On a humid Monday evening in October 2026, Meera Iyer, Vice President of the Consumer & Healthcare practice at MarkSc Ltd., stared at three spreadsheets on her screen. Each represented a different answer to the same question: how should MarkSc study the women that MamaHeart wanted to sell to? MamaHeart, a six-year-old direct-to-consumer (D2C) personal care brand built around expecting and new mothers, was preparing to launch its first colour cosmetics range, HeartGlow: a line of pregnancy-safe, fragrance-light tinted products. Its co-founder and CEO, Nidhi Sehgal, wanted a consumer survey to validate demand, pricing and pack sizes before committing ₹14 crore to manufacturing and launch marketing. MarkSc’s proposal was due on Friday. Inside MarkSc, opinions had hardened into three camps. Nidhi herself wanted speed: “We have 4.2 lakh customers who already love us. Survey them. Done.” MarkSc’s Head of Research Methods, Dr. Arvind Krishnan, insisted that anything short of a probability sample would produce “a very expensive mirror.” Field Director Farhan Qureshi argued that mothers of newborns were simply not where textbooks expected them to be: not on panels, not at home at 4 p.m., and certainly not in the mood for a 25-minute questionnaire. Meera had a budget of ₹42 lakh, a ten-week window, and a target population that, as Farhan put it, “is busy, sleep-deprived, and completely changes every nine months.” She needed a research design her client would accept and her methods head could defend.
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Here are learning objectives and learning outcomes for the MarkSc–MamaHeart case. The objectives say what the case sets out to teach. The outcomes say what students should be able to do afterwards, using Bloom’s taxonomy verbs so you can assess them.
Learning Objectives
The case is designed to:
- Introduce sampling design as a strategic decision, so students see that who is surveyed shapes what a firm can conclude, not just what is asked.
- Build working knowledge of probability and non-probability sampling (simple random, systematic, stratified, cluster, multi-stage, convenience, judgmental, quota and snowball) in a real, constrained setting.
- Expose students to the problems of hard-to-reach, low-incidence and sensitive populations, where no ready sampling frame exists.
- Develop quantitative skill in setting a sample size, including adjustments for design effect, incidence, response rate and subgroup analysis.
- Encourage critical thinking about research design, so students match exploratory, descriptive and causal designs to specific business questions.
- Show the trade-offs researchers negotiate between rigour, cost, time, ethics (consent under the DPDP Act) and client expectations.
Learning Outcomes
By the end of the session, students will be able to:
| # | Learning outcome | Bloom level | Case anchor | How to assess |
|---|---|---|---|---|
| LO1 | Define a target population precisely by element, sampling unit, extent and time, and explain how each choice changes the size of the universe and the cost of reaching it | Understand | Section 5; Exhibit 4 | Cold-call; short written definition |
| LO2 | Classify sampling methods as probability or non-probability, and identify the technique behind each of the three proposed designs | Apply | Exhibit 5 | Board exercise; quiz |
| LO3 | Diagnose sampling-frame error, coverage bias, self-selection, non-response and homophily in a given design | Analyse | Sections 5–6; Exhibits 4 and 6 | Group discussion; written critique |
| LO4 | Calculate the required sample size for estimating a proportion, then adjust it for design effect, incidence and response rate, and estimate the field effort and cost for each method | Apply | Exhibit 6; teaching note worked example | Numerical problem in class or as assignment |
| LO5 | Explain how weighting can align a sample with the population, and judge which biases weighting corrects and which it cannot | Evaluate | Exhibit 4 | Short-answer question |
| LO6 | Evaluate competing research designs against the business question, budget, timeline, ethics and data-protection constraints | Evaluate | Section 6; Exhibit 5 | Debate or role-play (Nidhi, Arvind, Farhan) |
| LO7 | Design and justify a hybrid research plan, sequencing exploratory, descriptive and causal phases within a fixed budget and timeline | Create | Exhibit 3 brief; ₹42 lakh and 10-week limits | Group proposal or 2-minute pitch; graded with a rubric |
| LO8 | Communicate methodological trade-offs persuasively to a non-technical client | Create | Section 6 dilemma | Mock client presentation; one-page memo to Nidhi |
Suggested programme-level mapping (optional): LO2–LO5 support analytical and quantitative reasoning, LO6 supports ethical and responsible decision-making, and LO7–LO8 support problem solving and communication. That should line up with most AACSB- or NBA-style assurance-of-learning goals.