The current technological advancements have aided enterprises in growing their business and profitability at a faster rate.
Data is abundant and sometimes is scattered across departments in disparate silos. Many enterprises are deploying predictive analytics for using various organizational data for predicting future events.
Historical data depends on your business’s nature, and the same data is used for building a suitable mathematical model for capturing various trends.
The predictive model is implemented on current data for predicting or suggesting the future course of action. This article will delve much deeper into the subject but keep it close to the subject matter and easy to grasp. So, without further ado, let’s get going.
First thing, first, “what is predictive analytics”?
It is a sub-category of data analytics used for making predictions about future outcomes derived from crunching historical data and analytics tools and techniques such as statistical modelling and machine learning.
The global predictive analytics market is projected to reach approximately $10.95 billion by 2022, growing at a compound annual growth rate (CAGR) of around 21 percent between 2016 and 2022. – Zion Market Research
As a business, you gain tremendous insights with significant precision for the next week, month, or year with the help of predictive analytics. This technology is increasingly becoming popular amongst a wide range of organizations.
Who’s using it?
As I mentioned earlier, the technology is being favored by various organizations who are using it for reducing risks, optimizing their operations, and increasing their revenue.
Here are the industries that are using this technology to gain better momentum in their respective market
- Health insurance
- Governments & the public sector
- Banking & financial services
- Oil, gas & utilities[SD1]
Why is predictive analytics important for your business?
Many modern-day business leaders turn to predictive analytics to solve their organizational problems and discover better and budget-friendly growth opportunities. Some of the most common uses of predictive analytics are mentioned below.
1. Fraud detection
Company officials are using multiple analytics methods for improving pattern detection. These analytical methods help them uncover and prevent various criminal behavior. With cybersecurity becoming a growing concern, many businesses are looking for high-performance behavioural analytics for detecting real-time actions on a network for spotting abnormalities that highlight
- Ongoing vulnerabilities
- Persistent threats
2. Optimization of marketing campaigns
Many retail and e-commerce businesses are getting help from predictive analytics for
- determining customer responses or purchases
- promoting cross-sell opportunities
- maintaining the stock
- optimizing the delivery
- streamlining their logistics
- deciding the delivery timeline
- creating their coupon and discount rates
Businesses are getting the much-needed insight and help from predictive models for attracting, retaining, and growing their most profitable customer base.
3. Improving the entire business operations
As mentioned above, businesses rely heavily on the predictive analytics model to forecast their inventory and manage their stocks. Many modern airlines are implementing predictive analytics for set ticket prices based on the current and historical data. Hotels use predictive models to derive the number of guests for any given occasion to maximize their occupancy and increase revenue. Predictive analytics is bringing more efficiency to the way organizations operate and helps them save significant resources that can be deployed somewhere else for generating more revenue.
4. Uncovering and reducing unnecessary risk
Hunch and risk have been the traditional driving forces in business. But it is not the most reliable way of managing a finance or banking institute. Many financial institutions rely on credit scores to assess a buyer’s likelihood of default for purchases. They are the most accurate example of modern-day predictive analytics implementation.
The predictive analytics models that various businesses might use
Models are the foundation of predictive analytics that allow business owners to turn past and current data into actionable insights. Here are some predictive models that businesses can use.
Grouping customers who might invest in products and services
Customer Segmentation Model
Group customers based on their characteristics, shopping, and purchasing behaviors
Predictive Maintenance Model
Forecasting chances breakdown in essential equipment
Quality Assurance Model
Spot defects and prevent high costs while providing products or services to the customers.[SD2]
How does predictive analytics work?
Predictive models utilize your historical data for continuously training a model for predicting values for varied data scenarios. Predictive modeling gives predictions based on an estimated significance from a set of input data variables.
This method is different from descriptive models as they help businesses understand what happened. They also help companies to create diagnostic models for deriving key relationships and determining the cause of any particular past event.
