INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue II, February 2025

www.ijltemas.in Page 39

Development of a Manychat Based Chatbot for Automated Customer

Support System

Lawal, Abidemi Saheed

Olabiyisi, Stephen Olatunde

Ismaila, W. O

1&2

Department of Computer Science Ladoke Akintola University of Technology, Ogbomoso,Oyo state Nigeria

Department of Information Systems Ladoke Akintola University of Technology Ogbomoso, Oyo state, Nigeria

DOI : https://doi.org/10.51583/IJLTEMAS.2025.1402005

Received: 16 February 2025; Accepted: 25 February 2025; Published: 07 March 2025

Abstract: In today’s fast-paced digital age, customer service demands have evolved with customers expecting prompt and accurate

responses to their inquiries and request. However, traditional customer service methods often struggle to meet these expectations. The rise

of artificial intelligence and natural language processing technologies present an opportunity to address these challenges through the

development of intelligent chatbot system. This research aimed to develop and train a chatbot system using ManyChat that can promptly,

accurately, and effectively address basic customer inquiries and handle simple request.

The methodology employed in this research involved a multi stage approach. Firstly, requirement analysis was conducted through

stakeholder meetings, user feedback, and scope definition. Next, the design phase utilized user flow mapping to visualize the customer

journey from the initial interaction to issue resolution. The chatbot was then developed on ManyChat platform. Following development,

testing, and refinement were performed based on feedback. The performance of the chatbot was evaluated after receiving feedback from

multiple users using user satisfaction, task completion rate, and ability to resolve customer inquiries.

The results of the evaluation reveal that the developed chatbot has a value of 92%, 87%, and 92% for user satisfaction, task completion

rate, and ability to resolve customer inquiries respectively.

The findings underscore the ManyChat based chatbot efficiency as a reliable means of providing prompt and accurate responses to

customer inquiries. This research proves how ManyChat can be used to build intelligent and scalable chatbot that can be tailored to

specific business requirement, and thereby improving the customer experience.

Key word: Traditional customer service, Chatbot, ManyChat, Customer, Evaluation.

I. Introduction

The integration of artificial intelligence (AI) and natural language processing (NLP) technologies has revolutionized the landscape of

customer service. Among the myriad of AI applications, chatbots stand out as a powerful tool for enhancing customer interactions and

operational efficiency (Jones and Taylor, 2023). Chatbots, or conversational agents, are software applications designed to simulate human

conversation and provide automated responses to user inquiries. These virtual assistants can operate 24/7, offering immediate assistance

and handling a high volume of queries simultaneously, which significantly reduces the burden on human customer service agents (Miller

and Roberts, 2022).

The rise of chatbot technology is closely linked to the proliferation of digital communication platforms, such as social media, messaging

apps, and company websites, where customers increasingly seek support. Traditional customer service methods, which often involve long

wait times and limited hours of operation, struggle to meet the demands of the modern, digitally-savvy consumer. In contrast, chatbots

provide a scalable solution that can respond to common customer queries instantly, ensuring that users receive timely and accurate

information (Evans et al, 2023).

ManyChat is one of the leading platforms for developing and deploying chatbot, particularly within the context of Facebook Messenger

and other messaging apps. ManyChat provides a user-friendly, no-code environment that allows businesses to create sophisticated chatbot

interactions without requiring extensive programming knowledge. The platform offers various features, including automated messaging,

audience segmentation, and integration with other business tools, making it an attractive choice for companies looking to enhance their

customer service capabilities.

Despite the potential benefits, the effective implementation of chatbots in customer service requires careful planning and execution. A

chatbot must be well-trained to understand and respond to a wide range of customer inquiries accurately. It should be able to handle

common questions, such as those related to product information, order status, and troubleshooting, while also recognizing when to

escalate more complex issues to human agents.

