1 00:00:00,000 --> 00:00:02,920 [Treehouse Workshops] ?[music]? 2 00:00:02,920 --> 00:00:06,240 Hi, I'm Pasan, and in today's workshop we are going to look at 3 00:00:06,240 --> 00:00:10,600 some important revenue metrics that you should be aware of when you run a web start up. 4 00:00:10,600 --> 00:00:13,990 Now whether it's an e-commerce site or a software as a service company, 5 00:00:13,990 --> 00:00:16,550 it is always important to keep an eye on certain metrics. 6 00:00:17,080 --> 00:00:19,430 Now there are many revenue metrics you can monitors, 7 00:00:19,430 --> 00:00:22,490 but we're going to focus on a few high-level metrics 8 00:00:22,490 --> 00:00:25,430 that will give you a great overall picture on the health of your company. 9 00:00:25,430 --> 00:00:29,130 But then you can also fine tune and get all those details that you need to. 10 00:00:29,710 --> 00:00:33,190 There are many areas of your company that you should be actively measuring-- 11 00:00:33,190 --> 00:00:37,010 acquisition, activation, retention, and so on. 12 00:00:37,010 --> 00:00:41,880 Revenue is one of the most important because it is absolutely essential to your company's survival. 13 00:00:41,880 --> 00:00:44,460 If your activation rates are dropping 14 00:00:44,460 --> 00:00:46,990 you still have existing customers that you can lean on. 15 00:00:46,990 --> 00:00:49,980 But the moment revenue starts dropping, you should be worried. 16 00:00:49,980 --> 00:00:53,190 Now that doesn't mean that you should ignore those other metrics. 17 00:00:53,190 --> 00:00:55,350 They are still very important. 18 00:00:55,350 --> 00:00:58,230 Now in this workshop, we're going to use an example 19 00:00:58,230 --> 00:01:01,250 of a generic software as a service--or SaaS company-- 20 00:01:01,250 --> 00:01:04,750 to walk through understanding and how to use these metrics. 21 00:01:04,750 --> 00:01:07,040 So let's build up our company. 22 00:01:07,040 --> 00:01:13,490 Now this company called, XYZ Incorporated sells an online data analytics software package. 23 00:01:13,490 --> 00:01:16,230 They break down their package by the number of data points 24 00:01:16,230 --> 00:01:18,620 that you capture per month that you can purchase, 25 00:01:18,620 --> 00:01:20,870 and their pricing model is as follows. 26 00:01:20,870 --> 00:01:24,220 So you have 10,000 data points captured. 27 00:01:24,220 --> 00:01:27,330 And that's the free individual account at $0.00 a month. 28 00:01:27,330 --> 00:01:32,400 Then you have the small business account which is 100,000 data points for $100 a month 29 00:01:32,400 --> 00:01:36,440 Mid-sized--which is a million data points--or $250 a month. 30 00:01:36,440 --> 00:01:41,250 And then their last account--the premium account--which is five million data points for $500 a month. 31 00:01:41,250 --> 00:01:46,870 So they have four tiers of pricing targeting four different types of customers. 32 00:01:46,870 --> 00:01:50,650 Every time a customer pays their monthly bill, you earn revenue. 33 00:01:50,650 --> 00:01:54,950 Now you can look at how revenue is growing by just checking your bank account. 34 00:01:54,950 --> 00:01:57,650 But there are several disadvantages to that. 35 00:01:57,650 --> 00:02:01,450 First, it doesn't give any indication of future earnings. 36 00:02:01,450 --> 00:02:06,010 You only know of past and current data, but you have your historical data 37 00:02:06,010 --> 00:02:09,500 which means you can't analyze your future data proactively. 38 00:02:09,500 --> 00:02:13,070 You don't know what's coming up, and you cannot prepare for unknown scenarios. 39 00:02:13,070 --> 00:02:17,260 Now it is very hard to interpret any meaningful relationships between 40 00:02:17,260 --> 00:02:22,250 your customers behaviors and revenue that is coming in when you are only looking at your bank account numbers. 41 00:02:22,250 --> 00:02:25,180 Now this can be remedied; by looking at metrics 42 00:02:25,180 --> 00:02:28,440 it will give us a better overall picture and allows us to make more 43 00:02:28,440 --> 00:02:33,050 informed decisions by connecting those dots between customer behaviors and revenue numbers. 44 00:02:33,050 --> 00:02:37,200 So the first metric we are going to talk about is recurring revenue. 45 00:02:37,200 --> 00:02:40,170 Recurring revenue is the amount of subscription revenue that your 46 00:02:40,170 --> 00:02:42,790 customers owe you over a certain time period. 47 00:02:43,520 --> 00:02:46,480 This can be measured annually, quarterly, or monthly. 