A lot of us are using some kind of real-time analytics and/or historical analytics in our everyday lives, but predictive analytics represents an advancement into new territory.

Predictive analytics is a branch of advanced analytics used to make predictions about unknown future events. It uses multiple sources and techniques–data mining, statistics, modeling, machine learning, and artificial intelligence–to analyze current data to make predictions about the future.

Collecting these management, information technology, and modeling business processes are what help put the predictions together. In fact, the patterns and trends found in historical and transactional data are what are used to pinpoint risks and opportunities for the future.

Type of data extracted and analyzed

What kind of data is used to make these predictions? There are 2 types of data to keep in mind.

  1. Structured data: Examples include age, gender, marital status, income, and sales.
  2. Unstructured data: Comprise of textual data in call center notes, social media content, or any other type of open text that needs to be extracted from the text along with sentiment.

All of this data is monitored, analyzed, and then applied into the predictive analysis.

For example, imagine your UC infrastructure and systems being the human body and the predictive analysis being the doctor. When the doctor gives you your check-up, they tell you what to do and not to do to stay healthy based on everything they learned about you. That’s exactly what the predictive analysis does! It gathers all the historical data and uses deep learning to tell you how to avoid running into issues with your systems in the future and what to continue doing to keep up its “health”.

Predictive analytics flow chart

Shows the overview process of how predictive analysis diagnoses what is going on in a system and what action you must take for positive results in the future.

How can we apply these predictions?

There are numerous ways predictive analytics can be applied today. A few ways we can use it are for:

  • Customer relationship management (CRM): These applications are used to achieve CRM objectives such as marketing campaigns, sales, and customer services. This analytical customer relationship management can be applied throughout the customers’ life cycle beginning at acquisition and relationship growth all the way to retention and win back.
  • Fraud detection: These applications can find false credit applications, fraudulent transactions done offline and online, identity thefts, and false insurance claims.
  • Risk management: These applications predicts the best portfolio to maximize return in capital asset pricing model and probabilistic risk assessment to yield accurate forecasts.

 

Why predictive analytics are the future

Predictive analytics are the extra hand decision makers always needed for envisioning new developments, capitalizing on future trends, and responding to challenges before they occur.

Specifically, for UC systems, predictive analytics will assist you in figuring out best management methods for your infrastructure to guarantee quality calls, videos, and messaging, when toll fraud is occurring and how you can avoid it from negatively affecting your business, and much more.

The deep learning curve this analysis provides is advancing every day and will help you figure out how to make a better tomorrow.

Unified communications video conferencing assessment

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