Digital Marketing Landscape and Strategy Assessment Presentation Brief November 2025

MSc Digital Marketing & Analytics

Objectives

• Write a presentation demonstrating understanding and insight into the  evolution of marketing practise as it  relates to a digital marketing platform,  technology or category

• Support conclusions with relevant statistics and respected thought-leader  opinions

Project Weighting

Presentation (written & verbal) – 100%

Assessment Criteria

Grading Criteria for Written submissions; (During the year)

Written Criteria: Presentation is well written, typed in a professional  manner, presented and structured – without any misspellings or  mistakes in grammar or English.

Referencing: Harvard Referencing System has been used  correctly and accurately.

Quantity of Research conducted: Extensive secondary research  has been carried out and referenced. Avoid plagiarism and /or  putting in facts without saying where you sourced them from.

Brief:

My Chosen Subject; Review of the Marketing Application of AI in Customer Retention in Telco’s

Application of AI in Customer Retention in Telcos:

Conference-Style Presentation Assignment**

This assignment mimics the early stages of producing a literature review for a dissertation. Students will locate, read, analyse, and cluster academic and high-quality industry sources on Artificial Intelligence (AI) applications in telecom customer retention, then present their findings in a conference-style presentation.

Step 1: Conduct a Systematic Literature Search

Using Google Scholar, JSTOR, or equivalent platforms, students must document:

Search Terms

(Examples — adapt as needed)

• “AI in customer retention” + “telecommunications”
  
• “machine learning churn prediction” + “telco”
• “predictive analytics” + “customer lifecycle management”
• “AI-driven personalisation” + “customer loyalty”
• “customer churn modelling” + “telecom industry”
• “CX automation” + “AI chatbots” + “telecom”
• “recommendation systems” + “telecommunications marketing”
• “AI” + “customer experience management” + “telco”

Inclusion Criteria

(e.g.,)

• Publication years: 2015–2025
  
• Focus on AI, machine learning, predictive analytics, customer retention, or telecom customer experience
• Peer-reviewed research or reputable industry reports (e.g., McKinsey, Accenture, Ericsson, Deloitte)
• English-language
• Must include marketing, customer management, or strategic applications — not purely technical optimisation

Exclusion Criteria

(e.g.,)

• Papers focused solely on network engineering, signal processing, or technical AI without customer implications
• Studies from outside telecommunications unless offering transferable customer retention insights
• Articles lacking methodological clarity or empirical evidence

Report Search Metrics

• Initial relevant hits: 20–30
  
• Final included articles: 6–8 peer-reviewed or high-quality industry papers

This replicates the early data-gathering stage of a dissertation.

Step 2: Identify and Cluster Thematic Areas

After reviewing the literature, group articles into themes.
Examples of typical clusters for this research:

1. Predictive Analytics & Churn Prediction Models

How telcos use machine learning, data mining, and behavioural models to identify at-risk customers.

2. AI-Enabled Personalisation & Customer Experience

How AI tailors plans, offers, messaging, and service journeys to increase satisfaction and retention.

3. Automation, Chatbots & AI-Driven Customer Service

Role of conversational AI, self-service systems, and automated support in improving retention and reducing frustration.

4. Customer Value Management (CVM) & Next-Best-Action Systems

How AI supports decision engines that determine personalised retention actions in real time.

5. Ethical AI, Data Governance & Customer Trust

How privacy concerns, algorithmic bias, transparency, and trust influence customer acceptance of AI-powered retention strategies.

Students may adjust or merge these based on their literature.

Step 3: Summarise Each Theme

For each cluster/theme, students should produce:

3–5 key insights from the selected literature

(e.g., accuracy improvements in churn prediction; impact of personalised offers; efficiencies from chatbot automation)

Gaps, tensions, or contradictions

(e.g., some studies show customer distrust of AI-driven personalisation; inconsistent results when telcos use black-box models)

Implications for customer retention strategy in telecoms, such as:

• Increasing retention via predictive churn scoring
  
• Designing personalised loyalty and reward programs
• Balancing efficiency with human-touch expectations
• Weighing transparency and ethics in AI-driven decisioning
• Integrating AI outputs across CRM, billing, and digital touchpoints

Students should explicitly connect insights to telco customer behaviour and retention management.

Step 4: Build a Thematic or Logic Map

The map should visually show:

• Key themes (e.g., Churn Prediction → Personalised Interventions → Customer Engagement → Retention Outcomes)
  
• Relationships between themes (e.g., churn prediction feeds next-best-action strategies)
• Dominant vs. emerging research directions

○ Dominant: e.g. predictive analytics, churn modelling
○ Emerging: e.g. generative AI, conversational CX, proactive network-based retention signals

• Conceptual overlaps
  ○  e.g., personalisation overlaps with ethics and trust

The final map should form a conceptual model of how AI supports customer retention in telecommunications.

Step 5: Final Output — Conference-Style Presentation

Students will deliver a 10–12 minute conference-style presentation including:

1. Systematic search process documentation

• Search terms
• Inclusion/exclusion criteria
• Article selection rationale

2. Thematic synthesis

• Summary of each cluster
  
• Key insights & gaps
  

3. Application to Telco Customer Retention Practice

• Strategic recommendations informed by the literature

4. A thematic/logic map

• Used as a main visual anchor for the presentation

5. Clear academic structure and professional communication suitable for a conference workshop.

Submission Instructions

• All work must be submitted to Turnitin. Failure to do so will mean your work is not accepted for grading.
• Please familiarise yourself with the IT Carlow Academic Integrity & Anti-Plagiarism Policy (available on Blackboard in the TLC Learning Development course).
• Harvard Referencing to be applied to all assignments – see TLC Learning Development course on Blackboard for  referencing guides.

Deadline

Deadline will be assigned in class.

• Deadline dates are final.
• Submissions can be made ahead of deadlines with prior arrangement.
• Please familiarise yourself with the IT Carlow Policy & Procedure on Late Submission of Assignments.

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