Last activity: 11 Jan 2022 9:52 EST
Adaptive learning on response level for email channel
With delayed learning in place our adaptive models are feeding with one negative or positive response once timeout happens. It is fine for most of the channels, however for email it doesn't look complete based on scenarios we have:
We have multiple email content where there is no clickable url to accept/like the offer, in those cases considering 'Open' as positive response (both open and clicks are defined as positive response in adaptive model rule) is enough but the same is a challenge to solve for an email with clickable links/buttons/urls. Because a customer can open an email and do not click it or open and click - in both cases delayed learning will output a positive response, however we can understand it's not the same.
Since we can't give a weightage to any response while feeding response to adaptive, we thought of having two adaptive model rule (one with only open as positive and one with only click as positive) and use them together with a weighting in prioritization formula. However challenge is how to trick delayed learning feature to output both click and open at the end of wait time window?
Or do you have a better solution to avoid the above situation and solve the problem?