0:00 In this video, I'm going to give you the 0:01 full system for converting YouTube 0:03 viewers into buyers, customers, clients, 0:06 and actual revenue for your business. 0:08 Now, how I know this works and why you 0:10 should listen to me. For context, my 0:12 YouTube growth company builds business 0:14 YouTube channels from scratch to 0:15 millions of dollars per year per 0:17 channel. We consult with and run some of 0:20 the top YouTube channels for leading 0:21 brands such as Gary Vee, Ali Abdal, and 0:24 many more. Over that time, we've 0:25 collected a lot of data from thousands 0:28 of videos, millions of views, thousands 0:30 of qualified sales calls and tens of 0:32 millions of dollars in attributed sales 0:34 across software, B2B service, coaching, 0:37 communities, physical products, medical 0:39 businesses, and plenty of other niches 0:41 as well. So, we've got a lot of 0:42 different data. From this, I've been 0:44 able to see what actually turns YouTube 0:47 viewers into buyers across a huge range 0:50 of offers, as well as how to actually 0:52 get YouTube viewers for these huge range 0:55 of offers in the first place, all 0:57 organically without paid ads. Now, this 0:59 is what my entire company is built 1:01 around. So, I know what I'm doing 1:02 because if I didn't, my company would 1:03 cease to exist. And so, it is literally 1:05 why we are called Views to Clients. That 1:07 is the name of my company. Now, here are 1:09 a couple screenshots of some of our 1:11 clients so you can see that I am legit 1:13 and I know what I'm talking about when 1:14 it comes to this topic. Glenoko went 1:15 from zero to $1 million collected from 1:17 YouTube in the first six months from 1:18 only 12 videos. Their first video 1:20 generated over $100,000 in new deals 1:22 within the first 30 days. And you can 1:23 see their growth trajectory there. In 1:25 the first 60 days, they went from 0 to 1:27 10,000 subscribers in the sales niche, 1:29 which made them the fastest growing 1:30 channel in that niche. We then have Sam 1:32 who went from 0 to $400,000 per month 1:34 directly added to his agency attributed 1:37 from YouTube all organically. He has no 1:40 brand elsewhere. YouTube drove all of 1:42 that growth and then he launched a 1:44 school community quite recently. Went 1:45 from 0 to $100,000 per month within the 1:48 first 30 days of launching it. I don't 1:49 actually even know where it's at now, 1:51 but I'll pop up a screenshot so you can 1:52 do the math. But yeah, he's generating 1:54 quite a bit from YouTube and he's the 1:56 top channel in the Facebook ads niche. 1:57 We also have Gary Vee. The first video 1:59 that we made for him actually outdid his 2:02 previous average videos by over 10x. So 2:04 the first video that we made for him, as 2:06 you can see here by the outlier 2:08 multiplier, it's a 10.9x compared to 2:10 what he was currently averaging at the 2:12 time. First video that we made from and 2:14 since then we have also had multiple 2:15 hits as well. Also, Instantly AI, who 2:18 went from zero to over a million dollars 2:19 annual recurring revenue added from 2:21 YouTube as a B2B SAS on YouTube. Again, 2:23 all organically. They rank at the top of 2:25 the search term lead generation on 2:26 YouTube. They've had multiple viral 2:28 videos like this one here, watch me 2:29 start and sell a service, where some of 2:31 these videos have generated multi-6 2:32 figures in attributed revenue just from 2:35 these videos alone. Those are just a 2:36 couple examples of the 100 plus channels 2:38 that we're running right now. So, now 2:40 that I've established that and you know 2:41 that you can listen to me and trust in 2:43 what I'm about to say, let me tell you 2:45 that YouTube is simple actually. Like, 2:47 it's genuinely simple. You see, most 2:49 businesses see YouTube as unpredictable. 2:51 Paid ads gives them CPMs, clicks, leads, 2:54 sales. Cold email gives them sends, 2:56 replies, booked calls. But with YouTube, 2:58 they publish, they look at their views, 3:00 and they just hope that buyers appear 3:02 because if a video doesn't get views, 3:03 they have no idea what even went wrong 3:06 with that video. Organic reach, I will 3:08 say, will never be perfectly 3:10 predictable. But that does not make the 3:12 commercial result impossible to measure. 3:14 And this is what most people get wrong. 3:15 And I'm going to show you how to do that 3:16 in this video so that you can identify 3:19 what is going wrong in your videos and 3:20 what is the bottleneck that is 3:21 preventing your videos from succeeding 3:23 and converting viewers into buyers and 3:25 maybe even capturing those viewers in 3:26 the first place. Across every single 3:28 YouTube channel, each buyer moves 3:29 through the exact same path. The right 3:31 person chooses the video, then they 3:33 watch long enough to either reach the 3:35 call to action or proactively seek the 3:38 next step themselves. If there is no 3:40 call to action, they then take that next 3:42 step and converts through the funnel. 3:44 And at each stage, some percentile of 3:47 that audience continues. And one stage 3:50 is going to limit how much cash actually 3:52 reaches the other side. Now, if the 3:55 right people do not even choose the 3:56 video in the first place, you're never 3:57 even going to get the views. If they 3:59 leave before the call to action, or if 4:01 they just do not proactively seek that 4:03 out themselves, then nobody is left to 4:05 convert, even if you get the views. If 4:07 the CTA feels bolted onto the video in 4:10 an awkward salesy way, it can hurt the 4:13 conversions and the video performance 4:15 simultaneously. In this video, I'm going 4:16 to show you how to solve each stage that 4:19 your video could potentially be going 4:20 wrong that's stopping you from getting 4:22 that end result of buyers, sales, and 4:24 revenue for your business from YouTube. 