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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