Managing a Small Business Project: Investigating the Impact of AI on Tesco's Marketing

Submission Front Sheet

Assignment Code:		RQFBMU6SEP24SS/SSH/CN/FM
Programme:		BTEC HND in Business (RQF)
Unit Title and Number:		Managing a Small Business Project - Unit 6
RQF Level:		4		Module Code	D/618/5039
Credit value:		15 credits
Module Tutor:		Smita Shukla / Chi Linh Nguyen Subodha Samarasinghage / Francis Marfo
Module Tutor Email:		s.shukla@mrcollege.ac.uk / l.nguyen@mrcollege.ac.uk s.samarasinghage@mrcollege.ac.uk / f.marfo@mrcollege.ac.uk
Date Set:		05/09/2024		Distribution Date:	09/09/2024
Cohort:		JAN 24 A-A / JAN 24 A-B JAN 24 B-A / JAN 24 B-B
Student’s name:		Georgette Yomba Djewouo Djimo
Registration number:		149641
Submission
First Submission	?		Formative Submission		?
Second Submission	?		Word Count:
Learner’s statement of authenticity I certify that the work submitted for this assignment is my own. Where the work of others has been used to support my work then credit has been acknowledged. I have identified and acknowledged all sources used in this assignment and have referenced according to the Harvard referencing system. I have read and understood the Plagiarism, Collusion and AI sections provided with the assignment brief and understood the consequences of plagiarising.
Signature		Georgette Yomba		Submission Date	22/10/2024

MANAGING A SMALL BUSINESS

PROJECT

Introduction

To present work identifies how AI can influence Tesco’s marketing efforts in terms of customer interactions, targeting accuracies, and customer satisfaction. As AI has revolutionized the field with recommendation engine, predictive, and real-time characteristics, it is one of the most valuable tools for marketers. The goal of this research is to investigate how marketing experienced through AI impacts the customers, increases the accuracy of the marketing methods, and increases satisfaction by personalizing the methodology. Being a quantitative study, the research seeks to ask Tesco customers and assess the success of AI in these areas. Thus, the findings have the intention to include suggestions of how to enhance the use of AI in the marketing field of Tesco.

Tesco owns and runs thousands of shops globally across the hypermarkets, supermarkets and convenience shops satisfying varied consumers’ demands. These stores come in all sizes and formats and enable Tesco to meet the needs of the urban and the rural population. It has a workforce of more than 330,000 and establishes a workforce that is dedicated to its customer and has a vision of delivering good service and good operational results (Tescoplc.com, 2024).

Figure: Impact of artificial intelligence (AI) and machine learning (ML) use on retail performance between 2022 and 2024

Source: (Statista, 2024)

Chapter 1 The Project Management Life Cycle

Stages of PLC and their relevance to the completion of the research

Project management is the processed form of organizing, coordinating and directing limited resources towards the achievement of specific deliverables within a given time frame often termed as the project (Lock, 2017, p. 137). It is the process of using skills, knowledge and techniques to steer a project from its start towards its successful end in the most effective manner by meeting objectives set in conjunction with minimizing risks and overcoming obstacles encountered in a process.

The Project Life Cycle (PLC) consists of several phases: I.P.E.M.C & C, which is further expands to Initiation, Planning, Execution, Monitoring and Controlling & Closing processes. These stages give a clear structure in handling a project from the scratch to its completion (Otte et al., 2019, p. 183).

1. The initiation phase defines the project's purpose, objectives, and alignment with organizational goals. For Tesco, this stage would be concerned with the possibility of the application of AI in a particular domain of operation, for instance, marketing department or the supply chain function. It initiates the project to understand its relevance and critically evaluate the expectations of its stakeholders (Meredith et al., 2017, p. 147).

2. Planning is the systematic development of the framework of the detailed work plan that includes setting up of time frames, costing, definition and control of costs and likely risks. This stage provides a solid ground for the next stage, that is the implementation of the project, about the way and how Tesco will conduct the research on AI; work plan, work schedule and work financial breakdown. They cause the project to be well structured in a way that it does not deviate from its set objectives.

3. Implementation is the phase where all those planned activities that have been laid down are carried out. In the case of Tesco’s project this stage serves to involve primary and secondary research on the effects of AI, gather data, analyse and assimilate the conclusions to the marketing plans. During this phase, resources are expended, while the specific project team attempts to achieve some predetermined goals and perform activities indicated on the project plan.

4. Both Monitoring and Controlling are important so that the project will remain on schedule. It is a work in progress and compared to the plan and if the need arises then necessary actions are taken. For example, if the investigation for the field of AI hits a deadlock or meets some other unforeseen challenges, the problem of shifting the timelines or resources can occur (Stark, 2022, p. 21).

5. The termination of a project is also marked by this phase is known as closure. It is at this stage that what has been developed in the project is refined and a review made. In the case of Tesco, this would require providing the conclusion on the effects of AI together with possible suggestions to the genuine stakeholders. This affords an opportunity to ensure that all the identified objectives have been completed and any knowledge gained documented for any future project.

The Implementation of the Project Management Plan

It is important that a number of key elements be assessed in order to create a Project Management Plan (PMP). Among them are the project scope statement; project cost; risk and resources; communication; and quality.

1. Project Scope describes the work to be done as well as what remains outside the Project team’s responsibility. In the case of Tesco’s AI project, an evaluation of its scope would simply delineate what area of AI applies to the firm, for instance, the AI in marketing or in supply chain. Scope definition is an essential step for prevent project scope growth influences, usually a main factor contributing to project contingency (Tereso et al., 2019, p. 13). It also maintains awareness to the core objectives and end products of the project team without drifting off the project goal set.