4:25 Also, track the original video through 4:27 to cash and find the exact bottleneck 4:30 holding back the result that you're 4:31 looking for. Organic reach will vary, 4:33 but once the path from viewer to buyer 4:35 is visible, it becomes a problem you can 4:38 diagnose and solve. So this is what I 4:39 call the views to clients equation. Now 4:42 you cannot control the exact number of 4:44 views your next video gets. Not 4:45 possible. But you can map every stage 4:48 between publishing and collecting cash 4:51 and measure where the result is being 4:53 lost and improve that stage 4:55 deliberately. And you can continue that 4:57 feedback loop until everything is 5:00 optimized. So what you're going to do is 5:01 you're going to map the full route from 5:03 video to conversion event. Whatever your 5:05 conversion event that you are optimizing 5:07 for is, whether it be cash all the way 5:09 at the end in sales or whether it be 5:11 qualified boot calls. So for example, 5:14 videos published multiplied by the 5:16 average views from potential buyers per 5:18 one of those videos equals your total 5:21 potential buyer views. Then the total 5:23 potential buyer views multiply by the 5:25 percentage who reach the CTA or take the 5:28 next step proactively multiply by the 5:30 percentage who take that next step in 5:32 exploring the funnel multiply by the 5:34 percentage who actually become leads 5:36 multiply by the percentage who v 5:38 multiply by the percentage who become 5:39 qualified sales opportunities multiply 5:41 by the percentage who buy multiply by 5:43 cash collected per buyer equals cash 5:45 collected from YouTube. That is the 5:46 equation. There is not a single step 5:48 that is missing from that. So, if you 5:51 can actually know the numbers that fall 5:53 into each of these stages, it becomes 5:55 incredibly easy to identify the 5:56 bottleneck. Now, this isn't just a 5:58 diagram like the diagram that I've 5:59 attached below. I've actually turned 6:01 this equation into a calculator that you 6:03 can use practically to help you identify 6:06 these bottlenecks. And so, you can put 6:07 your own numbers into this. And I'm 6:08 going to show you exactly how you can 6:10 use this once you understand what each 6:12 number is and what it's going to be 6:13 telling you. And there'll be a link to 6:14 this in the description as well, which 6:16 you can grab it completely for free. Now 6:17 every single number within this equation 6:20 multiplies the one before it and because 6:22 of that one weak stage restricts 6:25 everything else after it and that is 6:27 what you call a bottleneck. You can see 6:29 this visually here where the throughput 6:32 of a system that is optimized is the 6:34 width of this bar. But if there is a 6:37 bottleneck then the maximum throughput 6:39 that can exist through that system is 6:41 the width of what the bottleneck is. So, 6:44 for example, here, even though the width 6:46 is this high, you're ultimately only 6:48 going to be able to have a throughput of 6:51 this width here. And so, for example, 6:53 the bottleneck might be in attracting 6:55 the right viewers in the first place. 6:57 And so, no matter how good your CTA was, 6:59 no matter how good the video was, or no 7:00 matter how good your funnel is and your 7:02 sales process is, if you don't attract 7:03 the right viewers in the first place, 7:05 you're just not going to get any sales. 7:06 or maybe you actually get the right 7:08 viewers, but you make a real salesy CTA 7:11 that might actually hurt the performance 7:13 of the video, but your video still 7:14 performs quite well. But what it does is 7:16 it doesn't convert people effectively 7:18 because it's not the right sort of CTA 7:20 that you should use in your YouTube 7:21 video. And so then the CTA becomes the 7:22 bottleneck and not many people even make 7:24 it to the funnel. You get the point, 7:25 right? If your videos are not getting 7:26 views from potential buyers, it barely 7:28 matters whether your landing page 7:30 converts at 20% or 40%. that is not 7:32 going to be a needle mover if you make 7:33 that optimization because there is just 7:35 simply not enough attention actually 7:36 entering the system in the first place. 7:38 So what you would do is you would solve 7:39 that views problem first because that is 7:41 the area of highest leverage. Once the 7:43 right people are watching more views are 7:45 not automatically the answer. The 7:47 restriction might then be on the CTA, 7:49 the page, the lead quality or the sales 7:51 process. And so what you want to do is 7:53 you want to improve the one key 7:55 constraint and then run the maths again 7:57 and continue to that feedback loop. 7:59 First, map and measure the complete 8:01 route for each video as I've just 8:02 described. Secondly, find the evidenced 8:05 constraint with the greatest realistic 8:08 effect on cash. If you were to 8:10 realistically improve one of the metrics 8:12 by the same proportion as each other, 8:13 which one would have the greatest 8:15 downstream impact of what you're looking 8:16 for? And then three, change that one 8:19 thing, measure it again once you have 8:21 gone through the loop of actually taking 8:23 that action and getting the new result. 8:25 And then find the next constraint and 8:26 continue through that cycle. change 8:28 everything together and even a better 8:30 result will teach you almost nothing 8:32 about what caused it. That is a problem. 