MSc – Digital Marketing & Social Media Analytics

Module Introduction

This module develops advanced strategic understanding of digital marketing and customer experience management in data-driven environments. Students will critically evaluate digital channels, platforms, and ecosystems, and design integrated strategies that create measurable value. The module emphasises customer journey design, omnichannel orchestration, brand performance, and evidence-based decision-making. It is aligned with UK FHEQ Level 7 expectations and RSBT academic regulations, requiring critical judgement, strategic insight, and professional application.

Module Aims

• Develop advanced understanding of digital marketing strategy and customer experience frameworks.
• Enable learners to design integrated, data-informed digital marketing strategies.
• Build competence in journey mapping, value propositions, and omnichannel planning.
• Enhance critical awareness of platform dynamics, privacy, and regulatory considerations.
• Prepare learners to evaluate and improve digital marketing performance at strategic level.

Learning Outcomes

• Critically evaluate digital marketing ecosystems, platforms, and customer experience models.
• Design and justify an integrated digital marketing and CX strategy using appropriate frameworks and evidence.
• Analyse customer journeys and touchpoints to identify opportunities for value creation and optimisation. 2 4. Assess strategic, ethical, and regulatory considerations in digital marketing practice.
• Communicate strategic analysis and recommendations using appropriate academic and professional conventions.

Indicative Content

• Digital marketing ecosystems and platform strategies
• Customer experience (CX), journey mapping, and service design
• Omnichannel and integrated marketing communications
• Brand, performance, and growth marketing strategies
• Value propositions and audience segmentation
• Privacy, consent, and regulatory frameworks (e.g., data protection)
• Measurement frameworks and strategic KPIs
• Personalisation, experimentation, and optimisation
• Partnerships, influencers, and community strategies
• Future trends in digital marketing and CX

Learning and Teaching Strategy

Delivery will include lectures, case studies, workshops, simulations, and guided reading. Students will undertake substantial independent study, including analysis of brands, platforms, and campaigns. As a 30-credit module, the workload and depth of analysis are proportionately higher than 15-credit modules, with emphasis on strategic judgement and applied decision-making.

Assessment Strategy

CW1: Case-based analysis of a brand’s digital marketing and customer experience strategy. CW2 (60%): Individual Report (3,500–4,000 words) developing an integrated digital marketing and CX strategy for an organisation, including objectives, channels, journey design, measurement, and governance, with Harvard referencing.

Recommended Reading

• Chaffey, D. & Ellis-Chadwick, F. (2022). Digital Marketing. Pearson.
• Lemon, K. & Verhoef, P. (2016). Understanding Customer Experience. Journal of Marketing.
• Kotler, P. et al. (2021). Marketing 5.0. Wiley.
• Ryan, D. (2016). Understanding Digital Marketing. Kogan Page.

Module Introduction

This module develops advanced capability in marketing data management, social media analytics, and insight generation for strategic decision-making. Students will critically evaluate data sources, metrics, and analytical methods across paid, owned, and earned media, and translate analysis into actionable insights. The module emphasises data governance, experimentation, attribution, and ethical use of data. It is aligned with UK FHEQ Level 7 expectations and RSBT academic regulations, requiring analytical rigour, critical judgement, and professional application.

Module Aims

• Develop advanced understanding of marketing data ecosystems and social media analytics.
• Enable learners to design measurement frameworks and select appropriate metrics and KPIs.
• Build competence in data preparation, analysis, visualisation, and insight communication.
• Enhance skills in experimentation, attribution, and performance optimisation.
• Promote ethical, compliant, and responsible use of marketing and social data.

Learning Outcomes

• Critically evaluate marketing and social media data sources, metrics, and platforms.
• Design and justify measurement frameworks aligned to marketing objectives.
• Apply appropriate analytical methods to derive insights from campaign and social data.
• Interpret results to inform optimisation, experimentation, and decision-making.
• Communicate analytical findings and recommendations using appropriate academic and professional conventions.

