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Draft Measurable Marketing Goals with AI

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Coursera

Draft Measurable Marketing Goals with AI

Hurix Digital

Instructor: Hurix Digital

Included with Coursera Plus

Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

2 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

2 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Measurable goals prove marketing value—without clear metrics, impact and resource justification are impossible.

  • SMART goals align marketing creativity with business accountability and improve stakeholder communication.

  • Strong goals balance ambition and realism, stretching teams while staying achievable with available resources.

  • Time-bound goals create urgency and accountability, turning intent into action with clear milestones.

Details to know

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Recently updated!

January 2026

Assessments

7 assignments¹

AI Graded see disclaimer
Taught in English

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Build your subject-matter expertise

This course is part of the Google Analytics: Reports & Traffic Analysis Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 3 modules in this course

This module teaches selecting AI models that balance performance with interpretability requirements in regulated industries, using practical frameworks to analyze trade-offs between predictive accuracy and explainability constraints.

What's included

3 videos1 reading2 assignments

This module establishes statistical rigor to distinguish genuine algorithm improvements from random variation, teaching hypothesis testing frameworks, appropriate test selection for different scenarios, and multiple testing corrections to transform subjective algorithm selection into evidence-based decision-making that prevents costly deployment mistakes.

What's included

3 videos1 reading2 assignments

This module teaches ensemble modeling strategies—bagging, boosting, and stacking—that combine multiple algorithms to achieve superior performance beyond individual models.

What's included

3 videos1 reading3 assignments

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Instructor

Hurix Digital
Coursera
127 Courses3,202 learners

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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.