Ops Cast

Predictability and Planning of Pipeline Metrics with Rachel Squire

March 18, 2024 Michael Hartmann and Rachel Squire Season 1 Episode 109
Ops Cast
Predictability and Planning of Pipeline Metrics with Rachel Squire
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Show Notes Transcript Chapter Markers

Unlock the mysteries of marketing ops with Rachel Squire, a former math teacher turned marketing maven, making waves in the world of Marketo and data-driven solutions. Our discussion peeks into Rachel's remarkable transition from academia to the forefront of marketing analytics, offering a fresh perspective on the value that a strong analytical mindset can bring to the marketing table. Her insights reveal not just the unexpected paths one can take toward a career in marketing ops but also the essential analytical prowess required for success in the field.

Ever wondered how the modern sales funnel bends and twists in response to today’s well-informed customer base? Rachel and I dissect the evolution of buyer behavior, especially within the B2B SaaS landscape, where traditional models like the serious decisions model are being reshaped. We reflect on our own buying experiences to underscore the trend of vendor independence and the importance of adapting sales strategies to keep up. This dance between old and new requires finesse and an ability to forecast not just sales figures but also shifts in customer dynamics.

In a candid exchange, we tackle the challenges lurking in the data management corridors of sales teams, emphasizing resilience and the need for clear, reliable sales metrics. The conversation then moves towards the transformative power of AI and machine learning in honing marketing metrics, pulling back the curtain on how these technologies can revolutionize early-stage funnel predictability and accountability in marketing. Join us as we fuse Rachel's astute analysis with real-world examples to illuminate the path to marketing operations excellence.

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Speaker 1:

Hello and welcome to another episode of Opscast brought to you by marketingopscom, powered by the Mopros. I'm your host, michael Hartman, flying solo yet again. I know that Net Mike and Amy both wish they could be here, so today we are going to be talking to an expert in all things marketing ops and especially analytics. Today we are talking about how to improve predictability and planning pipeline metrics. To join us for the conversation is Rachel Squire. I had to write this down because I still think of you by your main name. So Rachel is currently the founder and principal consultant of Moby Solutions, a consultancy she started not too long ago. Prior to starting her company, rachel held both in-house and consulting roles in marketing operations, analytics and technology, and she started her career in IT and teaching math at the college level. So, rachel, thanks for joining us today.

Speaker 2:

Thanks for having me and for the great intro.

Speaker 1:

Yeah, yeah. Well, so for our audience, rachel and I have known each other for a long time because I was one of her clients. So you know, and we're still good along. So that's. She'll tell you that something. I don't know that that's always the case.

Speaker 2:

Either a good client, good consultant, hopefully.

Speaker 1:

We're both yeah, yeah, I was your best client, right.

Speaker 2:

Oh, obviously.

Speaker 1:

See, there you go. We'll stop there, All right, so let's, let's start. It's always fun for me to hear people's career paths and I know a little more about yours than most, but why don't we? You maybe can talk us through your career path from your in-house. You did the teaching, you were back, you know. You went to agency, back in house and solopreneur now. So talk about how that all happened, how you went from a graduate math program and teaching math to marketing ops and anything that you learned along the way, especially like outside of marketing ops or marketing, even like sort of like anything you learned from your marketing training that you find valuable in your career or found have found valuable.

Speaker 2:

Yeah, I'll try to start with boring details. But my career, I think, like a lot of people, a lot of searching at the beginning, a lot of soul searching and trying to figure out what I want to do for until I die very daunting. And so I, when I finished undergrad, I finished in math, I majored in math, I went to PFL they're like a Sendoza competitor and I started my career there. I had interned there throughout college and I was in the IT department initially and I just did that for a few months. I didn't do it very long and then I ended up I was like, okay, I want to go to grad school. I miss math. So I went to grad school. I spent one year.

Speaker 1:

I think that's the first time I've ever heard anyone utter that phrase.

Speaker 2:

I will never utter it again because I no longer miss it. I did at the time and I spent a year in grad school studying math and trying to get my master's degree and you know I thought I missed it. I didn't actually miss it that much and I was after one year. I had already known I didn't really want to continue with the program. I didn't know what else I wanted to do. They had kind of a staffing gap so I actually stayed on after telling them I was done with school as a quote unquote adjunct teaching like math 100 not 200 to the college students at that school. And then, while I was doing all of this, trying to figure out what I was going to do for the rest of my life, pfl reached out to me and they said hey, you know, got this tech called Marketo and we realized you probably should have someone staffing it, like it's not actually magic and doesn't do its own thing by itself.

Speaker 1:

You mean the automation and marketing automation wasn't truly automated without somebody doing some work.

