The Share Likelihood Score, calculated on the basis of previous activities, tells the probability of a customer sharing the brand content publicly on social media channels. For example, if your customer has shared the brand post publicly on their wall in the past, the likelihood score on the next post will be high.

The Share Likelihood Score will be visible within the Custom Properties section of the Profile pane in the third pane which can be accessed both through the agent and care console.

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​How Share Likelihood Score is Calculated

With the help of Sprinklr AI Model, the Likelihood score is calculated on each case depending upon the following metrics taking into account the data of the last 30 days for each activity :

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• Profile Followers Count - Number of social media followers (channel-wise) of the customer.

• Average Likes on User's Posts - Average number of likes on the user's post.

• Average Comments on User's Posts - Average number of comments posted by followers on the user's post.

• Average Posts in a Day - Average number of posts posted by the user in a single day.

• Average Posts in a Week - Average number of posts posted by the user in a week.

• Average Posts in a Month - Average number of posts posted by the user in a month.

• Number of Posts - Total number of posts posted by the user.

• Total Shared Posts - Total number of posts shared by the user, for example, retweets.

• Total Shared Posts of Brand Post - Total number of brand posts that are shared by the user, for example, sharing a discount offer post published by the brand on its Facebook wall.

• Total Posts Mentioning Brands - Total number of posts in which the user has mentioned the brand name or tagged the brand.

• Total Positive Posts Mentioning Brands - Total number of posts posted by the user by mentioning the brand name and has a positive intent.

• Total Negative Posts Mentioning Brands - Total number of posts posted by the user by mentioning the brand name and has a negative intent.

• Apart from the above-mentioned metrics, it will also take the number of positive/negative cases based upon the predicted CSAT Score, Case Sentiment, Case Survey Rating, and the total number of surveys filled by the user in the last 30 days.

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