Note: this is the second in a series of posts about community value. If you missed “A Perspective on Community Value“, I’d recommend starting there.
In the last decade, many companies have come to understand how valuable (and critical) their direct relationships with customers are. The most strategic organizations understand that these relationships are part of a larger network – the hub and spoke model, with the company at the center – is very much a thing of the past.
These “networks of relationships” amongst customer, prospects, partner and employees are often largely unactivated – primarily because companies don’t understand the potential value and how to begin to explore the possible opportunities. They often have trouble envisioning a future state of their community because a) they can’t see beyond the “traditional” model of support-based communities or b) they lack the internal capability and skill to lead a comprehensive discussion. In our work at Structure3C, we’ve found that understanding and discussing the following three contexts is a helpful way to begin the conversation.
In the simplest terms, the three contexts are:
- Customer lifecycle journeys: Where in the journey is community valuable?
- Criticality of product / service engagement: Which community experiences are valuable, based on use of product or service?
- Total addressable community: How many people can you expect to participate in your communities?
1. Customer Lifecycle Journey – Career Journey (as an initial model)
Understanding your customer relationship lifecycle, by persona, will provide helpful context to envision where in the set of journeys community may play a valuable role. We will use a career arc as a specific example here, but one can envision other scenarios beyond enterprise software, like the lifetime relationship a customer might have with a technology brand like Apple.
Example: Think about the career arc of an Industrial Designer using Autodesk’s Fusion 360 design software. Throughout her career, the designer will progress from primarily designing, to leading a small team of designers, to “owning” the design function at a company (the Skilled Practitioner arc in the diagram below). This designer’s peer may start off in design but decide she would prefer to focus on leadership and progresses through to become the CTO or CMO of the company (the Executive arc in the diagram).
- Number of distinct Customer Profiles (~Personas)
- Entire career journey, length of career, and the stages in that career journey
- What role might community play in each career transition point?
- How does / could the community support development and transition?
- Can your organization support the full Customer Career Journey, or does it make sense to partner with complimentary organizations?
2. Engagement with Product by Customer Profile, Over Time
Understanding the depth of product / service engagement by customer profile can give insight into the level of effort, the specific motivations, and the needed resources customers need to master your product, and by extension, advance in their career. This understanding can guide what community experiences you offer (and what community investments you make). Consider the previous example of an Industrial Designer who would be using design software tools most of her day early in her career, but would likely manage tool users later in her career. Her relationship with the tools changes over her career, and her needs related to skills development and learning change as well.
- Complexity of product / services
- Effort required to attain skills / mastery
- Amount of time spent using product / service
- Amount of time spent in surrounding ecosystem – courses, conferences, meetups, online content, expert communities, etc.
- How much time will the customer spend mastering product / services and necessary skills?
- How much time will the customer use the product in their work?
- How much time is it reasonable to expect a Customer to spend participating in your community weekly?
- What form factor and level of effort is required for quality participation?
3. Total Addressable Community & Crowd
Lastly, back to the point made at the beginning of this post: customer, prospect, partner and employee relationships are all part of a larger network. Understanding how big that network is creates your “denominator”, or gives you a sense of the largest possible size of your community. What if you were able to connect with 25% of your customers and prospects – what might that look like? How many customer types are represented in that percentage? Would they all naturally interact in one community experience, or might you need to support multiple experiences by customer type and / or stage in the relationship?
- Overall Market Size
- Current Customer Base
- Projected growth (ideally segmented by Customer Profile)
- Target vs Current Community Membership (again, segmented by Customer Profile)
- How big is the total addressable market?
- What % of active customers are targeted for community engagement?
- What business value can be realized at scale?
- How can the community business case be optimized by extrapolating investment vs return at scale? At what point does the investment vs return reach equilibrium? Go negative?
- How does the Customer value proposition change at scale? Is there a true Network benefit, or flat / diminishing return at a certain point in the growth arc?
Next Up: Strategy
I hope you found the ideas in this post useful, and to a certain degree novel. My intention with this series is to help open the aperture a bit on how community strategy is considered, developed and implemented. I hope it is now clear that I’m advising an approach that things about the entire lifespan of customer relationships, the complexity (and exponential value) of thinking about customer relationships in networks (vs 1:1), and considering the dynamic nature of a customers relationship with a brand and products (and therefor, any potential community) over time.
Next up in the series: My framework on community strategy + planning templates for your 2022 community initiatives.
Artificial Intelligence is arguably the buzziest of buzz words these days. Yet, there is a reason for the hype: AI could support a radical transformation of online community management and experience: automation of routine tasks, real-time insight, enhanced personalization and the enhanced agency of an individual in digital ecosystems.
For business leaders shaping online community strategy, AI holds promise to help solve two of the biggest challenges with online communities: 1) Quantifying the value of community investment and delivering timely and actionable insight and 2) Managing large networks of relationships at scale.
To Start: What is AI?
