ASSESSMENT BRIEF

BUS9010 Capstone Project

Assessment 2: Written Project Proposal

Overview of the Assessment Task

This assessment builds on the work you have undertaken in Assessment 1.

You are expected to take into consideration the feedback you received from Assessment 1 and then write a formal project proposal.

A project proposal at the higher education level is used to assess the quality and originality of your ideas, your understanding of the purpose of your project, and the ultimate feasibility of the project.

This assessment is worth 20% of the overall marks available for this subject.

Assessment Instructions

This assessment requires you to build on the work you have undertaken in Assessment 1.

Building on your research to date, and analysis of the identified business issues you have discovered, you are required to develop a formal project proposal.

The formal project proposal should be a 1,000-word document that addresses key questions related to the business issues identified in Assessment 1. It will typically describe something you intend to produce to address the identified issues – this might be a report, a web site, a film, a book, a computer game or an event – there are many possibilities.

While the actual elements of a project proposal differ depending on the project itself, there are several key elements of a project proposal. Building on your project proposal poster work you should, at a minimum, consider the following:

• Title: This is a brief heading that accurately captures the nature of the proposed work.
• Background: What problems, challenges, or opportunities exist that create a need for this project? You should provide the background context against which you will conduct your project, including a summary of what is already known about the issue or topic as well as some of the problem areas that directly relate to your proposed work. Literature / best practice should be explored in this section.
• Objectives: What are the intended outcomes of this project?
• Scope: What are the steps or stages of the project? What elements are included in this project? How will the objectives be reached through this project? This section highlights how you will contain your project to ensure it is focussed and achievable. It should also explain what is out of scope.
• Approach / Method: This section describes the methods and materials that you intend to use within your project. It will detail how you will go about acquiring the information you need and explain how the project will be managed. If a particular theory or model is to be used, this should be stated.
• Timeline: Over what period will the project be undertaken? What are the key deliverables on what dates?
• Outcomes: This section should include the expected impacts and benefits of the proposed project. It is important that these be specific and realistic.
• Conclusion:
• References: APA referencing style is to be used.

Your project proposal will be used to assess the quality and originality of your ideas, your understanding of the purpose of your project, and the ultimate feasibility of the project.

This assessment should incorporate APA 7th referencing style. For support on how to reference see the AIHE Learning Support Hub on canvas for further information.

Use of Gen AI is Allowed:

This assessment allows the responsible and ethical use of generative artificial intelligence (Gen AI) tools to assist with preparation. However, students must ensure that their final submission represents their own critical thinking, analysis, and synthesis of information. Any content generated by GenAI must be carefully evaluated for accuracy, appropriately acknowledged, and properly referenced. Students are required to declare the use of GenAI within the assignment. Any submission that includes content created by unauthorised use of artificial intelligence tools is a breach of academic integrity.

Marking Criteria

Please refer to the attached rubric for marking criteria and standards of performance. Constructive feedback will be provided within a timely manner in accordance with AIHE Assessment Procedure.

This assessment is not redeemable unless otherwise specified.

Limits for Assessments

Written submissions that exceed the word limit by more than 10% will cease to be marked from the point at which that limit is exceeded.

Time limits for in-person or video presentations that exceed the allocated time limit by more than 10% will cease to be marked from the point at which that limit is exceeded, and Lecturers may ask students to cease their presentation.

Assessment Variation and Special Consideration

Students may be eligible for a variation to assessment arrangements when unexpected or extenuating circumstances impact on their performance or their ability to complete their assessment tasks by or on the specified date. Students must complete the Application to Vary Assessment with evidence.

Students with identified special or specific needs may apply for variations to assessment in the subject. Students are required to contact the Student Support Officer or Student Learning Advisor to discuss their specific needs.

Late Assignment Submission Penalties

An assessment task is late for submission when it is not submitted by the due date and time as indicated on Canvas, or by an agreed extension date and time as confirmed by the subject lecturer.

Late assessment tasks will be penalised at the rate of 5% of maximum possible marks, per calendar day (i.e. 24 hours or part thereof). After seven (7) calendar days, assignments will attract zero (0) marks. Assignments submitted at any stage within the first 24 hours after the deadline will be considered to be one day late and therefore subject to the associated penalty.

For further detail see: AIHE Assessment Procedure.

Academic Integrity

Academic integrity is an essential quality for higher education and is a fundamental part of learning and teaching. AIHE is committed to promoting academic integrity and ethical behaviour. The reputation of AIHE and its graduates, and the academic standing of its qualifications rests with its ability to promote academic integrity and manage academic misconduct fairly and consistently.

All students must become familiar with, and understand the meaning and consequences of plagiarism, cheating in exams and tests, unauthorised use of artificial intelligence, collusion, contract cheating and other academic offences under the AIHE Academic Integrity Policy.

