Spring 2027

Digital Brand Building

Digital Brand Building examines the digital revolution's impact on the marketing mix and brand strategy, analyzing how organizations adapt to a digital-first environment and broader cultural shifts.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 4

Recent recorded grades — Fall 2025: 3.57 GPA, 79.7% A/AB (n=64 letter grades).

What you’ll be able to do

  • Explain how the digital marketing landscape has evolved over time, including specific disruptions and revolutions that have made the greatest impact.

  • Articulate how brand marketers adapted their strategies to compete in a changing digital ecosystem.

  • Develop critical thinking and communication skills that will require analysis and synthesis of class lessons and topics.

Source: UW Guide 1 UW Guide 2

Grade history

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average GPA
letter grades
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All recorded terms · compare terms & instructors

Prerequisites

Course map

MARKETNG 300,JOURN 201, or declared in the Business Exchange program

JOURN/MARKETNG 605

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

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SectionModeEnrolled / capacityWaitlist

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.

Meeting source records

Student experience

the class

Recent recorded grades — Fall 2025: 3.57 GPA, 79.7% A/AB (n=64 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • The digital landscape and its role in the marketing mix.
  • The digital revolution and its impact on building brands in a digital-first environment.
  • Effects of the digital revolution on organizational branding and culture.

Skills

  • Explaining the evolution of the digital marketing landscape and articulating how brand marketers adapt strategies to compete in a changing digital ecosystem.
  • Developing critical thinking and communication skills through the analysis and synthesis of class lessons and topics.

Grades

Latest available · Fall 2025— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

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AB
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BC
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More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Fall 2025 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1316 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

Selected offering source records

No offering records for the selected term.

Raw records
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Student reviews

Original comments behind the course and instructor summaries.

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Model & dataset provenance
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  "output_id": "0facc68cffeb5c49ac4d6102758c8971a4a05e40317a4a01a65f1cc92e94cfa2",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
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