Luna-Coffee-Co.-Customer-Segmentation-Analysis-—-Q1-2025.csv
CSVReference material for Luna Coffee Co..
Open resource
Luna Coffee Co.
Luna Coffee Co. is a mid-sized specialty coffee brand operating across the United States with annual revenue of $38 million. Founded in 2017, Luna built its reputation on ethically sourced single-origin beans and artisan roasting techniques, serving a loyal customer base through 22 company-owned cafés, wholesale partnerships with 140 independent retailers, and a growing direct-to-consumer e-commerce channel. The brand occupies a premium positioning in the specialty coffee market, competing against regional craft roasters and national chains. Luna's customer base skews older and traditional, with 68% of current revenue coming from consumers aged 40 and above who favor hot brewed coffee.
Project Brief
Review Luna Coffee Co.'s revenue challenge and your role as Marketing Analyst.
Manager Video Call
Watch Rachel Nguyen explain Luna's flat growth and segmentation priorities.
Task Email
Receive assignment to analyze customer data and recommend priority segment.
Review Resources
Examine Luna's customer data pack and identify key segmentation variables.
Written Task
Draft segmentation recommendation memo applying frameworks to Luna's customer data.
Feedback
Review detailed feedback on your segmentation analysis and prioritization decision.
Chat Defense
Justify your segment prioritization choice in conversation with Rachel Nguyen.
Key Moments highlight the workplace artifacts that make your topic feel real — like calls, emails, resources, and files.
Verify whether the student can accurately apply segmentation frameworks to Luna Coffee Co. customer data and make soun...
Sample Feedback & Report

Learner Role
Target Client
Luna Coffee Co. is a mid-sized specialty coffee brand operating across the United States with annual revenue of $38 million. Founded in 2017, Luna built its reputation on ethically sourced single-origin beans and artisan roasting techniques, serving a loyal customer base through 22 company-owned cafés, wholesale partnerships with 140 independent retailers, and a growing direct-to-consumer e-commerce channel. The brand occupies a premium positioning in the specialty coffee market, competing against regional craft roasters and national chains. Luna's customer base skews older and traditional, with 68% of current revenue coming from consumers aged 40 and above who favor hot brewed coffee.
The Challenge
Luna Coffee Co. has experienced flat year-over-year revenue growth for three consecutive quarters, missing its 12% annual growth target by a widening margin. Recent customer data reveals that Luna's core demographic—consumers aged 40-65—continues to purchase reliably but at static frequency, while younger cohorts (ages 18-24 and 25-39) represent only 19% of total revenue despite comprising 47% of the specialty coffee market nationally. Competitor brands have launched cold brew lines, mobile ordering, and sustainability-forward campaigns that resonate with younger consumers, while Luna's product mix remains 81% hot coffee with limited cold or ready-to-drink offerings. The marketing director has flagged this misalignment as a strategic risk: without attracting higher-growth segments, Luna faces margin pressure, declining retail partnerships, and long-term brand irrelevance in a market where specialty coffee consumption among younger demographics is accelerating at nearly 10% annually.
The Assignment
You will be responsible for analyzing Luna Coffee Co.'s customer data using segmentation frameworks, identifying the most strategically viable audience segment, and drafting a prioritization recommendation that addresses both growth potential and brand alignment. Your analysis will inform the marketing director's decision on where to allocate the next quarter's campaign budget and product development resources. You'll receive the specific assignment, materials, and next steps in the task brief.
Key Stakeholders
Rachel Nguyen
Marketing Director
Influence: High
Reporting ManagerDear there,
We've been tracking flat revenue growth for three quarters now, and it's clear we need to make a strategic shift in how we think about our customer base. Our core demographic continues to deliver steady results, but we're missing significant opportunities in segments that could drive the growth we need to hit our annual targets.
I'm attaching our latest customer segmentation analysis covering demographic, behavioral, and psychographic data across our current base. I need you to review this data carefully, identify our most viable customer segments, and recommend which segment we should prioritize next. Your analysis should evaluate segment size, growth potential, brand fit, and competitive dynamics — then make a clear call on where we focus our efforts.
This recommendation will shape our Q2 planning and resource allocation decisions, so I need your thinking to be thorough and well-supported. Please have your segmentation memo on my desk by end of week.
Regards,
Rachel
Marketing Director
Luna Coffee Co.
Luna Coffee Co. Customer Segmentation Analysis — Q1 2025.csv
CSV
Reference material for Luna Coffee Co..
Open resourceRachel Nguyen
Marketing Director
Hey, I skimmed your submission; why did you feel this approach was the right fit for Luna Coffee Co.?
