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Predictive Analytics

Market research 04 12

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Table of Contents


What is Predictive Analytics?

Predictive analytics involves identifying the likelihood of future outcomes based on an organization’s data. This is done with the use of historical and current data, statistical algorithms, artificial intelligence, and machine learning. 

Predictive models create predictions that are based on a lot of information and statistics, hence they are objective and can be easily repeated. The more data an organization has, the more accurate their predictions can be. This is one of the reasons organizations collect data from their customers and employees on an ongoing basis as this helps them make accurate predictions and better decisions.

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How does Predictive Analytics Work

Predictive analytics uses predictive models that are made through the construction of mathematical models and equations. These equations are created using information such as:

  • Target Outcome
  • Historical and Current Data
  • Relevant and Known Facts about the Scenario

To start developing the model, you first need to gather data on variables that have an effect on the outcome of interest. This data can be used to train the predictive model using machine learning.

Then, to train the predictive model you can introduce a “test data set” by inputting historical data that you already know the outcome of. The actual outcome of the event should then be compared to the outcome predicted by the predictive model in order to see how accurate the results are. Depending on the accuracy of the results, the model may need certain changes to improve its accuracy.

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Benefits of Predictive Analytics

The following are a few benefits of Predictive Analytics: 

Helps Predict Customer Churn

  • One of the most important benefits of predictive analytics is that it can create predictive models that learn behavioral patterns. This includes identifying behavioral patterns that precede customer churn. With this information, businesses can be notified when such behavior is exhibited, allowing them to take the measures required to retain these customers. 

Can Perform Text Analysis

  • Predictive technology can perform tasks such as text analysis. Text analysis can process text data from qualitative experience data and free texts. This is an information-rich pool of data that tends to be more tedious to process than quantitative data. With text analysis, this data can be processed efficiently using technology that identifies repetitively used words and phrases across responses. 

Promotes Efficiency

  • Predictive analytics technology cuts down the work involved in routine decision making as the machine does it instead. This frees up the organization’s human resources so that they can focus on more valuable tasks, and high-risk decision-making. 

Improves CX

  • With predictive technology, businesses can provide better and more personalised experiences to their customers by anticipating their future needs by analysing historical data and using predictive models.

Helps Plan Ahead

  • Predictive analytics gives organizations the ability to make assumptions about the future and make plans/decisions accordingly. This makes organizations more prepared and ensures that they invest their resources more wisely.
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Predictive Analytics Tools

The following are a few tools used in Predictive Analytics 

Text iQ: Unearths trends and patterns from texts from a range of sources such as reviews, surveys, and social media. 

Stats iQ: Stats iQ automatically selects and runs relevant statistical tests and delivers the information you need to make informed decisions. 

Voice iQ: Gains insights and identifies trends from voice interactions with customers. It can take unstructured voice data and use voice recognition and data mining to interpret and categorize voice interactions. 

Predict iQ: Uses advanced learning to create a detailed picture of customer behavior, allowing you to predict your customer’s moves such as customer churn.

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FAQs on Predictive Analytics

Answer: Predictive Analytics can be used by organizations in many ways. It can help determine customer responses or purchases, and customer churn. It can help retain customers or attract new ones to grow a business’ customer base. It can even be used to forecast inventory requirements in order to manage resources more effectively.

Answer: Data mining is the process of turning raw data into useful information by using softwares that can identify patterns and trends within large batches of data.

Answer: Artificial intelligence is a larger concept that refers to intelligent machines and technology that can simulate human thinking and behavior. Machine learning, on the other hand, is a subset of AI that enables machines to learn from data without being programmed.

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