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Understanding Predictive Analytics: A Beginner’s Guide

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Are you interested in predictive analytics and the solutions it provides? Or a student who needs a basic understanding of predictive analysis? If so, you're in the right place. Usually, technical jargon is used in explaining predictive analysis, making the comprehension process quite cumbersome. Such an approach doesn't favor individuals who are learning about it for the first time. That's why it's important to understand what predictive analytics is, how it works, and its benefits in the data management space.

What is predictive analytics?

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Predictive analytics is the processing and evaluation of data to facilitate predictions. It involves the practice of accumulating, processing, and evaluating historical data to predict future developments. It further utilizes artificial intelligence and machine learning-based solutions to analyze and unearth patterns and behaviors in data through mathematical processes.

Some common organizational data sources include; customer profiles (data from registered forms), customer interactions (customer service calls, e-mails, etc.), transactional data (customer purchases), and more. External data sources include social media data (YouTube, Twitter, Facebook, etc.) and other subscription services that most companies don't generate in-house.

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How does it work?

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Although acquired patterns alone can't be used in making deductions and projections, predictive analytics help leaders make smart data-driven decisions by basing on said patterns to point out trends, consumer and market behaviors, and industry changes.

Collecting data from various sources creates a connection between departments in the data region, the types of data acquired, and business processes that generate said data. However, this doesn't mean massing up data from varying sources guarantees accurate predictions.

There are two major statistical modeling methods utilized in predictive analytics, namely: classification and regression models. Their features include the following;

Classification models: This model is mostly binary and the simplest amongst all the other models. With this, data is categorized based on what it learns from the historical data.

Regression models: A regression model predicts actual numbers instead of ranges. It's trained in a way that enables it to project continuous numerical values as outcomes based on input variables.

What are the benefits?

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Now let's take a look at a few benefits of predictive analytics;

Predicting customer behavior: It helps organizations acquire and combine data on consumer behavior and purchase patterns, operation data, etc. They can then establish a predictive model to address projections. Knowing what your consumer wants is essential to business growth and business intelligence.

Ability to set desirable prices: Predictive analysis helps you set befitting price values for products and services through a thorough evaluation of consumer behavior patterns.

Prioritizing leads: Because you're given accurate predictions, you no longer have to choose human calculations to make decisions. Going through with leads that aren't guaranteed isn't a cost-effective choice for any business.

Customer targeting and segmentation: It provides businesses with data on consumer behavior to guide them in making strategic decisions.

Enhancing marketing campaigns: It adequately arms organizations with extensive information on consumer needs that help guide their marketing campaign strategies in the future.

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