A/B testing: How to calculate p-value on post test segments? My question on A/B testing is about doing post test segmentation analysis.

For example: 


  I run an A/B test on my website to track bounce rate.  On the
  treatment group, i put a video to explain my company. On the control
  group i put just plain text. I pick a segment of users who are first
  time users from USA to be split 50/50 into the 2 groups.


Metric that i am tracking is average bounce rate (assume 20%). 
Power effect (0.8)
effect size i expect to see(10% so bounce rate should fall to (20% - 0.10 * 20% = 18%))
Calculated sample size required is say 1000 for each group. 


Say i run the test for the correct amount of time. At the end of the test, i get a p-value of 0.06. i do not reject the null hypothesis. 


  However, when i do post test segmentation analysis, for example, i saw
  that users who signed up for a free trial, 44% of them played the
  video.


In this case, how do i calculate if the 44% was significant? (while taking into account the multiple comparison problem?)
Like in the Airbnb experiment, they did post segmentation analysis on the browser type and was able to calculate the p-value. 


My approach

Does this mean that for every segment that i want to analyze, i need to have at least 1000 samples? Also how would i recalculate the p-value given that the p-value of this A/B test was already generated above as 0.06?
I recently wrote about this in a blog post.
Given this is a rate evaluation metric, you will want to use the z-test. The basic steps are (more details in the blog post)

calculate the pooled standard error of your pairwise comparison
calculate z-statistic by normalizing the delta or lift by the standard error
look up the cdf value of the normalized delta
p-value = 1-cdf(z)
given that this is also an A/B/n test, you should also want to apply multiple testing correction using the bonferroni procedure or the benjamini hochberg procedure, when evaluating significance