A CMO's Guide to Pipeline Math, with Amanda Cole, CMO at Bloomreach | Ep. 439

0:01Welcome 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. we are back with another episode of the market 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 thanks for having me i want you to give a little background how you got into marketing and then we'll get into the conversation 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.

0:56I started working for a company called Business Intelligence Group. Michael Alisea was the founder there and he was looking for someone to help him process direct mail responses for a couple very large nonprofits that did, I mean, this was 20, 22 years ago. So 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. That is sounds like the like everybody. I swear I talk to so many marketers and it's always like such a interesting entry into marketing.

1:42And 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 is like helped helped a lot um but 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 like i want you to explain how you think about working backwards to get a pipeline number? Like what is the math behind it? Like, I don't think a lot of people dig into it because sometimes numbers are not the most exciting thing, but it's so important to make sure you, you win in marketing.

2:26So let's just like go into first of 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. 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, um, 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.

3:08And 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.

3:48And then you back up from there, 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, back up, 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 contact target for plans for a marketing team that a finance team and a sales team can understand. I want to take one step back because then we can get deeper. Like, I think some people get caught up with over promising, like the first number, which is like, let's talk about we want to 2x, 3x, 4x growth.

4:41So when you're going into that meeting with like the CFO and the CMO, I mean, the CMO with 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 of important. Cause some people would be like, oh, for sure. We could three X. And then you like do the math, start doing the math, going backwards. And it's like, wait, like, Like we have some of these channels are actually capped out and we're not going to, we might not hit this number. So I want to know that number first and then we could talk about detail.

5:22Yeah. I mean, I think that's why pipeline math is so important because 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?

5:59So 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 to 3X your growth, then you need to 3X your 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 startup or early stage companies can get into a debate like that with their teams.

6:38And it really comes down to, let's make the plan whatever we want to make it, but know that the investment needs to be appropriate size as well. What do you find is, which part of the pipeline, Matt, do you find is the hardest to forecast? Well, I mean, again, the great news is you don't start with forecasting, you start with actuals. Now, obviously, if you're in a startup and you have no actuals, you have no data, there's a ton of benchmarks. And I'm happy to share certainly 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, the number of opportunities that convert into sales qualified opportunities, and then closed ones.

7:26So 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, 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 benchmarks 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.

8:10Or 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 gonna be able to fill in the next six months. So you're gonna accept a lower than benchmark conversion rate, but that's what you're gonna base the plan on in order to hit your AR targets. So you don't necessarily have to get into an argument about feelings on those conversion metrics either. 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 pipe that that opportunity number but let's say like each icp might have like a different like if you're separating might have a different one rate but also there could be like a couple of sales reps that are way over performing 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.

9:21And then you have to get ramping reps and then you're like, so how do you like, do you take out outliers? Do you forecast in or do you lower it based on outliers? How do you do that when ready to make sure that like, if the top three sales rep decided to like, move to another the company and that win rate goes down three, 4%, that means like pipeline number is going to have to go up like three, four. That's right. Because exactly as you said, if a rep leaves, a new rep comes in, the rep who is rammed has a higher quarter percent. A new rep who's coming in is going to have a lower conversion rate.

9:56And certainly they're going to take a longer time to ramp. And so what you're, I mean, we're, 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, 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 the 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.

10:39And 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 seven 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 verticals.

11:18Similarly, on the rep side, we do use our median because you want to exclude the really bad performers and the top performers. i like that i mean because i think it always should be the nuanced 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's just wants like what is that percentage you 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 like you you want to grow in this vertical kind of do 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 vertical but the win rate number it 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 that stuff i think the other number that gets a little i'm interested in how you plan it 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 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 yeah i I mean, that is just part of the marketer's job is to understand the trends and what's impacting marketing.

13:28And 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 matter if LinkedIn is screwing you over. You gotta figure out how to be more efficient with your budget and generate more opportunities on a more efficient cost per opportunity.

14:06The 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 audience. 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 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.

14:45So that, that I think is the core of the strategic thought process of a marketer. What is, what is your philosophy on split on like budget split of like test budget versus like, or 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. 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.

