Mining demand chain knowledge of life insurance market for new product development

Abstract

Demand chain management (DCM) can be defined as “extending the view of operations from a single business unit or a company to the whole chain. Essentially, demand chain management focuses not only on generating drawing power from customers to purchase merchandises on the supply chain; but also on exploring satisfaction, participation, and involvement from customers in order for enterprises to understand customer needs and wants. Thus, customers have changed their position in the demand chain to assume a leading role in bringing more benefit for enterprises. This article investigates what functionalities best fit the consumers’ needs and wants for life insurance products by extracting specific knowledge patterns and rules from consumers and their demand chain. By doing so, this paper uses the a priori algorithm and clustering analysis as methodologies for data mining. Knowledge extraction from data mining results is illustrated as market segments and demand chain analysis on life insurance market in Taiwan in order to propose suggestions and solutions to the insurance firms for new product development and marketing.

Introduction

In manufacturing or business activities, the transmission between information flow, money flow and logistics flow is generally according to supply chain management (SCM). SCM focuses on using the above information to optimize the material flow through the successive steps of inbound logistics, operations and outbound logistics across the supply chain (Landeghem & Vanmaele, 2002). However, in the demand chain, the focus is clearly customer-centric, as defined early by Brace (1989), who explained the concept of a demand chain as “… the whole manufacturing and distribution process may be seen as a sequence of events with but one end in view: it exists to serve the ultimate consumer.” Demand chain management (DCM) can be defined as “extending the view of operations from a single business unit or a company to the whole chain. Essentially, DCM is a set of practices aimed at managing and coordinating the whole overall demand chain, starting from the end customer and working backward to raw material suppliers (Vollmann, Cordon, & Heikkilä, 2000). The information and communication infrastructure development has resulted in the continual evolution of the demand chain concept with a shift away from supply chains towards demand chains and DCM. The main stimulus behind this has been the shift in power away from the supplier and towards the customer (Soliman & Youssef, 2001). Striking a balance between good customer satisfaction and supply chain efficiency begins with understanding the situation and needs of distinct customer segments (Heikkilä, 2002). Thus, DCM is the management of supply production systems designed to promote higher customer satisfaction levels through electronic commerce (EC) that facilitates physical flow and information transfer, both forwards and backwards between suppliers, manufacturers, and customers (Williams, Maull, & Ellis, 2002). The generation of consumer-product ideas is usually “manufacturer-active”, rather than “customer-active” (Hippel, 1978). With this in mind, demand chain management tries to obtain more reliable and detailed information about (prospective) consumers (Landeghem & Vanmaele, 2002), and this is the practice that manages and coordinates the supply chain from end-customers backwards to suppliers (Frohlich and Westbrook, 2002, Vollmann and Cordon, 1998). Accordingly, demand chain management focuses not only on generating drawing power from customers to purchase merchandises on the supply chain; but also on exploring satisfaction, participation, and involvement from customers in order for enterprises to understand customer needs and wants. Thus, customers have changed their position in the demand chain to assume a leading role in bringing more benefit for enterprises.

In addition, as an enterprise asset, the customer has an important position. Most of the parties involved in the production chain, such as the manufacturers, suppliers and retailers, are aware of the importance and need for enterprises to acquire and share better customer knowledge. But this is easier said than done since customers’ knowledge is concealed within the customers. It is available but not accessible, and there is little possibility of exploring the full volume of data that should be collected for its potential value. Inefficient utilization would render the data collected useless, causing databases to become ‘data dumps’ (Keim, Pansea, Sipsa, & Northb, 2004). How to effectively process and use data is becoming increasingly important. This calls for new techniques to help analyze, understand or even visualize the huge amounts of stored data gathered from business and scientific applications (Liao, 2003, Liao and Chen, 2004, Liao et al., 2008). Among the new techniques developed, data mining is the process of discovering significant knowledge, such as patterns, associations, changes, anomalies and significant structures from large amounts of data stored in databases, data warehouses, or other information repositories (Hui and Jha, 2000, Keim et al., 2004). Customer knowledge extracted through data mining can be integrated with product and marketing knowledge from research and can be provided to up stream suppliers as well as downstream retailers. Thus it can serve as a reference for product development, product promotion and customer relationship management. When effectively utilized, such knowledge extraction can enable enterprises to gain a competitive edge through production of customer-oriented goods that provide better consumer satisfaction (Shaw, Subramaniam, & Tan, 2001).

Accordingly, this paper investigates the following research issues in Taiwan’s life insurance business: What exactly are the customers’ “needs” and “wants” for life insurance? Are insurers knowledge of the customers and the product itself reflected in the needs of the market? Can product design and planning for product mix be developed according to the knowledge of customers? Can the knowledge of customers be transformed into knowledge assets of the enterprises during the stage of new product development (NPD)? In addition, regarding the marketing methods, apart from the conventional supply chain marketing model, the demand chain marketing model can also be considered to ensure that the products developed are customer-oriented. Clustering analysis and the a priori algorithm are methodologies for data mining, which is implemented to mine demand chain knowledge from customers for NPD and marketing. The knowledge extracted from data mining results is illustrated as knowledge patterns and rules in order to propose suggestions and solutions to the case firm for NPD and marketing. The rest of this paper is organized as follows. In Section 2, we present the background of the current life insurance market in Taiwan. Section 3 introduces the proposed data mining system, which includes system framework, and physical database design. Section 4 presents the data mining process, including clustering analysis, a priori algorithm, knowledge extraction process, and result analysis for NPD and marketing. Managerial implications are presented in Section 5; and Section 6 presents a brief conclusion.