Ops Cast
Ops Cast, by MarketingOps.com, is a podcast for Marketing Operations Pros by Marketing Ops Pros. Hosted by Michael Hartmann, Mike Rizzo & Naomi Liu
Ops Cast
Moneyball for Lead Scoring with Lucas Winter
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On today's episode, we sit down with lead scoring consultant Lucas Winter to explore a refreshing, data-first perspective on building lead scoring models—one that challenges the conventional wisdom and AI hype alike. With storytelling flair and practical insights, Lucas discusses how marketers can uncover true buying intent and dramatically improve sales efficiency.
Tune in to hear:
- "Moneyball" Meets Marketing Ops: Lucas applies the Moneyball philosophy to lead scoring—focusing on what actually drives conversions versus what sales or execs think looks good. It’s about looking for patterns in customer behavior, not just traditional job titles or industries.
- AI’s Limitations in Lead Scoring: While AI has promise, Lucas outlines how AI-driven models often misinterpret causation (e.g., recommending “retired” contacts) and require human oversight to avoid absurd conclusions.
- Gold, Silver, Bronze > Arbitrary Scores: Ditch complex scoring ranges like “0-100” and opt for intuitive models like “gold, silver, bronze, junk”—making it easier for sales teams to understand and adopt.
- Why Gmail Isn’t Garbage: Contrary to common assumptions, personal email addresses like Gmail can indicate serious buyers—especially in early-stage startups. But to gain sales trust, these leads must “work harder” to earn high scores.
- Start Simple, Stay Iterative: Don’t wait for perfect data or fall into “overreactive” model changes. Build a solid draft, validate with real outcomes, and evolve based on performance—not opinions.
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Hello everyone, Welcome to another episode of OpsCast brought to you by MarketingOpscom, powered by all the Mopros out there. I'm your host, Michael Hartman, flying solo today. Joining me today to talk about what he calls money ball for lead scoring is Lucas Winter. Lucas is a lead scoring consultant joining us today from Oxfordshire I'm sure I'm mispronouncing that in the UK. He started his Mopro career in 2014 as a technical customer support rep for MarTech Professionals. Since then, he's managed his own team of Mopro professionals and has even flown solo as the whole marketing ops department for a company of 3,000 employees. He is now a lead scoring consultant at the Inspired Marketing Group. So, Lucas, thanks for joining me today.
Lucas Winter:Cheers, Michael. Yeah, this is a lovely way to spend a Wednesday night.
Michael Hartmann:Yeah, oh yeah, it's evening for you too, so how badly did I butcher the name of your city where you live? I?
Lucas Winter:don't think it really matters. But yeah, it's Oxfordshire, but I don't think anyone's going to be testing you on that again, Michael.
Michael Hartmann:Yeah, I think I'm going back to the Hobbit, you know, and the Shire, right, and I can't break that habit. Well, good, you'll just make fun of us, you know, as typical American mispronunciation. So before we get into this. So we talked about, like I said, this is going to be, we're going to be talking about money ball and money ball for lead scoring. So like, yeah, that was a term you came up with here when we were talking before. What do you mean by that?
Lucas Winter:Yeah, I mean you. You just slipped up on the pronunciation of Oxfordshire and now here I am, the Brit talking to you about baseball, so the shoe's immediately gone onto the other foot. In terms of me. I've never been to a baseball game, I've never watched baseball on TV, I've never played the game myself, but I am familiar with the concept of Moneyball and this is something that really got me inspired about league scoring.
Lucas Winter:For those of you who don't know, the story of Moneyball is about the Oakland A's, who are a baseball team with the second smallest budget in the league, and there was other teams that had a budget three times the size. But the Oakland A's consistently made the playoffs, consistently topped their division, and the reason that they managed to do that was because they were evaluating players differently to all the other teams in the league, and the question everyone was asking was why? But they weren't really liking the answer. So that's kind of the approach that I've taken, which is to look at the leads in companies' databases, find out not which contacts the sales team wants to speak to, but actually which customers are actually ready to be sold to, and kind of flipping that conventional wisdom on its head.
Michael Hartmann:Yeah, Well, I am familiar with Moneyball. I have played baseball, but very briefly as a young boy. I wasn't very good. I still love the game and I do not understand cricket.
Lucas Winter:I won't pretend to understand every facet of baseball.