48 00:02:46,480 --> 00:02:51,180 But in web start ups--which is a fast-paced landscape that changes quite quickly-- 49 00:02:51,180 --> 00:02:55,840 it makes more sense to look at a number that changes as quickly as the start up does. 50 00:02:55,840 --> 00:03:00,630 So our metric of choice here is a monthly recurring or MRR. 51 00:03:00,630 --> 00:03:06,210 MRR is a simple predictor of our growth, but not an actual measurement of our revenue. 52 00:03:06,210 --> 00:03:09,950 Even though it doesn't show how much money is going into our bank accounts, 53 00:03:09,950 --> 00:03:14,790 MRR is still a very important metric because it's an indicator of continued business 54 00:03:14,790 --> 00:03:17,400 where we are going to get paid in the future. 55 00:03:17,400 --> 00:03:20,990 Calculating MRR is relatively straightforward as well. 56 00:03:20,990 --> 00:03:23,920 For each active monthly subscription that we have, 57 00:03:23,920 --> 00:03:28,260 we determine what the subscription would be billed for a full month of service, 58 00:03:28,260 --> 00:03:31,380 disregarding any discounts or credits. 59 00:03:31,380 --> 00:03:33,480 So if you've given a customer a credit, you do not take 60 00:03:33,480 --> 00:03:36,070 any of those things into account; you take the number that they would 61 00:03:36,070 --> 00:03:38,980 pay if they were paying their month in full. 62 00:03:38,980 --> 00:03:42,500 And then the sum of all those figures across all customers 63 00:03:42,500 --> 00:03:44,830 gives us our monthly recurring revenue. 64 00:03:44,830 --> 00:03:49,970 Now MRR changes constantly as customers change their product or service selections, 65 00:03:49,970 --> 00:03:53,750 and if they leave the service, or if new customers add on. 66 00:03:53,750 --> 00:03:56,230 So if a customer upgrades to a higher account, 67 00:03:56,230 --> 00:03:59,150 naturally their contribution to MRR increases. 68 00:03:59,150 --> 00:04:02,160 If someone leaves their contribution decreases. 69 00:04:02,160 --> 00:04:04,190 Let's go over a few examples to clarify this. 70 00:04:04,190 --> 00:04:10,720 A group signed on to our small business plan, contributes about $100 a month to our MRR, 71 00:04:10,720 --> 00:04:15,860 even if they had a 50% discount--or any other discount that matter--when they sign up. 72 00:04:15,860 --> 00:04:23,060 A user signed up on our individual account--which is $0.00 a month--adds $0.00 to our MRR. 73 00:04:23,060 --> 00:04:27,090 So if a user receives a credit for 3 months off on a premium plan, 74 00:04:27,090 --> 00:04:34,060 they still contribute this $500 monthly amount to MRR for all 3 months that they would have had credit. 75 00:04:34,060 --> 00:04:37,130 So remember, even though they had a credit for 3 months you still take 76 00:04:37,130 --> 00:04:41,680 into MRR the full amount that they would be paying if they didn't have a credit. 77 00:04:41,680 --> 00:04:46,890 And finally--say a user upgrades from a small business account to a mid-sized account, 78 00:04:46,890 --> 00:04:53,260 their contribution increases by 150 which is a $150 to $250 a month increase. 79 00:04:53,260 --> 00:05:00,410 Because of the way that it is calculated, MRR isn't a great indicator of actual revenue being earned. 80 00:05:00,410 --> 00:05:04,830 Instead, it serves as a predictor of the potential revenue you can earn 81 00:05:04,830 --> 00:05:08,090 and its corresponding direction in magnitude. 82 00:05:08,090 --> 00:05:16,070 Suppose our MRR was $180,000; this doesn't mean that we actually made $180,000 in the past month. 83 00:05:16,070 --> 00:05:20,970 It means that if all of our active subscriptions in their current state, 84 00:05:20,970 --> 00:05:24,140 is everyone paid--all those accounts have the potential 85 00:05:24,140 --> 00:05:27,760 to generate us $180,000 every month. 86 00:05:27,760 --> 00:05:32,250 If this number goes up, it means our potential revenue stream is increasing. 87 00:05:32,250 --> 00:05:35,060 Let's take a look at some sample data. 88 00:05:36,250 --> 00:05:41,560 This graph shows the MRR for XYZ Inc. for the past 16 months. 89 00:05:41,560 --> 00:05:47,500 So while there are some dips like over here, here, and slightly over here, 90 00:05:47,500 --> 00:05:50,320 the graph is trending up wards in general. 91 00:05:50,320 --> 00:05:54,350 This means that we're increasing our earnings potential as the months go by-- 92 00:05:54,350 --> 00:05:56,350 which is a good thing. 93 00:05:56,350 --> 00:05:59,210 Now actual revenue is a complicated number 94 00:05:59,610 --> 00:06:01,610 that can be affected by a lot of different things. 