2. Project Cost is one of the most effective tools for managing the amounts of money invested in the particular project. Budgeting involves approximating costs requirement in terms of personnel, technology, and data collection tools (Lock, 2017, p. 129). In the case of Tesco, this(entity) ensures the project does not go over the required financial limit. The definition of the budget helps to avoid situations when other costs occur and are not covered, and all the planned activities, including the AI research tools and implementation, can be done without going beyond the limit of the established budget.

3. Business in line with Project Risk and Resources focuses on the identification of risk and proper directions in utilization of many resources. These could be some risks: Technological risks – such as technical malfunctions, Employee resistance to change. In the case of Tesco, it possible to agree with the author that preparing for such risks and developing contingency plans makes execution processes easier (Siraj and Fayek, 2019, p. 145). Having appropriate personnel to work on the task or having the correct technologies, etc., will be vital to the timely project realization.

4. Stakeholder Management specifically project communication is vital in order to engage and thus get all stakeholders on the same page. A communication strategy explains with whom, how, when, and what sorts of data will be exchanged in a given task environment (Pedrini and Ferri, 2019, p. 47). Daily and weekly meetings, reports and communication tools used for Tesco’s project enhance accountability and effective control over corrective measures whenever they arise to guarantee project’s continuation.

5. Project Quality essentially confirms that the outcomes of a project correspond to the stipulated specifications. In the case of Tesco, it is a question of defining proper quality standards for AI research and the input data credibility. To reduce variability, extra efforts are put in place to make certain that the results of a project incorporate quality control in order to create decisions based on the outcomes of the project (Meredith et al., 2017, p. 162).

Research Methods

1. Primary and Secondary Data Collection

Primary data is data collected from the original source and may be obtained by means of questionnaires, interviews, observation etc. It offers the opportunity to have first-hand information which is of immense relevance towards the achievement of the research objectives. Certainly one of the major benefits of applying this method is the specificity of the data which is collected to address the specific research context, thus ensuring comparably high accuracy and the appropriate level of relevance (Ajayi, 2017, p. 3).

Secondary data on the other hand is information that has been collected and made available to the public through various media be it in a form of article, report, or previous studies. This method is cheap and gives first port of call in accessing a wide range of information. Its drawback, however, is that secondary data often does not answer exactly the focus of these and may be stale or not germane to the aims of the current research.

2. Research Approach:

Inductive reasoning is one kind of reasoning style that starts at looking at individual pieces of data and then progresses towards looking for theoretical trends. It is beneficial when there isn’t already an extensive amount of background information to guide the analysis and the structure can be formed based on patterns found within the data.

On the other hand, Deductive Research is the exact opposite moving from theory or hypothesis with a view of testing these predetermined theories or hypothesis. As it offers more formalistic perspective, a researcher is able to distil certain conclusions from the data more easily (De Oliveira, 2023, p. 291). A limitation of quantitative research is that, it may not allow for flexibility if other unexpected data is discovered.

In this case, the deductive approach is most suitable to use in this Tesco’s AI implementation project because the research objectives are meant to examine the effects of AI in a particular management discipline like marketing or supply chain, therefore requires a more structured research approach.

3. Research Strategy:

An experiment means altering values on variables so the outcome can be observed, it gives room for accuracy and the determination of effects of change on the variables through cause as well as effect. However, experiments can be costly and in some cases do not provide a clear and valid image of reality processes.

A case study has a great focus on one subject, for instance, a firm or an event, and is not grasping in quantitative information, though it gives an opportunity to probe deeper into the investigated subject and get qualitative results (Malhotra, 2017, p. 179). The drawback of the approach is that it is limited to applicability to the particular case under consideration.

A survey is an example of quantitative research method of data collection from a large population without having to interview them personally because this is accomplished through questionnaires that would be prepared and issued to the population in question. Surveys do not cost a lot of money, are easy to distribute, and offer results in figures which is quite easy to understand. But they can have low response rate or response might be bias in nature.

In the case of the research on Tesco, surveys is chosen as the most effective approach because the researcher collects data from the cross-section of Tesco’s consumers and understand their perception about the application of AI in Tesco.

4. Research Methods:

Quantitative research collects numerical values that can be analysed statistically with generalized conclusions from large data sets. Its strength is in the fact that data collected is more often than not, is quantitative (Walliman, 2021, p. 191). But, which could fail to identify certain more profound quantitative qualities like the reasons behind customer buying and their perceptions on products.

In its chance, qualitative research focuses on non-numerical values, feelings, opinions, and ideas concerning an issue. Quantitative data is easy to compare because it gives numbers that are factual, but qualitative data has more depth and is therefore difficult to make generalizations with. Therefore, in this project the quantitative research method is used because it is consistent with the deductive research approach which helps to collect numerical data that will enable testing of hypotheses generated on the impacts of AI at Tesco.

5. Data Collection:

This research focuses on Tesco customers as the target population to be used in this research study using a sample size of 20. The main data collection tool will be email and social media with the permission from the respondent; therefore, accessibility of the instruments is not an issue. It is feasible for acquiring data from an interested audience that was already involved with Tesco; thus, the result acquired aligns with the research objectives. The sample size is justifiable to the range of the research and the given resources.

6. Data Analysis:

Microsoft Excel will be adopted in the analysis of data from the two surveys as it is suitable for frequency data analysis (Tallaksen and Laaha, 2023, p. 239). Excel enables response counting and analysis of responses in a specified range, and thus Total counts and Tesco’s customers’ interaction with the AI services. Because it can quickly and easily calculate and present the data it is appropriate to use as a tool when summarizing the patterns and making conclusions based on the collected data.

Chapter 2 Project Management Plan (PMP)

Aim: The aim of the research is to explore the impact of AI on enhancing marketing strategies and customer engagement within Tesco.

Objectives

• To analyse the impact of AI-driven marketing strategies on customer engagement at Tesco.