Michael Hartmann:That's all right. All right? Well, let's jump right in. So, yeah, when we talked before, one of the things you talked about was how, in many ways, lead scoring is transitioning to solutions that are AI driven, and, if I understand, your position on this is that you don't believe that AI-based solutions are quite ready, that there's some issues that they have today. So, first off, did I understand that correctly? Second, if so, what kinds of issues have you seen with some of the AI-enabled AI solutions for lead scoring?
Lucas Winter:Yeah, I mean one thing I'd say to the listener if you had four minutes on your sweepstake about how long it was going to take us to talk about ai, you can go to the pay window now. Um in in terms of um, where we've come from, which was kind of a feels right lead score to go into a data-driven lead score. You're absolutely right. The next stage in this transition would be to moving towards an AI-based scoring model. As it stands, there's some potential pitfalls to fall into. One of the examples that I really like to give is when looking at job title, and what I was finding is that AIs were picking out the best job title to contact is retired and basically what was happening was was a company was selling to someone who was in work, then they'd retire, then the ai would run the lead score model and go right, everyone who has retired has purchased. Therefore, going forward, we should be targeting retired people, which, if you just apply an ounce of common sense, you're going to go. Well, that's nonsense, yeah, but the AI wasn't able to do that and there's like countless of other examples of of AI is kind of just missing the point on this Um, there's a.
Lucas Winter:There's like um, product returns forms, customer. First thing they do is they purchase a product. Second thing they do is is they fill in a form that says I'd like to return that product. Therefore, the only people who can fill in the form are those that have already purchased. The AI goes I found you this really great demographic of leads. They're people that have returned your product and it's like no, we already knew about those. They've already purchased. So there's certain pitfalls to fall into. So, whilst AI can speed you up in a lot of circumstances, you do need someone who knows what they're doing, kind of babysitting, holding hands and checking the AI's homework.
Michael Hartmann:Yeah, it's interesting because I've thought for a while, like one of the things I think people get concerned about with AI is can it replace everything that we do as humans? And one of the scenarios that I think of where AI I think has a lot of potential is similar to that and like doing a deep analysis on data, looking for patterns that would otherwise not be obvious to us as humans or would take too much time and effort to get, to much time and effort to do it too. At the same time, it might identify patterns, like you said right, that actually are either kind of obvious and already known, so not really that helpful, or just simply not something that would make sense to take action on right, and you still need the human to evaluate that, even if you have an AI platform that can generate that and do a lot of heavy lifting on the analysis yeah, you've nailed it.
Michael Hartmann:Yeah, I mean, I've also, I know I have personally and I've heard many people also say like simply that and I think this is particular to the llms but yeah, that they're really terrible at even some basic math yeah, uh, yeah, I, I have.
Lucas Winter:I've seen that too. Um, where where they've kind of failed to work out basic multiplication, where you'll you'll be scoring in units of 10 but you're setting your thresholds in units of 33. Well, you're, you're, you're going to be points behind where you're actually at. So, um, sometimes, uh, uh, left hand's not talking to right hand within within the ai, but uh, you, you, I. The bit I'll find strange is is that people are often kinder to AIs than they are to humans. When a human makes a mistake, they're like oh well, you shouldn't have made that mistake, you should have been able to foresee this, you should have been able to document, you should have been able to troubleshoot ahead of time. And then, when the AI makes a mistake, we go well, the model's learning, and it's like why are we kinder to the AI than we are to the human?
Michael Hartmann:kinder to the ai than we are to the human. That's a really interesting thought and as soon as you said that, I was thinking to myself like I am when I use mostly I use chat, gpt, but um, I, I will, I will like, please, thank you, right, I'm doing all that and as if it's a human um, and maybe it's your point is sometimes maybe kinder than I do to actual humans.
Lucas Winter:Can I, can I just pick up on something that, um, this is something that I found fascinating. Um, when you start looking at contact us messages, what people are typing in and then um, sending out to sales something that I wish wasn't true, but I've seen this to be true on multiple customers is the people who are being polite and, as you mentioned, he's saying please and thank yous are often less likely to buy than those who choose not to use those. So I don't know if that's something to do with people more likely to progress in their careers and therefore more likely to be decision makers. I don't know the why behind that, but that's just a fascinating observation. I've found in lead scoring that the ruder someone is, the more likely they might be to sort of get to that sale.
