Marketing is no longer about gut instinct—it’s about data, attribution, and strategic optimization. Yet, many marketing students and CMOs still struggle with how to analyze multi-channel data, measure ROI, and optimize their marketing spend effectively.
To bridge this gap, we set out to create a real-world case study—one that would challenge students and professionals alike to think critically, make data-backed decisions, and optimize a marketing budget for maximum impact.
This is the story of how we built the Elysian Fertility & Surrogacy case study, designed to teach marketing professionals how to analyze and optimize their marketing mix across multiple channels.
Identifying the Knowledge Gap
As we spoke with marketing professors, business leaders, and CMOs, a few recurring challenges emerged:
- Marketing attribution is complex. Most professionals rely on built-in analytics from Google or Meta, which often favor their own platforms.
- Students and marketers struggle to connect marketing data with business outcomes. Clicks and traffic are easy to measure, but understanding which channels actually drive revenue is far more challenging.
- There was no hands-on case study that simulated a real-world marketing challenge. Many marketing courses teach theory, but few provide an interactive, evolving dataset that mirrors actual business decision-making.
Solution: We needed a case study that would simulate real-world marketing analysis, where students could test assumptions, make decisions, and refine their strategy based on progressive data disclosures—just like in real life.
Selecting a Realistic Business Model
The fertility and surrogacy industry presents a complex customer journey and long sales cycles, making attribution and budget allocation critical. Elysian Fertility, a hypothetical business in Northern California, became the perfect case study candidate.
Business Background
- Founded in 2016, Elysian Fertility quickly became one of the leading providers in the surrogacy and egg donation industry.
- 2023 Revenue: $10M with a 16.5% net margin.
- Marketing Budget: $1.44M (~14.6% of total revenue).
The Strategic Challenge
The company faced two critical marketing decisions:
- Should Elysian increase its marketing spend? Would more investment result in proportional revenue growth?
- How should Elysian allocate its marketing budget? Which digital channels would provide the best return on investment (ROI)?
To tackle these challenges, Elysian implemented Galileo, a data-driven marketing attribution platform that tracks multi-channel performance without the biases of platforms like Google and Meta.
Designing the Case Study: A Structured Learning Experience
To create an impactful case study, we structured it in three progressive phases, each revealing new layers of data and challenges:
Phase A: The First Look at Marketing Data
Students should start with basic marketing data--traffic and leads across different digital channels.
- They analyze which channels generate the most leads.
- They calculate cost per visit and cost per lead.
- They identify early patterns but also notice missing data (conversion & revenue metrics)
Key Lesson: The first phase simulates how many brands today make decisions based on surface-level data. It forces students to recognize what’s missing before making major budget changes.
Phase B: Measuring Conversions and Revenue
Now, students receive additional data on conversion rates and revenue per channel.
- They calculate Customer Acquisition Cost (CAC) and ROI per channel.
- They realize that some high-traffic channels aren't driving revenue efficiently.
- They must now reallocate their budget based on conversion efficiency, not just traffic.
Key Lesson: More data doesn’t always mean clearer answers. Students learn to refine their approach based on both efficiency (CAC) and volume (leads & conversions).
Phase C: Advanced Optimization with Predictive Modeling
In the final phase, students gain access to AI-driven marketing insights, including:
- ROAS (Return on Ad Spend) and ROMI (Return on Marketing Investment).
- Predictive modeling to test future marketing mix scenarios.
- A clearer understanding of diminishing returns on high-spend channels
Key Lesson: The best marketing strategy is data-driven but flexible. AI-powered attribution tools, like Galileo, help marketing leaders make smarter, unbiased decisions.
The Challenges of Creating a Realistic Case Study
Creating a case study that is both educational and true to real-world marketing wasn't easy. Some challenges we faced:
- Balancing Complexity & Simplicity: We had to make the case detailed enough for CMOs but accessible for students.
- The Attribution Dilemma: Many marketing teams implicitly trust Google or Meta's built-in attribution models, which often overvalue their own platforms.
- Realistic Budget Constraints: In real-world marketing, you can't just spend infinitely--diminishing returns exist. We made sure students had to consider budget ceilings and market realities.
By integrating Galileo’s AI-driven marketing analytics, we were able to introduce a platform-agnostic view of marketing performance. This removed the bias from Google and Meta, providing a clearer picture of where marketing dollars actually generate revenue.
Testing and Refining the Learning Experience
After designing the case, we tested it in different settings:
- Business School Classrooms: MBA and EMBA students used the case to simulate real-world marketing decision-making. Their feedback helped refine the case's complexity.
- CMO & Executive Workshops: Marketing executives used the case to train their teams on how to think strategically about marketing mix and ROI.
- Marketing Agencies & Internal Teams: Some companies even tested the case as a hiring and training tool for new marketing managers!
Key Takeaway: Marketing students and CMOs have different approaches—but both benefit from learning how to evolve their strategy as new data emerges.
The Final Product: A Case Study with Real Impact
Who Is Using This Case Study Today?
- Business Schools; Teaching marketing strategy, attribution, and digital marketing optimization.
- CMOs & Marketing Teams: Training employees on how to allocate budgets effectively.
- Executive Education & Workshops: Helping professionals understand the real value of AI-driven marketing insights.
Why This Matters for the Future of Marketing
As AI and analytics reshape the marketing landscape, marketers must move beyond guesswork. This case study forces learners to test assumptions, analyze data deeply, and make smarter investment decisions.
Key Lessons:
- Marketing is no longer just about creativity—it’s about data-driven decisions.
- Uncritically following Google or Meta’s recommendations leads to wasted ad spend.
- AI-driven attribution tools like Galileo can give CMOs an edge in budget optimization.
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