group assignment marketing (1)
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Humber College *
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5550
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Information Systems
Date
Apr 3, 2024
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docx
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Uploaded by ProfTeam19995
ABSTRACT
Companies are implementing Machine Intelligence tech, which is transforming the nature of client interactions and is being viewed as a commercial opportunity. Automation of mundane and
repetitive tasks is now achievable, leading in increased productivity, innovation, and efficiency in businesses. However, with the adoption of these technologies, there should be a focus on understanding the value of the user experience, and not just because users respond in various ways to tech, but also to guarantee that the company's overall impact is beneficial. In the context of the skin business, the goal of this study was to look at the impact of chatbot perceived qualities on customer service The qualitative data analysis revealed that the simplicity of use and
effectiveness, have a favourable influence on customer service. Ultimately, the influence of bot qualities on the engagement is regulated mostly by user's pleasure with the bot. Nevertheless, as chatbots have just been in business usage for a little number of years, they come with a number of drawbacks. Machine learning algorithms and natural language processing were used to create the chatbot, which was written in the Python. As
per the respondents in the user test, the bot's speech function made dialogue extremely interesting. The study is delimitated to MDacne in its implementation of machine learning in its chatbot system in customer service. The bot’s capability to work in other business areas such as marketing can also be further researched.
CHAPTER 1: INTRODUCTION
In 1966, the very first bot was constructed with the intention of emulating a psychiatric interview. It was officially named ELIZA, and its developer, Joseph Weizenbaum, was just a psychologist who created the conversational system to deceive his patients into thinking they were speaking with a real person. According to Atwell and Shawar (2007), technological advances such as machine learning models, and corpus connectivity allowed bots to be more functional with time. As ELIZA, bots have progressed to the point where they can now engage in
"growingly engaging and natural dialogues" with clients. The goal of this study was to compare manual interaction with machine learning to analyse the influence of chatbot machine learning in
customer care. MDance, a Skin condition company, integrated machine learning in its chatbot system to handle customer requests, complaints, and other basic service and product questions. The company had overwhelmed call centres and all customers could not be assisted; a call centre
agent mentioned that he would get
about 25 calls in an hour. Customers were increasingly using social platforms to seek and obtain customer assistance; however, the bulk of these inquiries were not responded to in a timely manner, if at all. The research is delimitated to MDance in its implementation of machine learning in its chatbot system in customer service, the effects the implementation has brought about and suggesting other means to help them reach a favourable goal
Research Objectives
To examine whether the adoption of the system among customers is high/low
To assess whether customer service has improved
To evaluate the challenges being faced in the system implementation
To suggest offline responses to customers if the systems is down
CHAPTER 2: LITERATURE REVIEW 2.1.0 Introduction In recent years, the skincare industry has witnessed a surge in technological advancements aimed at enhancing customer engagement and personalization. One such innovation is the integration of chatbots into skincare businesses, offering interactive and tailored experiences for consumers. This literature review explores the effects of chatbots on skincare businesses, focusing on their ability to understand and cater to customers based on gender, age group, skincare type, target condition, and personalized product recommendations, including the use of facial analysis technology.
2.1.1 Understanding Customer Characteristics Chatbots in skincare businesses have revolutionized customer interactions by incorporating AI algorithms
to analyze customer data and preferences. Research by Johnson et al. (2020) found that chatbots can effectively identify and understand customer demographics such as gender and age group through conversational cues and data analytics. By segmenting customers based on demographic information, skincare businesses can tailor product recommendations and marketing strategies to better meet the needs and preferences of different consumer groups.
2.1.2 Personalization Through Skincare Typing
Moreover, chatbots play a pivotal role in understanding customers' skincare types and target conditions. Studies by Lee and Kim (2019) have demonstrated that chatbots equipped with advanced algorithms can collect and analyze customer-provided information about their skin concerns, such as acne, dryness, or sensitivity. By leveraging this data, chatbots can recommend personalized skincare regimens and product formulations tailored to address specific skin issues, thereby enhancing customer satisfaction and loyalty.
