I am working on an early phase software startup to help other SaaS (software as a service) companies retain new customers. We will be sending out automatic emails designed to look like they are written in a one-off personal fashion. The contents of the email will be tailored to reflect how much time they have invested in the setup of their SaaS accounts.
It's our goal to send about three emails per new customer per week. Our email app sends emails during "waking" hours (8 a.m. to 8 p.m.) and it looks at the email send history for each new customer's account once per hour. So it's looking at each customer's email history 84 times per week (7*12). Upon checking the email history, it decides whether to send an email.
My approach to coding this function is just to use a probability to trigger an email, with some degree of confidence that over a week the total number of sent emails is going to be about three. This will provide a lot of consistency while at the same time still appearing to be "human" in that it is random in its timing.
I've tried calculating this a few times, and my best calculation
has returned a probability of .04713, but I've been running a simulation for about three weeks with 2500 customer profiles and only a handful of accounts have produced 3 emails in a week. Clearly my calculations are wrong.
It's been a while since I took a stat course and I'm not entirely confident I'm going to come up with the right answer :)
Your thoughts are appreciated.