95 00:06:01,610 --> 00:06:04,740 It includes things like--you know--your traditional monthly billing 96 00:06:04,740 --> 00:06:09,670 to accounts that are paid yearly--even if you are doing monthly subscriptions some could be paid yearly. 97 00:06:09,670 --> 00:06:13,460 And then there's also groups sales; there's other places you can earn money 98 00:06:13,460 --> 00:06:17,660 if you have a large cash surplus sitting in the bank that will give you interest revenue 99 00:06:17,660 --> 00:06:21,430 that adds to your total number--all these things add on. 100 00:06:21,430 --> 00:06:24,510 So when you are looking at a revenue graph, it's very hard to understand 101 00:06:24,510 --> 00:06:28,150 the relationships between revenue earned and customer behaviors. 102 00:06:28,150 --> 00:06:34,290 So we can't really say that we're doing a great job of bringing new customers by looking at a revenue graph. 103 00:06:34,290 --> 00:06:37,000 So lets look at this example to understand it better. 104 00:06:37,000 --> 00:06:42,300 Now in this graph--around the 8th month or 7th month mark and the 105 00:06:42,300 --> 00:06:47,400 12 month mark again--there's a significant increase in revenue for XYZ. 106 00:06:47,400 --> 00:06:49,750 This is obviously good news for the company. 107 00:06:49,750 --> 00:06:52,950 But what if we wanted to replicate this data? 108 00:06:52,950 --> 00:06:55,660 Do we know whether the revenue increased as a direct result of 109 00:06:55,660 --> 00:06:58,390 increased acquisition and activation methods? 110 00:06:58,390 --> 00:07:00,680 Or did we bring in more customers? 111 00:07:00,680 --> 00:07:03,530 Or did we simply decrease our expenses in some way? 112 00:07:03,530 --> 00:07:07,030 Did we pay off a loan or maybe we cut back our marketing budget? 113 00:07:07,030 --> 00:07:10,590 Consolidated staff and reduced wages? There are lots of different ways. 114 00:07:10,590 --> 00:07:14,550 By just looking at revenue, it's hard to say whether we are making more 115 00:07:14,550 --> 00:07:16,870 because we sold more accounts or if there is some other 116 00:07:16,870 --> 00:07:18,950 underlying reason that we don't understand. 117 00:07:20,040 --> 00:07:23,750 By monitoring MRR, we can see how our customer acquistion, 118 00:07:23,750 --> 00:07:28,870 activation, and retention efforts direct affect our potential revenue. 119 00:07:28,870 --> 00:07:34,200 Monthly recurrent revenue is easy to calculate when you have a monthly subscription business. 120 00:07:34,200 --> 00:07:37,490 You just use the actual billed, invoice, or paid numbers; 121 00:07:37,490 --> 00:07:41,440 you just pull that directly off your software to plug into your MRR calculations. 122 00:07:41,440 --> 00:07:44,770 But when you have term subscription models it is slightly different. 123 00:07:44,770 --> 00:07:48,160 And these models are something like--you know--if you allow the customer 124 00:07:48,160 --> 00:07:53,670 to pay annually or semi-annually, then you don't have a direct monthly figure that you can plug in. 125 00:07:53,670 --> 00:07:57,650 So with a term subscription to calculate MRR growth, 126 00:07:57,650 --> 00:08:03,060 you just take the monthly value derived by normalizing the term transaction value. 127 00:08:03,060 --> 00:08:07,700 For example, a $12,000 contract has a monthly value of $1,000. 128 00:08:07,700 --> 00:08:13,190 So to calculate the term contribution to MRR--if it's an annual term 129 00:08:13,190 --> 00:08:15,230 you divide the annual value by 12. 130 00:08:15,230 --> 00:08:19,180 If it's a semi-annual term, you divide that total number by 6. 131 00:08:19,180 --> 00:08:21,550 And for a quarterly term, you divide it by 3. 132 00:08:21,550 --> 00:08:25,380 But to be even more accurate, we can fine tune our MRR number 133 00:08:25,380 --> 00:08:27,630 to get CMRR. 134 00:08:27,630 --> 00:08:30,210 Committed Monthly Recurring Revenue. 135 00:08:30,210 --> 00:08:33,610 Before we do that--however--lets go over our second metric. 136 00:08:33,610 --> 00:08:37,919 This will help us visit a few more concepts to understand CMRR better. 137 00:08:38,570 --> 00:08:42,020 Now this second metric we are going to go over is churn. 138 00:08:42,020 --> 00:08:46,170 Although it's a simple metric to calculate, churn is extremely important 139 00:08:46,170 --> 00:08:48,980 and can help determine the very future of your company. 140 00:08:48,980 --> 00:08:53,270 Churn is attrition; basically when a customer leaves your company, 141 00:08:53,270 --> 00:08:55,700 and stops being a customer, they have "churned." 