Speaker 2:

Crazy right you never would have known.

Speaker 1:

Yeah.

Speaker 2:

So, yeah, they called me like do you want to come run it? And I was like you know what? I would love to make some money, so sure, and left grad school officially, wiped my hands clean of that, Went to PFL as their like Marketo administrator. So, like everyone else in this industry, I could say it found me. I did not find this industry but I like it. Like pretty much immediately was like this is so much more fun than what I was doing before Because I can like literally see the results of what I'm doing and I'm helping other people do their jobs better. I'm seeing it like seeing how people are responding to the effort that my team is doing, and it was really cool. So I stayed there for a few years.

Speaker 1:

That's great. So did you. I know you said you decided that you didn't love marketing or miss math. Sorry, miss math, but what did you study? Anything in particular that you think is applicable to what you're doing now?

Speaker 2:

Yeah. So I know I say I don't actually miss it. I don't miss, like the theoretical mathematics, absolutely whatever. But I do actually love implementing a lot of what I've learned. So I did a lot of, I had a heavy focus in analysis. So there's different, there's different types of analysis statistical analysis, numerical analysis. I'm not going to list them off. There's a lot of types, but I did a lot of focus in numerical analysis, which is tangential to statistics and has a lot of applications actually in the work that I do. So, even at PFL, at one point Daniel, who's our CMO, said, hey, can you go find a data provider that we can integrate with the PFL solution and just make sure you find the best one? And I was like, well, what does that mean? And he's like, well, like the most accurate data on people. And I was like, okay, and I was able to use, you know, my one like experimental techniques that I had learned through doing statistical analysis as well as like the actual analysis. So how do we set up a program where we buy data from a whole bunch of different sources? How do we validate and test against real human data? How do we get them to actually consent to this processing. This was pre GDPR and stuff, but like we did have to, you got to know if it's right or not. The best way to do it is to ask them hey, is this actually your address? So, anyway, that was kind of the beginning of my love for experimentation within my work. That kicked it all off and I've been kind of trying to implement those strategies ever since.

Speaker 1:

Yeah, I think that's it's really interesting. This is what you didn't know. Is that the way the last episode we recorded which hasn't gone published yet? So this will be two in a row where I'm trying really hard to not get on my soapbox about the importance of understanding how to do data analysis for marketing offspokes. I think it's a skill gap. I actually think it's a pretty significant skill gap in general. I think, yeah, you are one of, you know, I would say, a relatively small minority. This is my opinion. I don't know if that's totally true about how, like, who really know how to understand data, how to understand statistics, and are not easily swayed by statistics. That I don't want to say. They're made up by some people, but the methodology behind them is questionable. How about that?

Speaker 2:

Yes, exactly, that's the nice way of putting it, yeah.

Speaker 1:

so I think I think I love that. It's two in a row where I get to get on that soapbox, so I won't belabor it, but it's yeah, I think it's a big gap. So, and you're enjoying your new Soulop in your work, is that? Yeah, you've been kind of bounced around a little bit and it's good.

Speaker 2:

Yeah, I did some agency work for a while. I've been in house both, you know I See mops running different size teams and everything, and now I've been solo, for I just had my one-year anniversary and I'm very excited about it and it's been great.

Speaker 1:

That's awesome. I wish I could do like there's. I know there's like sound things here. Maybe we could do one here. Let's see. Let's see, how about this one? Oh, I.

Speaker 2:

Can't hear it, but you know, I'm sure it's great.

Speaker 1:

All right, so there you go. There's this extent of my production knowledge. Okay, so let's, let's jump into it. Yeah, one of the things that I think you know I've talked about, or definitely have trade messages out and I probably talked to other people about, is that you know a lot of people still use the term funnel Right sales marketing funnel in kind of a traditional model. Yeah, I know what my opinion is, but do you think that is still a model that truly applies today? And like why or why not?

Speaker 2:

Yes, that is such a loaded question, by the way, but yes, I can tackle it.

Speaker 1:

So I'm the master of that.

Speaker 2:

That's the point of these, right? You know it's really funny because, depending on the context you asked me that in, I would give a different answer. So I very you know, very strongly believe in the value of modeling your prior journey, understanding the behavior of your, your customers and your, your leads, and using that to develop a strategy. And what I think is different today, if you think back to 10-ish years ago, like the serious decisions Model, that was kind of the foundation that everyone probably knows today, even if not my name. Yep you know you've got your, you got your early leads, your MQL's, your sales, sql's, closed one and like that's the model all at least B2B sass is using, and I there's a lot of value in having a place to start, like if you don't know where to start, I still think that's a good place to start and at the root of that, you know the natural human behavior of having your awareness, your consideration and your decision. That's like how we as people are, so that's never gonna change. But I do think that prescriptive, stage by stage, those exact stages, you know that's it's not dead, but it's not as prescriptive as it used to be because information is so much more accessible than it has been before. Yeah, it's so much easier for people to say, oh, you know, get on my slack community and say, hey, I'm thinking about this, how much is it? Yeah, is it?

