How predictive analytics works?

How predictive analytics works?

Predictive Analytics- Who and Why to use

Predictive analytics is the use of data, Machine learning, Business Intelligence and Artificial Intelligence techniques to identify and assess the historical data for the better results in future. Basically, historical data are captured to develop useful mathematical models that predict better results. After developing the mathematical model, they are processed and matched with the current data to predict what will happen next or to suggest possible actions to take necessary steps. predictive analytics has gained more popularity because of artificial intelligence and machine learning.

Predictive Analytics differ from traditional business approach because it adopts a modern approach to data and Business Intelligence. Predictive analytics state how the customer will behave or respond to the product after being introduced by the company in the market. Predictive analytics arms the business organisation to prepare and plan for the future.

Why is Predictive Analytics important:

Many small and large business organisation use predictive analytics to increase their revenue and relationship with customers. Descriptive and Predictive analytics together can solve and answer more than 90 per cent of the problems. predictive analytics is important because:

  1. Detecting Fraud and crimes Combining multiple analytics methods can improve records and prevent criminal behaviour. Today every country are spending huge amount of money on securing the data and preventing terrorism. As cybersecurity is a major concern, high-performance behaviour calculates and examine all actions on the network in real-time and spot all abnormalities that can indicate any fraud, persistent threats and all vulnerabilities to the society.
  2. Optimising Marketing Campaigns Predictive analytics are used to determine customer response, purchase and behaviour, and promote different products. Using predictive analysis, businesses can attract the customer, grow potentially in the market and increase their profitable customers.
  3. ?Improving operationspredictive analysis is used to forecast the inventory and manage resources. Airlines use predictive analysis to set the fare of the air tickets. banks use predictive analysis to assess the customers, reduce frauds and illegal transfer of money. hotels use predictive analysis to predict the number of guests for any day to maximise the sales and increase their revenue. Predictive analytics is used by many government agencies to minimise the crimes and detect threats easily. Predictive analytics helps the organisation to functions more smoothly and efficiently.
  4. ?Reducing risk- Credit scores are easily predicted and calculated by using predictive analytics. Credit scores are used to identify the potential of buyers to make a default for a purchase. A credit score is a number generated by using predictive analytics that incorporated all data to a person’s ability to repay the debt. Predictive analytics is also used in financial institutions such as Insurance Company to assess claims and collections.?

Application of predictive analytics

The business organisation uses predictive analytics to reduce risks, optimise operations and increase revenue. Here is a few application of predictive analytics:

Marketing companies-?Predictive analytics use customer segmentation is the process which divides a customer base into different categories that are similar in specific ways based on marketing and sales, such as age, sex, personal interest, daily routine and speaking habits.

Predictive analytics helps the companies to accurately target specific marketing message based on their choice to customers who are actually looking to buy the products. It has been proved that predictive analytics can identify the customer demands much better than any other tools.

A direct example of customer segmentation can be easily identified in the field of bankings. Banks target customers through emails and contact number because of predictive analytics. Predictive analytics goal is to predict if the client will subscribe to the mentioned scheme or not. Attributes include information of the customer, the product, the contact number and another context.

The advantage of using predictive analytics in the banking sector is better communications and relationships with customers, saving much money in marketing and increasing profitability.

Predictive analytics analyse Input variables and Target variables.

Input Variables:

  • Socio-demographic factors which include age, marital status, education qualification and job.
  • Bank relationship factors which include balance, loan defaults and credit.
  • Past campaign factors which include day, month, time duration and contact type.

Target Variables:


Banking and financial companies– Predictive analytics allows users to compute and analyse the possible problems linked with the business. The aim of predictive analytics is to build a strong support system that can accurately predict the next move of the business whether the decision is having a good or bad impact on the company.

The example in the financial sector is to determine which customers will take the credit and which customers are likely to make default. Using predictive analytics we use different types of models to select if an application is perfect to receive credit. More specifically, predictive analytics uses the probability that the customer will not pay the loan and therefore we can reduce the options of making a default.

Predictive Analytics uses different models to study customers personal details and financial conditions to pay their liabilities. Different variables most commonly used by Predictive analytics include:

Input variables:

  • Socio-demographic factors which include age, marital status, education qualification and job.
  • Bank relationship factors which include balance, loan defaults and credit.
  • Product details which include credit amount and bill statements.
  • Customer behaviour which includes repayment status and previous payment.

Target variables


Predictive analytics make binary classification tests for this application. Thus, the accuracy of the predictive model is about 80-85 per cent, which is very good in this field. Therefore, predictive analytics make a model which is ready to assess the default risk of new customers. The classification accuracy ratio helps to analyse the data.

Telecom companies- Customer churn means the percentage of customers that have stopped using your company product or service after some course of time. Using predictive analytics, we can aim to predict why and when, which customers end their relationship with the company.

Churn prevention is very expensive and is typically avoided because the cost of retaining an existing customer is less as compared to that of acquiring new customers. By using the power of big customer database, companies can develop predictive models that will help to protect the customer before its too late.

Here, we have evaluated the telecom customers based on information about their account. It is believed that 40 per cent of people are disloyal to their operators. Hence, predictive analytics can help to prevent such a big loss to have occurred in the company.

Predictive analytics has correctly evaluated that about 80 per cent of the customers which will be lost. The telecom company then assess and analyse the main reason for churn and take necessary actions to retain such customers.

Retail– Calculation of prior history, market trends and seasonality results in true prediction sales which has been planned by the company. Predictive analytics could anticipate customer response and changing market behaviour by looking at all types of factors. Sales forecasting is applied in short term, medium-term and long-term forecasting.

An example is to accurately predict power demand of electric heater during winters by the electric industry. Predictive analytics analyse different variables before coming to some important conclusion.

  • Calendar data which includes season, bank holidays, the hour.
  • Weather data includes temperature, humidity and rainfall.
  • Company data which includes the price of electricity, marketing campaign and promotions.
  • Social statistics which include economic and geopolitical factors.
  • Demand data which includes historical consumption of electricity.

Oil, Gas and Utilities- Predictive analytics helps in predicting equipment failure, forecast resource needs, business management, mitigating safety and improving the overall performance of the industry. Many projects are now being build and assessed under predictive analytics. Many large and important projects like Salt river project of Arizona uses predictive analysis for the analysis of data of machine sensors and turbine