Overview

• Understanding Problem Definition
• Frameworks for Structured Problem-Solving
• Enhancing Critical Thinking and Logical Reasoning
• Advanced Case Study Analysis Techniques
• Real-world Applications and Examples

1. Defining the Problem

Why is problem definition critical?

A well-defined problem ensures clarity, alignment, and efficiency in deriving solutions. Without a precise definition, efforts can be misdirected.

Components of a strong problem statement:

• Context: Background of the issue
• Complication: Key challenge faced
• Question: Core issue that needs resolution
• Impact: Implications of not addressing the issue

Techniques for effective problem definition:

• 5 Whys Analysis: Iterative questioning to uncover root causes
• Problem-Goal Alignment: Clarify objectives before proceeding
• Situation-Complication-Resolution (SCR) Framework: Contextualize challenges for structured problem-solving
• SMART Problem Statements: Specific, Measurable, Achievable, Relevant, and Time-bound

2. Frameworks for Problem-Solving

MECE (Mutually Exclusive, Collectively Exhaustive)

A structured way to organize problems ensuring completeness and non-overlapping segments.

• Mutually Exclusive: Each category is distinct
• Collectively Exhaustive: Covers all possible areas

Issue Trees for Problem Breakdown

Additional Frameworks:

• Hypothesis-Driven Approach: Begin with a hypothesis and test iteratively
• Pareto Principle (80/20 Rule): Focus on key drivers
• Root Cause Analysis: Identify underlying causes before proposing solutions

3. Critical Thinking and Logical Reasoning

Principles of Strong Analytical Thinking:

• Data-driven approach: Base conclusions on evidence
• Skeptical inquiry: Question assumptions and biases
• Systematic analysis: Follow structured thinking methods
• Inference and evaluation: Draw meaningful conclusions

Common Logical Fallacies to Avoid:

• Straw Man: Misrepresenting an argument
• False Dichotomy: Presenting only two options
• Post Hoc Fallacy: Assuming causation from correlation
• Confirmation Bias: Selecting data to fit preconceived beliefs

Improving Logical Reasoning:

4. Advanced Case Study Analysis Techniques

Effective Case Analysis Steps:

1. Define the Key Problem: Establish scope and objectives
2. Analyze the Data: Utilize frameworks for structured evaluation
3. Identify Alternatives: Compare viable solutions
4. Evaluate Trade-offs: Consider benefits, risks, and feasibility
5. Formulate Recommendations: Provide actionable insights

Business Frameworks for Case Studies:

• SWOT Analysis: Evaluate Strengths, Weaknesses, Opportunities, and Threats
• Porter’s Five Forces: Analyze industry competition
• Business Model Canvas: Understand business dynamics
• PESTLE Analysis: Consider external macro-environment factors

5. Real-World Applications

Applying These Frameworks in Consulting:

• Corporate Strategy: Identifying new market opportunities
• Operational Efficiency: Process improvement and cost reduction
• Financial Modeling: Evaluating investment decisions
• Policy Making: Assessing economic and regulatory impact

Conclusion

Key Takeaways:

• Well-defined problems lead to structured, effective solutions
• Analytical frameworks provide clarity and direction
• Critical thinking ensures logical and unbiased decision-making
• Case study techniques enhance problem-solving in real-world applications

Mastering these skills will significantly enhance your consulting impact!

Class Case Study

Title: The Case of Prestige Retail’s Struggling Loyalty Program

Background

Prestige Retail, a national luxury department store chain, recently launched a new loyalty program designed to increase customer retention and lifetime value. However, after six months, executives are disappointed with the program’s results.

They have engaged your consulting firm to help analyze the issue. You and your team will meet with the VP of Marketing, Director of Customer Analytics, and CFO to gather initial insights before conducting a deeper analysis.

Your goal is to identify the real problem based on the client’s statements and concerns, separating their assumptions from underlying business issues.

Client Interview Transcript

Meeting Date: February 15, 2025
Attendees:
- You (Consultant)
- Julia Reynolds (VP of Marketing)
- Kevin Wong (Director of Customer Analytics)
- Richard Tanner (CFO)

Transcript

Consultant: Thank you for meeting with us. Let’s start with your key concerns. What seems to be the problem?

Julia (VP of Marketing): The loyalty program simply isn’t working. We were expecting a 15% increase in repeat purchases, but instead, returning customer purchases are flat. Worse, our competitors launched their own loyalty programs recently, and we fear they’re pulling our customers away.

Consultant: That’s concerning. What specific data makes you believe the program isn’t working?

Kevin (Director of Customer Analytics): Well, if you compare customers who signed up for the loyalty program to those who haven’t, their average spend per month is about the same. We even saw some drop-off in certain customer segments, particularly in Gen Z shoppers.

Consultant: Interesting. Are Gen Z shoppers a significant portion of your target audience?

Julia: They’re growing—around 22% of our revenue now. But our core base has always been high-income professionals in their mid-30s to 50s. Still, we can’t afford to lose younger shoppers entirely.

Consultant: Have there been any noticeable shifts in customer behavior overall?

Kevin: Yes. The program was supposed to drive more frequent shopping, but instead, many customers are only making purchases when they hit a reward threshold. It seems like they’re holding back spending, waiting for rewards to kick in.

Richard (CFO): And that’s a problem because we’re offering discounts to these customers, cutting into our margins, but we’re not seeing enough volume to make up for it. We thought this program would pay for itself with increased purchases, but now it’s just costing us money.

Consultant: I see. Have you tested different reward structures?

Kevin: We experimented with bonus point days and double rewards for specific categories, but the results were mixed. Customers are using rewards on items that already have high markdowns, which is driving profits down further.

Julia: Exactly. The original intent was to create loyalty, not discount-driven behavior.

Consultant: Understood. One last question—do you have any data on customers who signed up but haven’t engaged much with the program?

Kevin: We do. About 35% of signups have barely used the program. Many signed up but don’t seem to redeem points regularly or change their shopping patterns.

Your Task as a Consultant

1. Identify the core problem(s).
   • What is the client assuming the issue is?
     
   • What do the data and customer behavior suggest?
2. Differentiate symptoms from root causes.
   • Are customers truly disinterested, or is something about the program structure causing unintended behavior?
     
   • What role do customer segmentation, pricing strategy, and competitor actions play?
3. Develop an initial hypothesis.
   • Why might the loyalty program be underperforming?
     
   • What additional data would you request to validate your hypothesis?
4. Propose next steps.
   • What analytics techniques would you use to diagnose the problem further?
     
   • What strategic questions should the client be considering?

Discussion Questions for Students

1. What are the assumed problems the client has?
   
2. Based on the transcript, what are some alternative explanations for why the program isn’t working?
   
3. What additional data or metrics would help you validate the true issue?
   
4. How might behavioral economics and consumer psychology explain customer actions?
   
5. What recommendations would you make for short-term fixes and long-term strategy adjustments?