10:30 AMI focused on what would be most practical for Luna Coffee Co. right now, especially the parts that connect the recommendation back to the current challenge.
10:32 AMThat makes sense; what tradeoff did you consider before choosing that direction for Luna Coffee Co.?
10:33 AMI weighed the impact against how quickly the team could act on it, then prioritized the option that felt clearest to implement.
10:35 AMActivity Instructions
Draft a segmentation recommendation memo for Luna Coffee Co.'s Marketing Director identifying which customer segment to prioritize next, supported by framework-based analysis and data-driven justification.
Type your answer here...
Evaluate how clearly and accurately the student identifies and supports the 3 key drivers of Harbor Roasters' Café Group's profit decline, based on internal and external factors described in the scenario.
This submission was rated 2 because the student identifies some relevant drivers but does not clearly distinguish internal from external factors or provide sufficient supporting evidence from the scenario. The response mentions labor costs, QuickCup competition, and reduced foot traffic, which are valid drivers, but the causal logic connecting these factors to the 22% profit decline is not explicitly developed. For instance, the student does not link the 14% transaction decline (350 to 300 daily) or the 15% coffee bean cost increase to specific profit margin compression. A stronger response would name exactly three drivers, label them as internal or external, and cite scenario data points to substantiate each claim with precision.
How effectively does this submission analyze the given questions and provide a thoughtful response?
This submission was rated 2 because it shows some attempt at analyzing Harbor Roasters' profitability challenges but lacks depth and coherent structure. The student identifies relevant issues like labor costs, competitive pressure from QuickCup, foot traffic decline, and variable cost increases, demonstrating basic awareness of the case factors. However, the analysis is superficial and disorganized—jumping between topics without clear logical flow or systematic examination. The response fails to provide thoughtful integration of the scenario data (such as the specific 15% coffee bean cost increase, 14% transaction decline from 350 to 300, or customer satisfaction drop from 4.8 to 4.2). Stronger responses would demonstrate critical thinking by systematically connecting internal operational issues with external market pressures, using specific data points to build a coherent argument about root causes and their interrelationships rather than listing factors in a fragmented manner.
Segmentation Recommendation Memo
I would prioritize the approach that best serves Luna Coffee Co. and can be defended to its stakeholders. My rationale connects the choice back to the current issue rather than treating it as a generic preference.
The recommendation is grounded in the specific evidence from the scenario, weighing the competing interests before landing on a position.
I acknowledge the main trade-off this approach accepts, and outline a clear first step so the plan is actionable.
Rachel Nguyen
Today at 2:30 PM
Good opening. Make the connection to Luna Coffee Co.'s current issue sharper so the reader immediately sees why this answer matters.
After a learner completes Prioritizing Luna's Next Customer Segment, Cartedo generates a full report scoring each of the 10 assessment dimensions with evidence-based rationale. Run the simulation to see your own report.
Experience as learnerCartedo can generate a benchmark model answer for this task, so learners see what an exemplary response looks like against the rubric. The model submission is produced live inside the simulation.
Experience as learnerHow learners are assessed across each dimension
Domain / Competency
Measures how well learners apply domain knowledge in their written submission.
Question: Does this person correctly identify that Luna's 40-54 age cohort represents demographic segmentation, the 68% cold brew preference among 18-24 customers represents behavioral segmentation, and the 82% sustainability importance rating among younger customers represents psychographic segmentation?
Question: Does this person systematically evaluate Luna's customer segments using the four criteria in proper sequence—segment size, growth potential, brand alignment, and competitive intensity—with specific evidence for each criterion before reaching a prioritization conclusion?
Question: Does this person adapt their segment evaluation to Luna's specific context—a $38M specialty coffee company with ethical sourcing positioning and flat revenue for three quarters—rather than applying generic segmentation principles?
Question: Does this person catch weak assumptions in their segment prioritization—like overestimating Luna's ability to compete for the 18-24 segment given the $42 acquisition cost and 78% competitor cold brew penetration—before finalizing their recommendation?
Question: Does this person structure their recommendation with a clear logical flow—connecting Luna's customer data to segment definitions to evaluation criteria to final prioritization—using specific evidence at each reasoning step?
Soft Skills
Measures transferable skills like critical thinking, adaptability, and metacognition.
Question: Does this person move beyond surface-level demographic descriptions to build a multi-layered argument comparing Luna's segments across revenue contribution, behavioral patterns, psychographic alignment, and competitive dynamics with specific data justifications?
Question: Does this person consider how prioritizing one Luna customer segment affects the other segments, brand perception among non-targeted groups, and Rachel Nguyen's need to justify the strategic choice to Luna's leadership?
