Gen AI assisted research: Starting with end goal in mind

Making strategic business decisions in today's dynamic market requires more than gut instinct – it demands a methodical, evidence-based approach. Imagine building a house: you wouldn't start without blueprints. Similarly, effective market analysis begins with a clear vision of your destination. What decisions need to be made? What critical information will drive those decisions? This post explores a reverse-engineered research methodology, demonstrated through a case study: evaluating a retail bank's potential entry into the student checking account market. By starting with the end goal – in this case, determining the feasibility and strategy for launching a student banking product – I'll show how working backwards can creates a more focused and efficient analysis process. Think of it as plotting your journey on a map: instead of exploring every possible route, you start with your destination and work backwards to find the most efficient path. We'll demonstrate how this approach not only streamlines the research process but also ensures that every piece of analysis directly contributes to your final decision-making needs. Also, use of Gen AI has made it possible to bridge the gaps and consolidate information quickly to make this option viable.

The Research Framework: Working Backwards

This approach starts with a simple but powerful premise: define what decision-makers need to know before working backwards to gather and analyze the necessary information. Here's how this methodology played out in practice. Using Gen AI define the set of questions relevant and important for making a decision. In this case venturing with new Product Student Checking Account. Then next define a business use case template that is relevant for this problem. Here any existing template use in the organization can also be used. End goal is to define the agenda and topics that to be covered under a business case.

Step 1: Defining Decision Criteria

Under this identifying the critical questions to be answered before decisions can be made.

1.1 Top Questions from Decision Makers:

• Strategic alignment with bank's vision
• Market opportunity assessment
• Competitive positioning Implementation feasibility
• Risk evaluation

1.2 Business Case Requirements

• Market size and growth potential T
• Target market segmentation
• Financial projections Implementation roadmap
• Risk assessment

Step 2: Framework Analysis

With our end goals defined, we conducted business analysis. In this case, using three complementary frameworks. Below some snippets of the analysis done by Gen AI using those framework.

SWOT Analysis Results:

• Identified internal strengths (existing infrastructure, brand trust)
• Uncovered weaknesses (limited student banking experience)
• Spotted opportunities (growing digital banking adoption)
• Highlighted threats (fintech competition, regulatory challenges)

Porter's Five Forces Findings:

• Highly competitive rivalry in student banking
• Growing threat from digital entrants
• Strong bargaining power of university partners
• Moderate supplier power in technology vendors
• High buyer power among tech-savvy students

PESTLE Analysis Insights:

• Political: Increasing focus on student financial protection
• Economic: $3.55-4.53B market opportunity
• Social: Growing demand for digital banking among Gen Z
• Technological: Rapid adoption of mobile payment solutions
• Legal: Complex regulatory environment
• Environmental: Push toward paperless banking

Step 3: Consolidation Phase

In this phase bringing together information from various types of business analysis and market analysis information together. Some key points those stood out are following:

The consolidation phase revealed several key themes:

• Digital innovation is crucial for success
• University partnerships are strategic differentiators
• Customer acquisition costs are a critical factor
• Long-term relationship building is essential

Step 4: Data Review

One of the powerful capabilities of Gen AI is to ask it to play role of reviewer. By playing that role review data extracted in analysis process and it uncovers several important adjustments:

• Market size calculations needed revision
• Growth projections were initially too optimistic
• Revenue assumptions required adjustment
• Competitive analysis needed updating

Step 5: Addressing the key analysis objectives

In this step we leveraged Generative AI to transform our comprehensive research into two essential outputs: a detailed business case and answers to critical strategic questions. After those two documents were created some key points that stood out are following:

5.1 Business Case Components:

• Detailed market analysis showing $3.55-4.53B opportunity
• Segmentation strategy targeting three distinct student groups
• Financial projections with revised 5-year growth targets
• Comprehensive risk assessment and mitigation strategies

5.2 Strategic Question Responses:

• Clear alignment with digital transformation goals
• Specific market gap identification
• Timing rationale based on market dynamics
• Differentiation strategy through innovation
• Portfolio impact assessment

Step 6: Review and Feedback

In this step, we bring is Gen AI as another reviewer to share feedback on the above two documents created. The review process identified several areas requiring additional analysis:

• Bank-specific strategic context
• Internal capability assessment
• Portfolio impact details
• Implementation requirements

Step 7: Iteration and Updates

Continue analysis in those identified areas with the help of Gen AI and do the final iterations. Some feedback that came out of the process are following:

• Refining financial projections
• Strengthening risk mitigation strategies
• Developing detailed implementation timelines
• Creating specific success metrics

Key Learnings from the Process

1. Start with the End in Mind Understanding decision-makers' needs helps to focus on key aspects of the problem during research Clear objectives lead to more focused analysis
2. Multiple Framework Benefits Different frameworks provide complementary insights Cross-validation improves analysis reliability
3. Data Validation is Critical Regular review of assumptions and calculations Market dynamics require constant updates
4. Iterative Improvement Feedback loops enhance analysis quality Flexibility to incorporate new insights

Conclusion

This systematic approach to market analysis proved highly effective in evaluating the student checking account opportunity. By starting with the end in mind and following a structured process, we were able to provide comprehensive insights for decision-making while ensuring all critical aspects were thoroughly examined.

The methodology's success lies in its ability to:

• Maintain focus on decision-makers' needs
• Provide comprehensive market understanding
• Ensure data accuracy and reliability
• Allow for continuous improvement

For future market analyses, this framework offers a reliable template that can be adapted to different products and markets while maintaining analytical rigor and strategic focus.

Reference Prompt template used for the work.

https://docs.google.com/spreadsheets/d/1AnssaWKmn5k9s1nFj9b1UoKTd8WTO6nxvk5Gmj8Iu9c/edit?usp=sharing