The constant challenge of building enterprise communities is that much of the value lies in places few people check.
Often, that value goes unrealized because the organization doesn’t know where to look for it, or has no system to capture it.
Let’s use retention as a good example.
How Do Communities Really Drive Retention?
If you believe the 2026 CMX Community Industry Report, retention is the primary goal for organizations building enterprise communities right now.
But if you ask people why that retention happens, you often get a fuzzy answer.
This is often something along the lines of:
“When people are in a community, talking to others about the brand, they feel better about the brand and become more loyal.”
That might work for Harley-Davidson and a tiny handful of consumer brands, but fuzzy feelings don’t cut it outside of that.
The real mechanism is that people have a better customer experience because of the community.
As a result, they want to remain customers.
This comes through the knowledge and expertise shared in the community. For example:
- They can resolve their frustrations faster.
- They can configure and set up their products/services the right way.
- They discover novel use cases and examples which help them get more from the product.
- They understand where the product and roadmap are going and want to stick around.
All these things do impact attitudes (and are worth measuring), but fundamentally it’s about using customer knowledge to improve the ability of other customers to use the product.
The more you generate, capture, and deploy that community knowledge, the higher the retention.
How Do We Successfully Deploy Customer Expertise?
The problem is that many communities generate tens of thousands, even millions, of answers, and other members have to search for the right one and find it.
If they search for the right thing, they find it. If they don’t, they don’t. And while LLMs and AI are reconstituting this knowledge in interesting and exciting ways, they aren’t really designed to capture and harness the full value of customer knowledge.
That job falls to us, and most of us could be doing it far better than we are now.
We can divide this into five levels.
Level One: The Standalone Q&A
At level one, you have a community, but it’s not really connected to anything else.
People come, ask a question, get an answer, and leave. That can be pretty good for call deflection. We’ve done studies in the past that have shown that communities have a huge impact on call deflection.
And, generally speaking, the more people that see the answer, the higher that deflection rate is. This isn’t bad. It’s where many organizations are at right now with their community. They try to optimize the quantity and quality of answers in the community.
But if that’s the extent of what you do with that answer, then you’re capturing only a fraction of the value the community could and should provide.
Level Two: Integrate It With Support Tickets
The first step up is connecting your community to a system like Zendesk, where unanswered questions become tickets.
And just like any other ticket, they will (eventually) get answered by a customer support rep.
The key is that the answer is then pushed back to the community.
This means not only will the person who asked the question receive the answer, but hundreds, even thousands, of others can see it (and search and bots can see it, too).
In my experience, this also encourages support staff to search the community for answers to support tickets and customer calls, especially for the more difficult, contextual, edge-case problems.
This makes their answers far more valuable than replies to individual customer queries.
It helps to design the community as part of the support hub from the start.
Level Three: Community Knowledge Powers Your AI Support
This should be the minimum for any enterprise community today. Community knowledge should be retrievable by your RAG systems and AI support bots. This is when customer expertise becomes far more valuable.
This means that when someone asks a question on any support portal or AI assistant, the customer’s answer can appear alongside other relevant information via RAG (retrieval-augmented generation).
Community knowledge should be ingested into your support systems.
Yes, there are arguments about the reliability of customer answers, but that’s like arguing about the reliability of Wikipedia articles.
Will there occasionally be bad, poor information? Probably.
Will the useful, accurate information far outweigh the bad? In almost every case, yes.
The challenge here is that integration isn’t easy. Discussions aren’t designed to be retrieved like other articles in a database. To ingest community content, you either have to crawl it like a webpage, export and reshape it into something that resembles a help center article, or reduce it to a question-and-answer format. All of which strips out useful metadata. But this is still far better than nothing.
This should be an immediate project for the vast majority of enterprise community professionals out there.
We’ve long argued that keeping community as a separate, standalone department limits its impact. That argument now applies to community expertise. If that data isn’t combined with other systems, it delivers only a fraction of its value.
Level Four: Feed Community Content Into Courses And Product Content
A constant problem in many enterprise communities is repetitive questions.
I’ve never measured it, but I wouldn’t be surprised to see the 80:20 rule popping up here, where 80% of questions concern 20% of topics.
There’s a lot of knowledge being shared on a comparatively small range of topics. The obvious question is what happens if we pre-empt questions by giving members the knowledge up front?
There’s plenty of discussion about combining community with education, but largely it’s about integrating the platforms, reducing friction, and letting people display course qualifications on their profiles.
But the real win is feeding community content into your courses. When the same topic and answers show up in your community, that should feed the courses, onboarding material, and product documentation itself.
This turns community knowledge that disappears into a void into a growing body of content that improves satisfaction and retention across the board.
Veterans might remember when ‘tribal knowledge bases’ were a thing (essentially turning community content into wiki articles). The problem with TKBs is that motivation to contribute was low, and the content wasn’t plentiful enough to make them broadly useful. Feeding community knowledge into existing and new lessons doesn’t have that problem. Members are already creating the content; you just need to put it to work.
One of the fantastic things about AI is that you can automate this. You can have agents that review discussions in your community, look at popular answers, and review courses and product documentation to see where the knowledge can and should be updated. It obviously helps here if they are on the same platform, but it’s not a decisive factor.
Level Five: Account Signals To CSMs
Perhaps the most powerful way community drives retention is through the signals and information it provides about customers.
Unfortunately, in most cases, this knowledge goes to waste.
In a community, you will often see customers asking questions in a certain category, appearing frustrated, or not getting answers.
Wouldn’t this be incredibly useful for the relevant customer success managers to know? (And for the organization as a whole to know?)
If you’re responsible for a customer, and you get a notification saying that customer has had two unanswered questions in the community in the past two weeks, you can set up a call with that customer and check what help or support they might need.
This requires the community to be integrated with Salesforce, Gainsight, HubSpot, or another CRM. You need that data to show up.
The funny thing about level five is that it probably has the most direct impact on retention (making contact with unsatisfied customers before they decide to leave), but it’s also incredibly hard to measure and wouldn’t show up on most dashboards.
The problem here is matching. Members often register with personal email addresses. This means a significant percentage of community activity might not link to anyone in the CRM. So before you build the integration, check what percentage of active members you can actually match to an account.
The Systems We Build Define The Results We Get
Much of our work in recent years has shifted away from programs to incrementally improve the community and towards systems that get far more value from community knowledge.
Even today, too many community teams focus on individual programs, still often using engagement metrics to track success. The real win, especially for the larger, more mature community, is building connected systems that ensure community knowledge flows back through the organization.
Customer retention doesn’t increase because people experience warm, fuzzy feelings from talking to one another. It increases when people benefit from other customers’ expertise. That means going beyond Q&A and integrating community knowledge deeply into the broader customer journey.
Many organizations still lack processes to make that happen. This information, and these incredibly powerful signals, simply get lost.
If you want to make this happen, an easy step to take is to let us review the existing shape of customer knowledge. We can help you understand what expertise exists, where it is being deployed at the moment, how valuable it is, and what’s holding deployment back.
Based on that, we can put together a clear plan, technology requirements, and the processes to bring that expertise to the forefront of your customer experience.
If you would like guidance, inquire about our support.