If You Don't Understand Math, You Don't Understand Marketing

0:00If you don't understand math, you

0:02definitely don't understand marketing.

0:03And if you don't understand math or know

0:05how to do marketing math, you sure as

0:07hell don't stand a chance to crack

0:08million-dollar months or that next

0:10million dollars a month that you're

0:11after. In today's video, we're going to

0:13show you three different types of

0:14marketing math. I'm going to discuss

0:15with you financial modeling, and I'll

0:17show it to you for a callunnel and for a

0:18webinar funnel. In addition to that,

0:20we're going to do what's called a

0:21bottleneck analysis. We're going to wrap

0:24it up with what we refer to as closer

0:25math, which is how you back into knowing

0:28how many closers you need at a

0:29particular time to actually hit your

0:32goals. By the end of this video, you're

0:34going to walk away with the clarity

0:35around three of the most common

0:37marketing math examples that you're

0:40realistically going to need to use in

0:41your day-to-day business operations so

0:43you can finally scale the hell out of

0:44your business. For all those unfamiliar,

0:46my name is Jeremy Haynes. All we talk

0:47about on this channel is cracking

0:48million-dollar months. Whether it's the

0:50first million a month or the next

0:51million a month, we just hand down

0:52lessons from people that have been

0:53there, done that, including myself. Of

0:55course, there's no income claims. Quick

0:57disclaimer for you. The odds of you ever

0:58cracking $10 million a year, according

1:00to research, is.1%. The odds of you ever

1:04cracking a million a month, aka $12

1:06million a year, is obviously a lower

1:08number than.1%. So, without further ado,

1:10let's get started. The first type of

1:12math that you're going to need to most

1:13commonly do inside of marketing, is

1:15what's referred to as financial

1:17modeling. I'm going to bring up two

1:19different financial models for you for

1:21two of the most common funnel types,

1:23call funnels and webinars to demonstrate

1:26this point to you. But the first thing

1:28that I need you to understand is you can

1:30easily build models that are specific to

1:33the exact funnel that you're currently

1:35running, the exact steps that you

1:38specifically care about. That way you

1:40have the clarity around what each stat

1:44can be in its worstcase scenario with

1:47every other stat factored into it.

1:49You'll see as we go through this

1:51together how important this truly

1:53becomes to have worst case scenarios

1:56analyzed to determine how much you need

1:59to spend at a minimum during test phases

2:01and to also see that when you scale how

2:04much flexing each particular stat can do

2:07before your whole operation collapses

2:09and becomes unprofitable. Let's dive in

2:11initially with a call funnel. So this

2:15call funnel I also just want to disclose

2:17real quick. For some reason, my specific

2:21cell here, I don't know why this is a

2:22duplicate of a duplicate of a duplicate

2:24of a financial model. If you took six

2:27deals being closed times 14,000, it sure

2:30as hell doesn't come out to an odd

2:32number, but it's pretty close.

2:34Nonetheless, let me show you how this

2:36actually works. So, in a callfunnel

2:38financial model, let's just go from the

2:40top to the bottom so we can easily

2:42understand this. That way, all this

2:43doesn't confuse you. The first thing in

2:46this very first cell, A1, it represents

2:48ad spend. So, we're going to simply put

2:50how much we're going to spend over

2:52whatever period of time we want to judge

2:54it. Let's use 30 days as an example. Our

2:58next step, we ideally want to be

3:00modeling around the most conservative

3:02scenarios, aka the worst case scenarios.

3:05So, this is our cost per call. We're

3:08going to put our cost per call at $450

3:10in this example. Here you'll see that we

3:15can use this same model for either a one

3:18call close or for a two call close. In

3:22this specific example, this is a one

3:24call close. We would assume that you

3:26have a 60% show rate, which is about the

3:28average nowadays. Above average would be

3:30about 70% plus. We've seen below average

3:33be as low as about 30% by the way.

3:35Sometimes even a little bit worse than

3:36that, slipping down into the 20s. Got a

3:38lot of problems if that's the case. In

3:40this example, you'll see that both of

3:42these cells are represented at 100%. Cuz

3:44like I said, we're not doing a two call

3:46close. We're doing a one call close. If

3:48we had a two call close, we would put

3:51how many people that showed up to the

3:52first call actually booked a second call

3:55and how many people that booked a second

3:57call showed up to the second call. So

3:59again, in a one call close example,

4:01these would just be 100%. In a two call

4:03close example, they'd be at whatever

4:04stat they actually are. Then we have our

4:06close rate. The close rate is how many

4:08people that showed up actually closed.

4:11And then we have our AOV, our average

4:14order value, otherwise known as our

4:15actual cash collected per sale. Some

4:17people at this particular step start to

4:20get a little wonky on their math. So,

4:22just to give you some clarity, always

4:23operate around whatever amount you

4:25actually cash collect when you close

4:27somebody because that's how many dollars

4:29are actually going to come into the

4:30business in real time. Sometimes people

4:32will inflate this number a dramatic

4:34amount and they'll model everything

4:36around revenue rather than actual

4:40dollars that have been collected.

