Skip to content

Commit

Permalink
fix typo and re-format sql
Browse files Browse the repository at this point in the history
  • Loading branch information
@Jstein77
Jstein77 committed Jan 19, 2024
1 parent 318b1cd commit 76b60c3
Showing 1 changed file with 74 additions and 86 deletions.
160 changes: 74 additions & 86 deletions website/docs/docs/build/conversion-metrics.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -105,19 +105,18 @@ This step joins the `BUYS` table to the `VISITS` table and gets all combinations
The SQL generated in these steps looks like the following:

```sql
select
v.ds,
v.user_id,
v.referrer_id,
b.ds,
b.uuid,
1 as buys
from visits v
inner join (
select *, uuid_string() as uuid from buys -- Adds a uuid column to uniquely identify the different rows
) b
on
v.user_id = b.user_id and v.ds <= b.ds and v.ds > b.ds - interval '7 day'
SELECT v.ds,
v.user_id,
v.referrer_id,
b.ds,
b.uuid,
1 as buys
FROM visits v
INNER JOIN (SLECT *, uuid_string() as uuid
FROM buys -- Adds a uuid column to uniquely identify the different rows
) b
ON
v.user_id = b.user_id and v.ds <= b.ds and v.ds > b.ds - interval '7 day'
```

The dataset returns the following (note that there are two potential conversion events for the first visit):
Expand All @@ -134,19 +133,17 @@ The dataset returns the following (note that there are two potential conversion
Instead of returning the raw visit values, use window functions to link conversions to the closest base event. You can partition by the conversion source and get the `first_value` ordered by `visit ds`, descending to get the closest base event from the conversion event:

```sql
select
first_value(v.ds) over (partition by b.ds, b.user_id, b.uuid order by v.ds desc) as v_ds,
first_value(v.user_id) over (partition by b.ds, b.user_id, b.uuid order by v.ds desc) as user_id,
first_value(v.referrer_id) over (partition by b.ds, b.user_id, b.uuid order by v.ds desc) as referrer_id,
b.ds,
b.uuid,
1 as buys
from visits v
inner join (
select *, uuid_string() as uuid from buys
) b
on
v.user_id = b.user_id and v.ds <= b.ds and v.ds > b.ds - interval '7 day'
SELECT first_value(v.ds) over (partition by b.ds, b.user_id, b.uuid order by v.ds desc) as v_ds,
first_value(v.user_id) over (partition by b.ds, b.user_id, b.uuid order by v.ds desc) as user_id,
first_value(v.referrer_id) over (partition by b.ds, b.user_id, b.uuid order by v.ds desc) as referrer_id,
b.ds,
b.uuid,
1 as buys
FROM visits v
INNER JOIN (select *, uuid_string() as uuid
from buys) b
ON
v.user_id = b.user_id and v.ds <= b.ds and v.ds > b.ds - interval '7 day'
```

Expand All @@ -168,19 +165,18 @@ To resolve this and eliminate duplicates, use a distinct select. The UUID also h
Instead of regular select used in the [Step 2](#step-2-refine-with-window-function), use a distinct select to remove the duplicates:

```sql
select distinct
first_value(v.ds) over (partition by b.ds, b.user_id, b.uuid order by v.ds desc) as v_ds,
first_value(v.user_id) over (partition by b.ds, b.user_id, b.uuid order by v.ds desc) as user_id,
first_value(v.referrer_id) over (partition by b.ds, b.user_id, b.uuid order by v.ds desc) as referrer_id,
b.ds,
b.uuid,
1 as buys
from visits v
inner join (
select *, uuid_string() as uuid from buys
) b
on
v.user_id = b.user_id and v.ds <= b.ds and v.ds > b.ds - interval '7 day';
SELECT DISTINCT first_value(v.ds) over (partition by b.ds, b.user_id, b.uuid order by v.ds desc) as v_ds,
first_value(v.user_id) over (partition by b.ds, b.user_id, b.uuid order by v.ds desc) as user_id,
first_value(v.referrer_id)
over (partition by b.ds, b.user_id, b.uuid order by v.ds desc) as referrer_id,
b.ds,
b.uuid,
1 as buys
FROM visits v
INNER JOIN (select *, uuid_string() as uuid
from buys) b
ON
v.user_id = b.user_id and v.ds <= b.ds and v.ds > b.ds - interval '7 day';
```

