Most of the Institutions are yet to use basic best practices for marketing spending and effectiveness for allocation of marketing resources. They traditionally allocate their marketing budget either based on historical allocation level or based on product level priorities. Marketing has come under increased pressure as the marketing managers are constantly challenged with the problem of how to allocate the limited marketing budget across customers and show return on their marketing spending. As a result, there has been an increased interest and proliferation of work on customer lifetime value (CLV) and efficient allocation of market resource. Hence, prediction of CLV and efficient allocation of marketing resources are critical. In this paper, the successful application of artificial neural network (ANN) algorithm and its usefulness in prediction of CLV is done. The sample data of Business to Business customers from Non-Life Insurance Industry is used for the prediction of CLV. Also the paper compares the ANN with other data mining algorithms for its prediction accuracy.
[...] The paper contributes in terms of providing analytical tools and models to profile the customer segments in to financial portfolio by measuring its value. Customers are the financial assets of the company hence they should be valued and analyzed and profiling has to be done for efficient allocation of resources. In this light, the managers should carefully select the customer profile before they want to target. We clustered the PCLV data set using EM and Simple K-means. Here based on various trials we have arrived at five clusters for applying the clustering algorithm. [...]
[...] Bradlow, and Howard Kunreuther Modeling the "Pseudodeductible in Insurance Claims Decisions, Management Science,” Vol No.8, pp 1258- S.Gupta and D.R.Lehmann, “Customer as assets, “Journal of Interactive Marketing,” Vol No.1, pp S.Gupta, D.R.Lehmann and J.A Stuart, “Valuing customers. Journal of Marketing Research,” Vol No.1, pp Sunil Gupta, Dominique Hanssens, Bruce Hardie, Wiliam Kahn, V. Kumar, Nathaniel Lin, Nalini Ravishanker, and S. Sriram, “Modeling customer lifetime value,” Journal of Service Research, Vol No.11, pp Sunil Gupta and Thomas J. Steenburgh. "Allocating Marketing Harvard Business School Working Paper,” No.2, pp 08- . [...]
[...] In most CLV models the cost of goods in some form are known at the time of transaction In contractual settings like Insurance, the full cost is not known until the customer uses the service Insurance companies in order to fix the optimal premium for the customer, needs to determine the claim costs and the effect of changes in premiums on termination rates III. Methodology The research study focuses on business-to-business customer segments in non-life insurance industry. Based on the extensive literature review related to Non-life Insurance Industries, an appropriate mathematical model will be developed for predicting CLV using data mining approach. [...]
[...] In order to improve the predictive quality of the test data, we have simulated the trained data for a period of 5 years in line with the test data received from non-life insurance company. In order to prepare the model, the data obtained from the firm with relevant attributes were taken for building the model. A. Customer Revenue: The revenue from the customer is a linear function of premium, claim, acquisition cost, retention cost and cost of claim. In our equation we are using the terminology revenue not in the income sense but as the operating net income after the due expenses captured for the individual customer. [...]
[...] Customer Life Time Value in Insurance Industry Insurance is an extremely competitive industry where a combination of market growth and profitability are seen as imperatives to success. Market growth and profitability are result of making the right pricing decisions relative to claim costs. Therefore, predicting claim costs and frequency of claims has impact on profitability. The effect of pricing on customer retention patterns and in relation to the market growth is important. Jackson writing about the insurance industry defines “policy owners life time value is the present value of future stream of net contributions to overhead and profit expected from the policy owner”. [...]
using our reader.