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Since reach is a time-dependent summary of aggregate audience behavior, reach figures are meaningless without a period associated with them: an example of a valid reach figure would be to state that "[example website] had a one-day reach of 1565 per million on 21 March 2004" (though unique users, an equivalent measure, would be a more typical ...
TURF analysis, an acronym for "total unduplicated reach and frequency", is a type of statistical analysis used for providing estimates of media or market potential and devising optimal communication and placement strategies given limited resources. TURF analysis identifies the number of users reached by a communication, and how often they are ...
Social media reach is a media analytics metric that refers to the number of users who have come across a particular content on a particular social media platform. [1] Social media platforms have their own individual ways of tracking, analyzing and reporting the traffic on each of the individual platforms.
The media engagement framework is a planning framework used by marketing professionals to understand the behavior of social media marketing [1]-based audiences. The construct was introduced in the book, ROI of Social Media. [ 2 ]
RFMTC – Recency, Frequency, Monetary Value, Time, Churn rate is an augmented RFM model proposed by Yeh et al. (2009). [6] The model utilizes Bernoulli sequence in probability theory and creates formulas that calculate the probability of a customer buying at the next promotional or marketing campaign.
However, total reach is not always 100%. If an average of 12% of the people view each episode of a television program, and an ad is placed on 5 episodes, then the campaign has 12 × 5 = 60 GRPs. If 50% view three episodes, that's 150 GRPs.
Customer engagement can come in the form of a view, an impression, a reach, a click, a comment, or a share, among many others. These are ways in which analytics and insights into customer engagement can now be measured on different levels, all of which are information that allows businesses to record and process results of customer engagement.
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).