Product Case Study: Hatch AI Report Generator

Product Overview

The AI Report Generator is a Streamlit-based application that leverages large language models to transform raw marketing analytics data into polished client reports. As Product and Growth Consultant at Hatch Technologies, I identified this opportunity to improve operational efficiency while creating employment opportunities for disadvantaged youths.

Problem Identification

Working with Hatch's digital marketing agency division, I observed several critical inefficiencies:

• Time consumption: Agency staff spent 3-4 hours per client manually compiling weekly/monthly marketing reports
• Skill mismatch: High-skilled marketing professionals spent time on low-value report formatting tasks
• Consistency challenges: Reports varied in quality and structure based on which team member created them
• Scaling limitations: Report creation time created a ceiling on how many clients the agency could support
• Training bottleneck: New team members required extensive training on report creation, delaying productivity

Product Approach

Discovery Phase

• Process analysis: Shadowed team members through report creation process, identifying specific pain points
• Time tracking: Measured actual time spent on different aspects of report creation (data gathering: 20%, analysis: 30%, formatting: 50%)
• User interviews: Conducted structured interviews with both report creators and clients receiving reports
• Output review: Analyzed 20+ existing reports to identify patterns, common elements, and value-adding components

Product Definition

• Success metrics: Defined primary metric (time saved per report) and secondary metrics (client satisfaction, report consistency)
• MVP scope: Prioritized features that would deliver immediate time savings while maintaining report quality
• Technical constraints: Selected Streamlit for rapid development and accessibility to team with limited technical background

Workflow Design

• Process mapping: Created a structured 4-step workflow to standardize report generation:
1. Pre-sales conversation capture → Initial context gathering
2. Foundational marketing unit analysis → Checking for key marketing elements
3. Actionable insight extraction → Defining key recommendations based on presence/absence of marketing units
4. Automated report assembly → Combining introduction, insights, conclusion, and detailed breakdown into PDF report

• Validation: Tested workflow with actual client data to ensure it captured all essential report elements

Development Approach

• Prototype testing: Built functional prototype in 2 weeks and tested with actual client data
• Iterative refinement: Conducted 3 rounds of user testing and refinement before full deployment
• Training integration: Developed documentation and training materials for new youth hires

Key Features & Rationale

Structured Report Workflow Engine

• What: Standardized 4-step process from pre-sales conversation to final PDF report
• Why: Created consistent methodology that could be taught to team members with limited marketing experience
• Outcome: Enabled youth trainees to produce professional-quality reports within 2 weeks of onboarding

Marketing Unit Analysis Module

• What: Automated assessment of foundational marketing elements across platforms
• Why: Standardized evaluation criteria that previously varied between analysts
• Outcome: 95% improvement in consistency of marketing recommendations across different team members

Actionable Insight Generator

• What: LLM-powered system to analyze marketing performance and generate recommended actions
• Why: Automated the most expertise-dependent part of report creation
• Outcome: Created consistent, high-quality recommendations while reducing analysis time by 85%

Report Assembly Engine

• What: Automated system to compile introduction, insights, conclusion and detailed breakdown into PDF format
• Why: Eliminated the most time-consuming, low-value aspect of report creation
• Outcome: Reduced report formatting time from 2+ hours to under 5 minutes

Results & Impact

Efficiency Gains

• Time savings: Reduced report creation time from 4 hours to 15 minutes (93% efficiency gain)
• Capacity increase: Enabled team to handle 2.5x more clients with same headcount
• Quality improvement: 87% of clients rated new reports as equal or superior to previous manual reports

Business Outcomes

• Revenue growth: 4-figure monthly recurring revenue from web development agency services
• Youth employment: Created entry-level positions accessible to program graduates
• Recognition: Received Google AI Trailblazers Innovation Award 2024
• Client acquisition: 10 prospective clients specifically cited the rapid report turnaround time as a key factor in their decision to engage with Hatch

Social Impact

• Skills development: Tool created pathway for disadvantaged youth to gain marketable data analysis skills
• Employment opportunities: Lower barrier to entry created jobs for 4 program graduates
• Client education: Improved reports increased client digital marketing literacy

Product Decisions & Tradeoffs

Streamlit vs. Custom Web App

• Decision: Built using Streamlit rather than developing a custom web application
• Rationale: Streamlit enabled faster development and easier maintenance by team with limited technical skills
• Impact: Deployed functional solution in 3 weeks versus estimated 3+ months for custom solution

Semi-Automated vs. Fully Automated

• Decision: Designed for human review and enhancement rather than full automation
• Rationale: Maintained quality control and created employment opportunities for program graduates
• Impact: Balanced efficiency gains with employment creation objectives

Modular Design vs. Integrated System

• Decision: Created modular components that could be used independently or together
• Rationale: Enabled gradual adoption and flexibility for different client needs
• Impact: Increased team buy-in by allowing partial implementation based on comfort level

Lessons Learned

What Worked Well

• User-centered design: Regular testing with actual report creators ensured practical utility
• Balanced automation: Finding the right level of automation created both efficiency and employment
• Knowledge documentation: Built system that captured and standardized agency reporting expertise

Challenges & Adjustments

• Initial resistance: Some team members were hesitant about AI-generated content
• Training requirements: Needed more comprehensive training than initially anticipated
• Data inconsistencies: Had to develop more robust data validation than originally planned

Future Product Direction

• Client portal: Developing client-facing dashboard for real-time access to metrics
• Advanced analytics: Incorporating more sophisticated trend analysis and forecasting
• Expanded integrations: Adding connections to additional marketing platforms and CRM systems

Relevance to Public Sector Products

The AI Report Generator experience demonstrates my ability to:

1. Balance efficiency with social impact (crucial for public sector technology)
2. Identify and eliminate process inefficiencies (applicable to government operations)
3. Implement AI solutions that augment rather than replace human work (ethical AI deployment)
4. Create immediate value while building toward larger vision (essential for government innovation)
5. Design systems that consider varied technical capabilities (important for inclusive government services)

Reference

Victor Zhu - CEO @ Hatch Technologies