# Fuzzy Logic and Decision Making Applied to Customer Service Optimization

^{*}

## Abstract

**:**

## 1. Introduction

- The availability of a methodology based on fuzzy logic and multi-criteria decision-making that allows real-time prioritization of tickets according to variables such as customer value, impact, urgency, and the emotional nature of each interaction.
- Contextualize the model in different usage scenarios, considering additional variables such as waiting time and contact center workload. This allows a reordering process to be carried out according to these variables, and thus comply with the established SLAs.
- Develop a model that allows the weighting of variables in real time, and therefore, a dynamic adaptation to the particularities of the business.
- Extend a working methodology based on multi-criteria decision-making in the Customer Service area.

## 2. Literature Review

## 3. Methodology

#### 3.1. 2-Tuple Model (LD2T)

**Definition 1.**

**Definition 2.**

**Definition 3.**

**2-tuple linguistic comparison operators**. Given two 2-tuple language values $\left({s}_{n},{\alpha}_{1}\right)$ and $\left({s}_{m},{\alpha}_{2}\right)$ representing amounts of information:

- If $n<m$, then $\left({s}_{n},{\alpha}_{1}\right)$ is less than $\left({s}_{m},{\alpha}_{2}\right)$.
- If $n=m$, then
- (a)
- If ${\alpha}_{1}={\alpha}_{2}$, then $\left({s}_{n},{\alpha}_{1}\right)$ and $\left({s}_{m},{\alpha}_{2}\right)$ represent the same information.
- (b)
- If ${\alpha}_{1}<{\alpha}_{2}$, then $\left({s}_{n},{\alpha}_{1}\right)$ is less than $\left({s}_{m},{\alpha}_{2}\right)$.
- (c)
- If ${\alpha}_{1}>{\alpha}_{2}$, then $\left({s}_{n},{\alpha}_{1}\right)$ is greater than $\left({s}_{m},{\alpha}_{2}\right)$.

**Negation operator of a 2-tuple linguistic value.**It is defined as:

**Aggregation operators for 2-tuple linguistic values**. The aggregation operation used in our model are depicted below:

**Definition 4.**

#### 3.2. AHP Method

- The number of criteria. If the number of criteria is greater than one, we are faced with a multi-criteria decision making (MCDM) problem. The MCDM problems are much more complicated to solve than problems involving a single criterion, because they require a step of information unification, and in many cases this information is heterogeneous.
- The decision environment. If we know exactly all the factors involved in the decision problem, we are talking about an environment of certainty. On the other hand, if the information available to us is imprecise or not very specific, we are talking about a decision problem with uncertainty. Moreover, if any of the factors responds to chance, the environment is one of risk.
- The number of experts. In the case of several experts participating in the decision making, the problem becomes more complicated; we must be able to aggregate the information from all the experts to solve the problem. However, different points of view provide the problem with a more satisfactory solution—it is known as group decision making (TDG).

#### 3.2.1. Structuring the Decision Model in a Hierarchical Process

#### 3.2.2. Setting Criteria and Weighting

#### 3.2.3. Evaluate Each Alternative against the Criteria

#### 3.2.4. Making a Decision

#### 3.2.5. Sensitivity Analysis

#### 3.3. Treatment of Heterogeneous Information

#### 3.3.1. Numerical Domain

**Definition 5.**

#### 3.3.2. Interval Domain

**Definition 6.**

#### 3.3.3. Linguistic Domain

**Definition 7.**

## 4. VIUE, Proposed Model

- CRM data collection.
- Determine the CBTL expression domain for each criterion.
- We apply the 2-tuple model on the data obtained in the previous step.
- We obtain the global valuation of each interaction by applying the AHP model.
- If necessary, we establish a reordering of priorities according to the SLA and workload values of the Contact Center, thus applying an adjustment to the model.