142 00:08:55,700 --> 00:08:58,700 To monitor churn, we calculate the churn rate, 143 00:08:58,700 --> 00:09:01,790 which is the rate at which we're losing customers. 144 00:09:01,790 --> 00:09:05,560 To calculate churn, you divide the number of customers who cancelled 145 00:09:05,560 --> 00:09:07,670 by the number of customers. 146 00:09:07,670 --> 00:09:10,920 Churn kills growth at a SaaS company. 147 00:09:10,920 --> 00:09:14,580 Since churn is the percentage of customers that leave your service, 148 00:09:14,580 --> 00:09:19,180 the more customers you have, the more customers you will have will leave the service. 149 00:09:19,180 --> 00:09:22,930 In simpler terms, total churn--which is the churn rate-- 150 00:09:22,930 --> 00:09:26,530 times the number of customers is directly proportional 151 00:09:26,530 --> 00:09:28,680 to increases in your customer base. 152 00:09:28,680 --> 00:09:33,650 So the more customers you have, the more chances you have of losing those customers. 153 00:09:33,650 --> 00:09:39,450 In this graph, we have total number of customers against time in years. 154 00:09:39,450 --> 00:09:45,760 Let's say that you can take over 5 years to get 88,000 customers 155 00:09:45,760 --> 00:09:47,760 without any churn. 156 00:09:47,760 --> 00:09:52,740 Growth at this point along the line is 17,300 customers per year. 157 00:09:52,740 --> 00:09:57,060 If you introduce churn into the picture--and churn is a natural fact of 158 00:09:57,060 --> 00:09:58,730 running a business--so you will have churn. 159 00:09:58,730 --> 00:10:01,300 It's naive to say that you will not have churn. 160 00:10:01,300 --> 00:10:05,140 You can see that it's harder to get to that number. 161 00:10:05,140 --> 00:10:07,340 In fact, you might not even get there. 162 00:10:07,340 --> 00:10:09,340 Churn can kill your growth rate. 163 00:10:09,340 --> 00:10:12,940 Looking back at this graph, in the beginning down here-- 164 00:10:12,940 --> 00:10:16,450 when you have fewer customers, churn is quite low. 165 00:10:16,450 --> 00:10:22,430 But as you increase your customer base, and the total number of customers is on the Y axis of the graph-- 166 00:10:22,430 --> 00:10:26,000 remember your total churn increases as well. 167 00:10:26,560 --> 00:10:29,750 This is total churn--again--not the churn rate. 168 00:10:29,750 --> 00:10:32,710 Your churn rate should hopefully never increase. 169 00:10:32,710 --> 00:10:37,580 Well with a constant churn rate of 20% as your total customer base increases, 170 00:10:37,580 --> 00:10:40,490 the total churn number will increase we well. 171 00:10:40,490 --> 00:10:45,480 In this sample graph, we have the line trends upwards initially. 172 00:10:45,480 --> 00:10:49,530 This is because we are acquiring customers faster than they leave us. 173 00:10:49,530 --> 00:10:52,730 Acquisition rate is greater than the churn rate. 174 00:10:52,730 --> 00:10:58,310 When the graph plateaus up here or straightens out, that's when total 175 00:10:58,310 --> 00:11:00,560 churn has equalled acquisitions. 176 00:11:00,560 --> 00:11:05,240 So your customers joining exactly equal the customers leaving. 177 00:11:05,240 --> 00:11:09,180 At this point the churn rate is equal to the acquisition rate. 178 00:11:09,180 --> 00:11:13,720 Growth starts to slow down and can even stop right here when the graph flattens out. 179 00:11:13,720 --> 00:11:19,960 So as I mentioned earlier, the acquisition rate was around 17,300 customers per year. 180 00:11:19,960 --> 00:11:24,800 If our churn rate is 20%, then our acquisition rates equal 181 00:11:24,800 --> 00:11:28,960 our churn rates at 86,500 customers. 182 00:11:28,960 --> 00:11:34,220 So we plateau at 86,500 customers, and unless we increase 183 00:11:34,220 --> 00:11:37,290 our acquisition rates, we won't be able to do better. 184 00:11:37,290 --> 00:11:40,280 This is very important looking forward. 185 00:11:40,280 --> 00:11:44,210 We need to know that we can either increase our acquisition rate, 186 00:11:44,210 --> 00:11:47,350 or decrease our churn rate, or both. 187 00:11:47,350 --> 00:11:52,010 If you look back at the graph--if we increase our acquisition rates, 188 00:11:52,010 --> 00:11:54,670 then this purple line dips below. 189 00:11:54,670 --> 00:11:59,160 If we decrease our churn rate, then the graph--the slope increases which 190 00:11:59,160 --> 00:12:03,060 means that we can go past that acquisition number. 191 00:12:03,060 --> 00:12:07,860 This is important if we want to increase our revenue past a certain point. 