# A Machine Learning Based Classification Method for Customer Experience Survey Analysis

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Related Work and Paper Contribution

#### 2.1. Works on Net Promoter Score-NPS

#### 2.2. Limitations of NPS

#### 2.3. Our Contribution

## 3. Net Promoter Score Survey

- NPS question [5]: “How likely are you to recommend [company x] to your friends or colleagues?” The response is provided in the range of 0 (definitely no) to 10 (definitely yes).
- Satisfaction scores (from 0 or 1 to 10) for a set of CX attributes like product experience (service quality, network coverage, tariff plan, billing, etc.), Touchpoint experience (e.g., call center, website, mobile app, shops), customer lifecycle milestones (e.g., contract renewal), etc. Some surveys also include brand image related attributes (e.g., trust, innovation)

#### NPS Survey Analysis

## 4. NPS Bias Classification

#### 4.1. Datasets Available for NPS Bias Analysis

#### 4.2. Distribution Analysis of the NPS Bias Catergories

#### 4.3. Regression Analysis of the NPS Bias Classes

#### 4.4. NPS Drivers’ Analysis Based on NPS Bias

## 5. Machine Learning Algorithms for CX Classification

#### 5.1. Problem Formulation

**x**, where $\mathit{x}\in X\subseteq {\mathbb{R}}^{K}$ and $\mathit{y}\in Y=\{{C}_{1},{C}_{2},\cdots ,{C}_{Q}\}$, i.e.,

**x**is in K-dimensional input space and y a label of Q different values. Q labels form categories or groups of patterns and the objective is to find a classification rule or function $y=r\left(\mathit{x}\right):X\to Y$ to predict the categories of the NPS index, given a training set of N points, $D=({\mathit{x}}_{i},{y}_{i}),i=1,\cdots ,N$

#### 5.2. Machine Learning Algorithms

#### 5.2.1. Decision Trees

#### 5.2.2. k-Nearest Neighbors

#### 5.2.3. Support Vector Machines

#### 5.2.4. Random Forest (RF)

#### 5.2.5. Artificial Neural Networks (ANNs)

#### 5.2.6. Convolutional Neural Networks (CNNs)

#### 5.2.7. Naïve Bayes

#### 5.2.8. Logistic Regression

#### 5.3. Applied Dataset

#### 5.4. Experimental Results

## 6. Personal Data Protection Rules and Ethical Issues

- Personal data of participants are strictly held confidential at any time of the research;
- No personal data are centrally stored. In addition, data are scrambled where possible and abstracted and/or anonymized in a way that does not affect the final project outcome;
- No collected data are utilized outside the scope of this research or for any other secondary use.

## 7. Discussion

- (a)
- A set of scenarios was tested by changing the mix of random and real data in the training data set. The results indicated that the contribution of randomly generated data led to similar results (in terms of the metrics presented in Figure 7 and Figure 8). Although the incorporation of the randomly generated data eliminated the potential overfitting effects, it did not increase the achieved performance of the models tested. To this end, the next research step in this direction will be to further enhance the random data generator through the application of Generative Adversarial Networks (GANs) [39].
- (b)
- The comparative analysis of all the examined models indicated that despite the differences observed in the performance metrics, at this stage we cannot identify a single model as the one with dominant performance for NPS classification analysis. It appears that linear and logistic regression exhibit similar performance with other ML algorithms.
- (c)
- The introduction of the NPS bias label delivers substantial improvement in the performance metrics of all the tested algorithms. The proposed method provides fertile ground for the better understanding of the NPS key drivers, which in turn will allow to apply targeted actions based on separate analysis of positively and negatively biased customers, as described in Section 3. The next research step in this case will be to verify whether the statistical results of this paper are associated with causality. This can be achieved through the comparison of the key drivers’ analysis results with the free comments that the surveyed customers are asked to provide (sentiment analysis).