This research aims to explore the development of a customer service chatbot using the ManyChat platform. The study focuses on training

the chatbot to handle basic customer service questions and simple requests, thereby improving response times and overall customer

satisfaction. By examining the capabilities and performance of the ManyChat chatbot, this research seeks to contribute to the

understanding of how businesses can leverage AI-powered chatbots to enhance their customer service operations.

The findings from this study are expected to provide valuable insights into the practical aspects of chatbot development and deployment,

highlighting both the opportunities and challenges associated with this technology. Furthermore, the research will offer recommendations

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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue II, February 2025

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for businesses considering the adoption of chatbots, ensuring they can maximize the benefits of this innovative solution in their customer

service strategies.

Statement of the Problem

In today's fast-paced digital age, customer service demands have evolved, with consumers expecting prompt and accurate responses to

their inquiries and requests. Traditional customer service methods, such as email and phone support, often struggle to meet these

expectations due to limitations in scalability, response time, and consistency. As a result, businesses face challenges in maintaining high

levels of customer satisfaction and operational efficiency.

The rise of artificial intelligence (AI) and natural language processing (NLP) technologies presents an opportunity to address these

challenges through the development of intelligent chatbot systems (Smith and Wang, 2022). However, despite the potential benefits,

many existing chatbot solutions fall short in understanding and accurately responding to customer inquiries, leading to frustration and

decreased user satisfaction.

This research seeks to address this problem by developing and evaluating a chatbot system specifically designed to handle basic customer

service inquiries and simple requests. The study aims to train the chatbot to understand frequently asked questions (FAQs) with a high

degree of accuracy, implement key functionalities such as password resets and order tracking, and assess the Chabot’s performance in

real-world scenarios. Additionally, the research will explore how the chatbot compares to traditional customer service methods in terms of

efficiency and user experience.

By tackling these issues, the research aims to contribute valuable insights into the development of more effective AI-driven customer

service solutions that can enhance both customer satisfaction and operational efficiency.

Aim and Objectives

The aim of this research is to develop a chatbot system using ManyChat that can effectively address basic customer service inquiries and

handle simple requests

The specific objectives are to:

i. Design and train a chatbot to understand and respond to frequently asked customer service questions (FAQs) with a high degree

of accuracy

ii. Implement functionalities within the chatbot to handle simple customer requests, such as resetting passwords, tracking order

status, or scheduling appointments.

iii. Evaluate the chatbot's performance in terms of user satisfaction task completion rates, and ability to resolve customer inquiries

effectively.

Significance of the Study:

The study's significance lies in demonstrating how leveraging the ManyChat platform to build a customer service chatbot can transform

customer support operations. By enhancing efficiency, consistency, scalability, and customer engagement, this research highlights the

potential benefits and strategic value of integrating chatbot technology into customer service frameworks.

Scope of the Study.

Purpose and Target user: Identify the specific tasks the chatbot will handle (e.g., answering FAQs, resetting passwords, scheduling

appointments).

Consider the demographics and technical savvies of your target users to tailor the chatbot's language and capabilities.

II. Literature Review

ManyChat Chatbot:

The rapid advancement of artificial intelligence (AI) and natural language processing (NLP) technologies has significantly impacted

various industries, with customer service being one of the most profoundly affected areas. Chatbots, in particular, have emerged as a

powerful tool for automating customer interactions, providing quick and efficient responses to inquiries, and handling routine tasks

without human intervention. This literature review aims to explore the development, implementation, and effectiveness of chatbots in

customer service, with a specific focus on the ManyChat platform.

The review is structured to provide a comprehensive understanding of the current state of chatbot technology and its application in

customer service. It begins with an overview of chatbot evolution, tracing their development from simple rule-based systems to

sophisticated AI-driven conversational agents (Lee and Zhang, 2021). The key components and functionalities of modern chatbots are

discussed, highlighting the technological advancements that enable chatbots to understand and process natural language (Robinson,

2023).

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The literature review then delves into the specific benefits and challenges associated with implementing chatbots in customer service.

