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[00:02]
A
Welcome to the Marketing Millennials the no BS Marketing podcast. I'm Daniel Murray and join me for unfiltered conversations with the brains behind marketing's coolest companies. The one request I tell our guests stories or it didn't happen. Get ready to turn the up.
[00:27]
B
We are back with another episode of the Mark Millennials podcast. I am here with Amanda Cole, CMO of Bloom Reach. I'm excited to chat with her today. Welcome to the podcast. Thanks.
[00:38]
C
Thanks for having me.
[00:39]
B
I want you to go give a little background how you got into marketing and then we'll get into the conversation.
[00:45]
C
Yeah, I got into marketing very much by chance. I was a very young mom and it was hard to find jobs with flexible opportunities. I started working for a company called Business Intelligence Group. Michael Alicea was the founder there and he was looking for someone to help him profit process direct mail responses for a couple very large non profits that that did. I mean this was 20, 22 years ago. So you we, we were getting direct mail responses for solicitations like you know at the holidays when you get stamps or return address labels and my job was to collect the mailed in responses and the paper checks and validate which option was got more donations.
[01:33]
B
That is sounds like the like everybody I swear I talked to so many marketers and it's always like such a interesting entry into marketing and that's like that's one of the interesting and people forgot like how how marketing used to be where it was just such a manual process. Luckily now we will talk a little bit about it but AI has like helped helped a lot. I want to go into the conversation we have today is and I think this is a pretty interesting conversation because I've never talked about this in my podcast but I want you to explain how you think about working backwards to get a pipeline number. What is the math behind it? I don't think a lot of people dig into it because sometimes numbers are not the most exciting things. But it's so important to make sure you, you win in marketing. So let's just like go into first like the starting point like how you think about it. Where does that number come from? And then we can go into the, the breakdown of it.
[02:37]
C
Oh yeah, I can geek out on pipeline math. It's. It is, I think it is one of the reasons why potentially I'm, I'm so good at my job. Maybe it's the only reason but the because when you can connect marketing back to the revenue number the we're all responsible for growth we're all responsible for outcomes in the business. But. But marketing, especially in the B2B space, is responsible for making sure that the sales team has enough to work with to have a really good chance of hitting that revenue number. And if every conversation you have as a marketer ties back to how does what I'm doing deliver a revenue number, your conversations, certainly with finance and sales, get a lot easier. And so pipeline math is the idea that starting with the revenue number and the growth targets, what are the levers that you need to be aware of that you can pull to work backwards from that revenue number? And so in B2B, it's things like, what is our win rate? Win rate is the number of closed one deals divided by the total number of closed deals in a quarter. So what's our win rate? That tells us how many open deals or open opportunities we need. And then from you back up from. From there, you're. How many. How many conversations does it take for us to get deals? And you back up from there. How many people do we have to talk to to get the right kinds of conversations and one more backup? How many. How much do we need to spend and how many channels to get those numbers of contacts? And that's how you essentially build out the funnel of pipeline math that turns into targets and a budget and a context target for plans for, for a marketing team that a finance team and a sales team can understand.
[04:25]
B
I want to take one step back because. And then we could get deep, deeper. Like, I think some people get caught up with over promising, like the, the first number, which is like, let's talk about. We want to 2x3x4x growth. So when you're going into that meeting with like the CFO and the cmo, I mean, not the cmo, but the CEO, and you're like saying, they're saying to you, Amanda, okay, like, how. How much growth do you think we could do this year? Like, what, how do you get that number first? Because that number is more a lot important because some people would be like, for sure, we could 3x and then you, like, do the math, start doing the math going backwards. And it's like, wait, like we have some of these channels are actually capped out and we, we're not gonna, we might not hit this number. So I want to know that number first. And then we could talk about detail.
[05:23]
C
Yeah, I mean, I, I think that's why pipeline math is so important, because I, I love a conversation. Every CEO and CFO wants to push as much growth as Possible. We all do. I mean, especially in the software world. A lot of us are incentivized with partial ownership in the company and our ownership becomes more valuable the more the company grows. And so we are incentivized to deliver growth. But growth also comes with investment. And that's why pipeline math is so awesome. Because you can actually show how much investment is required to hit those growth targets. Because the other element of this is how much did it cost us. So if you take what I said earlier, let's make it very simple. How much revenue do you need? So how many opportunities do you need? We track all the way down to the cost to generate an opportunity. So if you want a 3x your growth, then you need to 3 extra opportunities and that has a correlation to how much you actually need to spend and invest in order to generate that. So it does become a very mathematical conversation instead of a it would be great if kind of conversation which a lot of companies, especially in, in startup or early stage companies can get into a debate like that with, with their teams. And it, it really comes down to let's, let's make the plan whatever we want to make it, but know that the investment needs to be appropriate size as well.
[06:47]
B
What do you find is, which part of the pipeline math do you find is like the hardest to forecast?
[06:54]
C
Well, I mean again, the great news is you're, you don't start with forecasting, you start with actuals. Now obviously if you're in a, if you're in a startup and you have no actuals, you have no data, there's, there's a ton of benchmarks and happy to share, certainly some, some that I've used in the past, but there's certainly some pretty standard benchmarks of like MQL is dead now, but like let's just say a marketing contact or an ICP contact into a meeting conversion. The number of meetings that convert into opportunities, a number of opportunities that convert into sales qualified opportunities and then closed one. So there's, there's benchmark data available but I highly recommend if you even have at least a year of data that you use your actual data. You say what is our actual win rate? What is our actual conversion from opportunity into into or excuse me, from meeting into opportunity because that's the baseline and of course where you can get into conversations is on debates about where can we improve. And again you would use benchmar here. So if you're meeting to sales opportunity, sales qualified opportunity is below the benchmark. You could make a decision and say okay, we've identified some specific optimization areas and we want to plan our pipeline targets for next year, assuming that our win rate gets better. Or you could say, actually we don't have any plans to make this better. We didn't even know it was bad. Or there's some other dynamic, like maybe you're just in a very competitive market or maybe you're aware that your product has gaps that you're not going to be able to fill in the next six months. So you're going to accept a lower than benchmark conversion rate, but that's what you're going to base the plan on in order to hit your targets. So you don't necessarily have to get into an argument about, about feelings on those conversion metrics either.
[08:42]
B
Yeah, I'm also interested because I've seen this because I used to be a marketing office. But like, let's say like the one rate conversation of, okay, like we have, like, we need to work back to get that, that opportunity number. But let's say like each ICP might have like a different, like if you separate, might have a different win rate. But also there could be like a couple of sales reps that are way overperforming those win rates that are like kind of outliers that are lifting up that number that could screw you up if that person decides to go to the next company or that person quits and then you have to get ramping reps and then you, you're like low. So, so how do you like, do you take out outliers? Do you forecast in or do you lower it based on outliers? Or how do you do that one rate to make sure that like if the top three sales rep decided to like move to another company and that one rate goes down 3, 4%, that means that pipeline number is going to have to go up. Like.
[09:46]
C
That's right. That's right. Because exactly as you said, if a rep leaves, a new rep comes in, the rep who is ramped has a higher quota percent. A new rep who's coming in is going to have a lower conversion rate and certainly they're going to take a longer time to ramp. And so what you're, I mean, we're really geeking out now at this point. But the, you can use the mean or the median. But this is where a marketer who knows this, who is in the details and who understands the data and is able to come back with an argument on why we chose the win rate. Win rate. Now I can't even say it that we chose are for these reasons. These are our strategic objectives. These are the verticals that we want to be in. And part of that is a recommendation that you're making. One of the things that we did in our planning cycle this year, we wanted to get into some new strategic verticals, and we had a worse win rate in those strategic verticals than our core. And so I actually carved out a percent of our ARR. The finance team did not come back with a finance plan that was split by vertical that would be entirely too nuanced for us to build an entire financial plan around that. But we did. We took our ARR plan and we said, let's assume that 20% comes from strategic verticals, because last year it was 18%. And now we're going to make a concerted effort to increase the amount of revenue that comes from these new verticals. But our win rate is 7 points lower than in these strategic verticals than it is in our core verticals. And so we did actually split out the way that we look at targets based on the nuance of the win rates in those in those verticals. Similarly, we, on the rep side, we do use our median because you want to exclude the really bad performers and, And. And the top performers.
[11:30]
B
I like that. I mean, because I think it always should be. The nuance stuff should always be in a marketing. Marketing should be doing those numbers separately and not show, like, not getting it all messed up in a finance person's head. Because the finance person just wants, like, what is that percentage? You. You. What number are we doing here? Like, I don't want to. I don't care that there's a rep that's leaving. I don't care about the. That, like, you want to grow in this vertical. Well, kind of. They want to care. Some finance people want to say, like, this is how much revenue is coming from, let's say, this new vertical. But the win rate number needs to. You just need to. If you want to get more nuanced and say, like, rep base and stuff like that, you don't need to show finance stuff. I think the other number that gets a little. I'm interested in how you plan is so, okay, working backwards. So there's opportunities. But let's say, like, you know, as a marketer that, like Facebook or like, let's say meta or LinkedIn might have, like, some audience fatigue happening in this vertical. Like, we were, like, capping out, like, capping out audience fatigue or capping out this. And last year, like, the numbers show, like, we like cost per lead with this. But like let's say like LinkedIn is raising like prices for what like impressions or whatever's, whatever's happening. How do you, like, how are you thinking about like, because sometimes like actuals could screw you up because like, like how platforms work like numbers, audience fatigue, all that. So how are you working in those numbers to make sure like you, you're not getting screwed over by like the audience to see platform rising costs, all those crazy things that happen?
[13:20]
C
