sending time optimization

Emarsys in numbers

2.5B contacts

 

1.2B unique contacts

first idea

every day usage

~ 1 year

1. what & why

2. how

3. how much

truly personal

optimize within a day

vs.

available data?

  • historical sends

  • opens, clicks

  • mobile / desktop

can we optimize
based on past data?

opportunity for improvement?

differences in open rate
for one contact

systematic differences between campaigns by send hour?

differences in open rate for all contacts in reality

06:00 16:00 20:00 SD
contact_A 0.5 0.33 0.4 0.085
contact_B 0 0.25 0.25 0.144

reality vs simulation

how to choose personal sending times?

agile data

at time of last open

 

bandit learning algorithm

 

algo trained for 3 years

bandit algorithm

bandit algorithm

  • arms:       possible send times

  • winning: open of sent message

personal send hour for one contact over time

exploration ...

... exploitation

algo adaptation

  • priors

  • history of 12 months

  • 2 hour intervals

piloting starts!

what to measure?

present performance

and

future potential

50%

50%

?

uplift in first 2 hours

uplift in open rate

uplift in click rate

in customer engagement

2-12% increase

handle recent campaigns

distorting priors

priors from STO emails

more piloting

non-random A/B testing

weekend effect with shifted sending for control group

  1. send time opt. worth it

  2. bayesian bandit algo

  3. algo is only one component of success

startup safary send time optimization

By Czeller Ildi

startup safary send time optimization

Slides for 2017 Startup Safary about send time optimization at Emarsys.

  • 91

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