Benefits such as increased efficiency, cost reduction, scalability, and improved customer satisfaction are examined, supported by

empirical studies and case examples. Conversely, challenges including technical limitations, integration issues, and user acceptance are

also explored to provide a balanced perspective.

A critical examination of the ManyChat platform is included to understand its unique features and capabilities. ManyChat's ease of use,

integration options, and specific strengths in customer service automation are also discussed functionalities for customer services are

highlighted, along with examples of successful implementations. This section aims to establish why ManyChat is a suitable choice for

developing a customer service chatbot.

The review also incorporates an analysis of existing case studies and research findings on the performance and impact of chatbots in

customer service. These case studies offer valuable insights into real-world applications, showcasing how businesses have leveraged

chatbots to enhance their service operations. Metrics for evaluating chatbot performance, such as response time, accuracy, and user

satisfaction, are discussed. to provide a framework for assessing the effectiveness of chatbot implementations (Smith & Davis, 2021).

Conclusively, the literature review identifies gaps in the current research and suggests areas for future investigation. While significant

progress has been made in understanding and utilizing chatbots for customer service, on-going research is needed to address unresolved

issues and explore new opportunities. By synthesizing the existing literature, this review sets the stage for the subsequent chapters, which

will detail the development, training, and evaluation of a customer service chatbot using the ManyChat platform.

This literature review provides a thorough examination of the state-of-the-art in chatbot technology and its application in customer

service. It highlights both the potential benefits and the challenges of implementing chatbots, with a specific focus on the ManyChat

platform (Brown, 2023). The insights gained from this review will inform the development and evaluation of the chatbot in this research

project, contributing to a deeper understanding of how businesses can effectively utilize chatbots to improve customer service.

ManyChat platform:

ManyChat is a chatbot-building platform that enables businesses to create, manage, and deploy chatbots without extensive programming

knowledge. It offers a user-friendly, visual interface that allows users to design conversational flows and automate responses to customer

inquiries. ManyChat is primarily used for Facebook Messenger but supports other messaging platforms like Instagram, WhatsApp, and

SMS. Key features include the Visual Flow Builder, which simplifies the process of designing complex interactions.

ManyChat integrates seamlessly with multiple messaging platforms, allowing businesses to reach their customers on their preferred

communication channels. Automated responses are created to reduce response times and ensure prompt and accurate information.

Customization and personalization options allow businesses to tailor the chatbot's behaviour and responses to align with their brand voice

and customer needs.

ManyChat also supports broadcasting and campaigns, analytics and reporting tools, integration with third-party services, human handoff

features, and growth tools. Advantages of using ManyChat for customer service chatbots include ease of use, multi-platform support,

automation and efficiency, customization and customization, data-driven insights, seamless integration with third-party services, and

scalability. These features make ManyChat an ideal choice for businesses looking to automate customer service interactions on messaging

platforms like Facebook Messenger, Instagram, WhatsApp, Telegram, and more.

III. Methodology

This chapter details the methodology used to create an automated ManyChat chatbot for customer service, which includes a thorough

requirement analysis, design phase, development, testing, and deployment. The research design follows a developmental methodology,

focusing on qualitative and quantitative approaches. The design phase focused on creating a user-friendly conversation flow, including a

visual flowchart, conversation scripting, and persona development.

The development phase was carried out on the ManyChat platform, including account setup, a flow builder, and automation rules. Testing

procedures included internal testing, user testing, and full deployment. Internal testing involved simulating customer interactions to

identify potential issues, while user testing involved monitoring interactions with the bot in real-world scenarios.

After successful testing, the chatbot was deployed for public use, with a soft launch and regular updates. Post-launch, continuous

monitoring was conducted to assess chatbot performance and make iterative improvements. Analytics tracking was done using

ManyChat's built-in analytics, and customer feedback was collected through post-interaction surveys. Continuous optimization was also

conducted, adding new FAQs and automated responses as needed.

