8 Key Metrics for Running an Effective Social Care Program - Multilingual Contact Center | TELUS International Europe
  • 8 Key Metrics for Running an Effective Social Care Program

    Social customer service is rapidly becoming the new, critical channel to drive satisfaction and loyalty. Almost all organizations, however, struggle with measuring the impact of social customer service on important business metrics. In this post we will point your attention to a framework of 8 metrics measuring the effectiveness of your social care program.

    key metrics

    The purpose of an effective metrics framework is to ensure that the social care program is achieving its business goals and objectives.

    1.       Reach

    Description: Aggregate number of people who can possibly read agent responses for a pre-defined interval of time. Adding up the potential audience does not mean that the entire audience will see every message, but it can serve as a proxy for the number of direct impressions that can be made.

    Calculation: Reach is quantified by summing the number of people who are subscribed, following, friended, etc. on the social channels where agents are posting messages.

    2.       Potential Influence

    Description: Aggregate number of people who are connected to the Reach audience, or the sum of the second-level of impressions that could be made if messages are re-published to these audiences. This does not mean that a message will reach the second-level audience but is again used as a way to measure potential impressions.

    Calculation: Potential Influence can be calculated by summing the different influence metrics from each of the social networks. For example, Twitter publishes how many followers a Tweeter has and how often they tweet. Facebook publishes how many friends a given member has. Polling the application programming interfaces (APIs) of the different social networks and summing the connections an audience has provides a measure of potential influence.

    3.       Amplification

    Description: How likely an individual is to retweet or repost.

    Calculation: Quantitative score from Twitter and similar services on social networks.

    4.       Conversion Rate: Customer Satisfaction

    Description: The rate that dissatisfied customer is converted to a satisfied customer through a social care interaction.

    Calculation: Calculations can be made based on customer satisfaction surveys or tracking posts going from negative to positive sentiment.

    5.       Conversion Rate: Revenue Generation

    Description: The rate at which a sale is made because of a social care interaction.

    Calculation: Calculations can be made based on checkout analysis.

    6.       Brand Evangelist Index (BEI)

    Description: Indicator of how likely a customer is to evangelize your brand on social networks.

    Calculation: Historically BEI has been calculated by correlating and summing customer satisfaction surveys. The most satisfied customers are more likely to be brand evangelists. Sentiment analysis is a more efficient way to see who is most positive about your brand.

    7.       Loyalty

    Description: A customer who continually purchases from a company when alternative, comparable products are available from competitors. Loyal customers are often brand advocates and price insensitive.

    Calculation: Net Promoter Score (NPS) and Likelihood to Recommend (L2R) are accepted measures of loyalty that can be calculated through a survey.

    8.       Sentiment

    Description: The attitude, opinion, emotional state, or intended emotional communication of a social post, which can be used to route the post to the appropriate agent/department for resolution.

    Calculation: Statistical models can be used to calculate a poster’s sentiment. Traditionally sentiment analysis accuracy is low (less than 70%) but by having the agent properly classify the sentiment & feeding that classification back into the statistical model, accuracy will continually improve.

    Metrics such as reach, loyalty and conversion rate help measure how much more revenue is gained through social care interactions and how much brand equity is built through positive interactions. Effectiveness metrics such as influence, brand evangelist index and recency can help prioritize posts. Sentiment analysis, or keyword filtering, helps determine which posts needs to be routed to which group. Tracking social care effectiveness metrics will provide you data to understand the economic impact of your social care program.



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