0:00 Most e-commerce brands are looking at 0:01 the wrong number. They look at rorowaz, 0:03 they look at revenue, they look at me, 0:05 but very few of them actually understand 0:07 how cash moves inside an e-commerce 0:09 business, what financial models to use, 0:11 and how all of this ties directly into 0:13 paid media. And that gap between what 0:14 the ad account says and what the bank 0:16 account says is where most brands get 0:18 into trouble. Today, I'm going to walk 0:20 you through every financial concept that 0:22 you need to understand. This isn't 0:24 theory. This isn't MBA stuff. This is 0:26 core application that we use every 0:28 single day with the eight and nine 0:30 figure brands that we work with. Finance 0:32 knowledge ends up being the bottleneck 0:34 for a lot of agencies and a lot of 0:36 founders because the ad account isn't 0:38 the business. Platform metrics end up 0:40 being a proxy for performance. Most 0:42 teams end up misdiagnosing their problem 0:45 because they think they have an ads 0:46 problem or a media buying problem, but a 0:48 lot of the time it's a measurement issue 0:50 or it's a margin issue or it's a cash 0:52 flow problem. Good paid media strategy 0:54 should always reconcile directly to the 0:56 P&L. If the ad account looks good, but 0:58 the business isn't improving, something 1:00 is fundamentally broken. Better data 1:02 should lead to better decisions and a 1:04 lot of reporting done internally in 1:06 large 9-f figureure businesses to seues 1:08 as well as small six to seven figure 1:10 businesses between an agency 1:11 partnership. A lot of it is vanity. So, 1:13 we're going to strip it away in this 1:14 video. Firstly, we're going to cover the 1:16 P&L. We're going to be going end to end 1:18 on every component of the P&L and how it 1:20 translates into direct to consumer 1:22 e-commerce and changes the decisions 1:24 that you make on a day-to-day basis. 1:25 Then we're going to go into unit 1:27 economics. We're going to break down 1:28 everything in unit economics that is 1:30 important for day-to-day decision-m as 1:32 it ties into paid media and the business 1:34 as a whole. We're then going to be 1:35 touching on metrics. This is the metrics 1:37 that actually matter. I'm not going to 1:39 be giving you 20 different metrics that 1:40 aren't actually going to move the needle 1:41 or change your decisions. You want 1:43 metrics that fundamentally change your 1:45 behavior after you read them. We then 1:47 have cash flow verse profit. This is 1:49 something that most smaller founders 1:51 don't understand well enough. I 1:53 guarantee every agency that's either 1:55 watching this or an eight or nine figure 1:57 marketing manager that's working with an 1:59 agency. The agency doesn't understand 2:01 how to properly integrate cash flow 2:03 versus profit decision-making into the 2:05 ad strategy. And so we're going to be 2:06 breaking down exactly how you need to be 2:08 doing this, where the complexity of cash 2:10 flow gets introduced into an e-commerce 2:12 business versus any other type of client 2:13 that you're working with across paid 2:15 ads. And then lastly, we're going to go 2:16 and put it all together. We're going to 2:17 loop the P&L into unit economics, into 2:20 metrics, into cash flow versus profit, 2:22 so that you can make better decisions 2:23 and you can know everything that you 2:24 need within finance for ecom. Starting 2:27 off with the P&L, we need to start at 2:29 the very top of the P&L, which is 2:31 revenue. Now, there's two really 2:32 important components to understand here 2:34 with revenue. Number one, there is a 2:35 difference between gross revenue and net 2:37 revenue. And the differences in 2:39 decision-making are enormous between 2:40 them. So, we're going to break that 2:41 down. Number two is a little bit more 2:43 simple. A lot of people know this, but 2:44 people don't think through it because 2:45 they don't run a 10, 20, $30 million 2:47 business, which is that revenue is not 2:49 profit. And so, you can have a $10 2:51 million business that's running at 5% 2:55 net profit and you'll be making 500k 2:59 per year in profit or income. Now, 3:02 technically, this won't actually flow 3:03 through to income in an e-commerce 3:04 business cuz net profit does not equal 3:06 cash flow. Or you could have a $5 3:09 million business that's at a 20% net 3:12 margin, which is healthy in ecom. And 3:14 that means that you're making $1 million 3:16 in profit. I would much rather every 3:19 single day of the week own this business 3:22 over this one. And the reason being is 3:23 that a $10 million business is a much 3:25 more stressful asset to hold than a $5 3:27 million business. And this is doing 3:29 double the profit. I think in agency 3:30 land and in revenue land, a lot of 3:32 people overprioritize just arbitrary 3:34 revenue growth with the idea that margin 3:36 will expand. For a lot of early stage 3:38 founders, margin actually never ends up 3:40 expanding and they just get themselves 3:41 into a bigger and bigger and bigger 3:43 business with the exact same amount of 3:44 profit that they were making 3 to four 3:46 years ago. And let me quickly expand on 3:47 that statement. What do I mean by people 3:50 scale to expand margin, but it doesn't 3:52 actually work? Well, the logic when you 3:54 scale an e-commerce brand is that you 3:56 have three buckets of expenses. So you 3:58 have cost of delivery. You then have 4:00 marketing. And we're going to break all 4:01 of this down and I'll show you exactly 4:03 what's in each of them. And then you 4:04 have operating expenses. Now operating 4:06 expenses should stay relatively fixed in 4:09 an e-commerce brand because you don't 4:11 need to expand people that heavily. So 4:13 as you scale up, what should happen is 4:16 operating expenses should remain stable. 4:18 So the blue line here will be opex. 4:20 Marketing will go up as revenue expands. 4:23 Cost of delivery will go up as well. And 4:26 then here's revenue at the top here. So 4:28 when we then calculate out profit, 4:30 what's actually happening to profit? 4:32 It's going up over time. It's actually 4:35 expanding as a percentage on the P&L 4:37 because operating expenses are remaining 4:39 flat. Now the issue is is that for 4:41 pretty much every small e-commerce 4:44 founder that I've worked with, and for 4:45 context, in the first four years of the 4:47 agency, we worked with nearly 300 7 4:49 figureure e-commerce businesses. So I 4:51 have a lot of experience working across 4:53 that size of business. And what almost 4:54 always happens is that seven figure 4:56 founders are just not good capital 4:58 allocators. And so operating expenses 5:00 actually ends up going up faster than me 5:02 and cost of delivery and therefore 5:04 profit remains flat. It remains stable 5:06 as a dollar figure which is obviously 5:08 not a position that you want to be in. 5:09 Now let's get back to revenue and 5:11 breaking down the P&L. When it comes to 5:12 revenue, you have net revenue and you 5:15 also have gross revenue. Now the 5:17 difference here is that net revenue 5:19 accounts for returns, refunds, 5:22 chargebacks, sometimes a discount 5:23 allowance as well, whereas gross is 5:26 simply their cash collected on topline. 5:28 Now you actually see this within 5:29 Shopify, but in other platforms you 5:31 won't. It's also important to note that 5:33 the Shopify gross to net breakdown 5:36 typically doesn't flow through into 5:39 accounting softwares like Zero, like 5:41 QuickBooks. And so you will only get a 5:43 net or a cash read within a P&L 5:46 accounting software. You won't get a 5:48 gross read. And this becomes really 5:50 critical for using the P&L to actually 5:53 make decisions. So when we look at gross 5:56 to net, this is really the data that you 5:58 want to be looking at. And the reason 6:00 being is that if you just have net 6:01 revenue at the bottom here and you're 6:02 looking at this within your accounting 6:04 software, let's say you have 100K one 6:06 month and then you have 110K the next 6:09 month. When you look at these two 6:10 revenue numbers, there's not really much 6:12 that you can substantiate out of it. You 6:14 can go, okay, revenue increased. We 6:16 don't really know why. We need now need 6:18 to look elsewhere in the P&L. Let's go 6:19 and look at marketing. Did marketing 6:21 expenses increase? Let's go and look at 6:22 operating expenses. Did we add another 6:24 staff member that's driving some kind of 6:26 revenue in the business? We have to 6:27 start looking elsewhere. But the answer 6:29 could actually be above this net revenue 6:31 number. If we go and look above net 6:34 revenue, what we might actually see is 6:36 that gross revenue across these two 6:38 months is the same. Maybe it's 120K here 6:40 and 120K here. But when we then go down 6:43 to refunds, refunds in this month were 6:46 at 20K, but refunds in this month were 6:48 only at 10. Then we go down to 6:50 discounts. There was zero change in 6:53 discounts and maybe shipping charges. 6:54 All shipping is free on this store. And 6:56 so then when we look at the difference 6:57 in revenue for this month, the 6:58 difference in revenue is actually coming 7:00 from refunds, which is a line item that 7:02 doesn't get encapsulated in most 7:04 accounting softwares if you're not 7:05 actually pulling through the gross to 7:07 net revenue dynamic into the software. 7:09 So really, really important that anytime 7:11 you're looking at revenue, we're not 7:12 just looking at net, but we're looking 7:14 at gross through to net to understand 7:15 these different levers cuz I could give 7:17 a hundred different examples of this. 7:18 Okay, we could change the discount line 7:20 item. So actually refunds were stable 7:22 across both months but the core 7:24 difference was that there was actually 7:25 10k in discounts in the prior month and 7:28 so because we had some kind of discount 7:30 running or discount code that didn't get 7:31 encapsulated in the following month and 7:33 therefore that's why we saw revenue 7:35 expansion. These are fundamentally 7:37 levers that exist within the business to 7:40 increase profitability increase net 7:42 revenue. If we can decrease refunds we 7:44 make more money. If we can decrease 7:46 discounts we make more money. If we can 7:48 increase the amount of shipping 7:49 collected at checkout, we also make more 7:52 money. And so you want to be looking at 7:53 all three of these levers because often 7:55 there actually is profit to be unlocked 7:58 in better optimizing these three 8:00 numbers. If you can push refunds down a 8:02 little bit, if you can push discounts 8:03 down a little bit, if you can collect a 8:05 little bit more shipping at checkout, 8:06 which most people can do, most people 8:08 underolct at uh checkout, you can 8:10 improve the P&L. Next layer of the P&L 8:12 is cost of goods sold. This is where 8:14 people make a massive mistake in direct 8:16 to consumer ecom, which is that they 8:18 don't encapsulate all the actual 8:19 expenses associated with cost of goods 8:21 sold. Cost of goods sold isn't just the 8:23 cost that you paid to the manufacturer 8:25 for the item, but it's the total landed 8:27 cost of getting that product from the 8:29 manufacturer to yourself, then into the 8:32 customer's hands. And so we have the 8:33 product cost. We then have importing 8:35 taxes and duties. We then have the 8:37 landed cost. So this encapsulates the 8:39 freight to get it to your warehouse. And 8:41 then sometimes included in here, 8:42 sometimes categorized into shipping and 8:45 fulfillment instead is the cost of 8:47 shipping to the actual customer. So this 8:49 would be shipping charges. And this will 8:51 also encapsulate uh import taxes and 8:53 duties if you're shipping 8:54 internationally. Tariffs in as well as 8:56 an example. So sometimes you'll go and 8:58 encapsulate that here and just count it 8:59 in cogs. Sometimes you'll separate the 9:01 definition to shipping and fulfillment. 9:02 But overall both of these sit under a 9:06 category called cost of delivery. And so 9:08 whenever COOD is used or cost of 9:11 delivery, it is encapsulating all of the 9:13 cost of goods expenses and then all of 9:15 the shipping and fulfillment expenses. 9:17 And this is where people really mess up 9:18 product margin versus gross margin. This 9:21 definition is super misunderstood. When 9:23 you say these numbers to people, you'll 9:25 always get different answers. So let me 9:27 run through an actual worked example. 9:29 Let's say that you're selling a $100 9:31 t-shirt. What we will typically get from 9:33 a brand is they'll say, "Yep, on this 9:35 t-shirt or on this category, we have 70% 9:38 gross margin." But what they're 9:39 typically doing here is they're going to 9:41 someone internal within the wider team 9:43 if it's a 89 figure retailer or if it's 9:45 a smaller business, they're just going 9:47 and looking at their PO and they're 9:48 going, "What did we actually pay for 9:50 this t-shirt to the manufacturer?" And 9:52 we paid $30 per unit. And therefore, 9:54 they're going, "Well, 100us 30 is 70. 70 9:57 divided by 100 equals 70%." Now, this is 10:00 product margin. This is not gross 10:02 margin. And why this is so critical is 10:04 because this GM number will be used in 10:07 setting KPI. If we know what the gross 10:09 margin is, we therefore know what 10:10 percentage allocation we should be 10:12 putting towards marketing to be able to 10:13 ensure that we sell through this 10:15 product. Now, with 70% gross margin, we 10:17 would honestly probably be fine spending 10:19 up to 20, even 25% on marketing to drive 10:22 sales here. However, if this is actually 10:24 a much lower number and we don't know 10:26 about it cuz we're getting given product 10:27 margin rather than gross, well, all of a 10:29 sudden we're driving this product at an 10:31 unprofitable marketing efficiency. So, 10:34 we then actually find out that it's $11 10:37 on average to ship this to a customer. 10:39 It's then $4 in pick and pack fees at 10:41 the warehouse. There's a 3% transaction 10:44 fee here, which is actually something I 10:45 missed in the cost of delivery expenses 10:47 before, which is transaction fees. They 10:50 always need to be accounted for in cost 10:51 of delivery because it is a cost to 10:53 fulfill the product. You have to pay 10:55 that transaction fee. A lot of people 10:56 put transaction fees in operating 10:58 expenses which is a mistake because the 11:00 expense is variable with revenue. And so 11:02 we have $3 on transaction fees. We 11:04 actually also have tax implications 11:06 here. So of this $100, we were actually 11:08 including tax, which was $9, which is a 11:11 flow through expense. So we need to 11:12 remove that. We then have the actual 11:14 cost of landing this product to our 11:16 warehouse. So yes, it was $30 a unit, 11:18 but the actual PO cost a few thousand to 11:21 get to us and ship via air freight. And 11:23 so if we then cut that down into a unit 11:25 byunit cost, that's about an additional 11:27 $4 per unit. And then the last thing 11:29 here that we still haven't taken into 11:31 the picture is an assumed refund or 11:33 discount allowance. And this is 11:34 absolutely critical in the fashion niche 11:37 because in fashion refund rates can 11:39 range from 10% in some cases I've seen 11:41 up to 80%. And so gross margin 11:44 substantially changes when 80% of the 11:45 product is getting returned. And so we 11:48 also need to factor in a refund or 11:50 chargeback allowance. And then let's 11:52 also throw in a discount allowance too 11:54 because people might be using uh coupon 11:56 codes here at some particular rate. And 11:59 so let's have $15 of allowance here 12:02 because this allows for about $5 in 12:04 discounts to be used on average as well 12:05 as about a 10% refund rate which is 12:08 pretty standard within fashion. Now we 12:10 go and add all these expenses up and 12:12 we've got $46 in additional expenses to 12:16 actually deliver on this product. That 12:18 comes out to $76 12:21 in cost of delivery. So if we 12:23 recalculate our gross margin here, which 12:25 you do by taking price, you minus the 12:28 costs and then you divide by the price, 12:31 this equals 24%. 