The challenge
Our client, a top cloud-based CRM platform, came to us with the challenge of better understanding the developers in their ecosystem in terms of challenges, tool use, and satisfaction. Specifically, the client was interested in whether there were significant differences in satisfaction levels between developers using certain tools or making use of select in-house environments. Further, the client was also posed more exploratory questions such as: “Are there certain roles or profiles for developers in our ecosystem that are more or less satisfied? ” or “When we account for regional differences and varying experience levels, who are the developers that are most likely to use AI-based tools? ”
The approach
To address the various hypotheses, we needed to collect representative data and analyse it in a rigorous, reliable manner that would empower their team to make decisions. We crafted a questionnaire and designed an outreach strategy that allowed us to field a tailored, large survey that collected data from a representative sample of developers within our clients' community. We analysed the data in a statistically rigorous manner using various regression models and inferential statistics to test if there was evidence to support hypothesised trends and differences.
The result
We provided data-backed answers to each of the questions our client posed. By identifying which groups or profiles had significantly higher or lower satisfaction, we enabled our client to prioritise resources and investments. Further, using the various regression models we estimated, the client could also project a holistic profile of developers who are more likely to be satisfied or use select tools (i.e., AI-based tools) in their workflows within their ecosystem.
Why SlashData
Our survey design expertise and panel access enabled us to efficiently collect relevant data from a representative sample of developers working in our clients’ ecosystem. When used in conjunction with our quantitative research capacities, this enables us to analyse the data in depth and take into consideration the uncertainty or variability in our work.
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