Transcription
Hey everybody, Pat here. And in this video, I want to talk about the problem of using what I call spray and prey lead data and how it actually impacts the results of your cold email campaigns pretty negatively.
Now, I know it's always nice to get, you know, the cheapest price we can possibly get, but it actually comes at a cost. And I want to actually show you the math behind it because there's a big obsession obviously with with getting cheap lead data. And I understand it. You know, I've I've used cheaper lead data in the past, but as we've shifted more towards higher quality data, I want to talk about and show you the price differences and then talk about like the solutions that that we're implementing into Lead Engine that can help you with this.
So, let's just go through some scenarios here. So, under the spray and prey lead column here, if you enter in your cost per valid lead, um, when I say valid, I mean everything. So, the the cost of the lead itself, web scraping, the email validation, you know, maybe even catch-all scoring, maybe even AI lead scoring. So, big trend and we have this feature inside of the Lead Engine AI platform. But uh, there's a trend people are using clay for this where you go out and scrape the lead's website and then you use that data and basically rank and score the lead. So, by the time you factor in the cost of the lead, the email validation, maybe even catch-all scoring, and even the AI lead scoring, you're you want to enter basically your total cost for for every single lead.
Okay, so let's just say you're targeting 50,000 leads. I'm just throwing a number in here for now to illustrate this, but let's just say the accuracy of that is 75%. It's probably going to be on the lower end of that if you're not doing things like AI lead scoring and and things of that nature, which in that case, you're getting closer to this, you know, our our solution and what we have with more accurate lead data and things like that, which I'll talk about in a second. But let's just say it's 75%. I I've seen like 40% inaccurate data, meaning only 60%. I kind of picked a number as an average, and I ran some studies and I I used mostly Apollo data for it.
Now, I'm not knocking on on Apollo, but the the problem with these spray and prey leads is that you're entering basic filters, right? And inside of the lead databases, if you're there's certain data points of course that they have in them, but unless you're really really hyper like completing every single little filter line and excluding as many leads as possible. And you got to go through lead after lead after lead after lead. You got to keep adding exclusions, exclusions, exclusions in order to get accurate data. And the problem with inaccurate data is a number of things outside of the normal. You're going to spend more money on the data. You're going to have more bounce rates. You're going to have more spam complaint rates. So your your leads are going to mark you as spam higher because you're reaching out to irrelevant people. Okay?
And so to estimate like the actual true cost in order to send this kind of volume, you might need, you know, to spend around $300 in email accounts. Okay? Okay, so I'm kind of calculating this on a on a monthly basis here, averaging if you you know, if you send 20 emails a day and 21.67 business days in a month and you're spending about $5 per email account somewhere around there. That's where I'm coming up with the cost. Then in order to have enough domains, your domain costs are going to be $230. This is a onetime like domain cost, but you got to understand that you're going to burn through domains, you're going to burn through inboxes, you're going to have to reset these things up on a periodic schedule, right? So, the cost of this data, if it's 2 cents, is going to be around $1,000.
Now, I've got a a Fafo cost, which is a [ __ ] around and find out cost, meaning that there's a bunch of unintended consequences that come as a result of cheap lead data. As I mentioned, bounces, which is going to lead to bad deliverability, which means you're going to have to screw around with with more relevant replies in your inbox. You're going to get higher spam complaints. Your leads are going to tell you to [ __ ] off, right? Um, and you're going to have to spend labor and time building more lead lists and more lead lists and you're going to have to, you know, just do a bunch of things that you don't really intend to have happen, but actually happens with lower quality lead data. All right.
So, if you kind of look at your funnel metrics here, that's the total cost. So, the total costs are really going to be around $2,300. All right. Now, our cost per reply, if we hit say a 2% lead to reply ratio, you're going to have about a,000 replies. That means it's going to cost you about $2.27 to generate a real reply, an actual reply from somebody. Okay? If we have a 15% positive reply ratio of the replies, that means we're going to generate 150 positive replies, that's going to cost you about $15 per positive reply. Now, if of those positive replies, if we book 20% of them into a meeting or generate an opportunity, we're going to generate about 30 opportunities. is going to cost us about $75 for an opportunity. Now, if we take a close rate, let's just say cold outreach usually closes at a lower rate. So, I put in 10% of this for now. You know, if you're really good at managing your funnel, you build really good cold email style offers. And if you nurture your leads and the people who you generate opportunities with and for the long term, you you can increase this for sure. But usually your lead your closing rate on these irrelevant leads are going to be lower because they're not as targeted. Okay? And I'll talk about, you know, some of the things that we're doing to help increase better targeting with some of the lead data solutions we're baking into the Lead Engine platform.