Yeah, I mean that is just part of the marketer's job is to understand the trends and what's impacting marketing. And the reality is regardless of what's happening in the market, your cost per opportunity can't go up. Everybody in marketing we know we need to do more with less. We need that cost per opportunity to decline year over year over year because we want to see those efficiency gains at the top of the funnel and in demand generation while we also see an increase in that spread between how much we need to spend in order to generate opportunities versus grow in revenue is part of the job of marketing. And so honestly it doesn't, it doesn't matter if, if LinkedIn is screwing you over. You gotta figure out how to be more efficient with your budget and generate more opportunities on a, on a more efficient cost per opportunity. The good news is there are lots of resources, podcasts like this that help you think about and be more creative. I do think audience segmentation and being really intentional with your aud. I think with all the AI tools that we have coming out now where you can do a better job really identifying when people are potentially in market so that you're executing ads certainly in paid social at a more strategic time in, in the relationship. If you are doing more brand awareness ads, make sure that you're not doing it in super expensive channels but where you can get reach and visibility and some of that subconscious impression but without spending lots and lots of money to do it. So that, that I think is the core of the strategic thought process of a marketer.
[14:51]
B
What is, what is your philosophy on split on like budget split of like test budget versus like for trying to hit revenue budget versus and we can go into a little bit of like brand versus like actual like capture. But the first I want to go into like how you think about test and like actual hit number budget.
[15:17]
C
Yeah. And we, we don't carve out a specific test budget, but we do a ton of testing and it's certainly becoming one of the, one of my favorite ways to Test now is certainly with synthetic data or digital twins and to really like take replications or representations of an audience and test messaging and see how quickly we can actually gather insights and intelligence. We love. There's a tool called Winter that is great in B2B that we, that we love using from a message testing perspective. When it comes to testing approaches, we, we pretty much everything we roll out, we roll out as a test. So there's nothing there. If we roll out a new demo request or if we roll out a new interactive video format, we're always, I think it's just in our nature to roll it out as a test. And we, we, our technology is in the B2C space so we deliver marketing automation or CRM for B2C companies. And B2C is incredible at testing. I mean that, that is how they, how they live. And so I think we've, we've taken some pages out of their book.
[16:21]
B
My wife runs Digital for Salt and Stone. And the amount of testing they do there is a crazy.
[16:28]
C
And every thing is how big the dollar sign in the offer. I mean they test literally everything and
[16:35]
B
how good they are like reporting like incremental like and where things are actually coming. And I wish B2B could do it as well as they do it because they, they know like, oh, If I did YouTube and this, this, this like I have like a software or a math problem to solve any of these things. And it's, it's crazy to see but B2B like fell behind a long time ago because we, we just stuck with it. But I want to know your philosophy. Like how are you like tracking where things are coming, coming from? Like what is your attribution philosophy? Or like. Because every, I think every market has a different attribution philosophy or maybe like way they do it.
[17:22]
C
Yeah, we've all done U shaped, W shaped, weighted. We've all done curve. We've done all the different attribution models that you can possibly do. And at the end of the day, very rarely did they contribute in a, in a significant or meaningful way to, to outcomes. And the benefit of B2C is that the purchase window is, you know, a long one is seven days. There are certainly some more considered purchases like furniture and things like that. But for the most part in, in the B2C world, your, your conversion points are, are sub 48 hours. In B2B it's a much longer extended window for us. We're in the enterprise and so ours can range nine to 12 months and on average we have 20 people involved in an opportunity. And so when you think about putting attribution against a marketing activity across 20 people in nine to 12 months, it's almost impossible to say, here's what our strategy needs to look like in order to be able to replicate this and further the organizations themselves. Because if you look at the, you know, the data points of, of individuality in a company dynamic and then 20 people inside of that company, it's really, really hard to replicate the attributes of what made that an opportunity to begin with. So what I think about in attribution is the micro and the macro. So we look at very micro is the channel, is the campaign, is the message doing what it's supposed to be doing? And that's the micro. And then in the macro, it's is the customer journey moving in the shape and timeline and urgency that we expect it to. And so that's where you are able to kind of triangulate both things that I know that channel my campaign, my message, is performing at baseline or better and that we constantly consistently optimize for that in, in the micro and then in the macro is the collective of all of these things driving opportunities forward enough at the right clip. That's our win rate and the timeline that we needed in the age of our opportunities.
[19:22]
B
I also, I think we've done a great job and not mentioned AI for 20 minutes. But I wanted to ask you like, I want to ask you like how, like with AI, how is it optimizing this pipeline math and how are you getting data faster to you to make quicker decisions? And what are you doing internally to do that or what are you doing? Let's just ask that question and I'll ask you more AI questions after that.
[19:52]
C
We are super aified. So one of my favorite tools, which I think is an augmentation for OPS teams, it's made our marketing and our revenue ops teams 40 to 50% more efficient. Is called von von and it essentially all of the random questions, all the reports, all the dashboards, all of the things that you need to understand about a customer and opportunity that before you would need to run a Salesforce report for or you'd have to wait for somebody from OPS to come back to you. You can get it conversationally in minutes. With Vaughn, it's trained on our data. We did need to make sure that it understood our context and our language and the way that we have information. In Salesforce, it is fully integrated with all of our marketing and marketing channels and it is also integrated with dream data, which does that customer journey element, like what are the elements of the various contacts and customers and how do they map together across this customer journey? But that's all available and accessible within one question and a few minutes. In Vaughan, how we execute marketing, we're using Claude and Claude design to throw up landing pages pretty quickly. We have a tool called Character Quilt that we build full end to end campaigns with in a matter of minutes instead of, you know, maybe a week or two. I mean we, we have AI fied all the. We are also an AI company, so there is an expectation that we are using these tools and that we're living and breathing it. So that definitely makes it easier. I have counterparts who are being told they're not allowed to use it, which blows my mind. But we've, we've input it into every part of our business.
[21:27]
B
I just had a conversation with someone that there's like such a two ends of the spectrum, like companies that are really just like controlling what AI you use. And then there's like AI paled companies where like everybody's using the AI and there's a little bit in the middle. But like I hear this both ends of the spectrum more than just the middle stuff. But I want to ask you, so what are you asking Vaughn on a daily basis? Like what are the questions you're asking? Like, like I just want people to know from the CMO lens. Like what? Like you go to Vaughn and I'm asking these few questions because I want to get these answers today. Like what is an example?
[22:07]
C
Oh man, I think I'm the number one user of it in the company. If I'm not, I'm going to keep, I'm going to try. I'm going to have to up my numbers. But yesterday, for example, we lost a deal to a competitor and I asked Vaughn to tell me how often was it a pattern? How often had we lost to the same competitor. I asked it to give me actual quotes from customers on calls about why they chose somebody else over us. And I asked it to also analyze the sales process. Like how well run was the sales process. It came back in 15 minutes with a report that was shareable. I do still click in and verify. I still go to Salesforce and make sure it's pulling the right stuff because I'm still a little bit paranoid. I don't want to ever share something that was a hallucination. Luckily Vaughn is right so far all of the time. But it was able to do something that would have probably taken a month before and provide an artifact that I was able to use to conversationally talk to my team and the sales team about what do we need to do differently. I converted that asset into an updated competitor battle card with some specific quotes about why it's really important that we change our positioning and messaging in one area. And then I sent it to Cloud design and converted into a landing page and gave it to our ads team and said by the end of the day or by the end of the week, I'd really like us to have some ads live in this category. And that's like, that's literally how everyone works. We had a. Our team yesterday launched an event for the U.S. open. We wanted to start prospecting and getting it out. There were some optimizations that we needed to make on the page. We had a back and forth slack. The team updated it, went live with it. This was all within an hour and a half.
[23:46]
B
Yeah, I mean, I mean, the old way is you submit a ticket to Ops or Analytics. Like analytics will say you're fifth in line to get this. Then you have to fight and say, could you make this a priority? And then even if it's a priority, I get two to three, four days, you'll get a report and then you want more and then you have to dig in for more. And then you're like three weeks away out and you lose another deal to the same competitor like four more times. And you're like, why did I. Like, I could have got this answer now. Like with AI, you get the answer in 15 minutes, which is I just want people to like know like that, like the ticketing, like your ticket is you, you asking Vaughn, that's your ticket request. And so I love the. I love the AI pill. What are some things that you had to do as a marketing org to make sure you had like clean data, clean reports to make sure Vaughn was spitting out like the right things for you. Because I feel like there was probably had to do some like lifting before. Vaughn was not like pulling out a report that like maybe have been dead five years ago or like data that's been bad. I mean, every company has a little bit Bloomreach probably is probably at the high end of doing the right way. And then there are people who aren't. So for people who have a messy stack or something, how would you recommend to make sure you do it before you implement a POM?
[25:26]
C
Well, I mean, this is the incredible news about LLMs is their ability to understand unstructured data. I mean, I think a lot of us have heard this by now, so I doubt I'm saying anything revolutionary, but LLMs ability to understand and build and draw context between unstructured data is pretty incredible. So with Vaughn, what we're really doing is teaching it our context layer, our taxonomy. So when we say opportunity, what is an opportunity? How do you actually identify that in the system? When we say closed one, what does that actually mean? Like, is it a stage, is it a status, is it those kinds of things? And so it's really like you're teaching the same way that you would a new hire. You're teaching them how to look at and understand the data itself, the cleanliness of the data. Again, because it does such a great job with processing unstructured data and understanding context between various points of data means that you don't really have to do a lot of cleaning up in the way that you would in the old days in order to get a report that was accurate. Because remember, in a report you're selecting data fields and there's no intelligence behind that. It's just identifying whether or not the data field is populated with the response that you gave it and if it is it, send it to you. With Vaughn, for example, even if sales reps have entered a close loss reason, I can say I don't want you to use the closed loss reason that the rep is used. I want you to actually listen to the call recordings or read the notes and the opportunity and come back and tell me what you think the closed lost reason is. So I think there's actually a much lower bar of data clean, cleanliness and quality. And actually if you have a quality and cleanliness problem, move faster on getting something AI embedded. But you definitely need to take time on the context on the training.