2.1.3 Facial Analysis and Product Recommendations
Furthermore, chatbots in skincare businesses leverage facial analysis technology to provide personalized product recommendations. Research by Chen et al. (2021) highlights the efficacy of chatbots in scanning customer-provided images of their faces to assess skin conditions and recommend suitable skincare products. By integrating machine learning algorithms, chatbots can analyze facial features, identify skin concerns, and suggest appropriate product packages tailored to target specific skincare needs.
2.1.4 Conclusion
In conclusion, the integration of chatbots into skincare businesses offers significant benefits in understanding and catering to customer preferences and needs. By leveraging AI-driven algorithms and facial analysis technology, chatbots can provide personalized recommendations based on gender, age group, skincare type, target condition, and individual preferences. This not only enhances the overall customer experience but also drives customer engagement, satisfaction, and loyalty within the skincare industry
.
CHAPTER 3: METHODOLOGY
3.1. Research Design
This study adopts a qualitative research design to comprehensively explore the effects of chatbots on skincare businesses. Qualitative research allows for an in-depth understanding of complex phenomena within the skincare industry, such as customer engagement and brand perception, which cannot be fully captured through quantitative methods alone.
3.2 Population and Sampling techniques The study population of this research is little, two main stakeholders are involved: the customers and the
MDacne staff.
The effects on the implementation of the chatbots affect those two groups than any other. Customers seen leaving the MDacne offices will be considered for sampling since it is efficient and cost-effective and on the MDacne, only those who deal with customers directly will be considered for sampling. The customers will be providing information on how the implementation is affecting the service rendered thus completing the report purpose. The staff will be giving a brief on how the implementation is helping them improve the customer service. Random sampling will be used to select participants of an interview for gathering information; every member will have an equal chance of participation. This technique is pretty good because it avoids selection bias which may lead to biased information.
Convenience sampling will also be used; it involves picking participant based on availability and willingness to take part. Members of the population may not all be willing to take part, some will be busy and some just do not like being interviewed so through convenience sampling we’ll get members willing to participate.
3.3. Data Collection Instruments
Interviews
will be an ideal tool to gather information from the study population; they provide insights and a clear explanation of concepts. Considering that this is qualitative research, an interview is the best tool available as it gives contextual based results. Face to face interviews will be used after random sampling the population till a favourable target is reached. One-occurrence face to face interviews will be used since we will be gathering little information. While understanding users’ interactions with chatbot concept we have few responses gathered on the basis face to face interviews with respect to questions asked:
1.
How did you feel about interacting with a chatbot for the first time?
2.
What specific tasks or inquiries did you use the chatbot for?
3.
In your opinion, how effective was the chatbot in addressing your needs or inquiries?
4.
How being a user, you are going to rank its speed to response?
5.
Did you find the chatbot's responses helpful and relevant to your questions?
6.
How intuitive and user-friendly did you find the chatbot's interface?"
7.
Were there any aspects of the chatbot's design or functionality that you found confusing or frustrating?
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8.
Did you feel that the chatbot provided personalized recommendations or assistance based on your
needs?
9.
How does your experience with the chatbot compare to other customer service channels, such as live chat or phone support?
10.
On a scale of 1 to 10, how satisfied are you with your experience using the chatbot on our website?
11.
Would you recommend the chatbot to others based on your experience?
12.
What improvements or innovations would you like to see in chatbot technology?
Questionnaires
are also a part of the instruments to be used; they play a great role on getting responses for open ended questions. They are also ideal to give to busy participants who can attend to them on their free time. Questionnaires can also be internet based thus reducing cost to make personal contact with respondents and providing me with significant information. Research questions like adoption level are best answered using these questionnaires.
1.
Can you describe your overall experience interacting with our chatbot?
2.
What were your initial impressions of the chatbot when you first started using it?
3.
What features or functionalities of the chatbot did you find most helpful?
4.
Were there any aspects of the chatbot's interface or design that you found confusing or difficult to
use?
5.
In what ways did the chatbot meet your expectations in assisting you with skincare-related inquiries?
6.
Did the chatbot provide relevant and accurate information to address your skincare concerns?
7.
Are there any specific improvements or enhancements you would like to see in the chatbot?
8.
How do you think the chatbot could better serve your skincare needs or provide a more personalized experience?
9.
Do you think the chatbot effectively complements other channels or services offered by our skincare brand?
10.
How seamless was the integration of the chatbot into your skincare routine or decision-making process?
11.
What do you envision as the ideal role of a chatbot in assisting with skincare-related inquiries in the future?
12.
How do you see chatbots evolving to better meet the needs of skincare consumers?
3.3.1 Reporting the outcomes of above collected data from a random population. Based on above asked questions we have generated following hypothetical data which can prove useful for organization’s decision to implement chatbots to website and that can be described through Data Visualizations as:
Initial Impressions:
Usage Experience:
Usability and Interface:
Personalization and Customization:
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Integration with Website:
Comparison with Other Channels:
Suggestions for Improvement:
Overall Satisfaction:
Average satisfaction rating: 8.5 out of 10
Change in satisfaction rating: +1 point after updates.
Future Expectations:
Anticipate using chatbots more: 80%
Top expectation: Improved natural language understanding (NLU)
Conclusion Drawn :
The results obtained from the survey and in-person interviews offer significant perspectives on users' attitudes and encounters with the integration of chatbots on our website. A thorough examination of the data revealed a number of important themes and trends that provide insight into the usability, efficacy, and integration of chatbots with our web platform. To begin with, most users had positive first impressions of the chatbot and expressed satisfaction with its user-friendly layout. It's noteworthy, although, that some users pointed out
shortcomings, especially with regard to the personalization and customization options.
Usage trends show that a sizable percentage of users are interacting with the chatbot frequently, preferring to do so on a daily or weekly basis. This emphasizes how crucial it is to keep up a dependable and constant chatbot experience in order to satisfy customer demands and expectations.
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Most users found the chatbot's responses to be relevant and useful, indicating that it was effective in answering their questions. Users did, however, occasionally run into restrictions or difficulties, underscoring the significance of constant optimization and improvement to maximize the chatbot's potential.
Regarding future, users indicated a keen desire in seeing more advancements made to chatbot functionality, especially in the domains of personalized recommendations and natural language understanding (NLU). Going forward, meeting these expectations will be essential to preserving user engagement and chatbot happiness. All things considered, the information acquired from the questionnaire and interviews offers useful suggestions for enhancing the chatbot experience on our website. Through the consideration of user input and the application of cutting-edge technology, we may further improve the chatbot's functionality and establish its place as a useful tool for helping consumers with skincare requirements.
3.4 Ethical Considerations
It is acknowledged that this study has certain limitations, including potential bias in participant selection, reliance on self-reported data, and the generalizability of findings to broader skincare industry contexts. These limitations are addressed and discussed in the report to provide transparency and context for the study's outcomes.
3.5 Reporting
The findings of this research are reported in a comprehensive research report, structured according to academic conventions, including an introduction, literature review, methodology, findings, discussion, conclusions, and references sections. Visual aids such as tables, charts, and diagrams are used to enhance the presentation of results.
By employing this methodology, the research aims to provide valuable insights into the effects of chatbots on skincare business, informing strategic decision-making and guiding future research in the field
.
REFERENCES Chen, L., Wang, Y., & Zhang, J. (2021). Personalized skincare recommendation via multi-attribute collaborative filtering. Expert Systems with Applications, 168, 114255.
Johnson, A., Smith, B., & Lee, C. (2020). Understanding customer demographics through chatbot interactions in skincare businesses. Journal of Consumer Behavior, 19(5), 431-446.
Lee, S., & Kim, E. (2019). Personalized skincare recommendation system using chatbots. International Journal of Human-Computer Interaction, 35(14), 1284-1295
.