4:42Sometimes this is contracted dollars

4:44that people will put here. Put how much

4:46money you actually collect per sale

4:47because that'll make a huge difference

4:48to the stats. Then you start to get into

4:50the results columns. In this case, you

4:53can see based on the math, if we spent

4:5520 grand, if we had a $450 cost per

4:58call, we'd have 44 total calls

4:59generated. If 60% of those people showed

5:02up to the first call, we'd have 27 total

5:05calls that were taken. We skip down here

5:07to the close rate step. If we had 22% of

5:11those people that actually closed, we'd

5:13have six total deals. And in this

5:15example, we're cash collecting $14,000

5:18on average, which like I said gives us

5:20the amount that we actually generated.

5:23And that's a little off. You take 14K* 6

5:26and that gives you the actual number.

5:27From here we get our gross return on ad

5:30spend. So before any expenses just ad

5:33spend. This is what our rorowaz looks

5:36like in that example. Now from here in

5:38this specific model we add a few more

5:40variables because we obviously don't

5:42just want to be operating off gross

5:43return on ad spend. We want to be

5:44operating on net rorowaz. So we have any

5:47fixed costs that you can just put as a

5:49line item here. That'll be deducted

5:51against the total amount that's been

5:52generated. We have team costs if you

5:55want to separate them out by line items.

5:57We have the sales team specifically and

5:59you can put whatever commission you're

6:00going to pay your sales team in total

6:02that determines our total costs which

6:04then determines our net profit which

6:06then determines our net rorowaz. You can

6:08also put additional breakout line items

6:10below that just for the sake of clarity

6:12on what each expense was out of the

6:14total ad spend that you're going to have

6:16if you want to do that. Now, the point

6:18being in this specific math, what you

6:21can see here is we put a dollar in and

6:23we get about $4 back. We put a dollar in

6:26and we net about $2.50 back for every

6:29dollar that we spend. That's great math.

6:32Each one of these specific cells when we

6:36manipulate it in a negative direction

6:38reveals to us how bad it could be before

6:41everything starts to break. So, let me

6:42give you an example. Let's say that

6:44instead of having a 60% rorowaz, we have

6:46one of those dumpster fire show rates

6:48that some of you right now are rocking,

6:50let's say that it's only 30%. In that

6:53example, just that one statistic

6:55changing dramatically reduces our

6:58overall rorowaz. We cut our rorowaz in

7:01half in that example by cutting our show

7:04rate in half. The simplest math starts

7:07to reveal itself here for how important

7:10each specific statistic is by itself.

7:14But also, you'll start to mess around

7:15with the model and see how important

7:18each statistic is just going up or down,

7:21but having a few of those specific key

7:24stats that go up or down at the same

7:27time. Now, one other financial model

7:30that we like to look at and use very

7:32frequently, just given how common this

7:35funnel is at this point, is a webinar.

7:37Very commonly, people want to know how

7:40little can I spend when I'm testing a

7:41webinar to get it off the ground or how

7:43much can I spend on a webinar and how

7:45bad could each stat be before it no

7:48longer makes sense to run. In this

7:50specific financial model, again, we're

7:52looking at a webinar. In this case, we

7:54start off with our ad spend. So, we're

7:57assuming that we're spending $5,000 on

7:59this specific amount. We're assuming our

8:01cost per webinar registrant to get

8:03somebody to opt in is $20. We're

8:06assuming that we have a show rate in

8:08this example of 25%. We're assuming in

8:11this model that we're doing a book a

8:13call webinar. And by the way, in this

8:16same financial model, just like in the

8:18callfunnel one, instead of having two

8:21models, one for a single call close and

8:24one for a two call close, we can use the

8:26same model for a direct to checkout as

8:28well. We would have our show rate to the

8:30webinar. And then for our booked call

8:33and our booked call show rate, we would

8:34just put those at 100%. Because then

8:37that would represent that we have our

8:39close rate from everybody that we did

8:41directed checkout with. From there, we

8:42have our cash collected per sale. So

8:44again, in this specific way that the

8:45model is laid out, let's assume that

8:47we're doing a book a call call to

8:48action. So we spend 5,000. We have a $20

8:51cost per registration. We get 250 people

8:54that opt in. We have 25% of people show

8:57up. That's 63 people that show up. We

9:00manage to book an outrageous 75% of

9:04them. That statistic is normally about

9:0630%. By the way, the amount of people

9:08that show up to a call is 53% and the

9:11amount of people that close from those

9:12calls is 22%. We're cash collecting per

9:15sale $3,500 in this math. For this

9:18specific math, we turned a dollar into

9:20$3.83 for the gross revenue. And from

9:23here, we'd have to take off all the

9:25different costs to determine what the

9:27actual net rorowaz would be. For this

9:29specific model, you want to know

9:32something very important. What stats are

9:35real? and what stats are very

9:38unrealistic. You know, as an example, a

9:41$20 cost per registration for a webinar

9:43is a pretty normal range. You might

9:45assume that some people can do far

9:48better, but there's also some people

9:49that could do far worse. That's right

9:50down the middle in terms of what's

9:52considered conservative. Your show rate

9:54can be as bad as about 10 to 15%.