8:33 You might start changing all these 8:34 things and then the next video it does 8:36 great and you're like yes and it 8:37 generates so many sales but then the 8:39 next video doesn't perform as well or it 8:41 doesn't generate many sales and then you 8:42 just don't know why it's happening again 8:43 and so you're again back to flying 8:45 blind. And so we'd much rather be 8:46 methodical about this and solve one 8:49 thing at a time. Isolate the variable, 8:51 treat it like a science experiment. The 8:53 goal is not to guess what might improve 8:56 YouTube performance as a whole. It is to 8:58 know which number needs to move next 9:01 towards your goals. The smallest 9:03 percentage is not automatically the 9:04 bottleneck. And asking what would happen 9:06 if every number doubled does not solve 9:08 the problem either. If an uncapped 9:10 multiplier doubles, the output roughly 9:12 doubles. that proves that every stage 9:15 matters, but it does not tell you what 9:17 stage is realistically fixable or which 9:20 result is based on enough evidence to 9:22 trust. And so what you want to do is you 9:24 want to prioritize realistic headroom, 9:26 downstream cash impact, and enough 9:28 volume for the number to actually mean 9:30 something. A key takeaway is that you 9:32 cannot force a specific upload to win. 9:34 YouTube is unpredictable like that. But 9:36 what you can do is you can force the 9:38 system to keep improving and over time 9:40 that will make you tend towards winning 9:43 to the highest probability and to the 9:45 highest level of consistency. And so the 9:46 first place to inspect is the input to 9:48 the entire system which is bottleneck 9:50 number one or enough potential buyers 9:52 watching. The first conversion happens 9:54 before the viewer even exists which is 9:56 when somebody from your target market 9:57 sees the video and chooses it over every 10:00 other option on their screen. Their 10:02 first input is therefore not simply 10:03 views, but views from potential buyers. 10:06 Because 1 million irrelevant views can 10:08 be worth less than 10,000 views from the 10:10 exact target market you want. We're not 10:13 optimizing for views. We're optimizing 10:15 from views from the right people. And so 10:17 qualified attention requires three 10:19 things. One is that your video must have 10:22 proven buyer demand. First, you need to 10:24 find the exact problem or outcome that 10:27 buyers have already shown that they want 10:28 to watch on YouTube. And you want to 10:30 express that via title and thumbnail in 10:33 the language that they have proven to 10:35 respond to because what language they 10:38 respond to outside of YouTube, trust me 10:40 from experience, is different oftent 10:43 times from what they resonate with on 10:45 YouTube because they are different types 10:47 of people with different preferences 10:49 because they are viewing things on 10:50 different platforms via different 10:51 mediums. Now number two is the chosen 10:53 packaging that you've selected. You want 10:55 to make the right person recognize the 10:57 video is for them at a single glance 11:00 within an instance of noticing it on 11:02 their feed. And it actually needs to be 11:03 attention grabbing enough to get the 11:05 attention of them in the first place 11:07 over the other videos. And then in an 11:08 instance, it has to make the right 11:09 person recognize the videos for them. 11:11 And then once they've actually 11:12 recognized that it is for them, choosing 11:13 it over every other alternative because 11:15 YouTube is a win to take all platform. 11:16 Your video lands on the feed with 20 11:18 plus other videos on the feed at the 11:19 same time. And so your video has to get 11:21 the attention of that target viewer 11:22 before they click on another video. It 11:24 has to get them to recognize actively 11:26 that they should spend more time 11:27 considering should I watch this video 11:28 which all happens in a split second by 11:30 the way and then they actually have to 11:31 choose your video and click on it before 11:33 their eyes wander elsewhere and 11:34 potentially consider another video and 11:36 then click on that instead. That is what 11:37 is going through a viewer's mind all in 11:39 a split second. Number three is the 11:40 fulfilled promise. So you want to 11:42 quickly fulfill on the click promise the 11:44 promise that they clicked for and you 11:46 want to retain the viewer throughout the 11:47 video to give them the value and then 11:49 give YouTube the evidence to find more 11:51 people like them. YouTube will push 11:52 videos based on the satisfaction level 11:54 of the average viewer that watch that 11:56 video. And so what we want to do is 11:57 create an experience where the average 11:59 target viewer that clicks on your video 12:01 has such a great experience and is 12:03 transformed so drastically through the 12:05 value you give and just in general the 12:07 video that YouTube just can't help but 12:09 push your video to more of them because 12:11 YouTube knows which users of their 12:12 platform are similar to the ones that 12:14 just watched it and had extreme 12:15 satisfaction with it because YouTube's 12:16 owned by Google which has more data on 12:18 anyone than we can even fathom. And so 12:20 you can see the process here goes from 12:21 proven demand to chosen packaging to 12:22 fulfilled promise. The topic of the 12:24 video that you choose to center your 12:26 video around will determine who YouTube 12:28 can show the video to. And the packaging 12:30 of that video will determine whether 12:32 they choose it. The video itself will 12:35 determine whether YouTube keeps showing 12:38 it to more of them. So to diagnose the 12:40 views constraint, there's a few 12:41 different scenarios here as examples. So 12:43 one is very low impressions from the 12:45 target market. What you want to do there 12:46 is you want to inspect the topic that 12:48 you chose, the demand language that you 12:50 use to package that and actually try and 12:52 resonate with that target market, the 12:54 audience fit as well and whether similar 12:55 subjects have already proven demand on 12:58 YouTube because if you pick the wrong 12:59 topic, then you can't actually get that 13:01 traction from the algorithm. The other 13:03 thing as well to consider is with very 13:06 low impressions, it can also be as a 13:08 result of your video just being terrible 13:09 too. So you need to actually understand 13:11 why have we got very low impressions 13:12 here? What could be the reason? Also, if 13:14 you are starting a new YouTube channel, 13:16 this is a little bit of a noisy 13:18 situation because when you start a new 13:19 YouTube channel, YouTube can take some 13:20 time to actually push that out. It's 13:22 very safe with the way it does that. It 13:23 wants your channel to build trust first. 