After Sales Service Case: Biao Wang Enhances Brand Influence Through After-Sales Innovation

After sales service is a critical component in the lifecycle of any product, and it plays a significant role in brand perception and customer loyalty. Biao Wang, a prestigious watch company, innovatively improved its after-sales service, which led to a notable increase in brand influence and customer satisfaction. This case study delves into Biao Wang's approach, leveraging a dynamic combination of academic understanding, mathematical modeling, and empirical evidence to validate the effectiveness of their innovative service strategy.

Academic Understanding and Foundation

Academic literature on after-sales service has long recognized its impact on brand value. A study from "Journal of Product Innovation Management" (2025) found that high-quality after-sales service could enhance customer perception and brand loyalty. Moreover, the Service Innovation Index (SII) developed by Song et al. (2023) provides a framework for evaluating service innovation, which Biao Wang adapted to its specific context.

Mathematical Modeling

To better understand the relationship between after-sales service and brand influence, Biao Wang employed a mathematical model that integrated customer satisfaction, service quality, and brand reputation metrics. The Service Quality-Brand Influence Model (SBI Model), as derived (2025), was based on the premise that service quality directly impacts customer satisfaction, which in turn affects brand influence. The model is articulated as follows:

[ \text{Brand Influence} = f(\text{Customer Satisfaction}) \times \text{Service Quality} ]

Where:

• Customer Satisfaction is a composite measure derived from consumer surveys and feedback.
• Service Quality is quantified based on delivery performance, customer communication, and service responsiveness.

The SBI Model was then used to predict the expected improvement in brand influence based on the implementation of innovative after-sales strategies by Biao Wang.

Algorithmic Process and Implementation

To translate the SBI Model into an actionable plan, Biao Wang utilized an algorithmic framework that included several key steps:

1. Data Collection: Customer feedback and service records were gathered from multiple sources, including customer service logs and online reviews.
2. Model Training: Historical data was used to train the predictive model, ensuring that the model could accurately forecast the impact of service improvements.
3. Service Improvement: Based on model outputs, specific after-sales service enhancements were implemented, such as extended warranty options and proactive maintenance reminders.
4. Monitoring and Evaluation: Ongoing monitoring ensured that the implemented services aligned with the predicted improvements. Key performance indicators (KPIs) were set to track the effectiveness of the service improvements.

A flowchart representing the process would look like this:

+------------------------+----------------+------------------------+------------------------+| Collect Data           | Train Model     | Implement Service       | Monitor & Evaluate      || +----------------+   | +----------------+   | +----------------+     | +----------------+   || | Customer      | --> | Historical      | --> | Service          | --> | Ongoing Monitoring and Evaluation || | Feedback &     |      Data       |      Improvement        |                      || | Service Records|                |                      |                      || +----------------+                +----------------+     +----------------++------------------------+                +----------------+     +----------------+|<-- Model Outputs used for improvement strategies|+------------------+| Track KPIs for effectiveness

Experimental Data Verification

To verify the model's effectiveness, Biao Wang conducted an empirical study over a two-year period, comparing the service and brand influence metrics before and after the implementation of innovative after-sales strategies. The results were striking:

• Customer Satisfaction: Increased by 15% (2025 data)
• Service Quality: Improved by 20% (2025 data)
• Brand Influence: Enhanced by 25% (2025 data)

These improvements aligned closely with the predictions made by the SBI Model, validating the model’s accuracy and the effectiveness of Biao Wang’s after-sales innovations.

In conclusion, Biao Wang’s decision to enhance its after-sales service through innovative strategies directly contributed to an increase in brand influence and customer satisfaction. This case study demonstrates the value of leveraging academic understanding, mathematical modeling, and a systematic implementation approach in improving service quality and brand perception.