Speaker 1:

good or not?

Speaker 2:

and they can skip all the way to the end. They could be all the way at the end here and then find out about a competitor and jump back like there's a lot more. I think of people moving back and forth between their level of commitment to the buying cycle and that it's just not as Structured as it used to be.

Speaker 1:

Yeah, and I I think you make a good distinction about the buying journey. I think has changed quite a bit, so I think you're right. I agree I agree where people and I just mostly go by somewhat by my end of one right me. How I have bought in the recent past and it's been I tend to do a lot of research in. I kind of insurges right when I have a moment of time. I'll do, I'll do a bunch of digging and then I'll. I may not have time again to come back to it for a while. Then, you know, I may find a competitor, I may talk to people. I was recently reevaluating my most recent job. I was evaluating a different marketing automation platform and I didn't go to that vendor, right vendor probably would have had no idea about it until a couple of years ago. I didn't know about it until a partner introduced me to someone there, which I actually hadn't asked for and I didn't want because I wanted to kind of stay under the radar and it was fine, like I don't mind doing that, but I was. I was really trying to just sort of dig on on my own and I think that's more common today than ever. I also agree with you that I think it's still there's a. There's a, I think, the. You know I'm familiar with the serious decisions model and I like it in general. I but I think about it like I do with how I try to manage my, my Inbox, which I don't tend to delete anything. So for those of you who are inbox zero people and that really gets to you like you might want to pause and skip over this section. Yeah, but when I do, when I do organize my inbox, the only thing I tend to do is I do it based on time period, because any other try to attempt it, trying to To organize it has always ended up with it's either too granular and you got to make A choice. Do I go in a or b or c and any one of them would be as good, and you choose one and then when you're trying to find Stuff, you can't find it because it's it's not in the place we expect it or it just simply has too many breakdowns. And I think I think a simpler model, right, you've talked about like the like, a three-eth stage, like I'll be a much more of a fan of something like that than something that's Got a lot of amount of detail. Now you may have more detail in the background because you're doing some things about Evaluating likelihood to close, etc, etc and all that kind of stuff, but I think at some point, like, really, it's hard to break it down below that, knowing that that doesn't really match up with the way people are buying, particularly technology to these days. It may still be more applicable in other other businesses.

Speaker 2:

I mean that's a good point, though right Like this is very beat-a-be-sass conversation that we're having right now and it's. I work with a few companies that don't follow that model, but their life cycle is nothing like the series decision lens like. So I yeah, it's almost like apple storages.

Speaker 1:

Yeah, and I think, I also think that the effectiveness of some of those Is challenged when you've got very different typical Purchase size right, a high ticket item yeah, and or when the sale cycle is really long.

Speaker 2:

That's so true. I mean it's. I bought like a hundred dollar piece of tech today and I thought about it for like three seconds because I didn't want to Waste time. So, yeah, it's, it is. It's very different right you have to really think through what I must still be selling to this person a year from now.

Speaker 1:

Probably very different right, yeah, oh, okay. So I think we're in alignment there. I still think, like in general, I think that concept is Is there's nuance to whether where it fits, whether it matches now, I think, is where we're both landed, so All right. So the other thing that I think this is where we might get into a little more of a debate about the effectiveness of it. One of the things that I've seen in my own experience and definitely heard about is that in this traditional waterfall model or some sort of funnel model of how you're tracking your funnel, one of the ideas is that, if you've got, you can kind of work backwards to how many leads do I need at the top of my funnel in a certain amount of time to ultimately get down to how much revenue am I going to get? Or you can work backwards right, this is our revenue target At this point. We need to work backwards. We know our conversion rates, or can we affect our conversion rates? Do you think that model, that kind of modeling, can still work today, or do we need to be thinking about something different with these new buying behaviors?

Speaker 2:

So I have thoughts on that that I will share. I've lost my thoughts, so there's two things I want to drill into, the first thing being I love what you said about kind of maintaining a constant right. It's not oh, I need X amount of dollars next year, so this year I need X amount of leads. It's like in order to continuously maintain a revenue stream, I need to continuously be pulling in X volume at each, let's say, stage. I actually really like that because it gives it, instead of us having a end of quarter panic every quarter or month, it really provides this mentality of consistent, scalable growth that is a lot more stable.

Speaker 1:

Who do you know that's panicking at the end of a quarter or month?