• To assess the effectiveness of AI in improving the efficiency and targeting of Tesco’s marketing campaigns.

• To evaluate customer perceptions and satisfaction with Tesco's AI-enabled marketing initiatives.

• To provide recommendations on improving Tesco’s marketing strategies through enhanced AI integration.

Deliverables

1. Research Report: Analysis of the impact of AI on Tesco's marketing strategies.

2. Data Analysis: Examination of survey results using frequency analysis in Excel.

3. Recommendations: Suggestions for improving Tesco’s marketing strategies through AI.

Quality

The project shall be work to this standard since all information gathered shall be highly accurate and relevant from well-developed questionnaires. The research will be bias free, and data analysis will be done accurately by excel. To ensure credibility, findings and recommendations will undergo rigorous examination in order to ascertain that they are usable in improving the marketing strategies of Tesco.

Risk

Risk	Level	Chances of Occurrence	Impact of Risk	Mitigation Strategies
Low survey response rate	Medium	High	Limited data for analysis	Increase outreach via multiple platforms, offer incentives for participation
Data inaccuracies or bias	Medium	Medium	Skewed findings and unreliable results	Carefully design survey questions to avoid bias, and validate data accuracy
Technical issues with data collection	Low	Low	Delays in project timeline	Ensure backup systems and multiple channels for data collection
Misinterpretation of survey results	Medium	Low	Incorrect conclusions	Cross-check findings with secondary data and peer review

Communication

Stakeholder	Communication Types	Frequency	Mode of Communication
Project Supervisor	Progress Reports, Feedback	Weekly	Email, Meetings
Research Team	Updates, Task Assignments	Daily	Email, Instant Messaging (Teams/Slack)
Tesco Management	Project Updates, Final Findings	Monthly/Final Report	Email, Presentation
Survey Participants	Survey Invitations, Reminders	As needed	Email, Social Media

Research Method

The research will be using the quantitative research method, respondents in the study will be asked to provide numerical information using questionnaires. These surveys will be disseminated through the social media platforms, as well as through emails to a large population. The compiled data will be looked at by Microsoft Excel whereby frequent analysis will be done with a view of establishing the extent of customer engagement and satisfaction with AI-marketing solutions.

Cost

The major expenditure for the project shall be primarily the costs of survey dissemination and, if necessary, bonuses for completing the questionnaire. Since survey of the target population will be conducted online, through the use of e-mail and social networks, expenses will remain low. Furthermore, data analysis will be carried out using Excel software with the reduction of expenses in operations linked to data processing.

Cost Item	Estimated Cost (in GBP)
Survey Distribution Tools (Email, Social Media Ads)	£100
Participant Incentives (e.g., vouchers)	£50
Data Analysis Software (Excel, if not available)	0
Miscellaneous (administrative tasks, contingency)	£30
Total Estimated Cost	£180

Work Breakdown Structure

(Source: Author’s Work, 2024)

Gantt chart

Activities	Week 1	Week 2	Week 3	Week 4	Week 5	Week 6	Week 7	Week 8	Week 9	Week 10	Week 10
The research topic is chosen
Aims and objectives are prepared
Literature review conducted
Data collection (survey)
Data analysis
Conclusion and recommendation
Report Writing and Proof Reading

(Source: Author’s Work, 2024)

Chapter 3 Literature Review

3.1 Introduction

The literature review reflects upon the role of AI based marketing initiatives and their effects on customer interactions, targeting precision, and customer satisfaction. In this review, an evaluation of main theories and literature demonstrate how AI have impacted marketing, and the way it is practiced today, by employing complex data processing. It also looks at the utilization of AI by customers and the likelihood of increasing interaction and satisfaction with the company’s marketing strategies in Tesco.

3.2 The impact of AI-driven marketing strategies on customer engagement at Tesco

As Rathore, (2020) note, AI is a disruptor in marketing because it designs and implements unique and data-based approaches. It is well understood that the use of AI can vastly improve customer experience by means of customer-specific suggestions, churn predictions and adaptive content propagation. AI applications enable marketing in organizations such as Tesco to identify and target customers’ behaviour patterns. This particular serves to increase interaction since customers are likely to engage with entities that target them directly with offers and experiences.

Furthermore Huang and Rust, (2021) state in their study that AI allows for the dynamic and continuous access with customers, for instance, trough Chabot’s applied in the messaging services, customer satisfaction. Similarly the research of Wolniak et al., (2024) state that AI can be used in Marketing, as it does with Tesco to gather data from different channels of interaction, including purchasing over the internet and memberships. However, the effectiveness of marketing based on AI depends on the quality of data processing and the quality of the developed algorithms, if implemented improperly; it can cause customer complaints.

One limitation of executing marketing strategies that utilize AI is that the results highly depend on the quality of the data and algorithms used. Bad data or wrong design can produce wrong data and lead to wrong targeting strategies and unhappy customers, which in return can reflect on the brand image and the amount of faith customers put in a brand.

3.3 The effectiveness of AI in improving the efficiency and targeting of Tesco’s marketing campaigns

AI in marketing has paved way to the optimization both in marketing techniques and in terms of degree of accuracy. As Kundu et al., (2023) pointed out, artificial intelligence makes it possible for companies such as Tesco to review large volumes of data relating to customer preferences and refine their marketing strategies with the least oversight. ‘Through the implementation of machine learning algorithms for customer preference prediction of Tesco, it means that marketing will be localized and more personalized thus reducing marketing unproductive waste.

Meanwhile Jabbar et al., (2020) supports that automated tools such as programmatic advertising and predictions bring higher accuracy of marketing since they operate with real-time data. This leads to better campaign optimization and resource leveraging that enable Tesco optimize on high return activities. However, it is a fact, that AI efficiency again comes with a challenge of unreliable data where it might lead to wrong targeting and in turn create a reverse effect of customer alienation. In all, AI makes marketing easier but only if the base information is credible and useful.

