Good-Planet’s research and analysis services will answer critical questions for our stakeholders in order to improve program design, management, and effectiveness.

• Data Science (Including Machine Learning, Forecasting, And Geospatial Research)

• Survey Research

• Gender and Social Inclusion Analysis

• Social Network Analysis

• Sector Assessments (E.g., Political Economy Analysis)

• Economic Analyses (Cost Benefit, Cost Effectiveness,)

• Systems Analysis

Approaches such as assessments, survey research, and data science are often used to inform program design, helping to identify target groups, priority needs, and intervention approaches. However, along with analyses of gender and social inclusion, political economy, and cost-benefit, these tools can also stand alone or support evaluations in assessing activity results and informing programming decisions. Whether used for program design, management, or evaluation, our research and analysis are driven by tailored, focused questions to support evidence-based decision making. Good planet International will partner with expertise with decades of experience leading development-focused research and analysis to help clients make programming decisions, whether in design, implementation, or evaluation. 

From employing machine learning to develop a streamlined poverty measurement tool. Good planet uses innovation to derive value, quality, and efficiency for stakeholders. We will survey hundreds of thousands of individuals, in nearly all contexts, including households, businesses, schools, and health centres etc, with in-house capacity for electronic data collection programming. Our work leverages high quality quantitative and qualitative approaches, recognizing the important value and complementarities each brings. Our Research and Analysis leverage innovation and efficiency to provide stakeholders with rigorous evidence for decision-making. Using these techniques allows Good planet to drastically reduce the time required to collect data, which in turn reduced overall costs and the time burden on survey respondents.