To analyze a business or marketing case study, clarify the assignment, diagnose the core problem, structure the analysis with proven frameworks (e.g., SWOT, Five Forces, unit economics), develop and compare options, then recommend a plan with an implementation roadmap, risks, and measurable outcomes.

Understanding the Brief and the Real Problem

Strong case work starts before you read a single exhibit. Your grade (and managerial impact) depends on solving the right problem, not summarizing the packet. Begin by decoding the brief and pinning down the decision you must make.

Clarify the assignment. Read the prompt twice and annotate the verbs: analyze, evaluate, recommend, justify, quantify. These verbs tell you what your instructor will grade. Note any fixed constraints (budget caps, timing, geography) and the decision-maker’s perspective (CMO, product lead, founder), because the same facts look different from each seat.

Formulate the case question. Turn the prompt into a single choice: Should Company X enter Segment Y in Q4? or Which of the three pricing options maximizes contribution margin without harming churn? If the prompt is vague, write a one-sentence “north star” and keep it visible while you work. Everything in your analysis should trace back to this choice.

Surface symptoms vs. causes. Declining sales, margin squeeze, customer complaints—these are symptoms. Ask “why” three to five times until you hit a root cause (e.g., channel conflict, weak positioning, rising CAC, operational bottleneck). You will later test these hypotheses with data.

Set success criteria up front. Decide how the final recommendation will be judged: profitability (NPV, IRR), growth (LTV/CAC, market share), risk exposure, brand fit, and feasibility. Clear criteria prevent hand-wavy conclusions and make your write-up feel inevitable.

Running example used throughout. Imagine BeanBurst, a mid-size coffee chain considering drive-thru expansion in suburban markets. The case packet includes store P&Ls, foot-traffic heatmaps, competitor placements, survey data on wait times, and capex estimates for drive-thru retrofits. Your north star: Should BeanBurst invest in drive-thrus now, and if so, where and how?

Building a Structured Analysis (Frameworks + Data)

Structure creates speed. Use a small toolkit well, not every framework you’ve ever learned. The goal is to organize the problem MECE (mutually exclusive, collectively exhaustive) and quantify the few numbers that matter.

Market & Competitive Landscape

Five Forces (fast read). Assess rivalry (local coffee density and quick-serve overlap), buyer power (price sensitivity around convenience), supplier power (bean contracts, labor), substitutes (home brewing, convenience store coffee), and barriers (permits, drive-thru lanes, lot size). In our BeanBurst case, rivalry is high in city cores but lower in outer suburbs; substitutes are strong in mornings but weaker in afternoon snack occasions.

Segmentation that matters. Instead of demographic labels, split by occasion and mission: “commute coffee” (speed), “study session” (dwell time), “family errand loop” (car convenience). Drive-thru disproportionately serves mission types 1 and 3—so the segment to model is suburban morning commuters near arterial roads.

Customer & Journey Insight

Jobs-to-be-Done lens. Customers hire a drive-thru to deliver predictable speed and no-friction ordering. Map the micro-journey: approach → queue → order → pay → pickup → re-merge into traffic. Each step has a measurable metric (queue length, seconds/order, error rate). A case often hides one choke point (e.g., order accuracy causing re-makes, which inflate wait times and crush perceived speed).

Survey and review data. Pull out quantified sentiments: acceptable max wait time (e.g., ≤4 minutes), tolerance for price premiums when wait is short, the menu items with the highest “regret” (items often returned). Don’t paraphrase—extract numbers and connect them to throughput or margin.

Economics & Unit-Level Math

Contribution margin is the spine. Build a quick unit model to see what must be true for a drive-thru to pay off. Focus on throughput, check size, and capex.

• Throughput: orders/hour × hours/day × days/year.

• Average check: item mix; drive-thru may skew toward higher-margin beverages and prepacked snacks.

• Variable costs: ingredients, payment fees, franchise royalties.

• Incremental labor: add a runner or dedicated order-taker if it increases orders/hour more than it costs.

• Capex & payback: retrofit cost, permitting, lane construction, headset/board systems.

Example calculation (simplified). Suppose a current suburban store does 65 orders/h in the a.m. rush with no drive-thru. The retrofit enables 95 orders/h (+30), adds $0.80 contribution per order after ingredients and payments, costs $220k in capex, and requires $60/day extra labor. If the morning rush runs 3 hours on 250 weekdays, that’s 30×3×250 = 22,500 extra orders/year → $18,000 contribution. Add non-rush uplift (smaller): say +10 orders/h for 4 hours/day, 300 days → 12,000 orders → $9,600. Annual uplift ≈ $27,600, minus $21,900 added labor (365×$60) ≈ $5,700. Payback on $220k is poor unless you either push throughput more (dual-lane) or grow check size (bundles) or restrict retrofits to higher-traffic lots. This simple math directly informs your recommendation.