The dataset returns the following:
Expand All @@ -201,38 +197,28 @@ You now have a dataset where every conversion is connected to a visit event. To
Now that you’ve tied each conversion event to a visit, you can calculate the aggregated conversions and opportunities measures. Then, you can join them to calculate the actual conversion rate. The SQL to calculate the conversion rate is as follows:

```sql
select
coalesce(subq_3.metric_time__day, subq_13.metric_time__day) as metric_time__day,
cast(max(subq_13.buys) as double) / cast(nullif(max(subq_3.visits), 0) as double) as visit_to_buy_conversion_rate_7d
from ( -- base measure
select
metric_time__day,
sum(visits) as mqls
from (
select
date_trunc('day', first_contact_date) as metric_time__day,
1 as visits
from visits
) subq_2
group by
metric_time__day
) subq_3
full outer join ( -- conversion measure
select
metric_time__day,
sum(buys) as sellers
from (
-- ...
-- The output of this subquery is the table produced in Step 3. The SQL is hidden for legibility.
-- To see the full SQL output, add --explain to your conversion metric query.
) subq_10
group by
metric_time__day
) subq_13
on
subq_3.metric_time__day = subq_13.metric_time__day
group by
metric_time__day
SELECT coalesce(subq_3.metric_time__day, subq_13.metric_time__day) as metric_time__day,
cast(max(subq_13.buys) as double) /
cast(nullif(max(subq_3.visits), 0) as double) as visit_to_buy_conversion_rate_7d
FROM ( -- base measure
SELECT metric_time__day,
sum(visits) as mqls
FROM (SELECT date_trunc('day', first_contact_date) as metric_time__day,
1 as visits
FROM visits) subq_2
GROUP BY metric_time__day) subq_3
FULL OUTER JOIN ( -- conversion measure
SELECT metric_time__day,
sum(buys) as sellers
FROM (
-- ...
-- The output of this subquery is the table produced in Step 3. The SQL is hidden for legibility.
-- To see the full SQL output, add --explain to your conversion metric query.
) subq_10
GROUP BY metric_time__day) subq_13
ON
subq_3.metric_time__day = subq_13.metric_time__day
GROUP BY metric_time__day
```

### Additional settings
Expand All @@ -249,7 +235,7 @@ Use the following additional settings to customize your conversion metrics:
To return zero in the final data set, you can set the value of a null conversion event to zero instead of null. You can add the `fill_nulls_with` parameter to your conversion metric definition like this:

```yaml
- name: vist_to_buy_conversion_rate_7_day_window
- name: visit_to_buy_conversion_rate_7_day_window
description: "Conversion rate from viewing a page to making a purchase"
type: conversion
label: Visit to Seller Conversion Rate (7 day window)
Expand Down Expand Up @@ -329,22 +315,24 @@ In this case, you want to set `product_id` as the constant property. You can spe
You will add an additional condition to the join to make sure the constant property is the same across conversions.

```sql
select distinct
first_value(v.ds) over (partition by buy_source.ds, buy_source.user_id, buy_source.session_id order by v.ds desc rows between unbounded preceding and unbounded following) as ds,
first_value(v.user_id) over (partition by buy_source.ds, buy_source.user_id, buy_source.session_id order by v.ds desc rows between unbounded preceding and unbounded following) as user_id,
first_value(v.referrer_id) over (partition by buy_source.ds, buy_source.user_id, buy_source.session_id order by v.ds desc rows between unbounded preceding and unbounded following) as referrer_id,
buy_source.uuid,
1 as buys
from {{ source_schema }}.fct_view_item_details v
inner join
SELECT DISTINCT first_value(v.ds)
over (partition by buy_source.ds, buy_source.user_id, buy_source.session_id order by v.ds desc rows between unbounded preceding and unbounded following) as ds,
first_value(v.user_id)
over (partition by buy_source.ds, buy_source.user_id, buy_source.session_id order by v.ds desc rows between unbounded preceding and unbounded following) as user_id,
first_value(v.referrer_id)
over (partition by buy_source.ds, buy_source.user_id, buy_source.session_id order by v.ds desc rows between unbounded preceding and unbounded following) as referrer_id,
buy_source.uuid,
1 as buys
FROM {{ source_schema }}.fct_view_item_details v
INNER JOIN
(
select *, {{ generate_random_uuid() }} as uuid from {{ source_schema }}.fct_purchases
SELECT *, {{ generate_random_uuid() }} as uuid FROM {{ source_schema }}.fct_purchases
) buy_source
on
v.user_id = buy_source.user_id
and v.ds <= buy_source.ds
and v.ds > buy_source.ds - interval '7 day'
and buy_source.product_id = v.product_id --Joining on the constant property product_id
ON
v.user_id = buy_source.user_id
AND v.ds <= buy_source.ds
AND v.ds > buy_source.ds - INTERVAL '7 day'
AND buy_source.product_id = v.product_id --Joining on the constant property product_id
```

Expand Down

0 comments on commit 76b60c3

Please sign in to comment.