Spring 2027

Marketing Analytics

Marketing Analytics studies how analytics impacts marketing decisions, covering metrics, digital analytics, response models, segmentation, product design, experimentation, and big data for careers in marketing, product management, and.

offering recorded2–3 credits
Recorded instructors · Spring 2027 Neeraj Arora

Summary

1 / 3

Recent recorded grades — Spring 2024: 3.45 GPA, 62.6% A/AB (n=91 letter grades); Spring 2025: 3.38 GPA, 58.1% A/AB (n=43 letter grades); Spring 2026: 3.40 GPA, 70.0% A/AB (n=50 letter grades).

What you’ll be able to do

  • Apply online, and offline behavior-based metrics to make effective marketing decisions

  • Apply marketing mix models, multivariate statistics, experimental analyses and machine learning tools to a variety of marketing problems

  • Acquire a portfolio of quantitative methods commonly used in business practice

  • Explain the impact of marketing analytics in areas such as market research, product management and consulting

  • Utilize data in compiling brand recommendations.

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

Graduate/professional standing or declared in graduate Business Exchange program

  • Graduate/professional standing or declared in graduate Business Exchange program
MARKETNG 815

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

Prerequisite text tree
  • Graduate/professional standing or declared in graduate Business Exchange program

Professors

Spring 2027

Recent recorded grades — Spring 2024: 3.45 GPA, 62.6% A/AB (n=91 letter grades); Spring 2025: 3.33 GPA, 54.5% A/AB (n=33 letter grades); Spring 2026: 3.40 GPA, 70.0% A/AB (n=50 letter grades). Includes jointly taught sections.

Historical instructors & teaching patterns

NEERAJ ARORA is recorded teaching in Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2014, Spring 2015, Spring 2016, Spring 2018, Spring 2019, Spring 2020, Spring 2022, Spring 2023, Spring 2024, Spring 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

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

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 300

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

Meeting source records
Download part 1

Student experience

the class

Recent recorded grades — Spring 2024: 3.45 GPA, 62.6% A/AB (n=91 letter grades); Spring 2025: 3.38 GPA, 58.1% A/AB (n=43 letter grades); Spring 2026: 3.40 GPA, 70.0% A/AB (n=50 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Marketing Metrics, Digital Analytics, Marketing Response Models, Segmentation, Product Design, Experimentation, and Big Data
  • Careers in marketing analytics, product management, and consulting

Skills

  • Applying online and offline behavior-based metrics to marketing decisions
  • Using marketing mix models, multivariate statistics, experimental analyses, and machine learning tools
  • Compiling brand recommendations using data

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · 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

1280 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.

Download part 1
Selected offering source records
MARKETNG 815 · Spring 2027

Marketing Analytics

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:237:003186",
    "course_id": "MARKETNG 815",
    "course_uid": "course_c250647266ab882424f3ce0d",
    "term_id": "1274",
    "source_course_id": "003186",
    "source_subject_id": "237",
    "title": "Marketing Analytics",
    "credits_min": 2,
    "credits_max": 3,
    "typically_offered": "Occasionally"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews

No records available.

Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "0b7c9133f129a4bd35ea140138103c65a68c1320e5a70a93a73f461f93eb352d",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}