Strategy & Positioning Fit

SWOT that drives action. Keep SWOT tight and downstream of facts.

• Strengths: barista training, signature cold brew, suburban brand familiarity.

• Weaknesses: limited lot sizes; single makeline causing delays.

• Opportunities: dual-lane layouts in outparcels; app pre-pay lanes.

• Threats: QSR giants already optimized for drive-thru speed.

The output of SWOT is what to do, not four quadrants. For instance, a single makeline suggests pre-batching bestsellers during peaks and menu simplification at the window.

Operational Bottlenecks

Little’s Law in plain English. Average wait time ≈ items-in-queue ÷ processing rate. Attack it in two ways: reduce incoming variability (predictable pre-orders) or raise processing rate (parallelize order and payment). Tie each proposed fix to a metric (e.g., seconds saved/order).

Evidence Checklist

Students often over-summarize and under-evidence. For a compelling case, your analysis should include:

• A concise market snapshot (size, growth, local density).

• A unit economics model (what must be true to break even in ≤24 months).

• A customer journey insight that explains the performance gap.

• A competitive reality (why your plan wins vs. incumbents).

Generating Options and Selecting the Best Strategy

Strategy is choice under constraints. Enumerate mutually exclusive options that plausibly solve the problem, define evaluation criteria, then score each option transparently. You do not need perfect numbers—directionally correct and consistent beats speculative precision.

Construct 2–3 Real Options

Using BeanBurst, assume three paths:

1. Retrofit drive-thru at existing suburban stores with sufficient lot depth.

2. Open new satellite drive-thru kiosks near commuter arteries; minimal seating, high throughput.

3. Digital-first “order-ahead express” without physical drive-thru—dedicated curbside lanes + app-only bundles.

Each option ties back to the core problem (convenience deficit vs. rivals) but differs in capex, speed, and risk.

Set the Decision Criteria

Choose 3–5 criteria that reflect your earlier success metrics. Keep them stable across options:

• Unit economics (margin uplift, payback period).

• Scalability (sites available, permitting feasibility).

• Customer impact (wait time reduction, NPS).

• Operational complexity (training, layout changes).

• Strategic fit (brand, long-term differentiation).

Score with a Simple Decision Matrix

Use a weighted matrix to compare options. Weighting forces you to state priorities clearly (e.g., unit economics and customer impact matter most).

Table 1. Option Evaluation (1–5 scale; higher is better).

How to read this: multiply each score by its weight and sum. In this example, satellite kiosks win due to strong unit economics and peak-hour impact, despite operational complexity. Your exact numbers will vary; defend your weights using facts from the packet.

Build a Financial Backbone

Even a coarse P&L helps your recommendation feel inevitable. Tie assumptions to data points revealed earlier.

• Revenue model: forecast traffic by daypart × average check; model uplift from bundled fast movers (espresso + pastry).

• COGS & variable costs: reflect menu mix; kiosks often carry tighter menus and higher beverage mix → better margins.

• Capex & depreciation: kiosks may be cheaper per site; retrofits can balloon due to permits.

• Labor: quantify throughput benefit from roles (order-taker outside, runner) and technology (dual screens).

• Sensitivity: stress-test against two variables that actually move outcomes (e.g., traffic 15% lower than plan, capex +20%). Present breakeven months under base and downside cases.

Risk & Mitigations

No plan is risk-free. Call out three real risks and how you will contain them:

• Permitting delays: pre-qualify locations; keep a rolling pipeline 2× target sites.

• Queue spillback to road: design S-curves and dual merge; deploy line management during breakfast peak.

• Operational errors under speed pressure: simplify window menu; pre-stage SKUs; tighten bar flow.

The key: your recommendation should survive the downside case with acceptable payback or have kill-switch thresholds defined in advance.

Writing the Case Analysis and Presenting It

Write for a busy executive. Your reader should grasp the decision and logic in the first minute. Structure and tone matter as much as analysis quality.

Suggested Structure (max five core sections)

1. Executive summary (≤200 words). State the decision, the “why,” expected impact, and top risks with mitigations. Make it skimmable—bold the decision.

2. Situation & problem diagnosis. One to two paragraphs that lay out the facts, market context, and the root cause behind the symptoms.

3. Analysis highlights. Show the 2–3 exhibits that actually drive the decision: a unit economics snapshot, a customer-journey bottleneck figure, and a location heatmap summary or store-level payback chart.

4. Options considered & decision matrix. Briefly show how each option performs on the chosen criteria; include the simple table from above and one paragraph of rationale.

5. Recommendation & implementation plan. Present the chosen path with a sequenced roadmap, KPIs, and risks.

Voice and Formatting Tips

• Lead with the answer. The first line of your summary should state the decision: “Launch 20 satellite drive-thru kiosks over 12 months, starting with the top quartile commuter corridors.”

