BUSINESS-08 · Undergraduate · ENGLISH · PLANNED
Study an original business case, compare options, make a decision and defend implementation.
Study time is an initial activity estimate, excluding extra reading, revision and extended laboratory or project work. Videos and question bodies have not been produced by adding this outline.
Complete these courses or demonstrate equivalent knowledge. These are recommended prerequisites, not enforced enrolment locks.
YouTube playlist: ARK Business Administration · BBA/MBA | Digital Marketing, Sales and Customer Experience
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Sign in to save this learning pathPractical outcome: Plan a fictional campaign with a USD budget and measurable objectives.
Explain search intent using a visual and one worked real-world example. Context: Plan a fictional campaign with a USD budget and measurable objectives. End with a prediction or check-for-understanding.
Explain search optimisation using a visual and one worked real-world example. Context: Plan a fictional campaign with a USD budget and measurable objectives. End with a prediction or check-for-understanding.
Explain paid-search concepts using a visual and one worked real-world example. Context: Plan a fictional campaign with a USD budget and measurable objectives. End with a prediction or check-for-understanding.
Explain social content using a visual and one worked real-world example. Context: Plan a fictional campaign with a USD budget and measurable objectives. End with a prediction or check-for-understanding.
Use the first concepts from this section. Plan a fictional campaign with a USD budget and measurable objectives. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain email marketing using a visual and one worked real-world example. Context: Plan a fictional campaign with a USD budget and measurable objectives. End with a prediction or check-for-understanding.
Explain landing pages using a visual and one worked real-world example. Context: Plan a fictional campaign with a USD budget and measurable objectives. End with a prediction or check-for-understanding.
Explain conversion funnels using a visual and one worked real-world example. Context: Plan a fictional campaign with a USD budget and measurable objectives. End with a prediction or check-for-understanding.
Explain attribution limitations using a visual and one worked real-world example. Context: Plan a fictional campaign with a USD budget and measurable objectives. End with a prediction or check-for-understanding.
Plan a fictional campaign with a USD budget and measurable objectives. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Search intent, Search optimisation, Paid-search concepts and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Role-play a needs-based sale without pressure or deceptive claims.
Explain prospecting using a visual and one worked real-world example. Context: Role-play a needs-based sale without pressure or deceptive claims. End with a prediction or check-for-understanding.
Explain qualification using a visual and one worked real-world example. Context: Role-play a needs-based sale without pressure or deceptive claims. End with a prediction or check-for-understanding.
Explain discovery calls using a visual and one worked real-world example. Context: Role-play a needs-based sale without pressure or deceptive claims. End with a prediction or check-for-understanding.
Explain value-based proposals using a visual and one worked real-world example. Context: Role-play a needs-based sale without pressure or deceptive claims. End with a prediction or check-for-understanding.
Use the first concepts from this section. Role-play a needs-based sale without pressure or deceptive claims. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain objection handling using a visual and one worked real-world example. Context: Role-play a needs-based sale without pressure or deceptive claims. End with a prediction or check-for-understanding.
Explain sales pipelines using a visual and one worked real-world example. Context: Role-play a needs-based sale without pressure or deceptive claims. End with a prediction or check-for-understanding.
Explain forecasting sales using a visual and one worked real-world example. Context: Role-play a needs-based sale without pressure or deceptive claims. End with a prediction or check-for-understanding.
Explain account management using a visual and one worked real-world example. Context: Role-play a needs-based sale without pressure or deceptive claims. End with a prediction or check-for-understanding.
Role-play a needs-based sale without pressure or deceptive claims. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Prospecting, Qualification, Discovery calls and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Map a service failure and design an evidence-based recovery.
Explain journey mapping using a visual and one worked real-world example. Context: Map a service failure and design an evidence-based recovery. End with a prediction or check-for-understanding.
Explain service touchpoints using a visual and one worked real-world example. Context: Map a service failure and design an evidence-based recovery. End with a prediction or check-for-understanding.
Explain customer support using a visual and one worked real-world example. Context: Map a service failure and design an evidence-based recovery. End with a prediction or check-for-understanding.
Explain complaint analysis using a visual and one worked real-world example. Context: Map a service failure and design an evidence-based recovery. End with a prediction or check-for-understanding.
Use the first concepts from this section. Map a service failure and design an evidence-based recovery. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain service recovery using a visual and one worked real-world example. Context: Map a service failure and design an evidence-based recovery. End with a prediction or check-for-understanding.
Explain retention using a visual and one worked real-world example. Context: Map a service failure and design an evidence-based recovery. End with a prediction or check-for-understanding.
Explain experience experiments using a visual and one worked real-world example. Context: Map a service failure and design an evidence-based recovery. End with a prediction or check-for-understanding.
Explain customer-data governance using a visual and one worked real-world example. Context: Map a service failure and design an evidence-based recovery. End with a prediction or check-for-understanding.
Map a service failure and design an evidence-based recovery. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Journey mapping, Service touchpoints, Customer support and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Combine all three sections: Map a service failure and design an evidence-based recovery. Submit an original worked artifact, evidence or tests, assumptions and a reflection. Rubric: correctness, method, practical usefulness, reproducibility and communication. Self-reviewed, not certified.
6–12 balanced questions sampling every section of Digital Marketing, Sales and Customer Experience, including interpretation, calculation or reasoning, a misconception and a new application. Passing is 70%; review errors before retrying.
Case discussions, business plans, management simulations and original consulting projects. Guided work leads to an independent attempt, with a diagram, explanation, trace, calculation or tested artifact. Question bodies are generated when opened in your private learning path. Programming submissions are stored as text; the platform does not execute submitted code.
Use original or appropriately licensed datasets. Practical science activities use models or supplied data unless a qualified supervisor has reviewed a physical experiment.
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