13:25 So, take that with a pinch of salt, too, 13:27 and just factor that into your 13:28 decision-making. Now, another scenario, 13:29 you have impressions, but you have a 13:30 very low click-through rate. So, with 13:32 this, you want to inspect the title and 13:33 thumbnail and the packaging of the 13:34 video. And the topic may have demand, 13:36 but your packaging is the thing that's 13:38 making it lose the choice of actively 13:40 being clicked on. a healthy 13:41 click-through rate but a sharp retention 13:43 drop. So with this you want to inspect 13:45 the hook and you want to inspect whether 13:47 that hook immediately fulfills on the 13:50 title and thumbnail meets the 13:51 expectations that were set and sells the 13:54 viewer and convinces the viewer enough 13:55 that they are actually going to get the 13:57 thing that they want in the rest of the 13:58 video. And if there's a healthy 14:00 click-through rate, there's also no 14:01 obvious early retention failure, but 14:03 then distribution still stalls, then you 14:05 want to inspect overall retention. Is 14:07 there any massive drops in retention 14:09 later downstream? Assess your video on a 14:11 more qualitative level of 14:12 self-reflection. Did I really make this 14:14 a really good video? Was there any low 14:16 quality points? Was the value really of 14:18 substance? Did I give a super hard call 14:21 to action? Basically, the most common 14:23 case with this specific situation is 14:25 that the video itself just wasn't good 14:27 enough in whatever respect. When you're 14:28 assessing a YouTube video post upload 14:30 and looking at its performance, you want 14:31 to read the impressions, the 14:32 click-through rate, the retention, and 14:34 the traffic sources all together. You 14:35 need to look at everything and you want 14:37 to compare like forl like videos instead 14:39 of reaching a universal benchmark. Like 14:41 an average click-through rate for your 14:42 channel is very different to an average 14:44 clickthrough rate for another channel. 14:46 And once you've been running your 14:47 channel for a while, you'll be able to 14:48 establish a somewhat accurate KPI is to 14:51 you will know what bad clickthrough rate 14:53 looks like for your channel and good 14:54 clickthrough rate looks like. But again, 14:56 there's no universal benchmark for this 14:57 because we've had videos with a 2% 14:59 click-through rate that have gotten 15:00 hundreds of thousands or millions of 15:02 views. And we've had videos with 6% 15:04 retention that have generated hundreds 15:05 of thousands of dollars completely 15:06 organically from YouTube. One of which 15:08 was Glen Koko's first video, which got 15:09 300,000 views, their first video 15:11 completely organically, starting from 15:12 zero. That video made $100,000 in the 15:14 first 30 days. And that channel went 15:15 from 0 to 10,000 subscribers in the 15:17 first 60 days and 0 to $1 million 15:19 collected from YouTube in the first six 15:20 months from only 12 videos with no 15:22 existing audience or nothing. Right? And 15:24 some of the videos that were doing that 15:25 were getting 6% retention, 3 4% 15:27 clickthrough rate, which a lot of people 15:28 would consider as bad, but it's not. 15:30 Now, bottleneck number two is does the 15:32 CTA add to the video or interrupt it? 15:35 And so, this is where we start to really 15:37 get into once you've got the viewers, 15:38 how do you actually convert them 15:40 effectively into buyers the right way? 15:42 Because the normal CTA will pick a time 15:44 stamp and then pause the education and 15:46 start a pitch. And what happens is 15:48 viewers leave, trust falls, and the 15:50 video can perform worse. As you can see 15:51 from this graph here, you have viewer 15:53 retention and you have the video 15:54 duration and the video time. And what 15:56 happens when you have a bolted on pitch 15:57 is you see a sharp drop in retention. 15:59 The retention tells us directly that 16:01 that means we're losing a ton of viewers 16:02 at that stage and we're therefore going 16:04 to have less people consume the rest of 16:05 the video. But also it tells us that 16:07 we've also really hit the satisfaction 16:09 of that video because people have felt 16:11 so disgusted to where they've clicked 16:12 off the video because of the CTA that 16:14 was given. And of course less people 16:15 convert as a result of this too. 16:16 Whereas, if we give a good call to 16:18 action, we don't really see that drop. 16:20 Or if we do, it's super negligible. And 16:22 at the end of the day, we have to be 16:24 able to make a trade-off with business 16:25 where we need to be able to give CTAs to 16:27 some degree to be able to effectively 16:29 convert. And that trade-off is worth 16:30 making if you can make it right. And 16:31 sometimes it's not even a trade-off. And 16:33 so, what you want to do is you don't 16:34 want to ask where should I insert my 16:36 CTA. What you want to ask is what does 16:38 this viewer naturally need next? And how 16:41 can I give it to them? I'm going to get 16:43 into this. And so now let's get into how 16:45 you should actually do a call to action 16:47 in your videos. So this is what I call 16:49 the value ad CTA rather than a value 16:51 subtract CTA. Your typical CTA in the 16:54 middle of the video. Join my free 16:55 newsletter to get some free gifts D 16:57 where I get free value blah blah. It 16:59 doesn't come across as a value ad. It 17:01 comes across as a take, not a gift. Or 17:03 if you want my help, my help blah blah 17:05 blah. You want to buy my services blah 17:07 blah blah. Click the link in the 17:08 description, book