We are living in a data-centric world, where every sixty seconds, the internet is generating a massive volume of data. The effective utilization of giant data sets to get better future insights and make smart and well-informed decisions is important for businesses to accomplish their goals

A data compilation carried out by Lori Lewis and published on the All-Access site highlights 60 seconds on the web in 2021 that comprises nearly 70 million messages shared via Facebook Messenger and Whatsapp whereas 695,000 stories are shared on Instagram and more than 500 hours of content is uploaded on Youtube.

What is Predictive Analytics?

As Albert Einstein said, “If you want to know the future, look at the past.” The urge to foresee upcoming events and learn new things can be channelled through retrospecting techniques, taking a close look at the past to uncover trends and patterns for estimating results.

Definition: The prediction of accurate and reliable future outcomes through current as well as historic data is possible through predictive analytics, a branch of advanced data analytics that makes forecasts using artificial intelligence, data mining and deep learning techniques, statistical modelling and automated machine learning algorithms.

Companies rely on predicting techniques to explore and uncover patterns in data to gather opportunities and risks and get proactive regarding the future. Business intelligence tools are extensively used in advanced analytics to determine upcoming events.

Predictive Analytics Examples

Different predictive models and techniques are effectively used in following domains:

  • Weather Forecasting: Decision Tree and Linear regression are the two most robust machine learning techniques that are used to predict the weather.

  • Insurance & Risk Assessment: Several methods like AI and machine learning algorithms, data mining techniques, statistical and predictive modeling are being used to generate authentic reports that uniquely and precisely identify risk levels and assist in underwriting and defining policies for the insurance companies.

    The benefits of using the AI technique is not limited to insurance, in fact, it also helps us in claim management. When a customer makes a claim, predictive analytics facilitates in flagging up related concerns and potential queries and then it carefully examines the legitimacy of the claim, making claim assessment simpler and transparent.

  • Supply Chain & Customer Behaviour: The surplus amount of sales and marketing data is used to analyze customer behavior for deriving a possibility of forecasting revenue more precisely. It also helps in anticipating the future demand of buyers.

    For example, automobile companies have utilized past purchase data to anticipate vehicle demand; and now they are effectively overlapping the collected data sets with current web searches information to gauge and predict sales.

    The inefficiencies and poor supply chain management lead to a leaking bucket of profits, but thanks to rewarding AI solutions, that breaks down complex tasks into small chunks.

    For instance, ranking dealers to analyze, who is most likely to exhibit fraudulent behavior or tracking failure equipment at any stage of supply chain operations.

  • Social Media Analysis: The scope of predictive analytics in social media analysis is broad and its significance cannot be overlooked when it comes to playing in identifying patterns and trends in data generated by social media.

    Though social media analytics is still paving its way in the industry, AI solutions are robust in providing a beneficial aspect for accomplishing a wide range of business goals and objectives. Forecasting consumer’s demand, examining marketing campaign performance, targeting relevant audiences by reaching accurate consumer demographics, and monitoring customer churn rate are some of the relevant examples.
    In order to have a strong presence on social media with attractive brand awareness, companies are investing highly towards it.

  • Internet of Things: IoT is being revolutionized by the application of predictive analytic techniques which has not only helped us in attaining our goal of creating IoT devices, in fact, it has managed to minimize the time used to configure and sync IoT appliances.

    Though Iot pours out a humongous amount of data, it is of no good use if it’s not reviewed properly. It should go through a thorough research and analysis phase for deeper understanding. The whole procedure incorporates deploying classification and clustering tools to manage huge Iot of data sets.

Benefits of Predictive Analytics

  • Accuracy and Reliability: Decision making is a crucial process for any organization at a strategic level and in order to make informed and better decisions for fueling a company’s growth, one cannot compromise on the accuracy and reliability of the data. Thanks to the AI techniques that come up with a perfect degree of precision.

