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Subscription Growth and Churn Forecasting

  • 22 October 2020
  • 1 reply
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Hi there! New to Looker and wondering if anyone has any tips for looks or dashboards that are helpful when forecasting retention, churn and revenue for an e-comm subscription business.


Am currently planning 2021 revenue and would love to use Looker to pull data and make my life easier, but I am not sure where to start.


One thing specifically I’d like to see: Churn by # of months subscribed aggregated no matter what month they joined the subscription.


Would love any helpful tips or feedback on how to optimize Looker for a subscription business.

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Best answer by Eric_Lyons 5 November 2020, 22:27

Hey @nikkadial,

Thanks for the note! For the visualization a common trend is to use our column visualization to show the total number of users (we could group this by number of months since they have signed up) and then, we can go to the series tab and show churn as a percent with a line visualization (We would most likely want to separate these in two axises so the percent is easier to see).

We could also show another column for revenue by cohort. This article shows us some more details on how to implement this. For your specific use-case, we could potentially use a duration dimension_group type with a start date and end date. This should give us the number of months (or other timeframes) since they signed up. 

We do have a retention analytics block and cohort analysis block. You could leverage some of the LookML patterns in these as a framework for your own analysis or try to fully implement one of the blocks on your Looker instance. 

Please let us know how everything is going or if you have any other questions!

 

Thanks,
Eric

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Hey @nikkadial,

Thanks for the note! For the visualization a common trend is to use our column visualization to show the total number of users (we could group this by number of months since they have signed up) and then, we can go to the series tab and show churn as a percent with a line visualization (We would most likely want to separate these in two axises so the percent is easier to see).

We could also show another column for revenue by cohort. This article shows us some more details on how to implement this. For your specific use-case, we could potentially use a duration dimension_group type with a start date and end date. This should give us the number of months (or other timeframes) since they signed up. 

We do have a retention analytics block and cohort analysis block. You could leverage some of the LookML patterns in these as a framework for your own analysis or try to fully implement one of the blocks on your Looker instance. 

Please let us know how everything is going or if you have any other questions!

 

Thanks,
Eric

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