a call, we'll chat 17:09 soon. D. That's a take, not a gift. The 17:12 value ad CTA is structured like this. 17:14 First you create the need and then you 17:17 supply the missing value to that need 17:20 and then you explain the action briefly 17:23 and then you return to the content. So 17:24 let me get into this in a bit. So 17:26 basically the way you want to think 17:27 about this is you want to make your 17:28 desired action the most useful next step 17:31 for what the viewer now wants. Desired 17:34 action being the call to action. You 17:35 want to make your desired call to action 17:37 the most useful next step for what the 17:40 viewer now wants present in that moment 17:42 watching that video. So if you sell a 17:44 product or software, I'm going to walk 17:46 you through a live example of this on 17:48 how we did it and how we were able to 17:50 generate multi6 figures from an 17:52 individual video for a software company 17:54 using this exact strategy. So this is 17:56 our client instantly AI. They're a cool 17:58 email sending platform/leg generation 18:00 platform for businesses who want to 18:02 generate leads, create marketing 18:04 campaigns, etc. Okay. And their most 18:07 popular video, ignoring this one, which 18:08 is an advertisement, is this video here, 18:11 all organically, watch me start and sell 18:12 a service in 10 hours. So, I could break 18:15 down this full video in an entirely 18:17 separate video. And in fact, I might 18:18 because it would be very valuable. But 18:20 to cut to the chase, we got this video 18:22 idea from a video in a completely 18:24 different niche, which was watch me make 18:26 and sell a digital product in 10 hours 18:27 by this channel there. This was over 2 18:29 years ago now when I found this video 18:32 quite near to when it was uploaded. this 18:34 video stood out massively in terms of 18:35 its performance to a level that like was 18:37 something that that channel would not 18:40 usually see. And then I made my 18:42 hypothesis on why this was. And what 18:44 this video is is a challenge video where 18:45 she literally makes and sells a digital 18:47 product in 10 hours, right? So it's like 18:49 this remarkable challenge that gets the 18:51 viewer bought into the stakes, the 18:52 storyline, is she going to hit it? So on 18:54 and so forth. And so we just did this 18:56 video but took it to the general online 18:59 business space with watch me sell a 19:01 service in 10 hours. And so we took this 19:02 exact format and did this video where we 19:04 start and sold a service in 10 hours. 19:06 And there were different steps of this 19:08 where first we had to come up with the 19:09 offer, the business, and then we had to 19:11 actually pick how we were going to get 19:13 clients. Once we had the landing page, 19:15 we had the offer, we had the funnel 19:16 built out, we had everything built out 19:18 in preparation. It was then time to 19:19 actually get the clients in the 19:21 challenge. And we had to decide, okay, 19:22 how are we going to get these clients? 19:24 Because we've only got 10 hours to do 19:25 this, less than 10 hours at this point. 19:27 And so we went through each option 19:29 content. Oh, that's going to take too 19:30 long. you know, it takes too long to get 19:32 traction with organic content. It's not 19:34 predictable enough is the common belief. 19:36 And then paid ads, okay, paid ads, yes, 19:38 I can get traffic straight away, but 19:39 paid ads is very difficult to crack in a 19:41 10-hour time frame. So, that's 19:42 eliminated. And so, we went through all 19:43 these options and then eventually we 19:44 landed on cold email because with cold 19:46 email, I can send a million emails 19:48 today. And if I can just reverse 19:50 engineer from the goal down of, okay, I 19:52 need this many deals closed. I close at 19:54 this rate, I need to book this many 19:56 sales calls therefore and I need to send 19:58 this many emails therefore all the way 20:00 at the top, right? And so it was very 20:02 simple to convey to the viewer as to why 20:05 cold email was the right option there. 20:07 But then the problem was with cold 20:09 email, if you know the industry, you 20:11 have to warm domains up for two weeks 20:13 because domains, if you don't warm them 20:15 up, they land in the spam folder and 20:16 therefore we wouldn't get results. And 20:18 so now we have this big pitfall in the 20:20 story where we're at this low point 20:22 because we're like, "Oh no, are we going 20:23 to be able to do this?" But luckily 20:25 instantly launched this new feature 20:27 which was their pre-warmed domains 20:30 feature. And you can see this here when 20:32 this came into the storyline where we 20:35 basically were like, "Oh, but instantly 20:36 have just released this new feature 20:38 called pre-warmed domains where you can 20:39 buy domains that are already warmed up. 20:41 So you don't have to wait two weeks. You 20:42 can launch cooled emails today." And so 20:45 instead of this being some hard 20:46 promotion for instantly where it was a 20:49 self-interested call to action, we 20:51 literally had to do it. It was the only 20:54 option. It was the obvious choice to do 20:56 this. And in fact, instantly actually 20:58 became the hero of the story because we 21:00 would not have been able to actually 21:02 achieve the result and complete this 21:04 challenge if it wasn't for that 21:06 instantly feature that had just 21:08 released. It's funny because the way we 21:10 actually came up with this video idea 21:11 was because instantly came to me. were 21:12 like, "We've launched this new feature. 