Michael Hartmann:That's interesting. Maybe it's something about being decisive. That's a fascinating one, too. Which we've talked about before is something that people in marketing ops probably need to understand better, right, both in terms of customers, but also how to work more effectively with other people within their organizations, but that's a whole topic in and of itself. Okay, so there's some potential challenges with AI. Maybe it'll get better how quickly, we don't know. Probably faster than I think many people expect, but still some work to be done there. So is it safe to say that your preference or your recommendation is to use a non-AI-based approach or solution, and have you seen those outperform other models, right? Yeah, so what's your take on all that?
Lucas Winter:yeah, I mean the chachi bt boom. Was that christmas 22? Now um, and in terms of how far that we've come, talking now in 2025, the leaps and bounds. Improvement isn't where I would have expected it to be. So, in terms of being able to do everything entirely as AI first, that's a tricky problem. So, yes, I definitely would suggest that you go with a data-driven solution, but that's not synonymous with an AI solution. Will it get better? It's got to. It's not going to get worse. Solution Will it get better? It's got to, it's not going to get worse. But if you're coming from a position of I've got no scoring model to jump straight to AI, you're probably going to find yourself with a model that's not performing as well as it can. And in terms of what you can do, just by doing some best practice scoring, you can find yourself where the position that I find myself in quite regularly, where I'm able to get a lead in front of a salesperson which is four times more likely to turn into a paying customer.
Michael Hartmann:Okay, yeah, so I mean, is that the kind of? It's almost not a metric, but are those the kinds of metrics that you look at in terms of the overall effectiveness and quality of a lead scoring model? Is it like leads that will turn into an opportunity or speed to close and win, like? What are some of the metrics you look at?
Lucas Winter:It's really important that it's really kind of meta question. You've got kind of how do you report and whether your reporting is any good, and, similarly, how do you score, if your lead scoring is any good. Ultimately, the dollars that it puts back into the business is going to be a great metric at that. It's about improving salespeople's performance, and you touched on psychology, and it is a really difficult thing to ask a salesperson to acknowledge that they've managed to increase their sales ability because they've been given better leads as opposed to because they've been performing well as a salesperson. When it comes to sort of that moneyball mentality, one of the mistakes that the scouts were making was they were going to see a player once, they'd watch them once and then they'd go right so this player's good or this player's bad and it's not representative of the entirety of their college career.
Lucas Winter:And when a salesperson is failing at their job somewhere between 80% to 99% of the time, I mean that's a terrible failure, right? You wouldn't accept that from any other job. If there was a bus driver who failed 99% of the time, you'd be like, okay, you're not a bus driver anymore. But sales only has to have a very low success rate to actually be very good at their job so they can find themselves going well.
Lucas Winter:I've had a success here and I now know what a looky like success looks like. But it's the same problem the scout had. They're doing it from sort of a one viewing of a player, one viewing of a lead, as opposed to being able to have the sort of zoomed out view of the entirety of every customer that's ever existed, and that's something that you can do with scoring, and that's something that you can do with scoring. And then, when it comes to that reporting and that metrics, you've got this very difficult thing where you put something on a bar chart that goes from 0 to 100. And in the old world the salesperson has a success rate of 1% and then you move them to a success rate of 2%. On that bar chart it doesn't look like they've really increased by that much. What you've actually managed to do is double that salesperson's ability to sell to customers. So it's a really difficult thing to sort of prove that value when it doesn't look like a massive spike, even if you've managed to double the sales they're making.
Michael Hartmann:Yeah, I mean, what you're just describing also is part, part of why I encourage regularly our listeners, our audience and anybody to learn the basics of statistics and understand them, because that difference between one percent, two percent, is one percentage point but, as you're pointing out right, it's a hundred percent increase on that rate which is significant. And if that you know, if the, then if you add in right, what's the value of a, of a, of a win is, you know, say, a hundred thousand dollars or a hundred thousand pounds, and you know one percent, uh, close rate, or close one weight rate, is five wins and now you're getting 10 wins. You know that's. You're going from half a million to a million. You know from that, and so that's significant and so that's there's.
Michael Hartmann:The other part of this is like no way that, like understanding that, but also understanding that there's more to any one of these metrics, and sometimes there's a basket of metrics that matter and they, they are interrelated in some ways, and understanding how that interrelationship works and understanding that sometimes there's there's. You can't get all of them to move in the ideal direction you would for any one metric as a basket of those, but you can try to optimize for them overall. So long winded way of saying like, this is like, but it's worth understanding that if you don't yeah, again, using the money ball philosophy.
