Realized LTV6:15 with Michael Watson
We introduce Realized Lifetime Value, or RLTV, a LTV calculation that is based on cash collections.
Here we are looking at our monthly Cohort Report, 0:00 showing the average realized lifetime value of our customers. 0:03 Depending on what month they signed up for our service. 0:07 Realized lifetime value means that we've actually collected cash from the customer. 0:10 As supposed to projecting or 0:16 making a forecast of cash that we will eventually collect. 0:18 So for our 58 customers in the June, 2018, 0:22 cohort, each customer on average paid us $1,536. 0:26 We could use our LTV formulas to predict what their LTV is going to be. 0:31 Or we could make an observation of historical cohorts to predict 0:36 what this cohort's LTV will be. 0:41 For example, we could use churn data to forecast the LTV six months out. 0:43 Or we could use a function and calculate what the average LTV for 0:48 historical cohorts was in their sixth billing cycle. 0:52 Let's do that calculation now together. 0:56 =AVERAGE, parentheticals. 1:00 So the average realized lifetime value for six months for 1:08 all historical cohorts that have gotten to that billing cycle was $4,921. 1:13 It's a good idea to use the median function, as well, 1:19 just to make sure that no outliers are really impacting this. 1:22 So I'm gonna change the average to median, And we see there's a $20 difference. 1:26 That could be a big difference, but it's not that much. 1:33 It's less than a percent difference. 1:38 Let's look at the realized LTV, or RLTV, in Month 1 for the January, 2017, cohort. 1:42 $1,600. 1:48 We could potentially think of this as the ARPU input in our LTV formula. 1:50 If we didn't have access to all the information we see in front of us, 1:57 that could very well end up happening. 2:01 But fortunately, we do have this information. 2:04 Because we can see that the numbers start to change in the second month. 2:07 That's true for all our cohorts. 2:11 By looking at this report, we can't be sure what is driving that. 2:14 It might be because of customer churn, 2:19 reduction in consumption, a combination of the two, or potentially something else. 2:21 But of the 83 students who paid us in their first month, 2:26 in the second month, a certain amount of them either churned and stopped paying for 2:31 our service, or consumed less, and as a result, paid us less. 2:35 With a product where you have the option to consume varying amounts and/or churn 2:38 out completely at any given month, it adds more complexity to LTV forecasting. 2:43 What is a potentially less risky way to forecast LTV in these situations? 2:49 This is where realized LTV can be helpful. 2:53 You can look at how historical customers have typically behaved and 2:56 use that to predict how much you'll get from a customer, 3:01 as opposed to using churn and ARPU. 3:05 Let's say you really don't feel like extending any sort of 3:07 customer acquisition cost payback period beyond 12 months. 3:11 Well, in that case, you might consider looking at what the average realized LTV 3:16 is for all your historical cohorts that have gotten to the 12-month billing cycle. 3:22 So you can see that on average for each of your cohorts that have made it 3:34 to the 12th billing cycle, your average customer has paid you $6,774. 3:39 You could use this to inform your decision making on what to spend to acquire 3:45 a customer. 3:49 There is one question we still need to answer. 3:50 Does this LTV data include cogs or not? 3:53 If it does, we're good to move on. 3:57 If not, we need to adjust down our numbers to account for cogs. 3:59 Realized LTV isn't a perfect metric, to be sure. 4:05 For some large corporations with healthy cash reserves, 4:09 utilizing a 12-month RLTV is perhaps too conservative. 4:12 Particularly if you have a strong track record of upselling and 4:17 expanding your relationship with customers. 4:21 Let's just talk through an example of that. 4:24 We'll go back to our hypothetical project management software company 4:27 from section one. 4:30 The company has observed what is often called a land and expand strategy. 4:31 So they typically start Year 1 with a small contract value, 4:37 perhaps with one team. 4:41 Then, from there, through some combination of sales, 4:43 support, amazing product, whatever it might be, 4:46 they typically grow the account by seeing increased adoption in the organization. 4:49 And the creme de la creme would be if those expanded customers rarely churn out. 4:54 We retain our users. 4:59 In that type of holy grail of business situations, 5:01 we might start with an account in Year 1 that is worth $10,000 for 10 users. 5:05 Assuming this is a big organization with lots of employees, it's highly 5:11 possible that over the years we continue to expand our user base in that account. 5:16 By Year 5, we may well have grown that account to 100 users a year, 5:21 worth $100,000. 5:26 In Year 2, we increase to 15, Year 3 to 50, Year 4 to 80. 5:27 Our price never changed, and we have no volume discount or pro rata charges. 5:33 So our realized LTV in Year 5 for this account would be $255,000. 5:37 Limiting our acquisition spend on this type of account dynamic based on 5:45 Year 1 RLTV data would, all else equal, probably not make sense. 5:49 All right, wow, we've covered a lot in section two. 5:55 We've introduced what LTV is, why it's important, and how ARPU, churn, and 5:58 potentially other metrics can impact how we calculate our LTVs. 6:03 Very importantly, we've all remembered to never exclude cogs 6:07 from the LTV metrics we use for decision making, 6:12
You need to sign up for Treehouse in order to download course files.Sign up