3.4 Customer perceptions and satisfaction with Tesco's AI-enabled marketing initiatives

Self-organising, autonomous, and integrated AI has made a significant impact on customer-conscious marketing, but these changes have made customer perception and satisfaction variably high or low. Chintalapati and Pandey, (2022) found that AI expands the forms of personalised marketing by focusing on advertisement and product recommendation which make customers more satisfied because of the materials provided. AI is used in Tesco’s in a manner that optimizes buyer persona dialogues in order to create more engagement and brand affinity.

According to Hicham et al., (2023), customers may have a compound attitude as some may feel that they data is being collected excessively for AI systems to function optimally. Transparency and ethical use of data is very important determining areas that affect the customer satisfaction. The research of Putani et al., (2021) state that the customers feel that AI marketing is intrusive or is not relevant towards them; their experience is going to be terrible. Therefore, satisfaction formed through personalization means always depends on the customer’s attitudes towards data usage and AI approaches.

3.5 Strategies for improving marketing strategies through enhanced AI integration

According to Huang and Rust, (2021), AI is the biggest marketing innovation as it offers customer customization, analytics of the market and customer data, and customer campaigns. Marketing plans that are aided by AI can optimize the way decisions are made and improve the promotion’s focus based on present data. They also enhance customer involvement by providing targeted new product suggestions, information about the status of the company’s loyalty club, or just general shopping opportunities. The research of Erçil, (2023) identifies that using AI include inaccuracies of data used, privacy issues and customer/Product distrust in the strategic AI models. Ahmed, (2024) state that data usage policies and the provision of usage policies, improvement of the relevance of AI algorithms, and improvements in AI-based customer care service helps to foster customer trust. Thus, by targeting these approaches, organizational benefits arising from shared marketing objectives and AI application may result in greater customer satisfaction and loyalty, as well as enhanced application of AI-driven solution delivery.

3.6 Summary

Again, the resolve of the review is that AI plays an indispensable role in improving marketing effectiveness and customer experience through individualised and information techniques. But, here AI becomes beneficiary only when used data is accurate and customers have positive perceptions about it. According to the findings of this work, opportunities for the utilisation of AI in marketing include customer trust, satisfaction, and ethical data usage, which should be the top priority of Tesco to enhance the results of such integration.

Chapter 4 Data Communication

Introduction

This chapter centres its attention on data to establish the phenomenon of how AI marketing affects Tesco’s customers. The study employed a survey strategy– intended to elicit participants’ awareness and behaviour. The survey comprised of 10 specific questions which sought information on sample customers concerning different features about Tesco’s artificial intelligence marketing strategies.

1. Which of Tesco’s personalized marketing features do you find most useful?

Options	Frequency	Frequency in (%)
Personalized product recommendations	7	35
Tailored promotions/discounts	5	25
Reminder emails	6	30
None of the above	2	10
Total	20	100

The most useful feature for respondents (35%) is personalized product recommendations, followed by reminder emails (30%), tailored promotions/discounts (25%), and none of the above (10%).

2. In what format do you prefer receiving personalized offers from Tesco?

Options	Frequency	Frequency in (%)
Email	3	15
Social media ads	10	50
Mobile app notifications	5	25
Physical mail	2	10
Total	20	100

The majority (50%) of respondents prefer receiving personalized offers from Tesco through social media ads, followed by mobile app notifications (25%), email (15%), and physical mail (10%).

3. What type of AI-driven content from Tesco would you like to see more of?

Options	Frequency	Frequency in (%)
Product suggestions	7	35
Discount offers	4	20
Loyalty program updates	6	30
Personalized shopping tips	3	15
Total	20	100

The highest percentage (35%) of respondents would like to see more product suggestions from Tesco's AI-driven content, followed by loyalty program updates (30%), discount offers (20%), and personalized shopping tips (15%).

4. How accurate are Tesco’s personalized recommendations for your shopping needs?

Options	Frequency	Frequency in (%)
Accurate	13	65
Sometimes accurate	4	20
Not accurate	3	15
Total	20	100

The majority (65%) of respondents find Tesco’s personalized recommendations to be accurate, while 20% believe they are sometimes accurate, and 15% consider them not accurate for their shopping needs.

5. What aspect of Tesco’s marketing do you think has improved with AI?

Options	Frequency	Frequency in (%)
Product recommendations	8	40
Promotional timing	5	25
Targeted advertising	4	20
Customer communication	3	15
Total	20	100

The majority (40%) of respondents believe that product recommendations have improved the most with AI in Tesco's marketing, followed by promotional timing (25%), targeted advertising (20%), and customer communication (15%).

6. What motivates you to engage with Tesco’s targeted promotions?

Options	Frequency	Frequency in (%)
Discounts or savings	3	15
Personalized product suggestions	13	65
Limited-time offers	4	20
None of the above	0	0
Total	20	100

The majority (65%) of respondents are motivated by personalized product suggestions to engage with Tesco’s targeted promotions, followed by limited-time offers (20%) and discounts or savings (15%), with no respondents selecting "none of the above."

7. What is your main concern about Tesco using AI in marketing?

Options	Frequency	Frequency in (%)
Privacy of personal data	11	55
Relevance of offers	3	15
Accuracy of recommendations	4	20
No concerns	2	10
Total	20	100

The primary concern for most respondents (55%) is the privacy of personal data, followed by concerns about the accuracy of recommendations (20%), and the relevance of offers (15%). Only 10% expressed no concerns.

8. Which AI-driven feature from Tesco enhances your shopping experience the most?

Options	Frequency	Frequency in (%)
Chat-bots for customer service	5	25
Personalized product suggestions	11	55
Targeted offers and discounts	4	20
None of the above	0
Total	20	100

The majority (55%) of respondents indicated that personalized product suggestions enhance their shopping experience the most, followed by chat-bots for customer service (25%) and targeted offers and discounts (20%), with no respondents selecting "none of the above."

