Statistics explained

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Statistics Explained

For every in-app message or email that you send using Intercom, you'll see some stats for it on the message page like this:. The stats you see in this example are sent, opened, clicked, goal, and replied. Messages sent in full are marked as opened as soon as they are displayed to the user.

Note: If customers have any previously unread messages, we will not count these types of messages as opened. This is why some in-app chat messages can sometimes have very different opened rates VS sent rates. Another reason for opened rates to be lower than expected is if a user navigates away from a page in the short gap between when the message is sent to them and when it is automatically displayed.

The Usual Suspects

This gap is typically of the order of seconds. This is pretty rare. When creating an auto message, you can set a goal to measure its effectiveness. You can see each user that matched the goal after receiving the message.

Confidence intervals and margin of error - AP Statistics - Khan Academy

Goal stats are based on messages sent. Reply rate is simply the number of people who replied to the message.

EdgeSight End User Experience Monitoring Statistics Explained

This figure is based on how many messages were sent, not on how many were opened. As soon as a link or button that automatically redirects through Intercom inside the message has been interacted with it gets marked as clicked. Clicks in embedded videos won't get marked as clicked.


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Manual messages come with 'Audience' stats too. Your 'Audience' stats show you the number of people who match the rules for that message. All hypothesis tests ultimately use a p -value to weigh the strength of the evidence what the data are telling you about the population.

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The p -value is a number between 0 and 1 and interpreted in the following way:. Always report the p -value so your readers can draw their own conclusions. You conduct a hypothesis test because you believe the null hypothesis, H o , that the mean delivery time is 30 minutes max, is incorrect. Your alternative hypothesis H a is that the mean time is greater than 30 minutes. You randomly sample some delivery times and run the data through the hypothesis test, and your p -value turns out to be 0.

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In real terms, there is a probability of 0. Since typically we are willing to reject the null hypothesis when this probability is less than 0. Of course, you could be wrong by having sampled an unusually high number of late pizza deliveries just by chance. How to Calculate Percentiles in Statistics If all you are interested in is where you stand compared to the rest of the herd, you need


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