we would send them a VP of infrastructure, our MQL scoring would pick up and see VP technology title cool MQL.

But then sales would come back and say, we can't sell that person. We can never sell that person. They don't buy you know, chromebooks in this particular case. So that that feedback loop was really key.

And you know t L d R there is you know, my team, and we ended up going through thousands and thousands of m q ls manually based on all of these rejection reasons from the sales team and and basically figured out that, you know, our m QL scores weren't as good as we thought they were, and we had to, you know, kind of take one on the chin and say, okay, we've got to go back to. The drawing board and fix some stuff on the marketing side lot. And was the hindsight what would you say, is the road treason?

Is the road problem of all of this? Right, because it doesn't start, let's say, when we have the fight for the for the even for the quality of that specifically, right would say that? Is it kind of the default GM playbook or anson else? Yeah, it's a question.

I think two things come to mind, and one is the the traditional enterprise marketing and sales, you know, the GTM motion I think is fundamentally. Not broken, but flawed. Right. I think the idea of marketing.

Services this first half of the funnel and they work towards a metric like an MQL, and then they throw it over the fence to sales and then you know, sales takes it from there and they own the back half of the funnel. And then you can even think about customer success. If we're talking about a software company, I think that that linear progression is not it's flawed, right, because it allows the teams to just get focused on that one thing that they're responsible for and then they can forget about it.

And so in this case, you know, I was I was guilty of doing that, right, Like I had to actually humble myself as a market leader and say what I'm doing is wrong. I've got to. Trust my peers on the sales side. I've got to take their feedback, and then I have to do something about it.

And so at a macro level, I think that is present. And then at a micro level, especially in enterprises or more mature go to market teams where let's just say you're you're dealing with, you know, thousands of leads on a monthly basis like five ten, you know, even more, some of these enterprise teams are dealing with millions per quarter, and so when you have that much volume, it's easy to just like sit back and hide behind all the screens and the dashboards and the trends and the graphs and not actually get into the data.

So one thing I advise all time is whether you're dealing with a thousand leads or a million. Just go into the data, like you've got to go in and look at the lead profile. You have to look at the sales notes. You have to look at the behavior of those contacts that they conducted with marketing and the different activities and that sort of thing.

Like you have to get close to it because that's where the real insights are. I think, like the ones we're talking about that really matter, and you'll notice you'll notice the good, the bad, and the ugly. If you do that, love it. And they're saying AI can help with this, I know that you go and maybe just a quick announcement for everybody, So we'll hostile as Savans Annual Virtual Full Funnel signment next week where Still would be one of the speakers and he'll be talking exactly about the topic of AI workflows for each stages of your ABM funnel.

So we'll leave it for that part, right, But anyhow, I would love to ask you, considering your experience of playing with A either think AI can help with this initial qualification and score them? I do, Yeah, And to give everybody a quick sneak peek, that's one of the workflows we'll talk about just in general, the idea of using AI for account scoring, leads scoring, intense scoring, really scoring in general is incredible And if you think about it, you know, even when I was at Google, right, one of the biggest companies on the planet, I was restricted by resources that were technical to build things like propensity models, you know, dynamics scoring and things like that, which are based on statistics and machine learning.

And now we all have access to that skill set at our fingertips, right, especially if you've used even. Just general prompting. Right, Like my first introduction to call it like statistical analysis was was. Just with chat GPT.

Right. I don't really know that much. I'm fairly technical as a marketer, but I could go back and forth with chat GPT and basically build you know, a machine learning expert, right, and then started to push further and say, okay, well, what kind of machine learning methods could you use? Which ones are.

Best for things like lead scoring or finding trends within data? And then you can go and you know, build that base of prompts and put that off to the side, eventually evolve that into more of like an agentic use of AI, where you can feed data in more real time and dynamically and then you can get an output at that point in time. And so it's incredible because I don't. Know too many off the shelf software programs today that have that level of customization and can deliver lead scoring at that at that scale and that level of personalization, right, And so I think that's that's the really the most amazing thing to me is we can we can We're pretty close to being able to build our own ABM tech stacks that are completely unique to our team, our product, our market, our motion, and we can, like you know, execute a lot of the steps that we use traditional software for we can now do with AI.