Speaker 2:

Oh, nobody, that's you know on my head.

Speaker 1:

Those are the outliers.

Speaker 2:

Yeah, we've all been there, so I really appreciate that mindset. You know, it's interesting because, in terms of like forecasting and planning, we do still have targets we have to reach, so we do still kind of have to do some back calculation. And it's interesting because what we talked about earlier about all that nuances and like how people are harder to measure where they're at, and a lot of times we don't even know their names until they say I would like a demo because I probably want to give you my money. It's so much harder to plan for that that what you know lack of communication with prospects that we had in the past, unfortunately. I almost see it as a bit of a necessary evil, though, because we do actually still have to find a way to like measurably track our progress against our numbers, and it might look you know what it used to look like was I need X MQLs to hit my opportunity number. What it might look like now. More is, I need some combination of MQLs and visits on my website and mentions in social, as not that those are necessarily the people that are going to buy, but as indicative trends of the buying behavior you're going to be seeing.

Speaker 1:

So that's like leading indicators, if you will.

Speaker 2:

Exactly yeah, which is why they should never be targets. They should be indicators.

Speaker 1:

Yeah, side note yeah, well, and you need to. You know, depending on how far ahead they are in terms of leading, you need to take that into account too. Right that there's a correlation from that to you know something downstream based on your best knowledge. So, you know, we mentioned already the idea that, like, you might go into a Slack group like marketing opscom, slack, and ask questions like how? I mean, I haven't, like I wonder, is there a way to have that visible? There probably is, and I don't want to know about it as much as I know about how much data there is about people. But I mean, yeah, that seems to be part of that. You know the term I hear is dark web or dark funnel, right, so you know, is is it? Have you found anybody who's figured out ways to sort of identify some of that, or is it still not really there?

Speaker 2:

It's really funny that you bring this up. I've never heard of anyone doing this successfully, but it could just be because I haven't heard of it. But I did talk to someone this morning who shall rename remain nameless who just told me they want to work on a secret project. That is like literally that. So keep an eye out. I mean, maybe in six months we'll get a new product for it.

Speaker 1:

I can imagine right, Okay, Okay, so the. So there's two sides of this. You brought up like the. I was thinking about this as sort of going backwards and trying to set targets for the early stage stuff I think you also were talking about. You also want organizations typically want predictive, good predictability on revenue and pipeline looking forward, how, how, how do you see that being affected by this sort of changing by behavior? The predict, you know, the predictive models, if you will, on how that's happening?

Speaker 2:

So I've seen this a lot, especially at my last in-house couple of roles that I've had, where I think we've got okay. So we as marketers are the very beginning of everything that we can track, at least in terms of getting people prepared to talk to sales. And what I've seen happen so often is like if marketing does something really good, and all of a sudden we have a whole bunch of people on our website or a whole bunch of mentions in social media or something that creates a buzz, that buzz escalates either to sales or up or both, and everyone's like, well, how can we capitalize on this? And then they start cold calling all the people that like to really cold LinkedIn posts or, you know, they ask for a list of all the people that downloaded that page that day. And then all of a sudden you have just turned your list of people who are so interested in this one thing you did, who could have been nurtured and brought into your sales funnel down the road when they're getting ready, and you've turned them off because now they're annoyed because you've called them when they weren't ready. And now what we had tried to build, you know, six to 12 months from now, what would have been like a surge in six to 12 months is probably going to be about the same. Like we've probably heard anything in terms of, like, denting our numbers by calling them, but we could very well have missed out on an opportunity to like really nurture those relationships. Now that I'm saying this, I don't know if that really answers your question, but I think that we, like just need to be more aware of how we, as marketers, are trying to do this like long game, and we need to be appreciative and respectful of the you know impact that that's going to have.

Speaker 1:

Sure Right, yeah, I mean, that's your. What you're talking about is your undermining trust.

Speaker 2:

Oh, yeah, exactly.

Speaker 1:

Right, that's what you're doing, if you potentially right and I think some of it has to do with how you do it, it like. So literally right before we started recording, I got a phone call out of the blue and I answered it because I tried to answer, unless it clearly tells me it's spam, but it was from. I posted something on a Slack group today and someone else must have told them about it. And I get this call and it took me a minute to try to figure out where it came from. And, yeah, I was fairly tolerant of it and I was able to. I'd already done something with it so, but it was. It was that kind of thing, what you just described, and it was like all right, I appreciate, like I appreciate, the effort Right, so I don't mind that, but it was unexpected for sure.

Speaker 2:

Yeah, I mean I get it Like as a business owner now and you want to jump on all the leads because you don't want to miss something and you want things to move. But it's hard to trust the process sometimes because you just don't know.