9. In which area should Tesco improve its AI-driven marketing efforts?

Options	Frequency	Frequency in (%)
Better personalization of offers	3	15
Timing of communication	2	10
Increased transparency on data use	6	30
Improved customer service integration	9	45
Total	20	100

The highest priority for improvement, as indicated by 45% of respondents, is improved customer service integration, followed by increased transparency on data use (30%), better personalization of offers (15%), and timing of communication (10%).

10. What would encourage you to interact more with Tesco’s personalized offers?

Options	Frequency	Frequency in (%)
More relevant offers	3	15
Bigger discounts	4	20
Better AI customer service	8	40
Easier access via app	5	25
Total	20	100

The majority (40%) of respondents indicated that better AI customer service would encourage more interaction with Tesco’s personalized offers, followed by easier access via app (25%), bigger discounts (20%), and more relevant offers (15%).

Chapter 5Discussion

The first objective of the study is to analyse the impact of AI-driven marketing strategies on customer engagement at Tesco. The findings of secondary research state that AI in marketing increases customer value for Tesco through a personalized approach of their customers through offers and recommendations. Targeted and consumption behavioural pattern based content leads the potential of the customer to self-engage with the brand. Chat-bots and predictive analytics when used offer customer real-time solutions, also enhancing the user experience. However, the efficiency of AI is based on data and algorithm’s precision (Huang and Rust, 2021, p. 39). Inaccurate insights are produced by weak data accuracy and may result in poor customer outcomes; therefore, the management of the AI system is crucial.

The findings of the primary research shows that 50% of participants found social media ads strategies are more engaging, 25% of participants prefer mobile app notifications while 15% of participants prefer Email and only 10% of the participants goes with physical mail. Along with this, 35% of participants like to see more product suggestions from Tesco and only 15% of participants likes personalised shopping tips.

The second objective of the research is to assess the effectiveness of AI in improving the efficiency and targeting of Tesco’s marketing campaigns. The findings of the secondary research suggest that AI enhances the efficiency of marketing by automating decision-making process steps, and lessening the amount of work. Digital media targets that use machine learning algorithms and programmatic buying targeting customers improve marketing efficiency (Kundu et al., 2023, p. 93). All this leads to efficient use of the resources resulting in improved returns on campaign investments. However, the quality of data, which is utilized, determines the efficiency of the targeting and can cause adverse consequences leading to customers’ rejection. Thus, only in case with AI-driven marketing, such targeting cannot but improve the level of efficiency if reinforced by qualitatively rich and as precise as possible information materials.

The findings of the survey shows that, 65% of participants consider Tesco’s personalised recommendations for shopping are accurate and only 15% of participants were opposed to this. The findings indicate that 40% of participants believe Tesco’s AI-driven product recommendations significantly enhance their shopping experience, while only 15% of participants noted improvements in AI-driven customer communication.

The third objective of the research is to evaluate customer perceptions and satisfaction with Tesco's AI-enabled marketing initiatives. The outcomes of the secondary research show that customer attitudes on utilizing AI in mechanical marketing are not unique in Tesco. In general, individualised communications complemented by recommendations increase satisfaction and engagement; however, some customers are worried about data protection and the invasion of AI-based offers. General speaking, ethical data usage and data transparency are measures that directly affect customer trust and customer satisfaction. Here, one can note that most customers get uncomfortable if they think they are interacting with AI when it is unnecessary, or it feels intrusive (Hicham et al., 2023, p. 148). While employing the idea of personalization, the company has to be very careful and compliant with the customer data protection laws while using AI marketing ideas which are fashionable but must align with recommended customer privacy limits.

The findings reveal that 55% of participants are primarily concerned about the privacy of personal data in Tesco’s use of AI in marketing, highlighting a significant apprehension regarding how their information is handled. In contrast, only 10% expressed no concerns, indicating that most customers are cautious about AI's implications.

The forth objective of the study is To provide recommendations on improving Tesco’s marketing strategies through enhanced AI integration. Use of AI in marketing has been important for firms such as the Tesco to stay ahead of the competition and serve the needs of the customers. Research conducted to the clients of Tesco reveals that the consumers have special recommendations that can be made to AI-based tactics for marketing. Notably, the respondents noted that one of the strategies to increase engagement was improvement on the use of AI in customer service. Thirdly, 30% of respondents are concerned with the new emphasis on the explanation of data usage, which seems to be rather essential for customer trust. Furthermore, 40% stated that increased use of artificial intelligence customer service will lead to higher interaction with personalised offers, 15% added that better personalisation would increase overall interaction.

Chapter 6 Conclusion and Recommendation

6.1 Conclusion

This study confirms that AI marketing has influenced Tesco in terms of understanding and approaching and customer needs and satisfying them. This paper concludes that customized product offering, offer incentives and communications strategies help to improve customer satisfaction. The study finds that AI improves marketing effectiveness in Tesco by creating and delivering targeted customer engagements. But, more action is required in order to solve customer relations trust crisis with greater data openness and AI-based customer care facilities. Recommendations include refining AI algorithms and promoting clear data policies. In all, AI has worked on Tesco’s marketing proposition but on-going optimisation of the techniques will be essential to maintain customer confidence and gain higher response.

6.2 Recommendation

Increase data transparency: Explain how Tesco responds to customers’ data privacy concerns when collecting, storing, and using customers’ information to reassure customers and reduce their concerns through corporate policies, emails and customers service department.

Refine AI algorithms: Enhance the relevance of product suggestions and promotions by tuning up AI processes, which would provide customer with more suitable marketing content according to his/her taste (Zid et al., 2020, p. 155).

