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The principle of ubiquity should be traded off with the need to keep interfaces low-profile and low-overhead to avoid undue stress on the customer’s use context or the business environment.
They should be simple and reliable having only the functions required for users to tap the utility of the service (following the principle of Ockham’s Razor).
Service interfaces should be self-reliant, requiring little or no intervention from service agents other than the dialogue necessary to carry out the service transaction.
8.2.2 Types of service technology encounters
Advances in communication technologies are having a profound effect on the manner in which service providers interact with customers. Airport kiosks, for example, have changed the interaction between airlines and their customers. There are four modes in which technology interacts with a service provider’s customers (Figure 8.5).
Figure 8.5 Types of service technology encounters35
Mode A: technology-free – technology is not involved in the service encounter. Consulting services, for example, may be Mode A.
Mode B: technology-assisted – a service encounter where only the service provider has access to the technology. For example, an airline representative who uses a terminal to check in passengers is Mode B.
Mode C: technology-facilitated – a service encounter where both the service provider and the customer have access to the same technology. For example, a planner in consultation with a customer can refer to ‘what if’ scenarios on a personal computer to illustrate capacity and availabilitymodelling profiles.
Mode D: technology-mediated – a service encounter where the service provider and the customer are not in physical proximity. Communication may be through a phone. For example, a customer who receives technical support services from a Service desk is Mode D.
Mode E: technology-generated – a service encounter where the service provider is represented entirely by technology, commonly known as self-service. For example, bank ATMs, online banking and distance learning are Mode E.
Encounters should be designed while considering customer assets.
Are customer employees technical or non-technical?
What are the implications of the technology encounter to the customer?
What are the customer expectations and perceptions?
For example, Mode E may be less effective than Mode B or C in cases where the encounter is complex or ambiguous. When the encounter is routine and explicit, as in password resets, Mode E may be preferred. Other modes may have secondary considerations. Mode D, for example, may have language or time-zone implications.
8.2.3 Self-service channels
Automation is useful to supplement the capacity of services. Self-service channels are increasingly popular among users now accustomed to human–computer interactions, devices and appliances. The ubiquitous channel of service delivery is the internet with browsers acting as service access points that are widely distributed, standardized and highly familiar through constant use. Advances in artificial intelligence and speech recognition have improved the capabilities of software-based service agents in conducting dialogue with customers. The richness of the dialogue and the complexity of the interaction continue to increase.
The capacity of self-service channels has very low marginal cost, is highly scalable, does not suffer from fatigue, offers highly consistent performance, and is offered on a 24/7 basis at a relatively low cost. Additionally, users perceive the following disadvantages with human-to-human interactions with respect to incidents and problems:
The emotional burden that the user is asked to carry in complaining about the service
Variability in the experience, competence and emotional state of human agents
Limited capacity of human resources, which causes uncertainty in wait times
The need to schedule certain interactions with staff
The fees associated with certain human resources.
Self-service channels are effective when appropriate knowledge and service logic is embedded into the self-service terminal. Service Design should ensure that Use Case analysis is performed to ensure usability, efficiency and ease in interactions through the automated interface.
Another example would be the use of the productive capacity of customers through self-service channels. Advances in human-computer interaction and the richness of interaction technologies, such as touch-screens, scanners and signature capture devices, allow for certain service activities to be completed without the presence or intervention of service staff.36 This is a very intelligent way to adjust capacity that is highly sensitive to the presence of demand. Each customer brings one additional unit of productive capacity, instantly added and removed from the system without inventory-carrying costs to the service provider.
It is necessary to evaluate the level of control users are expected to assume with self-service options. The level of control should be commensurate with the proficiency and experience level of the users.12 In almost every population of users there are differences in levels of experience, skills, aptitudes and work environments that determine preferences for methods and modes of interaction. The attributes and functions of service interfaces should take these differences into account. There will be trade-offs as different segments of users expect to be served according to their preferences. Some prefer step-by-step guidance while others prefer efficiency and flexibility. Advances in artificial intelligences and machine learning are creating a new level of sophistication for service interfaces, which are context-aware, forgiving of new users, and capable of dialogue embedded with inquiry. The principle of forgiveness requires that the design of a service helps users avoid errors. When the errors do occur, the design should minimize negative consequences.
8.2.4 Technology-mediated service recovery
According to the peak-end rule, whereby the service providers recover well from service incidents, customers may actually retain a more positive perception of service quality than they had before the incident. This behaviour provides justification for investment in superior service support systems, processes and staff. While the strategic intent may be to reduce the occurrence of service incidents, the tactical goal would be to recover well from service incidents that are not avoided or foreseen.
Under certain conditions, the use of automation allows for quicker service recovery through fast resolution of service incidents. Users often expect nothing more than quick resolution of their problems without tedious policies and procedures. This provides a business case for simplifying, standardizing and automating certain service activities or interactions. However, when poorly designed or implemented, automated or self-service options can be especially aggravating for a user who may have suffered from a service incident. The challenge is to pick the right type of interface for a particular interaction.