In summary, this methodology ensures that the chatbot meets business objectives, is user-friendly, and improves efficiency of traditional

customer service operations.

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Figure 3.1: Templates on Many Chat

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Fig 3.2: The Default Reply on Manychat

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Fig. 3.3: Results On Social Media Page

Ethical Considerations

Building the chatbot involved several ethical considerations:

i. Data Privacy: Since the chatbot collected customer data such as names and other details, it was critical to ensure compliance with

data protection regulations (e.g., GDPR). Clear consent messages were implemented, and customer data was securely handled

within the chatbot system. Data Privacy: Data privacy is becoming a more significant ethical concern as chatbots engage with

consumers and gather data.

ii. User Consent and Transparency: When feasible, users were provided with the opportunity to opt out and were informed about

the types of data being collected and how they are utilised.

iii. Data Security: To prevent breaches or unwanted access to sensitive user data, chatbots employed encryption, access controls, and

secure storage procedures.

iv. Bias and Fairness: Chatbot interactions was fair since NLP models trained on biased datasets may unintentionally reinforce

exclusion or discrimination.

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v. Data minimisation reduces privacy hazards and complies with legal requirements by just gathering the information that is

required and avoiding unnecessary keeping.

vi. Compliance with Legal Frameworks: To guarantee the moral treatment of user data, I abide by laws like the CCPA, GDPR, and

HIPAA.

vii. Bias in Responses: Care was taken to ensure that the bot’s responses were neutral, free from bias, and inclusive of all customer

demographics.

viii. Transparency: The chatbot clearly identified itself as an automated system to users, ensuring transparency in the interaction.

IV. Results

The Chabot’s performance involves assessing how well it meets user needs, resolves inquiries, and completes tasks. The key performance

metrics such as user satisfaction, task completion rates, and effectiveness in resolving inquiries factor into a Chabot’s overall success.

User Satisfaction:

User satisfaction is central to measuring a Chabot’s effectiveness. High satisfaction rates indicate that users find the chatbot helpful,

efficient, and easy to use. This can be evaluated through:

User Feedback: Collecting ratings after interactions helps quantify user satisfaction. Many chatbots implement post-conversation surveys

where users rate their experience, typically on a scale of 1-5 or via sentiment analysis.

Sentiment Analysis: Analysing user messages for tone can highlight positive, neutral, or negative sentiments, allowing for real-time

adjustments.

Customer Retention: Repeat usage can indicate satisfaction, as users tend to return to services they find helpful. Analysing repeat user

metrics helps assess if users prefer the chatbot over other support channels.

Impact of High Satisfaction: When satisfaction is high, users are more likely to have confidence in the Chabot’s capabilities. A satisfied

user base can lead to improved brand loyalty, reduced need for human agent support, and ultimately cost savings for the business.

The table below indicate the result gotten from the users review based on the user satisfaction metrics:

Evaluation Period

Total Interactions

Satisfaction Score

(1-5)

Positive Sentiment

(%)

Negative

Sentiment (%)

Feedback

Collected (%)

5,000

4.2

85%

10%

60%

5,200

4.3

88%

65%

5,500

4.5

90%

68%

6,000

4.6

92%

70%

Task Completion Rate:

Task completion rate measures how often a chatbot successfully completes tasks or processes that user’s start. This is a critical metric for

goal-oriented chatbots that handle tasks like order placement, booking appointments, or answering product-specific inquiries.

Automated Process Success: A high task completion rate often indicates that the bot can handle repetitive or straightforward tasks

autonomously. If users consistently reach a successful resolution without human intervention, it implies a well-functioning system.

Fall-back Rates: If users frequently need to be transferred to a human agent or if the bot cannot complete the task, it indicates room for

improvement. Monitoring where and why these fallbacks occur can highlight specific tasks or intents that require optimization.

User Journey Tracking: By tracking each step a user takes within a task flow, one can identify where users drop off. High drop-off rates

may point to confusing steps or poor user interface design.

