A Guide to Predictive Data Analytics (Making Decisions for the Future)

By on

Click to learn more about author Ram Tavva.

Everyone wants to succeed in their business, but some might choose an unwise approach toward it, while others might mess with the wrong set of data. A lousy hit wastes a lot of time and energy predicting the future and understanding the newest trends. 

But those problems will be over when you have the right set of data with you. This blog post is your go-to guide for predictive data analytics for making decisions for the future. 

What Is Predictive Analytics?

Predictive analytics is a data analytics category that focuses on predicting future outcomes based on historical and advanced data analytics techniques – statistical analytics and machine learning algorithms. The best part about predictive analytics is that it generates future insights that carry much more accuracy. 

The ultimate goal is to go beyond the past data that has happened and look for future trends that could be best for the organization and profit-earning. The Zion market report predicts the global market to reach $10.95 billion by 2022, a compound annual growth rate (CAGR) of 21% from just $3.49 billion in 2016. 

Yes, you heard that right. Predictive analytics is robust and plays a crucial role in predicting future data. For accuracy, it’s better to understand the objectives for the business you are doing, along with the proper techniques applicable for it to get you the best results. 

Predictive Analytics at Your Workplace

The power of predictive analytics widens your vision, helps you catch the newest trends to grow your business, and gain an edge over your competitors for the products and services you offer. It uses big data, data mining, statistical analysis, and machine learning algorithms based on mathematical processes. 

With predictive analytics, organizations can find and exploit the hidden patterns and various risk factors and new opportunities to maximize products based on their demand and build long-term relationships. 

Predictive analytics plays a prime role in understanding the ongoing trends in the market that most people are familiar with, customer lifetime value, and other measurements to ensure business growth, success, and relevance. Therefore, predictive analytics is a way to discover the next big thing to bring to the market to solve your customers’ problems. 

The Plus Side of Predictive Analytics

Predicting future goals and opportunities has never been easier with predictive analytics. The results are very accurate and more reliable than existing tools. It’s one of the essential tools for every business to bring holistic solutions and maximize profits. Here are a few benefits of predictive analytics:

Stay One Step Ahead in Performance and Competition

What businessman or CEO does not want to be at the top of the business ladder? With predictive analytics, you or your team can always plan, organize, test, and debug to reach a better product for your potential customers. And along the way, you will learn about various effective ways to procure these potential customers and nurture the leads. 

With thorough understanding and research, you can learn:

  • Types of products to be on demand and what tweaks can put your products into that list
  • What kind of pricing strategy can get you more sales
  • Ways to reach out to your customers and add more personalizations

Save Lots of Valuable Time and Energy

Being in the business for a long time is never an easy job –  your team needs to have a close eye on your competitors, which new products they are about to launch, features, benefits, and many other things. 

Your marketing team will look for effective ways to run various campaigns, make your products sell, get more leads, and nurture those leads into conversions. Marketing keeps on changing; the better you connect with their pain points and add personalizations, the more you sell and maximize your ROI. 

Avoid Spending Money on Needless Research 

Research indeed opens up new opportunities to sell better versions of products and services to the customers. With predictive analytics, you can even predict the type of products you can offer, plus tweaks you can make to your products to make them last longer.

Without further research into the products and services that don’t come under company policies or that don’t fall into your domain, you can look for how to add more value to your existing products and make them into best-selling products. 

A Few Classic Examples of Predictive Analytics

If you looking for endless possibilities in your business, coming out with better products, and being on the top of the business ladder, then predictive analytics can push you to new heights. It can get you very accurate numbers across diverse fields like finance, e-commerce, automotive, aviation, energy, manufacturing, and many others. 

  • Finance: You can develop credit risk models or add more security to online transactions by knowing the current threats.
  • E-commerce: Did you find one best thing when you do online shopping? Retailers notify you when the most demanding product arrives, run a flash sale, and products get out of stock. That’s the top reason why the e-commerce industry is one of the most successful business models today. 
  • Automotive: Though the technology keeps improving over the years, the craze and eyes are on battery-running vehicles to save renewable energy and rising prices against petrol and diesel. 
  • Aviation: Who doesn’t love flying high from one place to another in just hours? Airlines use predictive analytics for flight reliability, fuel availability, uptime, weather prediction, and more.
  • Manufacturing: Products can be defective to some extent rather than fully efficient, and with time they may fail. Using predictive analytics, you can predict failure and optimize the raw materials for future demands.  

Tools for Predictive Analytics

Predictive analytics tools are always handy – they give deep, real-time insights and opportunities for the business to grow with endless possibilities. The tools predict multiple behaviors and patterns to allocate your resources and expect the best time to launch your marketing campaigns based on various predictions and data collection over time. Here are a few predictive analytics tools to help you out:

  • Everstring
  • Radius 
  • Halo
  • SAS Advanced Analytics
  • RapidMiner Studio

Predictive Analytics Models (Making Data for the Future)

Choosing the right predictive analytics model is a major challenge to accurate and predictive analytics. The prime goal is to leverage the data to make insightful decisions based on the existing data. Here are five best predictive data models to consider for game-changing experience:

Classification Model

The classification model is the simplest among all the predictive models; it puts the data into categories and learns from the historical data. It is best for the problems of those who have a yes or no as an answer. It has a wide range of applications across many industries, with decisive guiding actions and simple programming algorithms.

Clustering Model 

Just as its name suggests, it works with nesting or grouping, or categorizing the data based on similarities. This model helps in effective decision-making among the items that show similar characteristics. Suppose you run an e-commerce website. You can easily categorize people based on their latest purchase to sell your future products or else when they look for similar items. Your website can show them varieties that fall under the same category. 

Forecasting Model

This model predicts the future data as per the metric value prediction, estimating the numeric value from the new data based on the learning from the historical data. Wherever there is historical data, this model fits the best in SaaS, e-commerce, etc. Forecasting models include multiple parameters, making them complicated at times.

Outliers Model

The outliers model is best for the strange data entries in the dataset, in conjunction with other numbers or categories. For example, you can consider the call records, transactions, insurance claims, and others. 

This model works the best for retail, e-commerce, and insurance sectors where your database tracks conjunction data, along with prime data. It can be fraudulent data, along with purchase history and locations. 

Time Series Model 

This model captures the sequence of data points using time as the input parameters – using past data as the reference and considering the present data as the input calculates future trends and patterns. However, in the time series analysis, the result is not always static or linear, but this will work the best for exponential data and align the best for the company’s growth. 

Final Words 

In this blog post, you got to know everything you wanted to know about predictive data analytics, plus a straightforward definition and practical applications. You also learned about some of the industry use cases, how different industries use predictive analytics to grow their business, and a list of few tools that you can always find on your future data trends. Finally, we shared the uses of five classic predictive data analytics models for predicting future data and being on top of the business.  

Leave a Reply