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