192 00:12:07,860 --> 00:12:10,110 Now why is churn important? 193 00:12:10,110 --> 00:12:13,980 Because churn has such a huge impact on revenue. 194 00:12:13,980 --> 00:12:16,800 This makes sense if you think about it--obviously when people leave 195 00:12:16,800 --> 00:12:19,860 you'll lose revenue because they are not paying you any more. 196 00:12:19,860 --> 00:12:23,000 So let's go back to our MRR graph. 197 00:12:23,000 --> 00:12:29,000 Right here--the orange curve represents our original MMR graph, right? 198 00:12:29,000 --> 00:12:33,160 This has no churn; so no customers were leaving monthly. 199 00:12:33,160 --> 00:12:37,040 This--the orange graph is our earnings potential. 200 00:12:37,040 --> 00:12:41,300 Now the second graph is with churn introduced into the picture. 201 00:12:41,300 --> 00:12:44,910 And this is--the blue line is a churn of 10%. 202 00:12:44,910 --> 00:12:47,910 As you can clearly see, our revenue drops quite a bit-- 203 00:12:47,910 --> 00:12:50,640 even with a churn of 10% 204 00:12:50,640 --> 00:12:56,170 In reality, 10% is very modest, and--you know--honestly it will probably be more. 205 00:12:56,170 --> 00:12:59,770 And the last line--this red line here--is our MRR graph 206 00:12:59,770 --> 00:13:02,000 with a churn rate of 20%. 207 00:13:02,000 --> 00:13:06,610 So with the churn rate at 20%, the drop in MRR is quite significant.. 208 00:13:06,610 --> 00:13:12,460 Looking at the month of February right here, our MRR--our earnings potential-- 209 00:13:12,460 --> 00:13:17,720 is around $155,000 a month with no churn. 210 00:13:17,720 --> 00:13:21,610 With 10% churn, it drops down to $140,000. 211 00:13:21,610 --> 00:13:26,510 With 20% churn, it drops down to $125,000. 212 00:13:26,510 --> 00:13:30,610 With 20% of our customer base leaving monthly, 213 00:13:30,610 --> 00:13:35,510 we are losing the chance to make $30,500 more a month. 214 00:13:35,510 --> 00:13:41,290 That works out to be around $350,000 yearly. It adds up quickly. 215 00:13:41,940 --> 00:13:46,330 Now when you have a small customer base and a lower MRR figure, 216 00:13:46,330 --> 00:13:50,310 10% or even 20% churn might not see like a big deal. 217 00:13:50,310 --> 00:13:53,700 If you go back to the graph, at the very low end-- 218 00:13:53,700 --> 00:13:56,990 in February--again--one-year ago from our hypothetical scenario, 219 00:13:56,990 --> 00:14:02,910 we're only losing a couple thousand dollars a month because our customer base is very small. 220 00:14:02,910 --> 00:14:11,530 So 20% churn means fewer people are leaving than our 20% churn one-year later in February again. 221 00:14:11,530 --> 00:14:16,040 Keeping churn constant thought, this grows to a much larger dollar amount 222 00:14:16,040 --> 00:14:18,940 as we grow our customer base and revenue stream. 223 00:14:18,940 --> 00:14:24,630 Churn provides another valuable insight that directly relates to revenue. 224 00:14:24,630 --> 00:14:28,200 First off--you should understand that the higher you churn 225 00:14:28,200 --> 00:14:31,410 the longer it takes to reach profitability. 226 00:14:31,410 --> 00:14:37,100 Let's look at another graph of MRR in dollars plotted against time in years. 227 00:14:37,100 --> 00:14:41,100 Now in this graph, the green line over here represents 228 00:14:41,100 --> 00:14:43,390 our MRR with no churn. 229 00:14:43,390 --> 00:14:46,340 Our total acquisition cost--for the sake of this example--are 230 00:14:46,340 --> 00:14:49,010 constant at $30,000. 231 00:14:49,010 --> 00:14:55,320 In this example, it takes XYZ close to 2½ years to reach break even. 232 00:14:56,120 --> 00:14:59,210 The point where revenues equal our cost as is indicated by the 233 00:14:59,210 --> 00:15:02,020 intersection of the white line and the green line in the graph. 234 00:15:02,020 --> 00:15:06,350 In this example again, we're not considering all costs just directly the 235 00:15:06,350 --> 00:15:10,480 cost of acquiring the customer against the revenue that those customers bring in. 236 00:15:10,480 --> 00:15:15,360 Now with a churn of 25%--as is indicated by the blue line here. 237 00:15:15,360 --> 00:15:20,570 So we're introducing a trend of 25% to this original MRR graph--the green line. 238 00:15:20,570 --> 00:15:22,570 So it brings it down here. 239 00:15:22,570 --> 00:15:26,380 The acquisition rate constant--again--at 30,000. 240 00:15:26,380 --> 00:15:29,920 The slope of the MRR graph decreases eventually. 