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Data is a hot topic in the community-building space right now, but in 15+ years in tech, I can count on one hand the organizations I know that use data effectively.
Here are 𝟭𝟴 product metrics and how to apply them to your community.
Core metrics are those most likely to define value for your members.
All social apps share these core metrics, but will include different feature metrics as they understand their value.
How many folks join your community, daily, weekly, or monthly?
Community Why: important to know your growth rate and plan for it.
Why Maybe Not: especially early days, it's important to satisfy existing members, rather than optimize for *more* members.
How many users logged in, measured daily (DAU), weekly (WAU), and monthly (MAU)
Community Why: an active user is a *chance* to engage, even if they don't take other actions.
Why Maybe Not: observers don't create relationships, the core of community.
The number of days a given user is active over a period of time, DAU/[days]
Community Why: important for understanding the usage and engagement patterns of your members.
Why Maybe Not: best used in an experiment, but you likely don't have enough members to gather meaningful data.
Days engaged for new members. Did they come back 1 day, 7 days, 1 month later?
Community Why: helps understand the quality of your onboarding and the likelihood that a given member will stick around long term.
Why Maybe Not: Yeah, just do this one 😂
How regularly members come back, measured DAU/MAU or WAU/MAU.
Community Why: stickiness helps you understand your community's rituals.
Why Maybe Not: when folks talk about social media addiction, it's that these companies are constantly trying to boost stickiness.
How many members will join from one, through invite/referral, >1 is viral.
Community Why: if your community grows through referrals, this is the best way to measure referral success.
Why Maybe Not: uncontrolled growth can overwhelm channels and relationships.
Beyond core metrics are other aspects of the community your members might engage with.
Some feature metrics will be important for understanding what members value and become core.
The count of activities within a digital community space.
Community Why: to understand the usage of your digital spaces.
Why Maybe Not: messages aren't inherently positive. Trolls post frequently and get a lot of replies.
The count and graph of 1:1 messages within a digital community space.
Community Why: DM counts help you understand connections and relationships you can't see.
Why Maybe Not: gathering this data can feel like a breach of privacy and misses connections that happen off-platform.
Whether members RSVP and then actually attend an event.
Community Why: this is a heavy/important action within a course or community.
Why Maybe Not: probably net good to understand, though important to understand seasonality around your member base.
Joins, posting, and engagement behavior within a channel or space of your digital community.
Community Why: behavior may help segment members based on interest.
Why Maybe Not: you could get caught in a comparison game, when different spaces might have different cadences.
If you attach learning content to your membership, tying it to progress within your LMS.
Community Why: can refine and smooth rough patches in your course materials.
Why Maybe Not: low completion rates don't necessarily mean low value.
Satisfaction metrics are standardized questions that help you understand whether members are getting what they want.
A 1-5 scale of satisfaction.
Community Why: helpful for quick interactions, speedy feedback right after an event, etc.
Why Maybe Not: satisfaction is considered a low bar.
NPS surveys ask the member how likely they are to recommend your community to their friends.
Community Why: NPS can be a great intro into a meaningful conversation about value.
Why Maybe Not: NPS, the score itself, is kinda snake oil. Over-used, people hate it and game the results.
A set of 12 questions, initially developed to gauge employee engagement.
Community Why: It's a good starting point, particularly the question about having a best friend in the community.
Why Maybe Not: questions about member value, ultimately, should be specific to your community.
Charts you look at that make you feel like you've learned something.
You might glean an insight or two from feel good data every once in a while, but turn into noise on a regular basis.
A graphic representation of the most commonly used words/phrases within your community.
Why Maybe Not: no one's ever learned anything useful from a word cloud.
Most engaged members, most active spaces, most yadda yadda.
Community Why: occasionally interesting.
Why Maybe Not: the ordering of a leaderboard will tend not to change, better to have actionable metrics based on that data.
A visualization of the connections within your community.
Community Why: it's interesting to see who knows each other and regularly interacts. They also look cool 😂
Why Maybe Not: Noise over time, also subject to troll influence. Connected people might not like each other.
Graphs that always go up. Total members, total messages, etc.
Why Maybe Not: pure feel good, aggregates really tell you nothing about success.
Do you need to optimize all 14 core and feature metrics to be successful?
Absolutely not!
Community is older than the internet and the ability to measure any of these things. The core of understanding your community is talking to your members.
Follow me for more about the intersection of product management and community every Thursday.
NEXT Thursday, I'm doing a deep dive on this subject. Would love to have you there!
In an upcoming post, we're going to dive into the types of community member profiles. Before you dig through these, it's important to understand the 5 stages of a community member....
You can now schedule direct messages to your community members using Burb!
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