What is a predictive analytics definition?
Definition of Predictive Analytics
Predictive analytics is an application of statistics which deals with extracting information from raw data and use it to predict the trends and behaviour patterns. Predictive analytics calculates statistical probabilities of future events online. Predictive analytics uses statical techniques which includes data modelling, data mining, deep learning and AI deep learning.
Predictive analytics can be applied to predict past, present and future. Predictive analytics finds a wide application to identify the suspects after some serious crime has been committed, or credit card frauds. The backbone of predictive analytics relies on capturing the relationship between explanatory behaviour and predicted variables from past incidents.
Predictive analytics is mainly defined at predicting the details of the data, i.e. it generates predictive scores for every single activity. This separates predictive analytics from forecasting.
Predictive analytics is the technology that uses experience (data) from the past to predict future behaviour in order to obtain better results. Predictive analytics helps to predict and prevent potential loss for decisions optimisation.
The process involved in Predictive Analytics:
- Defining project- Define the project outcomes, business objectives and identify all the data sets that are useful for Predictive analytics.
- Data collection- Data mining and data abstraction prepare data from multiple sources for data analytics. This provides the overall summary of customer interactions.
- Data Analysis- Data analysis involves inspecting, cleaning and modelling data with the objective of inventing and discovering useful information and to draw better conclusions.
- Modelling- Predictive modelling provides the ability to manually create a precise model for the future. There are several options with the best solution for multi-modal evaluation.
- Statistics- Predictive analytics helps to validate the assumptions, hypothesis and use them to draw better results.
- Model Monitoring- Models are managed and monitored to view better modal performance?
Types of Predictive model:
Generally, predictive analytics is used for predictive modelling and forecasting. However, Data analyst is using predictive analytics for descriptive modelling and decision modelling. ?
Predictive models: predictive modelling helps to analyse the relationships between the specific performance of a unit in a sample. The objective of the model is to analyse and access the likelihood that a similar unit in a different sample will have specific functions.
The predictive model often performs calculations during transactions. With advancements in computing speed, individual agents modelling system have become capable of simulating human behaviour or reactions.
?Descriptive models: Descriptive models elaborate on the relationship in data that are often used to classify people into different groups. Descriptive model analyses and identify the different relationship between customers. However, descriptive models do not a rank-order customer according to their likelihood of taking actions as a predictive model does.
Decision model: Decision model is the relationship between all the models. This model is used in optimisation, making certain decisions and minimising the errors. Decision models are used to develop decision logic or set of rules for the business that will produce better results.
Customer Relationship Management-?Customer relationship management (CRM) is the application of predictive analytics. methods of predictive analytics are applied to customer data to obtain CRM objectives, which involve have overall information of customers.
CRM uses predictive analytics for marketing campaigns, sales and customer service. They must analyse and understand the demand or the potential when there is very high demand and predict the nature of customer behaviour in order to promote their business.
Child protection- Over the last few years, some child agencies have started to use predictive analytics to mitigate the risk. This approach is widely appreciated by the Commission to Eliminate Child Abuse and Neglect Fatalities (CECANF), where the child agencies have been using predictive analytics tools and techniques to minimise the harm to the children.
Collection Analytics- many data have set of defaulter customers who do not make their payment on time. The financial institution has to undertake collection activities to collect these undue amounts. A lot of collection resources are laid down on customers who are difficult to track and difficult or almost impossible to recover.
Predictive analytics help to optimise the identify the most efficient collection agencies, legal actions and other strategies to reach customer which significantly increase the recovery and reduces the cost collection.
Customer retention- In this competitive era, businesses need to focus more on customer satisfaction, rewarding customer loyalty and minimising customer problems. Thus, a small increase in customer retention will help to obtain better results and increased profits margins.
According to one of the survey, it is concluded that a 10 per cent increase in customer retention rate will increase the profit margin by 25-30 per cent. Proper application of predictive analytics will help help to achieve a more proactive retention strategy.
By frequent analysis of customer past service usage, service performance and other behaviour predictive analytics can determine the likelihood of customer services. Predictive analytics can also predict the behaviour so that the organisation can take proper action to increase customer activity.
Direct Marketing- Apart from identifying products, predictive analytics can help to identify the most effective product versions, marketing targets and timing that can be used to target a given customer. Predictive analytics can help to lower the cost per order or cost per action.
Frauds detention- Frauds can be classified into several types: fraudulent transactions, inaccurate credit applications and false insurance claims. Credit card issuers, insurance companies, manufacturers, B2B suppliers are the most common victims of fraud detention. Predictive analytics can help to reduce such frauds and minimise such bad loans.
Predictive analytics can be used to easily identify criminals and fraud candidates in the public or private sector. The Internal revenue Service (IRS) of the United States also use predictive analytics to calculate tax returns and identify and access tax fraud.