12:34 Now, this is an incredibly extreme 12:36 example where all of the additional 12:37 variable costs here have significantly 12:39 added out outweighed the unit cost. But 12:41 you can see how there can be such a 12:42 drastic difference between the assumed 12:45 gross margin, which is product margin, 12:47 and the actual gross margin within 12:49 e-commerce. And it's because in 12:50 e-commerce, you have all of these 12:52 associated additional variable cost that 12:54 aren't there necessarily in brick and 12:56 mortar retail. So, as we move down the 12:58 P&L design here, we've gone through 12:59 revenue, we've gone through cost of 13:01 delivery, gross margin. Now, we're at 13:03 marketing. Marketing is relatively 13:05 straightforward, which is anything 13:07 associated with marketing and 13:09 advertising. The product falls into this 13:11 bucket right here. So, this is anything 13:12 on paid ads. This is any influencer 13:15 payments. This is any events, this is 13:18 any out of home. The big question here 13:21 becomes does content production go into 13:23 this bucket? And this is based on 13:25 whether it is a variable expense or 13:28 whether it is fixed within the business 13:29 to meet the content demand. And so what 13:31 typically happens through an e-commerce 13:33 direct to consumer business is that 13:35 content velocity or volume needs to flex 13:39 throughout the year in conjunction with 13:41 revenue expectations. And so a typical 13:43 e-commerce brand will look flat through 13:45 Q1. We'll have a spike at June for end 13:48 of financial year sales. Will then build 13:50 back up, have a big November, December, 13:51 and then fall back off. And so your 13:53 creative production or amount of content 13:56 entering into the ad account as well as 13:57 obviously investment in influences, 13:59 events, etc. needs to map to this. When 14:01 you try to map creative to this flexing 14:04 in demand, it becomes very difficult to 14:05 do with just an in-house team because if 14:07 you have four people, they're probably 14:08 underworked here. They're probably 14:10 substantially overworked here. And then 14:12 this is probably the only time a year in 14:14 which they have the right amount of 14:15 work. And so because of that, it's not 14:16 really an ideal model. So what you do 14:17 instead is you have in-house here and 14:20 then all of this additional flex up in 14:22 creative volume requirements is 14:24 typically done by agencies or you could 14:27 pull in short-term hires that are just 14:29 there to fulfill a 3-month period. Now 14:31 these agency expenses that flex this is 14:34 a variable expense that should go into 14:36 your me. These fixed costs down here 14:38 this should be associated with OPEX. 14:40 This is a fixed expense that needs to be 14:43 held for indefinitely into the future 14:45 while content is king for being able to 14:47 drive revenue. Now, as you then move 14:49 into flexing this throughout the year, 14:51 that's when you can make a variable. 14:52 Now, some people would also argue that, 14:53 hey, let's just put all of this in the 14:55 me bucket because all of this is revenue 14:57 driving, which I also understand, but 14:59 that's going to be where there's a 15:00 little bit of custom design within how 15:03 you're approaching the P&L at an 15:05 individual brand level. Once we now go 15:07 past ME, which is the second big expense 15:09 bucket within an ecom brand, the biggest 15:11 expense is typically cost of delivery. 15:13 Second biggest expense is me. We now get 15:15 to another margin, which is contribution 15:18 margin. This is one of the best metrics 15:20 for indexing performance over time. And 15:22 I'll tell you why in a second. Now, 15:24 contribution margin is relatively 15:25 straightforward looking at this P&L 15:27 design. It is total revenue minus cost 15:30 of delivery minus all marketing 15:32 expenses. And this will give you your 15:34 contribution margin. Now, this can all 15:36 be calculated as both a percentage and a 15:38 dollar figure. So, revenue is 100%, you 15:41 have cost of delivery at 30%, me at 20%, 15:43 therefore your contribution margin is 15:45 50%. Now, why is this contribution 15:47 margin number so helpful? Well, it's 15:49 because if we're looking at it as a 15:51 percentage, we are one step away from 15:53 net profit. To get to net profit, all we 15:55 need to do over here is minus off OPEX. 15:58 So if we know that opex in the business 16:00 is kept relatively stable at let's say 16:02 10%. Well then that means that 50% minus 16:05 10% is 40% net. Now we will have a net 16:08 target as a business. You might want to 16:09 be holding 10% net margin or 20% or 30% 16:12 net margin. You can then back math that 16:14 into a contribution margin target. 16:16 Right? So let me give you an example. 16:18 Let's say you have a 20% net profit 16:21 target within the business. Great. If 16:22 you know your operating expenses will 16:24 always sit at 15%. We then just need to 16:27 work our way up the P&L. So over here 16:30 we're starting at the bottom and we're 16:31 working our way up and we can go okay 16:33 net plus opex means our contribution 16:36 margin needs to hit 35%. So we 16:38 effectively have a contribution margin 16:40 target that's really far up the P&L that 16:42 ensures that we hit a net profit target. 16:44 Same thing with dollar values too, 16:46 right? So we might have a net profit 16:47 target which is a million. We might have 16:50 a million dollar in operating expenses. 16:52 And so our contribution margin target is 16:54 these two added together, which is $2 16:57 million. And so we know for the year we 16:59 need to produce $2 million in 17:01 contribution margin to be able to get a 17:03 million dollars in net profit. Now, one 17:04 further piece of clarification here is 17:06 that you'll actually hear contribution 17:08 margin being used, not in this 17:10 definition. So technically, this 17:11 definition is what's called contribution 17:14 margin 3. Contribution margin one is 17:17 product margin, which is what we talked 17:18 about at the start. Now why people call 17:20 this contribution margin one I have no 17:22 idea. People are just making it 17:23 complicated. Okay you can just call this 17:25 product margin but sometimes people will 17:27 call this CM1. CM2 is gross margin. So 17:30 we also went through that at the start. 17:31 This is including all variable expenses 17:33 associated with cost of delivery. And 17:35 then CM3 is exactly what we just went 17:37 through here. This is typically called 17:39 contribution margin. Just really 17:41 important delineation because sometimes 17:42 people will say contribution margin and 17:44 they'll be referencing one of these 17:45 other definitions which is a weird thing 17:47 to do. I don't know why people do it, 17:48 but just make sure you understand those 17:51 definitions. The third largest expense 17:53 bucket is operating expenses. Now, how 17:55 to think through what actually sits in 17:57 operating expenses is very simple. It's 17:59 just anything that doesn't go in cost of 18:00 delivery or marketing. Now, the typical 18:03 three big expense buckets here within 18:05 direct to consumer is going to be people 18:07 at number one. It's going to be software 18:09 at number two, and then it's going to be 18:11 some kind of office or fulfillment 18:14 center here. Now it's important to 18:15 delineate that actual product 18:17 fulfillment like a 3PL or a warehouse 18:20 the objective is to fulfill actually 18:22 sits in cost of delivery. Same thing 18:24 technically with warehouse staff. So if 18:27 you have staff that are pickp packing 18:29 and shipping product, you actually 18:30 technically want that defined in cost of 18:32 delivery. What you do is you take their 18:34 salary and let's say take their salary 18:36 per day. So maybe it's like $400 per day 18:38 and then you divide by how many packages 18:40 they ship per day. Let's say they manage 18:42 100. Well, then you would take a $4 per 18:45 order expense and you would move that 18:47 over to your cost of delivery in your 18:48 actual accounting software. You can tag 18:51 particular staff and your bookkeeper can 18:53 do this and then they can reconcile into 18:54 a custom P&L report that will have 18:57 warehousing staff in your cost of 18:59 delivery. So, you'll have an accurate 19:00 gross margin number on a month-by-month 19:02 basis that encapsulates people that are 19:04 actually fulfilling the orders. And then 19:06 who you would want in operating expenses 19:07 for people is everyone that's not 19:09 associated directly with the actual 19:11 fulfillment of orders. So this would be 19:13 like your head of marketing, your head 19:15 of operations, list goes on. Now the 19:17 real key to operating expenses and where 19:19 most 7 figureure e-commerce brands 19:21 actually get this very very wrong is 19:23 that they overinflate operating expenses 19:25 believing that this is the way to grow 19:26 the business. Cuz you commonly hear 19:28 reinvest into the business. That's how 19:29 you grow. But in e-commerce, the way 19:31 that you reinvest into the business is 19:33 actually to invest in marketing. This is 19:35 the primary growth lever that exists 19:37 within the P&L for a business like this. 19:39 Going and simply hiring more people 19:42 generally isn't a revenue generating 19:43 exercise. Going and simply getting more 19:45 software generally isn't a revenue 19:47 driving exercise. Getting a bigger 19:49 office generally isn't a revenue driving 19:51 exercise. None of these expenses 19:53 generally drive much revenue. Now people 19:56 are by far the greatest leverage that 19:58 exists in any business. In fact, the 19:59 ceiling of revenue within a business is 20:01 generally the ceiling of the summation 20:02 of skills and talent within the people 20:05 that exist within that business. So, I'm 20:06 very much so of the opinion that there 20:10 is nothing more important on this entire 20:12 P&L than the people line item. However, 20:15 7Figure e-commerce brands generally 20:17 don't have the skill set to hire 20:19 incredible people yet. Number one, they 20:21 don't have a big enough business to 20:22 attract incredible talent. Number two, 20:24 they simply haven't hired, trained, and 20:26 gone through enough interviews to be 20:27 able to identify what those people look 20:28 like. And I say that from personal 20:30 experience. I've personally hired over 20:32 60 people in the last 5 years. And at 20:34 the start, I had no idea what to look 20:36 for. I was just going through interviews 20:37 trying to figure it out. And over time, 20:38 as you do more and more interviews, and 20:40 to date, we've probably done near 500 to 20:42 a,000 interviews. We have a pretty good 20:43 idea of what a really good high 20:45 performer looks like verse not. But when 20:47 you're getting started, you don't. And 20:48 so, people overinflate their people 20:50 expense. They overinflate software 20:52 because they think this is going to 20:53 drive revenue. They end up overinflating 20:54 office and tertiary expenses, and it 20:56 ends up destroying the P&L. So this is 20:58 just a really important one to watch 21:00 because honestly this is the in a larger 21:02 business this is the CFO in a smaller 21:04 business this is the owner's 21:06 responsibility to be able to keep under 21:08 control. So we can now run through the 21:10 actual waterfall of the P&L here. So 21:13 starting at the top and I'm going to use 21:15 small numbers to keep this simple. We 21:16 have $100 in revenue and let's just 21:18 think about this as a daily revenue on a 21:20 tiny business so it makes sense. Then we 21:22 move down to cost of delivery which is 21:25 $40. And so this business ends up with a 21:28 $60 gross margin. Then the actual 21:31 marketing expense here is 20% of 21:33 revenue. They maintain a 20% me, which 21:35 means that contribution margin is now 21:38 $40. Operating expenses, these guys are 21:40 keeping it at 20% as well, which is $20. 21:43 Meaning all the way down the bottom 21:45 here, we get to $20 in what we can 21:48 define as net profit. Now, what you'll 21:50 notice here is that I'm calling it net 21:52 profit here, but over here it's called 21:54 IBIDA. The reason for that is that 21:56 technically this $20 here isn't actually 21:58 net profit. Net profit sits after we 22:01 take out interest, tax, depreciation, 22:04 and amotization. Now, a lot of words. If 22:06 you're not familiar with finance, it's 22:07 not really something that we need to get 22:09 too deep into in this video. The only 22:10 caveat that's really worth noting is 22:12 that typically to fund an e-commerce 22:14 business, most people rely financial 22:17 tooling like loans. And the reason for 22:19 that is it's very capital intensive. To 22:21 actually be able to grow quickly, you 22:22 need capital to make future inventory 22:24 purchasing whilst also continuing to 22:25 spend on media. And now with that, you 22:27 have interest repayments on those loans. 22:29 Question becomes, where does interest 22:31 repayments go? A common mistake I've 22:33 seen on a lot of seven figure and 22:35 actually 8 figure P&Ls is that interest 22:37 expenses will go into OPEX. This is not 22:40 true. Interest should not go into OPEX. 22:43 Now, the reason being is that interest 22:45 actually isn't an operating expense. 22:48 This is leverage to be able to 22:50 accelerate the growth of the business. 22:52 And therefore, if a buyer or a third 22:54 party wants to look at the true profit 22:57 generation of this business, they do not 22:59 want interest included in the IBITA 23:01 number. Hence, you get interest before 23:03 earnings, tax, depreciation, and 23:05 amotization. Then your interest expenses 23:08 go down here. And so, let's say that 23:09 there was $10 in interest expenses 23:12 associated with loans. Then you would 23:13 have one last line item down the bottom 23:15 here, which would be $10 in net profit. 23:18 So there is technically two definitions. 23:19 There's IBIDA, which is the capability 23:21 of the business to generate earnings if 23:23 it didn't have loans outstanding, which 23:25 is what a buyer wants to understand 23:27 because a buyer will just zero out the 23:29 loans. And then net profit is the actual 23:31 profit that the business ends up 23:33 generating after these repayments are 23:35 made. And this is the basic P&L 23:36 structure at a high level. We can also, 23:39 and this is a very valuable exercise 23:40 that I'd always do, allocate percentages 23:43 to each level of the P&L to understand 23:46 relative percentages throughout the year 23:48 because we want to be looking at these 23:49 on a month-on-month basis to be able to 23:51 understand how these percentages are 23:52 changing. So, for example, our cost of 23:54 delivery is 40%, our gross margin is 60, 23:57 our me is 20, our contribution margin is 24:00 40%, this is 20%, IBIDA is 20%, and then 24:03 net profit is 10. And so then you can 24:05 look at these percentages over time and 24:07 go, are they expanding? Are they 24:08 contracting? What is our target 24:10 percentage allocations here? And this 24:11 becomes how you can use the P&L 24:13 effectively on a day-to-day or 24:15 month-to-month basis to be able to make 24:17 decisions. Okay. If me is creeping up, 24:20 well, we know our marketing efficiency 24:21 is declining and that's deteriorating 24:23 and compressing the bottom of the P&L. 24:24 So, we need to go and fix that. If our 24:26 gross margin is compressing, we need to 24:28 look into why that's the case. Is this a 24:30 discounting issue, a returns issue? like 24:32 what above the P&L is causing the gross 24:34 margin compression. So the way to look 24:36 at this is that if a target percentage 24:38 or a target number on this P&L is not 24:40 hitting target, the fault is everything 24:42 above it. So if we're not hitting our 24:43 IBIDA target, it's an issue with either 24:46 or all operating expenses me cost of 24:48 delivery. If we're not hitting our 24:50 contribution margin target, it's an 24:52 issue with me and cost of delivery. If 24:54 we're not hitting our gross margin 24:55 target, it's an issue with cost of 24:56 delivery or revenue. Because remember, 24:58 revenue isn't just net revenue, but it's 25:00 also gross. And so there's a few 25:01 different levers in here that we need to 25:03 look at as well. So this is how you 25:04 troubleshoot the P&L. If you're not 25:06 hitting numbers that you want, you want 25:07 to look upwards and look at the levers 25:08 that exist there that we need to pull on 25:10 and that we need to diagnose. So that is 25:11 the P&L explained. Moving into unit 25:14 economics. So unit economics, how unit 25:16 economics differs from the P&L is the 25:18 P&L is a zoomed out view of the entire 25:21 business. Unit economics is diving into 25:23 a specific unit. Now what this would 25:26 typically look like in retail is we 25:27 would be looking at a single unit. And 25:30 so if we're selling a t-shirt, we would 25:31 be looking at this t-shirt and breaking 25:33 down what is the price, what is the cost 25:35 of goods sold, what is the cost to 25:36 acquire a customer for this t-shirt, and 25:38 therefore what is the net profit on this 25:39 individual unit. Now, in e-commerce, in 25:41 direct to consumer, this actually 25:43 changes a little bit. And we're actually 25:44 looking at baskets. And so when we talk 25:47 about unit economics, we're actually 25:48 typically talking about the basket 25:51 because an average cart doesn't just 25:53 have one unit in it. Most people will 25:55 buy 1.5 things or 1.7 things. That's 25:58 called your units per transaction. And 26:00 so because of that, we want to 26:01 encapsulate multiple different products 26:03 into any unit economic calculation. So 26:05 the single most important component of 26:07 unit economics is something that we 26:08 haven't spoken about yet, which is CAC. 26:10 This is your cost to acquire a customer. 