Indicative Content

• Marketing data ecosystems and data governance
• Social media platforms, APIs, and data collection
• Measurement frameworks, KPIs, and OKRs
• Descriptive, diagnostic, and predictive analytics
• Attribution models and incrementality testing
• Experimentation and A/B testing in marketing
• Dashboarding, visualisation, and storytelling with data
• Sentiment analysis, social listening, and text analytics (foundations)
• Privacy, consent, and regulatory compliance (e.g., data protection)
• From insight to action: optimisation and performance management

Learning and Teaching Strategy

Delivery will include lectures, hands-on labs, case studies, and guided reading. Students will undertake substantial independent study, including analysing real datasets and building dashboards and reports. As a 30-credit module, the workload and depth of analysis are proportionately higher than 15-credit modules, with emphasis on applied analytics, critical evaluation, and professional reporting.

Assessment Strategy

CW1: Analytics portfolio demonstrating data preparation, KPI design, dashboards, and insight narratives. CW2 (60%): Individual Report (3,500–4,000 words) analysing a brand or campaign’s data, presenting an insight-led optimisation plan, and justifying methods and metrics used, with Harvard referencing.

Recommended Reading

• Provost, F. & Fawcett, T. (2013). Data Science for Business. O’Reilly.
• Chaffey, D. & Ellis-Chadwick, F. (2022). Digital Marketing. Pearson.
• Kaushik, A. (2010). Web Analytics 2.0. Wiley.
• McKinsey Global Institute (latest). The State of Analytics.

Module Introduction

This module develops advanced understanding of content strategy, performance marketing, and growth frameworks in digital environments. Students will critically evaluate channel strategies, experimentation, conversion optimisation, and lifecycle marketing to drive sustainable growth. The module emphasises evidence-based decision-making, testing and optimisation, and ethical and compliant marketing practice. It is aligned with UK FHEQ Level 7 expectations and RSBT academic regulations, requiring strategic judgement, analytical rigour, and professional application.

Module Aims

• Develop advanced understanding of content strategy and performance marketing frameworks.
• Enable learners to design and evaluate growth strategies across paid, owned, and earned channels.
• Build competence in experimentation, conversion optimisation, and lifecycle marketing.
• Enhance skills in budgeting, forecasting, and performance management.
• Promote ethical, compliant, and responsible growth marketing practice.

Learning Outcomes

• Critically evaluate content, performance, and growth marketing strategies across channels.
• Design and justify an integrated growth plan using experimentation and optimisation frameworks.
• Apply appropriate methods for conversion optimisation, retention, and lifecycle marketing.
• Assess financial, ethical, and regulatory considerations in performance marketing.
• Communicate strategic analysis and recommendations using appropriate academic and professional conventions.

Indicative Content

• Content strategy, editorial planning, and brand storytelling
• Paid media, SEO/SEM, marketplaces, and influencer strategies
• Performance frameworks: funnels, cohorts, and LTV/CAC
• Experimentation, A/B testing, and optimisation at scale
• Conversion rate optimisation (CRO) and UX principles
• Lifecycle marketing: acquisition, activation, retention, monetisation
• Budgeting, forecasting, and performance dashboards
• Attribution, incrementality, and media mix considerations
• Compliance, brand safety, and platform policies
• Sustainable growth and long-term brand value

Learning and Teaching Strategy

Delivery will include lectures, workshops, case studies, simulations, and guided reading. Students will undertake substantial independent study, including designing experiments, analysing performance data, and developing growth plans. As a 30-credit module, the workload and depth of analysis are proportionately higher than 15-credit modules, with emphasis on strategic experimentation and performance management.

Assessment Strategy

CW1: Performance audit and experiment design for a chosen brand or campaign. CW2 (60%): Individual Report (3,500–4,000 words) developing a content and growth marketing plan, including channel strategy, experimentation roadmap, budget, metrics, and governance, with Harvard referencing.

Recommended Reading

• Ellis, S. & Brown, M. (2017). Hacking Growth. Crown Business.
• Chaffey, D. & Ellis-Chadwick, F. (2022). Digital Marketing. Pearson.
• Croll, A. & Yoskovitz, B. (2013). Lean Analytics. O’Reilly.
• Kotler, P. et al. (2021). Marketing 5.0. Wiley.

