Driving Growth with Predictive Analytics
Predictive analytics is the modern-day crystal ball, providing invaluable insights into what lies ahead for businesses. It enables decision-makers to make informed decisions, optimize operations, and even foresee potential challenges.?
More likely, it is the key to unlocking growth potential for businesses, whether they have two decades of experience in the industry or are emerging startups.
The global predictive analytics market size would be $38 Billion with a CAGR of 20.4% during 2022-2028 according to The Insight Partners. It is being driven by an increase in the adoption of big data technologies and an increase in the use of predictive analytics tools. Moreover, 52% of the companies worldwide are using predictive analytics according to MicroStrategy. With these insights, It's clear that demand for predictive analytics is growing exponentially.
?It is a journey where you exploit the power of analytics to predict trends, make innovative strategies, and take a step toward growth. In this digital era, everything leaves a trace back whether it’s a like, share, or click. It’s the digital fingerprint that cannot be wiped off. Hence to collect all this information we call it data and it is referred to as the most important piece for any business.
Approaches for Predictive Analytics
The most popular methods for predictive modeling are decision trees, regression, text analytics, neural networks, and time series analysis.
Decision Trees: Decision trees are categorization models that divide data into subgroups based on input variable categories. This helps you grasp someone's decision-making process. A decision tree resembles a tree, with each branch indicating a selection from a set of options and each leaf representing a categorization or decision. This approach examines the data and attempts to identify the one variable that divides it into the most distinct logical groupings. Decision trees are often used because they are simple to comprehend and interpret. They also tolerate missing values effectively and are handy for the pre-selection of variables. So, if you have a lot of missing values or want a response that is rapid and easy to understand, you may start with a tree.
Regression: (linear and logistic) is a prominent statistical approach. Regression analysis analyzes the relationships between variables. It detects essential patterns in huge data sets and is frequently used to evaluate how much particular elements, such as price, impact the movement of an asset. It is intended for continuous data that can be assumed to follow a normal distribution. We aim to predict a number, known as the response or Y variable, using regression analysis. One independent variable is utilized to explain and/or forecast the result of Y in linear regression. Multiple regression predicts the outcome using two or more independent variables. Unknown variables of a discrete variable are predicted using logistic regression based on the known values of other variables. The response for this variable is categorical, which means it can only take a restricted amount of values. A response variable in binary logistic regression has just two values: 0 or 1. A response variable in multiple logistic regression might have numerous levels, such as low, medium, and high, or 1, 2, and 3.
Text Analytics: It is used to get deeper insights from unstructured text, such as recognizing a pattern or trend. It predicts numeric values based on statistical and linguistic techniques. Sentiment analysis and Named entity recognition (NER) are some of the widely used terms in text analytics. For example, this technique may be used to analyze a bad surge in customer experience or the popularity of a product.
Neural networks: Neural networks are advanced techniques that can model exceedingly complicated relationships. They are popular due to their strength and adaptability. The power stems from their capacity to manage nonlinear data relationships, which are becoming more prevalent as we collect more data. They are frequently used to validate results from simple approaches such as regression and decision trees. Pattern recognition and various algorithms for artificial intelligence that graphically "model" parameters are the foundations of neural networks. They function best when no mathematical formula relating inputs to outputs is known, prediction is more essential than explanation, or there is a large amount of training data. Hence, It is a method inspired by human brains that tries to replicate tasks as a human is doing it. Hence trained to make decisions like a human.
Time series analysis: Time series data is time-stamped and gathered continuously at certain points in time. It can be seasonal or annual or in any other format. Traditional data mining and forecasting approaches are used in time series data mining. Data mining techniques such as sampling, clustering, and decision trees are applied to historical information to improve predictions.
Predictive analytics can be used to:
Source: Analytics animation
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Use cases for predictive analytics
Organizations today employ predictive analytics in practically limitless ways. It is being deployed in almost every industry from banking, healthcare, e-commerce, hospitality, pharmaceuticals, automotive, oil and gas, and manufacturing.
Here are a few examples of how Synchronous Services has helped? businesses in utilizing predictive analytics:
Real Estate: For a UK-based Real estate client we helped them in predicting the market value of the property on various factors like location, amenities, and market trends and also helped them predict the rental value.
E-commerce: We helped in predicting churn rates and provided recommendations about how to retain customers. Secondly, we determined which marketing channels and messages were most effective for different customer segments.
FinTech: We utilized predictive models to assess loan applicants' creditworthiness, particularly those with minimal or unusual credit histories for a FinTech startup.
Healthcare: We optimized bed availability, streamlined patient flow, and enhanced monthly patient visits while forecasting doctor revenues to improve overall hospital operations in the UAE.
Hospital In UAE: Total Revenue vs Target Revenue
Conclusion
It has changed the ways of doing business and every industry has seen improvements with this technology. Hence it is becoming a norm as people understand the correct use of it and its benefits. Moreover, it's not the solution to the exact problem but with proper management, data, and planning it offers help and leads to countless results.
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