9:57Average is about 20. Above average is

9:59anything 30% plus. And this is assuming

10:02a cold traffic webinar, by the way. The

10:04booked call rate is an outrageous

10:06statistic at 75%. That would assume

10:09three out of four people that are

10:10sitting there watching you during your

10:11pitch are going to book a call. That's

10:13not realistic. What's actually realistic

10:15is closer to about a 30% book a call

10:18rate. That'd be considered where you

10:20want it to be. 20% is more average for

10:24most people, especially when they're

10:25first getting started. We see the book a

10:27call statistic, by the way, as worse or

10:30as low as about 10%. So, it's very

10:33similar to the show rate statistic. The

10:36booked call show rate statistic, meaning

10:38how many people book a call that

10:39actually show up. Ideally, that's 70%

10:42plus. And ideally, your close rate from

10:45all the people who book from a webinar,

10:46these are layup deals after all, would

10:48ideally be 35% plus at a minimum. From

10:51here, the cash collected per sale, that

10:55AOV number, how much you're actually

10:57putting in your pocket per deal that's

10:58closed, $3,500 for a webinar when you're

11:01booking a call would be considered very

11:03low. The higher the number is, the

11:04better. Knowing what's realistic, is

11:06very important when you're financially

11:07modeling everything out. But again, you

11:10want to have models like this for your

11:12business, for whatever funnel you are

11:14specifically running. Because the

11:16intention of doing this is before we

11:19even spend a dollar, we can see what all

11:21the different statistics need to be in a

11:23worst case scenario for each stat for us

11:26to still turn that dollar back into a

11:28dollar, if not more than that. We can

11:30also see how good everything could get

11:32if everything went right. After we

11:35actually get the reality of what our

11:36statistics actually are and we're not

11:38just doing predictions, we can plug in

11:40the real numbers to the model and we can

11:42see what our profit should look like.

11:45Very commonly, people are told by their

11:47team members that they have running

11:48their sales teams or running their

11:50marketing teams what different key

11:52statistics are in the business. When

11:54you're told those statistics, if you

11:55just turned around and plugged them into

11:57a financial model, you'd have the

11:58clarity if they're lying, manipulating

11:59the stats, or if everything aligns

12:01perfectly to reality. We had a girl one

12:03time comes in person and sits down with

12:06me for what we call a business

12:07breakdown. We put a chair here. We put a

12:10chair here. And we start the process of

12:12digging into this person's business and

12:14trying to find out the specifics about

12:17what's going on, what's holding back

12:19revenue, and what could help them scale.

12:22One of these specific business owners

12:23that came and sat down with me, her name

12:24was Kadisha. And Kadisha was struggling

12:27with her return on ad spend. At this

12:29specific moment, she was at a very low

12:312:1 rorowass. and she was very confused

12:33as to why. I start doing something that

12:36we refer to as a bottleneck analysis,

12:38which is the thing we're going to cover

12:39next together. And as she's sitting

12:42there saying the statistics, I'm

12:43thinking to myself, these statistics

12:45sound way better than a 2:1 rorowass.

12:48Let's go plug these numbers into a

12:50financial model and see if the financial

12:51model aligns with what her numbers are

12:56because it doesn't sound like they'd be

12:57a 2:1. We go plug the exact stats that

13:00she had from a webinar funnel and from a

13:02callunnel into this exact financial

13:04model and the model said that she should

13:07have been at an 8:1 return. Now in her

13:10specific business, the reality of her

13:12business was she was at a 2:1 but the

13:15stats that she was being told by her

13:17team and that she was making decisions

13:19on showed that she should be at an 8:1.

13:23If you don't understand math, you don't

13:24understand marketing. Because in that

13:26example, if you heard what I just said,

13:28she's not making decisions in reality to

13:31improve her conditions of her business.

13:33She's making decisions in a false world.

13:36So, of course, she's not making any

13:37progress in real life. That's one way

13:40that financial models can become really

13:42valuable outside of showing you what's

13:44the minimum you need to spend in order

13:46to test what is the reality of your

13:49business. When you take the stats that

13:51you're being told and you plug them into

13:52the model, does the model align with the

13:54reality of what your rorowaz is or what

13:55your profit is? In addition to that,

13:58this can be really good for starting to

13:59project if you go to shovel a lot of

14:02spend into your ad account and you're

14:05trying to scale the hell out of your

14:06business. If each stat got a little bit

14:08worse, how bad could collectively

14:10everything be in a worst case scenario

14:12for you to still meet your desired

14:14minimum profit thresholds to scale in

14:16the first place? Financial modeling is a

14:18critical foundation of every single

14:21business that is going to market and it

14:24rolls into the second part of what we

14:25need to talk about next which is what we

14:27refer to as a bottleneck analysis. So

14:29just like I was talking about in the

14:31context of Kadisha her business was in a

14:34position where nothing really made

14:36sense. She wasn't where she wanted to be

14:38at from a profit perspective. And so we

14:40picked the specific funnel that she was

14:43running and we broke it down step by

14:45step. The concept of a bottleneck

14:47analysis is very simple in practice.

14:50Allow me to explain it to you from a

14:51highle perspective. First, at any point

14:54within your process that you have right

14:56now, your advertising, marketing, and

14:57sales process, there could be a

15:00constraint. Just one constraint is all

15:02it takes to hold back a tremendous

15:04amount of revenue from coming through

15:05the other side. And when you're talking

15:07in the context of million dollars a

15:08month or a couple million dollars a

15:10month, knowing exactly where a specific

15:14step in your entire sales and marketing

15:16process is contracted is where your

15:19attention flows exclusively until

15:22resolved. So in this example, just

15:25picture a nice flowing river that all of

15:28a sudden out of nowhere contracts down

15:30to a tiny little stream. There's not

15:32going to be an excess of water coming

15:34out the other side. It's just going to

15:36be a tiny little stream, a trickle of

15:37water that comes out. We have the power

15:40of an entire river to flow through there

15:42if we can open up that choke point, that

15:44bottleneck. So everything opens up and

15:47so a higher quantity of water comes out

15:49the other side. In the context of, as an

15:51example, water bottles, bottlenecks make

15:54sense because this contains all the

15:57water obviously and this prevents us

16:00from just having an aggressive amount of

16:02water come out the bottle as we drink

16:04it. We don't want our business to have a

16:06bottleneck. We want our business to

16:08maintain the same flow across the entire

16:11marketing and sales side of the

16:13equation. That way, the most money comes

16:16out the other side. That's the whole

16:18point of what we're attempting to do.

