Never opened

Marketing

Importing and joining tables

First, as usual, let’s load and join the data used by our marketing team. Mailchimp shows our users’ demographics and when they joined us. Postgres has the users’ last active date, what plan they are on, how much revenue they are generating. To see all the information is in one place we will need to join the tables together using emails.

Top locations by revenue 🌍

Top three are big countries, no surprises here: China 🇨🇳 Indonesia 🇮🇩 and Brazil 🇧🇷. They are followed by France 🇫🇷 and Portugal 🇵🇹. These last two punch above their weight, they have a loyal market of users we should retain.

For active users 🙋‍♂️

Does this change if we only consider users that were active in the last 90 days?

Oh no, France 🇫🇷 and Portugal 🇵🇹 have slipped down. That’s valuable information to know! Our next marketing campaign should target users in these countries.

Marketing campaigns 🎲

Speaking of marketing campaigns, are they all equally effective?

Looks like some campaigns are driving revenue twice as much as others. Guess you gotta keep paying the marketing team to devise clever campaigns…


Marketing

Importing and joining tables

First, as usual, let’s load and join the data used by our marketing team. Mailchimp shows our users’ demographics and when they joined us. Postgres has the users’ last active date, what plan they are on, how much revenue they are generating. To see all the information is in one place we will need to join the tables together using emails.

Top locations by revenue 🌍

Top three are big countries, no surprises here: China 🇨🇳 Indonesia 🇮🇩 and Brazil 🇧🇷. They are followed by France 🇫🇷 and Portugal 🇵🇹. These last two punch above their weight, they have a loyal market of users we should retain.

For active users 🙋‍♂️

Does this change if we only consider users that were active in the last 90 days?

Oh no, France 🇫🇷 and Portugal 🇵🇹 have slipped down. That’s valuable information to know! Our next marketing campaign should target users in these countries.

Marketing campaigns 🎲

Speaking of marketing campaigns, are they all equally effective?

Looks like some campaigns are driving revenue twice as much as others. Guess you gotta keep paying the marketing team to devise clever campaigns…


Marketing

Importing and joining tables

First, as usual, let’s load and join the data used by our marketing team. Mailchimp shows our users’ demographics and when they joined us. Postgres has the users’ last active date, what plan they are on, how much revenue they are generating. To see all the information is in one place we will need to join the tables together using emails.

CSV

CSV

Find and replace

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Top locations by revenue 🌍

Group

by
| and aggregate
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and
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Sort

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Top

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Top three are big countries, no surprises here: China 🇨🇳 Indonesia 🇮🇩 and Brazil 🇧🇷. They are followed by France 🇫🇷 and Portugal 🇵🇹. These last two punch above their weight, they have a loyal market of users we should retain.

For active users 🙋‍♂️

Does this change if we only consider users that were active in the last 90 days?

Filter

where
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Group

by
| and aggregate
as
and
as
and
as

Sort

inorder

Top

5
rows
Loading…

Oh no, France 🇫🇷 and Portugal 🇵🇹 have slipped down. That’s valuable information to know! Our next marketing campaign should target users in these countries.

Marketing campaigns 🎲

Speaking of marketing campaigns, are they all equally effective?

Sort

inorder

Group

by
| and aggregate
as
and
as
and
as

Sort

inorder

Find and replace

/^/
with
No.

Chart

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Looks like some campaigns are driving revenue twice as much as others. Guess you gotta keep paying the marketing team to devise clever campaigns…