Why Media cannot ignore Predictive Analytics!

Predictive Analytics

When was the last time you searched for something on Google? Sounds rather rhetorical right? As soon as you were beginning to type, you would have got options as to what you might be looking for. Google actually predicted your thoughts or rather completed it and literally read your mind!

It’s spooky isn’t it? But that’s how things work in today’s world. It is not only Google, but most Apps and websites use predictive analytics. Through the application of machine learning models and statistical algorithms, your historical data can be analysed and your future actions predicted. These metrics can help drive growth for businesses by reaching the people most likely to use your product/service.

There is a lot of competition when it comes to media and the sub-sectors are equally crowded with very few dominant businesses. With so much happening already, there is also the concern of changing consumer preferences. New paradigms can emerge very quickly and we need to keep up.

This is where Predictive analytics can help.

There are so many aspects of media that predictive analytics can help with. Here are a few of them:


Predictive analysis not only factors in a variety of inputs but predicts a specific future behaviour. For example, you can predict the type of content that interests the age group between 15-25 based on data from the last couple of years. It can give you the most sought-after location for tourists based on search analysis that can help companies in allocating more budget in marketing those places. Travel and entertainment sectors can bring out relevant content for their target groups leveraging media.


In this age of hyperconnected customers, companies need to deliver a highly personalized experience to succeed. In order to do this, we first need to segment the customers. Without segmentation, marketers tend to rely on intuition and guesswork and there is no real way to understand whether their efforts paid off. Predictive segmentation helps to put your target audience into groups. These groups can be based on many aspects, for example, their likelihood of buying your product or service in the next few weeks, their inclination to take a specific action, chance of subscription, choice of product/service and more. In marketing, this data can help in driving successful campaigns and targeting the right group. Predictive segmentation thus helps marketers in delivering the right message to the right person at the right time.

Natural language processing

Natural language processing (NLP) is all about the interactions between computers and human language and how to program computers to process and analyse large amounts of this data.  It plays a very important role in helping text analytics tools to understand the data that is entered into it over time. NLP helps companies create and collect data from various sources including social media profiles, customer surveys, employee surveys, and more. This can eventually lead to actionable insights. From supporting a multilingual environment to topic segmentation, entity and character recognition to answering customer queries, NLP has many use cases. 

Content analytics

Here’s how content analytics works. First, there is a scan and study of the available data. Once this is done, content analytics helps in pin pointing the sought-after topics. These recommendations can help when planning campaigns and also while creating all types of content. So, this tool helps in telling us what type of content people are seeking. There will be more successful campaigns thanks to the use of content analytics. 

In conclusion, Predictive analytics can help in bridging the gap between the end-users and the creators. This allows media to understand their consumers at an individual level and provide a much better online experience for the user.  It is of great advantage to the industry that thrives on the preferences of the consumers. 

In today’s world, with everyone spending more and more time online, digital has become the preferred means of collaboration, research and buying a range of products and services. Media can leverage the power of predictive analytics to utilize their budgets more effectively and build better relationships with their customers.

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