26:13 It's one of the most important metrics 26:15 on acquisition and it's a metric that 26:17 most brands calculate completely wrong. 26:18 How you calculate this is you take total 26:21 advertising spend over any given time 26:23 period. So this could be on a daily 26:24 level, a weekly level, a monthly level. 26:26 And then we divide by the total amount 26:28 of new customers that we acquired in 26:31 that period. This is not the same as CPA 26:33 in the platform because CPA is running 26:36 off attributed numbers and will end up 26:38 showing you a better number than is 26:40 actually reflective within the business. 26:41 And then people will also try to 26:43 delineate CAC down to a channel by 26:45 channel level, which is also impossible 26:46 because you're relying on attribution. 26:48 and attribution has a plethora of issues 26:50 which is why we have this whole finance 26:52 video together because attribution is 26:54 just unreliable. Now the first question 26:56 that I typically get when I pull a 26:58 trailing CAC calculation for most 27:00 businesses is okay well what is a good 27:02 CAC? You have access to over 68 n figure 27:05 brands. I've personally consulted on 27:07 well over a thousand brands to date. I 27:09 have a pretty good reference what a good 27:10 CAC looks like. The answer to what a 27:12 good CAC is is that it's fundamentally 27:14 actually the wrong question because CAC 27:16 means nothing without contextualizing it 27:19 to a pairing metric. And that pairing 27:21 metric is gross profit on first purchase 27:23 because you could have a $100 cost to 27:26 acquire a customer. But if you're making 27:28 $10,000 27:30 on the order, this is an unbelievable 27:32 deal. You're paying $100 and you're 27:34 making $10,000 on the order. That's 27:35 insane. And so this could be like super 27:37 high ticket furniture as an example. Or 27:39 vice versa, you could be super low 27:41 ticket selling lollies online, which is 27:43 probably not a very good niche, and 27:44 you're making $20 in GP. This is a 27:47 terrible exchange. You're actually 27:48 losing $80 on each new customer acquired 27:51 here. And so, you need to understand 27:52 what is your gross profit on first 27:54 purchase, and it's really important on 27:56 first purchase. Cuz what people will do 27:58 here is they'll take their average order 27:59 value, which on Shopify, let's say it's 28:01 $100, and then they'll take their gross 28:03 profit percentage, which is typically 28:05 wrong, calculated wrong. We spent a lot 28:07 of time going through that and they'll 28:08 go, "Okay, our gross profit on average 28:10 is 70%. Therefore, we have a $70 GP, so 28:13 we're happy with $35 CAC." Well, is this 28:17 on new customers? Because returning 28:19 customers always have a higher average 28:21 order value, which pulls your average 28:23 order value number up. And so, if you 28:25 actually delineate down into new 28:26 customers, you might find that this is 28:28 actually 90 and that your GP is actually 28:30 even lower because maybe you have more 28:32 discount orientated front-end offers. 28:34 And so your GP compresses all the way 28:36 down to maybe 50. And so now your CAC 28:38 targets are completely wrong in 28:40 conjunction with your true GP. And so 28:42 this is really just the important of 28:43 data integrity within the business. 28:45 These numbers need to be calculated 28:46 correctly. You need to trust the 28:48 calculations or else you're just going 28:49 to scale off fundamentally flawed 28:52 metrics. So as a part of unit economics, 28:54 let's go into pricing strategy because 28:57 pricing becomes an enormous lever for 29:00 profitability of the business 29:02 particularly when products are 29:03 underpriced. So, as a new e-commerce 29:06 brand, what people will typically do is 29:08 they will use keystone pricing. Now, I 29:10 think anyone that's ever run a business 29:12 or started an ecom brand has done this, 29:14 which is that you take your cost of 29:16 product, so you go to the manufacturer, 29:18 product cost $10, and you just double 29:20 it. This is what's called keystone 29:21 pricing. So, as an example, a $6 cost of 29:24 goods just gets 2xed and becomes a $12 29:27 price. So many people price this way. I 29:29 reckon 40% of the startup market just 29:31 prices in this way. This is probably the 29:32 worst way you could ever price. Now, 29:34 it's because this cost of goods doesn't 29:35 encapsulate all the variable expenses 29:37 associated with fulfillment. And so, you 29:39 end up with way more compressed margins 29:40 than just 50%. I think the reason why 29:42 people use Keystone pricing is number 29:44 one, it's simplicity, but number two, 29:46 the fact that people don't want to 29:47 overpric. People think that margin is 29:49 bad. We can't have too much margin or 29:51 else we're just ripping people off. But 29:53 because of that, they don't take into 29:55 consideration all of the expenses 29:56 associated to actually fulfill within a 29:58 business. And so because of that, people 30:00 underpric and they can never actually 30:02 scale. Whereas the actual cost to grow a 30:04 business in all of the variable expenses 30:06 associated with fulfillment as well as 30:07 all the marketing expenses associated 30:09 and then operating expenses taking up 30:11 15%. It ends up being a lot. And so you 30:13 end up needing to price way higher than 30:15 just a doubling using keystone pricing. 30:17 So the next pricing model that people 30:19 jump to is IMU pricing, which is initial 30:22 markup pricing. So this is effectively 30:24 the same thing as Keystone, but rather 30:25 than doing a 2x, maybe we do a 3x or we 30:28 do a 4x or a 5x. So we're effectively 30:30 taking the cost of goods once again, and 30:32 we're just applying a multiple to this 30:34 number. Now, that might seem better, 30:36 right? You're like, well, Nathan said 30:37 that the big issue with Keystone is 30:39 you're not increasing by enough. So if 30:40 we just increase to 34 5x, then surely 30:42 that fixes the issue. It doesn't fix the 30:44 issue because this is just an arbitrary 30:46 multiple on a product cost, which has 30:48 nothing to do with the price elasticity 30:50 of demand within the market. It has 30:51 nothing to do with the competitors and 30:53 it has nothing to do with the actual 30:54 cost structure associated with 30:56 delivering that particular product. And 30:58 so, yes, we could just 4x it now and the 30:59 price is $24. Amazing. But this might be 31:02 substantially overpriced compared to 31:03 competitors. This might still not 31:05 encapsulate enough margin to be able to 31:07 actually fulfill on this business model. 31:09 And so, all of these things have to be 31:10 taken into consideration. The real core 31:12 takeaways here is that you want to use 31:15 waterfall pricing from bottom to top to 31:19 be able to identify the price that you 31:21 need to actually price the product to 31:23 make money. And then from there, you 31:24 make the decision of do we even sell 31:26 this product? So here's all the expenses 31:28 associated with actually delivering on 31:30 the product. We need to start at the 31:31 bottom. So what is our actual 31:32 contribution profit target here? Okay, 31:34 is it 20%, 30%, 40% based on our 31:36 existing P&L understanding and how much 31:39 operating expenses we have. So you could 31:40 really start all the way down the bottom 31:41 here, right? And take this a step 31:43 further and say that our net profit 31:45 target is 10%. We know that our 31:47 operating expenses 10%. Therefore, our 31:50 contribution target is going to be 20%. 31:53 Okay, great. Now we need to start moving 31:55 up. How much do we think it's going to 31:56 cost to acquire a customer on this 31:58 product? Now, based on our 31:59 understanding, based on the ad account, 32:00 based on other product, we think our CAC 32:02 is going to sit at $30. Now, what's the 32:03 return allowance? This is also called a 32:05 shrink allowance, which is that we're 32:07 allowing for shrink in discounts, 32:09 returns, etc. Well, we probably want a 32:11 3% return allowance, probably a 3% 32:13 discount allowance. Shipping and 32:15 fulfillment on this product, we've gone, 32:16 we've reached out to Opost or the 32:17 shipping courier, and we know that this 32:19 is going to be $11. Then, we have the 32:21 cost of goods, which we know is $6 over 32:23 here. And now we can work our way all 32:25 the way up to pricing. So, we need to be 32:27 priced at 30 + 11 + 6 plus 3% 3% of this 32:31 total price. And then we need to come 32:32 out to a 20% contribution margin. And so 32:35 we can run all of this math, which I'm 32:36 not going to do for the sake of this 32:37 video, but let's say this takes us up to 32:39 a $100 price cuz 100 - 6 - 11 takes us 32:42 down to about 85. Then we minus another 32:44 6% which takes us down to 79. Take us 32:47 off another 30 that takes us to 49. And 32:51 so down the bottom here, we have a $49 32:53 contribution profit, which is actually a 32:55 really high contribution margin, way 32:57 above our actual target. So if we just 32:58 keep reworking these numbers, I believe 33:00 this should come out to probably like a 33:01 $70 price. So to hit this contribution 33:04 margin target with this CAC with these 33:06 unit economics, we come out to a $70 33:08 price at the top. This is how you price 33:10 correctly and you then go and you do 33:12 competitor research. And guess what? If 33:14 this $70 price is ridiculous, if 33:16 everyone else is pricing at $30, $40, 33:18 well either number one, we need to 33:19 figure out, can we position into a blue 33:21 ocean where no one's actually selling a 33:23 premium version of this product? Can we 33:25 position this as premium? Can we sell at 33:27 this price? And if we can't, we don't 33:28 sell the product. The unit economics 33:30 don't work end to end. If we can't hit 33:32 our profitability targets by selling 33:33 this product any less than $70, no one 33:36 will buy it at $70. Let's cut the 33:38 product entirely and not sell it. So 33:39 that is how you need to be thinking 33:40 through pricing strategy. Now you need 33:42 to understand the unit economics of 33:45 discounting. This is where most brands 33:47 destroy their margin without even 33:48 realizing it. Now I'll give you a really 33:50 quick worked example. So if we have a 33:52 $100 average order value or product 33:55 price, this means we're going to come 33:56 out to a $40 gross margin. Now what we 33:59 can do here to calculate our break even 34:01 return on ad spend. So what return on ad 34:03 spend do we need to be to break even is 34:05 we take our gross margin which is 40%. 34:08 So break even return on ad spend equals 34:10 1 divided by 40%. And this equals 2.5. 34:14 So we need to achieve at least a 2.5 34:16 return on ad spend to break even and 34:19 therefore make money. So this is the 34:20 lowest we can be. Great. This is a 34:22 really important number for not only 34:23 your internal team and your agency to 34:25 know because if let's say an ad set or a 34:26 campaign is below this you're losing 34:28 money. It's really important to 34:29 understand what this number actually 34:30 sits at, but more important to 34:31 understand where this number sits at 34:33 during a discount period. So, if we take 34:36 this exact same example, you're on 30% 34:38 discount. So, you get slashed to a $70 34:41 price. $70 price, your cost of goods 34:43 stays the same. And so, we have our $60 34:46 cost of goods here. That then comes out 34:48 to a $10 gross margin. If we then 34:50 recalculate our break even return here, 34:53 our break even return becomes 1 / 10%, 34:56 which is 10. So, our break even return 34:58 on ad spend, the efficiency that we need 35:00 to operate is 5x what it was up here off 35:03 just a 30% off discount, which is crazy. 35:05 And it's because discounting 35:07 exponentially increases the break even 35:09 return or the efficiency that needs to 35:11 be hit within paid media. And so, 35:12 anytime you're discounting, it is really 35:14 important to run this exact calculation 35:17 to be able to understand what efficiency 35:18 level we need to be at. Now, all of this 35:20 math can also be done based on a CAC or 35:22 a CPA number. So rather than doing this 35:24 division to calculate break even return, 35:26 all you do is this gross margin number 35:28 is your break even CAC. So your break 35:30 even CAC just simply equals gross 35:31 margin. So here break even CAC is 40. 35:34 Here break even CAC is 10. Obviously a 35:36 $10 CAC is absolutely insane. You're 35:38 probably never going to hit that across 35:39 paid platforms. Therefore, this discount 35:41 will never work for acquiring new 35:43 customers. So this shouldn't go out 35:44 publicly. Now this isn't to say that you 35:46 can't discount. You can't do blank 35:47 discounts. Of course you can. You just 35:48 need to understand how it affects the 35:50 efficiency targets within paid and 35:52 whether you can actually hit those 35:53 efficiency targets. You always need to 35:54 just run that math which is that if 35:55 we're going to do a 20% off discount, 35:57 how does our efficiency on paid media 35:58 need to change to maintain the same 36:00 level of profitability or improve 36:01 profitability? And if it's insane, if 36:03 it's like we need to go from a 2x row to 36:05 a 12, like well that is physically 36:07 impossible. The discount isn't going to 36:08 create that much of an uplift in demand 36:10 and conversion rates. Therefore, we need 36:12 to rethink the discount approach and 36:13 what we're actually doing with this 36:14 offer. And so, let me give you some 36:16 other options. Well, bundling is a very 36:18 big one. Everyone knows this. This is 36:20 pretty generic, which is that if you 36:22 bundle, you get economies of scale 36:24 because you might go from one unit in 36:26 the cart, too. But this doesn't increase 36:27 your shipping and fulfillment cost from 36:29 1 to two. Generally, you will get maybe 36:31 a 20% inflation in the shipping and 36:33 fulfillment cost of the product. And so, 36:34 therefore, you actually get better gross 36:35 margin when you bundle. And you can give 36:37 that margin away to the customer, and it 36:39 doesn't affect your GM percentage. 36:40 Bundling also increases average order 36:42 value substantially, which allows for 36:44 you to have a higher cost of acquiring a 36:46 customer on the platform and still make 36:48 more money. And so bundling is still a 36:50 really effective way to provide a 36:52 discount to the end consumer, provide an 36:54 offer that seems to have a value 36:56 discrepancy in the market that allows 36:58 someone to get over the line and buy, 36:59 but it is beneficial to you in regards 37:02 to the unit economics of that bundle. 