Skills Assessed
All dimensions assessed in this simulation. Coverage shows which dimensions have at least one rubric question mapped.
Does this person correctly identify that Luna's 40-54 age cohort represents demographic segmentation, the 68% cold brew preference among 18-24 customers represents behavioral segmentation, and the 82% sustainability importance rating among younger customers represents psychographic segmentation?
Does this person systematically evaluate Luna's customer segments using the four criteria in proper sequence—segment size, growth potential, brand alignment, and competitive intensity—with specific evidence for each criterion before reaching a prioritization conclusion?
Does this person adapt their segment evaluation to Luna's specific context—a $38M specialty coffee company with ethical sourcing positioning and flat revenue for three quarters—rather than applying generic segmentation principles?
Does this person catch weak assumptions in their segment prioritization—like overestimating Luna's ability to compete for the 18-24 segment given the $42 acquisition cost and 78% competitor cold brew penetration—before finalizing their recommendation?
Does this person structure their recommendation with a clear logical flow—connecting Luna's customer data to segment definitions to evaluation criteria to final prioritization—using specific evidence at each reasoning step?
Does this person move beyond surface-level demographic descriptions to build a multi-layered argument comparing Luna's segments across revenue contribution, behavioral patterns, psychographic alignment, and competitive dynamics with specific data justifications?
Does this person consider how prioritizing one Luna customer segment affects the other segments, brand perception among non-targeted groups, and Rachel Nguyen's need to justify the strategic choice to Luna's leadership?
Domain / Competency
Measures how well learners apply domain knowledge in their written submission.
Question: Does this person correctly identify that Luna's 40-54 age cohort represents demographic segmentation, the 68% cold brew preference among 18-24 customers represents behavioral segmentation, and the 82% sustainability importance rating among younger customers represents psychographic segmentation?
Question: Does this person systematically evaluate Luna's customer segments using the four criteria in proper sequence—segment size, growth potential, brand alignment, and competitive intensity—with specific evidence for each criterion before reaching a prioritization conclusion?
Question: Does this person adapt their segment evaluation to Luna's specific context—a $38M specialty coffee company with ethical sourcing positioning and flat revenue for three quarters—rather than applying generic segmentation principles?
Question: Does this person catch weak assumptions in their segment prioritization—like overestimating Luna's ability to compete for the 18-24 segment given the $42 acquisition cost and 78% competitor cold brew penetration—before finalizing their recommendation?
Question: Does this person structure their recommendation with a clear logical flow—connecting Luna's customer data to segment definitions to evaluation criteria to final prioritization—using specific evidence at each reasoning step?
Soft Skills
Measures transferable skills like critical thinking, adaptability, and metacognition.
Question: Does this person move beyond surface-level demographic descriptions to build a multi-layered argument comparing Luna's segments across revenue contribution, behavioral patterns, psychographic alignment, and competitive dynamics with specific data justifications?
Question: Does this person consider how prioritizing one Luna customer segment affects the other segments, brand perception among non-targeted groups, and Rachel Nguyen's need to justify the strategic choice to Luna's leadership?
Skills Assessed
All dimensions assessed in this simulation. Coverage shows which dimensions have at least one rubric question mapped.
Does this person correctly identify that Luna's 40-54 age cohort represents demographic segmentation, the 68% cold brew preference among 18-24 customers represents behavioral segmentation, and the 82% sustainability importance rating among younger customers represents psychographic segmentation?
Does this person systematically evaluate Luna's customer segments using the four criteria in proper sequence—segment size, growth potential, brand alignment, and competitive intensity—with specific evidence for each criterion before reaching a prioritization conclusion?
Does this person adapt their segment evaluation to Luna's specific context—a $38M specialty coffee company with ethical sourcing positioning and flat revenue for three quarters—rather than applying generic segmentation principles?
Does this person catch weak assumptions in their segment prioritization—like overestimating Luna's ability to compete for the 18-24 segment given the $42 acquisition cost and 78% competitor cold brew penetration—before finalizing their recommendation?
Does this person structure their recommendation with a clear logical flow—connecting Luna's customer data to segment definitions to evaluation criteria to final prioritization—using specific evidence at each reasoning step?
Does this person move beyond surface-level demographic descriptions to build a multi-layered argument comparing Luna's segments across revenue contribution, behavioral patterns, psychographic alignment, and competitive dynamics with specific data justifications?
Does this person consider how prioritizing one Luna customer segment affects the other segments, brand perception among non-targeted groups, and Rachel Nguyen's need to justify the strategic choice to Luna's leadership?
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