Now, if you've got a total lifetime value, I'm just throwing a number in here of $10,000. Um, of those three customers, of course, we generate about $30,000. So, it seems cheap, but now let's let's compare that to more expensive lead data. And I say expensive sort of loosely because the expense really just comes at the cost of the lead data. So, let's say our cost of lead data is 5 cents. And what I mean by more expensive data, so we're building in data into the platform that are, you've probably heard the term lookalike leads. Okay. Now, lookalike leads basically look like your best customers and your le your best prospects. So, you can start a campaign inside of the platform using lookalike leads, but actually we have a feature called lookalike lead refuel. And what that does is it takes your positive replies, your positive interests, and then it finds more leads who look like those people. So, it's going to weed out a whole bunch of the bad leads that you wouldn't normally have to sift through. So instead of reaching out to 50,000 leads, let's say our lead accuracy over here was 75%. Basically simple formula, we're taking 75% times 50,000. It's probably closer to, you know, 37 and a half thousand. Might actually be less than than that, but just for the sake of of this comparison. Now, let's just say some of those aren't going to be accurate, right? There there's still going to be inaccuracies there, but it's going to be far more accurate than your spray and prey leads. Okay, so we'll throw this in at 95%.
Now to estimate our cost, we're going to have slightly lower costs on email accounts, slightly lower cost on domains. We don't have to have much as much infrastructure, but our lead data cost is absolutely going to cost more money. Okay? And our FAFO costs, [ __ ] around and find out costs are going to be significantly lower because you're not exporting importing lists. You're not dealing with supplies in the inbox. you're you're spending less labor costs, managing, you know, some of those, you know, stupid little activities that you wouldn't normally have to do and experiencing the unintended consequences of bad spray and prey lead data. All right.
So, our cost per reply is actually slightly higher. Okay? Our our co total cost is of course slightly higher, but we start to see a a better improvement and lower cost when it comes to positive replies. So, let me show you a little bit of the math. So, instead of getting a 15% positive reply, we can confidently assume that we're going to have a lift on our positive replies. And so, basically, the lift of that is basically the accuracy here on our our better lead data divided by the accuracy over here times our positive reply rate. So, we see a slight improvement on positive replies, which makes sense, right? We're we're targeting better leads who look like our best customers and our best prospects instead of inaccurate spray and prey leads. Makes sense, right? Logically speaking, it makes sense that to see a more poser positive reply rate because we're targeting better prospects. So, even though we're getting slightly less replies here, and by the way, same reply our reply rate will increase proportionally as well, just like our positive reply rate. Sorry, I skipped through this. I missed this line. So, we'll get less replies, but by the time we get to our positive replies because we're targeting more accurate leads and we are experiencing less problems with our infrastructure, our deliverability, all that kind of stuff, we're going to see more positive replies.
Now, we're going to start to see a lower positive reply rate. Same can be said for opportunities, okay? Because we're targeting opportunities and building lookalike leads around people who are actually wanting to meet with us, right? We're going to see the same increase in opportunities that we've seen in replies and we've seen in positive replies, meaning we can go from 30 opportunities in this scenario to 46. All right. And then our cost per customer, right? So, if we're targeting people that more closely match our ideal customer profile by building lead lists based on our best customers, our best prospects, we're also going to see a slight increase in close rate, meaning our cost per customer is going to go down as well. And by the time you factor in the funnel math, okay, so instead of three customers under this scenario, we'd be getting 5.8 customers. And if we have the same exact lifetime value, that's going to lead to an increase in total lifetime value because our we're going to close more deals based on the higher relevance, the better targeting. Okay? We're weeding out the bad leads and also targeting leads who look like our best customers, our best prospects. So, we can reasonably based on all of this math, there's a significant increase in what we can generate in terms of total revenue. But it does take all of this does take a mindset shift away from the total spray and prey lead concept over to a higher targeted lead list building strategy.
