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Application of Predictive Analytics in Business
Predictive analytics has become an important concept in the field of data analytics, with interest rapidly raising during the past few years according to Google. Predictive Analytics has a wide application in Business.
Predictive Analytics uses Historical Data, Machine Learning, Business Intelligence and Artificial Intelligence to predict and access what will happen in the future. These data are fed directly into mathematical models that calculate the necessary trends and patterns of the data. Thus, this data is then applied to current situations to predict what will happen in the future.
Using the information from predictive analytics, companies can calculate the strength and weakness of their business and suggest actions that can affect positively to their operations, sales and marketing strategies. Data Analyst uses predictive analytics to forecast if these small changes can help them to reduce risks, improve operations and increase sales and revenue. Data Analyst solves the biggest problem of the business that is, What is my current position according to my data and What can I do to change its outcomes?
Real-world application of Predictive Analytics:
Customer Segregation– Customer segmentation is the process which divides a customer based 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:
- Conversation
?Risk evaluation– Risk evaluation 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
- Default
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.
Churn Prevention– 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.?The variables used for this assessment are:
- Locality Pin-code
- International roaming plan
- Voice mail plan
- Account length
- Today duration of the conversation
- Today day calls
- Total nights calls
- Total evening calls
- Total international calls
- The number of customer service calls.
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.
Sales Forecasting– 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, an 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.
Financial modelling– Financial modelling is the concept of building a pilot representation (a model) of a real-world financial situation. Predictive analytics is a mathematical model which is designed to build the performance of a financial asset or portfolio of a business or any other investment.
Financial modelling is the term which has a different meaning for different users. It specifically related to accounting and corporate finance applications. Financial modelling has been gaining popularity over the past few years.
An example of Financial modelling is to forecast the next day return of India stock exchange SENSEX with other international indices: stock market return of Germany, Stock market return of China, Stock market return of UK, Stock market return of Brazil, Stock market return of the US and many emerging markets.
Market analysis– Predictive analytics help in the analysis of the market survey to help companies to address customer requirements. Thus, predictive analytics helps to increase the profit and reduce the risk of losing potential customers.
Predictive analytics can be used to model medicine in pharmaceutical companies. The inputs include physicochemical tests (eg- pH values) and the output includes sensory data and product quality. Once the predictive model has been designed, the medicine components can find best fitting as per demand.?