User Loyalty Articles

According to Shapiro (1999, p.44), companies recognise the individuals’ desire for control and start to give customers the ability to personalise their experience with the company. He argues that personalisation on the Internet does not only help customers to interact with companies or persons, furthermore it helps to filter useful information.

Cunningham (2001, p.118) describes the task of personalisation as ‘delivering the right content to those who need it when they need it’.

A study conducted by Karat et al (2003) identifies a function of two variables in the value of personalisation for customers and a function of two variables for the provider: For the customer it is, ‘the cost of divulging personal information and the perceived resulting benefits’, while it is, ‘the cost of gathering information and the perceived benefits’ for the provider. The benefits for providers are usually measurable, while ‘the customer’s value proposition is more complex and can involve factors such as security, privacy, trust, and the value of business relationships’.

According to Kasanoff (2003, p.121), value through personalisation can be provided by remembering information about a person and using this information to deliver unique benefits to that person. One way of delivering unique benefits is by providing content-based recommendations (Adomavicius et al., 2003). These recommendations are similar items to the ones the user preferred in the past.

The data for personalisation is derived from web mining.

The process of web mining for personalisation is divided into three steps, data acquisition, data analysis and data output (Markellou 2004; Albanese 2004).

In web mining, several fields of data are defined. User data is separated into explicit data, which is gathered with knowledge of the user through manual input, and implicit data, which is gathered without direct interaction with the user by utilising web-usage mining to record and accumulate data about user interactions and behaviour whenever a web server receives a request for resources (Zhu, 2004). Both of these forms of data have their drawbacks. Explicit data can be influenced by negative attitudes of the user and implicit data can raise privacy concerns and thereby lead to loss of trust (Scime 2004, p.27 ff; Schubert et al. 2000; Eirinaki et al.2003).

The data is scanned for patterns and rules about users’ navigational behaviour, user and page clusters and can also be combined with other data, such as data from a database with additional information. Discovered rules and patterns can be used for personalising a website or are integrated in a user profile for a different purpose. (Adomavicus et al., 2001)

Examples for these rules can be:

Content Rules to select, sort or modify the information on a website.

Navigation Rules to add, remove, activate or sort any links in the user navigation. Presentation Rules to modify the structure of the published website or acquisition rules that determine how data is collected (Garrigós et al. 2003; Eirinaki et al. 2003).

Adomavicius et al. (2003) describes three delivery methods for personalised information. The ‘pull’ method notifies the user that there is personalised information available but displays it only when the user requests it. The ‘push’ method sends the personalised information to the user and the ‘passive’ method provides the information along with other information without interaction with the user. He also describes de-personalisation, which is the status when a personalisation system is not producing valuable results for a customer any more and therefore they stop using it. The effectiveness of personalisation is a topic of an ongoing debate. (Business Wire 2000) One[EB2] opinion is that ‘You can’t reduce a person to a rule’ (Calvacca 2001; Kastner 2003) because data does not show the reasons why a person acted as they did. The Jupiter Research report, “Beyond the Personalization Myth (2003),” confirms the statement of usability guru Jakob Nielsen (1998) that ‘Web personalisation is much over-rated and mainly used as a poor excuse for not designing a navigable website’. The report also concludes that it is cost ineffective to operate a personalised website because it costs four times more than a normal website (Festa 2004).

The success of online retailer Amazon shows a different picture. Amazon, which is the 74th most valuable brand, according to Businessweek (2004), and has the highest rating of 88 in the American Customer Satisfaction index (Allen, 2004), relies heavily on personalisation. It has spent $800 million since it was founded in 1997 on technology and, according to Jeff Bezos ‘enabled products to find customers’ (Kohavi 2004; Bezos et al. 2002).

In a recent study about on-line customer experience it also had the highest ranking of 8.0 among all companies (Britt 2005). Furthermore, Amazon also has the most loyal customers of all on-line bookstores (Brand Keys 2004).

Previous studies suggest there is a link between customer satisfaction and customer loyalty, even though they differ.

Meyer et al. (2001) assume, on the basis of a comparison of satisfaction levels of customers and rebuying behaviour, a progressive relationship between customer loyalty and customer satisfaction, while Müller et al. (1991) prove with empiric results that the relationship has a plateau phase in which customer loyalty remains the same even with increased customer satisfaction.