In the context of Community, AI can be thought of as an agent, or set of agents that
- is / are connected to real time data sources;
- has / have the ability to act in the community (or admin interface); and
- has / have specific goals to make progress towards.
From the Wikipedia entry on AI:
“In computer science AI research is defined as the study of “intelligent agents“: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”. “
“Isn’t this just an algorithm?” is the next natural question, and the answer is “well, not really.” Algorithms are complex sets of bounded instructions, and they aren’t (typically) designed to learn from their environment and evolve.
Where are we on the map?
Clearly, interest, investment and experimentation in AI by corporations is increasing year over year. According to Harvard Business Review, which surveyed over 3,000 organizations, 20 percent of companies used AI in a core part of their business model, and 41 percent were experimenting or piloting in 2017 (a total of 61 percent).
Narrative Science partnered with the National Business Research Institute and found the same numbers: 61 percent of surveyed respondents utilized AI in their corporations in 2017 (up from the 38 percent in 2016). The study also found that 35 percent of respondents use AI for interaction with customers (a.k.a. potential community members).
A recent study by Constellation Research found that 70% of the organizations they studied were already investing in AI and that 60% were expecting to increase their investment by 50% or more this year.
Community Leaders and Community Platform Providers have been leveraging simplistic AI tools for more than a decade, primarily for automating community moderation tasks and supporting member personalization. An early example: we launched TechRepblic.com in ’99 with an overly-complex community and content personalization function and wound up pulling back on the functionality in subsequent releases because of the technical overhead.
Emerging Use Cases for AI
We (Stucture3C) are in the midst on a year-long research project, C3/A3, studying how organizations are using / planning to use AI in their online communities. In our first wave of research with 40 Community Professionals at large organizations, we asked what types of advanced technologies they are considering or implementing, including AI and related technologies. Personalization, bots / agents and analytics topped the list.
- Advanced personalization based on profile / activity
- Recommendations of people and content
- Conversational interfaces, including chatbots
- Agents (acting on behalf of a member)
From the write in responses:
“(We are evaluating)… Machine Learning that automates personalization for content, news, interaction models.”
- Influencer & Advocate identification
- Escalation identification – ID’ing people who need help, like Facebook’s suicide threat technology
- Moderation of content and member behavior
- Suggested actions (what to do next in the community)
- Suggested content (to produce, based on member behavior and other signals)
From the write in responses:
“(We are)…Leveraging machine learning in our peer to peer support community to predict certain kinds of moderation needs, such as suicidal escalations or harassment etc. Better sentiment/text analysis.”
“(We are piloting)…AI text analysis to draw insights from unstructured data feeds (with reduced dependency on tagging)”
- Community health
- ROI measures
- Areas of investment
- Identifying customer behavior trends
- Gleaning insight for product / service enhancement
From the write in responses:
“Predictive – I want to present our users with timely and relevant content, before they even know they need it in some cases. If we know what you’re doing with our products and what your behaviors are in community, we should be able to activate that data into meaningful upgrades to the experience in both places.”
#TeamHuman vs. the Machines
Swiss Futurist Gerd Leonard characterizes the broad adoption of AI and related technologies as a battle of “Technology vs. Humanity”. The statement is hyperbolic, but the intent is spot in: we have to act now to ensure enabling human agency and purpose remains at the heart of any broadly deployed technology, including AI. Australian Online Community pioneer Venessa Paech says it best in a recent article:
“Instead of being replaced, community experts will upgrade. We’ll work to help businesses set up bots and intelligent interactions. We’ll plot behavioural frameworks for machine learning. We’ll spill into HR, marketing, IT, innovation – anywhere there’s a need to understand and optimise social intelligence. Leveraging AI for communities demands we extend our capabilities as social systems engineers. If we get it right, we can see to it that AI augments our best natures, not our worst.“
Participants in Wave 1 of the C3/A3 project are also optimistic about the possibilities of AI:
“I’m excited about the shift that AI could bring – instead of being reactive, let’s be proactive. I’d also like to use this tech to identify the things that we can flatly stop doing and redirect those efforts into more valuable activities.”
“I’m really excited to see how AI & ML augment and enhance a community member’s experience rather than replace any of the human aspects!”
Essentially, we think the value of AI is threefold for Community Professionals:
- AI will allow for the automation of routine community tasks and processes so that focus can be put on more valuable activities;
- AI will provide real-time analytics, insight, and specific and contextual suggestions;
- AI will shape the community experience for all stakeholders, including members (onsite), prospective members (externally), Community Managers and Executive Stakeholders.
We think future communities will thrive with AI if the ultimate goal of the community is enabling member agency and purpose. Perhaps paradoxically, the future of community management will likely depend on Community Managers becoming comfortable with, and knowledgable about, intelligent agents and automation, while doubling down on the art and science of human interactions and group facilitation.
Have questions, or interested in a briefing? Please reach out.