37:03 Another example here is a gift with 37:06 purchase. And so this is where you just 37:09 need to become good at calculating unit 37:11 economics correctly on offers because 37:13 this is what allows you to validate 37:15 whether an offer will work. It allows 37:16 you to bake in the assumptions and then 37:18 you can go and actually run it in 37:19 public. So an example of this would be 37:21 if you buy two beach towels, you get a 37:23 free bag that actually holds the beach 37:25 towel. Now the beach towels might be 37:26 $100 each is what they're priced at and 37:28 they're pretty high margin. Let's say 37:29 70% or something. So you go and when 37:31 people buy two, it's $200, but they get 37:34 a free bag. And you can say the free bag 37:36 is valued at or sold on the website at 37:38 maybe $50, $60. You can price it really 37:40 high. It can be seen as a premium bag, 37:42 but the reality is is that the cost of 37:43 goods on this bag for you is maybe $4. 37:45 And so you're going to forgo $4 of 37:47 margin to get an extra $100 in revenue. 37:50 Really good exchange. Okay, your gross 37:52 margin is pretty much going to stay the 37:53 exact same as a percentage, but you're 37:55 doubling average order value by giving 37:56 this free bag away. So gift with 37:58 purchase ends up working really well if 38:00 the offer is crafted well and if the 38:03 gift is relatively low cost. And then 38:05 the last is straight uh discounting 38:08 which I just told you compresses margin 38:10 a lot and you have to be really careful 38:12 about. But the caveat here is that you 38:14 can do this on grade C inventory. We'll 38:16 talk about stages of inventory later in 38:18 this video and we'll break down actual 38:20 strategies to be able to move inventory 38:21 and focus on cash versus net profit and 38:23 the marketing strategies associated. But 38:25 just as a call out, if you are going to 38:27 do flat discounting, you want to do it 38:28 on inventory that isn't moving. And then 38:30 you can move that inventory back into 38:32 cash. Even if it is at break even, it 38:34 doesn't really matter because the 38:35 inventory wasn't going to sell anyway. 38:37 Now, I can't talk about unit economics 38:39 and finance without talking about LTV 38:41 and repeat purchase rates. And the 38:43 reason this is so critical is because 38:45 acquisition is expensive. Fundamentally, 38:47 it is expensive to acquire customers. 38:49 Particularly if we start going into an 38:50 industry like CPG, consumer package 38:52 goods, you're not going to probably even 38:54 be profitable on first purchase. And the 38:56 reason being is that you just have 38:57 competitors that will outspend you, 38:59 outbid you at auction on Meta, on 39:01 Google, and they can go and pay $200 to 39:03 acquire a customer because they have 39:04 this massive lifetime value that they 39:06 can then realize on second, third 39:08 purchase. And if you don't have that, 39:09 you will lose because your competitors 39:11 will just spend more than. And so 39:12 lifetime value becomes a critical 39:14 component in profitability of most 39:17 business models. I can give you a 39:18 completely different example of this 39:20 outside of e-commerce as a whole, which 39:22 is actually the agency model, which is 39:23 something that I'm very familiar with. 39:25 In the agency model, most agencies won't 39:27 run profitable on first purchase or 39:29 first invoice on month one. Most 39:31 agencies will run at a 3 to six month 39:33 CAC payback period. So they will spend X 39:35 amount to be able to get a client, 39:36 whether that's associated with 39:37 marketing, sales costs, etc., etc., 39:40 networking, events, and then once they 39:41 have a client, they will only start 39:42 making money after 3 to 6 months due to 39:44 the CAC. Same thing in CPG, same thing 39:47 in fashion, retail, etc. Except in these 39:49 other industries, you need to carefully 39:51 and meticulously understand what your 39:52 LTV actually is so that you can operate 39:55 that model profitably. Because if you 39:57 are an agency, let's say, and you don't 39:59 understand all the associated cost to 40:00 get the customer and you miscalculate 40:02 CAC or you miscalculate LTV, you are 40:05 actually in a very different 40:06 profitability position and you can 40:08 really negatively impact yourself. So, 40:09 let's run through a quick example. Let's 40:12 say you're doing 200k a month in 40:14 revenue. of that 200k a month 140,000 is 40:17 new customer revenue and let's say this 40:19 is coming from 50k in ad spend that then 40:22 means you have $60,000 in RC revenue and 40:25 let's say that the cost here is like 40:27 2.5k which is associated clavio costs 40:30 and maybe an agency associated with 40:32 driving repeat revenue now your new 40:34 customer profit contribution which isn't 40:36 a metric that we've gone through yet but 40:37 it's just contribution margin but we're 40:39 doing it on new customer revenue only 40:42 this is 140 minus 50 So, we have 40:45 $90,000, but we obviously also need to 40:47 minus cost of delivery and gross margin. 40:49 So, let's assume a 50% gross margin. 40:51 That means that this is going to come to 40:52 70 - 50. This is going to be $20,000 in 40:55 profit contribution that we're making 40:57 each month on new customers. Now, what 41:00 about RC revenue? Well, 30 - 27, this is 41:04 27,500 41:06 on return. And so this business, which 41:08 is a pretty typical business in terms of 41:10 new customer revenue to returning 41:11 customer revenue percentages at this 41:13 kind of size, this business is making 41:15 more on returning customers in profit 41:17 per month than they are on new. And this 41:19 is super typical because majority of 41:21 your profit ends up coming from 41:22 returning customers. Now, why is that 41:24 the case? Well, because it's very hard 41:25 to get a new customer. You have to pay 41:27 money to get them. Returning customers 41:29 come back due to the product, the 41:31 experience, and the brand affiliation. 41:32 So you actually don't need to spend much 41:34 at all on getting a returning customer 41:36 and they drive tons of profit 41:38 contribution in the business. Now once 41:39 you start getting into this position, 41:41 that's where you start needing to 41:42 understand, well, can we actually push 41:43 up new customers even harder and 41:45 subsidize this acquisition cost with all 41:47 the profit that we're making on 41:48 returning and continue to grow the 41:49 business that way. And that's where I 41:51 just want to provide a massive 41:52 hesitation to most people, which is that 41:54 most people calculate LTV wrong. Number 41:57 one, they will calculate LTV based on 41:59 just infinity. And so when we look at 42:01 lifetime value, uh, as you extend the 42:03 time period in which you're looking at 42:05 how much a customer is worth to you, it 42:06 increases forever. And so we can look at 42:08 how much a customer is worth to us after 42:10 6 months and it might be $100. And then 42:12 we can look at 12 months and it might be 42:13 110. Then we can look at 18 months, it 42:15 might be $120. And as you just keep 42:17 extending that time horizon, LTV 42:19 increases forever. Now it does somewhat 42:21 asotope. So it will look something like 42:24 this. But still, if we measure from here 42:26 to here, there is still an increase. And 42:28 so what you want to be very careful of 42:30 is not just looking at an LTV 42:32 calculation based on total customer data 42:35 over forever and instead you want to 42:37 restrict it to a particular time period. 42:39 Now a good way to do this is to look at 42:41 90day or 180day LTV. The second 42:45 delineation you want to make is you want 42:47 to go to LTGP. You want to be looking at 42:49 gross profit, not value. Now, LTV in the 42:53 traditional value sense actually is 42:55 lifetime gross profit. When we measure 42:56 this against software because this is a 42:58 software metric and in software, 43:00 lifetime value is typically very close 43:01 to lifetime gross profit and therefore 43:03 it's the same thing. But in e-commerce, 43:04 we want to make this clear delineation 43:06 because some people and a lot of 43:08 softwares actually will give you LTV as 43:09 a revenue number. And so we want to 43:11 understand what the gross profit are of 43:12 these customers at 90 180 days and then 43:15 we can use that to be able to understand 43:16 what we can actually pay to acquire a 43:18 customer. Now, let's take unit economics 43:20 and apply it at a product level. So, on 43:23 product level economics, let's say you 43:25 have a hoodie that you sell and you sell 43:27 it at $90 retail with a $21. So, you 43:30 have a hoodie at $90, cost of goods 21. 43:32 So, you have gross margin of $69. Our 43:35 cost to acquire a customer through the 43:36 hoodie on our advantage plus scaling 43:38 campaign is $45 right now. So, that's 43:40 our CPA. We're just going to call it 43:42 CAC. So, our contribution margin here is 43:43 $24. Now, we have a t-shirt that's 43:46 priced way less. Cost of goods is about 43:48 the same. Actually, our gross margin is 43:50 only 27, but we have a CAC of 15 and we 43:53 have a contribution margin of $12. So, 43:55 right now, the hoodie is driving 2x the 43:58 contribution margin to the business per 44:00 sale. So, this firstly is an important 44:02 number for us to be across. Okay, 44:03 understanding what actual products 44:05 within the portfolio is driving us the 44:07 most contribution margin when we break 44:09 our unit economics down at a product 44:11 level. The second component that becomes 44:13 important here is understanding how we 44:16 should structure our campaigns 44:17 accordingly. Should we be splitting 44:19 these out? Because if we're using a 44:21 bidding strategy on Meta, Google, Tik 44:24 Tok, which is maximize conversions, and 44:26 this is the default bidding strategy on 44:28 every advertising platform. When you use 44:30 maximize conversions, what it's 44:32 optimizing for is the lowest CPA. So, 44:35 whatever product is driving the lowest 44:36 CPA, whatever ad is driving the lowest 44:38 CPA, that will get prioritized, that 44:40 will get the spend. Now, if we look at 44:42 this example here, the t-shirt actually 44:44 has the lower CPA. And so, Tik Tok, 44:48 Meta, Google will distribute all your 44:50 spend here, but this product is driving 44:52 a lower contribution margin per sale. We 44:54 would actually prefer Meta to put all of 44:56 our spend up here. Yes, it's going to 44:58 cost us a little bit more money, but 44:59 we're going to drive more contribution 45:00 margin per product sold. So this is the 45:03 better place to put cash right now. 45:05 Maximize conversions won't do that. Now 45:07 you can fix this in the platforms in a 45:08 number of ways. You can have 45:09 segmentation across product categories 45:11 based on product level economics which 45:13 is really important to do. You can 45:14 change the bidding strategy to maximize 45:16 for conversion value and therefore we 45:18 will actually prioritize this because it 45:20 has a better rorowaz. This actually has 45:21 a worse rorowaz too. So maximize 45:23 conversion value won't fix this issue 45:24 either. So segmentation is the way that 45:26 you actually need to fix this. Now the 45:27 other reason why this is an important 45:29 exercise is particularly in fashion what 45:31 you were trying to do is sell through 45:33 all of your product highest contribution 45:35 margin possible. What we need to start 45:37 thinking about in this instance is this 45:39 t-shirt will this sell anyway without us 45:42 even pushing it in paid cuz the cost to 45:44 acquire is so low here that it seems 45:46 like there is some kind of virality 45:49 component. there is some kind of product 45:50 market fit that's just getting this 45:52 t-shirt to sell kind of regardless of 45:54 our paid media spend because this is so 45:56 hyperefficient. So the question we need 45:58 to ask particularly if we're an omni 45:59 channel business with retail stores or 46:01 we're just a very large business in 46:02 general is would people have bought this 46:04 product anyway if we didn't spend? And 46:07 if that's the case well then guess what 46:09 let's not spend. Let's then take our 46:11 contribution margin up on this order by 46:13 $15 to $27. and let's actually 46:16 reallocate all the spend to the hoodie 46:18 which might not be selling naturally or 46:20 organically and we need the paid spend 46:22 to be able to drive sellrough rate. So 46:24 this is where product level economics 46:25 becomes incredibly important. It's 46:27 something you should be breaking down. 46:28 You can obviously build Google sheets 46:30 around this. Have visibility build it 46:32 into your decision-m. Now moving into 46:34 the metrics that matter. How we think 46:36 through this internally is through a 46:38 pyramid. We have platform level metrics 46:40 down the bottom. We then move into 46:42 finance grade metrics which we've been 46:43 covering a lot in this video. And then 46:45 up the top we move into incrementality 46:47 testing which probably isn't applicable 46:48 for 80% of people watching this but for 46:50 the 20% that's doing over $10 million a 46:53 month that has a presence in the US or 46:55 multiple different markets might be omni 46:57 channel this is going to be critical for 46:59 ensuring measurement within the business 47:00 and it's going to tie into understanding 47:02 how finance ties into actual platform 47:04 metrics. So if we start down the bottom 47:06 we have rorowaz CPA CTR multi-touch 47:09 attribution like triple whale etc. Now 47:12 why is rorowaz down the bottom of the 47:13 pyramid? Why is rorowaz not good? The 47:15 reason why return on ad spend isn't a 47:17 good metric is not because calculating 47:19 ROI is bad, calculating return on 47:21 investment is a great thing to do. The 47:23 reason why rorowaz is unreliable is 47:25 because it relies on attribution. And 47:28 attribution is fundamentally an 47:30 unknowable reality where you're trying 47:32 to connect correlation within platforms 47:35 with faulty data to be able to prove 47:37 causation. And what do I mean by all 47:38 that complex language? When someone gets 47:40 served an ad and then they click on the 47:42 ad and then they go to the website and 47:44 they purchase. You might think that this 47:46 is causation that this ad click caused 47:48 the purchase. But in a lot of cases 47:50 particularly in large omni channel 47:52 retailers that actually is not the case. 47:54 This is correlation and in fact all 47:56 attribution is just correlation. We are 47:59 saying that these two events are 48:01 correlated. The traditional way to 48:03 explain correlation versus causation is 48:05 that at the same time throughout the 48:07 year, swimming deaths go up and ice 48:09 cream sales go up. And you go, well, is 48:11 swimming deaths causing ice cream sales? 48:13 Is ice cream sales causing swimming 48:15 deaths? No, they're not causal. They're 48:17 correlated to summer. When it is summer, 48:19 more people die when they're swimming in 48:21 the ocean and more people buy ice cream. 48:23 And it's the same thing here. Ad clicks 48:25 is not necessarily causal to purchasing. 48:28 It is simply correlated. And so because 48:29 of this, return on ad spend ends up 48:31 lacking validity and congruency to the 48:34 P&L. What you see a lot of the time is 48:37 that people will go and say, "Rooraz is 48:39 unbelievable. Wow, but my business is 48:41 dying. What is going on?" And it is 48:43 because rorowaz has a few dynamics to 48:45 it. Number one, because of the way that 48:46 it attributes, it will always 48:48 overattribute to the bottom of funnel. 48:50 Because people that for example might 48:52 see a billboard up here and this is 48:53 obviously a super extreme example but 48:55 let's say someone sees a billboard and 48:56 they become aware of uh the brand. They 48:58 then see a TV ad which reinforces that 49:00 they actually really want to buy your 49:01 stuff because there was an influencer in 49:03 there who they connect with. Then 49:04 eventually they get an ad on Facebook. 