Simple and routine incidents should be recovered using automation when all other factors are equal. Software-agents with diagnostic capabilities can interact with users to resolve basic technical problems. Online knowledge bases with search and navigation capabilities are useful examples of such recovery.
The approach is necessary knowledge from service management processes into automated solutions such as online technical support, self-service terminals, IVR units and software applications. Users are then presented with the self-service option as the first line of support to solve the most routine of problems. It also helps to raise the level of technical knowledge of users through well-designed documentation and self-help kits. Over time, this reduces the number of incidents that have to be handled by human resources (see example in Figure 8.4).
Example of leveraging intangible assets
The product installation and maintenance system of a major internet and telecom solutions provider generated £0.75 billion in savings (1996–98). The company made an extensive amount of technical knowledge about its solutions freely available online to its customers. Large amounts of workload were diverted away from its technical support staff and engineers, who could focus on tougher problems needing escalation. Most of the customers were themselves technical staff willing to attempt to fix problems on their own to the extent possible. This online knowledge base could be concurrently used by a large number of customers without degradation of quality or inordinate waiting times.
Baruch Lev37
The idea of making it convenient, quick and courteous for users to report service incidents and receive compensation is an important principle that should shape policies and guidelines. Good service culture requires it to be easy and fair for customers to file a complaint and have problems resolved, without undue burden on their time, effort, or emotion, all of which are forms of indirect costs and psychological costs of being a customer.38 The need for that becomes particularly important where the customer or users will not receive any financial compensation. At this level of maturity, the service provider has institutionalized the true meaning of providing warranty to the customer. Preventing simple failures from turning into negative feelings will help maintain higher levels of customer satisfaction. Such service providers also demonstrate to their customers certain ethics that contribute to long-term success in the relationship.
8.3 Tools for service strategy
8.3.1 Simulation
IT organizations often exhibit the counterintuitive behaviour resulting from many agents interacting over time. Long-term behaviour can be surprisingly different from short-term behaviour. System Dynamics is a methodology for understanding and managing the complex problems of IT organizations. It offers a means to capture and model the feedback processes, stocks and flows, time delays and other sources of complexity associated with IT organizations. It is a tool for evaluating the consequences of new policies and structures before putting them into action.
Just as an airline uses flight simulators to help pilots learn, System Dynamics offers simulation methods and tools available to help senior managers understand their organizations. These management flight simulators, based on mathematical models and computer simulation, can deliver useful insights for decision makers faced with enormous complexity and policy resistance.
The application of System Dynamics in the service and process domains has yielded remarkable insight for IT organizations. Some examples follow.
The Capability Trap – By pressuring staff to work harder, an organization unwittingly triggers a scenario where ever-increasing levels of effort are required to maintain the same level of performance.39
The Tool Trap – Although technology tools offer very useful help to an organization, they often require the development of knowledge and experience. When an organization adopts new tools, it triggers lower productivity in the short term. The increase in workload from training, learning and practice activities may unwittingly push a resource-constrained organization over its tipping point.40
The Firefighter Trap – When an organization rewards managers for excellence in firefighting, they may unwittingly create a dynamic harming the long-term performance of the organization. The long-term performance is instead improved by not rewarding excellence in firefighting.40
8.3.2 Analytical models
Analytical models are very useful where the complexity is manageable, and there is no policy resistance or interacting feedback loops. They are effective when objectives are clear, the options are well defined and the critical uncertainties are measurable. They are easy to develop when there is a fair amount of clarity on a problem or situation, the cause and effect relationships are clear and persistent, and patterns are recognizable patterns (Figures 3.6, 4.8, 4.9, 4.13, 8.2, 9.9). They also need enough historical information for assumptions on certain variables, such as costs, processing times and the load factors of resources.
Good examples of the use of analytical models are Service Desk and call centre staffing, which can be visualized as a system of queues. It is possible to gather data on the rate of arrival of requests (or incidents), how long it takes to process them on average, and how many requests are waiting to be handled. This level of knowledge is sufficient to build simple analytical models. Figure 8.6 shows an example for a single-stage, single-agent queue at a Service Desk, with certain assumptions about the arrival pattern of requests and the processing time.
Figure 8.6 Example of simple analytical model for the Service Desk
Service Deskmodelling can become quite complex with the addition of numbers of service channels, multi-stage processes, dependencies and delays. However, it is useful to start with basic models and progressively elaborate them to reflect closely the reality of a problem or situation.
The following are commonly used sets of tools useful for decision making in Service Strategy:
Decision trees, payoff matrices, analytic hierarchy process, etc.
Linear programming (Figure 8.7) and integer programming, goal programming, etc.
Queuing and network flow models (Figure 8.8)
Clustering, forecasting, time-series analysis, etc.