Abstract: System and method for measuring the performance of brands across online and offline consumer conversations. Raw conversational data of online and offline conversations is received and analyzed using proprietary algorithms to extract, for each type of data (online and offline), relevant metrics that reflect the performance of the brand. In a non-limiting example, these metrics include: volume, sentiment, brand sharing and social influence. One of the problems with using raw data is the extreme volatility that will cause the results to sharply vary between a week and the other depending on the most recent activity or the lack of activity. To solve this problem, the embodiments compute for each metric a moving average (MA) formula specific to that metric to reduce the volatility of the results. The formula is computed so that the metric values produced by the formula would have the best/most correlation with factual data/business outcomes (e.g. sales/sale figures, stock price, purchase or dumping of stocks etc.

Abstract: System and method for measuring the performance of brands across online and offline consumer conversations. Raw conversational data of online and offline conversations is received and analyzed using proprietary algorithms to extract, for each type of data (online and offline), relevant metrics that reflect the performance of the brand. In a non-limiting example, these metrics include: volume, sentiment, brand sharing and social influence. One of the problems with using raw data is the extreme volatility that will cause the results to sharply vary between a week and the other depending on the most recent activity or the lack of activity. To solve this problem, the embodiments compute for each metric a moving average (MA) formula specific to that metric to reduce the volatility of the results. The formula is computed so that the metric values produced by the formula would have the best/most correlation with factual data/business outcomes (e.g. sales/sale figures, stock price, purchase or dumping of stocks etc.

Abstract: A system and method for measuring the efficiency of social media campaigns. The system collects searchable activity data of members of a social network and processes this data locally to extract interaction data happening on the profile page of the social media campaign on the social network. The interaction data is then weighed in accordance with its type. The system may also determine a reach of the campaign and a responsiveness score of a user. The system may also determine an effort score representing a monetary value of the profile page based on the volume of contribution by the owners of the social media campaign. The efficiency score may then be determined based on a relationship between the number of interactions, the weight associated with each interaction, the reach, the responsiveness score, and the effort score.

Abstract: A system and method for measuring the efficiency of social media campaigns. The system collects searchable activity data of members of a social network and processes this data locally to extract interaction data happening on the profile page of the social media campaign on the social network. The interaction data is then weighed in accordance with its type. The system may also determine a reach of the campaign and a responsiveness score of a user. The system may also determine an effort score representing a monetary value of the profile page based on the volume of contribution by the owners of the social media campaign. The efficiency score may then be determined based on a relationship between the number of interactions, the weight associated with each interaction, the reach, the responsiveness score, and the effort score.