49:05 They click on the ad and they buy. Now 49:07 did that ad on Facebook cause them to 49:08 buy? Probably not. Like yes it got it 49:10 over the edge at that specific moment 49:12 but they were going to buy regardless at 49:13 the next point of activation. It was the 49:15 billboard and the TV that actually 49:17 warmed them up at the top of funnel but 49:18 these got no attribution. Now, it's the 49:20 same thing across platforms. And so, a 49:22 really evident example of this actually 49:23 right now for us, and this might change 49:25 depending on when you're watching this 49:26 video, is Tik Tok to Meta to Google. 49:29 Now, this isn't applicable for everyone. 49:31 This is a very unique circumstance, but 49:33 it at least gets the point across, which 49:34 is that we're finding at the moment for 49:36 some very large retail brands that Tik 49:38 Tok is actually underattributing 49:40 substantially because it's getting 49:41 impressions. It's getting in front of 49:42 people in new markets, but they don't 49:44 actually click off the platform much. or 49:46 if they do click off the platform, they 49:47 click off, they look at the product 49:48 page, but they don't buy. Then once we 49:50 have them in the pixel, Meta goes and 49:52 follows up and retargets them. And we 49:53 end up capturing a lot of increased 49:55 demand on Meta when we increase our Tik 49:57 Tok spend. And then finally, people that 49:59 still haven't bought after Tik Tok and 50:01 Meta, they end up going to Google. They 50:03 search for the brand name, they click 50:04 and buy. And so when we increase Tik Tok 50:06 spend, our meta rorowaz goes up and our 50:09 Google rorowaz goes up. Tik Tok doesn't. 50:11 If we're using rorowaz as the indicator 50:13 for performance and budget allocation 50:15 across the business particularly and 50:17 this is where this becomes a very large 50:18 issue is when this is reporting up to a 50:21 seuite a seuite will see return on ad 50:24 spend numbers across these different 50:25 channels and go okay tik tok we should 50:27 cut it's not driving returns meta is 50:30 okay let's decrease budget and team 50:32 let's put more budget into Google that 50:33 would be a fundamentally terrible idea 50:35 because what we're effectively saying 50:36 there is let's cut all the top of funnel 50:38 generation and move to just bottom of 50:39 funnel bottom of funnel will stop 50:40 working unless there is top ofunnel 50:42 generation. And so rorowaz ends up 50:43 misleading people in terms of decision-m 50:45 unless there's a lot of additional 50:46 nuance in understanding what is actually 50:48 driving impact in the business. And so 50:50 we need to go to better measurement 50:51 systems to be able to actually 50:53 encapsulate this, understand it, and 50:55 then communicate it to a seauite or if 50:56 you're a small business just to you as 50:58 the founder. Now we could go on and on 50:59 about the other limitations of 51:01 attribution and return on ad spend here, 51:03 but this is fundamentally the crux of 51:04 why this metric misleads you in terms of 51:07 decision-making. So then we move one 51:09 stage up in the pyramid and we get to 51:11 financial grade KPIs. Now these are 51:13 metrics like acquisition me. You can 51:15 throw me in here as well. We've got 51:17 profit contribution which we've ran 51:19 through. We have CAC which we've ran 51:21 through. So these are all metrics that 51:23 aren't relying on the inplatform 51:24 attributed numbers to be able to 51:26 calculate them, but instead they're 51:27 using the actual financial metrics that 51:29 exist within the business. So the actual 51:31 amount of new customers that you're 51:32 acquiring, the actual new customer 51:34 revenue, and then we're dividing by the 51:36 actual ad spend. So this is all metrics 51:38 that aren't relying on any kind of 51:39 correlation calculation but instead are 51:42 directly associated to financial metric. 51:44 And so because of that the reliability 51:46 is much better as we move up the pyramid 51:48 reliability improves. However, speed 51:51 decreases and so what ends up being the 51:53 case with these metrics is them going up 51:55 and down. You need to look at this on 51:57 slightly longer time periods to be able 51:59 to make correct decisions. Whereas you 52:01 will get faster feedback loops generally 52:03 speaking on your inplatform metrics. So 52:06 why would we even use this bottom half 52:08 of the pyramid at all? Why would we even 52:10 look at rorowaz CPA? They are helpful in 52:12 directional campaign feedback and ad 52:14 feedback within the siloed platform. So 52:17 what we do not want to do, what would be 52:18 a big mistake is comparing a rorowaz 52:20 number on a Google campaign to a rorowaz 52:22 number on a meta campaign because they 52:24 have different attribution models. 52:25 They're sitting at different points in 52:27 the funnel. They're driving different 52:28 incremental impact to the business. If 52:30 we took both of the campaigns and we 52:32 pushed budget up, one of them would 52:33 drive excess return compared to the 52:35 other irrespective of the rorowaz number 52:37 on the campaign. So these are helpful 52:38 directionally within the own platform. 52:40 So if we're looking at two meta 52:41 campaigns next to each other, we can 52:43 compare rorowaz, we can use that to make 52:44 decisions. But if we start comparing 52:45 platforms, if we start zooming out and 52:47 making larger business level decisions 52:48 based on these numbers, that's where we 52:50 can start to mislead ourselves and go 52:52 into using low reliability, low data 52:54 integrity level metric. And then all the 52:56 way up the top here, we have 52:58 incrementality testing. We won't dive 52:59 into this in too much detail in this 53:01 video, but the crux of the way that this 53:03 works is that you take an area, 53:05 generally the whole country. You split 53:07 down by states. In Australia, you have 53:08 to split down by commuting zones rather 53:11 than states because there's not enough 53:12 state selection. And from there, you 53:14 hold out a particular area. Now, 53:16 typically it might even be two states. 53:17 So, we can go and grab two different 53:18 states that are next to each other. And 53:20 then we hold them out. Now, a hold out 53:22 means that we increase spend everywhere 53:24 else except here. and we see what is the 53:25 differential or we can just increase 53:27 spend here in the control group or we 53:29 can cut spend entirely. So we have a lot 53:31 of different options in terms of test 53:32 design here. But the idea is that when 53:34 we isolate a large control verse 53:36 treatment group, we can start to measure 53:39 the incrementality of changing different 53:41 campaign types. And so we might go and 53:43 take that Tik Tok campaign I was talking 53:44 about before that doesn't seem to be 53:46 doing well and we might double budgets 53:47 in these states. And then over a 30-day 53:49 period, we measure the revenue 53:50 realization difference over that 30 days 53:52 and maybe new customer revenue doubles. 53:54 And so as a function of that we can take 53:56 the lift in new customer revenue against 53:58 the control. We can do an incremental 53:59 return on ad spend calculation and we 54:01 end up getting a very accurate read on 54:03 the impact that these campaigns are 54:05 making. Now that is an unbelievably 54:07 simplified explanation of how this test 54:09 design actually works. It's actually a 54:11 lot more complicated. I could make a 54:12 2-hour video just on the data science 54:15 and approach to building incrementality 54:16 tests which I think we actually might do 54:18 at some point. That is the crux of the 54:19 idea as to how it works. I do not 54:22 recommend that you go away from this 54:23 video and think, "Oh, that was a really 54:25 interesting idea. Let me just make a 54:26 Google sheet and start doubling budgets 54:27 in states." If you do that, you won't 54:29 get good results. It'll pretty much 54:30 always say states aren't incremental 54:32 because the test design isn't set up 54:33 correctly. You'll end up way 54:34 overspending or under spending. You 54:36 won't get statistically relevant 54:37 results. You need at least a fundamental 54:39 understanding of data science to be able 54:41 to start to approach this portion of the 54:43 pyramid. Um, but that is ultimately the 54:45 most reliable option. It's just very 54:46 slow. You can't run a lot of them and 54:48 you're quite restricted. Now, I said 54:49 before that I was a little bit hesitant, 54:51 put me in here. And the reason why I was 54:52 a little bit hesitant is that me is 54:55 revenue divided by ad spend. Sometimes 54:57 people will do this the other way round 54:59 and they'll do ad spend divided by 55:00 revenue which will give you a percentage 55:02 number. This instead will give you a 55:04 multiple number. And so if we for 55:05 example have $100 in revenue and $50 in 55:08 ad spend, our me would be a two. So 55:10 every dollar we spend on ad spend we get 55:11 in revenue. You could also look at this 55:13 as an ROI calculation. Now, this looks 55:15 really good and this looks like a good 55:16 way to index the performance of total 55:18 spend across all of our media channels 55:19 against total revenue. But the reason 55:21 why it's fundamentally a terrible metric 55:22 for indexing the performance of paid 55:24 media over time is that it includes a 55:27 large bucket of revenue that has very 55:29 little to do with paid ad, which is 55:30 returning customer revenue. And so, we 55:32 really want to strip returning customer 55:33 revenue out of this calculation because 55:36 we don't want marketing taking credit 55:38 for all these returning customers that 55:39 would have returned anywhere. So that's 55:40 why we always want to delineate all of 55:42 our finance grade metrics into new 55:44 customerbased metric. And so rather than 55:46 looking at me, we want to look at a me, 55:49 which is new customer revenue divided by 55:51 ad spend. And this might actually give 55:53 us in this example a 1.4, which might 55:56 not actually be profitable at all for 55:57 us. And so we might want to be 55:58 rethinking our whole acquisition 56:00 strategy based on this delineation to a 56:02 better number. Now, there is a metric 56:03 that's better than everything that I 56:04 just put down in the finance grade 56:06 section of that pyramid. because I 56:07 wanted to spend some time on it by 56:09 itself and give you benchmarks. Now, 56:11 this is LTGP to CAC, similar to what we 56:14 talked about before when we had CAC and 56:15 I said that we needed a pairing metric 56:17 to be able to contextualize it. This is 56:18 contextualizing those two numbers. So, 56:20 we're taking the gross profit on a 56:22 customer and we're dividing by the cost 56:23 to acquire that customer. And this will 56:24 give us an integer. And so, as an 56:26 example, if we make $100 on a customer, 56:28 it costs us $33 to actually get that 56:31 customer. This would be 100 / 33, which 56:34 is 3.33 56:35 LTG picac. Now, where we need to be 56:37 really careful with this metric is that 56:39 once again, just having an unrestricted 56:41 time on lifetime value is a terrible 56:43 idea cuz this can be measured across 6 56:45 years and so we end up way overspending 56:47 but we don't actually realize this cash 56:48 for like 4 years into the future. And so 56:50 you want to put a time constraint here. 56:52 Now generally what we will do is we will 56:53 measure this across two time horizons. 56:55 We'll measure it across 30-day LTGP to 56:58 CAC and we will do 90day LTGP to CAC and 57:00 then sometimes we'll do 365 days as well 57:03 depending on how aggressive the 57:04 acquisition strategy is. Now, as 57:06 benchmarks here, what we want to be 57:08 seeing on either of these metrics, 57:10 depending on how aggressive the 57:11 acquisition is in the business, is that 57:13 there is multiple different levels that 57:14 you can be at. So, you can be at sub 57:16 one, you can be at 1 to two, you can be 57:18 at 2 to three, you can be at 3 plus. If 57:20 you're under a one, this is generally 57:22 speaking a terrible position to be in 57:24 because it means that you are actually 57:26 losing money on acquisition. You are 57:28 losing profit because you're paying more 57:29 to acquire a customer than the gross 57:31 profit on first purchase that you're 57:33 making or within the first 90 days. You 57:35 have to have incredible retention after 57:37 this point to be able to support losses 57:39 on acquisition. Most brands that I audit 57:42 that are losing on acquisition do not 57:44 have good enough retention to support 57:45 it. So this typically is not an actual 57:48 acquisition strategy most of the time. 57:50 This is poor efficiency on acquisition. 57:53 So this is simply a fact that your ads 57:54 in your acquisition funnel isn't good 57:56 enough and this is a bad position to be 57:57 in. Some people 0.01% 01% of people can 58:00 get away with this and they have the 58:01 finance capability and the modeling to 58:03 be able to actually uh operate at this 58:05 scale and they have really good LTV. 58:06 Most people can't do this. You do not 58:08 want to be below a one on 30-day or 58:10 90day. It is worth noting that being 58:12 below a one isn't necessarily just a CAC 58:14 issue. It isn't necessarily just poor 58:16 efficiency on acquiring, but it could be 58:18 poor uh gross profit. So, if you only 58:20 have like, let's say, less than $70 in 58:21 gross profit on first order, that's 58:23 probably a problem. you just don't have 58:24 enough gross profit to be able to 58:26 substantiate acquisition at scale on a 58:28 paid platform. We then have 1 to2. This 58:30 also isn't a great position to be in for 58:33 most people. Now, if you're in CPG and 58:35 you have really good retention dynamics, 58:37 and I'll give you some benchmarks on 58:38 that later on, then you can operate in 58:40 this area. But for most people with 58:42 mediocre retention, that's okay. And if 58:44 you think your retention is good, it's 58:46 usually mediocre. You will know if you 58:48 have excellent retention because the 58:49 numbers become very evident and you can 58:50 push acquisition like crazy. So, one to 58:52 a two. You also generally don't want to 58:54 be here. This is generally not good. 58:56 Dash average if you're really wanting 58:58 aggressive growth. If you're financing 59:00 hard, if you have investors and you need 59:01 to just uh throttle up revenue, sure you 59:03 can scale on this, but it's not ideal. 59:05 Two to three, this is optimal. This is 59:07 where you want to be. This is a great 59:08 zone. You should be scaling up budgets. 59:10 Now, to give you a little bit of context 59:12 here, what does a two elig look like? 59:14 Well, if you have $100 in gross profit 59:16 on first purchase, that's simply a CAC 59:18 of 50. And so take whatever your gross 59:20 profit number is on first order, divide 59:22 by two, and that's what your CAC would 59:24 need to be to hit this optimal range, to 59:26 hit the bottom end of the optimal range. 59:28 Now, greater than a three, this is 59:29 actually also a big mistake. Being less 59:31 than a one is is a mistake. Being 59:32 greater than a three is a mistake 59:34 because this means you're just leaving 59:36 money on the table. You could scale very 59:38 aggressively here, be unbelievably 59:40 profitable, and you're probably 59:41 underleveraging paid media or whatever 59:43 marketing channels you're using to drive 59:44 this efficiency. Now, let's add two 59:46 quick notes here before we move on. Note 59:48 number one regarding attribution and 59:50 incrementality and everything that we 59:51 discussed on the pyramid. Uh when it 59:53 comes to attribution, what you want to 59:55 make sure of is that you're not using 59:57 view through conversions in the 59:58 platforms. And so when you're looking at 1:00:00 meta specifically, you want to be making 1:00:02 sure that you're using 7-day click as 1:00:04 your optimization or your reporting. 