Predictive Analytics

An essential part of advanced analytics consists of predictive analytics, which is responsible for making predictions and outcomes based on historical data and current statistical figures using data mining techniques in machine learning. In addition, it helps to evaluate data patterns, which can significantly help identify the relative risks and opportunities for a business. 

This article deals with an in-depth evaluation of the concept of predictive analytics and understanding the underlying concepts. It will also talk about the types of predictive analytics and their applications.

Fundamentals of Predictive Analytics 

Predictive analytics is a technology that uses statistical data algorithms and relevant machine learning techniques to predict future outcomes in a given situation based on historical data records. The main objective of predictive analytics is to determine what is about to take place based on what has already happened in the past, including other relevant factors. 

The benefits of predictive analytics include: 

• It leads to improved customer retention for businesses to keep bringing in new customers. It is also necessary to replace existing customers to avoid any significant loss or reduction in revenue.

• Predictive analytics is important for marketers because it helps them identify the right customers and increases their customer base. It usually results in more profit for their business, especially in the long term. Such informative and deep insight is possible only through the technique of predictive analytics which allows businesses to optimise their marketing and focus on genuine efforts to acquire customers.

• Predictive analytics also helps businesses with diverse requirements to segment their customer base depending on the requirements that are critical to their operations. Using predictive analytics, businesses can also optimise their existing data and focus on the right target audience, extend the target segment, and improve opportunities for better markets.

• Predictive analytics is also helpful in improving decision-making processes apart from defining and identifying the most profitable customer segment. Since the analytics process is concerned with analysing all aspects of consumer behaviour and gaining in-depth insights, it is quite helpful in improving existing decision-making processes. 

Types of Predictive Analytics

Some important predictive analytics models are: 

Forecast models 

• It is one of the most common predictive analytics models.
• It performs metric value predictions by estimating the values derived from new data based on the output from historical data.
• It is also used to generate numerical values in historical data if there are none. 
• It can easily input multiple parameters.
• It is used in different industries and for business purposes.
• These models are incredibly versatile. 

Classification models

• These models work by categorising data based on historical information.
• It is used in different industries because they are easy to restrain with new data.
• It can provide a broad analysis to answer questions.
• It is most commonly used in the finance and retail industries. 

Outliers models

• These models work with anomalous data entry within the limits of a data set.
• It also identifies and analyses unusual data, either separately or concerning different numbers and categories.
• These models are helpful in industries (such as retail and finance) where the identification of anomalies is an important function to save resources.
• It is most effective in detecting fraud or any other similar fraudulent transactions by evaluating the amount of money lost, the location, purchase history, the date and time, and the nature of the purchase. 

Time series model

• The time series model focuses on data concerning time as an input parameter.
• It works by using different data points by deriving them from historical data to develop numerical metrics to predict trends within a duration.
• An example includes a scenario where a business would need to see how a given variable changes over time. 
• This model is superior to other models because it can simultaneously forecast different regions and projects. It can also focus on a single input region or project, but it depends on the organisation’s requirements. 

Clustering model 

• This model takes data and classifies it into different groups based on standard features. 
• Due to this feature, it is helpful in specific applications such as marketing and technical aspects.
• It works using two clusters: hard and soft clustering.
• Hard clustering classifies data based on whether it belongs to the data cluster.
• Soft clustering classifies data based on probability when it joins a cluster. 

Application of Predictive Analytics 

Predictive analytics is helpful in direct marketing by identifying the most practical combination of product versions. It can also help in risk management by conducting risk assessments based on the prediction of maximising returns. Furthermore, predictive analytics is helpful in fraud detection by spotting inaccurate credit applications and other details. 

Predictive analytics is used in cross-selling by identifying touchpoints connected to the customers and using purchase patterns to evaluate customer behaviour. It is also widely useful in the healthcare industry to determine and prevent possible cases and risks of developing critical health complications. 

Conclusion 

Predictive analytics is an important branch of advanced analytics that is used to make predictions concerning uncertain future events. It uses machine learning, statistical figures, modelling, data mining and artificial intelligence to evaluate its existing database and predict future possibilities. 

Different types of predictive analytics models include forecast models, classification models, outliers models, time series models and clustering models. There are several applications of predictive analytics, including in the healthcare sector, fraud detection, cross-selling, risk assessment, direct marketing, and customer behaviour analysis.