Yeah, that makes perfect sense. I totally agree. And just to wrap it up, so one of the solutions what I have heard from you and just correct me if I'm wrong, kind of to make the first step towards alignment with sales, even if we play let's say the MCL playbook and we can't do anything, is just manually qualifying all like, really do the market and lead qualification before transferring innocent to sales. Right, So this is the first thing and the second one, and you can do it, say yeah, you can do it manually whatever you prefer, but also what I feel is really important and I would like to get your take on this.

When we do this qualification, we just need to think about that this is just an individual contact, right, and this content might have completely different journey compared to the account. Right, So if we want to make sense of that engagement, we need to pair the signal, right, or the engagement that we captured it with that content was the historical interactions with the account, all the signals, so all the information that is available about this account, account research, et cetera, right, engagements with other Buying Committee members and then together with sales, make sense of that engagement.

Right, what could it mean in terms of like account activities and strategic initiatives they have and what could be our best next action that would add value for this specific individual? Right? What do you think about this? I'm just complicated to my chance.

Oh you need just to say, hey, guys, you got to buy and commit to member engaged, go get them? Yeah no, no, this. Is fantastic because you've actually just set the stage really well for what the talk next week will be about. And so I know the title is you know AI workflows for ABM.

But really what we're going to go through in that. Session is my philosophy on ABM and how it's evolved. And you now hear contact. Based marketing right, So there we actually need to sort of reinvent what the demand creation and capture process looks like and map that to account based funnels and contact based funnels.

And when we do that, we can then look at the intersection of signals and data and activity between accounts and contacts, and I think that's really where the where the magic is. And so so one, if you if you have the infrastructure and the architecture to collect that data, and you do are able to collect signals, you now have the sort of m VP for AI. In my mind, you need to have a good. Base of data that you're that you're building any of these AI workflows off of.

So that's number one, which is fantastic. Number two is you now have extremely unique data across third party, first party owned operated sources that you can use for personalization, which is which is also exciting because we've all gotten those messages that are like half personalized, like they they scraped something from our most recent LinkedIn post. And then slapped it in an email and blasted it out to us. That's not personalization.

We need. We need we need. Dozens of signals at a contact level, so we can build a profile of who that person is, what their pain points are, and what they care about, and then come up with a completely unique message that you know, almost gives them that aha moment at that point in time when we deliver something to them. And with those two things in place, then the third is that next best action piece, which is something I remember I heard that term like six or seven years ago.

I was talking to a machine learning engineer. It's fairly common in software. Right. You think about you buy a hat on Amazon, it's then going to show you a product for hat cleaners or hat storage or hat hangars.

Right, that's just next best action, Right, it's taking your existing behavior and trying to predict what you'll do or buy or need next based on that historical behavior. So we can now do that. We can now do that dynamically based on the scoring that we. Can evolve when it comes to an account and.

Contact level, and then again bringing those two data sources together so we can get a view into what is generally happening in this account, and then who within the account is most likely driving that decision, and then we can act accordingly and leverage all of the data that we have from prior customers and prospects to say, Okay, this is the next best thing we should do, right. And that's when you. Get tactically into the effectiveness of AI. Right and thinking about a recommendation engine that your marketing team is now powered by.

I just get pretty fired up thinking about that. That's pretty cool, right, and we can you can build that now exclusively for teams. Love it? Thank you?

By the way, guys, Yeah, welcome to ask us any questions. As always, we'll do this whole final life. We're going to cover all of them, so feel free to drop them in the chat and I will pick them up. And what I would love to move next is to ask you about the process you used to create this share OKAYRS with SDRs, right of course, I mean this is the first step to the cross functional alignment.