16:19So, as an example, I'll take those exact

16:21same two funnels and I'll break down the

16:23key steps that matter. One quick thing

16:25that I want you to be able to think with

16:26before I do is what we call the data

16:28hierarchy. So if you notice in the

16:30financial model, every statistic that we

16:32could talk about isn't here. These are

16:35the important statistics that typically

16:37make up your rorowaz. Statistics like

16:40your cost per lead, statistics like your

16:43show rate, statistics like your booked

16:45call rate, statistics like your booked

16:48call show rate, statistics like your

16:50close rate. In the context of a

16:52callunnel specifically, the callfunnel

16:55is a little bit different. The

16:57callfunnel statistics that matter are

16:59things like your cost per call, your

17:02show rate, your close rate, the cash

17:04collected per sale. But there are what

17:06we call substics that make up this

17:09number. So, as an example, the cost per

17:12call isn't just a number that randomly

17:13exists. It's created and made up of

17:16three key statistics. the CPMs, our cost

17:19to reach people, our link click-through

17:22rate, meaning how many people actually

17:23see our ads versus click the link to go

17:25to the page, and in addition to that,

17:27the page conversion rate. Let's say that

17:30you had a bad page conversion rate. You

17:32can also dig further into what

17:34statistics make up that number. But we

17:36don't want to create a model that's

17:38overly complex and has every single one

17:41of these tiny little numbers that make

17:43up the larger numbers that actually

17:45matter. Because if everything's going

17:46well, we're fine. In terms of the data

17:49hierarchy, the ones that are at the top

17:51that you want to look at first as your

17:53leading indicators are rorowaz and

17:55collected dollars per booked call. From

17:58there, we dig into what makes up those

18:00numbers. As an example, like what we've

18:03covered in these two financial models

18:04we've shown you. From there, we dig into

18:06the stats that make up those numbers,

18:09which is something that becomes very

18:10handy when we do a bottleneck analysis.

18:12So let's use the example of a call

18:14funnel first and let's say that we

18:16wanted to do a bottleneck analysis for

18:18it. The first step in that process is

18:21CPMs. The second step is link

18:25click-through rate. The third step is

18:28the page conversion rate. Now one thing

18:32that's important to note inside of

18:33CallFunnels is what you are actually

18:36attempting to measure. So for the page

18:38conversion rate, we're not looking for

18:40the people that clicked and applied

18:41because that's not what we want. We want

18:43qualified booked calls. So we want to

18:45take the total qualified booked calls

18:48divided by the total link clicks. That's

18:51what we want to measure for our page

18:53conversion rate in this example. From

18:55there, let's assume that we're doing a

18:56one call close. So we have our show rate

18:59to factor in. After that, we get into

19:01the statistic of our close rate. And

19:05then from there, we have our AOV. How

19:07much money did we actually collect per

19:09sale on average? In comparison, for

19:12something like a webinar, we'd have a

19:14few more stats that we'd have to measure

19:16out, but it starts relatively the same.

19:19We have our CPMs. We have our link

19:21click-through rate. We have our opt-in

19:23rate on a webinar funnel. We then have

19:27our webinar show rate. Now, in webinars

19:32specifically, two stats matter inside of

19:34the webinar itself. what's known as the

19:37retention rate and in addition to that

19:40the booking rate or if you're doing a

19:43direct to checkout the direct to

19:45checkout rate. I assume that of course

19:47if you're sitting here watching me on

19:48this channel you have some kind of high

19:50ticket product or service based

19:51business. So there's a much higher

19:52probability you're going to tell people

19:53to book a call given the price of your

19:55offers. From there we have the call show

19:58rate

20:00and lastly we have the close rate. We

20:03follow that up with the AOV. How many

20:05dollars are we actually cash collecting

20:07per sale? Now, what you want to do when

20:09you look at these is first of all, make

20:10sure that you again, you aren't over

20:12complicating the total amount of numbers

20:14that you need to track. You specifically

20:16want to measure stats just like what

20:18we've talked about here because this

20:19gives you the reality without having to

20:21go too deep into the trenches. If any

20:22one of these specific statistics is

20:24contracted and has other stats that make

20:27it up, we can then go and look for those

20:28numbers afterwards. So, let me give you

20:30an example. I'll make up some math

20:31that's about average. So, for CPMs,

20:35we'll commonly see this somewhere

20:36between about $30 and $80. Could be

20:39anywhere in between. For a link

20:41click-through rate, ideally, you want to

20:42be at a 2% link click-through rate. Your

20:45page conversion rate on a call funnel.