241 00:15:29,920 --> 00:15:32,920 And the curve flattens out because--as I explained earlier-- 242 00:15:32,920 --> 00:15:38,330 with acquisition rates constant, your churn rate could eventually meet up it. 243 00:15:38,330 --> 00:15:41,640 So your churn numbers equal your acquisition numbers. 244 00:15:41,640 --> 00:15:46,320 So customers are leaving as fast as XYZ is bringing in new customers. 245 00:15:46,830 --> 00:15:52,500 So originally with the green line in the no churn MRR, it took us 246 00:15:52,500 --> 00:15:54,650 2½ years to reach break even. 247 00:15:54,650 --> 00:16:00,580 With 25% churn which is realistic, then you see it takes longer to get to profitability. 248 00:16:00,580 --> 00:16:02,760 We're almost taking 5 years.. 249 00:16:02,760 --> 00:16:08,650 That's twice the amount of time it takes to reach profitability with just a 25% churn rate-- 250 00:16:08,650 --> 00:16:10,730 which is a huge time gap. 251 00:16:10,730 --> 00:16:14,400 And then there's a potential third scenario that's even more negative. 252 00:16:14,400 --> 00:16:20,120 So this third orange line here represents churn rate of 35%. 253 00:16:20,120 --> 00:16:24,090 Now that is 10% higher than this one, but in this case because 254 00:16:24,090 --> 00:16:28,070 the churn rate is higher, the graph flattens out earlier than this one. 255 00:16:28,070 --> 00:16:33,770 So we're reaching our acquisition rate number or compared to this graph, 256 00:16:33,770 --> 00:16:40,480 then--as you can see--the graph flattens out before he even reached the total customer acquisition costs. 257 00:16:40,480 --> 00:16:45,730 At this churn rate, MRR never has a potential to grow above acquisition costs. 258 00:16:45,730 --> 00:16:52,300 What does this mean? It means that XYZ can never reach profitability with the trend rate of 35%. 259 00:16:52,300 --> 00:16:56,690 Now looping back over to MRR, when you include your churn figures 260 00:16:56,690 --> 00:17:02,190 in MRR calculations, you are now measuring committed or contracted MRR. 261 00:17:02,490 --> 00:17:07,119 CMRR only calculates the recurring portions of the subscription revenue 262 00:17:07,119 --> 00:17:10,470 and removes all thees cancellations that churn brings into account. 263 00:17:10,990 --> 00:17:15,760 To calculate CMRR, you just take the total number of paying accounts 264 00:17:15,760 --> 00:17:19,690 that you have in a month, you subtract the number of accounts that you think 265 00:17:19,690 --> 00:17:22,119 are going to cancel denoted by a churn number. 266 00:17:22,119 --> 00:17:25,800 And then you multiply that by the relevance monthly subscription amounts 267 00:17:25,800 --> 00:17:33,310 based on the tier these accounts are in to get CMRR or Contracted Monthly Recurring Revenue. 268 00:17:33,310 --> 00:17:37,330 So, what have we learned from the past 2 metrics? 269 00:17:37,330 --> 00:17:41,120 MRR is an indicator of our earning potential. 270 00:17:41,120 --> 00:17:45,890 It's not actual revenue earned, but combined with cancellation figures 271 00:17:45,890 --> 00:17:50,120 it gives us a great idea of what we can expect to earn in the upcoming months. 272 00:17:50,120 --> 00:17:52,950 So this takes away from--you know--when we mentioned earlier 273 00:17:52,950 --> 00:17:55,580 revenue numbers are either current or historic. 274 00:17:55,580 --> 00:17:57,840 You can never see future revenue numbers. 275 00:17:57,840 --> 00:18:00,320 MRR gives you that opportunity. 276 00:18:00,320 --> 00:18:02,490 Secondly, churn is bad. 277 00:18:02,490 --> 00:18:04,560 Constantly aim to lower churn. 278 00:18:04,560 --> 00:18:06,590 There's a couple reasons why. 279 00:18:06,590 --> 00:18:09,990 So churn--as you saw on these graphs--lowers your MRR. 280 00:18:09,990 --> 00:18:12,600 This is bad in general. 281 00:18:12,600 --> 00:18:17,290 If your earnings potential is being lowered, then obviously so are your actual earnings. 282 00:18:17,680 --> 00:18:20,860 Churn--as we saw in the very most recent graph--means a longer 283 00:18:20,860 --> 00:18:23,950 time to break even or reach profitability. 284 00:18:24,300 --> 00:18:28,080 Churn rates should finally always be lower than your acquisition rates. 285 00:18:28,080 --> 00:18:32,310 If they are equal--as we saw in multiple scenarios--you plateau, 286 00:18:32,310 --> 00:18:36,290 the graph straightens out, and you will always bring in the same amount of money 287 00:18:36,290 --> 00:18:39,920 regardless of how many new customers you bring in because that same 288 00:18:39,920 --> 00:18:42,220 number of customers is exiting the company. 