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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:00,000 --> 00:00:01,649 So, to calculate your growth metrics, 2 00:00:01,649 --> 00:00:04,214 you would actually get the data from your data team. 3 00:00:04,214 --> 00:00:06,240 So, for daily active users, 4 00:00:06,240 --> 00:00:12,929 this would be the number of users who were active in the particular 24-hour time window, 5 00:00:12,929 --> 00:00:19,394 and the particular 24-hour time window is defined by the date you give the data team. 6 00:00:19,394 --> 00:00:21,074 For monthly active users, 7 00:00:21,074 --> 00:00:27,600 that would be the number of users who were active within the month-long time window. 8 00:00:27,600 --> 00:00:33,270 So, let's assume for our mock data we have here, 9 00:00:33,270 --> 00:00:37,785 we have the daily active users provided based on the last day of each month, 10 00:00:37,784 --> 00:00:40,559 and the monthly active users is building off of that. 11 00:00:40,560 --> 00:00:42,730 So, for the whole month, 12 00:00:42,729 --> 00:00:44,959 give us the number of users who were active. 13 00:00:44,960 --> 00:00:49,969 I created the bar graphs here to depict the numbers we had up here, 14 00:00:49,969 --> 00:00:53,554 and you can see that there is a dip in the number of 15 00:00:53,554 --> 00:00:59,000 active users on October 30th compared to September 30th, 16 00:00:59,000 --> 00:01:01,850 but it steadily improves after that. 17 00:01:01,850 --> 00:01:04,370 But the monthly active users count, 18 00:01:04,370 --> 00:01:10,125 which is the number of users who were active in the last month, were steadily increasing. 19 00:01:10,125 --> 00:01:13,049 A better interpretation of the DAU and monthly 20 00:01:13,049 --> 00:01:16,189 active user metric is using the stickiness ratio. 21 00:01:16,189 --> 00:01:18,605 So, let's go ahead and calculate the stickiness. 22 00:01:18,605 --> 00:01:20,525 I'm using the formula provided here, 23 00:01:20,525 --> 00:01:22,840 and I'm using it for all of the months. 24 00:01:22,840 --> 00:01:27,719 So, this would be daily active users over monthly active users, 25 00:01:27,719 --> 00:01:30,140 and I have a graph that I will go over in a minute. 26 00:01:30,140 --> 00:01:34,314 So now, we have the stickiness ratio calculated here. 27 00:01:34,314 --> 00:01:39,649 A stickiness ratio of one means that every user who visited the website in 28 00:01:39,650 --> 00:01:42,650 the last month was also active on 29 00:01:42,650 --> 00:01:45,830 the day on which the daily active user account was generated. 30 00:01:45,829 --> 00:01:52,579 You can see here that our stickiness ratio range between 0.1 to 0.21. 31 00:01:52,579 --> 00:01:56,329 This is the proportion of monthly active users who 32 00:01:56,329 --> 00:02:00,189 came back to your product in that 24-hour or day window. 33 00:02:00,189 --> 00:02:03,209 So, 0.2 ratio means 20 percent of the month. 34 00:02:03,209 --> 00:02:06,299 So six days out of the last month, 35 00:02:06,299 --> 00:02:10,204 the user was back online looking at the website or app. 36 00:02:10,205 --> 00:02:12,525 If you're ratio is say 0.01, 37 00:02:12,525 --> 00:02:13,830 that's less than a day, 38 00:02:13,830 --> 00:02:15,375 and that's a problem scenario. 39 00:02:15,375 --> 00:02:19,099 So, engagement metrics are useful metrics to gauge how 40 00:02:19,099 --> 00:02:23,284 often your website is being used and a good guide for your business. 41 00:02:23,284 --> 00:02:25,750 This line chart is a visualization tool 42 00:02:25,750 --> 00:02:28,909 business analysts use to show the stickiness ratio. 43 00:02:28,909 --> 00:02:31,579 It can be used to show email campaigns or 44 00:02:31,580 --> 00:02:34,870 experiments when your website features were introduced. 45 00:02:34,870 --> 00:02:39,335 They coincide with high or low stickiness ratio and 46 00:02:39,335 --> 00:02:44,070 can help guide future plans to introduce or revisit specific effort. 3948

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