# Social Currency Metric System (SCMS)

Question: How does one measure the value of community interactions and accurately gauge “trust” within a community as evident from qualitative sentiment?

## Description

The Social Currency Metric System is an aggregate or composite metric.

[Social currency or Social Capital](https://s3.amazonaws.com/academia.edu.documents/48651612/Why_Social_Currency_Becomes_a_Key_Driver20160907-13657-17yglte.pdf?response-content-disposition=inline%3B%20filename%3DWhy_Social_Currency_Becomes_a_Key_Driver.pdf&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWOWYYGZ2Y53UL3A%2F20191023%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20191023T062647Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=881a75764634522f4dcf1a77d2079c538f86bb38c0c97b83b830075572b35302) is a social scientific theory that broadly defines intangible factors of human interaction that lead to closer relationships and trust with peers and community leaders.

More specifically, a community is marked by increasingly interpersonal relationships ([Levinger’s Relationship Model](https://theadminzone.com/ams/levingers-stage-theory.1272/), and [Social Penetration Theory](https://psycnet.apa.org/record/1973-28661-000)). In our model of social currency a member’s sense of personal identity and sense of group identity grows as they place more stock in their relationships among members. As members build shared values, accumulate a sense of trust, encourage cooperation, and garner reciprocity through acts of [self-disclosure](https://www.google.com/url?q=https://en.wikipedia.org/wiki/Self-disclosure&sa=D&ust=1571815237962000&usg=AFQjCNHKOwpb9Glo4bE8kwPs5hWc1j271g), we hold that there also grows in them an increased and measurable sense of connection. The measure of these characteristics that are shared across community members and their affiliation with that community is called social currency.

The Social Currency Metrics System is a collection of individual metrics measured through qualitative data methods and analysis. This system provides different dimensions that help measure the sentiment of community interactions. These metrics, when put together, produce a “bank” of social currency that can be used to approximate the reputation and level of trust a community has with each other, and more critically, with leaders in a given community.

The Social Currency Metrics System is an objective and efficient way to sort through a fire hose of qualitative data produced by community interactions. A central premise of this approach is that community members’ interactions have an impact beyond personal value to themselves. These comments can ultimately inform the community. The Social Currency Metric System turns the intent and sentiment from those interactions into concrete and observable metrics.

Whereas the [Net promoter score](https://docs.google.com/document/d/1J55dt_wHVWgIuqBLYMXuu8689kPEuDoc6-tZmG-1Euk/edit) or state of community surveys are one-off analyses and give insight to sentiment only periodically, the Social Currency Metric System is a perpetual and continuous conversion of sentiment into metrics. The Social Currency Metrics System continually records passive, situational comments measuring the strengths of a community and offering insights on how to alleviate weaknesses as conversations occur.

**Assessing the levels of social currency outlined in the SCMS provides us with …**

1. A formalized and simplified structure for defining important, but traditionally difficult to define social currency metrics

2. Individual metrics that provide a way to balance and assess how different community efforts impact overall community health.

3. A codex of tracked terms that communities are already using, so it truly reflects their community.

4. A focused and concrete methodology based on years of academic research for understanding, tracking, analyzing, and acting on qualitative data at scale.

5. An analytics system for qualitative data that mirrors and compliments the traditional quantitative data metrics that analysts are accustomed to, while shifting the focus from “behavior” metrics to more ethical “sentiment” metrics.

6. A pipeline that provides community initiative ideas around content, development, marketing, and social interaction based on the data coming through.

## Objectives

From the perspective of the metric system proposed, the health of a community is indicated by the value members find in that community. This value is observed by measuring their comments across several dimensions. A community can be high in some dimensions and lack positive sentiment in others, creating discrepancies among community members’ motivations for participating. The Social Currency Metric System provides insight to guide how communities balance these motivations.