[27:13]
B
Yeah. So basically just like for me, for, I mean audience, I think just like anybody, any new hire, you need to like set up like an onboarding doc or multiple onboarding docs that have like text like taxonomy. Like what, what we want you to do here. Like here's your role. Like I'm, I'm just simplifying. But you need some ways to like make sure like AI knows what, what you're talking about and knows your company and knows your contacts. Otherwise it's going to hallucinate a little bit. Because they could say you say closed one and could say like 15 different definitions in the ether what closed one means to and different orgs have. Some people have MQL, some people have MDL and they have different acronyms and everything. Everybody's different with how they define things in their organization. So that's like the heavy lifting. But lucky you could. It's probably easy because you could just like get AI to help you do that because you could just. Someone in the company knows all those answers. They could talk to it. Like, I don't know if I would
[28:24]
C
call it heavy lifting. I'd probably call it more like hiring a mover. Pretty low effort on your. On your part.
[28:33]
B
But I do think you said something really important though. It's like now that with AI, the multiple data sets that you could pull from where it took so long, where you like, had to trust an AE or you had to trust someone else to like, give you an answer that might not have been true or give a. Like, you could go back and say, like, in the sales call, they said they heard about us in a podcast, but this person says this. You could say there's multiple conversations happening and before it would take hours to look at closed loss for all the data, then go listen to all those calls and then also go look at the reporting to see what the reports say. But now you could just ask it all these questions and make sure it comes back in a clean report however you like it to make sure things are clean, which I think that's. And to be able to ask, like to go into sales calls and stuff to do these things is super important. Is there any other things that. Are you like when building reports, how are you like now thinking about, like when you're building a reports to show the CEO or cfo, like how marketing is doing and they ask you a quick question, Are you. Are you asking Vaughn to like, spit something up and send it to them or.
[29:58]
C
No, not even that. I gave our CEO Vaughn and I said, you want to know how marketing's doing? Ask Vaughn. You want to know how sales is doing? As Vaughn, you don't want to know about Pipeline as one, because I am completely comfortable being accountable for what the. What our outcomes are. And the. Our CEO is also our founder. So this company is. Is his baby and he's got the right to hold. Hold me and anybody else accountable. And so giving him access to a tool to be able to interact and ask questions and get data on what we're doing right, what we're doing wrong. Where is marketing messing up? Where is it not? And using that as a. As a conversation starter rather than waiting on me to provide him a data set, I think is. Has been incredibly powerful and going.
[30:45]
B
Going way back. I know we. You went through it for us like your Pipeline math. But what are like the, like what would you see is like the top three or four most important numbers to make sure you get right in that pipeline math to make sure that you're doing it this the right way? Because I think there is one or two. I think of pipeline math as just lever pulling and then you can do a campaign to fix that lever pulling. I like to simplify marketing. When I was in marketing I was like you either need a pull lever to conversions to MQLs back in the day or conversions MQL to book meetings or conversion like there's different levers that you could pull to like make sure your numbers. So what are like the most top three numbers that you think are the most important in that pipeline?
[31:38]
C
Yeah, I mean I, I always go back to revenue. So 100 start with revenue. That is the most important number. And I can't, I can't say that enough marketing should care. Care the most about revenue. And then. And that that comes from new as well as your existing customers and making sure that your customers aren't turning. There's three levers to revenue. How do I grow net new revenue? How do I grow revenue with existing customers and how do I make sure I keep the customers I have? So make sure that you very well understand your revenue plan as a company and how you're going to grow. The next one is how. How good of a job are we doing winning these deals and what do those deals look like? That's your win rate. But are we in the right kinds of companies? Are we talking to the right kinds of people? Is our positioning and our messaging and the way that we're making ourselves sound like the better option against our competitors? Is all of that tight and are there opportunities for us to optimize our win rate based on what we understand about that? And then obviously the next one is how do we, how do we have enough of top of funnel in order to generate those. The meetings that we need for those opportunities? So meetings to sales qualified opportunity rate. Obviously then you would look at. You've got to increase this, this contact universe, this number of people that we're having meetings with that you cannot sacrifice on the quality metrics of that the meetings are good, they're with the right people with the kinds of deals that you will eventually be able to close when. Because the most important metric is revenue.
[33:10]
B
It also seems like which some morgues aren't like this. Like you have a really good understanding with like your sales partner to get that number. Like because I think Like I've been in, I've seen the, the sandbagging side of it where like you, you get, you ask the sales reps, what do you think? Like this, the head of sales, like, what is the, what do you think that win rate is going to be? And they give you like 12% but you know that, you know they could get 18 or like something. Or like something. It's not that bad. But like they say 12% but then you know it's like 14. But it seem you have like a good like relationship with like your internal leaders to be able to get that revenue number. Because it's hard for like only marketing to come up with the one. Right. Because there's some sales. Unless you're owning sales.
[33:58]
C
No, I do not. I do not.
[34:01]
B
How, how did you build that relationship with like those leaders to make sure like you're on the same page, like you understand what they understand, you're on the same page, what quality looks like. You understand on the same page of like revenues, a team game, like, what are the things you did to make sure, like you build a relationship of trust on both sides that will deliver you quality if you do X. I
[34:25]
C
think when the, when the sales leader knows that I know what I'm talking about, like they understand that I, I understand the business when they, when they recognize that I am their team member, I am here to help them hit revenue and my only goal is revenue. I don't celebrate hitting our pipeline target if we don't hit our revenue target. Because again, at the end of the day my job is to create enough at bats for the team to hit their revenue target. It doesn't mean we don't have difficult conversations about what could sales be doing better or why is our win rate not improving. We also have conversations around do we have enough money? Because often when you look at the GTM line item in or an S and M line item in a financial plan, sales and marketing are bucketed together. And so we have a, we have a percent of revenue that is pretty standard for companies to invest in sales and marketing across the board. And so the conversation I can very easily have with the sales leader is like, okay, if you want to say win rate is 10% and that's what you, that's. You want 10x created pipeline coverage is essentially what you're asking me for. This is how much that would cost, which comes out of our combined budget, which means you won't be able to hire sales heads. Now if we can get that up to 15% then I'm at 6x coverage, 20%, 4x coverage. This is how much it would cost. That leaves budget for hiring solutions consultants and partner managers and AES. And so I do constantly think that math, the pipeline math, makes it a very unemotional conversation. We're centered on the same goal, which is revenue. We have the same boundaries, which is budget. And we figure out how to, how to make that math work together.
[36:05]
B
I like how you said, but I think that's the way I think saying, like any sales leader, if you say like, okay, I need 10x coverage for like that run rate. And then you say like, if you just get a little lower, like you can hire a couple more reps, they're like, okay, fine, I could get it to 12%. But you say like, math is very unemotional, logical. Like it's like very logical to work, work your way back from that number. And I think that like the whole point of this conversation is like, you have to know these numbers and work back and make sure you're getting these numbers because otherwise like this is where you can get into trouble where you've like either over promised because you didn't do the numbers. You've like, you didn't have the conversation with sales. So you didn't fight for like you, you didn't fight for that 10x coverage of the win rate. And but if you know these numbers behind like, you know, like in the back of your hand, it's easy to have these conversations with any leader because it's a math problem. Not like we need to look pretty, like pretty in the market problem.
[37:12]
C
It is. Yeah, yeah. Marketing is a math problem. That is absolutely true.
[37:18]
B
I want to ask you, because I ask everybody in this podcast, what is a marketing hill you would die on?
[37:23]
C
I, I think that I feel like I have lots of hills that I would die on. A marketing hill that I would die on. Briefs don't matter anymore. That's one that I talk about a lot. Don't get so attached to the idea that you lose the idea of the outcome. That's a marketing hill that I would die on. Creativity does not trump delivering performance.
[37:47]
B
I'll go a little dude, but like, what is your opinion? Like, why? Because I'm kind of in agreement with the briefs conversation. So why do you believe briefs don't need to exist?
[37:59]
C
I think the world with AI has changed so much. So a brief was a way to communicate an idea and a concept and a strategy so that other people could execute things. And we've really been able to, with AI, truncate what execution looks like. And so an idea can now be actually produced as an artifact. A brief can be produced as an artifact. You can go back and forth with Claude or Vaughn or tool of your choice and actually create visually the concept of what you're trying to do and why and even argue a little bit like, is this going to work to achieve this kind of objective? Can you connect to my data and help me understand if this is something that, that will, that will succeed? So I think briefs, or maybe, maybe not even specifically briefs, but this concept of very structured, very intention, very theoretical, almost cerebral communication in order to activate or test out an idea. I just, I think that's unnecessary in today's world.
[39:04]
B
Yeah. Because you could pretty much show like, I would say, like whatever the level up from a mood board is to like someone and be like, this is what, like this is exactly what I want. Like, if you, if you want to like, obviously like Claude code could get you so far, but you do need engineers to do some things. But this is like I want my website to look like, close to. Because I could now just brief AI. And it also takes out a lot of steps if you, if you take out the briefing because then it's like back and forth, back and forth, back and forth, back and forth. So I like that, I like that take. Lastly, where could people find you and what you're doing and all that good stuff.
[39:45]
C
Yeah, I'm on, pretty active on LinkedIn. You can find me. I think it's, I think it's backslash. Amanda Joycole. But I'm, I'm on LinkedIn a lot and otherwise you can shoot me an email, Amanda Coleoomreach.com and feel free to give us feedback on our marketing. After all this talk about how marketing is math and send me an email about it.
[40:08]
B
Well, thank you so much. Yeah, let's go. And since marketing's about, let's go. Like judge creativity now. Just kidding. But thank you so much for coming on and I really appreciate the conversation.
[40:22]
C
Yeah, thanks for having me.
[40:25]
A
Thanks so much for listening. Keep tuning in to hear more great insights from the coolest marketers from around the world. If you haven't already, make sure to subscribe and follow the Marketing Millennials podcast on Apple Podcasts, Spotify, YouTube or wherever you get your podcast. And if you like what you hear, I would greatly appreciate you giving us a five star rating. It helps bring more marketers into our community.