Module Introduction

This module develops advanced understanding of marketing technology (MarTech), artificial intelligence applications in marketing, and end-to-end campaign orchestration. Students will critically evaluate MarTech stacks, data integration, automation, and AI-driven decisioning to design scalable, compliant, and high-performance campaigns. The module emphasises systems thinking, architecture choices, experimentation at scale, and governance. It is aligned with UK FHEQ Level 7 expectations and RSBT academic regulations, requiring strategic judgement, technical literacy, and professional application.

Module Aims

• Develop advanced understanding of MarTech ecosystems, platforms, and integration patterns.
• Enable learners to design AI-enabled campaign orchestration across channels.
• Build competence in automation, personalisation, and lifecycle journey management.
• Enhance skills in data integration, experimentation, and performance governance.
• Promote ethical, compliant, and responsible use of marketing technology and AI.

Learning Outcomes

• Critically evaluate MarTech architectures, tools, and data flows for different organisational contexts.
• Design and justify AI-enabled campaign orchestration and journey automation.
• Apply appropriate methods for personalisation, experimentation, and optimisation at scale.
• Assess privacy, security, and regulatory implications of marketing technology deployments.
• Communicate technical and strategic recommendations using appropriate academic and professional conventions.

Indicative Content

• MarTech stacks: CRM, CDP, DMP, CMS, MAP, and analytics platforms
• Data integration, identity resolution, and consent management
• Campaign orchestration and journey automation
• AI in marketing: recommendation, prediction, and decisioning
• Personalisation, segmentation, and real-time activation
• Experimentation platforms and optimisation at scale
• Measurement, attribution, and incrementality
• Architecture patterns, APIs, and vendor ecosystems
• Security, privacy, and regulatory compliance
• Operating models, governance, and change management

Learning and Teaching Strategy

Delivery will include lectures, workshops, architecture labs, case studies, and guided reading. Students will undertake substantial independent study, including evaluating MarTech stacks, designing orchestration flows, and analysing platform trade-offs. As a 30- credit module, the workload and depth of analysis are proportionately higher than 15- credit modules, with emphasis on systems thinking, integration, and strategic implementation.

Assessment Strategy

CW1: MarTech stack audit and campaign orchestration design for a selected organisation. CW2 (60%): Individual Report (3,500–4,000 words) proposing an AI-enabled MarTech architecture and campaign orchestration plan, including data integration, governance, experimentation, and measurement, with Harvard referencing.

Recommended Reading

• Chaffey, D. & Ellis-Chadwick, F. (2022). Digital Marketing. Pearson.
• Gartner (latest). Magic Quadrant for Digital Experience Platforms.
• Kotler, P. et al. (2021). Marketing 5.0. Wiley.
• Provost, F. & Fawcett, T. (2013). Data Science for Business. O’Reilly.

Module Introduction

This module is the capstone of the MSc Digital Marketing & Social Media Analytics programme and represents a substantial, independent piece of work at Level 7. Students will undertake a research-focused dissertation or an applied industry project addressing a complex problem in digital marketing, analytics, or social media strategy. The project requires critical engagement with literature, appropriate methodology, ethical compliance, and professional-quality reporting. As a 60-credit module, it demands a high level of autonomy, depth of analysis, and originality, aligned with UK FHEQ Level 7 expectations and RSBT academic regulations.

Module Aims

• Enable students to design and execute an independent research or applied project in digital marketing and analytics.
• Develop advanced skills in problem definition, research design, and project management.
• Strengthen critical engagement with academic literature and professional practice.
• Ensure ethical, compliant, and evidence-based research and analysis.
• Produce a substantial, coherent, and professionally presented dissertation or project report.

Assessment Strategy

Single major submission: Dissertation / Project Report (12,000–15,000 words, excluding references and appendices), demonstrating independent work, critical analysis, methodological rigour, and professional presentation. The submission may be accompanied by artefacts (e.g., dashboards, datasets, campaign assets, or code) where appropriate.