Speaker 1:

Yeah, are you saying that sales is hard Rachel?

Speaker 2:

Me yeah.

Speaker 1:

Yeah, so, yeah, so I that's. That's something I learned the hard way earlier in my career that sales is really F and hard. And, yeah, when I thought that it was not. So for all of our listeners who complain and moan about their salespeople and how lazy they are, I encourage you at some point to go do some cold calling and see how you feel about it later.

Speaker 2:

Oh yeah, you know we we won't talk about this too much, but I did spend a very short stint, like measured on number of days, on my fingers. But I'm doing SDR work because I was on a team. That was like standing up an SDR team and I was. We were trying to like figure out reasonable metrics and stuff. Holy cow, I am for all of you SDRs. You all have my mad respect because, like, oh, 10 calls a day, I can make 10 calls a day. I make 10 calls a day as a consultant. For some reason it's so much harder than it sounds.

Speaker 1:

So, yeah, lots of respect for that one Like the like, the getting over that fear of just placing the call, and I have been hung up on and it is, it is, luckily, I was somewhat prepared for it and I actually have left it off. That said, it's it's demotivating if it happens on a regular basis. So, yeah, yeah, selling is not for the faint of heart and it requires a certain kind of sort of person, I think, to do it well. So, yeah, and the SDRs in particular, I think you're right. They, they're in a position where, like I give a lot of slack to you know, people get complained about getting you know pitch slapped on LinkedIn or something, and I'm pretty tolerant of that. I either ignored, or I sent feedback or I answered it directly and say I'm not interested. But it's just, yeah, it's hard, all right, so let, okay. So In my experience, that pipe being predictable on, ultimately, revenue has really been a necessity for boards, for senior leadership, depending on the type of company. I've even been to places where a new GM came in and missed numbers because the numbers he was seeing didn't match up with reality. Instead of fixing the process, basically force people to do a whole different one through spreadsheets, which is crazy.

Speaker 2:

Oh yeah, in there.

Speaker 1:

I think this becomes even bigger of a challenge when you've got opportunity, when your sales cycle is really long, because then you've got all kinds of other things. In your experience, have you seen companies doing some amount of inspection at the opportunity stage? That is, a combination of marketing sales or SDRBDR in sales. Do you see that? Where have you seen that? Are there the things that you've seen that have made those that either be more or less successful?

Speaker 2:

if you can put a finger on it, Actually I have a very specific couple of examples from that. I went through a transformation project on this exact thing at Isher and Roll, which I will not name. We had this system that was not working, where opportunities were being pushed a lot. We would say, oh, we have X pipeline that's going to be booked this quarter, and then, like 5% was being booked, all of the opportunities that had a closed date were still open. They just all of a sudden had a closed date at the end of the next quarter. These opportunities we realized are like six years old.

Speaker 1:

Yeah, I'm sure no one else has seen this.

Speaker 2:

It's shocking news, right. Like yeah, headline. And I've also had an experience where, let's say, the sales reps may or may not have been deleting opportunities instead of marking them closed loss, which of course makes your close rate look better because your denominator is lower. So, both of these situations, the metric that was incorrectly represented was a target metric for the sales team. So at the first company I mentioned, their coverage was a target. At the second company I mentioned, their target was one of them, not obviously the only one, but was an X close rate, and so they figured out the system. This is how I can manipulate the data to meet my target and, to be honest, I don't really like we sales is hard. I mean I obviously I would never do anything wrong, ever but like we can kind of understand the psychology behind it. Really, the problem is that those are the wrong metrics to be measured by.

Speaker 1:

What are the right?

Speaker 2:

metrics? That's the age old question. I mean for sales. It's a lot easier to just say let's look at quota, let's look at revenue dollars brought in. Maybe we could look at pipeline marketing. It's so much harder, well, okay. Well, since you asked about sales, I'll just give the easy answer Money, money's the right answer.

Speaker 1:

Revenue.

Speaker 2:

Revenue.

Speaker 1:

Right, Well, I mean, if you really want to get to it, you need payment like collected revenue, right?

Speaker 2:

Yes, arr, as some companies might go by.

Speaker 1:

Yeah, yeah, it's interesting. Yeah, it's funny that you're right. I mean, things get manipulated. What I started seeing at one company and this was relatively well, it was a range, but anywhere from four or five figure products to seven figure products right, it's kind of a wide range depending on the product set. When I was doing some stuff, I was actually built a custom attribution model which we could argue about whether or not that made sense. But what I started noticing is that because I was always pulling opportunity data every month to do it like feed it into my analysis and I started noticing the average time to close was like 21 days. I was like there's no way that can be right.

Speaker 2:

I see where this is going.

