Social Currency Metric System (SCMS)
Question: How does one measure the value of community interactions and accurately gauge “reputation” of a community as evident from qualitative sentiment?
Social currency or Social Capital is a social scientific theory. It broadly considers how human interactions build relationships and trust in a community. The Social Currency Metric System represents the reputation of a community as measured via community trust, transparency, utility, consistency, and merit.
Interpersonal relationships are the social fabric of communities. This is shown in the Levinger’s Relationship Model and Social Penetration Theory. Community members' sense of personal and group identity grows as they interact. Members build shared values, accumulate a sense of trust, encourage cooperation, and garner reciprocity through acts of self-disclosure. These interactions build an increased and measurable sense of connection. The measure of these characteristics is called social currency.
The Social Currency Metrics System is a way to sort through a fire hose of qualitative data from community interactions. A central premise of this approach is that community members' interactions have an impact on the community. The Social Currency Metrics System continually measures the sentiment from those interactions. It illustrates the reputation and level of trust between community members and leaders.
Analyze the qualitative comments in community interactions. Gain an overview of sentiment in a community. Get metrics that show at a glance how a community is and was doing. Use lead metrics from continuous measurements for proactive community strategy development. Instill trust in community members that their thoughts and opinions are valued.
Set up a Data Collection Platform of your choice as described in the “Tools” section below. Ensure it has a minimum of 4 dimensions and 3 communication channels. Once it is set up, the following method is used to collect, analyze, and interpret results:
Collect Communication Traces -- Identify online platforms that your community is communicating on. Set up data funnels from the primary platform to your SCMS tool. The critical data for the system is user generated content.
Standardize How Communication Traces Should Be Assessed -- Use a codex to define important concepts as a “tracking keyword” or “category” in the focal community. This unified codex of terms ensures consistent analysis as different people read and tag community sentiment. Formalizing the revision and addition structure to this codex on a regular basis is a must.
Analyze the Communication Traces -- Community sentiment is analyzed in the SCMS tool by tagging data with codex terms. If the tagging is done by a team of people, it is recommended that everyone gets together regularly to discuss trends and ensure consistent tag use. If the tagging is done by an artificial intelligence algorithm, then a human team should supervise and retrain the AI as necessary.
Share and Visualize the Aggregated Analysis -- Visualize the quantitative count of codex terms over time, e.g., in a dashboard. This is where the qualitative analysis results produce an easy to observe dashboard of trends. Share analysis with team members.
Benchmark, Set Goals & Predict Future Growth -- After getting enough data to form a benchmark, take stock of where your community stands. What are its strengths and weaknesses? What actions can be taken to make the community healthier and more robust? Then form community initiatives with well-defined goals and execute on these projects to affect the social currency metrics for next week.
Repeat the Process -- In regular evaluation meetings, discuss the shortcomings of the dataset or collection methods. Come up with methods to address these shortcomings in the future. Work solutions into the system and move forward. Truth is in the trend, power is in the pattern.
- Channel: Sort by where the data was collected from.
- Tag: Show data based on what codex tags were used to identify sentiment in comments.
- Time: Show trends in the data over time and pull specific data-sets.
- Most impactful comments: Sort and filter by flags that can be placed in the data to highlight specific data points and explain their importance.
- AI vs. Human tagged: Filter by whether tags were applied programmatically or by a person.
- Weighted currency: Weight the “importance” of certain comments based on any one individually selected criteria. A resulting weighted view is simply a re-order of information based on weight.
Dashboard visualizing the aggregate metrics:
Example SCMS tool: On the left, raw community comments are shown and tags are added in columns immediately to the right. On the right, a pivot table shows in numbers how often tags occurred in combination with other tags.
Expanded comments view: remove the “quantitative” from the fields and provide the best possible way to read the different comments.
Tools Providing the Metric
To implement the metric any MySQL, smart-sheet, excel, or airtable-like excel datasheet program works fine. This data should be simplified enough to interact with other data interfaces to ensure that data migration is simple, straightforward, and can be automated (such as google data studio). This requires that systems used to implement the SCMS work with CSV and other spreadsheet files, and we heavily recommend open source programs for its implementation.
Once you have this, create a data set with the following data points:
|Date of entry||Date data was imported to SCMS tool|
|Date of comment||Date comment was made on original platform|
|Comment Text||Qualitative data brought in. Decide on how large you want these chunks ported. Some may port an entire email while others will be broken into one row per sentence. It should only have one “sentiment”|
|Data channel||Originating data channel the comment came from|
|Tags (created on codex document below)||Based on the unified codex of terms, decide what tags to track. There can be two kinds of tags. On the one hand, tags can be based on “themes” or recurring sentiment that people voice (e.g., gamer gate, flamewar, or thank you notes). On the other hand, tags based on “categories” can describe different aspects of a community that members comment on (e.g., events, release, or governance).|
|Social Currency Metric||The social currency being awarded or demerited in the system. This will directly affect numbers.|
|Weighted Score||Once you’ve decided what your “weight” will be, you can assign a system of -3 to +3 to provide a weighted view of human-tagged metrics (AI will not assign a weight for several reasons). This enables the “most impactful comment” filter.|
Create a second sheet for the Unified Codex of Terms which will define terms. It should look like this:
|Category Term||Definition||When to use||When not to use|
|[Custom Tags - themes and categories]|
|[Community specific jargon]|
|Social Currency Dimensions:|
|TRANSPARENCY||Do people recognize and feel a connection to your community?||When they have the "words" to pinpoint why they feel you are authentic or personalble.||This is not about good customer service, or doing well. That is utility. This is about whether they understand who you are as a business and show they are onboard with it.|
|UTILITY||Is your community doing something useful or is it contributing value?||Provide parameters that exclude when the term is used so that people know when the category tag should not be implemented.||This is not about good customer service, or doing well. That is utility. This is about whether they understand who you are as a business and show they are onboard with it.|
|CONSISTENCY||Do you have a history of being reliable and dependable?||When they suggest they have used your brand, or interacted with you several times||If they've only provided their comment to suggest you were useful once, use utility instead.|
|MERIT||Does your community merit respect and attention for your accomplishments?||When the social currency garnered from customers seems it will continue for a while, and will impact other people's opinions.||When they suggest they will use you again in the future use trust instead as that is a personal trust in the brand. Merit is external.|
|TRUST||Can people trust that your community will continue to provide value and grow in the future?||When they suggest they trust you well enough to continue conversations with you in the future||When there is not substantial enough evidence to suggest they will continue to work with and trust you as a loyal customer or community member.|
|INTERNAL REPUTATION||Do people believe these things strongly enough to warrant conversation or action?|
|EXTERNAL REPUTATION||What amount of your reputation in your community is transferable to strangers outside of your community (cold audiences)?|
The codex is filled in by stakeholders on a regular basis by specific communities and forms the basis for analysis of the data. This is the MOST IMPORTANT part. Without this the subjectivity of qualitative data does not follow the rule of generalization:
“A concept applies to B population ONLY SO FAR AS C limitation.”
Data Collection Strategies
Community member comments are available from trace data. The SCMS ideally imports the comment text automatically into a tool for tagging. Trace data can be collected from a communities' collaboration platforms where members write comments, including ticketing systems, code reviews, email lists, instanct messaging, social media, and fora.
Legal and Regulatory Considerations
Points of destruction: Detailed data is destroyed after xx months has passed. Quantitative calculations are kept for up to 250 weeks per GDPR compliance. Data older than 250 weeks becomes archived data you cannot manipulate but can see. Users can negotiate the primary statistic.