CRM, while focusing on three additional key areas, i.e., the process’ operational, managerial and strategic dimensions. Its unifying question is how change can be introduced into the company’s operations such that machine learning can be merged into its business processes.
Action research differs from traditional research in terms of how its subjects and objects are defined (Coghlan & Brannick, 2001). More specifically, action research is “an approach in which the action researcher and a client collaborate in the diagnosis of the problem and in the development of a solution based on the diagnosis” (Bryman & Bell, 2011).
For Coughlan and Coghlan (2002), the four broad characteristics of action research are that it is (1) research in action, rather than research about action; (2) participative; (3) concurrent with action; and (4) a sequence of events as well as an approach to problem-solving. Action research was selected for the present case because the members of the target organization participated actively in a cyclical process of interactive actions and collaborations.
3.2. Research process
As the originator of action research, Lewin (1946, p. 5) conceptualized its implementation as “a spiral of steps, each of which is composed of a circle of planning, action and fact-finding about the result of the action”. Coughlan and Coghlan (2002) also proposed a framework for the action-research cycle, comprising a pre-step – aimed at establishing the rationale for both action and research – and six main steps (Fig. 1; see also Maestrini et al., 2016). The six main steps are (1) data gathering through interviews with organizational members, collection of internal documents, and observation; (2) data feedback, in which the researcher provides the information collected in step 1 to the studied organization’s managers for their analysis; (3) data analysis, which is performed collaboratively by the researcher and managers, thus crucially facilitating the effective implementation of an action or actions; (4) action planning, in which activities are scheduled, and roles and responsibilities assigned to organizational members; (5) implementation, in which the organization executes the planned action(s) with the researcher’s support; and (6) evaluation: reflection on the outcomes of the implemented action(s), so that the next cycle may benefit from the cycle that has just been completed; this is the key to continuous learning.
Each action-research cycle leads to another cycle, and thus continuous planning, implementation and evaluation occur over time; and monitoring can be described as a
‧
(Reference: Coughlan & Coghlan, 2002; Maestrini et al., 2016)
Table 2. Action-research explanation
Steps What How Results
Data gathering Define business problems
Interviews and meetings Options of the actions
Data analysis Feasibility analysis of the action plans
Feasibility analysis
Discuss with members and managers
Feasible action plans
‧
國立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
10
Steps What How Results
Action planning Schedule the follow-up actions
Meetings with managers Action plan
Implementation Action execution Collaborate with relevant members
Execution
Evaluation Effectiveness evaluation
Metrics or measures Performance evaluation
3.3. Research analysis
As the above discussion implies, action research proceeds in an orderly, step-by-step fashion, and thus has the potential to provide a holistic yet detailed picture of a company’s CRM implementation process. The next section reports this case’s action-research process, and is organized according to the six main steps discussed above. After introducing the background of the case company and the business problems, the main section will focus on demonstrating the last three action-research cycles – action planning, implementation, and evaluation.
‧
4.1. T-Company and its maintenance service: Background
T-Company is a leading car dealership in Taiwan, with a market share of more than 30%, 2.6 million customers, and an operating revenue of over 110 billion TWD (3.41 billion USD) in 2016. In the same year, in addition to its showrooms, T-Company operated 122 vehicle maintenance plants. CRM is critical to T-Company, given that it provides warranties on every car it sells. Usually, each warranty covers 120,000 kilometers or a period of four years, whichever is shorter. T-Company’s maintenance service generates considerable profits through routine maintenance fees and parts replacement, as well as a considerable amount and array of information about customers’ habits and needs.
To retain existing customers and prevent churning, T-Company provides them with products not only within the warranty period, but also when they exceed it. These products were traditionally promoted in person by technicians to car owner who went to T-Company plants for maintenance. Those within their warranty periods would be pitched product A, which extended the warranty to cover a fifth year or 140,000 kilometers, while those who had already exceeded it were offered product B: three different types of cash cards with built-in discounts for car-maintenance services.
T-Company had also set sales targets and sales-performance-related key performance indicators (KPIs) for its maintenance plants. As a result, every technician in those plants was required to promote the service program alongside his/her maintenance tasks, which was not only very time-consuming but raised questions about whether the sales role/mindset was incompatible with that of maintenance professionals. For customers seeking maintenance service, safety is the main concern, and if the technicians appear to be using hard-sell tactics, it could create a bad impression and decrease customer satisfaction. Therefore, T-Company’s management wanted to find out who was most likely to purchase the promoted services, as a means of increasing the success rate of promotion while decreasing the overall duration and frequency of technicians’ involvement with promotional activities. It was hoped that this, in turn, would boost the efficiency and effectiveness of frontline service in the company’s vehicle-maintenance plants, and render customers’ perceptions of such service more positive.
4.2. Data gathering
The current study’s dataset included information on T-Company’s maintenance customers, the customers’ cars, and whether they had bought product A or B (or neither) after having such products promoted to them by technicians between January 2009 and September 2016. In all, this consisted of around 2.73 million rows of data, which T-Company’s IT