289 00:18:42,960 --> 00:18:47,870 Finally, if churn is higher than acquisition, you are just going to hemorrhage money over time. 290 00:18:47,870 --> 00:18:50,900 That brings us to the last metric in this workshop. 291 00:18:50,900 --> 00:18:53,180 Customer lifetime values. 292 00:18:53,180 --> 00:18:57,270 Customer lifetime values refers to the revenue that a customer brings in 293 00:18:57,270 --> 00:19:02,690 for the entire amount of time that they use your service or their lifetime using your product. 294 00:19:02,690 --> 00:19:06,340 The development and retention of profitable customer relationships, 295 00:19:06,340 --> 00:19:09,340 is crucial to the success of any business. 296 00:19:09,340 --> 00:19:12,770 But by understanding customer lifetime values, you can focus on 297 00:19:12,770 --> 00:19:16,150 long term revenue generating capabilities of each customer, 298 00:19:16,150 --> 00:19:19,700 And not just short term profits that get month to month. 299 00:19:19,700 --> 00:19:24,240 Lifetime values affects the execution of many activities across the organization 300 00:19:24,240 --> 00:19:27,900 including customer acquisition, activation, and retention, 301 00:19:27,900 --> 00:19:31,430 and it ultimately drivers shareholder value in company equity. 302 00:19:32,520 --> 00:19:37,820 Now calculating lifetime values or LCV is not so straightforward unlike churn. 303 00:19:37,820 --> 00:19:41,320 I am going to use a very simple method here to calculate it, 304 00:19:41,320 --> 00:19:44,700 but in reality determining some of the numbers that go into calculating 305 00:19:44,700 --> 00:19:48,840 your lifetime values, can be complicated for certain companies. 306 00:19:48,840 --> 00:19:52,550 You have to decide what contributes towards lifetime values for example. 307 00:19:52,550 --> 00:19:58,360 Like if Customer B signs up for XYZ small business account through a referral, 308 00:19:58,360 --> 00:20:01,030 they are obviously bringing in revenue. 309 00:20:01,030 --> 00:20:04,210 Does this increase the lifetime value for customer A? 310 00:20:04,210 --> 00:20:08,480 So the person who sent out that initial referral invite, does it increase 311 00:20:08,480 --> 00:20:11,600 their customer value because they indirectly increased 312 00:20:11,600 --> 00:20:14,230 our revenue through a referral activity. 313 00:20:14,230 --> 00:20:17,150 But those are decisions that--you know--each company should make 314 00:20:17,150 --> 00:20:20,120 relevant to their situation and how their customers behave. 315 00:20:20,120 --> 00:20:23,830 To get the average revenue per user, divide the total revenue 316 00:20:23,830 --> 00:20:26,100 by the number of paying accounts. 317 00:20:26,100 --> 00:20:29,410 So--you know--XYZ has a free tier that I mentioned earlier. 318 00:20:29,410 --> 00:20:33,010 These obviously don't contribute any sort of revenue to XYZ, 319 00:20:33,010 --> 00:20:38,510 and so we really don't have a lifetime value for them in our simple scenario, so let's just exclude them. 320 00:20:38,510 --> 00:20:43,480 For this example--also let's assume our average revenue per user is $2.97. 321 00:20:43,480 --> 00:20:46,400 Now the way you can get that is just take the number of paying accounts 322 00:20:46,400 --> 00:20:49,490 across all your customer base, and then divide the total 323 00:20:49,490 --> 00:20:53,750 revenue by the number of accounts to get average revenue per paying user. 324 00:20:53,750 --> 00:20:58,890 To determine the average length of a customer relationship, you take the inverse of churn. 325 00:20:58,890 --> 00:21:03,090 So say our monthly churn rate is at 20%. 326 00:21:03,090 --> 00:21:07,130 We can use this to calculate the average time our customers stay with us 327 00:21:07,130 --> 00:21:09,130 or stay with our service. 328 00:21:09,130 --> 00:21:12,050 So you divide one--one divided by 20% is 5. 329 00:21:12,050 --> 00:21:15,910 So since we measure churn in months, this means the average 330 00:21:15,910 --> 00:21:18,250 length of a relationship is 5 months. 331 00:21:18,250 --> 00:21:21,230 And this is a simple calculation because the rate of which people are leaving us 332 00:21:21,230 --> 00:21:25,490 is churned, the inverse of that will give us the people that we retain. 333 00:21:26,040 --> 00:21:30,800 So our LTV is 5 months--the average length of a customer relationship 334 00:21:30,800 --> 00:21:33,510 times the average revenue per user which is $2.97, 335 00:21:33,510 --> 00:21:37,380 so that gives us $1,485. 336 00:21:37,380 --> 00:21:43,030 So on average each customer brings in approximately $1,500 of revenue. 