Lucas Winter:It's not about one single metric, it's about understanding what you want to do and what your objectives are, and the example I would give is sort of when it comes to nearly said bowling. When it comes to pitching, it's not all about miles per hour. It's not just about how fast you can throw the ball. It's about, well, do they get players out? And this is one of the things that the Oakland A's struggled with was they were saying, look, we need to get the best bowlers. And then they put a player up on the board and say I recommend this guy, and they'd say, well, he doesn't, he doesn't throw that fast. They'd be like, well, he gets people out. And this is, um, something that can be sort of looked at.
Lucas Winter:When I have a really common objection and I reckon this one's gonna resonate with a lot of people right now listening I don't want to look at any Gmail's. I don't want anyone who's a gmailcom. I don't want all these Gmail's because they're students and I'm sure, as, as everyone can agree with once you graduate from university, you have to hand in your hat, your gown and your gmailcom email address. There's no such thing as somebody with a Gmail that isn't a student. I hope it's clear that I'm being sarcastic. Now what I then do is, when I put Gmails in front of sales, I don't just say, okay, these, these are good. I make sure that there's enough behind it that they're better than good. So I might put a business email address in front of someone and say, okay, this is a certain level.
Lucas Winter:Now the Gmail has to work even harder. They have to have a great job title. We have to know their company name. They have to have been to our contact us form. They have to have visited the job title. We have to know their company name. They have to have been to our contact us form. They have to have visited the price list. They have to have requested a quote.
Lucas Winter:Then, when I put that Gmail in front of a salesperson, they're able to say, okay, well, this one looks all right. Now the data-driven thing to do would be to look at things pure and say this lead is equal to that Gmail because they have the same criteria. With Gmail, I make them work even harder than they needed to. I make them have even more criteria and that way, despite it not being perfectly data-driven, it's that psychology and that perspective that the Gmail has to work even harder or otherwise. People are going to lose faith in your model when they start looking at the leads that are coming in and going. All these Gmails are rubbish and it's about to change that narrative, because people are coming from a position of Gmails are bad, that they have to be better than everyone else.
Michael Hartmann:That's interesting. Yeah, I've worked at companies where that's been. Something that we were asked to do is to not allow personal email addresses Gmail, yahoo, msn, whatever and in some cases I would push back because I had a little more insight into the customer base. Like so, very often, if you're, if one of your target audiences is relatively small businesses start early, early stage startups.
Michael Hartmann:I mean, it is a cheap way to get an email address for your fledgling businesses, to just use a gmailcom email address, right, and it doesn't necessarily mean that you're not a qualified or potential qualified, you know, prospect or customer. It's just that you're you're. You're making decisions about how, where to spend your cash right, cause it's usually limited. So, um, I'm with you on that, that, that, that that's the case. It's interesting that you bring up that. Like the, the perception is that they're there, they won't, aren't as valuable period, and then you need to come up with a high like there's a higher bar to to, to go over to, to get the, the sales team, to pay attention to them. So it makes sense yeah definitely so.
Michael Hartmann:Um, we've talked a little bit about I just want to make sure we cover off a little bit so we talked about so I think you've even said it like a feels right, score right, basically kind of like what we think is are the right things to be scoring on something that's more like what you ascribe to a data-driven money ball kind of model. And then we've talked a little about ai. Are there any other model, like types of lead scoring models that you've seen and that would be competitors to these different ideas?
Lucas Winter:can I tell you about my favorite scoring system on the planet?
Michael Hartmann:Can I say no, all right, so let's go to the next question. No, no, no no, I'm being smart, it's all right.
Lucas Winter:So you go into a restaurant, you get a menu and you have a chili indicator on the menu and if it's got no chili, you know it's got no spice. If it's got one chili, you get a menu and you have a chili indicator on the menu and if it's got no chili, you know it's got no spice. If it's got one chili, you know it's a bit of a kick to it. Two chilies I know that's going to be quite hot for me and three chilies I'm just not going to order what's on the menu For me. This is the best scoring system that's ever been devised, because I can go into any restaurant and I understand it immediately. Other restaurants might use something which is just way too detailed and you'll read a description and it will say something like um, um, sour, hot to taste. It's like I don't know what that means, but just having the visuals of chili peppers and I'm sure I've lost some listeners at this point when they go, you wouldn't order something with three chilies oh, I'm afraid not, that's beyond my personal.
Michael Hartmann:Uh, yeah, yeah, or if it's some sort of um, well, I like, I like indian food, and sometimes you go to an indian restaurant and be like there's the one chili, two chili, three chili, and then there's indian hot, indian spicy, yeah, and I'm sure that's true in other cuisine, but yeah, anyway, but yeah, I, I'm with you, right, right, it's a pretty universal, easy to understand model.
