Mobile Customer Satisfaction Scoring Research Based on Quadratic Dimension Reduction and Machine Learning Integration
Abstract
:1. Introduction
2. Literature Review
2.1. Systematic Clustering
2.2. Exploratory Factor Analysis (EFA)
2.3. GBDT Algorithm
3. Establishment of Core Indicator System
3.1. Initial Indicator System
3.1.1. Data Cleaning
- 1.
- Quantitative processing of data
- ①
- Whether encountered network problems (yes → 1, not → −1)
- ②
- 4/5G User (2G → 2, 4G → 4, 5G → 5)
- ③
- Phonetic method (VONR → 6, EPSFB → 5(5G))
- ④
- Whether to care for the user (not → −1, yes → 1)
- ⑤
- Whether or not the user is a real – name registered user (not → −1, yes → 1)
- ⑥
- Client star rating logo (unrated → −1, semi – starred → 0)
- 2.
- Empty value processing
- 3.
- Outlier handling
3.1.2. Systematic Clustering
3.2. Core Indicator System
- 1.
- Standardizing the data. In this paper, the data are standardized using the Z-Score method, which uses the standard deviation as a ruler to measure the distance that a particular raw score deviates from the mean, which contains a few standard deviations and Z-Scores. Thus, the position of these data in the whole data is determined. Z is determined as
- 2.
- The KMO test and Bartlett’s test are performed, and the standardized data are brought through the SPSSPRO platform to determine whether factor analysis could be performed. The results of the tests are represented in Table 4.
- 3.
- Determining the number of principal factors.
- 4.
- The elbow rule corrects the model
- 5.
- Naming factor loading coefficients
4. GBDT Algorithm Predicts User Satisfaction
4.1. Data Re-Cleaning
4.2. GBDT Algorithm Prediction
5. Conclusions and Future Work
- (1)
- In this paper, we obtain eight core influence factors through the double dimensionality reduction combining systematic clustering and exploratory factor analysis, which is more reasonable and can obtain the most core influence factors compared with Zi Ye [22], who selects the core influence factors of mobile users of Wuhan communication through correlation. Core factor 1 has the highest weight of 24.952%, i.e., the status quo of indoor voice problems has the greatest influence. Factor 1 has seven core influencing factors. Therefore, if mobile operators want to improve user satisfaction, they need to improve these seven core impact factors, including whether they have encountered network problems, residential area, underground, no signal of mobile phone, sudden interruption during the call, inaudible intermittent and intermittent call with noise, and one party cannot be heard during the call.
- (2)
- General machine learning prediction algorithms have an accuracy of about 70% [23,40,41], and the prediction accuracy of this study can reach 99.96%, which is a very high accuracy when predicting. Highly accurate satisfaction prediction can help operators more accurately adjust their operational strategies, so as to improve their market competitiveness. In addition, improved user satisfaction can help promote the development of the communication industry and promote national informatization and economic growth.
- (3)
- Although this study is based on data from Chinese operators, it can be generalized to a certain extent to foreign operators or other related satisfaction rating studies. However, it should be noted that factors such as culture, social background, the level of economic development, laws and regulations in different countries and regions will have an impact on the user satisfaction evaluation system. Therefore, it is necessary to make corresponding adjustments when applying the research results to other countries or regions. If one would like to apply them for Amazon’s mobile marketplace in the UK, the following areas can be explored further:
- ①
- Cultural factors are crucial to user satisfaction. The UK and China have different cultural backgrounds that influence values, socialization and communication habits. Amazon, as a multinational company, needs to adapt to the cultural expectations of UK users. UK users may value privacy more and have different attitudes toward data use and sharing. Therefore, it is important to understand the cultural characteristics of UK users to accurately reflect their needs and expectations when evaluating user satisfaction.
- ②
- Economic factors are also important. The economic level, spending power and shopping habits in the UK are different from those in China, which will affect user demand for mobile services and satisfaction levels. Amazon’s pricing strategy and package selection in the UK market must take into account the purchasing power and preferences of UK users. Therefore, economic factors need to be thoroughly analyzed in the rating system to more accurately reflect user evaluation and satisfaction.