337 00:21:43,030 --> 00:21:49,160 Lifetime values should always, always be greater than customer acquisition costs. 338 00:21:49,160 --> 00:21:51,550 If it's not, you're doing something wrong. 339 00:21:51,550 --> 00:21:55,340 Because if we're spending $1,800 to bring in one account, 340 00:21:55,340 --> 00:21:58,720 then at a lifetime value of $1,500 approximately, 341 00:21:58,720 --> 00:22:02,090 we are obviously never making money off the customer. 342 00:22:02,090 --> 00:22:06,240 We spend more bringing them in than they give us during their entire lifetime. 343 00:22:06,240 --> 00:22:09,330 And this approximates the lifetime--this is the maximum lifetime 344 00:22:09,330 --> 00:22:11,620 that we think we can get out of a customer. 345 00:22:11,620 --> 00:22:14,500 Obviously some people could leave earlier, churn could be higher, 346 00:22:14,500 --> 00:22:19,170 and then they could give us a certain customers lifetime value--it could even be $100 347 00:22:19,170 --> 00:22:24,090 and we spend $1,800 bringing them in, and now we only get $100. 348 00:22:24,090 --> 00:22:26,310 And we're out $1,700. 349 00:22:26,310 --> 00:22:31,130 So like MRR, lifetime values are also related to churn. 350 00:22:31,130 --> 00:22:34,350 By decreasing churn, you can keep customers around longer 351 00:22:34,350 --> 00:22:38,570 and increase their lifetime values; by increasing lifetime values 352 00:22:38,570 --> 00:22:41,520 you get 2 things done--2 important things. 353 00:22:41,520 --> 00:22:44,830 First by increasing the amount of profit each customer gives you 354 00:22:44,830 --> 00:22:47,500 you've increased your MRR. 355 00:22:47,500 --> 00:22:50,000 This increases your actual revenue. 356 00:22:50,000 --> 00:22:52,620 Because if you're increasing your earnings potential, then obviously 357 00:22:52,620 --> 00:22:54,760 your actual earnings can increase as well. 358 00:22:54,760 --> 00:22:58,110 And this increases your long term profitability. 359 00:22:58,110 --> 00:23:02,000 Second, by working towards increasing customer lifetime values, 360 00:23:02,000 --> 00:23:05,940 you are essentially keeping your customers happy and persuading them to stay. 361 00:23:05,940 --> 00:23:09,350 So if you are trying to increase the lifetime value, you're decreasing churn, 362 00:23:09,350 --> 00:23:13,120 so you are digging in to why these customers are leaving you in the first place. 363 00:23:13,120 --> 00:23:17,110 And by trying to solve those problems you are just making the whole experience better, 364 00:23:17,110 --> 00:23:19,990 and you retain them longer, you earn more money off of them. 365 00:23:19,990 --> 00:23:24,500 And this is many benefits--the most important being increase referrals. 366 00:23:24,500 --> 00:23:30,100 There are several ways an increased relationship can lead to higher lifetime values.. 367 00:23:30,100 --> 00:23:34,690 By keeping customers around longer, you not only increase the base profit that they give you. 368 00:23:34,690 --> 00:23:42,080 So--for example--you have a customer who pays you $15.00 monthly for 3 months--$45.00 369 00:23:42,080 --> 00:23:45,350 or if you--you know--make them stay for 7 months, you increase 370 00:23:45,350 --> 00:23:48,220 that by 3 more months, that'd give another $45.00. 371 00:23:48,220 --> 00:23:50,420 So now you have $90.00 in total. 372 00:23:50,420 --> 00:23:55,000 So--but then you also open the door for profit from increased purchases. 373 00:23:55,000 --> 00:23:57,780 THey can buy more stuff from you; they can raise their accounts. 374 00:23:57,780 --> 00:24:02,440 And XYZ for example, if they're happy and they stay around for longer, 375 00:24:02,440 --> 00:24:05,480 and their company grows, they can go from a small business tier 376 00:24:05,480 --> 00:24:07,880 to a mid-size tier to a premium tier. 377 00:24:07,880 --> 00:24:11,440 You can also open the door for price premiums, referrals, 378 00:24:11,440 --> 00:24:15,040 and reduce operating costs over the lifetime of that customer. 379 00:24:15,040 --> 00:24:20,200 So there you have it--MRR, churn, and lifetime values. 380 00:24:20,200 --> 00:24:23,540 By monitoring these really important metrics, you should have 381 00:24:23,540 --> 00:24:27,890 a good understanding of what is affecting your long run profitability 382 00:24:27,890 --> 00:24:33,230 and what you can do to increase revenue, retention, and lower costs. 383 00:24:33,230 --> 00:24:35,490 Thanks for watching, and I hope you enjoyed this workshop. 384 00:24:35,490 --> 00:24:38,570 [Treehouse Workshops] ?[music]?