Steemit Churn Rates Q1 2018

Churn is a fact of life in business.  You will win new users and you will lose users.  Churn can be calculated by the following formula

Churn = Number of Active users at the beginning of the period + New registrations – Number of Active users at the end of the period.

Churn rate can be calculated using 

Churn rate = Churn / Number of Active users at the beginning of the period

“While churn is not necessarily a bad thing for all businesses, a high churn rate compared to your competitors can be a troubling sign. For social media and advertising platforms, it can suggest a lack of engagement and loyalty among the user base – and increased consumer dissatisfaction with the platform at large. Especially for companies that rely on advertising revenue, the lack of a consistent, engaged audience could jeopardize key business models and monetization strategies.”

Source https://www.vertoanalytics.com/chart-week-social-media-networks-churn/

The aim of this analysis is to calculate the churn rate on Steemit.  I have seen the question being asked a number of times now in different comments on posts, and most recently I had a direct request from @davemccoy for this information.

  Repository

https://github.com/steemit/condenser

All data for this analysis was taken from Steemsql held and managed by @arcange.  As to not distract from the data, I will first present the findings and then the queries used.

Weekly Churn Rates 2018

The chart below shows the weekly churn rate by line and by bar you can see the number of new accounts and the distinct user count for the week.

  

 

 

The table above shows the number of new accounts registered per week, the distinct count of votes, the discount of authors, the distinct user count (either voted or posted/commented), the weekly churn and the weekly churn rate.

The churn rate varies considerably from week to week as it is dependent on two variables, the distinct users and the number of new accounts.  As both of these can vary from week to week it is expected to see this variation reflected in the churn rates.

The average weekly churn rate is 22% and the median is 20%

Monthly Churn 2018

 

As with the weekly churn rates you would expect to see a variance on the monthly churn rates as the variable also change.  There are notable swings on the number of new accounts each month. However the distinct user is smoother when looked at monthly over weekly.  This smoothness in the distinct user reduces the swings on the churn rates

Both the average and the median monthly churn rates are 59%

Q1 2018 Churn rate

  In Q1 of 2018 Steemit lost more active users than gained. The churn rate was 121%  

Using this as a comparative figure against the social media sites in the image above.  Facebook was reporting a churn rate Q3 to Q4 of only 2.8%, Snapchat 23.5%, Instagram 19.1%, Twitter 25.3% and Kick 33%.  I was unable to source more recent data and I was also unable to source churn rates for Reddit.  If you do have this information, please do comment below as it would add value to this post.

Specific Turn Rates

In @davemccoy request he defined the

 "active users" as those members that post or comment in the preceding 2 week period of time

 

 Calculating churn rates this was results in a higher monthly churn, with January showing that Steemit lost more active users than gained.  Both the average and the median month churn rates are 79% in 2018

Conclusion

Churn is a fact of any business.  What is an acceptable level of churn is also a business choice but the reality is, the lower this number the better.  Calculating monthly churn in both ways as above show that Steemit has a monthly churn of between 59% and 79%.  However a Q1 turn rate of 121% is very disappointing to see.

What are your thoughts on the churn rates above?  What reflection do they give about Steemit?  Please do comment below 

The Queries

As mentioned I used Steemsql and PowerBI to gather and model the data.  The query used to get the unique voters was

SELECT voter, timestamp FROM Txvotes (NOLOCK) where timestamp >= CONVERT(DATE,'2017-10-01')             and         timestamp< CONVERT(DATE,'2018-05-01')

The query used to get the unique authors was

SELECT author, timestamp FROM Txcomments (NOLOCK) where timestamp >= CONVERT(DATE,'2017-01-01')             and         timestamp< CONVERT(DATE,'2018-05-01')

To get the total unique users, I combined both of the above queries and then ran distinct counts on the name.

The query used to get the number of new accounts was

Select name, created FROM Accounts (NOLOCK)

 I am part of a Blockchain Business Intelligence community. We all post under the tag #blockchainbi. If you have an analysis you would like carried out on Steemit data, please do contact me or any of the #blockchainbi team and we will do our best to help you...
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