- ③
- Regulatory differences also need to be taken into account. The UK and China have different privacy and user rights regulations. Amazon’s mobile services in the UK market must comply with local laws to ensure data processing and user privacy. This has implications for service design, data collection and user interface. Considering cultural, economic and regulatory factors together will help to better understand the scope and limitations of the findings. This in-depth research will provide guidance to multinational organizations worldwide to ensure high user satisfaction with services and products in diverse environments.
- (4)
- At the same time, thousands of data collected in the real communication environment contain hundreds of millions of users, corresponding to each user’s quality of experience influencing factors, and behavioral characteristics also present ultra-high-dimensional characteristics. In order to effectively cope with the task of analyzing hundreds of millions of data, distributed and parallel processing algorithms, as well as corresponding processing software frameworks, can be adopted to reduce the time complexity of data mining algorithms and improve the efficiency of the algorithms.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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User ID | Overall Satisfaction with Voice Calls | Whether Encountered Network Problems | Residential District | Offices | Colleges | Commercial Streets, Subways | Subways | Rural Areas | High-Speed Railways |
---|---|---|---|---|---|---|---|---|---|
1 | 10 | 1 | −1 | 2 | −1 | −1 | −1 | −1 | −1 |
2 | 2 | 1 | 1 | 2 | −1 | 4 | −1 | −1 | −1 |
3 | 10 | 1 | −1 | −1 | −1 | −1 | −1 | 6 | −1 |
4 | 6 | 1 | 1 | 2 | −1 | −1 | −1 | −1 | −1 |
5 | 5 | 1 | −1 | 2 | −1 | −1 | 5 | −1 | 7 |
6 | 7 | 1 | 1 | −1 | −1 | −1 | 5 | 6 | −1 |
7 | 8 | 1 | −1 | −1 | −1 | 4 | −1 | 6 | 7 |
8 | 10 | −1 | −1 | −1 | −1 | −1 | −1 | −1 | −1 |
9 | 10 | −1 | −1 | −1 | −1 | −1 | −1 | −1 | −1 |
Category | Indicators |
---|---|
1 | Residential district; offices; colleges and universities; commercial streets; metro; rural areas; high-speed rail; mobile phone no signal; have a signal can not be dialed; call suddenly interrupted during the call; call in the murmur, inaudible, intermittent; crosstalk; call during the call of one party can not hear; mos poor quality number of times; failed to connect to the number of dropped calls; 4/5G subscribers; voice mode; whether the 4G network customers (local) Excluding IoT); voice call-length (minutes); ARPU in the current month; MOU in the current month; MOU in the previous 3 months; whether or not a 5G network customer; customer star identification; amount of arrears in the current month; amount of arrears in the previous 3 months |
2 | Whether encountered network problems |
3 | Number of off-grid trips |
4 | Complaints about tariffs, caring for users or not, complaints about home broadband |
5 | ARPU in the first three months, ARPU (home broadband), whether or not you have been to a business office, extra traffic (MB), extra traffic fee (yuan), percentage of voice from abroad, inter-provincial roaming-hours (minutes), percentage of traffic from abroad, total GPRS-traffic (KB), GPRS-domestic roaming-traffic (KB), whether or not you are a real-name-registered subscriber |
Level 1 Indicators | Level 2 Indicators | Level 3 Indicators |
---|---|---|
User Issues | User scenarios | Residential district |
Office | ||
High School | ||
Commercial Street | ||
Subway | ||
Rural | ||
High Speed Rail | ||
User Calls Network problems during user calls | No mobile phone signal | |
Can’t get through with signal | ||
Sudden interruption during the call | ||
Noise, inaudible, intermittent calls | ||
Crosstalk | ||
One party cannot be heard during the call | ||