#### 4.1. Data Collection

- $RFI{D}_{i}$: represents the customer’s value from the perspective of the Contact Center. For the case at hand, it is defined on a linguistic scale in a 2-tuple domain.
- $ticket\_i{d}_{i}$: corresponds to the code that uniquely identifies each ticket, i.e., an incident opened by the customer ${u}_{i}$, with $i\in 1,\dots ,\#T$.
- $ticket\_dat{e}_{i}$: corresponds to the date when the service was originally required.
- $trouble\_i{d}_{i}$: identifies the type of request, complaint, or problem the customer is having.
- $ticket\_impac{t}_{i}$: ticket relevance is a standard feature of most CRMs. This variable is responsible for measuring the effects of the ticket on business processes. It is generally expressed on an ordinal and/or linguistic scale of n values, so that the higher the value, greater relevance of the ticket. In this article, and considering the use case, the scale used will consist of five values {very low, low, moderate, high, very high}. As this is a linguistic scale, we will consider its modeling with the set of $S$.
- $ticket\_urgenc{y}_{i}$: most CRMs include ticket urgency as a standard feature. It is a measure of how much damage the issue can do to the business. It is usually expressed in the same way as the impact on an ordinal and/or linguistic scale of n values. In this report, and considering the use case worked on, we will consider the scale to have five values {very low, low, moderate, high, very high}. As this is a linguistic scale, we will consider its modeling with the set of $S$.
- $ticket\_emotio{n}_{i}$: corresponds to the emotional value of the interaction, it is a measure of the “degree of anger” of the customer in his interaction with the brand. For which we will perform a sentiment analysis that will allow us to classify the interaction and the emotional nature of the interaction [22]. The sentiment analysis will be carried out, considering the use case worked on, to a fuzzy model with three values {low, moderate, high}.

#### 4.2. CBTL Domain, Scores Computation

#### 4.3. VIUE, Overall Score

#### 4.4. Contextual VIUE, Reordering

- Alternative 1. The interaction will be attended by a Bot in the corresponding channel.
- Alternative 2. The interaction will be attended by generalist personnel.
- Alternative 3. The interaction will be attended by specialized personnel.

- Definition of the CBTL domain, in this case, the criteria are defined as follows: priority $P$, obtained from the overall score VIUE model, described in the previous phase, which is in a fuzzy domain $F\left(\overline{S}\right)$ = {very low, low, medium, high, very high}; waiting time ($T$) and workload ($C$) are in a numerical domain ${T}_{N\overline{S}}:\left[0,1\right]\text{}\to F\left(\overline{S}\right)$, Equation (7).
- Unification of heterogeneous information to the defined CBTL domain ($S5$).
- Evaluate the weights of each criterion involved in the decision-making process $W={w}_{P},{w}_{T},{w}_{C}$.
- Prioritization and recommendations for customization of interactions according to the weights of each criterion and the overall rating of each interaction obtained as a function of the criteria priority, waiting time and workload.

## 5. VIUE Model, Practical Application

#### 5.1. Data Collection

#### 5.2. CBTL Domain, Scores Computation

#### 5.3. VIUE, Overall Score

#### 5.4. Contextual VIUE, Reordering

- First, Table 8 shows how the tickets are ordered by priority in the management of the incident by applying VIUE, and then we proceed to reorder them according to the criteria expressed in the previous paragraph.
- Second, the contextual VIUE score is obtained, Table 9, based on the initial priority (VIUE), waiting time and workload of the Contact Center.
- Third, the ratings of the contextual 2-tuple VIUE set are obtained, Table 10.
- Finally, we would obtain the overall assessment ordered, Table 11, and therefore the final priority and the recommendations for customization by applying AHP.

## 6. Discussion

## 7. Conclusions

- Reduce Contact Center TMO.
- Increasing customer perception, NPS.
- Automate repetitive agent actions through robotic process automation (RPA), use bots oriented to support the agent in their search and analysis efforts with the goal of better connecting emotionally with customers.
- Integrate collaborative workspaces, eliminating information silos, where experts can cooperatively solve problems.
- Apply AI (predictive) models to analyze and direct the customer to fast, real-time solutions.
- Reduce learning time for contact center agents by providing them with tools that enable them to obtain real-time information from the systems.
- Reduction of the abandonment rate, by reducing the TMO we relieve the agents of their workload.
- Increase in first call incident resolution (FCRR).

- Queue management.
- Routing.
- Service level.
- Training.
- Personalization.

## 8. Future Works

- The measurement of customer value can be considered an aggregate of several factors: $CEV=f\left(CLV,\text{}CKV,\text{}CIV,\text{}CRV\right)$ [6]. It is advisable to include one more parameter, the customer value from the contact center point of view, RFID, is what we call Customer Service Value (CSV), so that CEV is expanded with CSV, $CEV=f\left(CLV,\text{}CKV,\text{}CIV,\text{}CRV,\text{}CSV\right)$. An aggregated and weighted measurement process would strengthen the CEV model.
- The employee attrition rate is a metric that contact centers are concerned about due to the high turnover in the industry, which is often attributed to the demanding work and emotional requirements [52]. Using a procedure that allows, predicting and interpreting the abandonment rate of contact center personnel would be a very important challenge.
- Apply RFID and VIUE models to different business environments, focusing on retail, insurance, banking, services, healthcare, and tourism. Each applied case will contribute to strengthen and possibly expand each of the models with specific characteristics of each sector.
- Create a communication add-on based on the VIUE model, between the Contact Center platform and the CRM, to define the interaction prioritization parameters in a totally dynamic way.
- Use Artificial Intelligence (AI) in the recommendation process so that, based on the prioritization of the interaction and the customer’s value, personalized recommendations can be established.
- Extension of the current study incorporating multi-expert decision-making, applying the fuzzy AHP model (FAHP).

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 6.**Unification model in 2-tuple linguistic [14].

Category | Title |
---|---|

Queue and Routing | The Economics of Line-Sitting [24] |

A Model of Queue Scalping [25] | |

Queuing System with Two Types of Customers and Dynamic Change of a Priority [26] | |

Priority | Priority Service Pricing with Heterogeneous Customers: Impact of Delay Cost Distribution [27] |

Personalized Priority Policies in Call Centers Using Past Customer Interaction Information [28] | |

Clustering | Multi-attribute intelligent queueing method for onboard call centers [29] |

Quality Service | General Practice and the Community: Research on health service, quality improvements and training [30] |

The Dispositional Attribution of Customer Satisfaction through the Juxtaposition of QFD and Servqual in Service Industry Design [31] How Amazon went from an uncertain online bookstore to the leader in e-commerce [32] |

Category | Publications | % |
---|---|---|

Queue and Routing | 15 | 45.45% |

Service Level | 12 | 36.36% |

Training | 3 | 9.09% |

Personalization | 3 | 9.09% |

**Table 3.**Saaty Scale [41].

Degree of Importance | Definition | Description |
---|---|---|

1 | Equal importance | Equal weighting between the two criteria i, j. |

3 | Moderate importance | The weighting of criterion i, is moderately higher than the weighting of criterion j. |

5 | Strong importance | The weighting of criterion i is higher than the weighting of criterion j. |

7 | Very strong importance | The weighting of criterion i is very strong than the weighting of criterion j. |

9 | Extremely importance | The weighting of criterion i is extremely strong than the weighting of criterion j. |

2, 4, 6, 8 | Intermediate values | Intermediate weighting of criteria. |

Reciprocals | The inverse correspondence between i and j can be established, according to the above specifications. |

**Table 4.**Random consistency values [41].

n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|

Random ConsistencyIndex (RI) | 0.00 | 0.00 | 0.58 | 0.9 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 |

**Table 5.**Consistency limits [41].

Size of the Consistency Matrix | Consistency Ratio |
---|---|

3 | 5% |

4 | 9% |

≥5 | 10% |

Ticket_ID | u | V = RFID | I | U | E |
---|---|---|---|---|---|

91 | wRlM0 | (L, 0.120) | VL | VH | M |

113 | wRlth | (L, 0.027) | L | VL | (L, 0.33) |

135 | wRmUk | (L, 0.089) | M | H | M |

136 | wRmWM | (L, 0.089) | VH | VH | (L, 0.33) |

197 | wRqQO | (L, 0.090) | VL | H | (H, 0.33) |

33 | wRiEY | (VH, −0.299) | H | VL | (L, 0.33) |

45 | wRiro | (VH, −0.200) | M | L | (L, 0.33) |

71 | wRjqc | (VH, −0.267) | H | M | (H, 0.33) |

102 | wRljY | (VH, −0.382) | M | VH | (H, 0.33) |

104 | wRllZ | (VH, −0.424) | M | VH | (H, 0.33) |

6 | wRf6k | (M, −0.398) | VL | VH | (L, 0.33) |

65 | wRjYB | (M, −0.425) | H | L | (L, 0.33) |

214 | wRrLB | (M, −0.394) | L | L | (H, 0.33) |

310 | wRyOF | (M, −0.436) | M | L | (H, 0.33) |

317 | wRyni | (M, −0.436) | VH | M | (H, 0.33) |

1 | wRenT | (M, 0.445) | H | M | M |

38 | wRiOQ | (M, 0.429) | VL | H | (L, 0.33) |

44 | wRipv | (H, −0.028) | M | VL | (L, 0.33) |

47 | wRish | (M, 0.454) | L | H | (L, 0.33) |

56 | wRiyz | (H, −0.418) | VH | M | (L, 0.33) |

5 | wRf5G | (M, 0.498) | VL | M | M |

34 | wRiIG | (M, 0.434) | L | VL | M |

35 | wRiIV | (M, 0.392) | VL | VL | (H, 0.33) |

41 | wRinp | (H, −0.007) | M | M | (H, 0.33) |

54 | wRiwj | (M, 0.396) | VL | M | M |

Ticket_ID | u | V = RFID | I | U | E | P = VIUE |
---|---|---|---|---|---|---|

91 | wRlM0 | (L, 0.120) | VL | VH | M | (L, −0.135) |

113 | wRlth | (L, 0.027) | L | VL | (L, 0.33) | (L, −0.362) |

135 | wRmUk | (L, 0.089) | M | H | M | (M, 0.249) |

136 | wRmWM | (L, 0.089) | VH | VH | (L, 0.33) | (H, 0.370) |

197 | wRqQO | (L, 0.090) | VL | H | (H, 0.33) | (L, −0.437) |

33 | wRiEY | (VH, −0.299) | H | VL | (L, 0.33) | (L, −0.174) |

45 | wRiro | (VH, −0.200) | M | L | (L, 0.33) | (M, −0.159) |

71 | wRjqc | (VH, −0.267) | H | M | (H, 0.33) | (H, −0.254) |

102 | wRljY | (VH, −0.382) | M | VH | (H, 0.33) | (H, 0.118) |

104 | wRllZ | (VH, −0.424) | M | VH | (H, 0.33) | (H, 0.111) |

6 | wRf6k | (M, −0.398) | VL | VH | (L, 0.33) | (M, −0.107) |

65 | wRjYB | (M, −0.425) | H | L | (L, 0.33) | (M, −0.111) |

214 | wRrLB | (M, −0.394) | L | L | (H, 0.33) | (L, 0.253) |

310 | wRyOF | (M, −0.436) | M | L | (H, 0.33) | (M, −0.364) |

317 | wRyni | (M, −0.436) | VH | M | (H, 0.33) | (H, −0.198) |

1 | wRenT | (M, 0.445) | H | M | M | (M, 0.457) |

38 | wRiOQ | (M, 0.429) | VL | H | (L, 0.33) | (L, 0.369) |

44 | wRipv | (H, −0.028) | M | VL | (L, 0.33) | (L, 0.325) |

47 | wRish | (M, 0.454) | L | H | (L, 0.33) | (M, 0.024) |

56 | wRiyz | (H, −0.418) | VH | M | (L, 0.33) | (H, −0.179) |

5 | wRf5G | (M, 0.498) | VL | M | M | (L, 0.299) |

34 | wRiIG | (M, 0.434) | L | VL | M | (L, −0.100) |

35 | wRiIV | (M, 0.392) | VL | VL | (H, 0.33) | (L, −0.404) |

41 | wRinp | (H, −0.007) | M | M | (H, 0.33) | (M, 0.244) |

54 | wRiwj | (M, 0.396) | VL | M | M | (L, 0.283) |

Ticket_ID | u | V = RFID | I | U | E | P = VIUE |
---|---|---|---|---|---|---|

136 | wRmWM | (L, 0.089) | VH | VH | (L, 0.33) | (H, 0.370) |

102 | wRljY | (VH, −0.382) | M | VH | (H, 0.33) | (H, 0.118) |

104 | wRllZ | (VH, −0.424) | M | VH | (H, 0.33) | (H, 0.111) |

56 | wRiyz | (H, −0.418) | VH | M | (L, 0.33) | (H, −0.179) |

317 | wRyni | (M, −0.436) | VH | M | (H, 0.33) | (H, −0.198) |

71 | wRjqc | (VH, −0.267) | H | M | (H, 0.33) | (H, −0.254) |

1 | wRenT | (M, 0.445) | H | M | M | (M, 0.457) |

135 | wRmUk | (L, 0.089) | M | H | M | (M, 0.249) |

41 | wRinp | (H, −0.007) | M | M | (H, 0.33) | (M, 0.244) |

47 | wRish | (M, 0.454) | L | H | (L, 0.33) | (M, 0.024) |

6 | wRf6k | (M, −0.398) | VL | VH | (L, 0.33) | (M, −0.107) |

65 | wRjYB | (M, −0.425) | H | L | (L, 0.33) | (M, −0.111) |

45 | wRiro | (VH, −0.200) | M | L | (L, 0.33) | (M, −0.159) |

310 | wRyOF | (M, −0.436) | M | L | (H, 0.33) | (M, −0.364) |

38 | wRiOQ | (M, 0.429) | VL | H | (L, 0.33) | (L, 0.369) |

44 | wRipv | (H, −0.028) | M | VL | (L, 0.33) | (L, 0.325) |

5 | wRf5G | (M, 0.498) | VL | M | M | (L, 0.299) |

54 | wRiwj | (M, 0.396) | VL | M | M | (L, 0.283) |

214 | wRrLB | (M, −0.394) | L | L | (H, 0.33) | (L, 0.253) |

34 | wRiIG | (M, 0.434) | L | VL | M | (L, −0.100) |

91 | wRlM0 | (L, 0.120) | VL | VH | M | (L, −0.135) |

33 | wRiEY | (VH, −0.299) | H | VL | (L, 0.33) | (L, −0.174) |

113 | wRlth | (L, 0.027) | L | VL | (L, 0.33) | (L, −0.362) |

35 | wRiIV | (M, 0.392) | VL | VL | (H, 0.33) | (L, −0.404) |

197 | wRqQO | (L, 0.090) | VL | H | (H, 0.33) | (L, −0.437) |

Ticket_ID | u | P = VIUE | T | C |
---|---|---|---|---|

136 | wRmWM | (H, 0.370) | 6 | 66 |

102 | wRljY | (H, 0.118) | 1 | 37 |

104 | wRllZ | (H, 0.111) | 19 | 76 |

56 | wRiyz | (H, −0.179) | 6 | 62 |

317 | wRyni | (H, −0.198) | 16 | 22 |

71 | wRjqc | (H, −0.254) | 16 | 72 |

1 | wRenT | (M, 0.457) | 16 | 96 |

135 | wRmUk | (M, 0.249) | 19 | 88 |

41 | wRinp | (M, 0.244) | 13 | 29 |

47 | wRish | (M, 0.024) | 5 | 85 |

6 | wRf6k | (M, −0.107) | 7 | 20 |

65 | wRjYB | (M, −0.111) | 1 | 22 |

45 | wRiro | (M, −0.159) | 14 | 41 |

310 | wRyOF | (M, −0.364) | 4 | 81 |

38 | wRiOQ | (L, 0.369) | 20 | 31 |

44 | wRipv | (L, 0.325) | 16 | 39 |

5 | wRf5G | (L, 0.299) | 2 | 60 |

54 | wRiwj | (L, 0.283) | 19 | 26 |

214 | wRrLB | (L, 0.253) | 0 | 41 |

34 | wRiIG | (L, −0.100) | 8 | 94 |

91 | wRlM0 | (L, −0.135) | 18 | 76 |

33 | wRiEY | (L, −0.174) | 4 | 94 |

113 | wRlth | (L, −0.362) | 9 | 66 |

35 | wRiIV | (L, −0.404) | 14 | 28 |

197 | wRqQO | (L, −0.437) | 0 | 91 |

Ticket_ID | u | P = VIUE | T | C |
---|---|---|---|---|

136 | wRmWM | (H, 0.370) | (L, 0.05) | (M, −0.075) |

102 | wRljY | (H, 0.118) | (VL, 0.05) | (H, 0.037) |

104 | wRllZ | (H, 0.111) | (VH, −0.05) | (L, 0.05) |

56 | wRiyz | (H, −0.179) | (L, 0.05) | (M, −0.025) |

317 | wRyni | (H, −0.198) | (H, 0.05) | (VH, −0.025) |

71 | wRjqc | (H, −0.254) | (H, 0.05) | (L, 0.1) |

1 | wRenT | (M, 0.457) | (H, 0.05) | (VL, 0.05) |

135 | wRmUk | (M, 0.249) | (VH, −0.05) | (L, −0.1) |

41 | wRinp | (M, 0.244) | (H, −0.1) | (VH, −0.113) |

47 | wRish | (M, 0.024) | L | (L, −0.062) |

6 | wRf6k | (M, −0.107) | (L, 0.1) | VH |

65 | wRjYB | (M, −0.111) | (VL, 0.05) | (VH, −0.025) |

45 | wRiro | (M, −0.159) | (H, −0.05) | (H, −0.012) |

310 | wRyOF | (M, −0.364) | (L, −0.05) | (L, −0.012) |

38 | wRiOQ | (L, 0.369) | VH | (H, 0.113) |

44 | wRipv | (L, 0.325) | (H, 0.05) | (H, 0.012) |

5 | wRf5G | (L, 0.299) | (VL, 0.1) | M |

54 | wRiwj | (L, 0.283) | (VH, −0.05) | (VH, −0.075) |

214 | wRrLB | (L, 0.253) | VL | (H, −0.012) |

34 | wRiIG | (L, −0.100) | (M, −0.1) | (VL, 0.075) |

91 | wRlM0 | (L, −0.135) | (VH, −0.1) | (L, 0.05) |

33 | wRiEY | (L, −0.174) | (L, −0.05) | (VL, 0.075) |

113 | wRlth | (L, −0.362) | (M, −0.05) | (M, −0.075) |

35 | wRiIV | (L, −0.404) | (H, −0.05) | (VH, −0.1) |

197 | wRqQO | (L, −0.437) | VL | (VL, 0.113) |

Ticket_ID | u | P = VIUE | T | C | Contextual VIUE |
---|---|---|---|---|---|

38 | wRiOQ | (L, 0.369) | VH | (H, 0.113) | (VH, −0.355) |

54 | wRiwj | (L, 0.283) | (VH, −0.05) | (VH, −0.075) | (VH, −0.469) |

135 | wRmUk | (M, 0.249) | (VH, −0.05) | (L, −0.1) | (H, 0.190) |

41 | wRinp | (M, 0.244) | (H, −0.1) | (VH, −0.113) | (H, 0.178) |

104 | wRllZ | (H, 0.111) | (VH, −0.05) | (L, 0.05) | (H, 0.140) |

35 | wRiIV | (L, −0.404) | (H, −0.05) | (VH, −0.1) | (H, 0.016) |

317 | wRyni | (H, −0.198) | (H, 0.05) | (VH, −0.025) | (H, −0.012) |

91 | wRlM0 | (L, −0.135) | (VH, −0.1) | (L, 0.05) | (H, −0.043) |

44 | wRipv | (L, 0.325) | (H, 0.05) | (H, 0.012) | (H, −0.051) |

45 | wRiro | (M, −0.159) | (H, −0.05) | (H, −0.012) | (H, −0.311) |

71 | wRjqc | (H, −0.254) | (H, 0.05) | (L, 0.1) | (M, 0.493) |

1 | wRenT | (M, 0.457) | (H, 0.05) | (VL, 0.05) | (M, 0.299) |

6 | wRf6k | (M, −0.107) | (L, 0.1) | VH | (M, −0.233) |

113 | wRlth | (L, −0.362) | (M, −0.05) | (M, −0.075) | (M, −0.268) |

34 | wRiIG | (L, −0.100) | (M, −0.1) | (VL, 0.075) | (M, −0.296) |

56 | wRiyz | (H, −0.179) | (L, 0.05) | (M, −0.025) | (M, −0.411) |

136 | wRmWM | (H, 0.370) | (L, 0.05) | (M, −0.075) | (L, 0.342) |

47 | wRish | (M, 0.024) | L | (L, −0.062) | (L, 0.321) |

65 | wRjYB | (M, −0.111) | (VL, 0.05) | (VH, −0.025) | (L, 0.073) |

310 | wRyOF | (M, −0.364) | (L, −0.05) | (L, −0.012) | (L, 0.065) |

214 | wRrLB | (L, 0.253) | VL | (H, −0.012) | (L, −0.123) |

33 | wRiEY | (L, −0.174) | (L, −0.05) | (VL, 0.075) | (L, −0.221) |

102 | wRljY | (H, 0.118) | (VL, 0.05) | (H, 0.037) | (L, −0.299) |

5 | wRf5G | (L, 0.299) | (VL, 0.1) | M | (L, −0.304) |

197 | wRqQO | (L, −0.437) | VL | (VL, 0.113) | (VL, 0.223) |

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**MDPI and ACS Style**

Marín Díaz, G.; Carrasco González, R.A.
Fuzzy Logic and Decision Making Applied to Customer Service Optimization. *Axioms* **2023**, *12*, 448.
https://doi.org/10.3390/axioms12050448

**AMA Style**

Marín Díaz G, Carrasco González RA.
Fuzzy Logic and Decision Making Applied to Customer Service Optimization. *Axioms*. 2023; 12(5):448.
https://doi.org/10.3390/axioms12050448

**Chicago/Turabian Style**

Marín Díaz, Gabriel, and Ramón Alberto Carrasco González.
2023. "Fuzzy Logic and Decision Making Applied to Customer Service Optimization" *Axioms* 12, no. 5: 448.
https://doi.org/10.3390/axioms12050448