Lucas Winter:And then you've got these numbers which don't really correspond to any meaning to the salesperson, you end up with these questions where they look at two leads and they go what's the difference between a 53 and a 54? And the best answer you can give them is oh nothing, that's one point. Yeah, it's not really a satisfying answer for anyone just going yeah, it's one point. But if you could change that to exactly the same thing a chili pepper thing what's the difference between a 53 and a 54? They're both two chili peppers hot. This is how hot your lead is. It's two chili peppers hot. I take learnings from that and, in terms of my favorite way to go about scoring is to say gold, silver, bronze junk is to say gold, silver, bronze junk. And one of the hardest things is driving adoption If you have a scoring system that says these are the gold leads everyone's immediately going to grasp that concept straight away.
Lucas Winter:And if they run out of golds, silver still sounds pretty good. And when driving adoption is so hard, someone's going to turn around and say, yeah, I don't know, I'm not feeling it. I know what a good lead looks like. I just want to take the good ones, based off of my own definitions. At which point you can say, all right, fair enough, do you still want the junk? At which point they'll probably go well, I don't really want the junk. And then you can just send them gold, silver, bronze. Now, in an ideal world, you'll just send them the gold leads and they'll be able to increase their ability to sell by four times. If they don't want to use your scoring model, you can at least get them away from the junk. Now, even if they just increase their sales by avoiding the junk by 1.5 or two times, that's still a huge increase. So, yeah, open up your menu. There's a lot that can be learned from there.
Michael Hartmann:Yeah, so I'm with you on simplifying the way you communicate those tranches right of leads, um, tranches right Of of leads. I think what I would expect is a little bit of a pushback, and maybe you've had this is okay, but that means there's some sort of range for each of those tranches. Like how do I know which ones are at the top of the range or at the bottom of the range, Right? So do you run into that? Um, cause I know I've run into things, maybe not that exact scenario, but something like that before.
Lucas Winter:Yeah. So there's many different ways to play this. You can just go, and this easiest thing to do is just to put the numeric score back on and just go. Well, it's just sort, numerically. Other things you can do is you can put a filter over it where you then have meaningful contacts. So now you're saying let's have the gold leads that have a meaningful interaction on top of it. Have they gone and submitted a contact us form? Have they said actually, yeah, I do fancy being spoken to? Have they gone and requested that quote, have they? Maybe you've got a e-commerce element. Have they abandoned their cart recently? You can then put that filter on top to make sure that once they're gold, they have other things going on about them. And I was going to say something else that was going to be really fascinating, and it's gone yes, it'll come back to you, I'm sure.
Michael Hartmann:Yeah, I think that makes sense. Um, I mean, there's a part of me like this is the kind of thing to maybe this is where ai kind of models would need to go to which is, maybe there's also some level of a sort of a confidence score that goes with it. Right, we believe this is a, you know, a gold in the confidence level, and it is 90 right, versus a gold, one that we're 50% confident in, or something like that. That may make it more I'm talking myself out of it, maybe even, but this feels like now I'm adding another layer of complexity that may just get in the way of the salesperson moving quickly to the next lead and moving it on.
Lucas Winter:Yeah, it's technically a good idea. Um, what I like to do is just have one score and then not a score of the score, that. That way, you can say look these, these are goals, contact them first. These are silver, contact those second, you in a world of infinite capacity. Then, yeah, I'd love to say, and this is how confident we are in this goal, and this is how confident we are in this gold, and this is how confident we are in this silver, but that there's a stereotype that salespeople are lazy, and I don't think it's a helpful stereotype and it doesn't really matter if it's true or not Right.
Lucas Winter:What I think is more important is that I can tell you that a hundred percent of salespeople are people um, people like a good user experience. So if they know that when they log in they can just find out where the gold leads are, then that's really good. And then, on top of that, what I like to do is sort of show my workings, and by that I mean tell people what's going on. So I was working at an organization where someone was really struggling to drive adoption with their scoring model and they had this element to it whereby it would look at the website of the lead, then look that website up against a list of 100. And if it met any of those 100 websites, then it was one of the top 100 targets for that year and salespeople were getting these leads through and going.