But I assume that you need to do a lot of change change management internally, right of course. Typically you have different incentives, different processes, and again, as we poke, you can even have this credit conflict, so can you walk us through the entire process and just explain how did you do this? Sure? Sure, Yeah, it's a disclaimer.

Is easier said than done. Uh I would. I think of it in two components, right, there is the change management piece that you mentioned. That's like the people side, and then the other side is process.

And I think a lot of go to market organizations they jump right into process. So i'll share that first, Right, that's your typical Okay, we're going to use HubSpot for lead scoring. We're going to have a demographic score, we're going to have a behavioral score, and oh, you know sales has these s LA's and they're going to run a band script or use spin methodology, and you know, all these teams they have all of these wonderful processes in place, but then they don't have the people aligned and that will break.

And then in. Other cases, which I think is better, is you get the people aligned and they don't have the process yet, and that's okay. We got to get the people aligned first and then we can build process around that. And so for me, like one of the let's see, this probably took me.

This took me like. Eight months to be honest, of getting an enterprise sales and marketing team aligned, but a couple of the key components that were really critical, where I had autonomy and ownership of the entire demand generation process, the budget, the activities, the resources, the channels, you know, the website like those things, right, So I do think that is important. You don't, as a marketing leader need to own it, but you at least need to get alignment with your peers so that everything that demand generation touches or is adjacent to.

Is complete, completely aligned. Right. Without that, you're going to run into problems where you might have the best LinkedIn campaign on the planet, but your landing pages are terrible because your because your web team isn't bought in and they're just giving you like random, random stuff that doesn't quite fit. So you've got to get that alignment first.

The other piece was the person I worked with on the sales side was basically a mirrored version. Of me, but on the on sales. So so she led. The SDR team and her ownership was really on you know, think of like SQL too, like stage two opportunity.

And then depending on the deal side, there was an account executive or you know, maybe a senior sponsor or executive sponsor or something like that involved. But her role was on that core piece of SQL to opportunity creation and then progression into an actual qualified deal. And so we were both completely incentivized to work together because I essentially owned the steps prior to SQL and she owned all the steps after SQL. So when we got together, you know, one we aligned on like.

Overall principles like okay, marketing is going to be a you know, a generator of revenue. We're going to generate meetings, We're going to generate opportunities. We're going to connect that to revenue. We're going to be data driven in how we analyze the effectiveness of marketing campaigns and channels and tactics, that sort of thing.

And so we came together at that level. But then the other piece was we started by saying, what is the single most important metric that we should both be accountable for? And when I say accountable, I mean going on to a bi weekly pyce line review with the GM of Product, the VP of Sales, the head of marketing, and like going into the fire and together owning why sqls. Were what they were.

So that was the metric we picked that was the determinator, so I was held accountable to SQLS, so was she. And then the next level was what I talked about with the quality metric, was we first looked at SQLS going up or down, what was the delta, what was the change, and then why and then we used all the data to figure that out. So so that's one is like align on the like the ownership and the accountability of like your team and your area. Then choose a metric, become jointly accountable for that metric, and then you know the third thing is I actually like Google used the like the Smart Goal system.

Which I probably forget it now. It's like specific, measurable, attributable, I think, et cetera. So you can look it up. But smart system was the like criteria we use to create the okayrs and then OKRs themselves.

It's objectives and key results, so you had to write those in a way that were pretty descriptive. Like basically it was like I'm going to or my team is going to do X, and it's going to produce Y by this timeframe. So that's where you use that smart system to write these goals that will will live you know, throughout the year if you're doing like quarterly planning, so then all of those things come together, right and when you have bi weekly, monthly, quarterly pipeline reviews, this is very clear what you're reporting on and what you're accountable to.

A lot of I guess how much? So uh, what I would love to where I would love to pre seeds, and what I would love to touch Next is the account precression right kind the parts I have described from the beginning. I know you've been working a lot on this and on the framework that you have established out. Try to pull it in hopefully it will be visible for everybody, but guys, just in case you have a button two zoom in.