Networks, costs and remaining issues | Number of problems | Whether encountered network problems |
Off-network | ||
Poor mos quality | ||
Number of missed calls | ||
Remaining issues | Voice mode | |
Client Star Rating | ||
4/5G users | ||
Whether 4G network customer (local excluding IoT) | ||
Whether 5G network customer | ||
Voice Call—Duration (minutes) | ||
Cost issues | Amount in arrears for the current month | |
Amount in arrears for the previous 3 months | ||
ARPU for the current month | ||
Current Month MOU | ||
Previous 3 Months MOU |
KMO Value | 0.807 | |
Bartlett sphericity test | approximate chi-square | 285933.639 |
df | 465 | |
P | 0.0002 |
Factor | Explanatory Rate of Variance before Rotation | Factor | Post-Rotation Variance Explained | ||||
---|---|---|---|---|---|---|---|
Characteristic Root | Explanation of Variance (%) | Cumulative Variance Explained (%) | Characteristic Root | Explanation of Variance (%) | Cumulative Variance Explained (%) | ||
1 | 6.457 | 20.828 | 30.828 | 1 | 485.446 | 15.66 | 25.66 |
2 | 3.586 | 11.567 | 42.395 | 2 | 293.767 | 9.476 | 35.136 |
3 | 2.604 | 8.4 | 50.796 | 3 | 286.541 | 9.243 | 44.379 |
4 | 1.74 | 5.613 | 56.409 | 4 | 264.02 | 8.517 | 52.896 |
5 | 1.691 | 5.454 | 61.863 | 5 | 168 | 5.419 | 58.315 |
6 | 1.185 | 3.823 | 65.686 | 6 | 162.372 | 5.238 | 63.553 |
7 | 1.113 | 3.591 | 69.277 | 7 | 157.675 | 5.086 | 68.639 |
8 | 1.08 | 3.484 | 75.761 | 8 | 127.704 | 4.119 | 74.759 |
9 | 0.976 | 3.147 | 78.908 | ||||
10 | 0.959 | 3.094 | 80.002 |
Factor | Nomenclature | Factor | Nomenclature |
---|---|---|---|
Factor 1 | State of the indoor voice problem | Factor 5 | Status of the voice signal stability problem |
Factor 2 | State of the art of voice problems in traffic | Factor 6 | Status of voice route independence issues |
Factor 3 | Speech stability issues | Factor 7 | Current status of voice service level issues |
Factor 4 | Densely populated areas | Factor 8 | Current status of voice mode issues |
Core Impact Factor | Factor 1 Components (%) |
---|---|
Whether encountered network problems | −0.136 |
Residential district | 0.111 |
Subway | 0.102 |
No mobile phone signal | 0.095 |
Sudden interruption during a call | 0.101 |
I can’t hear any noise during the call | 0.105 |
One party cannot be heard during the call | 0.106 |
Parameter Name | Parameter Value |
---|---|
Data Slicing | 1 |
Data Shuffling | not |
Cross Validation | not |
Loss Function | friedman_mse |
Node splitting evaluation criteria | friedman_mse |
Number of base learners | 600 |
Learning rate | 0.3 |
Proportion of no-playback sampling | 0.8 |
Maximum proportion of features considered for splitting | None |
Minimum number of samples for internal node splitting | 2 |
Minimum number of samples in leaf nodes | 1 |
Minimum weight of samples in leaf nodes | 0 |
Maximum depth of the tree | 10 |
Maximum number of leaf nodes | 50 |
Threshold for impurity of node division | 0 |
MSE | RMSE | MAE | MAPE | R² | |
---|---|---|---|---|---|
reasonable dataset | 0.014 | 0.118 | 0.082 | 1.253 | 0.997 |
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Share and Cite
Zeng, F.; He, Y.; Yang, C.; Hu, X.; Yuan, Y. Mobile Customer Satisfaction Scoring Research Based on Quadratic Dimension Reduction and Machine Learning Integration. Appl. Sci. 2023, 13, 9681. https://doi.org/10.3390/app13179681
Zeng F, He Y, Yang C, Hu X, Yuan Y. Mobile Customer Satisfaction Scoring Research Based on Quadratic Dimension Reduction and Machine Learning Integration. Applied Sciences. 2023; 13(17):9681. https://doi.org/10.3390/app13179681
Chicago/Turabian StyleZeng, Fei, Yuqing He, Chengqin Yang, Xinkai Hu, and Yining Yuan. 2023. "Mobile Customer Satisfaction Scoring Research Based on Quadratic Dimension Reduction and Machine Learning Integration" Applied Sciences 13, no. 17: 9681. https://doi.org/10.3390/app13179681