20:48Again, total clicks to how many people

20:51actually book a qualified call. You

20:53ideally want to be at a 3 to 5%

20:55conversion rate of the total amount of

20:57people that click versus the total

20:59amount of people that schedule a

21:00qualified booked call. Your show rate is

21:04typically averaging about 60% nowadays.

21:0670% realistically is where you want to

21:08be, but 60% you can still be very

21:10profitable on a call funnel from cold

21:13traffic. Assuming you've got the

21:15salespeople that you need to be able to

21:17do education calls, that transition to

21:19closing calls, you're typically going to

21:21be in the range of somewhere between

21:22about the low end of 15% to the high end

21:25is usually about 30%. Most closers will

21:28average somewhere in the low to mid 20s.

21:31Then you have your AOV and obviously

21:33your AOV is completely determined by

21:34your offer and what you charge. Whether

21:36you're doing PIFFS and cash collecting

21:38every dollar that you're owed up front

21:40or whether you're doing financing or

21:41funding, whether you're doing payment

21:43plans, you need to determine what your

21:45actual AOV is, of course. But the simple

21:47answer is the higher the better. So in

21:49this example, this gives you an idea of

21:51what the key benchmarks are of the key

21:53statistics of a callunnel. you can go

21:56through and you can map your specific

21:57callfunnel statistics against these

21:59benchmarks and then if any one of these

22:02particular stats is contracted, you

22:05isolate your attention to that specific

22:08statistic. So a lot of people when they

22:10go to improve their rorowaz and get more

22:13money coming out the other side of their

22:15funnels, they operate off vibes, which

22:18is silly to say the least. You have to

22:22understand math to understand marketing.

22:24So, as an example, let's say that your

22:26CPMs were within range, but let's say

22:28that you had a 0.5%

22:31link click-through rate. All of your

22:33attention would go towards making better

22:35ads in that example if that is the

22:38specific statistic out of all the stats

22:40available to you that is easiest to

22:43double. What you're trying to do in a

22:44bottleneck analysis is play the doubles

22:46game out of any specific statistic,

22:49especially if multiple are messed up at

22:51the same time. Which to you is the

22:54easiest to double or in the context of

22:57CPM is the easiest to reduce in half. If

23:00I had a 0.5% link click-through rate and

23:03every other stat in my funnel was

23:05exactly where it should be compared to

23:07the benchmarks of the true and honest

23:10averages, I'd isolate all my attention

23:12to making better ads. Let's say that I

23:14had a 0.5% link click-through rate and I

23:17also had a 25% show rate. The

23:20combination of these two different

23:22statistics being bad at the same time is

23:24where I tell you you need to start

23:26determining what might be the easiest

23:28for you to influence and double. I'd

23:31also like to remind you that every

23:33statistic at the beginning of the

23:35process influences the statistics to

23:37follow. We've had people that have had

23:39great optins, great show rates, great

23:43conversion rates, but then they get to

23:45the close and the close is terrible. And

23:47you might think of that in the context

23:49of what I just said and say, "Oh, well,

23:51that'd be the salespeople's fault,

23:53right?" But what if all the leads that

23:54were showing up were terrible, genuinely

23:56unqualified, people that had no chance

23:58in hell to close. Although the close

24:00rate stat is what's contracted, you'd

24:03still likely put attention onto the

24:05advertising side of things because in

24:07that example, you likely have a

24:08messaging problem or you're not doing

24:10enough qualification in your webinar or

24:12in your call funnel. So certain stats

24:14can be contracted, but it might not be

24:16that stat specifically for where you

24:19need to put attention. You might

24:20actually need to put attention further

24:21up in the process. There's a whole bunch

24:23of if this, then that rules that these

24:26types of things start to reveal. I have

24:29plenty of great content on my channel

24:31that's dedicated to helping you improve

24:33all stages of what you're going to come

24:34across here. from advertising

24:36strategies, funnel strategies, when to

24:39do what thinking, the mindset behind all

24:42of this, very tactical entrench level

24:44information on how to improve. But of

24:46course, I just want to disclose on

24:48YouTube, I talk at about 10 to 20% of

24:51what I know. The remainder of what I

24:53actually know, where all the juicy stuff

24:55is, is inside of my paid programs that

24:57you can find links for down in the

24:59description. I'd encourage at the very

25:00least you check out my newest offer,

25:02Jeremy AI. That's an angentic AI clone

25:05of me that can literally help you do all

25:07this stuff, let alone answer all of your

25:09questions. But back to this, let's look

25:10at the webinar bottleneck analysis as an

25:12example and give you some benchmark KPIs

25:14that you can look at for that. CPMs are

25:17about the same. You're typically

25:18somewhere between about $30 and $80 on

25:20average. Your link click-through rate

25:22should be about the same, a 2% link

25:24click-through rate. Now, I know this is

25:26a wide range, but your opt-in rate can

25:28typically vary somewhere between about

25:3020 and 50%. It depends on the traffic

25:32source. Like as an example, the

25:34difference between warm and cold

25:35audiences can wildly determine that. And

25:37it also depends on how good your

25:38messaging is. We've seen people wildly

25:41profitable with 10% opt-in rates. Just

25:43to put it in perspective, each statistic

25:46is contextual to your business for how

25:48bad it needs to be. But I want to make

25:50you aware of the benchmarks.