Obviously, if you're watching this from the smart one, it won't be very convenient, but if you sit with your laptop you can try to zoom in and see the framework itself. I also Steve published it on LinkedIn, so you can download it from his LinkedIn profile. So I would love to ask you a couple of questions around this, right so, as we spoke with you one day. Account progressional account velocity is one of the most important metrics to track ABM efficiency for the long sales cycles.

For a simple reason, you have a delayed revenue outcome. You can't just expect if your Selle cycle ankss eighteen months, you can't expect revenue from a single program that you are run in for the first month of months number two. Right, But you need some leading indicators in place that kind of tell you that we are doing the right things. Right.

This is why the account velocity helps because it kind of shows how the accounts are procressing from one stage of the bar journey to another. Right, And I will impact on this as a team or no. So what I would love to ask you is, maybe, first of all, can you walk us through the entire framework and explain if I wanted to build or implement this framework from myself, right, how to do it step by step? Yeah?

Yeah, yeah, Let's. Let's get into it and I'll share first this framework actually came from some of the pain points I experienced with attribution conversations. And you mentioned it, Andrea at the beginning to call was you know, we become really obsessed with you know, multi touch, last touch, incrementality, and those things are all great, They're wonderful, especially at a more mature company that's perhaps spending a lot on demand generation. But I sat back and I thought, from an ABM perspective, what are we actually trying to do here?

Right? Like we're trying to take accounts from one stage in the journey and move them along. Like it's that simple, right, And that's what account progression is. And so in this framework, there are three components.

There's one which is your account progression table. This is basically like the inputs you're going to need from an account perspective to be able to produce this. And then the second is what I call the event layer, which this is really the kind of the dynamic real time data that you'll be collecting for each account and that's what you're going to use. Ultimately to build your reporting.

And then the last component is the impact and the velocity through reporting. And so when you have the like core table dynamics in place and then you're tracking those events real time, you can now report on what's happening within a select group of accounts. And importantly, you can go back and look at what the you know, like the origination or the originating place was, or the originating stage of that account was, and then you can compare it to baseline, So we now have the ability to report on, you know, just purely what is happening within an account, but second, what has been the impact of the events and the activities we ran and how does that compare against baseline?

And then we can look at and are we are these accounts progressing as quickly as we'd like? Is? Are they stuck somewhere? How do we?

How do we fix that? Right? So those are the three components. And then next you have the stages.

So these are the common ones that that I see and we use all the time with clients, but these can really be whatever you want them to be. So you have in a stage like on the left side, this is completely cold, right unaware, and then you move and you make an account aware and they become engaged and qualified and sales ready, customer growth, expansion, et cetera. Can keep going. But for you, for anybody really, you could you could simplify this down into three stages, four stages.

You can change the names of the stages. What's most important is how you define the stages. And this is where a little bit of data analysis comes into play and where you can actually use AI as your friend to do this. Analysis.

But as an example, like an unaware account, if you were to go and let's say you have a target account list of seven hundred and fifty accounts or five hundred accounts, and you're able to export and get let's say LinkedIn ad data or you know, Google or. Meta ad data, and then you have. Maybe some web data, and then you have like email service data from like a HubSpot or you know, you're like a marketo. You could export all of that data and then essentially map it to each account and then you can start to index those accounts by stage.

And so unaware, for example, for your definition, might mean that they have zero engagement. In the last ninety days. So any accounts last ninety days no engagement, they get applied to label as unaware versus aware. They might have you might like we.

Typically use like ad impressions and clicks and website visits kind of like top of funnel, some kind of vanity type metrics that are important to show that they might be aware. And the same process, you go and you index all of that and then you map it to that stage. And then when you do that across all of these stages, what you have at this step in The process is a complete indexing of where your accounts currently sit today, and this becomes your baseline. Everything builds on top of this.

And what we typically see is we run this report and it's humbling for clients because it's like eighty ninety percent are unaware, whereas they thought, you know, they had made more more effort with these accounts. But when you when you time constrain it, because awareness is fleeting right when you look at it within ninety days. We often find, hey, a lot of these accounts are unaware. And with that insight we can then we can change our tactics and our strategy quite effectively.

