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What are predictive analytics?

Predictive analytics use data to make predictions about customer habits. This optimizes product and service recommendations.

If we analyze them through the Gartner Analytic Ascendancy model we can break down their evolution into understandable parts by pairing each important analytic stage with a question to be answered: what happened, why did it happen, what will happen, and how can we make it happen.

If we look at Netflix as an example, we see that they compile a lot of data about their users. 

Including your searches, where you pause, rewind or skip ahead, your browsing behavior, and many more. These patterns and habits allow the algorithms to predict what you’re most likely to be interested in in the future.

Netflix uses this system to influence you constantly. In fact, 80% of what you watch is directly influenced by their recommendation system.

This brings an interesting ethical nuance of whether or not users are actually trapped into a bubble of predetermined content, without being able to consciously choose what they want to consume freely.

Next time you’re online, think about what traces you’re leaving behind for big businesses to use.

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Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to provide a best assessment of what will happen in the future.

They are a powerful marketing tool that help determine customer responses in order to utilize them.

Data has to be compiled, analyzed and then finally applied in future outcome scenarios.

Streaming companies like Netflix use them by monitoring:

• At which point you pause, rewind, or skip ahead. 
• What date and day you watch something
• Your zip code
• What kind of device you use to view
• Browsing and scrolling behavior
• Ratings
• Do the credits get skipped

They use this data to recommend movies and shows, ensuring that users spend the maximum amount of time on their website consuming their content. 

In the future, companies will have access to more data, making it easier for them to predict more individualized outcomes. The software necessary to produce these analyses will also become more accessible to smaller companies.

To conclude, it might also be interesting to understand that predictive analytics are not perfect. A problem often found with them, is that they make it really easy for companies to filter what they want to show their customers, confining them into isolated information and media bubbles and preventing them from actually choosing the content they see. Some people also have concerns about their privacy online as they feel that their data is being compiled excessively.