1:00:06 This is going to give you much tighter 1:00:08 congruency to acquisition me. In fact, 1:00:11 in most businesses when you look at 1:00:12 their acquisition me and then you look 1:00:14 at their 7-day click rorowaz on meta, it 1:00:16 is super correlated. And that's because 1:00:18 it doesn't include all these view 1:00:20 through conversions that is 1:00:21 overattributing in the platform. For 1:00:23 those that don't know what a view 1:00:24 through conversion is, it's when a user 1:00:25 sees your ad on Facebook, doesn't click, 1:00:27 but then buys within 24 hours, Meta can 1:00:29 claim the conversion. And a lot of those 1:00:31 people are going to buy anyway. They're 1:00:32 existing customers. The list goes on. 1:00:33 And so, you want to be not including 1:00:35 them in your reporting. You also don't 1:00:36 want to be including existing customers. 1:00:38 As I said before, existing customers 1:00:40 aren't that incremental on the platform 1:00:42 and so you don't want to be overspending 1:00:44 here. You want to go to breakdown 1:00:45 audience segments and look at where your 1:00:46 spend's going. Often people are putting 1:00:48 way too much spend to existing customers 1:00:49 and their frequency is way too high. So 1:00:51 you want to pull out the frequency 1:00:52 column and you want to make sure that 1:00:54 over the last 7 days it's under an 1:00:56 eight. Any higher than an eight and 1:00:58 you're definitely overspending because 1:00:59 you're serving to existing customers 1:01:00 more than eight times a month which is 1:01:02 not incremental. So there's a big 1:01:03 existing customer trap on the platforms. 1:01:05 The platforms always want to spend here 1:01:07 because they know they can overattribute 1:01:08 and they always want to expand their 1:01:10 attribution windows because they know 1:01:11 they can overattribute. And if they can 1:01:12 attribute more revenue, you will spend 1:01:14 more. Now, as a bit of a formula here, 1:01:16 cuz I get this question a lot, even from 1:01:18 700 800 million brands, I get this 1:01:21 question when when we come in and talk 1:01:22 to them and have calls with them, which 1:01:23 is what percentage should we be 1:01:25 allocating to existing customers? You're 1:01:27 saying that existing customers aren't 1:01:28 that incremental. Well, so then what 1:01:29 percent should we be allocating of our 1:01:31 budget? Should it be 20%, 30%, 40%? 1:01:33 Well, because 80% of our revenue as a 1:01:35 large 9 figure brand is coming from 1:01:36 existing customers because we have 1:01:38 almost full market saturation in 1:01:39 Australia. So, what are we doing? The 1:01:41 question isn't percentage. Thinking 1:01:42 about percentage allocation of ad spend 1:01:44 to existing customers is just a bad way 1:01:47 to look at it. What we instead want to 1:01:48 look at is total amount of existing 1:01:50 customers. So, how many existing 1:01:52 customers do we have? How many times do 1:01:55 we want to show them an ad? And then 1:01:56 from there, we can calculate how much we 1:01:58 need to spend. Because as long as we 1:01:59 know the CPM, we can get the total spend 1:02:02 per month. So, I'll give you the formula 1:02:04 and I'll give you an example, which is 1:02:05 let's say you have 300,000 existing 1:02:08 customers and you want to serve how many 1:02:10 ads to them. Let's say you want to serve 1:02:12 three times a month. Then you just times 1:02:13 by your CPM. So, go into the platform, 1:02:16 do an audience segment breakdown, look 1:02:17 at what's your CPM on existing 1:02:19 customers, and let's say it is $2.50. 1:02:22 Now, that's super low. It's probably 1:02:24 going to be a lot higher than that, but 1:02:25 let's just say that for the sake of this 1:02:26 example, that means that you're going to 1:02:28 have to spend $2,250 1:02:30 per month on existing customers to hit 1:02:32 them three times. Now, you can go and 1:02:34 change these variables and that will 1:02:35 change the outcome and tell you how much 1:02:36 to spend. Reality is you need to spend 1:02:38 way less than you actually think. Most 1:02:40 people think, "Oh, we're a massive 1:02:41 brand. We have multiple millions, if not 1:02:43 tens of millions of customers. We need 1:02:45 to spend hundreds of thousands a month 1:02:46 targeting them." You typically don't. 1:02:47 You could hit them with a three to six 1:02:49 frequency and you could just spend 10 1:02:51 maybe on the upper side $30 $40,000 a 1:02:53 month and you're completely fine. I see 1:02:54 tiny businesses spending $40,000 a month 1:02:56 on existing customers. And so this is 1:02:58 the math. This is what you want to be 1:03:00 doing. Let's move on to the final topic 1:03:01 here on metrics that matter, which is 1:03:03 the profit frontier. As a founder or a 1:03:05 head of digital or head of marketing, 1:03:07 the question you need to be able to 1:03:08 answer is if we were to spend an extra 1:03:11 $10,000 next month, where would we put 1:03:13 that budget? where would we get the best 1:03:15 incremental impact of that media spend? 1:03:18 Most people don't know that answer 1:03:20 reliably enough. They don't know 1:03:21 actually where they should be allocating 1:03:23 their spend and therefore they get into 1:03:25 a position where they continue to drive 1:03:27 spend up across platforms but they don't 1:03:28 see incremental returns. And so how you 1:03:30 need to be thinking through this problem 1:03:32 is what's called the next best dollar. 1:03:35 And so we want to have all of our 1:03:37 platforms, let's say Meta, Google, Tik 1:03:39 Tok, add in a bunch of other channels if 1:03:41 you're spending on them. Ideally, you 1:03:43 shouldn't be spending on a bunch of 1:03:44 tertiary channels, but let's say you 1:03:45 are. And then if we just take spend 1:03:47 here, we want to take Meta up to the 1:03:49 level of spend in which we stop getting 1:03:52 returns that we couldn't get on the 1:03:53 other platform. So, let's say this is 1:03:55 the current allocation of spend. Okay? 1:03:56 Maybe we're like 100,000 here, 50,000 1:03:58 here, 70,000 here. What we want to know 1:04:01 is if we went and put an extra 10,000 1:04:03 into Tik Tok off the top here, what 1:04:05 revenue return would we get of this 1:04:06 $10,000? Same thing for Google, same 1:04:08 thing for Meta. And this is what we're 1:04:10 constantly trying to solve for because 1:04:12 ultimately to grow the business we need 1:04:13 to continue allocating more marketing 1:04:15 dollars across the current marketing 1:04:17 channels and we need to do it in the 1:04:18 most efficient way possible. The only 1:04:20 way to really understand this is to 1:04:22 unfortunately run incrementality tests. 1:04:23 We would want to for example go and put 1:04:25 10 into meta but do it in a controlled 1:04:27 incrementality test where we can get a 1:04:28 read on what this is. Same thing for 1:04:30 Google, same thing for Tik Tok. Now we 1:04:32 can also just do this intuitively over 1:04:33 time. You could use MM as well, some 1:04:36 marketing mix models to be able to 1:04:37 identify where you should be putting 1:04:38 media spend at a lower revenue threshold 1:04:40 like seven figure brands. This should be 1:04:42 relatively intuitive if you're a good 1:04:43 performance marketer. But this is 1:04:44 ultimately the problem that you should 1:04:45 be solving for. And you should also be 1:04:47 thinking about this inverse. And so 1:04:49 could we pull 10,000 out of Google, 1:04:52 reallocate it to Meta, and get a better 1:04:54 ROI? Maybe this 10,000 here is only 1:04:56 driving us a 2x, but we could go and put 1:04:58 another 10,000 into Meta, and it would 1:05:00 give us a 4x on new customer revenue. 1:05:03 And so we should be rebalancing budgets 1:05:05 and reallocating and scaling across here 1:05:07 based on incremental returns and the 1:05:09 profit frontier. And so the profit 1:05:11 frontier is that you want to go all the 1:05:13 way up to the point in which you're 1:05:14 making no more profit past this nominal 1:05:17 dollar in spend. And so you want to find 1:05:18 the point in which your acquisition me 1:05:21 becomes the break even point. And once 1:05:23 you hit that break even point 1:05:24 incrementally that's where you stop 1:05:26 spending. And you're doing that across 1:05:27 all channels at all times. And only 1:05:29 until you've reached the profit frontier 1:05:30 on all of these three primary channels 1:05:32 do you go and move on to adding in 1:05:34 additional channels. So cash flow verse 1:05:36 profit. Profit doesn't equal cash. And 1:05:38 that's ultimately why paid media within 1:05:41 e-commerce is way more complex than in a 1:05:43 service-based business or than in a SAS 1:05:45 business or particularly in the info 1:05:48 space. And it's because when you scale 1:05:50 up, you need to commit cash to future 1:05:52 inventory purchasing which substantially 1:05:55 restricts the actual dividends that 1:05:57 could be yielded within the business. 1:05:58 And so if you see an e-commerce business 1:05:59 doing 100k in profit, reality is 1:06:01 founders probably taking no money. Even 1:06:03 a million dollars in profit, $2 million 1:06:05 in profit, respective to the total 1:06:07 revenue and growth rate, there may be 1:06:08 actually no profit available at the end 1:06:10 of the day cuz it all gets reinvested 1:06:12 into future inventory buying. So let's 1:06:14 say that the P&L here shows $2 million 1:06:17 in profit per year, but the balance 1:06:19 sheet has $3 million on it. So the 1:06:21 balance sheet will show you the assets 1:06:23 of the business. The assets in 1:06:25 e-commerce is typically unsold 1:06:27 inventory. Now, there could be buildings 1:06:29 on here if they own the office, if they 1:06:30 own the warehouse, etc. But typically 1:06:32 for most businesses, we're just talking 1:06:34 about inventory sitting on the balance 1:06:35 sheet as well as cash sitting on the 1:06:38 balance sheet. So, the reality of this 1:06:39 business right here is that they are not 1:06:42 in a good position. The brand actually 1:06:44 has negative cash flow depending on how 1:06:46 we're looking this across time and how 1:06:48 the cash is actually moving. But if they 1:06:50 made $2 million in profit, but $3 1:06:51 million in inventory didn't sell, well, 1:06:54 they actually made no money. They would 1:06:55 actually be cash flow negative. They 1:06:57 would be down a million in cash because 1:06:59 yes, they made 2 mil, but they bought 3 1:07:01 mil in inventory and it never moved. Not 1:07:02 good. So, this is where we get into the 1:07:04 inventory death spiral or what can also 1:07:06 be called as skew rationalization. So, 1:07:10 all of your inventory has a grade 1:07:13 associated to it. Most people are 1:07:14 probably aware of this, but the 1:07:16 marketers and the performance marketers 1:07:18 watching this aren't. And so this will 1:07:19 become incredibly helpful in making paid 1:07:21 media decisions and tying this into how 1:07:23 we should be approaching the ad account. 1:07:25 Grade A is fast moving inventory. This 1:07:28 is inventory that is selling quickly 1:07:30 that we honestly don't need to worry 1:07:32 about. We need to just be thinking about 1:07:34 how do we not go out of stock. Grade B 1:07:35 inventory is medium sell to rate. This 1:07:38 is slowm moving sell to rate and then 1:07:41 this is not moving at all. Now, Shopify 1:07:44 actually auto ranks your inventory 1:07:46 anyway. As long as the inventory is 1:07:48 within Shopify, you can actually see 1:07:49 this yourself. I think it's under the 1:07:51 products tab. Now, the key here really 1:07:52 is that people will do product launches 1:07:55 because product launches are one of the 1:07:56 best levers, one of the best ways to 1:07:58 scale an ecom brand. Ultimately, you 1:08:00 have brands like Grunes, like AG1, like 1:08:03 IM8. All of these brands have gotten 1:08:05 enormous, hundreds of millions of 1:08:06 dollars, close to a billion dollars with 1:08:09 just one product. But that is misleading 1:08:11 you in terms of how most e-commerce 1:08:13 brands need to grow because those brands 1:08:15 have done so well because they're in the 1:08:16 supplement space. And in the supplement 1:08:18 space, you have a very unique advantage 1:08:19 which is that you can reposition your 1:08:21 product into technically like different 1:08:22 products. And so if you're selling a 1:08:23 multivitamin, you can say that that 1:08:25 multivitamin helps with gut health, but 1:08:27 it also helps with hair health, but it 1:08:28 also helps with all these other 1:08:29 problems. And so because of that, you 1:08:31 have a really large total addressable 1:08:32 market that you can reposition the 1:08:34 product into. With most brands, you 1:08:35 cannot reposition your product enough to 1:08:37 gain a large total addressable market. 1:08:39 So you need to do future product 1:08:40 launches. Now when you do that, what 1:08:42 ends up happening is they don't work. A 1:08:44 lot of product launches fail. And so 1:08:46 they either land into grade B, grade C, 1:08:49 or you get an initial pop from organic 1:08:52 and existing customers and you can sell 1:08:53 through maybe the first 30 40% of 1:08:55 inventory and then it doesn't move at 1:08:57 all. And so then you get stuck in a 1:08:58 situation which is that every time 1:08:59 you're trying to grow through product 1:09:01 expansion, you were just adding more 1:09:02 cash onto the balance sheet that is 1:09:04 restricting the cash position of the 1:09:06 business and the ability for you to 1:09:07 scale. This is where skew 1:09:09 rationalization becomes incredibly 1:09:10 important, which is that as new products 1:09:12 are introduced into the product suite, 1:09:14 you need to be thinking about how can we 1:09:16 rationalize our skew count down always 1:09:18 to be able to decrease the amount of 1:09:19 available products, to be able to 1:09:21 decrease the amount of inventory on 1:09:22 hand. There also needs to be some 1:09:24 congruency here between the marketing 1:09:26 team and the finance team. And this is 1:09:27 where an enormous mistake is made, not 1:09:29 only on agency side, but internally a 1:09:32 lot of the time, which is that let's 1:09:33 play through an example of this, which 1:09:34 is that you go and you launch a new 1:09:36 product. it doesn't do very well. Okay, 1:09:38 it works on the organic list. You get a 1:09:40 bit of a pop out of it. Maybe you do 1:09:41 100K, but you ordered a million dollars 1:09:43 worth in sell value and then because of 1:09:45 that, it ends up over here in grade D or 1:09:47 grade C. It's barely moving. You look at 1:09:49 the sell through rate and you go, we 1:09:50 currently have 400 days of inventory on 1:09:53 hand, which means it's going to take 400 1:09:55 days for us to sell through all this 1:09:56 product, which isn't good. Okay, we want 1:09:58 to move this back to cash as soon as 1:09:59 possible. Now, what will normally happen 1:10:00 is the CFO or the owner or the manager 1:10:02 or whatever it is, will look at this and 1:10:04 go, "Ah, that's annoying. 1:10:06 and then try to solution it themselves. 1:10:08 But a lot of the time, we can actually 1:10:09 just solve this through paid media. 1:10:12 Okay, we can take this product that 1:10:13 didn't do too well and we can start 1:10:15 pushing spend behind it on paid media at 1:10:17 break even or maybe even a slight loss. 1:10:19 Dedicate isolated into its own adfunnel. 