  • Analysis of Competitors: Business Decisions are highly carved in observing rival products and competitor analysis. This is the junction where data analytics comes to the rescue. If companies could predict the future and grab the direction of which path buyer winds are propelling, staying one step ahead in the marketplace would be easy.

    AI coupled with predictive analytics, helps you determine your existing and potential customers’ attributes so you can generate qualifying leads that have similar traits and benefit accordingly.

  • Fraud Detection: Machine Learning algorithms are used to observe customers’ ongoing transactional methods, smart AI solutions are utilized to sense normal and abnormal behavior.

    A slight deviation can be detected that may demonstrate cyber threats by training machines through high-end software solutions.

  • Financial Modeling: Bloomberg is popular among financial analysts to get high-quality data at a macroeconomic level for making crucial financial decisions.

    Bloomberg knows the art of playing with the data and how the best use of analytics can change the course of the business with faster and smarter decisions. Data is the new oil of global financial markets, an essential asset for making influential decisions and the need of the hour is to gather valid data for analysis and evaluation. Clients are inclined to trust Bloomberg enterprise data products for fueling their growth prospects and stay abreast of the latest insights.

Industry Trends in Predictive Analytics

  • Marketing: Statistical modelling, machine learning techniques and data models are utilized to forecast future outcomes. Incorporating AI in sales and marketing reaps its own benefits in terms of gauging campaign performance, evaluating advertising results in terms of increased or decreased sales and taking measurable steps to improve overall strategy.

  • Health Care: Predictive analytics serves as an advancement technique for improving patient’s diagnosis by examining past records of patients showing similar symptoms and carrying out medical treatment and procedures accordingly.

  • Human Resources: In order to find the candidate for a job role that is a perfect fit for an organization, whether an employee should be hired for his current skill set or should the former employees be upskilled, what’s the likelihood of employees leaving the company or whether the current employees are productive for company’s growth or not; predictive analytics allows human resource team to make forecasts about different HR functions and how they should maintain the strategy in future for better growth prospects.

  • Retail: Customers highly regard product recommendations and buyer reviews before making any purchase, chances are that they are more drawn towards a personalized retail experience. For instance, Amazon comes up with product recommendations based on past purchases.

    It identifies your persona and serves up personalized categories for a friendly and interactive shopping experience.
    In the same way, Netflix suggests movies based on the user’s last watched genre. When running a promotional offer or launching a flash sale on an item, meaningful data can assist you in forecasting product demand, anticipating trends and carrying out market analysis.

    Predictive analytics methods are driven by a wide range of models and algorithms that can be applied to several real-world use cases. The key is to determine which predictive modelling technique suits best for your problem, a solution that is perfectly aligned with your requirements, as to leverage data for a profitable decision.

    For instance, if a buyer is up to reducing customer churn rate through AI technique, the predictive analytic model used by a hospital for predicting the inflow and outflow of patients admitted into intensive care unit room in the next ten days might not be the prototype for the retailer to employ for his customer churn rate.

  • Automobile: You want to get the best market price for buying or selling your car and this overwhelming process is not at all hassle-free. The best and sophisticated approach is to look up for a solution that gives you the estimated market price based on market trends. Data analytics is driving the automobile industry with CAROOGLE, an effective, user-friendly and easy to use AI solution, an automotive predictive model that assists car dealers in making quick decisions based on historical data.

    Automobile price evaluation is made easy and it allows the dealer to enter the vehicle’s information including all the specifications and get an estimated rate of that car. The tedious task to surf through the web to get more information is diminished.

    You can get hands-on complete pricing information with different parameters that too in a single click.


An annual growth rate for using predictive analytics in industries is estimated at 8-10% and indeed it is booming and flourishing by leaps and bounds.

A survey was carried out for deployment of AI solutions which stated that 85% of respondents are up to maximizing efficiency by using predictive analytics in their workflow while 51.5% want to go for deployment within six months.

In the long run, the top goal for executives is to progress and make the forecasting model mature and robust by deploying updated AI solutions for predicting behaviors to the extent that decision making is done in real-time, with sound analysis at hand.