In today's competitive business landscape, maintaining high-quality products and services is crucial for the success of any organization. To achieve this, companies often rely on quality tracking techniques to measure and improve customer satisfaction. Traditionally, these techniques involved collecting customer feedback through surveys or questionnaires, which could sometimes be intrusive and time-consuming. However, advancements in technology and the rise of the digital era have opened up new opportunities for businesses to monitor and track quality through digital Voice-of-Customers (VoCs) analysis. In this blog post, we will explore the concept of digital VoCs analysis and its potential for implementing an effective product/service quality tracking process.
Understanding Digital Voice-of-Customers Analysis
Digital VoCs can be defined as freely published reviews, opinions, or feedback on products or services by customers on publicly accessible online platforms such as social networks, blogs, and review aggregators. These digital VoCs provide valuable insights into customer experiences and perceptions. Analyzing this information can help businesses overcome the limitations of traditional quality tracking techniques. However, analyzing unstructured textual data from digital VoCs can be challenging. To overcome this, advanced data mining techniques, such as text mining and topic modeling algorithms, are used to extract relevant information from these digital VoCs.
Topic Modeling Algorithms and Quality Determinants
One of the most effective data mining methods for analyzing digital VoCs is the application of topic modeling algorithms. These machine learning algorithms can identify latent topics discussed within extensive collections of unstructured textual documents. When applied to digital VoCs, these algorithms have shown the ability to extract the latent quality determinants that influence customer perception of quality. By analyzing the topics discussed in digital VoCs, businesses can gain valuable insights into the factors that affect product/service quality.
Implementing a Quality Tracking Process Using Digital VoC Analysis
To implement a quality tracking process using digital VoC analysis, businesses need to consider several design parameters. These include determining the time window for analysis, analyzing newly captured data, defining the sampling period, and choosing the appropriate quality tracking scheme. Different approaches, such as global time window analysis, mobile time window analysis, and local time window analysis, can be used depending on the specific requirements of the business. By carefully considering these design parameters, businesses can effectively track and monitor the evolution of quality determinants over time.
Case Studies
To illustrate the practical application of digital VoC analysis for quality tracking, two case studies are presented. The first case study involves a restaurant chain in the UK, where the emergence of a new quality determinant related to COVID-19 regulations is observed. The second case study focuses on the analysis of digital VoCs from car-sharing providers, showing the evolution of quality determinants over a 14-year period. These case studies highlight the potential of digital VoC analysis in identifying emerging trends and areas for improvement in product/service quality.
Implications for Designing and Redesigning Products/Services
Digital VoC analysis can provide valuable insights for the designing or redesigning of products and services. By analyzing the trends and changes in quality determinants over time, businesses can better understand customer needs and preferences. This information can guide product designers and developers in making informed decisions to improve the quality of their offerings. The continuous improvement and redesigning process driven by digital VoC analysis can lead to enhanced customer satisfaction and increased competitiveness in the market.
Conclusion
Digital VoC analysis offers businesses a powerful tool for quality tracking and improvement. By leveraging advanced data mining techniques and topic modeling algorithms, businesses can extract valuable insights from digital VoCs to monitor and track the evolution of quality determinants over time. The case studies presented demonstrate the practical application and benefits of digital VoC analysis in identifying emerging trends and driving the designing or redesigning of products and services. As businesses continue to embrace digital transformation, integrating digital VoC analysis into their quality management processes will become increasingly important for staying competitive in the market.
Reference
Barravecchia F.; Mastrogiacomo L.; Franceschini F. (2023). Product quality tracking based on digital Voice-of-Customers. Total Quality Management and Business Excellence, 34(11-12), 1386-1409, DOI: 10.1080/14783363.2023.2177147.