Lucas Winter:I've got no idea why this is gold. And then they weren't working them and it was a case of what you need to do is to show your workings and say it's a gold lead and then have the box for show your workings why underneath and say look, this lead leads gold because it's part of this website. This website means it's this company name which is one of your top 100 targets for the year, and there's nothing wrong with showing your workings. I think it's actually a really good way to work. Like uh, michael, you're quite open and honest with this podcast. This isn't the first time we've had a chat. You've done some research and people aren't folding their arms going. I can't believe he's done some research before he's gone on this podcast.
Michael Hartmann:You're going oh right, they're going to put some effort in, they're explaining what's going on, they're being truthful about things, and then they kind of go oh, it kind of helps make a better experience for everybody yeah, yeah, no, I think I think that's good and, um, I mean for a, for a period of my career I've had like an inbound SDR team and you know, luckily I understood the inner workings of some of this stuff, so I knew where to look for those signals that could come in, show up in this case Salesforce but so it was kind of natural for me to do that.
Michael Hartmann:I'm not sure that's the case, and I think it would. When I had opportunities to talk to actual sales teams, I like I try to make take those opportunities to just kind of do one-on-one um training's not the right word but like help them understand, like how they could find this stuff themselves, because I don't think that their typical, you know, training and enablement things included that, because it wasn't something that was top of mind right there, like. So I think there's like there's a, there's a a change management component to this too, and a communication that needs to be a part of it needs to be repeated.
Lucas Winter:That's a natural part so, yeah, and I love myself, I'm with you. Um, I was thinking that nobody wants to be going on a treasure hunt for every lead that they get in. Okay, this one's a gold, is it? Now? Let me check three different databases to find out why. Just get to the point.
Michael Hartmann:Yeah, so you mentioned at some point earlier about the idea that there's like some best practices for lead scoring, and I know you've also said like there's some opinion-based models, often based on a sales leader's opinion or somebody else with a big title, right. So, um, you know, assume that's something you want to avoid. What are some of the do's and don'ts that you have as maybe sort of principles that you you lean on when you're building building out models? Yeah, you don't have to go through everything, because I'm sure you've got a, because of your experience you've got an extensive list, but maybe you know a couple of do's, a couple of don'ts, uh, whatever you think is most appropriate, sure.
Lucas Winter:I like to be really unpopular in the configuration and really popular in the results. So how do you get there? You look at what the existing mechanism is and you might find that a company isn't actually in the industry that they think they're in. And I'm going to steal a story from someone on the Mopros community, so this isn't my story, it's theirs, but it illustrates it really well.
Lucas Winter:There was an organization and they sold horse x-rays and an airport security looked at their x-rays and went it's big enough for a horse, is it big enough for a car?
Lucas Winter:Their x-rays and went it's big enough for a horse, is it big enough for a car?
Lucas Winter:And they went if we can x-ray cars, we'll be able to do that in the airport and find out what's going on inside and if there's any security concerns. And then it turned out that the company was then selling much better to airport security than they were to vets. Now what you can do with that is then when you're looking at what defines a good lead and they go okay, so we know what industries we're in. We'll actually have a look to see who you're really closing to and it might turn out that you're not selling to vets, you're selling to security companies and then with that information you'll be really unpopular in the configuration, but then you'll be really popular in the results. And there's going to be these gems out there that people haven't considered and they might not necessarily want to consider, because it might mean that their whole marketing, the whole brand identity, is completely wrong if they're targeting the wrong people, that there'll be some resistance to even think of that as being true.
Michael Hartmann:So could I? Just I want to make sure I'm doing this right, right, so, this being unpopular in the analysis, I think you're saying that to kind of tie it to the other side of this, but it feels like you're not saying you want to be intentionally antagonistic about this approach, but you want to make sure that you're as simple as it is right. You want to be data driven, you want to let the data tell you kind of what, where, where you should go with this is that. Am I understanding that right exactly?
Lucas Winter:it's. It's not a case of being confrontational for the sake of being confrontational, okay, and the what you'll find a lot of the time is who you think your customers are. They are who your customers are. Sure, a lot of the time, there'll be some things that aren't surprises.
Lucas Winter:I had this issue recently where the link between contacts and opportunities and close one deals it broke and I kind of had to do everything as it feels right, so I kind of did it in an isolated hole, waited until the bug was fixed and then went and checked retrospectively and it's like, oh okay, a lot of my assumptions were correct. A lot of them are wrong. I need to fix those. But you will find that a lot of the time, you're not really surprised. I've worked at one organization where online events were better than in-person events, and that is the exception to the rule. I've only ever seen that once, and most of the time, yeah, in-person events. Of course, they outperform online events, and you will find this sort of it's about being ready to be open to say this is unexpected, but unfortunately, this is the truth.