15:36We 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 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 is how they live and breathe. And so I think we've taken some pages out of their book.

16:20My wife runs digital for Saltonstone. And the amount of testing they do there is like crazy. and everything is how big the dollar sign in the offer i mean they test literally everything and how good they are like reporting like incremental like increment 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 crazy to see um 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 way they do it.

17:21Yeah, we've all done U-shaped, W-shaped, weighted. We've all done, 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 significant or meaningful way 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 the B2C world, your conversion points 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.

17:59And 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, what I think about in, in attribution is, is the micro and the macro.

18:40So 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 my channel, my campaign, my message is performing at baseline or better. and that we constantly, consistently optimize for that 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.

19:18And the timeline that we needed in the age of our opportunities. 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, Matt? 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. We are super AI-fied. 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, V-O-N.

20:05And 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 Von. 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.

20:37Like 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 Vaughn. 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 full end-to-end campaigns within a matter of minutes instead of, you know, maybe a week or two. I mean, we have AI-ified all that. 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.

21:15So 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 put it into every part of our business. 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-pilled companies where like everybody's using 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?

21:53Like what are the questions you're asking? 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? I mean, 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.

22:29And 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.

23:05I 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 Claude Design and converted it 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.

23:39We had a back and forth Slack. The team updated it, went live with it. This was all within an hour and a half. 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 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 insane i just want people to like know like that like the ticketing like your ticket is you you asking fawn that's your ticket request um and so i love the i love the ai pill what what are some things that you to do as a marketing org to make sure you had like clean data um clean reports to make sure von was spitting out like the right things for you because i feel like there was probably had to do some like lifting before von was not like pulling out a report that like maybe you've been dead five years ago or like data that's been bad i mean every company has a little bit you bloom reach is probably is probably at the the high the high end of like doing the right way and it's the other people who aren't so for people who are have like a messy stack or something how how would you recommend to make sure you do it before you like implement upon well i mean this is the good this is the incredible news about llms is like their ability to understand unstructured data i mean i think a lot of us have heard heard this by now so i i doubt i'm saying anything revolutionary, but the, but LLM's ability to understand and build and draw context between unstructured data is pretty incredible.

25:43So 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 a, 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.

26:26Because 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 sends it to you. With Vaughn, for example, even if sales reps have entered a closed 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 loss reason is. So I think there's actually a much lower bar of data cleanliness and quality.

26:59And 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. Yeah, so basically, I just like for me, for, I mean, the audience, I think just like any new hire, you need to like set up like an onboarding doc or multiple onboarding docs that have like taxonomy, like what you're 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 gonna hallucinate a little bit because they could say you say closed one and you could say like 15 different definitions in the in the ether what close one means to and different orgs have some people have mql some people mdls 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 um 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 yeah i i don't know if i would call it heavy lifting i'd probably call it more like hiring a mover pretty low effort on your on your part 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 the sales call they said like they heard about us in a podcast but this person says this like you could say more like there's multiple conversations happening and before it would take hours to like look at close loss for all the data then go and 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.

29:36Is there any other things that are you, like when building reports, how are you like now thinking about like when you're building a report to show the ceo or cfo like how marketing is doing like and they ask you a quick question are you are you asking vaughn to like spit something up and send it to them or like no 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 ask 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 our CEO is also our founder.

30:16So this company is his baby and he's got the right to 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 conversation starter rather than waiting on me to provide him a data set, I think has been incredibly powerful. and going 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 you see is like the top three or four most important numbers to make sure you get it right in that pipeline math to to to make sure that like you're doing it this the right way because i think like there there is like one or two like because i I think of like pipeline math as just like lever pulling, like, and then you can do a campaign to fix that lever pulling.

31:16I like to simplify marketing when I was in marketing, I was like, you either need to pull lever to conversions to MQLs back in the day or conversions to 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? Yeah. I mean, I, I always go back to revenue. So a hundred percent 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.

31:56There'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?

32:29And 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 have enough of top of funnel in order to generate 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 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.