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savadev/growth_accounting_and_ltv.sql

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Created November 19, 2023 23:56
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growth_accounting_and_ltv.sql
with
dau as (
-- This part of the query can be pretty much anything.
-- The only requirement is that it have three columns:
-- dt, user_id, inc_amt
-- Where dt is a date and user_id is some unique identifier for a user.
-- Each dt-user_id pair should be unique in this table.
-- inc_amt represents the amount of value that this user created on dt.
-- The most common case is
-- inc_amt = incremental revenue from the user on dt
-- If you want to do L28 growth accounting, user inc_amt=1.
-- The version here derives everything from the tutorial.yammer_events
-- data set provided for free by Mode.
-- If you edit just this part to represent your data, the rest
-- of the query should run just fine.
select
date(occurred_at) as dt,
user_id,
sum(user_type) as inc_amt
from tutorial.yammer_events
group by 1,2
),
-- First, set up WAU and MAU tables for future use
wau as (
select
date_trunc('week',dt) as week,
user_id,
sum(inc_amt) as inc_amt
from dau
group by 1,2
),
mau as (
select
date_trunc('month',dt) as month,
user_id,
sum(inc_amt) as inc_amt
from dau
group by 1,2
),
-- This determines the cohort date of each user. In this case we are
-- deriving it from DAU data but you can feel free to replace it with
-- registration date if that's more appropriate.
first_dt as (
select
user_id,
min(dt) as first_dt,
date_trunc('week',min(dt)) as first_week,
date_trunc('month',min(dt)) as first_month
from dau
group by 1
),
mau_decorated as (
select
d.month,
d.user_id,
d.inc_amt,
f.first_month
from mau d, first_dt f
where d.user_id=f.user_id
and inc_amt>0
),
-- This is MAU growth accounting. Note that this does not require any
-- information about inc_amt. As discussed in the articles, these
-- quantities satisfy some identities:
-- MAU(t) = retained(t) + new(t) + resurrected(t)
-- MAU(t - 1 month) = retained(t) + churned(t)
mau_growth_accounting as (
select
coalesce(tm.month, lm.month + interval '1 month') as month,
count(distinct tm.user_id) as mau,
count(distinct case when lm.user_id is not NULL then tm.user_id
else NULL end) as retained,
count(distinct case when tm.first_month=tm.month then tm.user_id
else NULL end) as new,
count(distinct case when tm.first_month!=tm.month
and lm.user_id is NULL then tm.user_id else NULL end
) as resurrected,
-1*count(distinct case when tm.user_id is NULL then lm.user_id
else NULL end) as churned
from
mau_decorated tm
full outer join mau_decorated lm on (
tm.user_id=lm.user_id
and tm.month = lm.month + interval '1 month'
)
group by 1
),
-- This is the MRR growth accounting (or growth accounting of whatever
-- value you put in inc_amt). These also satisfy some identities:
-- MRR(t) = retained(t) + new(t) + resurrected(t) + expansion(t)
-- MAU(t - 1 month) = retained(t) + churned(t) + contraction(t)
mrr_growth_accounting as (
select
coalesce(tm.month, lm.month + interval '1 month') as month,
sum(tm.inc_amt) as rev,
sum(
case
when tm.user_id is not NULL and lm.user_id is not NULL
and tm.inc_amt>=lm.inc_amt then lm.inc_amt
when tm.user_id is not NULL and lm.user_id is not NULL
and tm.inc_amt<lm.inc_amt then tm.inc_amt
else 0
end
) as retained,
sum(
case when tm.first_month=tm.month then tm.inc_amt
else 0 end
) as new,
sum(
case when tm.month!=tm.first_month and tm.user_id is not NULL
and lm.user_id is not NULL and tm.inc_amt>lm.inc_amt
and lm.inc_amt>0 then tm.inc_amt - lm.inc_amt
else 0 end
) as expansion,
sum(
case when tm.user_id is not NULL
and (lm.user_id is NULL or lm.inc_amt=0)
and tm.inc_amt>0 and tm.first_month!=tm.month
then tm.inc_amt
else 0 end
) as resurrected,
-1*sum(
case
when tm.month!=tm.first_month and tm.user_id is not NULL
and lm.user_id is not NULL
and tm.inc_amt<lm.inc_amt and tm.inc_amt>0
then lm.inc_amt - tm.inc_amt
else 0 end
) as contraction,
-1*sum(
case when lm.inc_amt>0 and (tm.user_id is NULL or tm.inc_amt=0)
then lm.inc_amt else 0 end
) as churned
from
mau_decorated tm
full outer join mau_decorated lm on (
tm.user_id=lm.user_id
and tm.month = lm.month + interval '1 month'
)
group by 1
),
-- These next tables are to compute LTV via the cohorts_cumulative table.
-- The LTV here is being computed for weekly cohorts on weekly intervals.
-- The queries can be modified to compute it for cohorts of any size
-- on any time window frequency.
wau_decorated as (
select
week,
w.user_id,
w.inc_amt,
f.first_week
from wau w, first_dt f
where w.user_id=f.user_id
),
cohorts as (
select
first_week,
floor(extract(DAYS from (week - first_week))/7.0) as weeks_since_first,
count(distinct user_id) as users,
sum(inc_amt) as inc_amt
from wau_decorated
group by 1,2
order by 1,2
),
cohort_sizes as (
select
first_week,
users,
inc_amt
from cohorts
where weeks_since_first=0
),
cohorts_cumulative as (
-- A semi-cartesian join accomplishes the cumulative behavior.
select
c1.first_week,
c1.weeks_since_first,
c1.users,
cs.users as cohort_num_users,
1.0*c1.users/cs.users as retained_pctg,
c1.inc_amt,
sum(c2.inc_amt) as cum_amt,
sum(c2.inc_amt)/cs.users as cum_amt_per_user
from
cohorts c1,
cohorts c2,
cohort_sizes cs
where
c1.first_week=c2.first_week
and c2.weeks_since_first<=c1.weeks_since_first
and cs.first_week=c1.first_week
group by 1,2,3,4,5,6
order by 1,2
)
-- For cumulative LTV data use this
select * from cohorts_cumulative
-- For MAU growth accuonting use this
select * from mau_growth_accounting
-- For MRR growth accuonting use this
select * from mrr_growth_accounting
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