And yeah, and then I can we can dive into this little bit more. But I've had I did some kind of high level steps for how you could go about implementing it, what some of the core data needs are, and then how you. Can actually look at success through metrics from this reporting. I have seen a question from LUSA Ojen and she asked about the tools that's being utilized to implement this general let's speak and if you can maybe highlight how a good stack that kind of enables implementation of that framework looks like that would be helpful.

Yeah, yeah, of course it's it's it's always fun to talk about the tools. So for something like this, like there's different levels of complexity. Let's say one of my favorites, which is can be really simple, but you can also build some complexity on it. Is you've got Clay or a similar tool like a common room or.

U you know, zoom info, like something. That is is robust with building lists and then mapping data to those lists. I think Clay is the most flexible currently. So clay on call it like a component one, right, like your progression table, so you'd have all of your target accounts in there, you'd have all of your basic demographic, firmographic type data in place in a tool like that, and then next would be think of it as like orchestration, like where do you execute marketing and sales activities?

So on the marketing side, if you're using you know, just straight in the channel, it could be in a you know, Google Meta LinkedIn, et cetera, where you're targeting at an account level, or you might have a tool in place. There's a tool I've looked at called Primer. There's a few others that have popped up that will allow for basically like LinkedIn grade targeting, but in Google Meta and Reddit, so you might have some data living in a tool like that which is going to get you that awareness level data at the account level.

Then I would say your CRM and whatever your email service provider is. So if you are sending emails from HubSpot or marketo or active campaign, you're going to want to get access to all of that data. And then your CRM is going to be where all of your sales activity is. So whether it's Salesforce, a t O pipe drive, like, there's so many of these that you know now can can even you can API connect or you can just export data.

But for building the actual report, I've yet to come across a tool that can do this out of the box. So when we build these for clients, it's actually completely through AI and you can do it honestly. You can do this with a twenty five dollars a month cloud or cloud subscription where you can have a dynamic dashboard produced in HTML bi weekly every month, whatever you want, and then you can just you know, create create all the underlying context and skills and information so that it's it's repeatable.

But yeah, those those right there is is kind of all you need because the the customization of the definitions of the stages and like even the with the sources of the data. It's it's usually unique, Like every company is different. Their mix of channels is different, their mix of tools is different, and so you know, maybe it's an opportunity for somebody if you've got an engineering background, go build this tool, uh, and then hit me up when you do, I can help you sell it. But yeah, right now, it's it's custom.

A lot of and just maybe your mansion cloned. Do I think, uh, one day it's just the ultimate solution to manage it all. And we probably just maybe talking a little bit of about trends because well I'm absorbing. Probably we all have heard about clase evaluation, right and all the tools that kind of appeared and martech and sales take landscape because of AI.

But if I just think about the development of cloud and cloud cowork and the integrations with your CRM, with your proprietary data, et cetera, I think I feel at least it's just a matter of time when they will either acquire or partner with the huge database platforms like think about domain for European providers right, and then they will have access to ever and then basically they just kill everyone. So when I'm heading with this, maybe I see it in completely wrong quay. But even despite of this, well I'm thinking, isn't it better to kind of simplify Everson for yourself and indeed start playing, for example Claude or any software that you'll select for example Google Build right, and especially if your organization is on Google right, and try to time that creates that solution internally, compare to develop let's say text tech, which you still need to integrate right.

There is still a learning curve and you need to train your people to leverage all of this assorts. Yeah, yeah, yeah, I mean here's the I think the reality is when you go on LinkedIn or you know, you look at Reddit, or you follow the news in the trends, it seems like the whole world is building completely unique applications with cloud code, and they just aren't right. There are if you're in SaaS and like software and kind of emerging tech, sure, I think it's something to pay attention to, but there is a whole universe of companies that honestly they don't even have subscriptions to AI.

