Predictive Analytics is a method through which we can extract information from existing data sets to predict future outcomes and trends and also determine patterns. It does not tell us what will happen in future rather it forecasts what might happen in future with acceptable level of reliability.It also includes what if-then-else scenarios and risk assessment. Applications areas of Predictive
CRM (Customer Relationship Management): Predictive analytics is useful in CRM for marketing campaigns, sales, customer services etc. The focus is to put the organization’s efforts effectively on analyzing product in demand and predict customer’s buying habits .
Clinical Decision Support: Predictive Analytics helps us determine which patients are at risk of developing certain conditions like diabetes, asthma, lifetime illness etc.
Collection Analytics: Predictive Analytics helps financial institutions for the allocation of collecting resources by identifying most effective collection agencies, contact strategies etc. to each customer.
Cross Selling: “An Organization that offers multiple products, Predictive Analytics can help to analyze customer’s spendings, their behavior etc”. This can help to lead cross sales that means selling additional products to current customers.
Customer Retention: As the number of competing services is increasing, businesses should continuously focus on maintaining customer satisfaction, rewarding loyal customers and minimize customer reduction. If Predictive Analytics is properly applied, it can lead to active retention strategy by frequently examining customer’s usage, spending and behavior patterns.
Direct marketing: When marketing consumer products and services, there is the challenge of keeping up with competing products and consumer behavior. Apart from identifying prospects, predictive analytics can also help to identify the most effective combination of product versions, marketing material, communication channels and timing that should be used to target a given consumer.
Fraud detection: Fraud is a big problem for many businesses and can be of various types: inaccurate credit applications, fraudulent transactions (both offline and online), identity thefts and false insurance. These problems plague firms of all sizes in many industries. Some examples of likely victims are credit card issuers, insurance companies, retail merchants, manufacturers,business-to-business suppliers and even services providers. Predictive analysis can help to identify high-risk fraud candidates in business or the public sector.
Portfolio, product or economy-level prediction: These types of problems can be addressed by predictive analytics using time series techniques. They can also be addressed via machine learning approaches which transform the original time series into a feature vector space, where the learning algorithm finds patterns that have predictive power.
Risk management: When employing risk management techniques, the results are always to predict and benefit from a future scenario. Predictive analysis helps organizations or business enterprises to identify future risk, Natural Disaster and its effect. Risk management helps them to take correct decision on correct time.
If you are a company that would like to develop in-house products and services, and would like to integrate predictive analytics into your products and services a good suggestion is to take professional advice from a data scientist who can explain to you which kind of predictive analytics is most suitable to your business . You may avail consulting services from Advanz101 Business Systems. Alternatively, if you want a professional to work with you one-on-one, Advanz101 Consulting is what you need to hire a consultant to get the job done.