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I am part of the team that build the EWT api. You are absolutely right that there are some cases where more recent data point give a better estimate on the handle time. We mainly chose a week to capture the average to smooth it out over different time period of the day, i.e. beginning and end are usually very hard to predict using recent data.
Let us know if the result of the current EWT is not accurate based on your observation, and we can take a look and see what's affecting it. As far as being able to change the interval of aggregation of those metrics, at the moment this is not being supported at the individual customer level.
That being said, we are putting in some effort in enhancing all of the data we use for calculating EWT with machine learning and AI tech. That's in the roadmap as part of the overall enhancement to EWT. This will take in all the pattern and behavioral changes (time of the day, day of the week, recent fluctuation, system load) into account when calculating EWT and make it that more accurate.
Angelia,Would love to dig deeper into this. Have you created a care ticket for us to look at. Keeping in mind that there's a lot of moving pieces to consider too when it come to a longer than expected Estimated Wait Time. Currently the system doesn't predict if an agent or more is going to be taken out from the queue, in this case for example a initially predicted EWT would be shorter than what the actual wait time will be with less staff.
If you have the reports, you can check if the AHT for the hour or day is significantly different from others. If not, most likely there's other factor that's in effect.
Going back to my original comment, we are working on a better solution that would also incorporate agent going in and out of queue, and availability probability of agents that serve multiple queue on a specify queue, etc.Thanks,
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