Referral vs Paid Search

Kindled's CFO wants one head-to-head before signing off next quarter's spend: the referral programme, which costs almost nothing, against paid adverts, which cost a great deal. Two figures per channel settle the argument — how many people it brought in, and how much they went on to do on the site. Everybody who arrived counts toward the headcount, including people who did nothing afterwards, because dropping them is how a bad channel is made to look good. The two remaining channels are not part of this comparison.

events — one row per tracked action. user_id points at a row in users. event_type is one of signup, view_product, add_to_cart and purchase. event_date is the day the action happened. Nothing guarantees a person produces all four kinds, or any particular number of rows.

users — one row per person who has created an account. source records how they arrived: organic means they found Kindled themselves, paid_search means they clicked a bought advert, and referral means an existing customer invited them. signup_date is the day they registered.

Both tables already exist in the database — there is nothing to create or load.

Task: Write a query that returns one row for each of the referral and paid-search channels, with columns source, users — how many separate people arrived through it — and events, how many tracked actions those people produced in total. Rows come back alphabetically by source.

Example output — shape only, on invented tallies. The source labels are the real stored ones, and the two rows are alphabetical rather than biggest first: