How to calculate a CS parameter

What is customer service in the context of logistics?

In the field of logistics, customer service focuses on ensuring that customers receive their products effectively and efficiently. This includes ensuring accurate delivery times, communicating any potential delays, and providing quick solutions to any issues that may arise during the shipping process. The goal is to optimize every customer touchpoint to achieve a satisfactory experience that encourages loyalty and repeat purchases.

How Python can help measure and improve customer service

Python is a powerful tool for analyzing data and optimizing processes in customer service. Here are some ways it can be useful:

1. Data Analysis: Use libraries like Pandas and NumPy to analyze large volumes of customer data, identify patterns, and predict trends.

By implementing these solutions, companies can offer more agile, reliable, and personalized service, thus achieving better customer relationships.

Definition of Customer Service Customer service refers to the set of activities, processes, and strategies that a company or business implements to meet the needs and expectations of its customers. It involves providing assistance, support, and a positive experience to customers before, during, and after a sale or interaction.

SC Objectives

1

Understand and effectively address customer needs

2

3

4

5

How is customer satisfaction measured in a company?#

Methods for measuring customer satisfaction:

1. Satisfaction surveys: These are direct tools that allow companies to obtain feedback on the customer experience. They can include questions about product quality, customer service, and the likelihood of recommending the company to others.

2. Social media comment analysis: Evaluating customer reviews and comments on social platforms can provide a clear view of their satisfaction. This method helps identify trends and areas for improvement.

3. Complaints and claims index: Tracking the number and nature of complaints can help companies measure customer satisfaction. An increase in complaints may indicate service or product issues.

4. Focus groups: Gathering a group of customers to discuss their experiences and opinions about a product or service can offer valuable insight into their satisfaction and expectations.

5. Customer retention and loyalty rate: Measuring how many customers return for repeat purchases can be a direct indicator of their satisfaction. A high level of retention suggests that customers are happy with the company's offerings.

ive consultores

Mathematical Tools to Measure Customer Satisfaction

To analyze and measure each item in a customer satisfaction survey, companies can use mathematical techniques that provide valuable insights into the customer experience. One of the most commonly used techniques is multiple linear regression, which helps understand the relationship between variables and measure their impact on satisfaction.

Satisfaction Survey

Satisfaction surveys are a fundamental tool for collecting direct data from customers. These surveys can include questions about various aspects of the service or product, allowing companies to capture customers' attitudes and perceptions about their overall experience.

How to Formulate and Evaluate a Customer Satisfaction Survey

To formulate an effective customer satisfaction survey,

Define Clear Objectives: Establish which aspects of customer satisfaction you want to measure (e.g., service quality, customer service, etc.).

Evaluating a survey involves reviewing the quality of the questions and the representativeness of the responses, ensuring that the conclusions obtained are accurate and useful for decision-making.

Statistical Tools for Validating Survey Questions

When creating surveys, it is essential to ensure that the questions are valid and reliable. Here are some statistical tools that can be used for this validation:

Reliability Analysis (Cronbach's Alpha): Measures the internal consistency of the survey questions, ensuring that all questions intended to measure the same construct are correlated.

Using these tools ensures that a survey collects meaningful and representative data, consequently improving the effectiveness of customer satisfaction analysis.

To explain Cronbach's alpha mathematically, let's consider a set of questions designed to measure a single construct. This metric assesses internal consistency and is calculated with the following formula:

$[ \alpha = \frac{N \cdot \bar{c}}{\bar{v} + (N - 1) \cdot \bar{c}} ]$

Where:

• $N$ is the number of items (questions) in the scale.

• $\bar{c}$ is the mean of the covariances between the items.

• $\bar{v}$ is the mean of the variances of the individual items.

The alpha value ranges from 0 to 1, with a higher value indicating greater internal consistency.

Multiple Linear Regression

Multiple linear regression is a statistical analysis technique used to model the relationship between a dependent variable (e.g., customer satisfaction) and multiple independent variables (e.g., service quality, scheduling convenience, etc.). Details of using regression include:

1. Variable Selection: First, all variables that may affect customer satisfaction are identified.

2. Modeling: A model is built that quantifies the relationship between the variables.

3. Interpretation of Results: The model coefficients allow for the interpretation of the individual impact of each independent variable on customer satisfaction.

4. Prediction: Based on the constructed model, the company can make accurate predictions and make informed decisions to improve its offering.

The combination of well-designed surveys and multiple linear regression analysis provides a solid foundation for understanding and improving customer satisfaction in any company.

The formula for multiple linear regression is:

$$[ Y = \beta_0 + \beta_1X_1 + \beta_2X_2 + \cdots + \beta_nX_n + \varepsilon ]$$

Where:

• $Y$ is the dependent variable.

• $( \beta_0 )$ is the intercept of the model.

• $( \beta_1, \beta_2, \ldots, \beta_n )$ are the regression coefficients.

• $( X_1, X_2, \ldots, X_n )$ are the independent variables.

• $( \varepsilon )$ is the error term.

Let's look at an example.

import pandas as pd

Bd = pd.read_csv('/Users/CRC/Bd.csv') #puedes usar tu propia base de datos de encuensta de clientes; fuente --> Kaggle.com

Bd.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 25976 entries, 0 to 25975
Data columns (total 25 columns):
 #   Column                             Non-Null Count  Dtype  
---  ------                             --------------  -----  
 0   Unnamed: 0                         25976 non-null  int64  
 1   id                                 25976 non-null  int64  
 2   Gender                             25976 non-null  object 
 3   Customer Type                      25976 non-null  object 
 4   Age                                25976 non-null  int64  
 5   Type of Travel                     25976 non-null  object 
 6   Class                              25976 non-null  object 
 7   Flight Distance                    25976 non-null  int64  
 8   Inflight wifi service              25976 non-null  int64  
 9   Departure/Arrival time convenient  25976 non-null  int64  
 10  Ease of Online booking             25976 non-null  int64  
 11  Gate location                      25976 non-null  int64  
 12  Food and drink                     25976 non-null  int64  
 13  Online boarding                    25976 non-null  int64  
 14  Seat comfort                       25976 non-null  int64  
 15  Inflight entertainment             25976 non-null  int64  
 16  On-board service                   25976 non-null  int64  
 17  Leg room service                   25976 non-null  int64  
 18  Baggage handling                   25976 non-null  int64  
 19  Checkin service                    25976 non-null  int64  
 20  Inflight service                   25976 non-null  int64  
 21  Cleanliness                        25976 non-null  int64  
 22  Departure Delay in Minutes         25976 non-null  int64  
 23  Arrival Delay in Minutes           25893 non-null  float64
 24  satisfaction                       25976 non-null  object 
dtypes: float64(1), int64(19), object(5)
memory usage: 5.0+ MB

Bd.describe()

	Unnamed: 0	id	Age	Flight Distance	Inflight wifi service	Departure/Arrival time convenient	Ease of Online booking	Gate location	Food and drink	Online boarding	Seat comfort	Inflight entertainment	On-board service	Leg room service	Baggage handling	Checkin service	Inflight service	Cleanliness	Departure Delay in Minutes	Arrival Delay in Minutes
count	25976.000000	25976.000000	25976.000000	25976.000000	25976.000000	25976.000000	25976.000000	25976.000000	25976.000000	25976.000000	25976.000000	25976.000000	25976.000000	25976.000000	25976.000000	25976.000000	25976.000000	25976.000000	25976.00000	25893.000000
mean	12987.500000	65005.657992	39.620958	1193.788459	2.724746	3.046812	2.756775	2.977094	3.215353	3.261665	3.449222	3.357753	3.385664	3.350169	3.633238	3.314175	3.649253	3.286226	14.30609	14.740857
std	7498.769632	37611.526647	15.135685	998.683999	1.335384	1.533371	1.412951	1.282133	1.331506	1.355536	1.320090	1.338299	1.282088	1.318862	1.176525	1.269332	1.180681	1.319330	37.42316	37.517539
min	0.000000	17.000000	7.000000	31.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	1.000000	1.000000	0.000000	0.000000	0.00000	0.000000
25%	6493.750000	32170.500000	27.000000	414.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	3.000000	3.000000	3.000000	2.000000	0.00000	0.000000
50%	12987.500000	65319.500000	40.000000	849.000000	3.000000	3.000000	3.000000	3.000000	3.000000	4.000000	4.000000	4.000000	4.000000	4.000000	4.000000	3.000000	4.000000	3.000000	0.00000	0.000000
75%	19481.250000	97584.250000	51.000000	1744.000000	4.000000	4.000000	4.000000	4.000000	4.000000	4.000000	5.000000	4.000000	4.000000	4.000000	5.000000	4.000000	5.000000	4.000000	12.00000	13.000000
max	25975.000000	129877.000000	85.000000	4983.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	1128.00000	1115.000000

print(Bd.iloc[:, 24])

0                      satisfied
1                      satisfied
2        neutral or dissatisfied
3                      satisfied
4                      satisfied
                  ...           
25971    neutral or dissatisfied
25972                  satisfied
25973    neutral or dissatisfied
25974                  satisfied
25975    neutral or dissatisfied
Name: satisfaction, Length: 25976, dtype: object

Bd['satisfaction_number'] = Bd.iloc[:, 24].replace({'satisfied': 1, 'neutral or dissatisfied': 0}).astype('int64')
Bd.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 25976 entries, 0 to 25975
Data columns (total 26 columns):
 #   Column                             Non-Null Count  Dtype  
---  ------                             --------------  -----  
 0   Unnamed: 0                         25976 non-null  int64  
 1   id                                 25976 non-null  int64  
 2   Gender                             25976 non-null  object 
 3   Customer Type                      25976 non-null  object 
 4   Age                                25976 non-null  int64  
 5   Type of Travel                     25976 non-null  object 
 6   Class                              25976 non-null  object 
 7   Flight Distance                    25976 non-null  int64  
 8   Inflight wifi service              25976 non-null  int64  
 9   Departure/Arrival time convenient  25976 non-null  int64  
 10  Ease of Online booking             25976 non-null  int64  
 11  Gate location                      25976 non-null  int64  
 12  Food and drink                     25976 non-null  int64  
 13  Online boarding                    25976 non-null  int64  
 14  Seat comfort                       25976 non-null  int64  
 15  Inflight entertainment             25976 non-null  int64  
 16  On-board service                   25976 non-null  int64  
 17  Leg room service                   25976 non-null  int64  
 18  Baggage handling                   25976 non-null  int64  
 19  Checkin service                    25976 non-null  int64  
 20  Inflight service                   25976 non-null  int64  
 21  Cleanliness                        25976 non-null  int64  
 22  Departure Delay in Minutes         25976 non-null  int64  
 23  Arrival Delay in Minutes           25893 non-null  float64
 24  satisfaction                       25976 non-null  object 
 25  satisfaction_number                25976 non-null  int64  
dtypes: float64(1), int64(20), object(5)
memory usage: 5.2+ MB

new_Bd = Bd.select_dtypes(include=['int64'])
new_Bd.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 25976 entries, 0 to 25975
Data columns (total 20 columns):
 #   Column                             Non-Null Count  Dtype
---  ------                             --------------  -----
 0   Unnamed: 0                         25976 non-null  int64
 1   id                                 25976 non-null  int64
 2   Age                                25976 non-null  int64
 3   Flight Distance                    25976 non-null  int64
 4   Inflight wifi service              25976 non-null  int64
 5   Departure/Arrival time convenient  25976 non-null  int64
 6   Ease of Online booking             25976 non-null  int64
 7   Gate location                      25976 non-null  int64
 8   Food and drink                     25976 non-null  int64
 9   Online boarding                    25976 non-null  int64
 10  Seat comfort                       25976 non-null  int64
 11  Inflight entertainment             25976 non-null  int64
 12  On-board service                   25976 non-null  int64
 13  Leg room service                   25976 non-null  int64
 14  Baggage handling                   25976 non-null  int64
 15  Checkin service                    25976 non-null  int64
 16  Inflight service                   25976 non-null  int64
 17  Cleanliness                        25976 non-null  int64
 18  Departure Delay in Minutes         25976 non-null  int64
 19  satisfaction_number                25976 non-null  int64
dtypes: int64(20)
memory usage: 4.0 MB

from sklearn.linear_model import LogisticRegression

X = new_Bd.drop('satisfaction_number', axis=1)  # Features
y = new_Bd['satisfaction_number']  # Target

model = LogisticRegression(max_iter=1000)
model.fit(X, y)

LogisticRegression(max_iter=1000)

coefficients = model.coef_
print(coefficients)

[[-5.18027052e-05 -1.16258989e-05 -2.80761473e-02  3.95433258e-04
   7.36834576e-02 -7.33795504e-02  2.20575820e-02 -5.02928419e-02
   3.44141436e-02  1.39971393e-01  7.60752048e-02  1.00156931e-01
   6.77428471e-02  6.52574668e-02  3.40855476e-02  4.14320968e-02
   3.04496795e-02  6.91197925e-02 -6.33084903e-03]]

intercept = model.intercept_
print(intercept)

[-0.01461123]

predictions = model.predict(X)
print(predictions)

[1 1 1 ... 0 1 0]