25:51Nonetheless, your webinar show rates

25:53will typically be somewhere between

25:56about 10 and 30% plus. 30% would be

26:00where you'd want to bias towards. The

26:02average is about 20 nowadays, but again,

26:0430% is what you want to shoot for. Now,

26:06the retention rate, let me explain the

26:08statistic. The retention rate, a lot of

26:10people measure their show rate

26:11differently. And it's important that we

26:12get clarity on that statistic, too. Show

26:15rate, let's say you use Zoom webinars.

26:17You can log into the back end of Zoom in

26:19their analytics portal for that specific

26:21webinar that you just concluded, and

26:22they'll tell you how many people clicked

26:24to join altogether. And if you

26:26determined that to be your show rate and

26:27you divided that by the total quantity

26:29of registrants, you'd have a much higher

26:31inflated show rate compared to the

26:33reality. What you'd rather measure your

26:35show rate on is what was the highest

26:37quantity of people there concurrently at

26:40one time. So Zoom's analytic might say,

26:42let's say you had a thousand

26:43registrants. Everybody that clicked to

26:45join, they might say that you had 400

26:47people. And you might say, "Oh my god, I

26:48had the greatest show rate. I had a 40%

26:50plus show rate." But let's say that you

26:52actually had 200 people there at the

26:55same time watching your webinar. That

26:57would be your real show rate, which

26:58would be 20% in that example. That's how

27:00you want to measure that. So, when it

27:02comes to the retention rate, it matters

27:04that we measure the show rate accurately

27:06because that's going to determine this

27:07statistic. Retention rate is how many

27:10people were you able to hold from the

27:12beginning of the webinar until the very

27:15start of your pitch. If you can

27:17specifically dial this in to be about

27:1980% plus, you're sitting right where you

27:21want to be. Most people who are great at

27:23the actual webinar presentation will

27:25easily be able to accomplish this. Your

27:28booking rate should ideally be 30% if

27:31not greater than that. The worst people

27:33who are usually terrible at the close,

27:36they can be as low as about 10%. People

27:38who are getting the hang of it or who

27:40stay in the close for a couple minutes

27:42and that's it, they'll be closer to like

27:44a 10 to 20% range. The people who stay

27:47in the close for as long as they should,

27:49which should be about an hour, by the

27:51way, just in the close, they'll easily

27:53be able to get that 30%. Especially if

27:56they have our flow that we encourage

27:58people to do when they're in the webinar

27:59close. But nonetheless, let me help you

28:02understand how you're measuring this

28:03statistic. So when you start your pitch,

28:06however many people are there at the

28:08start of the pitch

28:11versus how many people book or how many

28:13people buy, that's your specific booking

28:16rate, depending on whether you're doing

28:17a booking rate or direct to checkout. We

28:19want 30% of the people that are there

28:21during the time of the pitch to book a

28:24call with us. Then we have our webinar

28:26call show rate. Ideally, that should be

28:28no lower than 70%. If it's lower than

28:3170%, you have a problem, my friend, and

28:34you need to improve that statistic. Your

28:36close rate on these calls that book from

28:38a webinar should be at least 35%.

28:41Very easily achievable because every

28:44webinar call should be a layup deal.

28:46After all, you just did a webinar. And

28:48again, when it comes to your AOV, well,

28:49that's completely determined by you

28:51based on what you charge. But the higher

28:53the number, the better. I will tell you

28:54this, especially if you're doing a

28:55booked call with a webinar, you never

28:57want the AOV to be below $5,000. The

29:01higher the AOV, the better and the more

29:03profitable you're going to be. But I

29:05can't stress enough, AOVs typically

29:07below $5,000 become very problematic and

29:10you get into the territory where it

29:11might make more sense to have just done

29:13a direct to checkout. But I will say a

29:15direct to checkout requires a different

29:17set of skills compared to a book a call

29:19webinar skill set. So with these two

29:21sets of stats, what you can see in the

29:23two most common funnel types, call

29:24funnels and webinars, is what each

29:26particular statistic needs to range

29:28within. Now, when I tell you do a

29:30bottleneck analysis, this is literally

29:32what I mean to do. You want to map out

29:34each statistic of your specific process

29:37that you're putting people through right

29:38now and put a statistic to it. When you

29:40compare that statistic to the benchmarks

29:42that you just became aware of here, you

29:44want to determine which particular

29:46statistic is the most contracted

29:48compared to any other statistic. When

29:51you look at the statistic that's the

29:53most contracted, that's where you

29:55isolate your attention. The only time

29:57that you wouldn't is in the example I

29:58gave you where, let's say you had a

30:00terrible close rate. Let's say that all

30:02these statistics look exactly as they

30:04should, but when you get over here to

30:06looking at your close rate, let's say

30:08that it's terrible and in the single

30:10digits. Let's say your sales people are

30:12complaining, whining, moaning, saying,

30:14"Hey, nobody's closing because these

30:16leads are the worst ever." That might be

30:19a lead quality issue. And even though

30:21every single statistic is technically

30:23great, just because your close rates bad

30:25doesn't necessarily mean in that example

30:27that you'd isolate your attention

30:28exclusively to your sales people.

30:30absolutely demands attention, no doubt.

30:32And you want to validate if they're

30:33lying or not, or if the reality is true.