21:14 We need to somehow try and make a video 21:15 around it." And most software companies 21:17 launch these product demos and these 21:19 feature release videos that are just 21:21 absolutely terrible and they never get 21:23 results because they haven't framed the 21:25 videos correctly. They haven't 21:26 structured the videos correctly and they 21:27 come across as self-interested. And the 21:29 result of this was yes, hundreds of 21:30 thousands of dollars generated from this 21:32 single video attributed to this video 21:35 for instantly, but also hundreds of 21:38 people in the comments saying that this 21:39 was the best advertisement they had ever 21:41 seen. You can see this one here. The 21:43 best advert I've ever seen. Will 21:44 definitely start using instantly for my 21:46 campaigns. 100 likes. Wow. Did not 21:47 realize I was watching an ad until 1449. 21:50 Well done. 359 likes. And there's so 21:52 much of this. Right. This video is well 21:54 done. Clearly, this was an ad for 21:55 Instantly itself, but I didn't realize 21:56 that it was going to be that until the 21:58 product cropped up. Well said, hook, my 22:00 friend. So on and so forth, right? I 22:02 know this was an ad, but I couldn't stop 22:04 watching. Ads done, right? You get the 22:06 point. And so, you don't want to begin 22:07 with how do I promote my product? You 22:09 want to begin with where does my product 22:12 genuinely help the viewer achieve the 22:14 outcome and if it never becomes useful 22:16 to the outcome then do not force it into 22:18 the video because the viewer will 22:19 notice. Now that's fair if you have like 22:21 a physical product or software but if 22:23 you sell a service coaching or expertise 22:26 a service cannot usually become a 22:28 literal tool right like SAS is. And so 22:31 instead you want to take the same 22:32 framework but you want to supply 22:35 implementation value that the video 22:37 cannot provide through a specific 22:40 calculator scorecard template diagnostic 22:43 or workflow. And so I can demonstrate it 22:45 with this video right here. Right? This 22:47 video cannot take your numbers. It 22:49 cannot model every stage and calculate 22:51 which constraint deserves your attention 22:53 first. And so what I did was I built for 22:55 this video a free interactive calculator 22:57 that does it for you. And so you can 22:59 enter your numbers from views all the 23:01 way to the conversion event that you're 23:03 optimizing for, such as cache. And it's 23:05 going to map the journey and show you 23:07 the first constraint that's worth 23:09 testing. And I'm leaving a link to this 23:10 in the description so that you can get 23:11 it. And so if you notice where that call 23:14 to action appeared, it was after the 23:16 model created a need the video could not 23:19 fulfill by itself. Like you could, there 23:21 was no other way I could have reached 23:22 out through the computer and done that 23:24 for you. And so I'm giving additional 23:26 value that you are grateful for rather 23:29 than you having resent towards me for 23:31 because I gave a call to action if that 23:33 makes sense. And so a good call to 23:35 action does not collect payment for the 23:36 value that you already gave. It extends 23:39 the value further. And another thing I 23:41 want you to think about when giving a 23:42 call to action and figuring out where do 23:44 I give this and how do I get this is if 23:46 you had zero affiliation to your product 23:49 or the product that you're promoting, 23:50 would you promote it? How would you 23:52 promote it? When would you promote it? 23:53 What sort of energy would you have when 23:55 promoting it? That's how you want to 23:57 think about it because people when 23:59 making YouTube videos promote tools all 24:01 the time. I promote little extensions 24:03 that I use that are helpful here and 24:04 there. Sometimes you might just even see 24:06 them on the videos themselves and it's 24:08 never going to harm the video and stop 24:10 it from performing because I genuinely 24:12 just use these tools, right? And people 24:14 use these tools. And so the same way 24:17 that people walk around dayto-day and 24:19 they wear a logo on their shirt, nobody 24:21 sees that as promotional to that brand, 24:24 it's not like those people are sponsored 24:26 by those brands. It's just this is a 24:28 really good piece of clothing. This is a 24:30 brand that I really like and that many 24:32 other people also really like and it 24:34 just makes sense why I'm wearing this. 24:37 That is the frame that it should come 24:38 from in the YouTube video itself. And so 24:40 now we need to make sure that the 24:42 numbers going into the calculator 24:43 actually exist. Bottleneck number three, 24:45 can you see what happens after the view? 24:47 And so YouTube analytics tells you what 24:49 happened on YouTube, but it cannot tell 24:52 you which video created a qualify call, 24:54 which call became stale, or how much 24:57 cash that buyer specifically paid 24:59 initially and also over its lifetime. 25:01 And so you don't have the business 25:03 results that each of the YouTube videos 25:04 that you're putting out are actually 25:06 driving to an accurate degree. eight 25:08 videos and 10 sales still does not 25:10 reveal whether every upload contributed 25:12 or one generated nearly everything. And 25:15 if you don't have that, you're not going 25:16 to be able to replicate success and 25:19 double down effectively. Because if you 25:20 just see, yes, YouTube's working from 25:22 this data, but you don't actually know 25:24 what is working and therefore also what 25:27 isn't, you're still going to be flying 25:28 blind. But if you can see, oh, I did do 25:30 eight videos and I did get 10 sales, but 25:32 seven of the sales came from this one 25:34 video. This other video got two sales. 