It is more cost effective to retain current customers than to acquire new ones. This thesis explores the history of CRM and how its proper implementation can help identify areas of customer satisfaction and retention in the property and casualty insurance industry. Data were collected from a regional property and casualty insurer and analyzed to determine customer satisfaction standards. A factor analysis and several multiple regressions were conducted to determine whether satisfaction on identified standards was a predictor of stated likelihood to renew the policy.
The overall aggression examined independent variables under the control of the insurance company and showed a significant overall prediction, with 48. 0 percent of the variance explained. When looking at the significant unique contributors, satisfaction with premium/policy factor had the greatest influence, followed closely by people service factor and claims service factor. The second regression was conducted with customers of high-value agencies and explored variables under control of the agent. The model explained 33. Percent of the variance, and found satisfaction with the agent had the iratest influence, followed by ease of billing, and satisfaction with explanations of premium costs. The third regression looked at the same variables but with customers of low-value agents. The model explained 47. 4 percent of the variance, and found ease of the claims process had the most influence, followed by satisfaction with explanations of premium costs, and ease of billing. The goal was to investigate how variables identified through previous research would predict likelihood to renew with the insurer.
CRM is concerned with the creation, development, and enhancement of individualized customer relationships with carefully targeted customers, resulting in maximizing their total customer lifetime value (L TV) (Reinsert, Kraft, & Hoer, 2004). Companies want to avoid the mistake of not identifying a good customer, and subsequently, not rewarding the customer accordingly. Companies also want to avoid wrongful classification of a low-value customer as a high-value customer and subsequent overspending of resources.
The development of a reliable CRM approach aids in the measurement of customer value and therefore reduces the chance of these errors (Reinsert, Kraft, & Hoer, 2004). The concept of CRM entered the business world in the sass’s with a promise to change the way businesses interacted with their customers. However, there are some obstacles. CRM is a cumbersome process. It is expensive and difficult to track and 2 maintain the large database needed to run CRM effectively. However, recent technological advances have greatly improved CRM capabilities.
Despite Cram’s popularity, there is still confusion about what it is, what it can do, and the best situations in which to use it. When used properly, CRM can allow a company to better understand its valuable customers’ needs and ants, allowing measurable customer service standards to be created. It identifies the service components important to customers such as an acceptable wait time or time of transaction. The company can then implement customer service standards. Once the standards are in place, analysis can then be conducted to see if, by meeting the standards, customer satisfaction improves.
Further research could also explore the relationship between customer satisfaction and customer retention. Database marketing CRM is often confused with database marketing. Although both use databases to dude marketing strategies, the difference is the focus of the marketing. CRM is aimed at determining and influencing the behavior of individuals through one-on-one marketing. Database marketing is aimed at identifying customer segments and markets to them. Customer relationship management evolved in the 1 ass’s from database marketing and was made popular with mass mailers such as American Express and State Farm Insurance.
Both companies used their customer lists to build relationships with their customers after the initial sale, leading to retention and cross sales (Hughes, 2003). Database marketing assumes that through the collection and organization of information about a business, marketing costs can be reduced and profit can increase. Typically, the information is consumer focused: the date of the last purchase, what was purchased, and 3 other demographic information. However, an integrated approach would include information about products, suppliers, competitors, and other business areas.
As technology became more sophisticated and economical, database marketing became more accessible and practical to businesses. It became possible to tore and use information to build lasting relationships. As a result, it became possible to increase sales and profits by promoting cross sales, repeat sales, and upgrades, by computing customer LTV and using it strategically, and by creating customer loyalty programs (Aragua, 2001 One of the greatest benefits of database marketing is improved customer service.
When there is accurate information about the customer, the customer service representative (CARS) is better able to address questions and concerns, since they are provided with the customers past purchase behaviors (Bean, 1999). Information such as past purchases, times of purchases, amounts of purchases, along with any relevant demographic information about the customer, are available. The unique customer service also allows a special, individualized relationship to develop between the company and the customer.
Building a database Cram’s success is dependent on an accurate database. The integrity of the data is important. Not taking care to make sure the data are accurate is a major reason why marketing databases fail (Bean, 1999; English, 1998). Business costs of poor data can be significant, and the investment in data quality generates a payback greater than the initial investment. The true challenge of database marketing is the organization and transformation of numerous scattered data into meaningful customer information (Bean, 1999).
Building begins with identifying the sources of data, which include transactional 4 information, order entry systems, accounting systems, operational manufacturing systems, sales tracking systems, and outside lists. Customer Data Integration (CDC) is an area within data management that can organize various soloed systems into single customer view (McCormick, 2007). One method is through a hub and spoke customer integration model where a central integration point is created into which all source systems will link.
Master customer data is stored within the hub such as name, address, date of birth, e-mail address, telephone number, etc. A unique customer identifier is given to link the customer to different spokes of data sources. Next, the data must be organized and maintained in a meaningful way. Customer data can change, and it can be difficult to keep the information current and correct. One way this can be done is through the establishment of consistency keys, which make it possible to detect changes in various data sources.
This is part of the function of the unique customer identifier. Anytime data from the spokes of the model conflicts with the master customer data in the main hub algorithms are used to create the best match or determine if a new customer record should be created (McCormick, 2007). Consistency key management ensures recognition of the same customer over time (Bean, 1999). Types Of databases There are three main types of databases: operational, marketing and warehouse. Each database is quality controlled by a different department (Hughes, 2003).
An operational database is used to process transaction information and general business information, such as sales, shipments, and payments. The IT department often maintains the operational database since it is based on accounting principles. It is balanced to the 5 dollar and is externally audited. The operational database contains information only on current customers, and old data is archived. There are no data on prospective customers until they make a purchase. Warehouse. The marketing database receives information from the operational database and is managed by the marketing department.
It includes information on current, lost, and prospective customers and the company’s communication with them. It also contains data from preferences and profiles provided by the customer, a response history from marketing campaigns, and a customer lifetime value. A customer’s lifetime value is defined as a measure of the net profitability received from a given customer during their future lifetime as a customer (Hughes, 2003). The warehouse database combines the two databases into one centralized location and is the truly integrated database.