SayPro Research Process Documentation

Methodological Transparency | SCRR-5 June 2025 Market Sizing and Forecasting

To ensure that all findings in the SCRR-5 June 2025 report are grounded in evidence and derived through sound methodology, SayPro’s Research Office adheres to a clearly defined and consistently applied research process. This structured approach ensures accuracy, transparency, and strategic alignment in all research outputs.

1. Research Objectives

The SCRR-5 research process was designed to:

Measure current market size across SayPro’s strategic focus areas
Identify short- and medium-term trends influencing program demand
Forecast market changes and opportunities for Q3–Q4 2025
Ensure data-driven alignment with SayPro’s operational and growth strategies

2. Data Collection Process

A. Primary Data Sources

Customer & Consumer Surveys
- Administered between 1–15 June 2025
- Mixed-mode (online, phone, and in-person)
- Sample Size: 4,200+ respondents across 6 countries
- Focus Areas: Skills development, employment, healthcare, government training
- Tools Used: KoboToolbox, SurveyMonkey
Key Informant Interviews (KIIs)
- 18 interviews with local government officials, NGO partners, and SayPro trainers
- Used to validate assumptions and highlight emerging regional factors

B. Secondary Data Sources

Industry Reports & Market Briefs (e.g., UNESCO, WHO, AFDB, local government datasets)
SayPro Internal Platform Data
- Learner enrollments, course completion rates, usage analytics (Jan–May 2025)
Historical SayPro Reports
- SCRR-3 (Dec 2024), Forecast Outlook 2023–2025, Health Access Deep Dive (2023)

3. Data Analysis Methodology

A. Quantitative Analysis

Descriptive Statistics: Used to summarize survey results by age, region, sector
Cross-Tabulation: Identified relationships between demographic variables and preferences
Market Sizing Formulae:
- TAM, SAM, SOM calculated using population data, SayPro reach, and conversion rates
- Growth rates computed using historical trends and current quarter projections

B. Forecasting Techniques

ARIMA Time-Series Modeling: For education and employment sector forecasting
Monte Carlo Simulations: Used for risk-adjusted projections in high-volatility regions
SayPro Predict™ Engine: Proprietary machine learning tool for multi-variable forecasting
Confidence Intervals: Established using historical accuracy scores and scenario testing

4. Data Validation & Quality Assurance

Triangulation: Cross-verification between surveys, interviews, and secondary data
Peer Review: Draft findings reviewed by senior analysts and program directors
Error Checking: Automated scripts flagged data entry errors and inconsistencies
Ethics & Consent: All survey participants provided informed consent in accordance with SayPro’s Research Ethics Policy

5. Documentation & Archiving

Master Data Workbook: All raw data, cleaned sets, and final calculations are archived in
🔒 SayPro ShareDrive > Research > SCRR-5 > DataMaster_June2025.xlsx
Version Control: All analytical files are tracked with versioning logs to ensure reproducibility
Methodological Notes: Full technical annex available upon request, outlining modeling assumptions, formulas, and margin of error per sector

6. Limitations & Considerations

Internet accessibility influenced online survey reach in rural areas
Some sector forecasts (e.g., health education) are subject to external funding shifts
Data collection was limited in conflict-affected zones (flagged in risk scoring)

Conclusion

The SayPro research process is designed for clarity, consistency, and credibility. By documenting each stage—from data collection to forecasting models—SCRR-5 ensures that SayPro’s insights are transparent, evidence-based, and ready for stakeholder scrutiny or replication.

SayPro Research Process Documentation