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Improving customer-perceived service quality is a critical mission of telecommunication service providers. Using 35 billion call records, we develop a call quality score model to predict customer complaint calls. The score model consists of two components: service quality score and connectivity score models. It also incorporates human psychological impacts such as the peak and end effects. We implement a large-sized data processing system that manages real-time service logs to generate quality scores at the customer level using big data processing technology and analysis techniques. The experimental results confirm the validity of the developed model in distinguishing probable complaint callers. With the adoption of the system, the first call resolution rate of the call center increased from 45% to 73%, and the field engineer dispatch rate from 46% to 25%.

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The product line optimization plays a key role to increase the revenue by satisfying consumers' various needs. While the extant studies have made the approaches from the perspective of consumers, in this paper, we propose a product line design problem from the perspective of manufacturers. The concept of neighborhood is introduced that parameterizes consumers' tolerance on specification differences. In addition, the maintenance costs are considered in order to cope with the multi-periodic characteristics of the problem. We demonstrated the applicability of the proposed model to a major mobile phone manufacturing company in Korea. The case study shows that the chosen manufacturing company could have reduced the size of its product line by 64.2%, and saved 68.0% of its product line development costs.