Email is a key tool to keep customers engaged and get them back to the site when they're ready to buy.




Which email campaign is best for re-engagement?

Better emails trigger more visits and, as a result, more orders.

Ex: A custom t-shirt company identified the “Showcase your latest designs” emails as best for re-engagement and increased site traffic by 2%.

Build the Analysis
DATASET
ALL Opened Email
FIRST IN BETWEEN Started Session



ANALYZE
Increase Conversion Rate to Started Session Between (%)
by analyzing the influence of Campaign Type
by month and ☑️ filter data for people who can still convert




What is the optimal time to send emails to customers?

Your customer are busy. If emails arrive at an off time, they may be ignored.

Ex: An e-commerce company realized emails between 9pm - 3am were rarely engaged with.

Build the Analysis
DATASET
ALL Opened Email
FIRST IN BETWEEN Started Session
Add a computed column (Date Part) for Hour of Day


ANALYZE
Increase Conversion Rate to Started Session Between (%)
by analyzing the influence of Hour of Day
by month and ☑️ filter data for people who can still convert




How many emails should you send?

Too many emails can be a turn-off and too few emails can be ineffective.

Ex: A shoe retailer found diminishing returns after 14 emails. They stopped sending 15+ emails and they've cut down on email costs by 43%.

Build the Analysis
DATASET
ALL Opened Email
FIRST IN BETWEEN Started Session



ANALYZE
Increase Conversion Rate to Started Session Between (%)
by analyzing the influence of Activity Occurrence
by month




What is the right cadence for sending emails?

Too fast and the customer will be annoyed, too slow and they customer will move onto the next thing.

Ex: A men's clothing brand found that 2 days between emails was just right for their customers and increased engagement by 9%.

Build the Analysis
DATASET
ALL Received Email
FIRST IN BETWEEN Started Session

LAST BEFORE Received Email


ANALYZE
Increase Conversion Rate to Started Session Between (%)
by analyzing the influence of Days Since Last Before Received Email
by month






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