As we have already discussed on other occasions, predictive analytics uses statistical modelling techniques, Big Data and Machine Learning to extract historical data and make predictions. There are different types of predictive analytics but today we will focus on how predictive analytics based on Big Data works.
We will see some examples that not only affect the video playback service, but also other business sectors such as health, sports, finance, energy, etc.
How to Perform a Step-By-Step Predictive Analysis?
The predictive analytics process starts from a dataset and requires the following steps:
- Project definition
- Data collection
- Data analysis
- Preparation of statistics
- Creation of the predictive model: A model that allows you to modify parameters to adjust the results.
- Deploy the predictive model: Use the results to generate reports and metrics and make decisions based on them
- Follow-up of results: Finally, verify that the results correspond to the predictions obtained.
Benefits of Big Data Predictive Analytics
The main advantage of predictive analytics is that it allows companies to learn from their experience from their data and make decisions to apply what they have learned in the future for better results.
In addition, with predictive analytics, you will generate a competitive advantage that will improve the identification of trends, challenges, and opportunities; you will eliminate the burden of manual data analysis by minimizing errors thanks to artificial intelligence and other techniques such as machine learning. In addition, when launching any service, the product increases your chances of success, as you have the advantage of knowing in advance the interests and needs of the customer. Another important advantage is that you will identify the variables that most influence the behaviour of users and what drives them to buy, thus improving customer satisfaction.
Examples of Predictive Analytics
– Consumer Goods: An example of this type of predictive analysis is the one that Amazon does, when a purchase is made, it presents similar items that other customers bought.
– Health: The best example we have is COVID-19, where different predictive analyses have been carried out to help hospitals improve the management of ICU occupancy and hospitalization of those affected by the coronavirus.
– Weather: The weather forecast has potentially improved thanks to this predictive analytics, predicting 5 days into the future as accurately as it did years ago with a one-day forecast.
– Energy: Energy companies can predict when a customer’s bill is going to be high and send an alert to the user about this high consumption. And there are even applications where we can see the prediction of the times of the day where there is a higher cost to reduce consumption in certain periods of the day.
– Pay-TV Services: Thanks to this prediction, the company can predict the loss or not of subscriptions, what content may interest the audience more, what video will generate more views, etc.
From JUMP Data-Driven Video, a business data management platform designed specifically for video service players, we have solutions that enable our clients to predict both audience behaviour and personalize the user experience.
