We created the Community Engagement Framework to categorize engagement behaviors and help organizations understand how to differentiate and measure various engagement behaviors. Most engagement analytics are architected around content or transactions, not people. I want to know things like: What pathways do people take to successfully complete a workflow? Who is doing behavioral analytics well? This approach to analytics helps community practitioners see: The average cycle time between when people join the community and when they first exhibit certain engagement behaviors. Community platforms will struggle until they get analytics right. When the platform vendors do analytics right, their value will be self-evident. A good community strategy defines the key engagement behaviors you want and the workflows those behaviors enable. It’s time for community platform vendors to get analytics right. Let us know if we can help you get the most out of your community.
Everyone wants engagement, but few know how to measure it.
Organizations are realizing that in the age of options, engagement is a key indicator of attention, commitment, and ultimately, success.
Executives see the level of engagement on platforms like Facebook, SnapChat, and Twitter and they want that for themselves, both with customers and employees. Most organizations, however, don’t really understand the dynamics of engagement, how to deconstruct and measure it, and how to tie online engagement to business outcomes. This disconnect results in both poor applications of technology and uneven results.
Not surprisingly, the result is a mess.
Social technology vendors that support complex, high-value engagement environments are being sold for their parts because their complexity and value are not well-understood by the mainstream market. Vendors that support simple engagement objectives are getting more and more attention because they are easy-to-understand and straightforward. Organizations struggle to see how different engagement approaches impact their business objectives AND that not having access to engagement data will severely limit their ability to succeed. In short, most organizations don’t understand the range of engagement behaviors, how they connect to value, what behaviors they need to support their business objectives, and what technology best supports the range of behaviors they need to be successful.
This inability to show self-evident value has caused the social software market to rapidly commoditize. For most vendors, their front-end engagement functionally, back-end analytics, back-end governance and management, and business models are poorly aligned. This creates confusion and churn in the community platform space and hurts the market, making it slow to mature and hard to understand for stakeholders.
We created the Community Engagement Framework to categorize engagement behaviors and help organizations understand how to differentiate and measure various engagement behaviors. Many of these behaviors cannot be measured easily in existing community platforms and as a result, manual work is required to map existing data to these behaviors.
Engagement analytics are terrible.
There are two big issues with community platform analytics.
- Not all engagement behavior is supported, so measuring it is impossible and even when certain behaviors are supported, the data is not readily available.
- Most engagement analytics are architected around content or transactions, not people. This approach makes it very difficult to see people’s experience and change in behaviors over time.
What that means is that community platforms are great at displaying ‘vanity metrics’ like how many people viewed a page. Activity like this is obviously important – without it, nothing else happens – but it’s really insufficient at helping community practitioners make good decisions. For example, pageviews are unable to tell you that employees are much more likely to read your marketing content than your customers. Insights from pageview data is incremental and it can tell you that something triggered a click through. The insight from the behavior data is monumental by comparison: it can tell you if your content is not appealing to the people it should be – even if it is good content – and that you’ve got a business problem. The value difference between these two insights is enormous.
As a business analyst, I want to see how activities and content affect a workflow in terms of time, cost, or quality.
I want to know things like:
- What pathways do people take to successfully complete a workflow?
- How long does it take people to complete steps along that path and does it vary by demographic?
- What makes that path shorter for one demographic vs. another?
- Does adding a trigger help or hurt the cycle time?
- Does the person get more access/better information via one pathway vs another?
- What impact does the cycle time have on the profitability of the workflow?
Notice that none…