Automated Customer Data Enrichment Plugins
Budget: £250 – £750 GBP
I want to build a set of AI-driven plugins that plug straight into our LinkedIn connection database and continuously enrich each profile with deeper demographic details and behavioural insights. The core workflow is data updating: whenever a new customer record appears—or an existing one changes—the plugin should trigger, fetch the most relevant external signals, run light machine-learning scoring if needed, and write the additional fields back to our CRM in real time.
Scope
• Data domain: strictly customer data. The enrichment must cover both demographics (age band, location, income range, etc.) and behavioural patterns (engagement frequency, preferred channels, likelihood to churn).
• Automation focus: updating only. Collection pipelines already exist; I just need this layer to augment and refresh the data we already hold.
• Tech expectations: Python or Node, clean REST or GraphQL calls to third-party APIs, optional use of tools like Airflow, AWS Lambda, Zapier, Snowflake or similar—whatever achieves reliable, low-latency updates.
• Reliability: retries, error logging, and configurable scheduling (cron + webhook support).
• Privacy & compliance: GDPR-friendly design, no raw PII stored outside approved systems.
Deliverables
1. Source code for the plugin(s) with clear, commented functions.
2. A short README covering setup, environment variables, and deployment steps.
3. Unit tests and a mocked sample dataset to prove the enrichment logic.
4. A one-page report explaining which external data sources or models were used and how accuracy was validated.
Acceptance criteria
• Given a sample customer file, the script runs and returns new demographic and behavioural fields with ≥95 % completion rate.
• Running twice on the same input makes no duplicate or conflicting updates.
• All variables (API keys, endpoints, schedules) are configurable from a .env or dashboard.
If you have prior experience writing CRM extensions, ETL micro-services, or ML-powered enrichment tools, I’d love to see a quick demo repo or reference. Let’s make our customer records smarter, automatically.
Scope
• Data domain: strictly customer data. The enrichment must cover both demographics (age band, location, income range, etc.) and behavioural patterns (engagement frequency, preferred channels, likelihood to churn).
• Automation focus: updating only. Collection pipelines already exist; I just need this layer to augment and refresh the data we already hold.
• Tech expectations: Python or Node, clean REST or GraphQL calls to third-party APIs, optional use of tools like Airflow, AWS Lambda, Zapier, Snowflake or similar—whatever achieves reliable, low-latency updates.
• Reliability: retries, error logging, and configurable scheduling (cron + webhook support).
• Privacy & compliance: GDPR-friendly design, no raw PII stored outside approved systems.
Deliverables
1. Source code for the plugin(s) with clear, commented functions.
2. A short README covering setup, environment variables, and deployment steps.
3. Unit tests and a mocked sample dataset to prove the enrichment logic.
4. A one-page report explaining which external data sources or models were used and how accuracy was validated.
Acceptance criteria
• Given a sample customer file, the script runs and returns new demographic and behavioural fields with ≥95 % completion rate.
• Running twice on the same input makes no duplicate or conflicting updates.
• All variables (API keys, endpoints, schedules) are configurable from a .env or dashboard.
If you have prior experience writing CRM extensions, ETL micro-services, or ML-powered enrichment tools, I’d love to see a quick demo repo or reference. Let’s make our customer records smarter, automatically.
Related categories:
Python
Software Architecture
Amazon Web Services
Hadoop
Node.js
Aws Lambda
GraphQL
REST API