Michael Hartmann:Okay, that makes sense. So are there any? So you know, make sure you're driving by the data. So that's good. Anything that you say don't like avoid doing this.
Lucas Winter:Any don'ts on there? Yeah, don't overreact. Um, when you get, when you get feedback on your model, don't do these sort of wild swings. What you can fall into the trap of is getting feedback such as um, um, I'm trying to pick a pick on a different example than Gmail's. Now you might get some feedback of.
Lucas Winter:This is a bad lead, because they're not a decision maker and you'll have a certain job title or they'll say, right, so anyone of this level or below, they should be going straight to junk. Of this level or below, they should be going straight to junk. Now don't overreact and say, okay, now, anyone who's part of this job title, they go to junk. If anyone's part of one of these bad industries, they go directly to junk. Make sure that you're reacting appropriately so you can adjust the model.
Lucas Winter:It can be iterative and it can get a bit of a knock on the head and a bump downwards, but make sure that you're not taking them straight from gold down to junk because there was other attributes along the way that are of value and you don't want to end up in a situation where you're having these sort of wild swings based on sort of one or two things, that they end up going the wrong way. Uh, things that they. They end up going the wrong way. And that's true for the other side of things, which is where someone will say my marketing tactic is the most important marketing tactic. Therefore, anyone who goes to one of my events must be a gold lead by default. Yeah, just because they've attended your event doesn't necessarily mean that they're the single most important thing. So they don't go from a wild swing, from being junk straight to gold.
Michael Hartmann:Yeah, so I think like a single data point does not mean it's something you should react to is kind of what I the way I interpret that, and I agree with that.
Michael Hartmann:The other point that I just want to hone in a little bit on what you talked about is like there's this iteration idea.
Michael Hartmann:Right, and this is one of the things I've run into when I've done lead scoring at different companies is when you get the people in the room you think need to be involved with providing input on that, the, the scoring model. There's often this I sense often that those people think like this is my one and only chance to have input, and what I've learned to do in those scenarios is, like you know, I want us to get to at the end of this session. I want us to get to something that we can all, we all feel comfortable with we don't believe it's 100 but also something that we can. We know that we can give it a little bit of time to monitor and see what the results are and then um make changes over time based on the results, and I think that helps not only with the immediate building of it but also the ongoing, as long as they continue to bring them in when we're evolving it. That way they know about the changes.
Lucas Winter:I think that's true, and there's some advice out there which might sound contradictory, but I think it's also true. Two things can be true at the same time. Absolutely. One thing that you might discover if you start to look up lead scoring is that you should just get started, just start making it Now.
Lucas Winter:That doesn't mean that you take your first draft and put it in front of sales leaders and then say here's what I've started with, how do we go from here? It's a bit like I don't know scripting anything, a Hollywood movie. You wouldn't take your first draft to a producer. You'd make sure that it's draft five or six and then when that producer then starts making your movie, the one that ends up in the cinema might have little to no reflection at all of draft five or six that you originally sent to them. So, yes, it's true that it's iterative and it can be changed, but what's also true is what you said about having a model.
Lucas Winter:That's good, that you can then sit down and say this is what we've got, what do you like, what do you don't like? This is what we've got. What do you like, what do you don't like? One thing that I like to do is shadow sales and watch what they do on a normal basis. Which fields are they looking at first? If they're looking at sort of three fields, then one, you know what's important, and two, you know exactly where their blind spots are.
Michael Hartmann:Yeah, I will tell you, when I've done stuff like that, sitting with somebody, the hardest part is to keep my mouth shut, honestly, because I don't want to influence what they do. I really want to observe and that's actually really really hard, if you know it well. So you were kind of running close to a time when we're going to have to wrap up, kind of running close to a time when we're going to have to wrap up. But I do want to hit on one thing, because this is something that in our earlier conversation it sort of surprised me and not did surprise me a little bit. But you, if I, you told me that you like to use spreadsheets to build your lead scoring models, and I think you have a template too, or at least maybe a, a, a, a good structure for doing it.
Michael Hartmann:But like, so, first of all, I think that surprised me. I suspect it will surprise many of our partners, because most, like most marketing automation platforms have some sort of way of doing it. Um, I'm gonna assume salesforce has it. I don't, I'm less familiar with that but like, um, why it's the spreadsheet? I think Excel or Google sheets, whatever you use. And then, um, how do you like? I'm curious, like how do your clients react when you you start doing that?
