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Fifthly, the reason for the need of predictive analytics is Satisfy. This is done through meeting and satisfying consumer expectations.
Objective - Satisfy
By consumer or customer's satisfaction we mean one's experience when expectations are fulfilled with the offering. A customer becomes satisfied when the offering meets his expectations. One of the important goals in most of the companies is improvement of consumer satisfaction. Sales are evaluated on the basis of customer's satisfaction. Thus not only it is necessary to hit sales targets but also there is a need for consumer satisfaction. So one needs to know how to improve the satisfaction of the customer. Two important ways to improve satisfaction of the customer are as follows:-
Establishing appropriate expectations in minds of the customers.
Deliver on the expectations.
Also, it is likely that dissatisfied customers will tell others about the negative experience more than the satisfied customer telling about their good experiences. One can reduce the risk of buyers' dissatisfaction by establishing appropriate expectations. The challenge in business is to maintain customer loyalty. It may seem that emphasis is given on what company is giving instead of what the consumer is getting. But this is not the case. There are many companies who set up programs for measurement of customer or consumer's level of satisfaction.
Insurance marketing relies on traditional marketing approaches. Predictive modeling is utilized in insurance marketing and it represents an innovative approach to the fact that it was supposed to be a relationship based business. In marketing, predictive analytics is applied in various services and products. For identification of potential customers engaged in mortgages, loans, annuities and investments ,financial services makes use of predictive analytics. In order to analyze purchasing patterns for the insurance customer's predictive analytics can be used by property-casualty insurers. To increase the marketing functions retention and hit ratio all these information are used. Also, for planning differentiators relevant to every market segment, analytics plays a critical role. It will be an important message to make surety about investments to communicate correctly.
In the context of Pricing
Predictive analytics' use is less revolutionary and more evolutionary. The accurate result regarding prediction of future losses and the price of the products can be made with the help of predictive analytics. And thus this is a protection against all the losses. It acts as the next generation of powerful tools by means of which goals are achieved. Human error factors can be corrected with the help of predictive models. Through predictive analytics insurers can filter out the applicants not meeting a pre determined model score. Thus by means of such screening insurer's efficiency can be increased and this can be done by reducing the employee's time for analyzing applicants. The model score can be used on behalf of rating mechanism if the applicant's model score is sufficient for consideration.
Identifying a Fraud Claim
Estimating the actual percentage of fraud claims is a tough task. There are numerous forms of fraudulence have, building up of claims, staged accidents are some of them. Identification of relatively small number of claims that are fraudulent from the many claims filed annually is really a difficult job. This is where predictive analytics plays a key role. It helps the insurers to determine those claims that require additional review for fraud, and this is achieved through increase of the likelihood that fraudulent claims are to be discovered. This refers to and termed as a prevention to the occurrence of Type I and Type II error. Type I error occurs at the time when fraudulent is assumed for a legitimate claim. And when one fails to identify the claim which is fraudulent and considers it as legitimate claim, a type II error occurs.