For example, a community manager of a fictional forum for people who want to flash phones may be concerned about a high fluctuation of community members. People stop by for only a short interaction when they are struggling. After applying the Social Currency Metric System, the community manager may have learned that community members of the forum derive great value from the forum (a high utility score), but view interactions as transactional. With this insight, the community manager can better evaluate the community structure’s strengths and weaknesses, better assess community health, and launch activities tailored to those community members. These activities may include a weekly scheduled “spotlight” blog post/video/podcast meant to encourage periodic site visits that are not based on assistance queries, and community projects intended to stimulate conversation and self-disclosure between members.

**Knowing the changes of social currency over time allows …**

1. **Community managers** to connect with executive members and other leaders who are not aware of community impact efforts to quickly get on the same page.

2. **Data analysts** to get ahead of the curve by turning lag metric data about what people _have_ been thinking, into lead metric data about what community leaders need to do to improve the dynamics and discourse within their community.

3. **Researchers** to see qualitative data processed and analyzed for results faster so that they can more easily see the trends and patterns in the quantitative data confirmed, refuted, or conflict with it. This lets them see a larger part of the story.

4. **Developers** to see direct and aggregated feedback across larger passive channels of customer sentiment so that trends and patterns in the data will provide a more objective means of ranking important fixes and developments than direct ticketing systems. The Social Currency Metrics dashboard allows developers to avoid common feedback fallacies like the vocal minority, spiral of silence, sub-set generalization, and polarized opinions, helping them to quickly triage issues and dig into those issues’ impacts.

5. **Executive members** to more easily digest findings about community sentiment while still ensuring the expediency, objectiveness, scientific rigor, and clarity that modern behavioral tracking analytics has provided them, without sacrificing privacy.

6. **Thought leaders** to understand how their presence in communities affects their constituents, to help them better balance the difficult decisions they will need to make and avoid common missteps and pitfalls.

7. **Community members** to trust that their words and actions will have a more direct impact on initiatives power users and community leaders implement by forcing community leaders to take action based directly on user sentiment and feedback by way of data aggregation, analysis, and results.

## Implementation

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:

1. **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.

2. **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.

3. **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.

4. **Share and Visualize the Aggregated Analysis**
* Visualize in a dashboard the quantitative count of codex terms over time. This is where the qualitative analysis results produce an easy to observe dashboard of trends. Discuss results with team members.

5. **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.

6. **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.

### Filters

1. **Channel**: Sort by where the data was collected from.
2. **Tag**: Show data based on what codex tags were used to identify sentiment in comments.
3. **Time**: Show trends in the data over time and pull specific data-sets.
4. **Most impactful comments**: Sort and filter by flags that can be placed in the data to highlight specific data points and explain their importance.
5. **AI vs. Human tagged**: Filter by whether tags were applied programmatically or by a person.
6. **Weighted currency:** Weight the “importance” of certain comments based on any one individually selected criteria (cannot at the moment support more than one weighted metric, by design). A resulting weighted view is simply a re-order of information based on weight.

### Visualizations

**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:

| Data Points | Description |
|---|---|
| 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.

**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.

## References

- [An example implementation on airtable](https://airtable.com/invite/l?inviteId=inv8u49VVMtQTrfFU&inviteToken=c49b1ed3759c5cd736901fd81c9f460f86e8e9f462703c4f85a3bdd7250ca5a7)
- [An example implementation in data studio](https://datastudio.google.com/open/1X9UdQz8FtHHmjMBpjba3pFqE55lNpwg5)(report)
- [An example implementation in data studio](https://datastudio.google.com/open/1Z4EJ03898lZxm2NZVULaEoLS0bYqL79A) (data source)
- [An example implementation in google sheets](https://drive.google.com/open?id=1zi3JE0bwfEdRdc-wQEZn8GaB7sE8IvxeSeqvVywKnXw)
- [Implementation documentation](https://docs.google.com/document/d/1RlAedRBQbhq0oYMCB3VqdawOCZE2XT5R3teydjBZODM/edit#heading=h.8hyunaadfriq) (starts on page 33)