Summary7 min read

The Marketing Millennials – Ep. 439

"A CMO's Guide to Pipeline Math"

Guest: Amanda Cole, CMO of Bloomreach
Host: Daniel Murray
Date: August 12, 2026

Episode Overview

This episode dives deep into the "pipeline math" every modern B2B marketer must master in order to link marketing activities directly to revenue outcomes. Daniel Murray hosts Amanda Cole, CMO at Bloomreach, for a candid and analytical conversation about how to reverse-engineer revenue targets, forecast accurately, leverage benchmarks, adapt to channel changes, and embrace AI to optimize reporting, attribution, and team efficiency. Amanda shares hard-won lessons, actionable frameworks, and stories from the marketing trenches, breaking down the myth that marketing is more art than science: "Marketing is a math problem." (37:12)

Key Discussion Points & Insights

Amanda Cole’s Path to Marketing

Amanda entered marketing through a “by chance” opportunity as a young mom needing flexibility, starting with highly manual direct mail response work.
Quote:

"I started working for a company called Business Intelligence Group ... collecting the mailed-in responses and paper checks, and validating which option got more donations." (00:44)

Demystifying Pipeline Math

Amanda offers a step-by-step guide to pipeline math, focusing on tying every marketing conversation and investment back to revenue.

Core Concepts:

Start with your revenue goal and work backward to determine required opportunities, conversions, and costs.
Each funnel stage—contacts, meetings, opportunities, deals—must be quantified and tied to data.
Articulating these numbers with sales and finance builds trust and removes emotion from budget/resource requests.

Quote:

"If every conversation you have as a marketer ties back to how does what I'm doing deliver a revenue number, your conversations, certainly with finance and sales, get a lot easier." (02:36)

Pipeline Math Steps:
(03:00–04:25)

Define revenue target.
Calculate needed opportunities based on win rates.
Assess number of meetings/contacts required.
Work back to calculate spend and channel distribution needed to generate those contacts.
Result: actionable targets and justified budgets for planning.

Forecasting Challenges & Using Actuals vs. Benchmarks

Amanda stresses the value of using your company's historical data (actuals) for baseline numbers, turning to benchmarks only if you lack internal data (e.g., startups).

Quote:

"If you even have at least a year of data, use your actual data. ... So there's benchmark data available but I highly recommend if you have at least a year, that you use your actual data." (06:54)

Pinpoint where improvement is possible: if your conversion rate is below benchmark, analyze and plan optimization accordingly.
Acknowledge when external/internal dynamics (e.g., product gaps, market competitiveness) justify lower-than-benchmark rates.

Nuance in Win Rates & Outliers

Different ICPs and verticals have different win rates and need to be modeled separately.
Outlier reps (high/low performers) should be excluded by using the median win rate, making the forecast more robust to staff changes.
Amanda shares her approach to splitting targets by vertical based on historical contributions and differing win rates.

Quote:

"We took our ARR plan and said, let's assume 20% comes from strategic verticals... but our win rate is 7 points lower in those strategic verticals than our core verticals." (11:00)

Adapting to Channel Changes & Audience Fatigue

When costs rise or channels saturate:

Marketers must remain accountable to cost-per-opportunity metrics, even as paid channel costs rise.
Efficiency gains remain expected; when LinkedIn or Meta increases prices, "it doesn't matter ... you gotta figure out how to be more efficient" (13:19)

Strategies:

Leverage AI for smarter segmentation and in-market identification.
Focus brand awareness efforts on less expensive, high-reach channels.
Constantly optimize for cost and relevance.

Testing Culture: How Bloomreach Runs Experiments

No dedicated test budget; everything is rolled out as a test.
Heavy use of AI tools like Winter for message testing and rapid insight capture.
Adopts B2C-style rigor and velocity in experimentation, even in B2B contexts.

Quote:

"Pretty much everything we roll out, we roll out as a test." (15:17)

Attribution Philosophies in B2B vs. B2C

Amanda has used every attribution model but finds them less meaningful in complex, long-cycle, multi-stakeholder B2B sales.
Focuses on micro- (channel/campaign/message performance) and macro-attribution (is the overall customer journey advancing as expected?).