Marking Rubric & Standards of Performance – FORMAL WRITTEN PROJECT PROPOSAL

Approval Record & Document Control

Note: This report is provided as a sample for reference purposes only. For further guidance, detailed solutions, or personalized assignment support, please contact us directly.

Project Proposal: Enhancing Customer Engagement through AI-Driven Digital Marketing Strategies

Introduction

In today’s rapidly evolving digital landscape, businesses are increasingly challenged to capture and retain customer attention. This project proposal explores the development of an AI-driven digital marketing strategy aimed at improving customer engagement and conversion rates for small-to-medium enterprises (SMEs). The project investigates how artificial intelligence tools can personalize customer experiences, optimize marketing campaigns, and drive business growth. The purpose of this proposal is to outline the background, objectives, methodology, and expected outcomes of the project while ensuring its feasibility and practical relevance.

Background

With the rise of digital platforms, customer behavior has shifted significantly, requiring businesses to adopt more innovative marketing strategies. Traditional marketing approaches often fail to deliver personalized experiences, leading to reduced engagement and lower return on investment (ROI).

Recent studies indicate that AI-powered marketing tools, such as chatbots, predictive analytics, and recommendation systems, can significantly enhance customer interactions. According to contemporary marketing theories, personalization and customer-centric strategies are key drivers of engagement and loyalty.

However, many SMEs lack the knowledge, resources, or structured approach to implement AI effectively. This creates a gap between technological potential and practical application. The project addresses this gap by developing a structured AI-driven marketing framework tailored for SMEs.

The theoretical foundation of this project is based on:

• Customer Relationship Management (CRM) theory
• Digital Marketing Funnel Model
• Technology Acceptance Model (TAM)

These frameworks support the integration of AI into marketing strategies to improve efficiency and customer satisfaction.

Objectives

The primary aim of this project is to design and evaluate an AI-driven digital marketing strategy for SMEs. The specific objectives include:

1. To analyze current challenges faced by SMEs in customer engagement.
2. To explore the role of AI technologies in enhancing marketing effectiveness.
3. To develop a practical AI-based marketing framework.
4. To evaluate the impact of AI tools on customer engagement and conversion rates.
5. To provide recommendations for SMEs on implementing AI-driven strategies.

These objectives align with the overall goal of improving business performance through innovative and data-driven approaches.

Scope

This project focuses specifically on SMEs operating in the retail and service sectors. The scope includes:

In Scope:

• Analysis of current digital marketing practices
• Implementation of AI tools such as chatbots and analytics platforms
• Development of a structured marketing strategy
• Evaluation of customer engagement metrics

Out of Scope:

• Large enterprise-level AI implementations
• Advanced machine learning model development
• Financial investment analysis beyond basic feasibility

The project will be conducted in clearly defined stages, ensuring it remains achievable within the given timeframe. By narrowing the scope, the project maintains focus and delivers practical outcomes.

Approach / Method

This project adopts a mixed-method approach combining both qualitative and quantitative research.

Data Collection:

• Primary Data: Surveys and interviews with SME owners and customers
• Secondary Data: Academic journals, industry reports, and case studies

Methodology:

1. Literature review to establish theoretical foundation
2. Analysis of current SME marketing practices
3. Design of AI-driven marketing framework
4. Implementation of selected AI tools (e.g., chatbot simulation, analytics tools)
5. Evaluation using key performance indicators (KPIs)

Tools & Techniques:

• Google Analytics (for data insights)
• Chatbot platforms (for automation)
• CRM systems (for customer data management)

The project will follow project management principles such as Agile methodology to ensure flexibility and continuous improvement.

Timeline

The project will be completed over a 7-week period:

Key deliverables include research findings, framework design, and final recommendations.

Outcomes

The expected outcomes of this project are:

• A comprehensive understanding of SME marketing challenges
• A practical AI-driven digital marketing framework
• Improved customer engagement strategies
• Increased awareness of AI applications in marketing
• Actionable recommendations for SMEs

The project aims to deliver measurable improvements in customer interaction and business performance, making it highly relevant in today’s competitive environment.

Conclusion

This project proposal presents a structured plan to explore the integration of AI in digital marketing for SMEs. By addressing existing challenges and leveraging modern technologies, the project aims to bridge the gap between theory and practice. The proposed methodology, timeline, and scope ensure that the project is both feasible and impactful. Ultimately, this project contributes to the growing field of digital transformation and provides valuable insights for businesses seeking to enhance customer engagement.

References (APA 7th Style – Sample)

Chaffey, D., & Ellis-Chadwick, F. (2019). Digital marketing (7th ed.). Pearson.

Kotler, P., Kartajaya, H., & Setiawan, I. (2021). Marketing 5.0: Technology for humanity. Wiley.

Ngai, E. W. T., Xiu, L., & Chau, D. C. K. (2009). Application of data mining techniques in customer relationship management: A literature review. Expert Systems with Applications, 36(2), 2592–2602.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model. Management Science, 46(2), 186–204.