Impact of High Task Completion: A high completion rate boosts user confidence, as they’ll likely rely on the bot for future inquiries. It

also reduces the workload on human agents, allowing them to focus on more complex issues.

Task completion rate table:

Evaluation

Period

Total Tasks

Started

Tasks

Completed

Task

Completion

Rate (%)

Fallback Rate

(%)

User Drop-off

Rate (%)

4,000

3,200

80%

15%

4,500

3,700

82%

13%

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5,000

4,200

84%

12%

5,500

4,800

87%

10%

Ability to Resolve Customer Inquiries Effectively

Effective inquiry resolution is about how accurately and efficiently a chatbot answers questions, which is often more nuanced than binary

task completion.

Accuracy of Responses: Accurate answers show that the chatbot has a deep understanding of the domain and access to updated

information. Leveraging NLP (Natural Language Processing) allows the bot to understand nuances, like synonyms and contextual

meaning, which improves resolution accuracy.

Response Time: A quick response time is essential for a smooth user experience. Inquiries about common issues or FAQs should yield

immediate answers, enhancing user trust in the bot’s speed and accuracy.

Handling of Complex Queries: Not all inquiries are straightforward. For complex queries, the bot’s performance can be measured by how

well it handles multi-turn conversations or escalates the issue to a human when necessary.

Inquiry Resolution Effectiveness

Evaluation

Period

Total Inquiries

Accurate

Responses (%)

Avg. Response

Time (seconds)

Complex

Queries

Handled (%)

Escalation

Rate (%)

6,000

88%

3.2

70%

12%

6,200

89%

3.1

72%

10%

6,500

90%

3.0

75%

6,800

92%

2.9

78%

Overall Performance Summaries

Metric

User Satisfaction Score

4.2

4.3

4.5

4.6

Task Completion Rate (%)

80%

82%

84%

87%

Accurate Responses (%)

88%

89%

90%

92%

Average Response Time (s)

3.2

3.1

3.0

2.9

Escalation Rate (%)

12%

10%

Difficulties in Developing and Implementing Chatbots

There are several obstacles to overcome when creating and implementing a chatbot, which may affect its performance, integration, and

functionality. Some of the major constraints encountered during the process are examined in this section.

1. Limitations of Natural Language Processing (NLP)

Using natural language processing (NLP) to ensure correct and meaningful conversations is one of the biggest hurdles in chatbot creation.

Typical problems include:

Ambiguity in Language: Chatbots may have trouble deciphering ambiguous or imprecise user input, which could result in inaccurate

answers.

Understanding Context: It might be challenging to keep context during several conversational turns, particularly in intricate talks.

Managing Variations: The chatbot's ability to comprehend user intent may be diminished by variations in dialects, slang, or colloquial

language.

Entity Recognition Errors: It might be difficult to recognise and extract pertinent information from user enquiries, especially when there

are several entities in a single statement.

2. Difficulties with Integration

A chatbot frequently requires integration with databases, APIs, or external systems in order to operate efficiently. There are various

challenges in this process:

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Compatibility Problems: It can be difficult to make sure the chatbot integrates well with current platforms, such enterprise software or

customer relationship management (CRM) systems.

Security Issues: Strong security measures are necessary to handle sensitive user data safely and adhere to privacy laws (such as GDPR

and HIPAA). API Reliability: Relying on external APIs may expose you to risks, particularly if such services go down or undergo feature

changes.

3. Problems with Scalability and Performance

A chatbot needs to be able to respond quickly and effectively to a variety of loads. The following are the main performance-related

obstacles:

Latency and Response Time: Improving user happiness requires prompt responses. However, there may be delays in processing NLP

queries and retrieving pertinent answers.

Scalability: In order to preserve performance without deteriorating, the chatbot infrastructure must grow in tandem with the volume of

user interactions.