25:36 That's pretty good. This other video got 25:37 one, but the rest got zero. Then you're 25:39 probably going to look at the one and be 25:40 like, okay, probably not going to take 25:42 that as signal. You might take the two 25:43 as minor signal and be like, that could 25:45 be worth doing. The seven you're going 25:47 to take as a major signal and be like, 25:48 that is something that I need to be 25:50 doubling down on. And so that is why the 25:51 tracking is so important because without 25:53 this, you have no feedback loop. 25:54 Analytics tell you what happened on the 25:56 platform, but attribution tells you what 25:58 happened because of that video. And then 26:01 you want to make sure that hopefully you 26:02 have a CRM set up to manage all these 26:04 leads. And you should preserve the video 26:06 source inside that CRM through show, 26:09 qualification, sale, and payment so that 26:12 you're able to attribute everything back 26:14 to the video that that came through at 26:15 every single level. And then you want to 26:17 reconcile the accounts for one 26:19 comparable period. For every video, you 26:21 should be able to see views, tracked 26:23 clicks, landing page visits, leads, 26:25 bookings, qualified shows, sales, cash 26:28 collected at least. For your funnel, it 26:29 may vary. If you sell a software, you 26:31 might not need some of these metrics. 26:32 You might need other metrics, but you 26:33 can extrapolate from this what you need 26:35 for your specific funnel and your 26:37 product and service. So, you can see 26:38 here, for example, most people are 26:40 looking at this data. Oh, video A got 26:42 42,000 views. Video B got 39,000 views. 26:45 Okay, both of these perform well. Video 26:46 A actually a little bit better. Maybe 26:47 I'll double down on that. But both of 26:48 them did quite well. But once you have 26:49 that attribution layer, you see, oh, 26:51 video A only got $20,000 in sales, but 26:54 video B got $72,000 in sales. And so, 26:57 you're just able to make better 26:58 decisions with this data. You have to 26:59 understand the video with the most leads 27:01 may not attract the best buyers as well. 27:03 The one with the most good calls may 27:05 create lowquality calls and a bunch 27:07 might get disqualified. And a video with 27:09 fewer views may actually quietly be 27:11 generating far more cash than all of the 27:13 others. And so don't optimize YouTube 27:14 for the easiest conversion to count, 27:16 which is probably views. Optimize it for 27:19 the commercial outcome that you actually 27:21 want and get the system in place to 27:23 actually be able to optimize for that 27:24 and measure it. Now, the full setup that 27:26 I use for every channel that I run, just 27:28 so you know what a great attribution 27:30 looks like, not just a minimum viable 27:31 version. For every channel I run for 27:33 businesses and brands, I implement the 27:35 full version because it is incredibly 27:37 important for me to know as accurately 27:39 as possible where the bottleneck 27:41 actually is. And so for every video, we 27:43 preserve the video ID, channel, publish 27:45 date, topic, format, and CTA. And then 27:48 we join that to impressions, views, 27:50 clickthrough rate, retention at the CTA, 27:53 right? So which is basically the 27:55 retention rate and the retention 27:56 percentage at the point of us giving a 27:58 call to action so that we can even 28:00 measure that tracked clicks, page 28:02 arrivals, leads, bookings, 28:04 disqualifications, qualified shows, 28:06 sales, cash collected and recurring 28:08 value as well. So not just initial cash 28:10 collected, but then is YouTube getting 28:12 us customers that are actually staying 28:14 for longer than something like paid ads. 28:15 and is a certain video bringing in 28:18 customers that have a much lower refund 28:20 rate than other videos are bringing on 28:22 which is indicative of the quality of 28:24 the person who is coming through those 28:26 videos. And so that gives us a video 28:28 level view and a cohort level view. And 28:31 from that we can compare individual 28:32 videos, we can compare groups of similar 28:34 videos that might use a similar format 28:36 or a similar thumbnail style or a 28:38 similar topic and the full channel 28:40 overall of course to understand how much 28:42 of an ROI YouTube's actually driving. 28:44 How much cash collected did we do for 28:45 the month from YouTube? So on and so 28:46 forth. And we do that without losing the 28:48 original source as the buyer moves 28:49 through analytics, the CRM, the 28:51 calendar, and then the payment processor 28:53 and even through the actual client 28:54 journey when they become a customer or a 28:56 client. And then after that, this is an 28:58 important step to be honest, is we do 29:00 still have to reconcile unknown and 29:01 unattributed records instead of just 29:04 quietly pretending that the tracking is 29:06 perfect. Because even with the best 29:08 attribution solution, the truth is this 29:10 still happens and there's still those 29:11 nuance cases where you have to go 29:13 digging a little bit to be able to 29:14 understand where they came through. Was 29:16 it YouTube? If it was YouTube, which 29:17 video, what was their journey? You can 29:19 do this if you're actually directly on 29:21 the calls and conversations you have 29:22 with them and through a post sale on 29:26 boarding form where you ask what made 29:28 them buy, so on and so forth. Now, two 29:30 honest limitations with YouTube 29:31 attribution is call to action exposure 29:33 is estimated because we're using 29:35 retention at the CTA time stamp. And 29:38 also, attribution, as I said, is 29:39 imperfect because viewer changes 29:41 devices. They return directly and they 29:43 buy after multiple different touch 29:44 points. And we can track some of this to 29:46 a degree, but at the end of the day, you 29:48 have to understand that we're using this 29:50 model to make better decisions and not 29:52 just claim perfect observation. And you 29:54 don't need perfect observation. We're 29:56 using this in order to make more money. 