Quote:

"When you think about putting attribution against a marketing activity across 20 people in 9-12 months, it's almost impossible..." (17:21)

How AI is Transforming Pipeline Math and RevOps

Bloomreach uses an AI tool called Vaughn to drastically speed up analytics, reporting, and opportunity/deal analysis for marketing and sales teams.
Vaughn queries the CRM, journey analytics, and campaign data conversationally, eliminating typical reporting bottlenecks.
Amanda describes using Vaughn to instantly analyze lost deals, pull customer quotes, and update competitor positioning.

Quote:

"All of the things that you need to understand about a customer and opportunity that before you would need to run a Salesforce report for ... you can get conversationally in minutes." (19:52)

Daily AI Use Cases (22:06)

Deal loss analysis (frequency, patterns, customer feedback)
Pipeline health checks
Fast campaign deployment (generating landing pages/collateral based on insights)
Real-time updates that used to take weeks

Enabling Data Cleanliness for AI

LLMs ("large language models") excel at working with unstructured, messy data but require robust context and taxonomy training.
Amanda likens training AI to onboarding a new hire: define terms, outcomes, what "opportunity" or "closed won" means in your org.

Quote:

"Because it does such a great job with processing unstructured data ... you don't really have to do a lot of cleaning up ... just take time on the context on the training." (25:25)

Radical Transparency with AI

Amanda gave her CEO direct access to Vaughn for self-service marketing and pipeline data—building accountability and trust.

Critical Pipeline Math Levers: The Must-Know Numbers

Revenue (always start here—new, existing, churn)
Win Rate (optimize by improving messaging, product, market fit, sales performance)
Volume of quality top-of-funnel activity (meetings/opportunities)
Conversion rates between funnel stages

Quote:

"Marketing should care the most about revenue." (31:38)

Quote:

"Marketing is a math problem. That is absolutely true." (37:12)

Building a Math-Driven Relationship with Sales

Trust and partnership with sales are paramount; pipeline math objectifies the conversation (budget, milestones, outcomes), reducing finger-pointing.
Amanda avoids "celebrating pipeline" if revenue isn’t hit.

Quote:

"When the sales leader knows that I know what I'm talking about... I am here to help them hit revenue and my only goal is revenue." (34:24)

Notable Quotes & Moments

"If every conversation you have as a marketer ties back to how does what I'm doing deliver a revenue number, your conversations, certainly with finance and sales, get a lot easier." (02:36)
"I do constantly think that math, the pipeline math, makes it a very unemotional conversation. We're centered on the same goal, which is revenue." (35:00)
"Marketing is a math problem. That is absolutely true." (37:12)
On creative briefs:

"Briefs don't matter anymore. Don't get so attached to the idea that you lose the idea of the outcome. ... Creativity does not trump delivering performance." (37:23)
On leveraging AI, referencing the pain of the "old way" of reporting:

"The old way is you submit a ticket to Ops or Analytics ... and then you're three weeks out and you lose another deal to the same competitor... now with AI, you get the answer in 15 minutes." (23:46)

Timestamps of Important Segments

00:44 — Amanda’s unconventional entry into marketing
02:36–04:25 — Pipeline math explained and how to work backwards from revenue goals
06:54 — Actuals vs. benchmarks in forecasting
11:00 — Modeling win rates by vertical & excluding outliers
13:19 — Dealing with rising channel costs and audience fatigue
15:17 — Testing philosophy and how B2C outpaces B2B in experimentation
17:21 — The limitations of attribution modeling in B2B
19:52 — How AI (Vaughn) supercharges reporting and analytics
22:06 — Daily practical examples of AI-driven marketing operations
25:25 — Data preparation and context for AI implementation
31:38 — Top metrics for pipeline math and what matters most
34:24 — Building trust and transparency with sales through math
37:12 — Why marketing is a math problem, not just a creative pursuit
37:23 — Marketing hill Amanda will die on: "Briefs don’t matter anymore."

Concluding Thoughts

Amanda Cole provides a masterclass in translating marketing activity into business outcomes, making a persuasive case that the marketer’s most valuable skill is numeracy, not just creativity. Her frameworks, partnership mindset, and AI-powered processes illustrate the new standards for high-performing B2B marketing teams.

Find Amanda on LinkedIn: amandajoycole
Feedback? Email Amanda at amanda.cole@bloomreach.com

