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Welcome to the CommunityMaintenanceSupport wiki!

Notes from the first meeting:

Planning/Inception

Dashboard

Getting premium subscriptions

• Email (based on a template) 2500 --> 10 premium sign ups
• contact via slack - conversations started by others have lead to upgrades

End goal

increase revenue

what would increase revenue?

• premium subscriptions (including upselling) 3/4 (30+)
• organisation subscriptions
• sponsorship 1/4 (sponsor report) - data analysis
• grants/donations (repeating ...?)

identifying who to approach in community who might be interested in a premium subscription or an upgrade

(LinkedIn has an automated system to offer premium free trial to users who become more active)

Kafka (event stream and analytics pipeline)

Could be tracking events like votes in slack (which project, what they are voting on), which would be fed into data reservoir - and do analytics on it to work out who to approach

https://segment.com/ (marketing analysis platform)

Events

1. slack
2. website (DB)
3. email

separate data warehouse of events

Marketing Measures

Google $10,000 a month - 83K impression, 1000 click throughs, %convert (members, premium members) --> google analytics (adwords) Twitter --> %conversions LinkedIn --> %conversions Facebook --> %conversions

Possible Analysis

Slack History --> upgrades

__I---___IIIII----_P--F2F--__III
__I---_II_I----_P--PP---IIII
__I---III_I----P--MOB--P ___I---_III_I----_P?X___III---

+demographic data

time series analysis

risk report on premium members

AVAnalysis (CommunityMaintenanceSupport)

As an AgileVentures admin So that I can ensure the AV revenue stream continues to grow I would like to know which members to approach to suggest premium upgrades to Instead of guessing based on intuition from observing community activity

As an AgileVentures admin So that I can ensure the AV revenue stream does not decrease I would like to know which members are likely to be downgrading in order to offer them additional help and support Instead of guessing based on intuition from observing community activity

Initial work

take slack dump (add premium upgrade dates and match with user ids) and do some visualisation to look for patterns (hand rolled scripts, or existing options)

1. more data analysis (gathering more data, different types), to get something with strong significance (multi-variate) (upgrades/downgrades)
2. do some predicting, and acting, which generates data itself

Metric

#consecutive_activity_increase_weaks 1700 measure of consistency of increasing activity - ranking show top five ranks of increasing activity people (and show their premium or non-premium status)

activity in weeks preceding A1 A2 A3

2(A1-A2)/3 + (A2-A3)/3

]/AverageActivity

1500

a week (in which one person upgraded) * 12

A1-A2 A2-A3 A3-A4 A4-A5

A1 ==> individuals activity normalised for the activity that week

Action Items

• michael to try creating a ranking metric and see if is is superficially predictive of upgrade activity

  • provide a weekly ranking of user activity for a given week