SayPro Conduct internal SayPro M&E team interviews

Interview Objectives

• Assess internal alignment with SayPro’s Theory of Change and Results Frameworks
• Collect firsthand feedback on MEL tools, processes, and reporting effectiveness
• Identify capacity-building needs, success stories, and bottlenecks
• Inform development of future learning briefs, strategy updates, and team practices

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🧠 Interview Themes & Questions

Theme	Sample Questions
Role Clarity & Contribution	“How would you describe your current role in SayPro’s MEL work?” “What do you feel has been your biggest contribution in the past quarter?”
Tools & Technology	“What MEL tools or platforms are you using regularly? Are they helping you work effectively?”
Data Collection & Use	“How confident are you in the accuracy and timeliness of the data collected?” “What kind of data do you think is missing or underused?”
Challenges & Risks	“What are the biggest bottlenecks or challenges you’ve faced?” “How do you manage data fatigue or reporting overload?”
Learning & Impact	“Can you share an example of how M&E data led to a positive program shift or decision?”
Recommendations & Aspirations	“If you could change one thing about SayPro’s M&E approach, what would it be?” “What kind of support or training would you like in the next 6 months?”

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📋 Summary of Key Insights

Insight Area	Findings
Strong Alignment	Most team members feel well-aligned with SayPro’s mission, especially in evidence-based learning and youth engagement.
Tool Utilization	KoboToolbox, Power BI, and Google Sheets remain primary tools, though many request better integration and automation.
Data Bottlenecks	Common challenges include delays from field partners, inconsistencies in indicators, and limited time for reflection and analysis.
Learning Culture	Several members praised peer learning circles and internal workshops, but asked for more cross-royalty sharing.
Training Needs	High interest in advanced Excel, AI for M&E, and gender-sensitive data analysis.
Strategic Shifts	Suggestions include decentralizing MEL feedback loops and involving beneficiaries in validation more directly.

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👥 Voices from the Team (Selected Quotes)

🗣️ “Our reporting is strong, but we don’t spend enough time unpacking what the data really means for our programs.”
— M&E Specialist, Western Cape

🗣️ “The integration of AI into our content evaluation is exciting—but we need proper onboarding to make it sustainable.”
— Digital M&E Associate

🗣️ “I feel proud when program staff actually use the dashboards we build. That’s when I know the work is making a difference.”
— Data & Reporting Officer

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✅ Action Points Going Forward

1. Develop internal “M&E Knowledge Hub” – shared folder for tools, guides, best practices, case studies
2. Launch quarterly MEL learning sessions – rotate topics like indicator design, AI use, adaptive M&E
3. Follow up on training requests – begin planning for July/August skill-building workshops
4. Improve data turnaround time – pilot automated validation scripts and cleaner indicator banks
5. Include field staff in reflection loops – start testing rapid feedback templates and stories of change forms

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