There are two variations of predictive models.
The classification models help in predicting class membership.
For instance, you are trying to classify
- whether someone is likely to leave
- whether they will respond to a solicitation
- whether they are good or bad credit risk
The model results are made of 0 or 1, with 1 being the event you are targeting.
Regression models predict a number.
For instance, how much revenue will your customer base help you generate over the next year or the number of months before a machine component will fail?
What are the various modelling techniques?
Let’s discuss the most widely used predictive modelling techniques in the current business world.
The decision trees modelling technique
As the name suggests, a decision tree depicts a tree where every branch represents a choice.
The choice between alternatives. Each leaf represents a classification or decision.
These are classification models that divide data into subsets based on categories of input variables. They help you understand the decision-making path of your customer base.
The model, as mentioned earlier, crunches the data and derives a variable that splits the data into logical groups that are the most different.
The reason behind this modelling technique’s popularity can be credited to its ease of interpretation and understanding. This modelling technique allows businesses also to handle missing values well and are valid for preliminary variable selection.
This is the predictive analytics model for businesses that want to find various missing values or want a quick and easily interpretable answer.
The regression technique
In statistics, regression is known as the most popular method. In predictive analytics, regression analysis helps businesses estimate the relationships among variables. The regression analysis allows companies to find critical patterns in large data sets. This analysis is intended for companies with continuous data flow and wants to determine how specific factors, such as the price, influence the movement of their product or service.
For instance, let’s say a company wants to determine or predict a “Y” variable. In linear regression, one independent variable will be used to explain and predict the outcome of Y.
Multiple regression will use two or more independent variables for predicting the outcome.
By using logistic regression, unknown variables of a discrete variable will be predicted based on other variables’ known value.
The response variable is categorical, which means it can assume only a limited number of values.
Binary logistic regression gives a response variable that only provides two values, such as 0 or 1.
Multiple logistic regression gives a response variable with several levels, such as low, medium, and high, or 1, 2, and 3.
The neural networks
Neural networks are sophisticated techniques that businesses can use for modeling highly complex relationships. The reason these are very popular amongst companies is that they’re powerful and flexible. Their ability to handle nonlinear relationships in data is their most powerful trait. Neural networks are often used for confirming the findings from simple techniques like regression and decision trees. They are generally based on pattern recognition and AI processes that graphically “create” the required parameters. They even function awesomely well when
- there is no mathematical formula for relating inputs to outputs
- prediction is more important than explanation
- there is an abundance of training data
How can businesses define predictive analytics algorithms?
The adopters of predictive analytics can easily access a wide range of statistical, data mining, and machine-learning algorithms. These algorithms are designed for use in predictive analysis models.
Why is there a need for modelling algorithms?
Algorithms are developed for
- solving a business problem or series of problems
- enhancing any existing algorithm
- supplying some unique capability
Confused? Well, so for instance,
Clustering algorithms are generally suitable for
- customer segmentation
- community detection
- other social-related tasks
Classification algorithms help businesses
- improve customer retention
- develop a recommendation system
A regression algorithm is generally suitable for creating a credit scoring system or predicting the outcome of different time-driven events.
Concluding thoughts: Data-driven decision-making is the future. Why not profit from it!
Like every good thing, businesses need to give some time and ponder how they want to implement the predictive analysis. So, it is not that easy as a “snap.” But still, choosing predictive analytics is easier for a business committed to the approach and is ready to invest the time and funds required for the implementation of the same.
Just keep it simple for the organizational structure and yourself and start implementing a limited-scale pet project in a critical business area for cutting down costs. Once you have the model ready and you put the same into action, it needs to be crunched into actionable insights for the future.
Name: Himanshu Singh
Himanshu Singh is a Marketing consultant at Rapidops. He is a technology enthusiast and well versed in software development. He is also interested in domains like machine learning and data science. In his spare time, he enjoys guitar, badminton, and photography.