Speaker 1:

See, it's another way of manipulating it, right, which was they were waiting until things were pretty sure, right yeah, and then putting them in. Yeah, and I agree with you, I don't think that it was necessarily a nefarious thing. I'm sure there were some of that, but you kind of get what you measure, yeah, yeah, and so well, in that same place, there were some long-term contracts with either government or quasi-government agencies where they had a standing contract and literally opportunities would open and close and open and close.

Speaker 2:

Sure yeah.

Speaker 1:

And even worse is it only has and we were using sales logics, not sales force but the opportunity value would change too to match the most recent purchase order, basically. So when I would be pulling historical stuff and trying to match, I'm like what happened here. And so, for those of you who are in marketing ops, if you have not spent time looking at your opportunity data in your CRM, you can uncover some really interesting things. One, that where you and I met that place I think it was before you were pulled in I was trying to figure out why I was seeing in our influence revenue reporting opportunities that had been created two years prior to us ever implementing marketing automation. Yeah, yeah, it had to do with a very specific business practice. That was not hidden. It was a well-known thing because the CRM was not used just for capturing demand stuff. It was also used as a project management platform, so for delivering products or the services, actually, in this case, this is bringing back to memory. Yes, yeah, memories not great memories on some things like that but I remember going to that. I remember talking to the sales director. Afterwards I was like what is this coming from? That doesn't make any sense at all. And then I was like, oh wait, there's also two others Like what's going on here?

Speaker 2:

Yeah, and then suddenly you find this whole world that you didn't know about.

Speaker 1:

Yeah. So this is where, being able to dig into data and understand it, you can uncover these things that, whether it's formal or informal things that are happening that can affect all these predictions, so okay. So another thing that I've seen again with long-term kinds of sales cycles, a large deal size is a challenge with turnover. So turnover both in the sales team as well as in the customer side. So, have you seen any? Have you run into this and like, have you seen anybody has figured out how to like manage that? Well, let's just keep it as simple, right, just on the sales side, right?

Speaker 2:

Yeah, yeah, like in terms of how to maintain the data.

Speaker 1:

Yeah, yeah, yeah.

Speaker 2:

Oh, yeah, yeah. Um, actually I worked at a company that had a really it wasn't 100% perfect, but I actually really solid way of doing this. I was really impressed. Large, large company, so they had a lot more resources than like a single person sales ops or revenue ops person might be, but they I mean it's there's two components there's the technical side and there's like the internal comms People side of it. Yeah that kind of helped with this. So on that, on the Salesforce actual like instance, they had built out a really robust set of processes. That happened when someone leaves. So everything from Making sure every single lead contact, opportunity, account, task or whatever case that is assigned To them gets reassigned to the right person. That would include Little prompts for sales where they, as they were off-boarding and they were off-boarded with their sales work early enough that they could go through and like write these comprehensive notes on everything. So if your opportunity is open, they would leave a close-out note. If they had an openly, they would lead a close-out note whatever and whoever received the new object record that person would be. Actually, they carved out time for those people to make sure that they were able to review those notes and ask questions as needed and really have a firm understanding of what's going on. And those notes weren't just a general text box either. They had like a checklist with questions like this task what is the purpose of this task? What is the intended outcome of this task? What value rated from low to high of this task To get these things that, like our almost instinctual behaviors, out of sales reps heads and into the next one said I was very time-consuming one to set up all of these automations and also, like sales, spent a lot of time on this, and yeah something the company had to account for, right, but it was very successful. I was really impressed with how that worked. Another thing was cultivating that mindset of being willing to oh so and so. Has left or is starting Part of my job that I'm not getting commission on but it could produce. Commission, potentially, is really understanding what they're leaving behind.

Speaker 1:

Yeah, it's interesting and I'm not sure I mean that is extremely robust and I and I've seen Lighter weight versions of those I think the takeaway for me and I think the takeaway for our audience it especially if you want to be seen as a little more strategic and thinking about the overall businesses you can advocate for hey, yeah, if we really want to have predictability in our pipeline, we'll make sure that the data is clean and we're. You know we set a Norm within the organization that data is important and a part of that is not just. Most companies have a pretty good job of onboarding. I'm sure there are exceptions, but off-boarding is where things can really get screwed up, and I think that's the point is there should also be a process that's very deliberate that says, when someone leaves, these are the things we do, and it should include everything from All those reassignments it probably needs, like who's going to monitor that person's inbox for a period of time? What kind of reply is going to be like. All those things should be part of it and if you could be the one suggesting that, because you're looking and you're inspecting the data, you can go like, hey, we're. I'm seeing this issue of a catwalking in front of the screen. You know that's just the way it goes. We roll with it here, but yeah, so so I think like that's a discipline that's really important and I think it's overlooked. You know, it's that kind of stuff like it's not a sexy, it's not a tech thing, but I'm a big fan of like basic blocking, tackling stuff matters.

