One of the fields where analytics is not very popular is journalism. Though there resides tons of data on the daily happenings around the world from time immemorial, analytics hasn’t been used much there. Have you ever wondered why? It is because it requires a lot of pre-processing of data and it leaves not a very wide scope of applications. Also one another reason why analysts generally leave it untouched is that, they require advanced Machine Learning algorithms to be applied on them.
What are the possible applications of analytics in journalism?
We always have different reach for different contents published in a magazine. Once we analyse the proportion of emotions sticking around a particular article, we can analyse the patterns for the type of content which is received the most. Based on which, we can foresee the reach of an article in the magazine/newspaper.
With Text Summarization, Sentiment Analysis, Emotion Classification etc. (whose outcome we can imagine as the understanding of the article by an average reader) authors can cross-check whether their final content has turned out in-line with their empirical knowledge and tweak accordingly if need be.
Churn prediction and prevention:
A product will have various segments of customers like regular, occasional etc. The primary task of any existing business is to retain their old customers, post which only will they concentrate on acquiring new customers. Because your old customers themselves act like your brand ambassadors for the word of mouth campaign of your product, which automatically leads to arrival of new ones. Growth of loyal customers is usually due to the brand value of your magazine/newspaper which has been built over the years with reliability of the published information, presentation skills, kinds of information covered etc. With the subscription details, we can decrease the rate of churn and in turn strengthen the customer base, reputation and revenue.
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