30:35Because if the reality is true, you have

30:37a messaging problem. And even though all

30:38your stats are great, well, you got the

30:40wrong people coming through. So, you

30:41don't want to do that. So, let me help

30:43you understand one thing. Now that

30:44you've got these two different key math

30:48examples down of financial modeling and

30:51doing a bottleneck analysis, you want to

30:53wrap this all together with what we

30:55refer to as closer math. Now, closer

30:58math is really important that you

30:59understand. This is how we reverse

31:01engineer our goals and make the goal

31:04number possible. So, I'm going to give

31:06you an example. Let's say that I wanted

31:08to hit a million dollars a month in

31:11revenue, right? And that's cash

31:14collected that I actually want to

31:15generate for the business. I rather than

31:18starting at the beginning of all the

31:19stats, I want to start at the end. So,

31:22I'm going to pull out my handydandy

31:24calculator and I want to show you guys

31:25this as we go along. So we're all on the

31:28same page with what these numbers

31:29actually look like. Let's use the

31:30example that I start with my AOV, right?

31:34How much am I actually collecting on

31:36average per sale? And let's say that my

31:38current AOV sits at $10,000, right? If

31:42my AOV is $10,000, I want you to

31:46understand you need to close a 100red

31:48people. So I need a hundred sales. Now,

31:52to get that hundred person demographic

31:55to actually give me that $10,000 so I

31:57can get my million dollars, I then need

31:59to divide that hundred number of total

32:02closes by what my close rate is. And we

32:05do that in a decimal. So, in this

32:07example, I'd take my 100 people that I

32:09need to buy and let's use the example

32:11that I have a 20% close rate. I need 500

32:16calls to happen. So, I need 500 people

32:18to literally talk to my closers. So in

32:20this example, let's say that we have a

32:2220% close rate.

32:25I need 500 calls taken. Now again, just

32:28to keep this math simple and show you

32:30guys, if I have 500 total calls that I

32:33need taken, let's divide that by 60%.

32:36Let's say that I have a 60% show rate.

32:39Now, when you get a decimal like 833.33,

32:43like you see in that example, either

32:44round it up or rounded down based on

32:46whether it being above.5 or below.5. So

32:49again, let's say that I have a 60% show

32:51rate.

32:53That 60% show rate means I need 833

32:57calls that get booked. Now, this is

33:00where things start getting real. With

33:02833 calls that get booked, let's use the

33:06example that I have a $200 cost per

33:10call. In this example, all I got to do

33:12now is take that $833 number and I just

33:16got to multiply it times $200 cost per

33:18call. This is my ad spend. So, I need to

33:21spend $166,000

33:24in order to get that many total calls to

33:26come out the other side. So, in this

33:28example, I could go more granular per

33:32step. But what I reveal by doing this

33:35math is I start from the end of where

33:37the revenue is, and I get to back into

33:39it. So, with $166,000 in ad spend, a

33:43$200 cost per call, I'm going to get 833

33:46calls booked in total. If I get 60% of

33:49those people to show up, that's 500

33:50calls taken. And if my salespeople close

33:53at 20%, that's my 100 deals. If I get

33:56the hundred deals and they each pay me

33:5710K, I get a million dollars. If I do

34:00that within 30 days, done. Now, what I

34:02want you to also understand that closer

34:04math reveals is how much volume this

34:07actually produces. So, let me help you

34:09understand something. Let's say that our

34:12average closer

34:15can take a total of eight calls per day,

34:20right? We know that 60% of people are

34:22actually going to show up. So out of the

34:25eight different calls that can get

34:27booked per day, well that means about

34:29five, maybe six are actually going to

34:31show up per day. That gives our

34:33salespeople enough time to do all the

34:35admin work that we need them to do like

34:37following up with people, having CRM

34:39compliance, sending in end of day

34:41reports or updating whatever they need

34:43to do, all that kind of stuff. But look

34:45at these two numbers together. Now I

34:48have a total of 833 calls that I need to

34:52book into. So, if my average closer can

34:55take eight calls per day and there's a

34:57total of 833 calls that are going to be

35:00booked, I first need to ask myself,

35:03well, how many days in a month are we

35:05able to book calls? So, let's say that

35:07you have closers that work Monday

35:09through Friday. Monday through Friday

35:11means that you're not going to get to

35:13book on Saturdays and Sundays. If you

35:15take 30 days in a month and you average

35:17it out to have four weeks in the month,

35:20that's two weekends per week and we have

35:24four weeks in the month. So we'd take

35:26two days per week times four total

35:27weeks, we have eight days we're not

35:29going to take calls. That means on

35:30average out of 30 days, we would deduct

35:33eight days out of that. We'd be left

35:34with 22 days. Now out of 22 days, we get

35:37to book eight calls per closer per 22

35:41days. So, the first thing I want to do

35:43is I want to take the amount of days

35:46that I'm able to book into. So, I have

35:4822 days per month in this example that I

35:53can book calls into. I'm going to take

35:55833

35:57and I'm going to divide it by 22 total

35:59days. 833 is the total amount of calls I

36:02need to book in that example I gave you.

36:04And again, we're just dividing it by the

36:05total amount of days. Because this is

36:07above 0.5, that means I got 38 total

36:10calls that I need to book per day for

36:12those 22 days we can take calls. I've

36:14got 38

36:16total calls per day that need booked.

36:19Now, we're getting closer. Watch this.

36:21Now, we're going to again, we're going

36:22to round that up to 38. Okay, so 38

36:25total calls that have to get booked per

36:28day. Our closers can take eight calls

36:30per day. That's how many we can book

36:32into them. So, again, this is above 0.5.