Error Handling and Robustness: The chatbot's dependability may be adversely affected by unforeseen user input, technological

malfunctions, or server outages. Fallback procedures must be put in place to guarantee continuous usability.

In conclusion

Continuous advancements in NLP models, strategic integration planning, and performance optimisation methodologies are necessary to

meet these problems. Chatbot developers can improve user experience and guarantee more seamless deployment in real-world

applications by addressing these problems.

V. Conclusion

Evaluating the Chabot’s performance based on user satisfaction, task completion rates, and the ability to resolve customer inquiries

reveals a steady improvement across all measured metrics. Over the course of four quarters, the chatbot demonstrated a positive trend in

user satisfaction, with the score rising from 4.2 in Q1 to 4.6 in Q4. This improvement is further supported by the increasing task

completion rate, which rose from 80% in Q1 to 87% in Q4, indicating better efficiency in completing user requests. Additionally, the

Chabot’s performance in resolving customer inquiries was commendable, as evidenced by the consistent improvement in accurate

responses, with rates increasing from 88% to 92%. The reduction in escalation rates from 12% to 8% also indicates the Chabot’s growing

proficiency in handling complex queries without needing to refer users to human agents.

However, there are still areas where the chatbot can be optimized further. While the chatbot has shown improvement in handling inquiries

and providing timely responses, the average response time (although improving from 3.2 seconds in Q1 to 2.9 seconds in Q4) still

presents an opportunity for enhancement. The steady decrease in user drop-off rates and fallback rates indicates that users are finding

more value in the Chabot’s interactions, but addressing areas such as reducing response time even further and enhancing the resolution of

highly complex queries can lead to even higher user satisfaction and engagement.

VI. Recommendations

To further enhance the Chabot’s performance, it is recommended to focus on optimizing the natural language processing (NLP)

algorithms to better handle complex queries. While the escalation rate has decreased, continuous refinement in the Chabot’s ability to

resolve complex inquiries without escalation is key to improving efficiency and reducing reliance on human agents. Additionally,

investing in machine learning and user intent recognition models could enable the chatbot to better understand and predict user needs,

leading to faster and more accurate responses. Expanding the Chabot’s database of frequently asked questions and solutions will also help

in reducing fall-back rates, ensuring that more queries are resolved within the Chabot’s capabilities.

Also improve the Chabot’s overall response time. Despite a slight decrease over the quarters, reducing response time further can enhance

user satisfaction and engagement. This can be achieved by streamlining backend processes, optimizing server response times, and

possibly leveraging caching strategies for common queries. Furthermore, collecting and analyzing user feedback more proactively will

help identify pain points and areas where users experience difficulties, allowing for continuous improvement. By focusing on these areas,

the chatbot can become more effective at meeting customer needs, providing a smoother experience, and ultimately driving higher

satisfaction and task completion rates.

VII. Contribution to Knowledge

This research has contributed to knowledge by designing and training a chatbot system that leverage ManyChat platform to achieve a high

degree of accuracy in understanding and responding to customer inquiries. It highlights effective methodologies for addressing frequently

asked questions (FAQs) and handling simple customer requests

Possible Advancements in the Future

Future developments to improve chatbot efficacy and solve current issues could include:

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Better NLP Models: Using more datasets and more sophisticated AI models to enhance response accuracy, context recall, and language 
comprehension. 
Improved  Multimodal Capabilities:  Combining text input, image  processing,  and  voice  recognition  to  produce  a  more  engaging  and 
adaptable chatbot experience. 
Improved Personalisation: Applying machine learning to customise answers according to user preferences and previous exchanges. 
More Smooth Integrations: Creating standardised frameworks and APIs to make it simpler to integrate chatbots with various platforms 
and applications. 
More advanced encryption methods, zero-trust architectures, and enhanced data governance frameworks are examples of stronger security 
measures. 
Ethical AI Practices: Using explainable AI techniques and improved dataset curation to reduce biases and increase transparency in chatbot 
decision-making. 
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

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