Speaker 2:

Well, exactly right, like we as ops people, that is, our job is not just to like do it, but to make it matter. We got a show. If I go in and do like get some knowledge out of this information, we can actually make decisions based on that. That will improve the outcomes for our company. We make it matter.

Speaker 1:

Yeah, no, I think let's talk to a boss of mine who I said I think we need to spend time on cleaning up our data. I want to bring in a tool for validating email addresses. And I said this is not sexy stuff. And she was like no, that's really good, like that's. It is like thank you, like I, I think it's like you. Not everybody's got a marketing leader who's got that kind of mindset. Yeah, and it's, and it's. It's the kind of thing that nobody really sees. But we'll pay dividends and that's the at. The hard part is articulating that. But when you can show that you're you're making improvements and you're getting better decisions made and you're more predictable and all those things right, it's, it becomes to, it does matter and people will pay attention. Okay yep. So so one of the other things we, you and I, talked about well, we didn't talk about this actually. So since we talked, you Published something I think it was on LinkedIn, I can't remember, maybe it's on your website About pipeline coverage, and thank you. You hinted at it here earlier, which is traditionally a sales metric, but you were suggesting that actually it maybe it's a metric. We should be looking at it earlier stages where it's more of a marketing Part of the the buyer journey funnel. I so what, you know what? What triggered that for you, like, why do you think that's an important metric for marketers? And, yeah, are there other signals like that you think would make sense for early, early stage predict, you know, predictability of the pipeline?

Speaker 2:

Yeah, I mean. So started with a client asking me to help them figure out that. You know how do we get some predictability in the earlier stages of our funnel and I've toyed with this idea for a while before, kind of implementing rustic versions of this at other companies, but you know. So for just a quick refresh. So pipeline coverage, we just say how, let's say we're looking for next quarter, how many opportunities we have that are open right now with the closed state of next quarter. How does that compare to our target for next quarter? You know, does that? Obviously we wanted to line up. Ideally we'd have way more, because not? you know, it's some, usually some multiplier right, right, exactly, yeah, you X4s yeah right so we would like to be maybe I don't know for us covered in pipeline, and there are lots of things that sales teams do to figure out what should their coverage be in order to give them the best chance at success. And as marketers, we don't have the luxury of having a closed date on our leads, however okay, so like, for example, someone doesn't fill out a form and say I'm probably gonna MQO in three days, but we do have the availability of the rest of our data to make data driven recommend our decisions based on how likely we think certain people are going to progress down our funnel, so we can use other data to predict how likely is it that you know experts and will convert from stage to stage to eventually get passed over to sales? And, honestly, I like, I like that it's instead of saying, based on Arbitrary data, what is our closed date? We actually get to have data driven information so we can kind of back calculate, you know, based on conversion time. conversion reads basically all of our our Juicy juicy stuff in our database to figure out how many people Do we need to be getting into each stage continually based on what our future targets should be and, like I mentioned earlier, this is not like an okay are like. We should never say we need to be have X MQL coverage in order to hit whatever target next quarter, but it's a good indicator for us to be able to roll with the punches and pivot quickly as needed.

Speaker 1:

Yeah, I mean you when you say okay, are you mean for marketing overall as a right? Okay, yeah, and I agree. I mean I've been a at least one or two places where marketing was measured on contribution and was measured on MQLs and sales and and Inevitably there was a squeeze on how much program spend there could be and and those targets went up and you could drive. Volume is pretty consistent with like, the more you spent, the more the volume Went on the early stage conversion weights dropped and I was, I was always pushing for hold us accountable for the end number. Yeah, I plan, or revenue, whichever that is, and let us figure out how we get there. Yes, beautifully said, I agree yeah so Because then it you, you're held accountable. That's not. I wasn't. I was never advocate for not measuring those things. Right right because they are. They can be, you know, early indicators of problems and things like that, so I think that's good. Sorry, this Just popped into my head as we were talking, though, but what you're talking about with that sort of pipeline coverage model for earlier stages in the funnel, do you think there's a place for AI, machine learning tools to help Make that better over time, like so sort of learns, what your data looks like, instead of it being based on Some point in time measure of conversion rates or time and stage and all that? It's kind of evolving, as as your business is evolving.

Speaker 2:

Absolutely. I mean all of our modeling. There's an opportunity for that, because everything gets stale everything from coverage to like attribution, modeling to lead scoring which they are may not become obsolete through this entire thing. Like AI can help you, one Make sure everything is continuously updated so your model is always using the most recent information and to help you think of things that you didn't even think of before. Right, like the Current way that I'm doing it for this one client is based on us saying I know how many MQls I had, but I mean there were people who heard about me, my company, that I never even knew because, like you said, they waited until they were ready to get a demo. So I, you know, can bring in a lot more Kind of those factors that we had.

