McKinsey Case Interview Example

McKinsey is one of the most prestigious of the MBB consulting firms. It is also well known for having among the most challenging case interviews.

In this article, we will walk you through Beautify, an example McKinsey case interview: https://www.mckinsey.com/careers/interviewing/beautify

Understand the problem

The first step to any case interview is to clarify the problem and goal. In this case, the beauty retailer Beautify is facing issues selling their products amid a shift to online shopping compared to in-person sales.

The goal at hand to evaluate is whether training the sales consultants to use virtual channels to connect with customers will improve profitability.

Structure the approach

Now that you understand the problem at hand, the next aspect to address is structuring your approach.

One way to have a approach available it to use a framework. A framework is a model for solving certain types of problems. In this case a profitability framework would be a great fit given the problem at hand.

The profitability framework is simple, Profit = Revenue – Costs.

Besides the framework, there are also several other factors to consider such as:

Customer experience – How will virtual advisors work in place of in-person advice?

Technology and training – What platforms will the advisors use? Will they be user friendly for both advisors and customers?

Operational changes – How will inventory and retail partnerships adapt to the new model?

Competitive landscape – Are competitors already using virtual advisors? How can Beautify differentiate itself?

Financial impacts – What are the upfront and ongoing costs? How will revenue streams change?

You can put these together to form a framework suited for the exact case like the below example:

Area	Key Questions
Customer Impact	Will customers accept virtual advisors? What features would encourage them?
Operational Feasibility	Can Beautify train consultants and build the tech infrastructure?
Financial Viability	What’s the ROI? How long until profitability?
Competitive Analysis	How are competitors using AI/virtual tools? What can Beautify learn?

Gather data and analyze

After you have built your approach, the next phase is to ask the interviewer for clarifying facts and data. You do this by first explaining the reasoning for why you are asking for the data and facts.

For example, you might say:

Given that the issue faced by Beautify is related to profitability and given the decline in sales, I would like to first understand the current revenue of the company.

The interviewer might then give you the figures or ask you to make estimation based on common knowledge or what has already been provided.

Here is an example of the data you might obtain through your questioning and the analysis you could perform:

Customer Impact

Data: Current customers prefer in-store service for high-touch engagement.
Analysis: Virtual advisors must replicate this experience through:
- Personalized consultations (e.g., video calls, AI-driven recommendations).
- Convenience (e.g., 24/7 availability, easy reordering).
- Engagement (e.g., live tutorials, social media interaction).

Operational Feasibility

Data: Upfront costs include IT (€50M), training (€25M), remodeling (€50M), and inventory (€25M).
Analysis: Beautify needs to:
- Invest in user-friendly platforms for advisors and customers.
- Train consultants in virtual sales and social media engagement.
- Maintain some in-store presence (e.g., remodeled counters for hybrid experiences).

Financial Viability

Data:
- Incremental revenue: 10% of €1.3B = €130M/year.
- Upfront costs: €150M.
- Annual costs: €10M + 5% depreciation (€7.5M) = €17.5M/year.
Analysis:
- Year 1: Net profit = €130M – €17.5M = €112.5M; Cumulative = -€37.5M.
- Year 2: Net profit = €130M – €17.5M = €112.5M; Cumulative = +€75M.
- Conclusion: Profitable in Year 2.

Competitive Analysis

Data: Competitors’ AI chatbots (Clarice, Lena, Jaki, Lola) show varying success in satisfaction and conversion.
Analysis:
- High satisfaction (e.g., Clarice) doesn’t always mean high conversion.
- Beautify should aim for a balanced approach (e.g., like Lena or Lola) or use AI to complement human advisors.

Develop and Test Hypotheses

The next phase of the case is developing and testing hypothesis that might address the problem at hand.

Hypothesis 1: “Virtual advisors will increase revenue by 10% but require €150M upfront, becoming profitable in Year 2.”

Test: Financial calculations confirm profitability in Year 2.

Hypothesis 2: “Customers will resist virtual advisors unless the experience is highly personalized and convenient.”

Test: Customer feedback and competitor data suggest personalization and convenience are critical.

Hypothesis 3: “AI chatbots can enhance, but not replace, human advisors.”

Test: Competitor data shows AI improves conversion but may reduce satisfaction; a hybrid model is ideal.

Synthesize Findings

Key Insights:

Customer Acceptance: Virtual advisors can work if they offer personalization, convenience, and engagement.
Financial Feasibility: The investment is profitable in Year 2, but upfront costs are high.
Operational Readiness: Beautify needs to invest in tech, training, and a hybrid in-store/virtual model.
Competitive Edge: AI can complement human advisors, but Beautify should focus on balancing satisfaction and conversion.

Risks:

Customer pushback if the virtual experience feels impersonal.
High upfront costs may strain short-term cash flow.
Competitors may quickly adopt similar strategies.

Provide a Recommendation

Before giving a solution, summarize the problem, the insights you gathered and then provide a solution along with benefits and risks, as well as follow up actions.

Example final recommendation like so:

“Based on our analysis, Beautify should proceed with a phased transition to virtual advisors, starting with a pilot program. The financials show profitability in Year 2, and customer acceptance can be achieved through personalization and convenience. Competitor data suggests AI can enhance, but not replace, human advisors. We recommend a hybrid model, with ongoing monitoring of customer feedback and competitor trends.”

Short-Term Actions (0–12 months):

Pilot Program: Launch a small-scale virtual advisor program with a select group of consultants and customers.
Tech Investment: Build a user-friendly platform for virtual consultations and social media sales.
Training: Train consultants in virtual engagement, social media, and AI tools.
Customer Incentives: Offer discounts or exclusive content to encourage virtual adoption.

Long-Term Actions (1–3 years):

Scale Up: Expand the virtual advisor program based on pilot feedback.
Hybrid Model: Maintain a mix of in-store and virtual experiences to cater to all customer preferences.
AI Integration: Introduce AI chatbots for routine queries, freeing human advisors for high-value interactions.
Monitor Competitors: Continuously analyze competitor strategies and adapt Beautify’s approach.

After giving the recommendation, expect potential follow up questions and rebuttals, ensure your responses to those fall within your structured framework so that they are back up with the analysis you have performed.