Right, you have sort of like shadow. Use of AI within the company and people just using free accounts to play around and do stuff, and they're still you know, trying to create content or write emails or landing page copy for websites and things like that. So I think, you know, one thing is like don't I think for folks, I say, don't don't feel behind, but don't wait too long, like start playing around, start start trying some things, and the other pieces that what I've found is the differentiator for how good your output is is what you put into it and the thought that you put into your inputs.

So the phrase garbage in, garbage out is like so true. Everybody knows that, right, It's really really prominent engineering circles, and it's it's true for us too as marketers. And so it's you know, don't get caught by opening up. A chat window in Claude or Gemini or.

Whatever and you know, writing your single prompt and then you get an output and you're like, cool, it's great, Like that that single shot kind of prompting isn't going to get you to like the complex strategic outputs with some of the stuff we're talking here. You know, you're going to have probably anywhere from like ten to twelve artifacts or like input documents that are going to go into your final workflow. These are things that you need to build to train you know, that agent, that project, that specific chat to do exactly what you want.

And this is where the real marketing work happens, right, Like, this is your core stuff, this is your ICP, these are their pain points, These are the value props of your product and service. This is your your buying motion. Jobs to be done, Like, this is all of that information that has to be really good to get these things right. And then the third piece here, I think is that most marketers today are not opening terminal and running you know, cloud code or like a you know a companion AI that's helping them write and edit in QA code.

It's it's incredibly. Complex, and and even for me, like I'm lucky I have resources in my company that are really good at this stuff. And are like pushing me along to learn more. But I think it's it's worth trying, right, Like it's worth going in and experiencing it and and fail and like break things and see what happens.

And that happens to me all the time. I was just doing something yesterday and you know, I had all this code on my screen and I don't even know what the heck any of it says. But Claud's helping me figure it out. And you know, prompted this error is like line once and two has an error with this and that.

I'm like, what the heck does that mean? Right? And then you go and. You work through the process of fixing that, and then it breaks something else, and you know, sometimes you.

Get into these loops. But you know, that experience I think is pretty valuable, especially today, to just become familiar with the tools and how they work. I love it. Thank you.

I just want to ask you one practical question, if you'll If I look at this framework, and more specifically on the dashboards section. In one of the sections, I mean, why you explain the four types of dashboards. One dashboard is about the forecast support, So I think this is one of the most problematic eras, right if you have the long sale cycles. So I'm wondering, how do you use this framework for forecasting on pipeline or revenue.

And also you mentioned in that deskboard sounds like stage health wardance. So I think just highlights how the use for it for fort a custom and how do you define this kind of stage health wardance I think would be cool. Yeah, yeah, that's a good one. So it's it's uh, it's it's it's based.

On two parts of the initial data gathering process. Right. So one thing that's cool about this framework is you can you can retroactively put this into place, meaning if you have a bunch of unstructured data across marketing and sales, you can build the the the progression stages and that table, and then you can take all your existing data and map it into that that progression framework, and then you can. Understand some key metrics that'll.

Be useful for forecasting, right like how many accounts you know typically fall per stage, what is the delta or the velocity of how frequently they change? So you know, in simple terms, the questions of how long does it take to get an account from unaware to aware? You can answer that question by doing that retroactive analysis, and then you can you can peel back the onion further and say, okay, how many touch points did those accounts have? What was the average number of touch points?

Per week, What channels were those touch points in, what content did they engage with, Who were the contacts that engaged at what point in the journey, And so you can start to map all of these things across that progression journey, and so that that's one thing. So you can retroactively go back and do this analysis, and then you'll get an output of that, which. I usually called the baseline. Like, this is your baseline right where you now as a GTM team, you know it takes fifty five days on average to get an account from unaware to aware.

You know it takes thirty eight days to get them from aware and engage. You know that. Targeted Google ads are the best way to get them into an aware stage and the most cost effective, and you know that running webinar replays on LinkedIn is the best way of getting them from aware to gauge. So you now have all of these insights that you can build from, and you have a baseline of how things progressed in the past.