• Use numbers, not adjectives. “Reduce wait times from 6:10 to ≤3:45” beats “significantly faster.”

• Tight paragraphs. 2–4 sentences each. Every paragraph should earn its space by advancing your argument.

• Exhibits that speak. Title each chart with the insight, not the label: “Dual-lane cuts average wait by 38% at 90th percentile traffic.”

• One narrative, not four frameworks. You used SWOT and Five Forces to think, not to show everything you did. Only include the parts that alter the decision.

Example: Executive Summary (BeanBurst)

Decision. Launch satellite drive-thru kiosks in suburban commuter corridors; defer expensive retrofits to a second wave after flow improvements.
Why. Kiosks deliver better unit economics and bigger morning throughput where rivalry is moderate and lot availability is high. They also avoid complex retrofit permits that elongate payback.
Impact. Target 20 sites in 12 months, base-case payback <24 months, breakfast NPS +8 points, morning share +2%.
Risks & mitigations. Permits (build 2× pipeline), queue spillback (dual lanes + line marshals in first 90 days), operations (menu simplification + pre-batching).

Implementation Roadmap

Break your recommendation into a phased plan aligned to measurable milestones. This is where many student papers falter; they stop at “what,” not “how.”

Phase 1: Validate economics (0–60 days).

• Pilot three kiosks across different suburban profiles (commuter highway, retail cluster, school corridor).

• Track orders/hour, avg wait, check size, labor hours, refund rate.

• Run A/B on two menu variations: full vs. “fast five” (the top 5 high-margin beverages + 2 snack bundles).

• Gate to Phase 2 only if throughput uplift ≥25% and payback ≤24 months in base case.

Phase 2: Scale playbook (2–8 months).

• Standardize layout (dual order screens, dedicated handoff shelf).

• Launch app-first journey: “tap to repeat” orders, pick-up lanes, and pre-pay to cut dwell time.

• Build site selection model using traffic counts, ingress/egress, parking lot shape, and competitor proximity.

Phase 3: Optimize & expand (8–12 months).

• Add drive-thru-only bundles, measured by attach rate.

• Introduce external order-taker during peak hours; re-train team on speed-of-service.

• Evaluate select retrofits where lots and permits are favorable, using lessons from kiosks.

What to Cut (Common Student Pitfalls)

• Bloated literature dumps. Two pages of general marketing theory earn no points unless they move the decision.

• Unanchored surveys. If 68% of respondents “value speed,” translate that into design choices (dual lanes) and KPIs (≤4-minute service time).

• Framework tourism. Don’t parade models. Choose a few, do them well, and tie them to the economics.

Exhibit Craft: Make Your Numbers Believable

• State assumptions plainly near each figure. If you assume order uplift of 30%, say where it comes from (pilot results, comparable stores).

• Bound uncertainty with ranges or sensitivity. For example, show payback at traffic −15% and capex +20% to demonstrate robustness.

• Triangulate where possible (survey tolerance for wait time × observed throughput × average car occupancy).

Adapting the Approach to Other Case Types

The same step-by-step logic works beyond retail:

• B2B SaaS pricing. Diagnose the problem (low conversion or high churn), analyze unit economics (ARPU, gross margin, CAC), segment customers by use case, test tiered pricing options, and recommend with a rollout plan and guardrails (grandfathering, win-loss monitoring).

• Nonprofit program expansion. Define outcomes (clients served, cost per outcome), map constraints (funding cadence, staffing), compare program models using a weighted decision matrix, and propose a staged expansion with evaluation checkpoints.

• Marketing creative strategy. Identify the job (awareness or conversion), analyze channel economics (CPL, CPA), test two creative territories, and recommend the winner with an execution calendar and spend caps.

How to Work Fast Under Deadline

When the clock is ticking, time-box the steps: 20% scoping, 50% analysis, 30% synthesis and writing. Draft the executive summary first; it clarifies what you are solving. As you discover better evidence, update the summary. Keep a small parking lot for interesting but non-essential ideas; don’t derail your core argument.

Evidence to Carry Into Your Appendix

Your main text should flow; the appendix can carry supporting detail:

• Location shortlist with key metrics (traffic counts, ingress/egress score).

• Detailed unit economics by site.

• Survey instrument and summary stats.

• Sensitivity analyses and scenario trees.

Conclusion: A Case Method You Can Reuse

A high-scoring case study analysis is not a recap—it is a decision with a defendable path. Start by clarifying the brief and framing a single, testable question. Structure your thinking with a few proven frameworks and quantify the unit economics that actually drive outcomes. Generate real options, compare them with explicit criteria, and recommend one path with a staged implementation, metrics, and mitigations. Do this, and your professor (or manager) can follow your logic from first line to last slide without guessing what you mean.