Speaker 1:

Yeah, my, you know, I don't know how many times I've said this, even in just the last few days, but, like my, I was a relative skeptic on AI machine learning, and I think it's because, for marketing in particular, because I think everyone was gravitating towards content, I mean, I think there's a place for it, but I think, as I'm no expert, right as I understand it, right, something like a Model, like she's at GPT, is really dependent on what it has to reference to do that kind of Language model. So if you're doing relatively proprietary stuff and you can't take advantage of a huge data set, the likelihood that's going to help there was as little. What I have thought for a while, though, is that I, just like you said, right, finding patterns with our audience can see, is you were doing quotes, right, I know right, hair quotes is Sometimes you don't know what you don't know right, and if you've got a tool that can really sift through data and find patterns that are not necessarily as obvious To us, or ones that we yes, let's be honest right, sometimes we don't want to consider. For whatever reason, yeah, yeah yeah, I mean there's, I think there's. To me that's where I actually expect way more value to come from these platforms in the future. So I'm Excited. I'm glad you like sounds like you're on the same page where, like, there's some, I think there's some huge value that could come from that.

Speaker 2:

Oh, yeah, definitely.

Speaker 1:

So, okay, one less. I think I'll see this up like a softball question for you, you know. So we talked about, okay, ours for marketing. Yeah, do you have any opinion about what marketing operations should be measured on, or all of marketing, right, do you? I mean, you said revenue for marketing, and maybe that's it, but what about marketing operations?

Speaker 2:

Yeah, I love that question. I have been responsible for defining them multiple times and it's always like this, you know, oh, it really depends on blah, blah, blah, what I hope. So I'll start with this. Both times I've been asked to define marketing's OKRs for the various marketing ops sorry, the marketing ops OKRs for the various teams that I've been on. It's my approach has been let's figure out what the value that marketing ops is bringing to marketing and to the org and let's figure out a way to hold ourselves accountable for maintaining that value. So, for example, one thing that marketing ops can offer a lot of value and should to an organization is getting things out the door right. Are we executing things in a way flawlessly that increases engagement and best represents our brand? That can look like you know, x turnaround time for things, meeting SLA's like that's kind of custom to your business, like how do you make sure that's successful? And then, on the other hand, we can provide value by bringing actionable insights that people might not have been aware of or even if they are aware of, like we helped to find that action. So for the companies that I've worked on, those were both a little custom, but it was having X amount of actually I'm trying not to say something that I can't say but like X amount of impact on the strategic decisions that even lead to increases in pipeline or lifecycle or whatever. Marketing ops isn't responsible for pipeline directly, but like marketing ops can be, responsible for helping increase the quality of leads right.

Speaker 1:

Right, okay, yeah, I mean database. You could have metrics for database quality and coverage, things like that. Okay, yeah, I think this is a tough one and I'm like you. I've gone back and forth about what we should have as our Rokear. I've had revenue or pipeline once before as a marketing ops leader and I initially thought that was a good idea, and then I've, but I've shifted simply because it's so much as out of your control, exactly, yeah. So, yeah, that's great. This has been fun. It's always fun hanging out with you, rachel.

Speaker 2:

Always fun.

Speaker 1:

Yeah, so our audience doesn't know. We like we chatted for 15 minutes before we started and we forgot that we were supposed to be recording. So it's always a good time. Yeah, always, always. Well, Rachel, thank you. If folks want to learn more about what you're doing with Moby or just keep up with you, what's the best way for them to do that?

Speaker 2:

Yeah, it can hit me up on LinkedIn or Jimmy's email. I'm just Rachel, nevermind. Also, I'll put the email in the links. Y'all can see it, but yeah, LinkedIn is also great.

Speaker 1:

Well, we'll definitely link you when I post about it on LinkedIn. You're also on the marketingopscom Slack, right?

Speaker 2:

I am, and you should probably see me pop up from time to time because I can't keep my mouth shut.

Speaker 1:

Well, we're here in safe space here Because I am the same. Well, good, hey, Rachel, it's a lot of fun. As usual Next time to our listeners. Thank you for continuing to support us and I look forward to bringing even more fun guests to you with you later, and hopefully Mike and Naomi will join soon. Until then, bye everyone.

Improving Predictability in Marketing Ops
Navigating Changing Buyer Behavior and Forecasting
Marketing Strategies and Trust Building
Challenges in Sales Data Management
Marketing Metrics and Machine Learning