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S Jun 6, 2022 at 10:11 history suggested Alaa CC BY-SA 4.0
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Jun 18, 2018 at 6:24 vote accept Andrei
Jun 18, 2018 at 6:24
Jun 16, 2018 at 15:47 comment added Andrei There pretty much might be a seasonality pattern, that's what i'm trying to figure out. Some of them might have had 1's for a few years and afterwards are just trailing 0's that pretty much mean (based on the data available) that the customer will not be active anymore. Some of them have only a few 1's in the middle of the selected daterange (these are, as well, not very likely to produce new activity). Some might bring up recurring activity based on seasonality (these are the one's id like to determine). There might not have been any sight from them recently but the might be active again soon
Jun 16, 2018 at 15:40 comment added Spacedman Is there seasonality (a yearly pattern)? Is there serial correlation in the 1s (ie you are more likely to get 10 1s in a row than if they are completely random)? Do 1s "tail off" for all customers? Do some basic descriptive summaries before thinking all you need to do is throw it into a magic machine learning algorithm.
Jun 16, 2018 at 13:26 answer added Sharvari Gc timeline score: 0
Jun 16, 2018 at 9:59 review Close votes
Jun 18, 2018 at 17:40
Jun 16, 2018 at 9:58 comment added Andrei @Spacedman: Thanks for your input. To be honest i'm a bit lost (+ very new to this field) and haven't done anything yet. No, the data is only 1 and 0. The outcome would be, as you mentioned, "any activity". If there is at least one 1 in the next 6 months then I am not interested in this customer.
Jun 16, 2018 at 9:44 comment added Spacedman This question is way too broad. What have you tried already? Have you done any basic statistical analysis? Are the data any different from random 1s and 0s? Have you seen any patterns? Is your outcome "any activity (ie at least one "1") in the next six months"?
Jun 16, 2018 at 9:17 review First posts
Jun 16, 2018 at 17:46
Jun 16, 2018 at 9:15 history asked Andrei CC BY-SA 4.0