06 Jun What is Predictive Analytics & Why it’s important for your business?
Predictive Analytics is about identifying future business outcomes based on historical data. Read this article to find out more.
How do companies accurately predict their customer’s buying behavior or determine which strategy would lead to their subscribers renewing their online subscriptions? No, not through intelligent guesswork. Predictive analytics is the tool that can make this possible.
To state it simply, predictive analytics is the technology that can use existing data to develop informed predictions about future outcomes. For instance, companies can use predictive data analytics to forecast the future market demand for their products or services.
What exactly is predictive analytics – and why is it important for any business?
What is Predictive Analytics?
Simply put, predictive analytics is the application of machine learning techniques to business data to forecast future business outcomes. Predictive analytics models can examine large data volumes to identify trends such as customer behavior within an industry. Thus, predictive data can be further used to gather valuable insights, which can result in improving business decision-making and actions.
Also referred to as predictive modeling, predictive analytics combines mathematical (and statistical) techniques with deep learning technologies to make accurate predictions. For example, predictive analytics models can be used for weather forecasting, election results, climate change effects, and even disease spread.
Having said that, predictive data analytics have now been used increasingly in business functions like marketing, supply chains, logistics, and quality control. Next, let us understand why predictive analytics is important to business enterprises.
Why is Predictive Analytics important?
Predictive analytics solutions are used by organizations across industry domains. Here are some of the common business use cases that underline the importance of this technology:
With Predictive AI tools, marketing teams can now forecast market trends and make better decisions. For instance, they can now accurately identify customers who are most likely to make an immediate purchase – or identify which market will have the highest demand for their products or services. This can help in promoting cross-selling opportunities and creating personalized marketing campaigns.
Companies can now deploy predictive analytics models to improve their business operations including inventory and resource management. Manufacturing firms can predict how much inventory stocks to buy – along with when and from where they should purchase. Similarly, companies can forecast problems in supply chains and help in avoiding them.
3.Customer experience (CX)
In the age of personalized CX, predictive analytics can be used to predict customer behavior and improve their overall experience with the brand. For instance, financial products (or services) can be personalized based on the customer profile and expectations. Based on customers’ emotions, predictive AI tools can learn what individual consumers think about their brand – and respond appropriately.
4.Fraud and risk management
Predictive modeling enables companies to understand their customers better based on structured and unstructured customer data. Apart from identifying potential customers, companies can also detect high-risk individuals who are likely to commit financial fraud. An example of predictive modeling is credit scores, which can accurately measure the creditworthiness of any individual.
5.New business opportunities
Using predictive analytics, companies can obtain deep and valuable insights into the market conditions and customer expectations. Based on data patterns, companies can identify new business and revenue opportunities, which are likely to emerge in the future. This provides them with a distinct competitive edge and takes action to tap into these business opportunities.
Next, let us discuss how predictive analytics work.
How does Predictive Analytics work?
As mentioned before, predictive analytics uses historical data (or results) to make future predictions. Predictive analytics models work by using existing data (or results) to develop and train a predictive model, which can be used to forecast business events.
Predictive models are different from descriptive models, which primarily describe why a particular event is happening. Primarily, there are two types of predictive models, namely:
- Classification models where the result can either be 0 (or the event not being targeted) or 1 (or the event being targeted). This model can be used to classify if a customer will buy (or not buy) a product or determine if a user has good (or poor) credit ratings.
Here is an example of the use of classification algorithms to predict NFL outcomes.
- Regression models are used to predict a number. For example, the sales figure for a product over the next year – or the inventory stock number required for a particular month.
Predictive modeling uses a variety of techniques with the most popular being:
- Decision trees are a common type of classification model, where data is partitioned into multiple subsets. This helps in determining the decision path.
- Regression analysis is a form of regression model that determines relationships between data variables. It can track continuous data following a normal distribution – along with key data patterns.
- Neural networks are a complex technique for predictive modeling, which are capable of handling complex and non-linear data relationships. This technique is based on complex pattern recognition.
Predictive Analytics – Our perspective
At NuMantra Technologies, we have leveraged AI and machine learning technologies to extract valuable insights from business data and deliver them for accurate predictive analytics. Using predictive models, we have enabled organizations to simplify complex workflows and achieve desired business outcomes.
Combined with our technical expertise in process intelligence and analytics we have helped our business customers maximize their benefits from their technology-based investments. Our process mining applications can accurately identify & eliminate bottlenecks that prevent businesses from realizing revenue or margin goals and support the development of models on future business trends and patterns using machine learning algorithms.
We believe that process mining is a crucial step in predictive analytics as it can:
- Extract relevant information from large data sets, which can be used to remove bottlenecks and optimize processes.
- Provide the relevant data to be analyzed by the ML/AI and data analytics team in order to build their predictive models.
With over 20 years of industry experience and technology implementation, we can assist you to achieve your business outcomes with predictive analytics. Want to know how? Get in touch with us today!