Loading summary

Transcript48 lines

[00:02]
A
Welcome to the Marketing Millennials the no BS Marketing podcast. I'm Daniel Murray and join me for unfiltered conversations with the brains behind marketing's coolest companies. The one request I tell our guests stories or it didn't happen. Get ready to turn the up.
[00:27]
B
We are back with another episode of the Mark Millennials podcast. I am here with Amanda Cole, CMO of Bloom Reach. I'm excited to chat with her today. Welcome to the podcast. Thanks.
[00:38]
C
Thanks for having me.
[00:39]
B
I want you to go give a little background how you got into marketing and then we'll get into the conversation.
[00:45]
C
Yeah, I got into marketing very much by chance. I was a very young mom and it was hard to find jobs with flexible opportunities. I started working for a company called Business Intelligence Group. Michael Alicea was the founder there and he was looking for someone to help him profit process direct mail responses for a couple very large non profits that that did. I mean this was 20, 22 years ago. So you we, we were getting direct mail responses for solicitations like you know at the holidays when you get stamps or return address labels and my job was to collect the mailed in responses and the paper checks and validate which option was got more donations.
[01:33]
B
That is sounds like the like everybody I swear I talked to so many marketers and it's always like such a interesting entry into marketing and that's like that's one of the interesting and people forgot like how how marketing used to be where it was just such a manual process. Luckily now we will talk a little bit about it but AI has like helped helped a lot. I want to go into the conversation we have today is and I think this is a pretty interesting conversation because I've never talked about this in my podcast but I want you to explain how you think about working backwards to get a pipeline number. What is the math behind it? I don't think a lot of people dig into it because sometimes numbers are not the most exciting things. But it's so important to make sure you, you win in marketing. So let's just like go into first like the starting point like how you think about it. Where does that number come from? And then we can go into the, the breakdown of it.
[02:37]
C
Oh yeah, I can geek out on pipeline math. It's. It is, I think it is one of the reasons why potentially I'm, I'm so good at my job. Maybe it's the only reason but the because when you can connect marketing back to the revenue number the we're all responsible for growth we're all responsible for outcomes in the business. But. But marketing, especially in the B2B space, is responsible for making sure that the sales team has enough to work with to have a really good chance of hitting that revenue number. And if every conversation you have as a marketer ties back to how does what I'm doing deliver a revenue number, your conversations, certainly with finance and sales, get a lot easier. And so pipeline math is the idea that starting with the revenue number and the growth targets, what are the levers that you need to be aware of that you can pull to work backwards from that revenue number? And so in B2B, it's things like, what is our win rate? Win rate is the number of closed one deals divided by the total number of closed deals in a quarter. So what's our win rate? That tells us how many open deals or open opportunities we need. And then from you back up from. From there, you're. How many. How many conversations does it take for us to get deals? And you back up from there. How many people do we have to talk to to get the right kinds of conversations and one more backup? How many. How much do we need to spend and how many channels to get those numbers of contacts? And that's how you essentially build out the funnel of pipeline math that turns into targets and a budget and a context target for plans for, for a marketing team that a finance team and a sales team can understand.
[04:25]
B
I want to take one step back because. And then we could get deep, deeper. Like, I think some people get caught up with over promising, like the, the first number, which is like, let's talk about. We want to 2x3x4x growth. So when you're going into that meeting with like the CFO and the cmo, I mean, not the cmo, but the CEO, and you're like saying, they're saying to you, Amanda, okay, like, how. How much growth do you think we could do this year? Like, what, how do you get that number first? Because that number is more a lot important because some people would be like, for sure, we could 3x and then you, like, do the math, start doing the math going backwards. And it's like, wait, like we have some of these channels are actually capped out and we, we're not gonna, we might not hit this number. So I want to know that number first. And then we could talk about detail.
[05:23]
C
Yeah, I mean, I, I think that's why pipeline math is so important, because I, I love a conversation. Every CEO and CFO wants to push as much growth as Possible. We all do. I mean, especially in the software world. A lot of us are incentivized with partial ownership in the company and our ownership becomes more valuable the more the company grows. And so we are incentivized to deliver growth. But growth also comes with investment. And that's why pipeline math is so awesome. Because you can actually show how much investment is required to hit those growth targets. Because the other element of this is how much did it cost us. So if you take what I said earlier, let's make it very simple. How much revenue do you need? So how many opportunities do you need? We track all the way down to the cost to generate an opportunity. So if you want a 3x your growth, then you need to 3 extra opportunities and that has a correlation to how much you actually need to spend and invest in order to generate that. So it does become a very mathematical conversation instead of a it would be great if kind of conversation which a lot of companies, especially in, in startup or early stage companies can get into a debate like that with, with their teams. And it, it really comes down to let's, let's make the plan whatever we want to make it, but know that the investment needs to be appropriate size as well.
[06:47]
B
What do you find is, which part of the pipeline math do you find is like the hardest to forecast?
[06:54]
C
Well, I mean again, the great news is you're, you don't start with forecasting, you start with actuals. Now obviously if you're in a, if you're in a startup and you have no actuals, you have no data, there's, there's a ton of benchmarks and happy to share, certainly some, some that I've used in the past, but there's certainly some pretty standard benchmarks of like MQL is dead now, but like let's just say a marketing contact or an ICP contact into a meeting conversion. The number of meetings that convert into opportunities, a number of opportunities that convert into sales qualified opportunities and then closed one. So there's, there's benchmark data available but I highly recommend if you even have at least a year of data that you use your actual data. You say what is our actual win rate? What is our actual conversion from opportunity into into or excuse me, from meeting into opportunity because that's the baseline and of course where you can get into conversations is on debates about where can we improve. And again you would use benchmar here. So if you're meeting to sales opportunity, sales qualified opportunity is below the benchmark. You could make a decision and say okay, we've identified some specific optimization areas and we want to plan our pipeline targets for next year, assuming that our win rate gets better. Or you could say, actually we don't have any plans to make this better. We didn't even know it was bad. Or there's some other dynamic, like maybe you're just in a very competitive market or maybe you're aware that your product has gaps that you're not going to be able to fill in the next six months. So you're going to accept a lower than benchmark conversion rate, but that's what you're going to base the plan on in order to hit your targets. So you don't necessarily have to get into an argument about, about feelings on those conversion metrics either.
[08:42]
B
Yeah, I'm also interested because I've seen this because I used to be a marketing office. But like, let's say like the one rate conversation of, okay, like we have, like, we need to work back to get that, that opportunity number. But let's say like each ICP might have like a different, like if you separate, might have a different win rate. But also there could be like a couple of sales reps that are way overperforming those win rates that are like kind of outliers that are lifting up that number that could screw you up if that person decides to go to the next company or that person quits and then you have to get ramping reps and then you, you're like low. So, so how do you like, do you take out outliers? Do you forecast in or do you lower it based on outliers? Or how do you do that one rate to make sure that like if the top three sales rep decided to like move to another company and that one rate goes down 3, 4%, that means that pipeline number is going to have to go up. Like.
[09:46]
C
That's right. That's right. Because exactly as you said, if a rep leaves, a new rep comes in, the rep who is ramped has a higher quota percent. A new rep who's coming in is going to have a lower conversion rate and certainly they're going to take a longer time to ramp. And so what you're, I mean, we're really geeking out now at this point. But the, you can use the mean or the median. But this is where a marketer who knows this, who is in the details and who understands the data and is able to come back with an argument on why we chose the win rate. Win rate. Now I can't even say it that we chose are for these reasons. These are our strategic objectives. These are the verticals that we want to be in. And part of that is a recommendation that you're making. One of the things that we did in our planning cycle this year, we wanted to get into some new strategic verticals, and we had a worse win rate in those strategic verticals than our core. And so I actually carved out a percent of our ARR. The finance team did not come back with a finance plan that was split by vertical that would be entirely too nuanced for us to build an entire financial plan around that. But we did. We took our ARR plan and we said, let's assume that 20% comes from strategic verticals, because last year it was 18%. And now we're going to make a concerted effort to increase the amount of revenue that comes from these new verticals. But our win rate is 7 points lower than in these strategic verticals than it is in our core verticals. And so we did actually split out the way that we look at targets based on the nuance of the win rates in those in those verticals. Similarly, we, on the rep side, we do use our median because you want to exclude the really bad performers and, And. And the top performers.
[11:30]
B
I like that. I mean, because I think it always should be. The nuance stuff should always be in a marketing. Marketing should be doing those numbers separately and not show, like, not getting it all messed up in a finance person's head. Because the finance person just wants, like, what is that percentage? You. You. What number are we doing here? Like, I don't want to. I don't care that there's a rep that's leaving. I don't care about the. That, like, you want to grow in this vertical. Well, kind of. They want to care. Some finance people want to say, like, this is how much revenue is coming from, let's say, this new vertical. But the win rate number needs to. You just need to. If you want to get more nuanced and say, like, rep base and stuff like that, you don't need to show finance stuff. I think the other number that gets a little. I'm interested in how you plan is so, okay, working backwards. So there's opportunities. But let's say, like, you know, as a marketer that, like Facebook or like, let's say meta or LinkedIn might have, like, some audience fatigue happening in this vertical. Like, we were, like, capping out, like, capping out audience fatigue or capping out this. And last year, like, the numbers show, like, we like cost per lead with this. But like let's say like LinkedIn is raising like prices for what like impressions or whatever's, whatever's happening. How do you, like, how are you thinking about like, because sometimes like actuals could screw you up because like, like how platforms work like numbers, audience fatigue, all that. So how are you working in those numbers to make sure like you, you're not getting screwed over by like the audience to see platform rising costs, all those crazy things that happen?
[13:20]
C