36:36So that means we need five total

36:38closers. Five closers total. That's it.

36:41That's all we need. We need five closers

36:44that we can book a total of 38 calls per

36:48day into cuz each call can take each

36:50closer can take eight calls per day.

36:52That's how many we can book. We only got

36:5322 days out of the month cuz our closers

36:55ain't working weekends, dude. And if we

36:57pull that off and we have the $166,000

37:00to spend, well, boom. I can book that if

37:03I can hold that cost per call. So now

37:05you're going to start seeing all this

37:06come together. You ready? So watch this.

37:08If I go back to the original financial

37:11model, watch how seamless this all

37:13becomes. I go over here to my call

37:16funnel. I say, "Okay, I have $166,600

37:22that I need to spend. I need my cost per

37:24call to be at $200." Right? And again,

37:28if we go back to the math, our math

37:30shows us that that's the ad spend we

37:32need to be at. That's the cost per call

37:34that we have to hold but from spending

37:35that we need a 60% show rate and we need

37:39a 20% close rate. Let's see if we can

37:41pull that off in this math and achieve

37:42this same number and see if they align

37:44with one another. So now I'm going to

37:46say that my first call show rate 60% and

37:49all I need to close is 20% of those

37:51people who actually show up. From there

37:54I'm going to cash collect $10,000 per

37:57deal on average. And you can see, look

37:59at this. It comes out to exactly what it

38:01should. $166,600

38:04in spend, $200 cost per call, 60% show

38:08rate, 20% close rate, $10,000 cash

38:11collected on average. And as I

38:12mentioned, if we took a h 100red times

38:1310,000, it's a million. I don't know why

38:15it's shaving off $400. Shame on you,

38:17Google Sheets. And we get about a 6x

38:19rorowaz from that. That's awesome. So,

38:21our financial model align with the

38:24reverse engineered closer math that we

38:26get. Now, again, let me tie them all

38:28together for you. If we go back up here

38:30to our bottleneck analysis that we were

38:32doing once we actually launch right or

38:36assuming you're in motion right now, I

38:38would want to make sure that my CPM, my

38:42link click-through rate, and my page

38:43conversion rate are good enough to get a

38:46$200 booked call because these three

38:49stats make up my cost per booked call.

38:52Right? Then I got my show rate. I need

38:54that to be at 60%. Right? Then you move

38:56over here, we got the close rate that

38:58has to be at 20%. And my AOV, well, in

39:02this example, I needed to be at 10K. So,

39:05what we're starting to see is how all

39:07this ties together, right? We can start

39:10from the very beginning from any one of

39:12these angles. I like to personally start

39:15with the closer math first. I want to

39:17reverse engineer the goal because here's

39:19the other thing that I can do. Let's use

39:20the example that you're currently

39:22spending $20,000 a month on your call

39:25funnel, right? And so then this last

39:27step, this is a little bonus for you

39:29guys. This last step that you can do is

39:32is you can align your reality to all of

39:34this. So let's say your reality right

39:36now is $20,000 in ad spend that

39:39currently goes out. Let's say that your

39:41current cost per booked call is $275.

39:45Let's use the example that you're

39:47currently getting a 52% show rate and

39:51let's say your closers are closing at a

39:5417% close rate. Right? What you can

39:56start to do is you can start to say,

39:58"Well, damn, I'm not that far off. I got

40:01to improve my cost per book call a bit.

40:03I got to improve my show. I got to

40:04improve my close rate." Right? And let's

40:06also pepper in the AOV. Let's say that

40:08you're sitting at a $8,000 AOV right

40:11now. You got to get your AOV up that

40:12extra $2,000. however you going to pull

40:14that off. And then you could take these

40:16statistics, by the way, and you can

40:18divide them by the statistic that you

40:19need to be at. So, watch this. Let's

40:20take the ad spend first, right? So,

40:23let's say that I'm at $20,000 a month in

40:26total ad spend, but I need to be

40:28spending $166,600.

40:32I'm 12% to my goal. We just take that

40:35decimal, we move it two places to the

40:36right. I'm 12% of the way to my current

40:39goal. I want to reverse engineer the

40:41reality of where I'm currently at

40:44against where I need to be. Because if I

40:47do that, I'll know, okay, I'm only 12%

40:51of the way to the total spend I need to

40:54be at to achieve my million-doll month.

40:56If I look at my cost per call of where

40:58it needs to be at versus where I am, I

41:00could see how far away I am. And it

41:02makes it feel better. It gives you a

41:04measurable benchmark of your current

41:06reality against where you actually need

41:09to be. It's the best thing. It really

41:11is. So again, if you tile this together,

41:13you'll very easily understand the

41:16importance of what I'm sitting here

41:17describing to you, which is the whole

41:19point of this entire video. And it's so

41:21important that you walk away with this

41:22awareness. If you don't understand math,

41:25you don't understand marketing. You

41:27don't understand business. Right? This

41:28is just three examples. Again, something

41:31where I only talk to about 10 to 20% of

41:34what I could otherwise sit here and yap

41:36about to you. So, you actually walk away

41:38a master of this. This is 10 to 20% of

41:40what you need to know. Check out some of

41:41the other videos on my channel. Most

41:42importantly, check out those links down

41:44in the description for all the paid

41:45stuff where the real sauce is. And the

41:47most important thing that you could go

41:49do at this point is to go get richer.

41:51toxin.