.svg)
.svg)
Let’s make your tech stack work together
Don't see your use case here? We've likely built it.
.avif)
.svg)
Strategic Impact
Our Approach
Crowdfunding Data Extraction
37,000 project URLs were processed automatically rather than by hand. The extraction pipeline pulled community tab data including top contributing cities and percentage breakdowns across the full dataset without manual collection.
Geographic Diversity Enrichment
Geographic diversity became a queryable field rather than a lookup exercise. For every U.S.-based city in the dataset, a racial distribution index score was retrieved from a public data source and joined directly to the project record.
Ethnicity Estimation via API
Ethnicity estimates were appended at scale without manual processing. All entrepreneur names were normalized before batch submission through the Ethnicolr API, and predicted ethnicity fields were added to every record in the consolidated output.
Data Normalization and Standardization
The dataset arrived clean and ready for analysis, not requiring an additional cleaning pass. City names were standardized, geographic duplicates resolved, state mappings aligned, and field formatting enforced consistently across all merged sources.
Dataset Delivery and Documentation
The research team received a single file with everything in it. All required fields, from project URLs to racial diversity scores and ethnicity estimates, were delivered alongside pipeline methodology documentation for citation and replication purposes.
BabyBots Impact Assessment
The Impact
The research team received a structured, publication-quality dataset covering 37,000 records with demographic enrichment across three distinct data dimensions, all delivered within a two-week engagement. Work that would have taken months of manual collection and normalization was complete before the analysis window closed. The study proceeded on schedule with a dataset the team could trust.
- Automated scraping pipeline for approximately 37,000 crowdfunding project URLs
- Racial distribution index scores appended for all U.S.-based cities
- Ethnicolr API integration for entrepreneur ethnicity estimation across all records
- Normalized, deduplicated, and formatted consolidated dataset (Excel/CSV)
- Data dictionary documenting all fields and sources
- Post-delivery support for data-related questions
Project Journey
Project Timeline
Phase 1
Research Data Pipeline — Kickoff & Scope Alignment
Confirmed data requirements with the research team, reviewed the 37,000-URL dataset structure, and aligned on output format, field definitions, and delivery timeline.
Phase 2
Research Data Pipeline — Kickstarter Scraping Build
Developed and executed the automated pipeline to extract community tab data from approximately 37,000 Kickstarter project URLs, capturing top contributing cities and associated percentages.
Phase 3
Research Data Pipeline — Racial Diversity Enrichment
For each U.S.-based city identified in the dataset, retrieved racial distribution index scores from BestNeighborhood.org and appended them to the working dataset.
Phase 4
Research Data Pipeline — Ethnicity Estimation & QA
Batched all entrepreneur names through the Ethnicolr API, appended predicted ethnicity fields, and performed QA validation across sample segments to confirm accuracy and field integrity.
Phase 5
Research Data Pipeline — Dataset Delivery
Delivered the final consolidated dataset in Excel/CSV format with a data dictionary, covering all required fields: Kickstarter URL, top cities, diversity scores, and ethnicity estimates.
Education / Research
Research findings are only as credible as the dataset behind them.
The research team received a single file with everything in it. All required fields, from project URLs to racial diversity scores and ethnicity estimates, were delivered alongside pipeline methodology documentation for citation and replication purposes.
.svg)
.avif)
BabyBots is an enterprise technology partner with expertise across process automation, AI systems, data intelligence, and platform architecture.
.avif)