Yeah, I mean that is just part of the marketer's job is to understand the trends and what's impacting marketing. And the reality is regardless of what's happening in the market, your cost per opportunity can't go up. Everybody in marketing we know we need to do more with less. We need that cost per opportunity to decline year over year over year because we want to see those efficiency gains at the top of the funnel and in demand generation while we also see an increase in that spread between how much we need to spend in order to generate opportunities versus grow in revenue is part of the job of marketing. And so honestly it doesn't, it doesn't matter if, if LinkedIn is screwing you over. You gotta figure out how to be more efficient with your budget and generate more opportunities on a, on a more efficient cost per opportunity. The good news is there are lots of resources, podcasts like this that help you think about and be more creative. I do think audience segmentation and being really intentional with your aud. I think with all the AI tools that we have coming out now where you can do a better job really identifying when people are potentially in market so that you're executing ads certainly in paid social at a more strategic time in, in the relationship. If you are doing more brand awareness ads, make sure that you're not doing it in super expensive channels but where you can get reach and visibility and some of that subconscious impression but without spending lots and lots of money to do it. So that, that I think is the core of the strategic thought process of a marketer.
[14:51]
B
What is, what is your philosophy on split on like budget split of like test budget versus like for trying to hit revenue budget versus and we can go into a little bit of like brand versus like actual like capture. But the first I want to go into like how you think about test and like actual hit number budget.
[15:17]
C
Yeah. And we, we don't carve out a specific test budget, but we do a ton of testing and it's certainly becoming one of the, one of my favorite ways to Test now is certainly with synthetic data or digital twins and to really like take replications or representations of an audience and test messaging and see how quickly we can actually gather insights and intelligence. We love. There's a tool called Winter that is great in B2B that we, that we love using from a message testing perspective. When it comes to testing approaches, we, we pretty much everything we roll out, we roll out as a test. So there's nothing there. If we roll out a new demo request or if we roll out a new interactive video format, we're always, I think it's just in our nature to roll it out as a test. And we, we, our technology is in the B2C space so we deliver marketing automation or CRM for B2C companies. And B2C is incredible at testing. I mean that, that is how they, how they live. And so I think we've, we've taken some pages out of their book.
[16:21]
B
My wife runs Digital for Salt and Stone. And the amount of testing they do there is a crazy.
[16:28]
C
And every thing is how big the dollar sign in the offer. I mean they test literally everything and
[16:35]
B
how good they are like reporting like incremental like and where things are actually coming. And I wish B2B could do it as well as they do it because they, they know like, oh, If I did YouTube and this, this, this like I have like a software or a math problem to solve any of these things. And it's, it's crazy to see but B2B like fell behind a long time ago because we, we just stuck with it. But I want to know your philosophy. Like how are you like tracking where things are coming, coming from? Like what is your attribution philosophy? Or like. Because every, I think every market has a different attribution philosophy or maybe like way they do it.
[17:22]
C
Yeah, we've all done U shaped, W shaped, weighted. We've all done curve. We've done all the different attribution models that you can possibly do. And at the end of the day, very rarely did they contribute in a, in a significant or meaningful way to, to outcomes. And the benefit of B2C is that the purchase window is, you know, a long one is seven days. There are certainly some more considered purchases like furniture and things like that. But for the most part in, in the B2C world, your, your conversion points are, are sub 48 hours. In B2B it's a much longer extended window for us. We're in the enterprise and so ours can range nine to 12 months and on average we have 20 people involved in an opportunity. And so when you think about putting attribution against a marketing activity across 20 people in nine to 12 months, it's almost impossible to say, here's what our strategy needs to look like in order to be able to replicate this and further the organizations themselves. Because if you look at the, you know, the data points of, of individuality in a company dynamic and then 20 people inside of that company, it's really, really hard to replicate the attributes of what made that an opportunity to begin with. So what I think about in attribution is the micro and the macro. So we look at very micro is the channel, is the campaign, is the message doing what it's supposed to be doing? And that's the micro. And then in the macro, it's is the customer journey moving in the shape and timeline and urgency that we expect it to. And so that's where you are able to kind of triangulate both things that I know that channel my campaign, my message, is performing at baseline or better and that we constantly consistently optimize for that in, in the micro and then in the macro is the collective of all of these things driving opportunities forward enough at the right clip. That's our win rate and the timeline that we needed in the age of our opportunities.
[19:22]
B
I also, I think we've done a great job and not mentioned AI for 20 minutes. But I wanted to ask you like, I want to ask you like how, like with AI, how is it optimizing this pipeline math and how are you getting data faster to you to make quicker decisions? And what are you doing internally to do that or what are you doing? Let's just ask that question and I'll ask you more AI questions after that.
[19:52]
C
We are super aified. So one of my favorite tools, which I think is an augmentation for OPS teams, it's made our marketing and our revenue ops teams 40 to 50% more efficient. Is called von von and it essentially all of the random questions, all the reports, all the dashboards, all of the things that you need to understand about a customer and opportunity that before you would need to run a Salesforce report for or you'd have to wait for somebody from OPS to come back to you. You can get it conversationally in minutes. With Vaughn, it's trained on our data. We did need to make sure that it understood our context and our language and the way that we have information. In Salesforce, it is fully integrated with all of our marketing and marketing channels and it is also integrated with dream data, which does that customer journey element, like what are the elements of the various contacts and customers and how do they map together across this customer journey? But that's all available and accessible within one question and a few minutes. In Vaughan, how we execute marketing, we're using Claude and Claude design to throw up landing pages pretty quickly. We have a tool called Character Quilt that we build full end to end campaigns with in a matter of minutes instead of, you know, maybe a week or two. I mean we, we have AI fied all the. We are also an AI company, so there is an expectation that we are using these tools and that we're living and breathing it. So that definitely makes it easier. I have counterparts who are being told they're not allowed to use it, which blows my mind. But we've, we've input it into every part of our business.
[21:27]
B
I just had a conversation with someone that there's like such a two ends of the spectrum, like companies that are really just like controlling what AI you use. And then there's like AI paled companies where like everybody's using the AI and there's a little bit in the middle. But like I hear this both ends of the spectrum more than just the middle stuff. But I want to ask you, so what are you asking Vaughn on a daily basis? Like what are the questions you're asking? Like, like I just want people to know from the CMO lens. Like what? Like you go to Vaughn and I'm asking these few questions because I want to get these answers today. Like what is an example?
[22:07]
C
Oh man, I think I'm the number one user of it in the company. If I'm not, I'm going to keep, I'm going to try. I'm going to have to up my numbers. But yesterday, for example, we lost a deal to a competitor and I asked Vaughn to tell me how often was it a pattern? How often had we lost to the same competitor. I asked it to give me actual quotes from customers on calls about why they chose somebody else over us. And I asked it to also analyze the sales process. Like how well run was the sales process. It came back in 15 minutes with a report that was shareable. I do still click in and verify. I still go to Salesforce and make sure it's pulling the right stuff because I'm still a little bit paranoid. I don't want to ever share something that was a hallucination. Luckily Vaughn is right so far all of the time. But it was able to do something that would have probably taken a month before and provide an artifact that I was able to use to conversationally talk to my team and the sales team about what do we need to do differently. I converted that asset into an updated competitor battle card with some specific quotes about why it's really important that we change our positioning and messaging in one area. And then I sent it to Cloud design and converted into a landing page and gave it to our ads team and said by the end of the day or by the end of the week, I'd really like us to have some ads live in this category. And that's like, that's literally how everyone works. We had a. Our team yesterday launched an event for the U.S. open. We wanted to start prospecting and getting it out. There were some optimizations that we needed to make on the page. We had a back and forth slack. The team updated it, went live with it. This was all within an hour and a half.
[23:46]
B
Yeah, I mean, I mean, the old way is you submit a ticket to Ops or Analytics. Like analytics will say you're fifth in line to get this. Then you have to fight and say, could you make this a priority? And then even if it's a priority, I get two to three, four days, you'll get a report and then you want more and then you have to dig in for more. And then you're like three weeks away out and you lose another deal to the same competitor like four more times. And you're like, why did I. Like, I could have got this answer now. Like with AI, you get the answer in 15 minutes, which is I just want people to like know like that, like the ticketing, like your ticket is you, you asking Vaughn, that's your ticket request. And so I love the. I love the AI pill. What are some things that you had to do as a marketing org to make sure you had like clean data, clean reports to make sure Vaughn was spitting out like the right things for you. Because I feel like there was probably had to do some like lifting before. Vaughn was not like pulling out a report that like maybe have been dead five years ago or like data that's been bad. I mean, every company has a little bit Bloomreach probably is probably at the high end of doing the right way. And then there are people who aren't. So for people who have a messy stack or something, how would you recommend to make sure you do it before you implement a POM?
[25:26]
C
Well, I mean, this is the incredible news about LLMs is their ability to understand unstructured data. I mean, I think a lot of us have heard this by now, so I doubt I'm saying anything revolutionary, but LLMs ability to understand and build and draw context between unstructured data is pretty incredible. So with Vaughn, what we're really doing is teaching it our context layer, our taxonomy. So when we say opportunity, what is an opportunity? How do you actually identify that in the system? When we say closed one, what does that actually mean? Like, is it a stage, is it a status, is it those kinds of things? And so it's really like you're teaching the same way that you would a new hire. You're teaching them how to look at and understand the data itself, the cleanliness of the data. Again, because it does such a great job with processing unstructured data and understanding context between various points of data means that you don't really have to do a lot of cleaning up in the way that you would in the old days in order to get a report that was accurate. Because remember, in a report you're selecting data fields and there's no intelligence behind that. It's just identifying whether or not the data field is populated with the response that you gave it and if it is it, send it to you. With Vaughn, for example, even if sales reps have entered a close loss reason, I can say I don't want you to use the closed loss reason that the rep is used. I want you to actually listen to the call recordings or read the notes and the opportunity and come back and tell me what you think the closed lost reason is. So I think there's actually a much lower bar of data clean, cleanliness and quality. And actually if you have a quality and cleanliness problem, move faster on getting something AI embedded. But you definitely need to take time on the context on the training.