Enhance AI-powered customer service: Enhance AI-based customer support tools such as Chabot’s to present quick, proficient, and warm interface with the company services, thus increasing the satisfaction scores and engagements by the clients of Tesco.

Expand personalized offers through preferred platforms: Customized marketing messages can be sent via social media or via apps on customer’s phone, so that the customers actively engage with Tesco’s marketing campaigns.

6.3 Limitation

The main limitation of this study lies in the fact that it is based only on a sample of Tesco clients and, as such, may contain a number of peculiarities that makes it difficult to draw a definite conclusion regarding the overall demographic representativeness of the findings. The downsides are that survey data mostly reduce the capability of competitive advantage as quantitative surveys are used excluding qualitative factors such as customer attitude and experience. The research could also be enhanced using mixture of methodological approaches, such as interviews or focus group discussion to get enhanced understanding.

Chapter 7 Personal Reflection

Evaluation of Project Management Process:

The project management process involved applied orderliness with the right PMP to employ concerning the timelines and the goals to achieve. However this aspect of basing the process of data collection was somewhat limited, this was helpful to ensure that time to completion remains standard with the set specifications of the project.

Problems and Resolutions: There was also worry with non-response on the surveys done. In response to this issue, primarily in order to improve participants’ response and data returns whenever possible, more than one means of communication was employed, such as on-email notifications, social media posts, and follow-up reminders (Hsiao and Bentley, 2021, p. 19).

Learning from the Project: Therefore, I acquired skills such as management of the data received, and an assessment of the customers’ feedback on the AI, and the implementation of the AI in the marketing process. This project made me improve on the problem solving skills and flexibility in this research process.

Strength and Weakness: It was effective planning and timetable within the activities of the project. However, one of these weaknesses was over estimating the time to complete survey participants and as a result on the the first data collection process there was a negative impact.

Future Improvements: Regarding the future work, I will have to enhance on the participant engagement approaches regarding the mean sample size. Moreover, research variety and, in particular, the expansion of data collection methods will improve the methodology’s accuracy and address the shortcomings in the coverage of the results.

References

Ahmed, F. (2024) ‘Artificial Intelligence and its Impact on Customer Service: Enhancing Experiences or Eroding Trust.’ Review Journal for Management & Social Practices, 1(3), pp.10-19. Available at: http://www.rjmsp.com/index.php/Journal/article/view/14

Ajayi, V.O. (2017) ‘Primary sources of data and secondary sources of data.’ Benue State University, 1(1), pp.1-6. Available at: https://www.emerald.com/insight/content/doi/10.1108/QRJ-08-2022-0101/full/html

Alwabel, A.S.A. and Zeng, X.J. (2021) ‘Data-driven modeling of technology acceptance: A machine learning perspective.’ Expert Systems with Applications, 185, p.115584. Available at: https://www.sciencedirect.com/science/article/abs/pii/S0957417421009866

Chintalapati, S. and Pandey, S.K. (2022) ‘Artificial intelligence in marketing: A systematic literature review.’ International Journal of Market Research, 64(1), pp.38-68. Available at: https://journals.sagepub.com/doi/abs/10.1177/14707853211018428

De Oliveira, B. (2023) ‘Participatory action research as a research approach: Advantages, limitations and criticisms.’ Qualitative Research Journal, 23(3), pp.287-297. Available at: https://www.emerald.com/insight/content/doi/10.1108/QRJ-08-2022-0101/full/html

Erçil, A. (2023) ‘Enhancing Customer Personalization in Retail with AI.’ Journal of AI-Assisted Scientific Discovery, 3(2), pp.70-86. Available at: https://scienceacadpress.com/index.php/jaasd/article/view/193

Hicham, N., Nassera, H. and Karim, S. (2023) ‘Strategic framework for leveraging artificial intelligence in future marketing decision-making.’ Journal of Intelligent Management Decision, 2(3), pp.139-150. Available at: https://www.researchgate.net/profile/Nassera-Habbat/publication/373759557_Strategic_Framework_for_Leveraging_Artificial_Intelligence_in_Future_Marketing_Decision-Making/links/65dcba82c3b52a1170fbbff2/Strategic-Framework-for-Leveraging-Artificial-Intelligence-in-Future-Marketing-Decision-Making.pdf

Hsiao, J.C.Y. and Bentley, F. (2021) ‘Exploring Email-Prompted Information Needs.’ Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), pp.1-33. Available at: https://dl.acm.org/doi/abs/10.1145/3479861

Huang, M.H. and Rust, R.T. (2021) ‘A strategic framework for artificial intelligence in marketing.’ Journal of the Academy of Marketing Science, 49, pp.30-50. Available at: https://link.springer.com/article/10.1007/s11747-020-00749-9

Jabbar, A., Akhtar, P. and Dani, S. (2020) ‘Real-time big data processing for instantaneous marketing decisions: A problematization approach.’ Industrial Marketing Management, 90, pp.558-569. Available at: https://www.sciencedirect.com/science/article/pii/S0019850118307454

Kundu, N., Mustafa, F., Hemachandran, K. and Chola, C. (2023) ‘Artificial Intelligence in Retail Marketing.’ In Artificial Intelligence for Business (pp. 86-107). Productivity Press. Available at: https://www.taylorfrancis.com/chapters/edit/10.4324/9781003358411-6/artificial-intelligence-retail-marketing-nirmalya-kundu-farhan-mustafa-hemachandran-channabasava-chola

Lock, D. (2017) ‘The essentials of project management.’ Routledge. Available at: https://doi.org/10.4324/9781315239941

Malhotra, G. (2017) ‘Strategies in research.’ International Journal for Advance Research and Development, 2(5), pp.172-180. Available at: https://www.ijarnd.com/manuscript/strategies-in-research/

Meredith, J.R., Shafer, S.M. and Mantel Jr, S.J. (2017) ‘Project management: a strategic managerial approach.’ John Wiley & Sons. Available at: https://books.google.co.in/books?hl=en&lr=&id=ipZXDwAAQBAJ&oi=fnd&pg=PR5&dq=project+life+cycle+phases+in+project+management+&ots=Qyl7vHEDjR&sig=RWLlOkzUkR1G6SAoFxui47Av5QQ&redir_esc=y#v=onepage&q=project%20life%20cycle%20phases%20in%20project%20management&f=false

Otte, J.N., Kiritsi, D., Ali, M.M., Yang, R., Zhang, B., Rudnicki, R., Rai, R. and Smith, B. (2019) ‘An ontological approach to representing the product life cycle.’ Applied Ontology, 14(2), pp.179-197. Available at: https://content.iospress.com/articles/applied-ontology/ao190210

Pedrini, M. and Ferri, L.M. (2019) ‘Stakeholder management: a systematic literature review.’ Corporate Governance: The International Journal of Business in Society, 19(1), pp.44-59. Available at: https://www.emerald.com/insight/content/doi/10.1108/cg-08-2017-0172/full/html

Puntoni, S., Reczek, R.W., Giesler, M. and Botti, S. (2021) ‘Consumers and artificial intelligence: An experiential perspective.’ Journal of Marketing, 85(1), pp.131-151. Available at: https://journals.sagepub.com/doi/abs/10.1177/0022242920953847

Rathore, B. (2020) ‘Personalization and profits: The impact of ai on targeted digital marketing.’ International Journal of Transcontinental Discoveries, 7(1), pp.1-14. Available at: https://www.researchgate.net/profile/Amer-Taqa-3/publication/383942668_Personalization_and_Profits_The_Impact_of_AI_on_Targeted_Digital_Marketing/links/66e1714efa5e11512cb15b46/Personalization-and-Profits-The-Impact-of-AI-on-Targeted-Digital-Marketing.pdf

Siraj, N.B. and Fayek, A.R. (2019) ‘Risk identification and common risks in construction: Literature review and content analysis.’ Journal of construction engineering and management, 145(9), p.03119004. Available at: https://ascelibrary.org/doi/abs/10.1061/(ASCE)CO.1943-7862.0001685

Stark, J. (2022) ‘Product lifecycle management (PLM).’ In Product lifecycle management (volume 1) 21st Century paradigm for product realisation (pp. 1-32). Cham: Springer International Publishing. Available at: https://link.springer.com/chapter/10.1007/978-3-030-98578-3_1

Statista. (2024). AI and ML impact on retail performance 2024 | Statista. [online] Available at: https://www.statista.com/statistics/1453198/ai-and-ml-impact-on-retail-performance/ [Accessed 9 Oct. 2024].

Tallaksen, L.M. and Laaha, G. (2023) ‘Frequency analysis.’ In Hydrological Drought (pp. 233-304). Elsevier. Available at: https://www.sciencedirect.com/science/article/abs/pii/B9780128190821000035

Tereso, A., Ribeiro, P., Fernandes, G., Loureiro, I. and Ferreira, M. (2019) ‘Project management practices in private organizations.’ Project Management Journal, 50(1), pp.6-22. Available at: https://journals.sagepub.com/doi/abs/10.1177/8756972818810966

Tescoplc.com. (2024). About. [online] Available at: https://www.tescoplc.com/about [Accessed 8 Nov. 2024].

?Walliman, N. (2021) ‘Research methods: The basics.’ Routledge. Available at: https://www.taylorfrancis.com/books/mono/10.4324/9781003141693/research-methods-nicholas-walliman

Wolniak, R., Stecu?a, K. and Ayd?n, B. (2024) ‘Digital Transformation of Grocery In-Store Shopping-Scanners, Artificial Intelligence, Augmented Reality and Beyond: A Review.’ Foods, 13(18), p.2948. Available at: https://www.mdpi.com/2304-8158/13/18/2948

Zid, C., Kasim, N. and Soomro, A.R. (2020) ‘Effective project management approach to attain project success, based on cost-time-quality.’ International Journal of Project Organisation and Management, 12(2), pp.149-163. Available at: https://www.inderscienceonline.com/doi/abs/10.1504/IJ

Appendices

Work Breakdown Structure

Level 1	Level 2	Level 3
1 Small Scale Research Project	1.1 Initiation	1.1.1 Chosen research topic 1.1.2 Approval of research topic
	1.2 Planning	1.2.1 Creating Project Management Plan (Gantt chart, WBS, etc.) 1.2.2 Finalizing research design (quantitative approach) 1.2.3 Developing the survey questionnaire
	1.3 Execution	1.3.1 Literature Review (Weeks 3-5) 1.3.2 Primary Data Collection (Survey - Weeks 6-7) 1.3.3 Data Analysis and Interpretation (Excel) 1.3.4 Conclusion and Recommendations 1.3.5 Personal Reflection 1.3.6 Evaluation and Limitation 1.3.7 Fill up Logbook
	1.4 Monitoring & Control	1.4.1 Proofreading. 1.4.2 Submitting for formative feedback.
	1.5 Completion.	1.5.1 Final Submission of Assignment report

Gantt Chart

Activities	Week 1	Week 2	Week 3	Week 4	Week 5	Week 6	Week 7	Week 8	Week 9	Week 10	Week 11
The research topic is chosen
Aims and objectives are prepared
Literature review conducted
Data collection (survey)
Data analysis
Conclusion and recommendation
Report Writing and Proof Reading

Performance Review

1. What was the Project supposed to accomplish? The study was intended to assess the effectiveness of applying artificial intelligence in Tesco’s marketing and communication initiatives as well as customers’ experience in order to lay down an optimization plan on the use of the technology.