Homburg (2003) identified in his construct of customer satisfaction the connection between expectations that had been met, expectations that had been exceeded and unfulfilled expectations. The construct shows that exceeded expectations have enthusiastic customers as a result. Bitner (1998) and Costabile (1998) found evidence of a close connection between satisfaction over time and trust. They found that loyalty and trust were a result of the experience of satisfaction over a period of time.

The link between satisfaction, trust and loyalty could not always be confirmed. (Jacoby et al. 1978). Singh et al. (2000) findings show that trust is not the most important factor for creating loyalty. He finds that customers want extra value, which is the optimal combination of time, effort and cost savings. The different findings show the need for further research in this area, but in general, one can see a loose connection between customer satisfaction and customer loyalty. However, highly satisfied customers do not have to be highly loyal customers and vice versa.

Another driving force for loyal behaviour can be barriers. People might stay loyal to a company because of the high switching cost. In 2004 a study, conducted by InterUnity Group, showed that the IT company SAP had the most loyal customers, even though it might not have the most satisfied. In this case 30% of the respondents said they would feel trapped because of the high cost of change (Bailor 2004; McCue 2004).

Online loyalty involves special aspects. Chau et al. (2002) finds that the design of websites strongly determines the motivation of a user to use them.

Murray et al. (2003) suggests that the interface of a website attracts new users when it can be handled with skills a user already has. To establish long-term interface loyalty one has to encourage the development of non-transferable user skills.

Koufaris (et al. 2002) found that positive experiences with a website lead to increased trust in the company that runs the website and this has a positive impact on customer retention and intention to buy. Vatanasombut et al. (2004) suggests that retention initiatives for internet-savvy users ‘should focus on measures that create commitment and trust in the relationship’. Possibilities to do that include ‘implementing effective and proactive communications and ensuring perceived security’. Kim (2000) findings show similar results as he proposes that ‘comprehensive information, shared value, and diverse communications affect the level of trust, which in turn influence customer loyalty’.

The significance of this research is the increasing importance of loyalty on the Internet. For companies websites play a significant role in creating loyal customers.

A survey conducted by FGI Research (Kontzer 2005), in which 4000 consumers were questioned ‘about a range of shopping factors’, shows that those customers who did online research before buying a product in a store were more satisfied than those who did not. The most satisfied and loyal customers were the ones who did research and bought online.

Furthermore, studies show that preventing only 5% of a company’s customers from defection can result in an 85% profit gain over time (Reichheld et al. 1990). Word-of-mouth marketing, which is one of the most efficient ways to get new customers (Hof 2004; Pagado 2005), further increases the profit through recommendations from loyal customers to potential customers. Loyal customers also show an increased buying frequency and companies have reduced costs for data management of customer data, while the possibility of cross-selling products is increased (Elke 2003).

The same applies for non e-commerce websites. Online portals like MSN, Yahoo or AOL try to increase the stickiness of their sites (Hansell 2004) because their capital is users, even though they gain money from their customers who advertise on their sites (Tedeschi 2004).

Loyalty in the sense of the word which is a ‘feeling of allegiance’ (WordNet n. d.) is also described as emotional loyalty. For emotional loyalty it is important to exceed customers’ expectations and offer an over-all satisfactory experience from which customers really benefit (Newell 2000, pp 18-20).

Hallberg (2002) describes the Ogilvy Loyalty Index, which is a study conducted by Millward Brown about emotional loyalty. They have identified four levels of emotional loyalty and in the highest level, which they call bonding, a customer has eight times the value for a company than in the lowest level. They also claim that heavy buyers who are in the bonding level can have a 10 to 15 times higher value for a company than an average buyer at the base of the emotional loyalty pyramid.

Emotional loyalty is an expression that is not very often used in customer loyalty literature. As an example, Brandi (2005) includes the emotional aspect in her description of loyalty. She defines loyalty as ‘a genuine emotional attachment that occurs when your customers appreciate the value of your product or service, as well as the way you deliver it’.

To establish emotional connections with their customers companies have to create products and services with personality (Norman 2003, p.56).