Lucas Winter:I've uh, I've lost you on the technical. I'm going to guess what the end of that question was. I've lost you on the technical. I'm going to guess what the end of that question was. And essentially, when it comes to building the scoring model, the marketing automation platforms have all the technical stuff in there that you need to make things. Functionally, you can say 10 points for this, 5 points for that. What you can't do is actually evaluate whether you should be doing that or not, and that's kind of the element that you have to sort of take down. If you can do it in python, fantastic. If you have a data visualization tool, fantastic. Um, what I want to do is is, uh, crunch as many numbers as I can in in mic Excel, because I can just get there a lot quicker.
Michael Hartmann:Yeah, yeah, and so like full, full transparency to our audience. So in the middle of me asking my question, I think my connection was wonky and so Lucas didn't get all of it, so hopefully that will still work out. But yeah, I mean, I think the reason it surprised me is, I think, because I think a lot of people who are listening, you know, we have these other platforms and we kind of think in that model, but at the same time, excel continues to be one of the most common tools in the MarTech stack, if you will Right. So it makes sense and it's easy to share it, it's easy to communicate it, it's easy for other people who are familiar with Excel to understand how even a complicated Excel model might work.
Lucas Winter:Whilst we're on the topic of technology, I think it would be rude for me to wear an in-cycle t-shirt and not mention in-cycle for me to wear an in-cycle t-shirt and not mention in-cycle. So if you've got dirty data, I found in-cycle really helpful to sort of clean that up. In terms of when you're building that lead score model, one of the first products that people will want to go to is a data enrichment tool. But if your data isn't clean, if you don't have good data quality, then you're going to have to cleanse that data first. If you want to have a good, have good data quality, then you're going to have to cleanse that data first. If you want to have a good scoring model, make sure that you're cleansing your data and then make sure that you're enriching and cleansing again.
Michael Hartmann:Yeah. Yeah, I'm torn on that because I tend to believe that you can't wait for your data to be quote clean end quote right before you start on some of these things. Sometimes just the process of doing that will reveal where there's data issues and then you can go address them as they appear. But in general, ideally the better the data, the better the outcomes. I tend to agree with that.
Lucas Winter:Yeah, the better your data, the better you can have an impact on your scoring. If you're staring at your database and everything's blank I've been talking about industry if it's blank, that doesn't help you. I've been talking about job title. If your job title is blank, that doesn't help you. If you're looking at number of employees, etc. Etc. Etc. I wouldn't recommend just going out and getting a data enrichment provider if you've got a fully fledged database. If you're just staring at a bunch of blank fields, backfilling that is going to make you in such a stronger position. So you don't have a model that basically says what's your job title? Well, it's good if we know it. Like that's such a such a low level way of determining quality versus not.
Michael Hartmann:Right, interesting. Well, lucas, we are kind of coming up to the end of our time. We covered a lot here. Is there any big nuggets that we didn't cover that you want to make sure the audience hears about?
Lucas Winter:Well, that's flown by for me. If you want me to chat more about lead scoring, I'd ask the listener to put a comment down and just say bring him back, and I'll come and do this all over again.
Michael Hartmann:That'd be great. Well, so in that vein. So, first off, lucas, thank you. This has been a fun conversation and I think it'll be interesting for our audience to get this, because it's a little bit contrary to the common narrative, right? So it's a good example of what I would say. Is you know why? I say often that there's this fallacy of best practices, right? Because if everybody was doing things every way at the same time, no one would be differentiated. So, for saying thank you, if people do want to you know, don't want to wait and put in comments and ask for your input what's a good way for them to connect with you or learn more about what you're doing?
Lucas Winter:Nice, I'm not on social media. No, that is a lie. I am on social media. I'm on the MoPro Slack channel. You can find me there as Lucas Winter, and if you find yourself where you're liking what I'm saying and you want to build yourself a first lead score model and you're still not sure where to start, I'm lucas at scoremyleadscom. And if you're in a situation where you've had a lead score model for years but nobody wants to use it Again, lucas at ScoreMyLeadscom, give me a shout there. I'd love to connect.
Michael Hartmann:Terrific Well again, thank you, lucas. Thank you to our long time and first time listeners and audience. We always appreciate that. As always, we are open to ideas for topics and guests, and so if you, if you have an idea for a topic or a guest or want to be a guest, like Lucas did, reach out to us and let us know We'd be happy to talk to you Till next time. Bye, everybody.