1:10:22 So, it doesn't even have to sit on the 1:10:23 website. It can sit separately. We can 1:10:25 push it through whitelisting pages. What 1:10:26 we've also done for a client is we've 1:10:28 actually taken their grade D inventory 1:10:29 and we've pushed it into a new country 1:10:31 that we knew would perform well. we 1:10:32 could discount heavily, wouldn't erode 1:10:34 the brand equity within the primary 1:10:36 market, and then we can just move all 1:10:37 this stock using paid and turn it back 1:10:39 into cash. Now, people don't have this 1:10:41 conversation enough with the paid media 1:10:42 team because the paid media team, the 1:10:44 agency, is always KPI on profit, and 1:10:46 this can be really detrimental. If we're 1:10:48 trying to just maximize the profit 1:10:49 position of the business, what ends up 1:10:51 happening is all of our spend will 1:10:52 actually go up here and we'll end up 1:10:53 really messing the business up because 1:10:55 they'll have all this great BCD 1:10:56 inventory that doesn't actually get 1:10:57 prioritized in paid media at all cuz it 1:10:59 doesn't have a good ROI, a good row, a 1:11:01 good efficiency. We need to push grade 1:11:03 BCD inventory in paid to be able to turn 1:11:05 it back into cash. This is where you do 1:11:07 need an understanding of the inventory 1:11:08 position of the business so that we can 1:11:10 make better decisions on paid for the 1:11:12 overall health of the business. All 1:11:14 right, we can't talk about finance 1:11:15 without talking about cash conversion 1:11:17 cycles. The actual formula for cash 1:11:19 conversion cycle is DIIO plus DSO minus 1:11:22 DPO. So days in inventory, days sales 1:11:24 outstanding, days of payable 1:11:26 outstanding. Now this is a 20-minute 1:11:28 video on itself. In fact, we've actually 1:11:29 put out a lot of content on cash 1:11:30 conversion cycles, how to calculate it, 1:11:32 how to measure it, what's good, what's 1:11:33 bad, etc. So instead of going too deep 1:11:35 here, I instead just want to explain the 1:11:37 top level concept so that if you're in 1:11:40 marketing, you can understand how this 1:11:41 actually relates into the business and 1:11:43 where you might need to be understanding 1:11:44 of these components. If you actually own 1:11:46 the business or you're a CFO, I 1:11:47 recommend watching our other videos that 1:11:48 go into a little bit more detail on this 1:11:50 topic. But the fundamental idea here is 1:11:52 that if you sell 10 units of product and 1:11:54 you make $50 in gross profit here, and 1:11:57 once again, I'm just making numbers up, 1:11:58 but this will make sense. What ends up 1:12:00 happening here is because you only have 1:12:01 $50 left in your hand, you can only then 1:12:04 go and buy 11 units on your next 1:12:06 purchase order. And then you'll make $55 1:12:08 in gross profit. And then this will 1:12:10 allow you to maybe buy 12 units on the 1:12:13 next order. And then this goes on and on 1:12:14 and on. And so the limiter in this 1:12:16 business is not the actual selling of 1:12:18 the product. It isn't marketing. It 1:12:20 isn't ROI on Facebook ads. It's the fact 1:12:22 that they're just cash limited in their 1:12:24 ability to buy future inventory because 1:12:26 the amount of gross profit that is 1:12:27 generated off this purchasing only 1:12:29 allows a slight increase in future 1:12:31 purchasing. Now, what you need to also 1:12:32 add in here is let's say that there is a 1:12:34 90-day lead time from putting the 1:12:36 inventory in to actually arriving at 1:12:38 your warehouse to when you can start 1:12:39 selling. And let's assume that you have 1:12:41 very poor terms with the manufacturer 1:12:43 cuz these POS are so small. So, you 1:12:44 actually have to put the money down 90 1:12:46 days prior to the actual inventory 1:12:48 coming. then you're in a terrible 1:12:50 position because you have to buy these 1:12:52 11 units after you sell these units to 1:12:54 get the cash. And so you actually then 1:12:56 have to go out of stock for 90 days here 1:12:58 cuz once you have this cash, then you 1:12:59 can buy the units, then you can actually 1:13:01 go and sell again. And so what you would 1:13:02 want to do here in this example is you 1:13:04 would want to negotiate on better 1:13:06 supplier terms so you aren't out of 1:13:08 stock for 90 days. And so you might 1:13:09 actually negotiate that you only need to 1:13:11 pay 20% upfront here. And so after you 1:13:13 sell two units, you have enough money to 1:13:15 put in your next order. And then as 1:13:16 you're selling through these other 1:13:17 units, you're going through this 90-day 1:13:19 period. Then once these actually get 1:13:21 shipped, you pay the rest. And so maybe 1:13:22 the shipping time is 20 days. You're 1:13:23 actually only out of stock for 20 days 1:13:25 rather than 90. Now, another thing that 1:13:26 you could do is you could reduce the 1:13:29 lead time from the manufacturer. So 1:13:31 rather than it taking 90 days, you could 1:13:33 try get a different manufacturer that 1:13:34 can reduce this down to maybe 30. And 1:13:36 then that fixes the whole problem in 1:13:38 itself. Or the last one is you use uh 1:13:41 some kind of inventory financing. And so 1:13:44 you actually take a loan out to pay for 1:13:46 this inventory so that you can sell 1:13:48 through, get this profit. This profit 1:13:49 pays down the loan as you're going. And 1:13:51 then this inventory comes. You then 1:13:52 start selling this inventory. You open 1:13:54 up another loan and this loan pays for 1:13:56 the next purchase. And so you can get 1:13:57 ahead by opening up some kind of 1:13:59 financing. Now the issue is that 1:14:00 financing is very expensive. When 1:14:02 companies are loaning, you'll pretty 1:14:03 much always have to put yourself up as 1:14:04 collateral as well. And it's super 1:14:06 risky. anytime you're introducing a lot 1:14:07 of debt into the business, you're 1:14:08 banking on the fact that you could sell 1:14:10 these future units at an efficiency 1:14:12 that's going to allow you to pay down 1:14:13 the loan. And so if you are agency side 1:14:16 and you're working with like seven 1:14:17 figure businesses, they're almost always 1:14:18 using some kind of financing to be able 1:14:20 to actually grow. Um, and you need to 1:14:22 take into consideration that rapid 1:14:23 growth for these types of businesses 1:14:25 usually actually isn't possible. The 1:14:26 limiter isn't on how quick we can 1:14:28 increase ad spend or how efficient the 1:14:30 ads are. The limiter is the cash 1:14:31 conversion cycle. And so I have actually 1:14:33 seen single-handedly probably five times 1:14:35 now sevenf figureure businesses that 1:14:37 have gone to an agency. The agency has 1:14:39 absolutely crushed it. They've ramp 1:14:40 spend from $20,000 a month to $200,000 a 1:14:43 month. They haven't actually had any 1:14:45 visibility into any of this with the 1:14:47 client. The client in the background to 1:14:49 fuel this growth has gone and taken 1:14:51 millions of dollars out in inventory 1:14:53 financing to be able to fuel the growth 1:14:55 to be able to buy all the future 1:14:56 inventory required to hit that kind of 1:14:58 tripling per year in growth rate. And 1:15:00 then as they've done that, their 1:15:01 interest repayments get so large and the 1:15:04 ads start becoming inefficient to where 1:15:06 net margins get squeezed down to zero. 1:15:08 And now, yes, we've taken a business 1:15:09 from doing $2 million a year to $7 1:15:11 million a year. Like, amazing. Good job, 1:15:13 agency. But the business has gone from 1:15:15 cash flow positive founders actually 1:15:17 taking an income to the business is near 1:15:19 zero on net profit after all the 1:15:22 interest repayments and the compression 1:15:24 in marketing efficiency as they've 1:15:26 achieved that additional scale. And so 1:15:27 this is why this is so important for at 1:15:29 least people to be across so that they 1:15:30 can understand that you just like can't 1:15:32 triple an ecom business in a year. You 1:15:33 just can't because the cash requirements 1:15:35 on inventory are just so enormous. And 1:15:37 so you either need a lot of 1:15:38 self-funding. So the person starting the 1:15:40 business needs to have multi-millions 1:15:42 liquid. They need funding from someone 1:15:44 who's providing the multi-millions or 1:15:46 they need to go into some kind of 1:15:47 financing. Otherwise, you just can't 1:15:49 scale inventory orders fast enough to 1:15:51 keep up with the scale that you want to 1:15:53 achieve within the business. And a lot 1:15:54 of the time where this gets a little bit 1:15:55 dangerous is that uh people will pull 1:15:57 marketing efficiency down to achieve 1:16:00 larger scale but to do that it yes it 1:16:03 accelerates the growth rate but it also 1:16:06 increases the amount of debt that the 1:16:08 brand needs to take on which actually 1:16:09 compresses margins down to zero. And so 1:16:11 you would actually be better off growing 1:16:13 slightly slower at a slightly lower me. 1:16:16 So a better efficiency, less marketing 1:16:18 spend, having less interest repayments 1:16:20 and making way more money rather than 1:16:21 just trying to arbitrarily accelerate 1:16:23 your growth rate but increasing interest 1:16:25 payments and making marketing efficiency 1:16:27 worse. So that's just where you need to 1:16:28 be very very careful in your financial 1:16:30 modeling. Now this is where ultimately 1:16:31 there starts to become a CFO verse CMO 1:16:35 disconnect in a lot of businesses in 1:16:37 this core decision which is let's run 1:16:39 through an example. February arrives and 1:16:41 inventory from November is still unsold. 1:16:44 Maybe there was some new products in 1:16:45 here that didn't do so well and they've 1:16:47 fallen into grade C and D inventory. 1:16:49 Now, most CFOs will actually look at 1:16:52 this and go, "Okay, we need to cut media 1:16:55 spend because we actually don't have 1:16:56 enough cash right now to be able to fuel 1:16:59 the current marketing spend in Feb 1:17:01 through to April." And so, let's go and 1:17:03 take our marketing expenses and cut them 1:17:04 by 40%. And now this is quite logical 1:17:07 when you're just opening up and looking 1:17:08 at the P&L because when you look at the 1:17:10 P&L uh cost of delivery might be 30%, 1:17:13 marketing might be 25% and then opex is 1:17:15 15 and so profit in this business is at 1:17:18 30%. Now when revenue suddenly decreases 1:17:21 in Feb and we actually don't have a lot 1:17:23 of cash available me might go up and 1:17:26 spike to 30% which starts compressing 1:17:28 profitability down and profitability 1:17:31 dips to 25%. Now naturally the reaction 1:17:33 from the CFO should be this has gotten 1:17:36 out of control. ME has accelerated up. 1:17:38 We need to cut marketing spend. 1:17:40 Marketing team cut your budgets. Now 1:17:42 there's two issues here. Number one is 1:17:43 that in this business it might very much 1:17:45 so be the fact that the marketing is 1:17:48 driving the revenue. And so this 1:17:49 compression in marketing efficiency is 1:17:51 an efficiency issue on the current media 1:17:53 spend. We shouldn't be cutting media 1:17:55 spend because if we cut media spend, 1:17:57 revenue will dip further. And so you can 1:17:59 get into these uh cyclical situations 1:18:01 where marketing spend drops, revenue 1:18:03 drops, profitability decreases. To fix 1:18:05 profitability, marketing spend needs to 1:18:07 drop even more. And you keep dropping 1:18:08 marketing spend to try to fix the issue, 1:18:10 but it doesn't fix it. The other issue 1:18:11 is that to fix this grade CN inventory 1:18:13 issue for November, what we actually 1:18:15 need to do is quite counterintuitive. We 1:18:17 need to increase marketing spend even 1:18:18 further because we need to sell out of 1:18:20 the CND inventory and it's not going to 1:18:22 be profitable to do so. And so what we 1:18:24 actually need to do here is we need to 1:18:26 take me that's now inflated to 30% and 1:18:29 we need to say hey you know we actually 1:18:30 need to move this in the short term to 1:18:32 35%. And this additional 5% will be 1:18:35 purely for driving all the CND inventory 1:18:38 so that we can turn this back into cash 1:18:40 which is going to give us a more a 1:18:42 better position from a cash perspective 1:18:44 which is going to allow us to get back 1:18:45 into the old revenue position. So let's 1:18:47 then put it all together. First place to 1:18:49 start is in forecasting. Forecasting is 1:18:52 critical for anything financial related 1:18:54 because it allows us to set our KPIs and 1:18:56 our expectations that we're then 1:18:58 measuring off on an ongoing basis to be 1:18:59 able to call scribe. Anytime you are 1:19:01 forecasting, the number one rule of 1:19:03 forecasting in e-commerce direct to 1:19:05 consumer is that you always need to 1:19:07 forecast new customer revenue separate 1:19:09 to returning customer revenue. And the 1:19:11 reason being is that both of these 1:19:12 revenue buckets have different 1:19:13 underlying levers that impact the 1:19:16 realization of the revenue. So for new 1:19:18 customers, this is primarily going to be 1:19:20 marketing. Now, this might be just 1:19:21 advertising spend for some brands. This 1:19:23 might be advertising spend plus events 1:19:24 and influencers. This might advertising 1:19:26 plus influencers plus other tertiary 1:19:28 channels as well. When it comes to 1:19:30 returning customer revenue, yes, 1:19:31 advertising spend is going to drive a 1:19:33 little bit of returning customer 1:19:34 revenue. Yes, an influencer activation 1:19:36 might drive a little bit of returning 1:19:37 customer revenue, but primarily 1:19:39 returning customers is going to come 1:19:40 from uh product launches. It's going to 1:19:43 come from marketing events like discount 1:19:45 periods and it's going to come from 1:19:46 other activations that are going to prop 1:19:49 up and give returning customers a reason 1:19:51 to come back. There is also obviously 1:19:54 direct communication channels in here as 1:19:56 well like email and SMS. These are 1:19:59 different inputs compared to over here. 1:20:01 Therefore, when we are forecasting, what 1:20:03 are we doing? We're actually just 1:20:05 forecasting inputs and therefore getting 1:20:07 an output which is the forecast or the 1:20:09 budget. And so we need to look backwards 1:20:11 into the inputs to be able to then 1:20:13 determine how we're actually getting the 1:20:14 final number. For returning customers, 1:20:16 you want to look at all of your 1:20:17 returning customer cohorts. So every 1:20:19 month back to let's go Jan 2021 all the 1:20:23 way to Feb 2021. And this goes on and on 1:20:25 and on all the way to today. There was a 1:20:27 certain amount of new customers that you 1:20:28 acquired. Maybe back here it was 100, 1:20:30 then it was 110, then it was 150. All of 1:20:32 these customers repeat at a certain rate 1:20:34 over time. And you can see this in your 1:20:36 cohort analysis. And so people might 1:20:38 come back at 7% in the first month, then 1:20:40 6%, then 4%, then two, then one, and 1:20:43 then ultimately it asotopes down to 1:20:45 usually a pretty small number. What you 1:20:47 can then do is you can take all of these 1:20:49 old cohorts and the amount of customers 1:20:50 that you acquired then, and you can go, 1:20:52 okay, well, they're about to enter into 1:20:54 month 32 of them being a customer. What 1:20:56 is the repeat rate on average of someone 1:20:58 after month 32? And maybe it is 0.1%. So 1:21:02 then you take your 100, you times by 1:21:03 0.1%. You then times by whatever your 1:21:06 average order value is on returning 1:21:09 customers, which let's say it's $100. 1:21:11 And so we would expect this cohort to 1:21:13 give us $1 in returning customer revenue 1:21:15 next month. And then we do that for the 1:21:16 next cohort and the next cohort and the 1:21:18 next cohort and every single cohort that 1:21:20 we've had in the past. And this will 1:21:21 then give us a realistic extrapolation 1:21:24 of what we should expect returning 1:21:26 customers to contribute to next month. 1:21:27 This number ends up being usually within 1:21:29 about 10% accuracy. To get it within 1 1:21:32 to 2%, you do two things. Number one, 1:21:34 you bake on seasonality. And so you look 1:21:36 at uh average seasonality in the last 3 1:21:39 years across the calendar year and you 1:21:40 just apply a factor based on returning 1:21:42 customer revenue seasonality. Number 1:21:44 one. Number two is you then go and 1:21:46 superimpose marketing events that are 1:21:48 going to substantially change these 1:21:49 numbers. So obviously if you have a 1:21:51 major marketing event that's going to 1:21:52 occur this year that didn't occur last 1:21:54 year, there's going to be revenue driven 1:21:55 from that. You want to figure out what 1:21:56 is your uh expected revenue realization 1:21:59 and then you add that into the forecast. 