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QUESTION
 
QUESTION 1
Write a SQL query (MUST using Postgres or SQLite syntax – http://www.sqlite.org/index.html) on the orders table (orders.csv) that shows:
1. What % of users placed more than 1 order in the first 28 days
2. what % of users have an average days between order over 28 days
Note: An order is considered to be in the user’s first 29 days if it happened before OR on first_date + days(28). E.g. if the user’s first order was on 1/1/2018, an order on 1/29/18 would still count as a first_29_day_order but an order on 1/30/18 would not.
Process: Include your SQL code and answers in a single txt document.
QUESTION 2
Background on the Data:
We have a system that allows apps to send receipt alerts to the shopper in 3 channels (banner, push, sidebar). We log two separate events into a table called alerts (alerts.csv):
1. Whenever a shopper sees an alert (impression or ‘imp’)
2. Whenever a shopper clicks on an alert (‘clk’)
The table contains data for a single day. This logging is new and hasn’t been vetted yet. Trust nothing, callout bad data, and work to clean the data as necessary to get the best answer.
Columns in the table:
user_id: numeric, unique and persistent id for each shopper.
group_id: numeric, unique and persistent id for each store. Each userid belongs to exactly one group. app_id: numeric, unique and persistent id for each app
event: string with values ‘imp’ (as in impression, so it was seen) or ‘clk’ (the shopper interacted with it) primary_browser: string, the browser the user was using
alert_type: string, alert was in a banner/sidebar/push eventtime: the timestamp of when the interaction happened
Questions:
1. What is the best performing alert type?
2. What apps are the best and worst performing?
3. I’m curious about what the first alert a group clicked on in this day? For each alert_type, compute how many groups clicked an alert of that type as their first alert in a day.
4. What other research, experiments, or approaches around alerts would you recommend to improve engagement of the product
 
 

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