Machine Learning and Predictive Analytics Solutions

A typical example of Predictive analytics would store data from loyalty programs that can analyze past buying behavior to predict the coupons or promotions a customer is most to participate in or buy in the future. Predictive analytics could also be applied to customer website browsing behaviors to deliver a personalized website experience for the customer.

What is the Scope of Predictive Analysis?

Everyone has a curiosity about the future, wondering questions like what, how, when. What if someone says we can find such information based on our past or present behavior? Yes, this can be possible with one magical element. And that’s called data! In today’s tech-savvy world powered by vast numbers of gadgets we use on a regular basis, the amount of data we consume is incredible.

And since there are no certain methodology or future events predictors, Predictive analysis has come a long way when it comes to foreseeing the future based on several behavioral patterns and paradigms. Predictive analysis is nothing but a branch of advanced analytics which lets you predict the unknown events that tend to happen in the future. Different methods and techniques have been used to figure out such patterns. And Artificial Intelligence, Machine learning, Predictive Analytics, and statistical modeling are merely one part of it.

Predictive Analytics solutions and Services

• Data Science 
• Data warehousing
• Data Analytics
• Data Visualization
• Data Migration
• Big Data Implementation
• Predictive Analytics
• IoT Analytics
• Customer 360
• DevOps
• Microsoft Power BI
• Qlik Sense and QlickView

Predictive Analytics Using Machine Learning

One of the prominent objectives of Predictive Analysis is to provide trends data in multiple industries to business analysts to find precise patterns to make accurate forecasts. And for decision-makers, it is crucial to have the utmost accuracy when making any important decision for their organization. And predictive analysis makes the job easier for them by offering systematic reasoning and data analysis which is like a goldmine for predicting your future patterns.

Apart from accurate forecasting, one can have multiple benefits like risk reduction, competitive advantage, and increased efficiency in production, especially when you combine the two robust concepts, predictive analytics and machine learning solutions.

Here, let’s go through the two prime domains that describe the scope of predictive analysis in the most precise way.

• Industry Analytics
• Functional Analytics

Industry Analytics:

Predictive analytics have varied applications in numerous industries like Pharma, insurance, healthcare, retails, finance, and many more. For financial institutions, it will be easier to find the possible fraudulent transactions done online/offline, or it can figure out inaccurate credit applications. In the health sector, with proper charts, the patients who can be at certain risks of developing certain diseases can be identified before a significant period, so the cure becomes possible before the patient’s health condition worsens. Furthermore, it can be easier for manufacturing firms to find out shifting paradigms of consumer behavior. Thus, keeping the tab on manufacturing units becomes convenient, especially when you are producing multiple product lines.

Apart from that, predictive analysis can do wonders in the manufacturing department. In that case, one can imagine how far productivity can go if we utilize Predictive analytics and machine learning solutions in offshore operations and maintenance by maximizing its profits to the core!

Functional Analytics:

Predictive analytics has been utilized in various functions as well. For instance, the employees of an entity only generate a significant amount of data, and the HR department can use that data to plan the upcoming extensive policies of the organization. On the other hand, in the marketing department, the marketers can uncover the possible deviations or deficiencies in particular marketing mix channels. 

Basically, in simple terms, predictive analytics facilitates the process of studying the functional and operational challenges to effectively address the core incompetency of the business by restructuring your organization’s business model, providing utmost accuracy.

Moving ahead, it’s worth observing that predictive analytics tools are equipped with several algorithms and models. In order to predict the future, predictive analytics examines patterns of current and past data through advanced mathematics. Machine learning is one of the tools which automates such predictions by producing algorithms that work based on the commands to find something specific through those patterns. Sounds complicated? Let’s understand the concept by cutting it through different parts.

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Common Predictive Analytical Models

Predictive analytics have common predictive analytical models that fuel the algorithms by offering unconventional solutions to modern business problems. The top analytical models are,

1. Outliers Model
2. Clustering Model
3. Time series Model
4. Forecast Model
5. Classification Model

In general, one can distribute such Predictive analytics algorithms in two primary groups, Machine learning and deep learning. With only machine learning, one can find distinctive algorithms that you can apply at the base level, which describes the Fundamentals of machine learning for predictive data analytics with a comprehensive operational dashboard. Such algorithms include the Generalized linear model(GLM), Gradient Boosted Model (GBM), Random Forest model, and K-means. 

The concept of reporting real-time and historic data is not exclusive anymore. However, in the last decade, an enormous amount of data has been processed and changed significantly. In such circumstances, opting for the right tool that gathers the data becomes crucial in multiple ways. The tools work in specific ways to handle such type of reporting by combining the traditional ways with the modern predictive big data analytics. 

You, too, can get an edge over the enterprise that does not take a step further in this data-rich world by taking optimum advantage of Forrester wave predictive analytics and machine learning solutions. 

Use cases of Machine Leaning Based Predictive Analytics

• Banking and Financial Services
• Cybersecurity Apps
• Retail Apps
• eCommerce
• Customer service 
• Medical Diagnosis
• Sales and Marketing
• Financial Services
• Cybersecurity
• Demand Forecasting
• Marketing Campaign Optimization

Key Take Away

If you are looking for a helping hand to achieve unprecedented insights about your business by harnessing the ever growing volume to automate your human analytic processes. In that case, TechnoSIP can be your one-stop solution. We have years of experience in helping all the shapes and sizes of businesses to help them stay ahead of these challenges with ML-based predictive modeling solutions.