When preparing a case study presentation for an interview, first turn the prompt into a clear business problem; then connect your assumptions, findings, and recommendation. The goal is not simply to create slides, but to show clearly how you think and what evidence supports your decision. Before you begin, confirm the assignment, format, and time limit; then adapt the flow below to your case.
The purpose of a case study presentation
A case study is not the same as a general presentation about a topic or an introduction to a project you completed in the past. Ohio University’s interview guide notes the importance of showing your work in a case interview, or explaining why you made a particular choice and how you reached your conclusion; it also adds that there may not be a single right answer in every case (Ohio University). So explain not only your conclusion, but also the rationale behind your decision and its limitations.
If you are being asked to describe a past project, see the guide to presenting a project in an interview. If you are working on a case specific to an operations role, preparing a presentation for an operations manager interview may be more relevant.
Clarify the assignment and its boundaries
Read the case prompt carefully and note the following: What decision needs to be made? How will success be measured? Who will be listening to the presentation? Are there any explicit rules about time, slide count, use of sources, or file format? If the instructions are unclear, ask the recruiter for clarification if possible. If you do not receive an answer, clearly state your interpretation and assumptions in the presentation.
If you have been given a data file, check its definitions, time periods, missing values, and units of measurement. If no data is provided, do not invent figures and present them as real; say what information you would need and base your recommendation on clearly stated assumptions. Binghamton University’s data analyst case guide covers defining the problem, explaining assumptions, evaluating findings, and linking recommendations to business goals. These steps can provide a useful framework; however, the guide is aimed at data analyst cases, so follow the case instructions for your specific role as well (Binghamton University).
Build your decision logic
Before creating slides, write a one-sentence problem statement. Then ask, “What evidence could change my decision?” and define the scope of your analysis accordingly. Rather than listing many findings in succession, select those that matter to the decision. For each finding, make this connection: finding → potential business impact → recommendation.
Do not leave your recommendation at “what should be done?” State the first step, any people or teams to collaborate with if needed, the metric to track, and when you will evaluate the outcome. Make sure the metric aligns with the case question. If the data does not establish the outcome conclusively, present your recommendation as a hypothesis to test rather than guaranteeing success.
Hypothetical example: reducing cart abandonment in an online store
The scenario and findings below are entirely fictional; they do not represent a real company or research result. Suppose the case question is: “What is the first change you would test to reduce cart abandonment?” Assume you are given step-by-step conversion data for the past four weeks. The data shows that the purchase rate among people who proceed to checkout is lower than at the previous step; however, that difference alone does not explain why.
A six-slide flow could look like this:
- 1. Decision summary: “I recommend first running a limited test to confirm the reason for abandonment at checkout.” This sentence presents your decision and an important uncertainty together.
- 2. Problem and scope: State how you define cart abandonment, the period examined, and which users the analysis covers.
- 3. Approach and assumptions: Explain that you compared conversion rates at each step. If you did not examine differences such as device type or payment method, note that explicitly.
- 4. Key finding: Use a simple funnel visual to show at which step the drop occurs. Do not present fictional example data as a real result.
- 5. Proposed test: Review the explanations and error messages on the checkout screen; recommend testing a limited change suited to the observed issue. Since the cause has not yet been confirmed, do not call this a definitive solution.
- 6. Measurement and next step: Recommend tracking purchase conversion and checkout errors, and checking results across groups such as device type or payment method. State what additional data you would examine if the outcome differs from expectations.
The important thing in this example is not to invent fictional numerical results, but to preserve the distinction between a hypothetical finding and an inference. Explain your approach in the interview and say what additional information you would need to validate your recommendation.
Create your slides and rehearse
Where possible, write slide titles as short conclusion statements that convey the main message; for example, “The drop occurs at checkout.” Specify the unit of measurement, time period, and groups being compared on charts. Keep methodological detail to a level that does not interrupt the main narrative; have additional calculations and breakdowns in backup notes that you can refer to if asked.
Rehearse the presentation aloud within the allotted time. Then prepare brief answers to questions such as “What would your recommendation be if this assumption changed?”, “What other explanations are possible?” and “Which data would you validate first?” These are not questions every interviewer will necessarily ask; they are useful rehearsal prompts for checking uncertainties and alternative explanations in your work.
Common issues and how to address them
If the data does not support the recommendation: Do not claim causation. Describe the finding as an observation, distinguish possible explanations, and suggest a next step to test them.
If the case is too broad: Narrow the scope; state what you have left out and why. Focus on the first question needed to make the decision.
If figures or definitions are inconsistent: Go back to the source and recheck the time period, denominator, and filters. If you cannot resolve the discrepancy, do not hide it; explain which inference is affected by the uncertainty.
If the presentation does not directly answer the question: State your decision, its main rationale, and an important limitation on the first slide. Explain the detailed analysis on subsequent slides.
Pre-presentation checklist
- Does the presentation address the case question and follow the requested format?
- Are the problem statement, period, scope, and assumptions clear?
- Is the rationale for important recommendations understandable?
- Have the data, chart labels, and calculations been checked against the source?
- Are hypothetical examples and uncertainties clearly identified?
- Are the recommendation’s first step and the metric to track clear?
- Does the presentation fit the allotted time, and are notes ready for possible questions?
How many slides should a case study presentation have?
There is no single number that works for every case. First, follow any limit in the interview instructions. If no number is specified, use enough slides to explain the problem, approach, findings, recommendation, and measurement; move details to an appendix if needed.
Can you prepare a case study presentation without being given data?
Yes. State your assumptions clearly, define what data you would need to make the decision, and present your recommendation as an approach to test rather than a validated result. This lets you explain your thought process without hiding uncertainty.
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