29:58 Not necessarily know to the exact dollar 30:00 and cent of how much money we made. 30:03 Nobody cares about that. And so once 30:04 this chain is visible, you can stop 30:06 debating on whether YouTube works and 30:09 diagnose where the money is being made 30:11 and where the money is being lost and 30:13 optimize more for that and reject more 30:15 of that. What you need to do is you need 30:17 to find the constraint that actually 30:18 matters because the smallest percentage 30:20 is not automatically the bottleneck. It 30:23 may already be excellent for that stage 30:25 while a larger percentage has far more 30:27 room to improve. And a dramatic rate 30:30 based on five people might be actually 30:32 noise. And so the useful question you 30:34 want to ask yourself is if one stage 30:36 reached a realistic target while 30:39 everything else stayed constant, which 30:42 change would create the greatest 30:44 increase in cash? Okay, that is the 30:46 question you need to ask when assessing 30:48 where should you place your attention in 30:50 terms of removing a bottleneck and 30:52 optimizing the YouTube funnel. The 30:54 question is stronger than simply asking 30:56 which number you would double because it 30:58 forces you to account for the realistic 30:59 headroom, evidence volume, and also 31:02 whether the change is actually within 31:03 your control. You can't double every 31:05 single metric. And so just ask yourself 31:07 that question instead. Then what you 31:08 want to do is you want to run one 31:10 comparable period through the equation. 31:12 Enter one comparable period from videos 31:14 and views through to sales and cash 31:16 collectors. And then set targets using 31:19 your own history, comparable winners, or 31:22 a genuine business requirement that you 31:24 actually need, not universal YouTube 31:26 benchmarks. Ideally, you take a mixture 31:28 of both. And you might not have some of 31:30 them. Like if you're starting a new 31:31 channel, you're not going to have 31:32 history, right? But if you've been 31:33 running your channel for a while, you'll 31:34 have your history, but you'll also be 31:36 able to compare to winners. And also, if 31:38 you have a requirement, you can factor 31:39 that into consideration. It should be 31:41 multiple. You should choose these 31:42 targets based on three rules. Commercial 31:44 impact, enough evidence, and a 31:47 controllable change that you can 31:48 actually test. And if only a few 31:49 potential buyers are watching, the 31:51 calculator should not pretend it knows 31:53 whether your closing rate is the 31:55 problem. You should solve the upstream 31:57 constraint and then collect more 31:58 evidence and then run it again. Here, 32:01 for example, you can see the YouTube 32:02 funnel at a basic level and we can see, 32:04 okay, we're getting too few of the right 32:05 viewers here. And so, what we want to do 32:07 is we want to measure that. We want to 32:09 diagnose that. We want to change that 32:11 one thing. We want to measure it again. 32:12 And we go through this feedback loop 32:14 until we have solved that bottleneck. 32:16 For example, if the exposure of the call 32:18 to action that you actually give is 32:20 constrained, then you can change its 32:22 placement or setup in order to influence 32:24 that outcome. If action is constrained, 32:27 then change the relevance or value of 32:29 the next step. If somebody actually 32:30 taking action and clicking the link for 32:32 example is constrained then change the 32:35 relevance of the call to action and 32:37 maybe the lead magnet that you're giving 32:39 or the value of the next step and how 32:40 you communicate that. If qualified call 32:42 rate is the constraint then inspect who 32:45 the video and the funnel attract rather 32:47 than celebrating cheap leads because we 32:49 don't care about that. If your qualified 32:51 call rate is constrained, then inspect, 32:53 was the topic that I made this video 32:54 around really the right topic or did I 32:56 package this topic in a way that my 32:58 target viewers would actually resonate 33:00 with rather than lower quality viewers? 33:01 Or if it's not that, then assessing your 33:04 funnel and are you actually 33:05 communicating to the wrong audience via 33:08 your funnel even though you might be 33:09 attracting the right viewers through 33:10 your video. And so you go through this 33:11 deep analysis, you make the diagnosis, 33:14 you change that one thing, you iterate, 33:15 and you continue through that process. 33:17 That is the views to clients equation. 33:18 You earn attention from potential 33:20 buyers. You offer a next step that adds 33:22 value. You preserve the source and 33:24 improve the stage restricting the 33:26 commercial result that is showing as the 33:28 bottleneck. And you continue through 33:29 that cycle. Now again, YouTube and 33:31 organic content in general is never 33:33 going to become perfectly predictable. 33:35 But what going through this process will 33:37 allow you to do is stop from being post 33:40 and hope and instead you know what 33:42 happened and where the result was 33:43 actually lost and what to improve next 33:45 so that you can build a YouTube system 33:47 that does reliably get you the results 33:49 that you want to get for your business. 33:51 Now that said, the most common 33:52 constraint is actually the first 33:54 constraint, the first step, which is 33:55 earning enough views from potential 33:57 buyers. And so what I'm going to do is 33:58 I'm going to pop up a video here right 34:00 now that you can click on as the next 34:01 thing because what I'm going to do in 34:03 that video is I'm going to walk you 34:04 through exactly how we find the exact 34:07 topics that we choose to make videos 34:08 around and then how we frame the 34:11 language of those topics in order to hit 34:13 maximum resonance with our target 34:14 audience specifically on YouTube because 34:16 again what resonates with our target 34:17 audience off YouTube is not necessarily 34:19 what they resonate with on YouTube. And 34:21 then how we package it through title and 34:23 thumbnail proven to attract them before 34:25 we even make the video. So, we can go 34:27 into that video knowing that this video 34:28 can get the results that we're after.