So with that we can now forecast and know forecasting in a simple way is just looking at the past and predicting that the same thing will happen in the future. So let's say, like I said, this many days, it costs this much budget, is this channels, these campaigns, this creative, this content, etc. That's what we had to do historically to. Move you know, X number of a counts from aware to engage and you know, all the way through to a customer.

You can use all of that data now to then forecast what the next quarter will look like, the next half, the next year, the next three years, five years, et cetera. So that would be like the simple way. I think if you wanted to make it a little bit more advanced, you could you could apply some like some compound and growth indicators, like if you know, you can assume that you know, quarter after quarter, you'll get better at qualifying leads, the lead quality will get better, or sales will get better at running their discovery process, Like you can build that into your forecast model and that'll help, you know, increase the growth output substantially.

Another thing, probably the more advanced way to look at it would be if you if you're good on the. Data side, and you do have all of that data and you have. A robust account progression framework in place, you can actually then apply a more predictive layer to that, and you could use like more statistical modeling to try to show what that forecast could look like based on all of. That historical behavior.

And that's not too just difficult with either if you have data science resources or you're capable of building a data science and machine learning agent with AI. I love it. I guess so much for super detailed answer. I think it's super helpful.

I would actually try to replicate with my team what you have said and kind of crete an actionable step by step guide. So again then lay a shape as everybody because I think it's really really helpful. Thank you. Uh, maybe just to wrap it up, I would love to ask two questions that I received from from our community.

One is from me no, the question about best final metrics to track a BM at account level. Best funnel metrics for yes, yes, yeah, good question. It may be too small on the on the chart here, but I think for aware think mostly about like what are the first touch. Points that a your your I c P has to engage with you and your brand?

Right, So if you're running ads, you can look at impressions, clicks, reach, if you're sending emails. It might be. You know, maybe opens or clicks like kind of depends on how you want to define these things. Uh.

And then the other more prominent source would be your website. I like when you break down web into multiple kpi right, because like a visit to the homepage is very different than a visit to a pricing page or a visit to a conversion page or like a demo page, et cetera. So having some kind of hierarchy there on the website is important. So think of those those typical like top of funnel metrics.

For that like aware and engaged stages. And then for like engaged towards qualified, I would think about what are the actions that your ICP can take to become known known to you? Right? So they go from you know, basically like anonymous to identified.

That's things like registering for a webinar, downloading a piece of content, you know, a booth scan at an event, you know, any of those mechanisms where you're able to capture contact information for a lead is topically what I'll associate with with engaged and then and then qualified is actually usually more just like the volume of those activities, right, So qualified is like kind of think of it like your typical MQL score. It's like, Okay, the ICP fits this demographic criteria, great, Now what.

Was their behavior? And this is the one you can fine tune a little bit because it depends on your company. But for example, you know. Ad impressions don't mean you're qualified, but if there is enough of them and it's combined with ad clicks, website visits, content downloads, webinar registrations, all that stuff, right, then okay, now they are starting to show some qualification there.

And you know the cool thing about this is like what we're talking about is at the account level, right, so you have a you can't just apply take your lead scoring and apply it to an account, right, You're going to see a lot of activity at an account level if you are. Executing a b M properly. So you know, keep that in mind, and then you know sales ready is going to be you know that point in time when that i c P has like basically raised their hand and said, you know, hey. I'm interested in talking to your team.

So you know, a. Demo, a trial, contact, sales form, that sort of stuff is most important. There, perfect, thank you, and maybe just the last question from John McCree. He asked what about the main differences between working at Google Cloud and your agents in terms of being able to roll out such a successful demand gen process.

Oh man, that's a cool question. Thanks for asking it, because I think about this all the time. Google. To be honest, Google was a was an amazing place.

The culture there was was so rich and innovative, and and the people that I worked with really are are what made it. So. I was luckily lucky to be a part. Of a big team, and I had a team myself, and I still stay in touch with a lot of them.