2. Did the project succeed in its aim/how do you know? Specifically, please outline any evaluation and assessment undertaken. Yes, it succeeded. Using surveys as well as literature review showed that AI had the positive impact on the customer interaction and the marketing effectiveness taking into account that there are areas that necessary for work on them – data openness, and customer support.

3. What things do you think worked well and why? Evaluate all aspects of the project (e.g. initial inception, project activities and project outcomes) from a range or perspectives. Clear objectives and methodology ensured focus. Effective data collection via surveys provided valuable insights. Quantitative analysis using Excel produced reliable conclusions. Practical recommendations aligned with findings.

4. What problems emerged during the project and how were they tackled? Was there timely identification of issues and resulutions during the project process? Low survey response rate: Addressed via multi-platform outreach and incentives. Data accuracy concerns: Mitigated with unbiased questions and secondary data validation. Time constraints: Managed through prioritization and proactive scheduling.

5. What did you learn from undertaking the project? Learned about the application of AI in marketing and data ethics, and expanded knowledge of AI, study methods, statistical tools, and problem-solving techniques.

6. How would you rate your performance as a management consultant leading the project? 8/10 for great planning, analysis and recommendation but can improve on the factor that contributed to the challenges and degree of participants’ interaction.

7. What strengths and weakness of your performance did you identify? Strengths: Many people can check numbers, but few can manipulate them, present the case, and come up with meaningful solutions. Weaknesses: Issue identification at the wrong times and overdependence on numbers.

8. How will this inform and support your continuous professional development? Future projects will involve mixed-methods research designs, enhanced power for engaging participants, and enhanced faculty in AI and research approaches to capitalize on the research strengths and to overcome limits.

Project Logbook

Project Logbook for the chosen organisation (TESCO)
Name of Student: Georgette Yomba Djewouo Djimo	Name of Supervisor:
Project Title: The Impact of AI on Enhancing Marketing Strategies and Customer Engagement in Tesco	Date:
Update of weekly research/task achieved (Account for a minimum of six weeks with dates) What have you completed? Did you fulfil the task requirements? Are you on track and within the deadlines set? Did you need to make any changes to your project management plan?	Over the past six weeks, I successfully completed several key tasks: In week 1, the topic of research was selected, in week 2 the objectives were refined, the literature review process was conducted in week 3, 4 and 5 and the data collection by means of surveys was done in week 6 and 7. I was also able to ensure that all the created tasks met the requirements of the project plan, there were no delays, no changes were required to apply to the project plan.
Any risks and/ or issues identified? Did you identify risk/issues with a lack of skills required for undertaking research task? Did you identify and additional risk/issues that have an impact on the project management plan?	There were no issues that would relate to the skill impacts of ASMS’s past and present business processes. The only extra concern was poor survey response rate, which was tackled through incentives, response through different online platforms without much effect on the project timeline.
Problems encountered What barriers did you face? How did you overcome them?	The largest barrier was the problem of achieving low survey response rate. This was overcome by the use of incentives, targeting the use of multiple media and sending follow-up reminders to the participants.
New ideas and change of project directions	There were not changes in the projects, but I probed for fresh approaches for managing surveys, for example, through attempts to apply online advertising base on social media.
What have I learnt about myself this week? How did I feel when I had to deal with task/problems? Did I find it useful to complete the task? How well have I performed? What did I contribute? What Can I improve on next week? How might this learning apply in the future?	In week 6 of this service learning project, I came to understand that I can actually change again to different issues such as low survey response rates by writing back/reminding the participants and using more channels. These issues I had no doubt I could address and overall the task proved quite beneficial for the acquisition of relevant information. I did fairly well and expect to increase the efficiency in data analysis during the eighth week.
Tasks planned for next week Which tasks are the priority? Have you set aside sufficient time for completion?	In the next week (8th week), the priorities are data analysis of the survey and writing the results’ part. This means that I have had ample time to analyse data and write reports to quickly finish these works.
Project Plan Status to date (On, ahead, behind)	All the tasks have been implemented by the appropriate due dates hence the project is well on schedule. Preparation of survey, data collection and tabulation together with report writing is going on in an orderly manner, thus giving the project the right tempo.
Supervisors Comments to address
Signature of the Supervisor and date:

Survey Questions

1. Which of Tesco’s personalized marketing features do you find most useful?

1. Personalized product recommendations

2. Tailored promotions/discounts

3. Reminder emails

4. None of the above

2. In what format do you prefer receiving personalized offers from Tesco?

1. Email

2. Social media ads

3. Mobile app notifications

4. Physical mail

3. What type of AI-driven content from Tesco would you like to see more of?

1. Product suggestions

2. Discount offers

3. Loyalty program updates

4. Personalized shopping tips

4. How accurate are Tesco’s personalized recommendations for your shopping needs?

1. Accurate

2. Sometimes accurate

3. Not accurate

5. What aspect of Tesco’s marketing do you think has improved with AI?

1. Product recommendations

2. Promotional timing

3. Targeted advertising

4. Customer communication

6. What motivates you to engage with Tesco’s targeted promotions?

1. Discounts or savings

2. Personalized product suggestions

3. Limited-time offers

4. None of the above

7. What is your main concern about Tesco using AI in marketing?

1. Privacy of personal data

2. Relevance of offers

3. Accuracy of recommendations

4. No concerns

8. Which AI-driven feature from Tesco enhances your shopping experience the most?

1. Chabot’s for customer service

2. Personalized product suggestions

3. Targeted offers and discounts

4. None of the above

9. In which area should Tesco improve its AI-driven marketing efforts?

1. Better personalization of offers

2. Timing of communication

3. Increased transparency on data use

4. Improved customer service integration

10. What would encourage you to interact more with Tesco’s personalized offers?

1. More relevant offers

2. Bigger discounts

3. Better AI customer service

4. Easier access via app