Now let's just play around with some numbers. Let's say, okay, Patrick, I'm not paying 2 cents. I'm paying 1 cent. All right, in this case, like we're still going to spend more money. We're it's going to cost us more to generate a reply. These replies are going to be more targeted, more accurate. We're going to spend more money on our positive replies. We're going to spend more money on opportunities, but our customer acquisition cost is still lower. And we're still going to see the same lifetime value. Even though this cost went down, this doesn't isn't going to change our funnel math. Okay? Where it could change the funnel math, maybe you're like, "Okay, Patrick, my list building accuracy isn't quite that bad." Have you ever gone through the list and verified it? I've ran studies on this and as I mentioned I've seen up to 40% inaccurate lead data list building and honestly it's been much higher than that too. That's it's that's kind of you know you know the the average so to speak. I've seen as little as you know 15%. But it can go up from there. But let's just say our accuracy you know goes up a little bit. Let's let's say it's it's 80%. Okay. and we're at still at one per one cent per lead based on validation, based on the lead cost, based on, you know, everything. Okay, so we're still going to spend more money in leads, still going to cost us more for replies, more for positive replies, more for opportunities, even more for customers, but our lifetime value is actually still going to be higher.
So, how how low can this go, right? Like how low can you get this before it doesn't make sense, right? Your accuracy is going to need to be, let's see here, 90 about 93%. So, you got to have a 93% accuracy over here to match a 95% accuracy over here and still basically break even your acquisition. And that that's that's if you're paying a penny per lead. If you're paying more than that, your your costs over here are going to change. Your lifetime value won't because that's really driven off of accuracy. But to get a 93% accuracy at only one or two cents is almost impossible. it's going to be closer to like a 75 cents. Okay. And let let's just play around with some of these numbers. All right. Let's just say actually, you know, the cost per lead is, you know, let's say it's 7.5 cents. All right. It's still, of course, it's going to cost us more for the total cost for the in most of that's going to come in the form of our data cost, right? So, it is going to be significantly more. And of course, I'm using, you know, 50,000 leads, you know, 37 and a half thousand leads respectively in here. So, yeah, it's going to cost us more for data. It's going to cost us more for a reply, more for a positive reply, but we're going to start seeing our costs come down for opportunities and our cost per customers. And our total lifetime value is of course substantially higher.
Now maybe you're like, "Oh, Patrick, okay, 7 and a half cents per lead. You know what? If your lead accuracy over here isn't quite as good." Okay, let's start playing with this number. All right. If it goes down from 95% to 90%, yes, we're still going to have a little higher costs through the funnel until we get to our acquisition cost and we're still going to have a higher lifetime value because our metrics are that we're reaching more accurate leads. So, we're going to see a lift in reply rates still, right? We're going to we're going to see a slight lift and it's just not a crazy lift, but it's a lift by the time you factor that on your positive replies, your opportunities, right? Your customers, your closing rates, right? the more closer to your ideal customer profile that you can use in your outreach here, the more your funnel metrics are going to improve for every stage of your funnel.
Okay, so that's just that's just some math here. Put this calculator together, you can use it, you can play around with it, you know, tear it apart, ask me questions, so on and so forth. This is a type of data that we're building into the Lead Engine platform to basically have more relevant outreach, right? You experience less problems. You have less email accounts, less domains, less data. But this is the big one here. Okay? What I call the Fafo costs. All right? The the [ __ ] around and find out cost. But and this this is a real cost. Okay? This this isn't zero. All right? There's labor costs and all this kind of stuff. You got to factor that in. And the other thing this really isn't factoring in is the downtime of your campaigns. Okay? So, we have, like I mentioned, we have the refuel, the lookalike lead refuel. It keeps your campaigns running based on the positive replies. So, it's going to keep adding more and more leads. So, you you don't need to go and export, import list, build new lists, all this kind of stuff. It keeps your campaigns actually running instead of spending all that time. Your campaigns might shut off. You might not realize your campaign is done. And then all of a sudden a week or two weeks or three weeks later, you realize like, "Oh, shoot. I haven't added more leads. I need to launch a new campaign." Meanwhile, you haven't generated opportunities, which means you're not generating, you know, customers, you're not closing deals, right? And so, you have these up and down and up and down in the business, and it just doesn't make sense. All right?
So, if you want to learn a little bit more how we're running and doing these database free refueling concept inside of the platform with lookike leads and then eventually other style leads to narrow your lead list down to the highest 10 best possible prospects, then we'd love for you to book a call. There should be a link somewhere around here and I'd love to chat with you. Hope this made sense. Use the calculator and we'll see you.