And I imagine there are so many companies that are have tried to like replicate their culture off of you know, big tech like like Google and Meta and those sorts of companies. So so that was one part. But I will say as a an agency operator and having a team of you know, strategists and technical folks that are really experts in in what they do, whether it's like running ads or you know, building revops, workflows and that sort of stuff, I am learning at a speed unlike anything I've experienced in the past.

I mean, I feel like every day I'm learning something, I'm getting pushed. And when you work with you know, all kinds of different clients. They're always asking different questions, and everybody's unique and different, and so you know, there's a lot of cool things that. I'm able to see and we're able to do now that that wouldn't.

Fly at a company, a big company like Google or big tech. So that speed is really important. And then you know the ability to test and try things out right, especially when you have happy clients that are wanting to test as well, and we can bring ideas like like this account progression dram works or dynamic intent scoring systems and say, hey, we've got an idea, can we try this out. Let's do it together.

So I think that's kind of the pros and cons when you work in a big company get to work with a lot. Of great, smart people. Things move a little bit slower, though, and you can't be as agile and scrappy as you want. And you know, working with smaller company.

Really the opportunities to kind of seem endless these days. I can't just say it's kind of so much as a night's with my own experience, as I've been in consultant nine years already, after being eleven years in the corporate world as well, like you were at enterprises, but I was not in the tech space but at Kimberly Clark. And the truth is, right now you can connect the core frameworks that you kind of established in the enterprise, and then you have clients from multiple industries with different challenges, right, with different companies, they are target in different market conditions.

And then basically what happens is just strengths and your core framework. As I'm reflecting a lot about, I'll love to read Charlie Monger and what he says what is really important is to learn from multiple disciplines, right, and then connect the dots, because then you can see the big picture. The same is here. Right.

Then when you have this experience from this verticle, you know this is how it kind of applies in healthcare. But then you see the challenges, you can immediately see them from different angle in another space. Right. This is what kind of helps to kind of strengthen your frameworks.

See the gaps, follows them and prove and I think this is kind of the beauty of it. Maybe the last fun question from my side. I have never been at Google, but I watched the movement the in township was Oh, Wilson if the experience exactly like I was described in the moment, Oh. No, I love that.

Yes, and no. What's funny is the. The in the movie, you know the remember the scene at Vince Vaughan's like ordering a coffee in a pastry. So I mean.

That's true, right, Like you go up to a barista in your office and it's like, what do you want? You can have anything like coffee, you can have you can make a flat white with cinnamon and this and that, and you can get whatever you want. And then on the shelf is uh, you know, chocolate pastries, strawberry pastries, peach pastries. So you can it's going to test your your discipline.

So that's true. But I think too, like the. Movie if I remember, you know, not every day is like hackathons and like innovation, like it's it's still a regular company at the end of the day, and you. Deal with a lot of bureaucracy and stuff like that.

But I do think, especially my early years there when google Cloud was not as big and prominent as it is today, there was a real innovative, scrappy mindset to things, and I think, you know, Google has a lot of internal principles that help people and teams like be innovative, you know, use like design thinking. It was a big one to just produce like really amazing outputs. And h you know their marketing I think is you know some of the best, right you look at the certain marketing and like the year in search video that they put out every year.

You know, so there there's a there's a process, there's an equation behind the scenes to get to that level. So like that that stuff was cool, love. It, Thank you so much, really enjoyed our conversation and looking forward to your keynote at our signed next week. And as you guess, guys, next week we won't have a full final life.

Well, we have the entire sign at three days, fifteen keynotes, fifteen great speakers, so make sure that you have signed up. It's free. We keep it deliberately free because we're once in the era of AI slap of the kind of really really really bad content. We want to bring the practitioners who can share their actionable workflows have similar conversations like we had today with Steve.

You guys welcome to join and ask any questions because probably this is the only way to help our community to stay sane in the world of the nonsense that we are all experience and today, so thank you so much, Steve. Excited to have you on board on our signment next week and see you all guys. Have a good fest of the week, take care.