Data Science Interview: Conversion Dropped—Blame Checkout?
Avoid a product data science interview pitfall: blaming a checkout release solely on the total conversion rate. Define and validate the account-level metric, separate returning and first-time visitors, and reweight prior rates with the recent audience mix. The fictional example yields 17% to 11% from mix and a further 0.6 percentage-point within-group change to 10.4%. This is descriptive arithmetic, not proof of cause. Investigate missing returning traffic and first-time decline; a confirmed checkout failure would justify mitigation.
Read more