1:22:00 Then on new customer revenue. Now in 1:22:03 paid ads, this is relatively 1:22:04 straightforward when you have larger 1:22:06 media makes for if you have other 1:22:07 channels like influencers etc. This is 1:22:09 where you have to build your own 1:22:10 modeling around this. And this is 1:22:11 obviously where modeling and finance and 1:22:14 data science becomes pretty critical 1:22:15 once you once you achieve scale as an 8 1:22:17 to9 figure brand. To keep it simple for 1:22:19 paid ads, what you want to do is you 1:22:21 want to take your acquisition me on the 1:22:23 y- axis. You want to take spend on the 1:22:25 x- axis and you want to plot the last 1:22:27 let's for the sake of this video say one 1:22:29 year of daily data. What you'll see is 1:22:31 that every day there is a certain 1:22:33 acquisition me that is associated to 1:22:35 that day. And what should end up 1:22:36 happening is you should have something 1:22:38 like this. Now if you go and put a 1:22:40 logarithmic regression or a linear 1:22:42 regression whatever has the best fit for 1:22:43 the model you will get an average of the 1:22:46 expected efficiency at a certain spend 1:22:48 level. Now you will have outliers like 1:22:49 these outliers over here. These are 1:22:51 typically sales periods. So, we want to 1:22:52 actually remove these from the data set. 1:22:54 We also know that if we're only going to 1:22:56 be spending a minimum of $1,000 a month, 1:22:58 we could also just remove anything under 1:23:00 $1,000 a month out of the model, too. 1:23:02 And that's going to improve the accuracy 1:23:04 of modeling on these higherend spends. 1:23:05 Then, what you're also going to have is 1:23:06 all of these over here are going to be 1:23:08 November and Black Friday periods that 1:23:10 are significantly overinflating the 1:23:12 actual efficiency that you would expect 1:23:14 during BAU. So, you want to go and 1:23:15 remove all these two. And then what 1:23:17 you'll get is this line through all of 1:23:18 the days last year at certain spend at 1:23:21 certain efficiency. And then you know, 1:23:22 okay, if we go and spend $8,000 a day 1:23:25 here, we know roughly what efficiency we 1:23:27 should expect. And this is how you model 1:23:29 out spend and new customer revenue 1:23:31 expectations. You would actually back 1:23:32 propagate from your new customer goal. 1:23:34 So if you wanted to get like let's say 1:23:36 $100,000 in new customer revenue, uh, 1:23:38 and let's say you want to do it at a 4 1:23:40 a.m., you would just go to a four on the 1:23:42 graph, which might be here. You would go 1:23:44 across and you go, "Okay, it's right 1:23:45 here. Can we spend enough to get a 4 AM 1:23:48 in this much revenue? And the answer 1:23:49 might be no. Okay. Well, we 1:23:50 fundamentally have an issue yet. We need 1:23:52 to rethink what we're going to do 1:23:53 differently this year to generate 1:23:55 outsized returns compared to the average 1:23:57 of last year. Are we going to do some 1:23:58 kind of marketing event? Is there going 1:24:00 to be a new product launch? Is there 1:24:01 going to be a new channel? Are we 1:24:02 tripling creative production? Like what 1:24:03 actual input or lever is going to 1:24:05 generate this outcome? And this is 1:24:06 ultimately the exercise of forecasting. 1:24:08 The exercise of forecasting is to look 1:24:09 at the realistic expectation against the 1:24:11 target. There's going to be a delta. 1:24:12 There's always going to be a delta. the 1:24:13 board wants you to hit 50 million. You 1:24:15 do this and you go, the math says we can 1:24:17 only hit 40 million. And then you look 1:24:19 at, well, what are the inputs required 1:24:21 to achieve that $10 million difference? 1:24:23 We talked about the profit frontier. We 1:24:24 talked about how funnels overattribute 1:24:26 at the bottom, underattribute at the 1:24:28 top. But I want to reinforce this idea 1:24:29 because it's a really common mistake in 1:24:31 budget allocation, which is that you 1:24:33 will always see the best rorowaz at the 1:24:35 bottom. You will always see the worst 1:24:36 rorowaz at the top. And so if you have a 1:24:39 meta campaign that's attributing at a 2x 1:24:42 and you have a Google campaign that's 1:24:43 attributing at a 6x, don't simply go and 1:24:46 put more budget here. This is going to 1:24:47 be overattributing because it sits at 1:24:49 the bottom of funnel. It might have 1:24:50 branded key terms. It might be 1:24:51 retargeting. Even if you have a bunch of 1:24:52 exclusions in place, probably bottom of 1:24:54 funnel and you're actually going to see 1:24:56 better incremental impact putting 1:24:57 budgets here. Obviously, always do this 1:24:59 within a controlled test. Make sure that 1:25:00 you're on top of whether this is 1:25:02 actually true or not for your particular 1:25:03 business. But be very careful with 1:25:05 rorowaz reads in the platform because 1:25:06 it's going to usually make you 1:25:08 overallocate to bottom of funnel efforts 1:25:10 and then you'll wonder why you're not 1:25:11 growing when you're increasing budgets. 1:25:12 One thing that we haven't touched on at 1:25:14 all here and this starts to get a little 1:25:16 bit outside of finance but I think it's 1:25:18 a really important mention which is 1:25:20 brand at 1 million to I would actually 1:25:23 argue probably 20 million. You can just 1:25:26 brute force revenue through performance 1:25:28 marketing. Okay, performance marketing 1:25:29 through ads can get you here very 1:25:31 easily. You put $1 in, you get $4 out, 1:25:33 and then you continue to scale. But 1:25:35 eventually, what usually ends up 1:25:36 happening is you hit diminishing 1:25:38 returns, which is that as you try to put 1:25:40 more spend into these platforms, as you 1:25:42 try to start pushing past $30,000 a day, 1:25:44 $40,000 a day in ad spend, you just 1:25:46 can't get any further. And the way to go 1:25:48 further is through some kind of brand 1:25:49 effect. And I say this from personal 1:25:51 experience myself. Obviously, in the 1:25:53 early days, we worked with three 500 7 1:25:55 figure brands, either full-time or in 1:25:57 some kind of consulting capacity. And 1:25:59 these days we currently work with over 1:26:01 68 and 9 figure brands and close to 10 1:26:03 figure brands as well. And so we have 1:26:05 seen both sides of the spectrum. All 1:26:07 these small brands that are very reliant 1:26:08 on performance marketing and all of 1:26:09 these large retail brands that aren't 1:26:11 reliant on performance marketing at all. 1:26:12 And in fact, there's actually a huge 1:26:14 opportunity in performance marketing 1:26:15 because they don't do it very well. And 1:26:16 this is due to the brand that they have 1:26:18 within the platform. You can open up and 1:26:20 I do this all the time. You can open up 1:26:22 one fashion ad account that's doing 1:26:23 maybe $8 million a year and you can look 1:26:25 at all the core metrics in the Facebook 1:26:26 ad account. the click-through rates, the 1:26:27 CPMs, the CPCs, the conversion rate, and 1:26:30 then I can go and open up another 1:26:31 fashion ad account of a business doing 1:26:33 $300 million a year. And the crazy thing 1:26:35 is that the ad account over here has 1:26:37 better metrics on everything. They have 1:26:39 better CPMs. They have better 1:26:40 click-through rates. They have better 1:26:41 CPCs. They have better rows. Everything 1:26:43 is better. And you go, how is that even 1:26:45 possible? They're doing like 20x the 1:26:47 volume. They're doing 20x the ad spend. 1:26:49 That just doesn't make sense because as 1:26:51 you scale paid media, you hit 1:26:52 diminishing returns. So, why are they 1:26:54 not seeing all their numbers degrade? 1:26:55 And it's because of this overarching 1:26:57 brand effect that they have in the 1:26:58 market. They have so much market 1:26:59 saturation. They have so many 1:27:01 associations that have been built 1:27:02 through external marketing efforts that 1:27:04 sit outside of the ad account that 1:27:06 inside the ad account it looks really 1:27:07 good. But it's because of everything 1:27:08 that they're doing outside of the ad 1:27:09 account that makes it look good. And so 1:27:11 I can go in any day on a brand that 1:27:12 every single person knows and run ads 1:27:14 and I'll have incredible clickthrough 1:27:15 rates cuz everyone knows who they are. 1:27:16 But if I go in on a brand that no one 1:27:18 knows who they are and I'm trying to 1:27:19 push, obviously the performance 1:27:20 marketing has to be a lot better and 1:27:21 that's why you hit diminishing returns. 1:27:22 And so also just when you're thinking 1:27:24 about marketing expense allocation, I 1:27:26 would always be having some kind of 1:27:28 budget towards branding efforts. And by 1:27:31 branding efforts, it's making 1:27:32 associations within the market that is 1:27:34 going to put you in front of the 1:27:36 customer where they are. And so if your 1:27:37 customers are commonly in a particular 1:27:39 area or at a particular event or looking 1:27:42 at particular things, that's where you 1:27:43 want to show up to be able to build 1:27:45 associations. I'll give you two personal 1:27:47 examples of this, which is that I've 1:27:48 recently bought running gear from two 1:27:51 different brands. I bought from 2xU 1:27:52 which is an Australian brand. It's 1:27:54 actually a client of ours and then 1:27:55 another brand which is 247 represent. 1:27:59 And the reason I bought from this brand 1:28:02 was because all of the running 1:28:04 influencers that I follow, all the 1:28:06 people that I watch YouTube videos of 1:28:07 every week, all the people that I follow 1:28:09 on Instagram, they are all either 1:28:10 sponsored by 247 or they just wear it. 1:28:13 They make associations with it. And so 1:28:14 because of that, that natural 1:28:16 association that's been made within 1:28:17 market of where I end up showing up on 1:28:19 the content that I consume is the reason 1:28:21 why I bought it. Had nothing to do with 1:28:22 quality, had nothing to do with anything 1:28:24 except for the fact that I follow all 1:28:25 these running influencers, ended up 1:28:27 following the founder, following his 1:28:28 story, watching podcasts of him, and 1:28:30 that's ultimately what pushed me to the 1:28:31 purchase. I would argue that all of 1:28:33 those uh influencer deals, all of the 1:28:36 branding exercises, them showing up to 1:28:37 run clubs, etc., that probably doesn't 1:28:39 have direct profitable ROI. They're 1:28:41 probably not getting the coupon code 1:28:42 that the influencer has at checkout, 1:28:45 which I don't think they even do, but 1:28:46 let's say they did do it. Probably not a 1:28:47 profitable exchange, but is the 1:28:49 overarching branding effect of making 1:28:50 those associations that ends up pushing 1:28:52 tons of people to purchase. On 2XU, it's 1:28:54 the branding of premium. Now, 2XU's 1:28:57 products are incredibly premium. I think 1:28:59 they're probably one of the highest 1:29:00 quality products in Australia in this 1:29:01 market. But honestly, I don't think that 1:29:03 even matters for me in terms of my 1:29:05 purchasing decision. I didn't purchase 1:29:06 because I knew the product was quality 1:29:08 cuz I bought online. I hadn't seen it. I 1:29:10 bought because of the perception of 1:29:12 quality and so it is the brand 1:29:14 perception that they have built that 1:29:15 this is the highest quality uh 1:29:17 activewear clothing in Australia that is 1:29:19 causing me to buy. Now once again was 1:29:21 this through some kind of performance 1:29:22 marketing ad? No. Was this through a 1:29:24 Google ad? No. It was through the 1:29:26 overarching associations that they make. 1:29:28 It's through the messaging that they 1:29:29 have and it's through the way that they 1:29:31 show up in the creative as well 1:29:32 particularly in the campaign shoots that 1:29:35 makes the perception that it is super 1:29:36 high quality which ultimately drove me 1:29:38 towards that conversion. And so both of 1:29:40 these purchases likely wouldn't have 1:29:42 happened through any kind of performance 1:29:43 marketing effort. They actually occurred 1:29:44 through brand which is why that we can't 1:29:46 understate this and we need to have it 1:29:47 as a portion of the video because it is 1:29:49 unbelievably important particularly as 1:29:50 you continue to scale and it should be 1:29:52 thought through as a budget allocation 1:29:54 of an expense on the P&L. So wrapping 1:29:56 this up, if there are five things that 1:29:58 you should be walking away with as key 1:30:00 takeaways to take forward in your 1:30:03 business or working with a client, it is 1:30:05 number one, know your gross margin and 1:30:08 know how to calculate it correctly. You 1:30:10 need to understand variable costs. You 1:30:12 need to understand the difference 1:30:13 between product margin and gross margin. 1:30:15 You need to understand how that also 1:30:16 then flows through into contribution 1:30:18 margin. Number two is you need to 1:30:19 understand the definitions and you need 1:30:21 to have live dashboards that track 1:30:23 acquisition me profit contribution 1:30:26 ideally LTGP to CAC not just being over 1:30:28 here tracking rorowaz on a day-to-day 1:30:30 basis and having weekly rorowaz reports 1:30:32 this is not productive at all for core 1:30:34 decision-m and moving the business 1:30:35 forward the third is that you want to be 1:30:38 separating all new verse returning 1:30:41 customer metrics you want to be tracking 1:30:44 new customer economics acquisition me 1:30:46 new customer profit contribution new 1:30:48 customer revenue, new customer cohort 1:30:50 size separate from returning because 1:30:52 ultimately the levers underlying them 1:30:54 are different. This also obviously 1:30:56 applies into what we were just talking 1:30:57 about around forecasting. Number four, 1:31:00 you want to understand the difference 1:31:03 between a cash verse a P&L play. It 1:31:06 might very much so be the case within 1:31:08 the business that the current limiter 1:31:10 isn't marketing spend or marketing 1:31:11 efficiency, but it's the cash conversion 1:31:13 cycle. And so there is no point in 1:31:14 arbitrarily pushing budgets up and 1:31:16 trying to scale if it's just going to 1:31:18 cause an increase in interest expenses 1:31:19 on the P&L and a compression in me. 1:31:21 There also needs to be constant 1:31:23 communication between either the 1:31:24 internal marketing team or you and the 1:31:26 agency as to the inventory position 1:31:28 within the business across the different 1:31:30 SKUs so that there can be strategies 1:31:32 employed to be able to actually decrease 1:31:34 profitability, decrease acquisition me, 1:31:37 decrease efficiency, but prioritize the 1:31:40 turnover of inventory into cash to make 1:31:42 the business overall healthier. And then 1:31:44 number five is that you want to be using 1:31:47 the P&L and all of these other financial 1:31:50 tools to be able to identify the 1:31:53 constraint in the business. And so when 1:31:55 you can understand how to read the P&L 1:31:57 and structure it out, you can understand 1:31:59 how to KPI at each level. And then when 1:32:02 you start falling below KPI, you can 1:32:04 look above that level in the P&L to 1:32:06 understand, okay, what has changed? What 1:32:08 levers are there? And then how can we 1:32:10 pull on those levers to rectify and 1:32:12 course correct back to where the target 1:32:14 actually is. If you made it this far, 1:32:16 thanks for watching for an hour and a 1:32:18 half. And if you are an e-commerce brand 1:32:20 doing over $5 million a year, there'll 1:32:22 be a link somewhere in the bio to reach 1:32:24 there'll be a link somewhere below in 1:32:25 the description to reach out and get a 1:32:27 free audit from ourselves where we'll 1:32:28 run you through all of this financial 1:32:30 modeling, but we'll actually apply it to 1:32:31 your business. And if you're a 1:32:32 performance marketer that's gotten this 1:32:33 far, please reach out. We're always 1:32:34 hiring for a play of performance 1:32:36 marketers. Click on the website, reach 1:32:37 out to us somehow. You could also email 1:32:39 hiring bluesdigital.com.au