Work01
ICP Engine & Data Enrichment
On-demand ideal-customer-profile engine built directly on live CRM data, enriched with verified online-identity and ad-behavior signals.
- Built with
- Python, Microsoft Dataverse, Embeddings, FastAPI
The problem
A German architectural glazing manufacturer sells through three distinct sales channels, each with a different ideal customer. Profiling was manual, static, and disconnected from the CRM.
The system
The engine reads customer records from Dataverse, verifies each company's online identity, and enriches records with digital-footprint and advertising-behavior signals. From the enriched base, channel-specific ideal-customer profiles are compiled on demand using analyst-selected filters: a living profile instead of a quarterly slide.
How it is built
Python service against the Dataverse API; embedding-based similarity for profile matching, an approach first proven in a prototype that identified architecture firms likely to specify a specific timber product line; enrichment adapters feed a common schema. Output profiles are consumed directly by the lead-generation crawler.
- 1
Dataverse CRM
Customer records read from the live CRM.
- 2
Identity verification
Each company's online identity verified.
- 3
Signal enrichment
Digital-footprint and advertising-behaviour signals attached through per-source adapters feeding a common schema.
- 4
Embedding match
Embedding-based similarity scores profile fit.
- 5
Channel ICP
Channel-specific profiles compiled on demand from analyst-selected filters, then consumed by the lead-generation crawler.