The contributions of this study are both academic and practical. Academically, it has verified the advantages of machine-learning techniques over conventional analytic tools, and of the action-research approach to CRM problems. Practically, it has important implications for organizations who might wish to learn from T-Company’s experience of applying machine-learning techniques on CRM.
Apart from finance and telecommunications, scant CRM research has focused on the service sector, despite data-mining techniques’ demonstrated capability to uncover hidden patterns in large-scale data that can help CRM processes become more effective, and the critical importance of shaping customers’ perceptions of services – i.e., that the overall image created by service providers can significantly influence service quality, and thus further influence customers’ choices of the service providers (Ince & Bowen, 2011; Wei et al., 2013;
Xiao & Nicholson, 2011).
The present research used machine learning-based data-mining techniques and an action-research approach to improve the CRM process of a large car-sales and maintenance company. The decision-tree model selected to generate recommended-customer lists succeeded in helping the company’s frontline technicians better distinguish between customers, and thus improve their operating efficiency and effectiveness. The results of this case support the advantages of applying machine-learning techniques even in service industries where the number of transactions per customer are relatively rare.
Much as Croteau and Li (2003) suggested, the present results imply that before project implementation, all the required IT infrastructure and technical staff should be ready;
and from the outset, the organization should clearly define its problem and list all the necessary data sources precisely. Additionally, high-level managers should explain their CRM strategic plan to their employees, and link it to specific targets for them to follow.
Managers must address the benefits of using the new techniques to their business operations, and commit to providing the required support and resources. If a CRM project is cross-functional, different functions of the organization should be integrated and connected, ideally with the involvement of top-level managers. During implementation, monitoring and reporting is essential for model improvement. And the organization should strive to enhance model performance even as they implement it continuously.
The features of service-sector CRM applications are closely interrelated with human factors. Unlike in other industries, people are the most important element of service providers.
Therefore, when implementing new techniques, staff commitment and motivation are the key factors to success, and businesses should therefore allow ample time not only for training employees in the use of the new techniques, but also for convincing them that such techniques can bring advantages to themselves as well as to the company.
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This research has certain limitations. First, because the data used were all from one company operating in one country, its outcomes may not be generalizable to other companies or places. Future research should therefore explore additional cases. Second, only 27 vehicle-maintenance plants were utilized in the experiment, which lasted only five months, which may not have been sufficient to verify the reliability and validity of the model. Third, among a number of machine learning-based data-analytic models including neural networks, SVMs, nearest neighbor, and so forth, only decision trees were used, and this could further limit the applicability of the analytical results. And fourth, the experimental method and the methods of evaluating model performance were both designed specifically for this case, meaning that applications of a similar approach in studies of other firms will require adaptations.
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Appendix A. Information Attribute Inputted for Decision Tree Model
Customer Information Attribute Notes
I.D Card No. of the new license plate received person
I.D Card No. of the car user
I.D Card No. of the invoice recipient
I.D Card No. of the contact person
I.D Card No. of the maintenance service contact person Number of cars owned by new license plate received person
Number of cars owned by car user
Number of cars owned by invoice received person
Number of cars owned by contact person
Number of cars owned by maintenance service contact person Postal code of new license plate received person
Postal code of car user
Postal code of invoice received person
Postal code of contact person
Postal code of maintenance service contact person Gender of new license plate received person
Gender of car user
Gender of invoice received person
Gender of contact person
Gender of maintenance service contact person
Age of new license plate received person
Age of car user
Age of invoice received person
Age of contact person
Age of maintenance service contact person
Number of complaints From telephone survey
Car Information Attribute Notes
License Plate Number
Car Brand
Car Name
Car Type
Car classification 1.Private
2.Business
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Driving rate per 10,000 kilometers L: Low, H: High
Average kilometers driven per year
Maintenance Information Attribute Notes
Customer designated maintenance plants
Latest maintenance date
Latest maintenance plants
Latest maintenance recorded odometer
Latest panel-beating or car-paintng date
Latest panel-beating or car-paintng plants Latest panel-beating or car-paintng recorded odometer
Latest regular maintenance date
Latest regular maintenance plants
Latest regular maintenance recorded odometer
Regular maintenance reminder E: Email
S: SMS T: Telephone D: DM
Contact person 1. New license plate
received person
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Number of panel-beating and car-paintng
Panel-beating and car-paintng revenue
Regular maintenance every year or not Y: Yes, N: No
Latest maintenance date over 1 year but within 2 years Y: Yes, N: No The number of days passed since the latest panel-beating or
car-paintng date
The number of days passed since the latest regular maintenance date
Next regular maintenance date
The number of days passed since the next regular maintenance date
Number of online reservations for maintenance by app Number of online reservations for maintenance by web page Number of maintenance times due to inducing
Number of reservations due to inducing
Number of times for failed inducing
Number of times for successful inducing
Number of times for postponed reminder
Number of times for failed reminder
Number of times for successful reminder
Maintenance reservation type 1. Every 1,000
kilometers
Maintenance reservation source 1. Induced by
maintenance plants 2. Induced by agents 3. Reserved by customer 4. Reserved by sales 5. Online reservation
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Qualified to purchase product A Y: Yes, N: No
(The warranty period is within 4 years, or the odometer is under 120,000 kilometers) Average usage of customer's reward points per year
Have purchased product B or not Y: Yes, N: No Age of maintenance service contact person when product B sold The sum of used customer's reward points before purchased
product B
The sum of total customer's reward points before purchased product B
Number of purchase times of the car damage insurance Total premium for the car damage insurance Annual premium for the car damage insurance
Duration of the accident insurance
Number of purchase times of the accident insurance
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Total premium for the accident insurance
Annual premium for the accident insurance
Duration of the passenger insurance
Number of purchase times of the passenger insurance
Total premium for the passenger insurance
Annual premium for the passenger insurance
Duration of the compulsory insurance
Number of purchase times of the compulsory insurance Total premium for the compulsory insurance Annual premium for the compulsory insurance
Duration of the life insurance
Number of purchase times of the life insurance
Total premium for the life insurance
Annual premium for the life insurance
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Appendix B. Important Variables from Decision Tree Regression Results
Important Variables for Product A Correlation Coefficient
Annual premium for the Compulsory insurance 5.20%
Total Premium for the Compulsory insurance 1.50%
Average annual usage of customer's reward points 0.71%
Number of purchase times of the compulsory insurance -0.66%
Annual premium for the car Damage insurance 0.50%
Average miles driven per year -0.49%
Gender of New License Plate Received Person: Null -0.37%
Total Premium for the car insurance 0.32%
Keep Regular Maintenance every year or not: Yes -0.24%
Maintenance reservation type: Every 1,000 kilometers 0.23%
Number of Panel-beating and Car-paintng times 0.22%
Number of purchase times of the accident insurance -0.21%
Maintenance reservation type: Every 10,000 kilometers 0.19%
Car Name: S 0.19%
Gender of New License Plate Received Person: Female 0.16%
Important Variables for Product B Correlation Coefficient
Number of online reservation times for Maintenance by Web Page
13%
Customer Designated Maintenance Plants: C 0.14%
Gender of Maintenance Service Contact Person: Female -0.11%
Number of Panel-beating and Car-paintng times 0.084%
Gender of New License Plate Received Person: Null -0.056%
Customer Designated Maintenance Plants: D 0.028%
Driving frequency per 10,000 kilometers: Low 0.028%
Car classification: Null 0.028%