## 8. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Appendix A. Performance Metrics

#### Appendix A.1. Confusion Matrix

Actual Class | ||||
---|---|---|---|---|

Detractors | Passives | Promoters | ||

Predicted-Class | Detractors | ${C}_{1,1}$ | ${C}_{1,2}$ | ${C}_{1,3}$ |

Passives | ${C}_{2,1}$ | ${C}_{2,2}$ | ${C}_{2,3}$ | |

Promoters | ${C}_{3,1}$ | ${C}_{3,2}$ | ${C}_{3,3}$ |

#### Appendix A.1.1. Accuracy

#### Appendix A.1.2. Precision and Recall

#### Appendix A.1.3. F1-Score

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**Figure 7.**Comparison of the classification metrics for Dataset 1 results (input: CX attributes only).

**Figure 8.**Comparison of the classification metrics for Dataset 2 results (input: CX metrics plus NPS Bias).

Response Values | NPS Label |
---|---|

9–10 | Promoter |

7–8 | Passive |

0–6 | Detractor |

NPS Bias | Bias Category |
---|---|

$\mathrm{NPS}\_\mathrm{BIAS}\ge 0$ | Positively Biased |

$\mathrm{NPS}\_\mathrm{BIAS}<0$ | Negatively Biased |

Mix | Detractors | Passives | Promoters | Total |
---|---|---|---|---|

Negatives | 42 | 84 | 2 | 128 |

Positives | 3 | 155 | 165 | 323 |

Total | 45 | 239 | 167 | 451 |

Chi-Square Test (a = 0.05) | ||||
---|---|---|---|---|

Chi-Square | df | p-Value | Significance | Cramer V |

197.52 | 4 | $1.28\times {10}^{-41}$ | Yes | 0.467 |

Parameter | Values |
---|---|

Function measuring quality of split | entropy |

Maximum depth of tree | 3 |

Weights associated with classes | 1 |

Parameter | Values |
---|---|

Number of neighbors | 5 |

Distance metric | Minkowski |

Weights function | uniform |

Parameter | Values |
---|---|

Kernel type | linear |

Degree of polynomial kernel function | 3 |

Weights associated with classes | 1 |

Parameter | Values |
---|---|

Number of trees | 100 |

Measurement of the quality of split | Gini index |

Parameter | Values |
---|---|

Number of hidden neurons | 6 |

Activation function applied for the input and hidden layer | RelU |

Activation function applied for the output layer | Softmax |

Optimizer network function | Adam |

Calculated loss | sparce categorical cross-entropy |

Epochs used | 100 |

Batch size | 10 |

Parameter | Values |
---|---|

Model | Sequential (array of Keras Layers) |

kernel size | 3 |

pool size | 4 |

Activation function applied | RelU |

Calculated loss | categorical cross entropy |

Epochs used | 100 |

Batch size | 128 |

Parameter | Values |
---|---|

Maximum number of iterations | 300 |

algorithm used in optimization | L-BFGS |

weights associated with classes | 1 |

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**MDPI and ACS Style**

Markoulidakis, I.; Rallis, I.; Georgoulas, I.; Kopsiaftis, G.; Doulamis, A.; Doulamis, N. A Machine Learning Based Classification Method for Customer Experience Survey Analysis. *Technologies* **2020**, *8*, 76.
https://doi.org/10.3390/technologies8040076

**AMA Style**

Markoulidakis I, Rallis I, Georgoulas I, Kopsiaftis G, Doulamis A, Doulamis N. A Machine Learning Based Classification Method for Customer Experience Survey Analysis. *Technologies*. 2020; 8(4):76.
https://doi.org/10.3390/technologies8040076

**Chicago/Turabian Style**

Markoulidakis, Ioannis, Ioannis Rallis, Ioannis Georgoulas, George Kopsiaftis, Anastasios Doulamis, and Nikolaos Doulamis. 2020. "A Machine Learning Based Classification Method for Customer Experience Survey Analysis" *Technologies* 8, no. 4: 76.
https://doi.org/10.3390/technologies8040076