[27:13]
B
Yeah. So basically just like for me, for, I mean audience, I think just like anybody, any new hire, you need to like set up like an onboarding doc or multiple onboarding docs that have like text like taxonomy. Like what, what we want you to do here. Like here's your role. Like I'm, I'm just simplifying. But you need some ways to like make sure like AI knows what, what you're talking about and knows your company and knows your contacts. Otherwise it's going to hallucinate a little bit. Because they could say you say closed one and could say like 15 different definitions in the ether what closed one means to and different orgs have. Some people have MQL, some people have MDL and they have different acronyms and everything. Everybody's different with how they define things in their organization. So that's like the heavy lifting. But lucky you could. It's probably easy because you could just like get AI to help you do that because you could just. Someone in the company knows all those answers. They could talk to it. Like, I don't know if I would
[28:24]
C
call it heavy lifting. I'd probably call it more like hiring a mover. Pretty low effort on your. On your part.
[28:33]
B
But I do think you said something really important though. It's like now that with AI, the multiple data sets that you could pull from where it took so long, where you like, had to trust an AE or you had to trust someone else to like, give you an answer that might not have been true or give a. Like, you could go back and say, like, in the sales call, they said they heard about us in a podcast, but this person says this. You could say there's multiple conversations happening and before it would take hours to look at closed loss for all the data, then go listen to all those calls and then also go look at the reporting to see what the reports say. But now you could just ask it all these questions and make sure it comes back in a clean report however you like it to make sure things are clean, which I think that's. And to be able to ask, like to go into sales calls and stuff to do these things is super important. Is there any other things that. Are you like when building reports, how are you like now thinking about, like when you're building a reports to show the CEO or cfo, like how marketing is doing and they ask you a quick question, Are you. Are you asking Vaughn to like, spit something up and send it to them or.
[29:58]
C
No, not even that. I gave our CEO Vaughn and I said, you want to know how marketing's doing? Ask Vaughn. You want to know how sales is doing? As Vaughn, you don't want to know about Pipeline as one, because I am completely comfortable being accountable for what the. What our outcomes are. And the. Our CEO is also our founder. So this company is. Is his baby and he's got the right to hold. Hold me and anybody else accountable. And so giving him access to a tool to be able to interact and ask questions and get data on what we're doing right, what we're doing wrong. Where is marketing messing up? Where is it not? And using that as a. As a conversation starter rather than waiting on me to provide him a data set, I think is. Has been incredibly powerful and going.
[30:45]
B
Going way back. I know we. You went through it for us like your Pipeline math. But what are like the, like what would you see is like the top three or four most important numbers to make sure you get right in that pipeline math to make sure that you're doing it this the right way? Because I think there is one or two. I think of pipeline math as just lever pulling and then you can do a campaign to fix that lever pulling. I like to simplify marketing. When I was in marketing I was like you either need a pull lever to conversions to MQLs back in the day or conversions MQL to book meetings or conversion like there's different levers that you could pull to like make sure your numbers. So what are like the most top three numbers that you think are the most important in that pipeline?
[31:38]
C
Yeah, I mean I, I always go back to revenue. So 100 start with revenue. That is the most important number. And I can't, I can't say that enough marketing should care. Care the most about revenue. And then. And that that comes from new as well as your existing customers and making sure that your customers aren't turning. There's three levers to revenue. How do I grow net new revenue? How do I grow revenue with existing customers and how do I make sure I keep the customers I have? So make sure that you very well understand your revenue plan as a company and how you're going to grow. The next one is how. How good of a job are we doing winning these deals and what do those deals look like? That's your win rate. But are we in the right kinds of companies? Are we talking to the right kinds of people? Is our positioning and our messaging and the way that we're making ourselves sound like the better option against our competitors? Is all of that tight and are there opportunities for us to optimize our win rate based on what we understand about that? And then obviously the next one is how do we, how do we have enough of top of funnel in order to generate those. The meetings that we need for those opportunities? So meetings to sales qualified opportunity rate. Obviously then you would look at. You've got to increase this, this contact universe, this number of people that we're having meetings with that you cannot sacrifice on the quality metrics of that the meetings are good, they're with the right people with the kinds of deals that you will eventually be able to close when. Because the most important metric is revenue.
[33:10]
B
It also seems like which some morgues aren't like this. Like you have a really good understanding with like your sales partner to get that number. Like because I think Like I've been in, I've seen the, the sandbagging side of it where like you, you get, you ask the sales reps, what do you think? Like this, the head of sales, like, what is the, what do you think that win rate is going to be? And they give you like 12% but you know that, you know they could get 18 or like something. Or like something. It's not that bad. But like they say 12% but then you know it's like 14. But it seem you have like a good like relationship with like your internal leaders to be able to get that revenue number. Because it's hard for like only marketing to come up with the one. Right. Because there's some sales. Unless you're owning sales.
[33:58]
C
No, I do not. I do not.
[34:01]
B
How, how did you build that relationship with like those leaders to make sure like you're on the same page, like you understand what they understand, you're on the same page, what quality looks like. You understand on the same page of like revenues, a team game, like, what are the things you did to make sure, like you build a relationship of trust on both sides that will deliver you quality if you do X. I
[34:25]
C
think when the, when the sales leader knows that I know what I'm talking about, like they understand that I, I understand the business when they, when they recognize that I am their team member, I am here to help them hit revenue and my only goal is revenue. I don't celebrate hitting our pipeline target if we don't hit our revenue target. Because again, at the end of the day my job is to create enough at bats for the team to hit their revenue target. It doesn't mean we don't have difficult conversations about what could sales be doing better or why is our win rate not improving. We also have conversations around do we have enough money? Because often when you look at the GTM line item in or an S and M line item in a financial plan, sales and marketing are bucketed together. And so we have a, we have a percent of revenue that is pretty standard for companies to invest in sales and marketing across the board. And so the conversation I can very easily have with the sales leader is like, okay, if you want to say win rate is 10% and that's what you, that's. You want 10x created pipeline coverage is essentially what you're asking me for. This is how much that would cost, which comes out of our combined budget, which means you won't be able to hire sales heads. Now if we can get that up to 15% then I'm at 6x coverage, 20%, 4x coverage. This is how much it would cost. That leaves budget for hiring solutions consultants and partner managers and AES. And so I do constantly think that math, the pipeline math, makes it a very unemotional conversation. We're centered on the same goal, which is revenue. We have the same boundaries, which is budget. And we figure out how to, how to make that math work together.
[36:05]
B
I like how you said, but I think that's the way I think saying, like any sales leader, if you say like, okay, I need 10x coverage for like that run rate. And then you say like, if you just get a little lower, like you can hire a couple more reps, they're like, okay, fine, I could get it to 12%. But you say like, math is very unemotional, logical. Like it's like very logical to work, work your way back from that number. And I think that like the whole point of this conversation is like, you have to know these numbers and work back and make sure you're getting these numbers because otherwise like this is where you can get into trouble where you've like either over promised because you didn't do the numbers. You've like, you didn't have the conversation with sales. So you didn't fight for like you, you didn't fight for that 10x coverage of the win rate. And but if you know these numbers behind like, you know, like in the back of your hand, it's easy to have these conversations with any leader because it's a math problem. Not like we need to look pretty, like pretty in the market problem.
[37:12]
C
It is. Yeah, yeah. Marketing is a math problem. That is absolutely true.
[37:18]
B
I want to ask you, because I ask everybody in this podcast, what is a marketing hill you would die on?
[37:23]
C
I, I think that I feel like I have lots of hills that I would die on. A marketing hill that I would die on. Briefs don't matter anymore. That's one that I talk about a lot. Don't get so attached to the idea that you lose the idea of the outcome. That's a marketing hill that I would die on. Creativity does not trump delivering performance.
[37:47]
B
I'll go a little dude, but like, what is your opinion? Like, why? Because I'm kind of in agreement with the briefs conversation. So why do you believe briefs don't need to exist?
[37:59]
C
I think the world with AI has changed so much. So a brief was a way to communicate an idea and a concept and a strategy so that other people could execute things. And we've really been able to, with AI, truncate what execution looks like. And so an idea can now be actually produced as an artifact. A brief can be produced as an artifact. You can go back and forth with Claude or Vaughn or tool of your choice and actually create visually the concept of what you're trying to do and why and even argue a little bit like, is this going to work to achieve this kind of objective? Can you connect to my data and help me understand if this is something that, that will, that will succeed? So I think briefs, or maybe, maybe not even specifically briefs, but this concept of very structured, very intention, very theoretical, almost cerebral communication in order to activate or test out an idea. I just, I think that's unnecessary in today's world.
[39:04]
B
Yeah. Because you could pretty much show like, I would say, like whatever the level up from a mood board is to like someone and be like, this is what, like this is exactly what I want. Like, if you, if you want to like, obviously like Claude code could get you so far, but you do need engineers to do some things. But this is like I want my website to look like, close to. Because I could now just brief AI. And it also takes out a lot of steps if you, if you take out the briefing because then it's like back and forth, back and forth, back and forth, back and forth. So I like that, I like that take. Lastly, where could people find you and what you're doing and all that good stuff.
[39:45]
C
Yeah, I'm on, pretty active on LinkedIn. You can find me. I think it's, I think it's backslash. Amanda Joycole. But I'm, I'm on LinkedIn a lot and otherwise you can shoot me an email, Amanda Coleoomreach.com and feel free to give us feedback on our marketing. After all this talk about how marketing is math and send me an email about it.
[40:08]
B
Well, thank you so much. Yeah, let's go. And since marketing's about, let's go. Like judge creativity now. Just kidding. But thank you so much for coming on and I really appreciate the conversation.
[40:22]
C
Yeah, thanks for having me.
[40:25]
A
Thanks so much for listening. Keep tuning in to hear more great insights from the coolest marketers from around the world. If you haven't already, make sure to subscribe and follow the Marketing Millennials podcast on Apple Podcasts, Spotify, YouTube or wherever you get your podcast. And if you like what you hear, I would greatly appreciate you giving us a five star rating. It helps bring more marketers into our community.