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Digital Marketing / Marketing Analytics / Data-Driven Marketin
Data-Driven Marketing Analytics: Google Analytics 4 and Google Merchandise Store Campaign Analysis
This Data Driven Marketing assessment requires students to develop practical digital analytics skills and apply them to the evaluation of a real-world marketing campaign. The assessment combines professional training through LinkedIn Learning with a recorded executive-level presentation based on data from the Google Analytics 4 Demo Account for the Google Merchandise Store. The aim is to analyse campaign performance, interpret relevant marketing KPIs and present actionable recommendations that senior executives and managers can use for decision-making. CW 1_ LinkedIn Learning and Rec… The first task requires completion of two LinkedIn Learning courses: Google Analytics 4 (GA4) Essential Training and Advanced Google Analytics. Students must submit the certificates of completion as evidence of developing the foundational and advanced skills required for digital marketing analysis. CW 1_ LinkedIn Learning and Rec… The second task places the student in the role of a digital marketing executive for the Google Merchandise Store. Students select and analyse a digital marketing campaign using GA4 demo-store data and then create a 10-minute recorded presentation, with a tolerance of ±2 minutes, providing evidence-based recommendations for improving campaign performance. CW 1_ LinkedIn Learning and Rec… The main presentation is limited to 12 slides and should include an executive summary, key findings and insights, recommendations, actionable steps, and references or appendix material. An additional 4–6 appendix/reference slides may be used for supporting charts, graphs and data. Recommendations must be linked directly to analytical evidence and aligned with the campaign's overall marketing objectives. CW 1_ LinkedIn Learning and Rec… The analysis should examine relevant marketing performance indicators, including click-through rate, conversion rate, cost per acquisition, return on ad spend and user-engagement measures such as bounce rate, session duration, pages per session and retention patterns. CW 1_ LinkedIn Learning and Rec… Students are expected to move beyond descriptive reporting and develop strategic recommendations concerning budget reallocation, creative optimisation, audience targeting and funnel performance. The presentation should identify underperforming and high-performing channels, evaluate audience segments, examine points of user drop-off and propose practical interventions to improve conversion and campaign efficiency. CW 1_ LinkedIn Learning and Rec… The marking scheme gives 20 points for LinkedIn Learning certification, 35 points for executive communication and structure, 35 points for data analysis and insight generation, and 10 points for references and appendices. CW 1_ LinkedIn Learning and Rec… CW 1_ LinkedIn Learning and Rec… Overall, the assessment integrates GA4 skills, campaign analytics, KPI interpretation, data visualisation, strategic recommendation development and executive presentation skills within a practical digital-marketing context. The brief also requires Cite Them Right Harvard referencing. CW 1_ LinkedIn Learning and Rec…
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Digital Marketing / Marketing Analytics
2,483 words
Digital Marketing Analytics (BS783) — GA4 Performance Analysis and 90-Day Campaign Strategy for the Google Merchandise Store
This Level 7 report positions the writer as a digital marketing consultant engaged by the Google Merchandise Store, a live e-commerce operation. All quantitative work is drawn from the public GA4 Analytics Demo Account, so the analysis rests on real platform data rather than invented figures, and the report is structured in two halves that move from diagnosis to prescription. Part A is research and analysis. It opens by defining and evaluating the role analytics currently plays in shaping the store's digital marketing strategy — what is being measured, what that measurement is used for, and where the strategic gaps sit — grounded in marketing performance-measurement literature rather than description of the interface. The tactical analysis then assesses how effectively the store's digital channels and social content drove traffic and conversions across two defined quarterly reporting periods. Performance reporting follows, comparing the two periods in detail to identify the strongest customer segments, acquisition channels and products, and — more importantly — offering a reasoned explanation for why performance differed between them, distinguishing seasonality and campaign activity from genuine structural change. Part B turns to campaign strategy for a niche lifestyle market segment. A customer persona is developed and supported by the behavioural evidence visible in the GA4 data rather than asserted demographics, and a full journey map traces that persona from awareness through consideration, conversion and retention, identifying the friction points at each stage. Strategic recommendations then propose specific enhancements to channels and content aimed at that audience, each justified against the performance evidence from Part A and against the competitive landscape the store operates in. The final section builds the measurement architecture for the proposed campaign over the following ninety days. It sets goals and performance management parameters, selects the tools and metrics that will track them, defines the reporting cadence and the decision thresholds that trigger optimisation, and places particular weight on measuring return on marketing investment — connecting spend to sales, leads and customer satisfaction rather than to vanity engagement figures. Attribution limitations and the practical constraints of GA4 measurement are acknowledged where they affect confidence in the numbers. Throughout, screenshots and exported GA4 reports evidence the claims made, academic and practitioner sources support the analytical framing, and Harvard referencing is applied consistently. The submission is a single file through Turnitin.
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Digital Marketing and Analytics
2,974 words
Digital Marketing Analytics Consultancy Report — Accessibility, Social Listening and Web Analytics for an E-Commerce Storefron
This assessment places the student in a consultancy role advising a global brand's e-commerce storefront on its digital marketing strategy. It is written as a business report rather than an academic essay, in the third person, with appendices used only for supporting material that the main text explicitly refers the reader to. The work is built from four analytical layers, and the mark weighting tells students where the effort belongs. The first is an accessibility and user experience evaluation of the client site benchmarked against two self-selected industry competitors — the selection itself must be justified, and the analysis must cover both technical and content dimensions using recognised evaluation tools rather than impressionistic browsing. The second layer, carrying the smallest weight, is social listening: which networks the brand and its competitors are active on, what engagement looks like, which content types perform, and who the influential voices are. This is paired with qualitative sentiment or content analysis of actual social comments, on the premise that quantitative engagement metrics describe reach but not attitude. The third and heaviest layer is quantitative analysis using a web analytics platform, typically via a demonstration account that provides real traffic data. Students query the data themselves and extract performance insights. Comparison across time periods is what separates competent work from strong work here: a single-period snapshot describes, while period-over-period comparison explains. The fourth layer, weighted equally with the analytics, asks the student to synthesise everything into strategic recommendations for the coming year, covering areas such as customer segments, user behaviour, landing and exit page performance, search ranking positions, advertising budget allocation, marketing channels and e-commerce performance. This section is where most marks are lost. Recommendations that do not trace back to a specific finding from the preceding analysis read as generic digital marketing advice, and rubrics at this level penalise exactly that. Presentation requirements are prescriptive — specified font, size, line spacing and justified margins — and the report is expected to be concise despite the breadth of analysis, which makes ruthless selection of evidence part of the task. A draft submission point for similarity checking is usually provided separately from the marked final submission. Assessments of this type commonly require a signed declaration itemising any AI tool use, with an explicit confirmation that AI was not used to generate sentences, paragraphs or sections. Our support on assessments of this type is guidance-based. Typical areas of help include: explaining how accessibility evaluation tools are used and what their output actually shows, clarifying the difference between reporting analytics figures and interpreting them, showing how a recommendation should be traced to a specific finding, advising on report structure and appendix discipline, checking APA consistency, and reviewing a student's own draft against the published rubric.
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