The Gender Data Learning Series is an in-depth course that equips financial service providers with the skills to develop or enhance their women-centered strategies using sex-disaggregated data.
The Learning Series will feature guest experts with experience developing successful sex-disaggregated data strategies, presentations of use cases and best practice tools, and opportunities for peer learning with fellow participants working at FSPs around the world.
Learning Journey
Through a hybrid learning path combining online self-paced learning modules with live sessions, participants will be able to access tools and develop skills in their own time as well as meet virtually as a group to gain insights from global experts and engage with peer practitioners.
Time Commitment
The Learning Series runs from October 9 to November 6, featuring five live Zoom webinars taking place every Thursday from 1:00 to 2:30pm London time. Additionally, you’ll complete an online course on the Alliance learning management platform, requiring about 90 minutes per week between live sessions.
Languages
The live webinars will have live interpretation services from English to Spanish, Russian and French. The online course is in English, only.
Why Attend
This series will be of interest if you are looking to:
- Sign up for the WE Finance Code.
- Reach more women, and increase engagement.
- Cross-sell to and retain your individual women and/or WMSME customers.
- Develop or enhance your institution’s gender data journey from diagnosing to aligning, piloting, reporting, and refining your data.
- Define what you mean by WMSME clients, establish a baseline, identify KPIs, and report on sex-disaggregated data.
- Use sex-disaggregated data to improve business decisions and policy design.
Who Attends
The program is designed for teams looking to enhance their sexdisaggregated data analytics. This includes data practitioners (i.e. business intelligence team at FSPs and data aggregators) as well as Women’s Markets managers and senior business decision-makers at FSPs.
It is recommended that each institution attending the series nominate at least two staff members to participate: ideally a combination of the business intelligence/IT team and segment teams.
