Benchmarking NLP Pipelines for Lead Enrichment from Unstructured External Sources

Srikanth Balla; Arpit Jain

doi:10.36676/urr.v10.i3.1556

Authors

Srikanth Balla 1Christian Brothers University Memphis, TN, USA
Arpit Jain K L E F Deemed University Vaddeswaram, Andhra Pradesh 522302, India

DOI:

https://doi.org/10.36676/urr.v10.i3.1556

Keywords:

Natural language processing, lead enrichment, unstructured data, external data sources, NLP pipeline benchmarking, named entity recognition, relation extraction, sentiment analysis, CRM integration, data-driven sales.

Abstract

Enrichment of leads is the central theme of customer relationship management (CRM) and sales performance improvement by providing high-quality information to marketing and sales organizations. Despite dramatic improvements in natural language processing (NLP) application, there is vast research void for systematic benchmarking of NLP models expressly designed for lead enrichment from external, unstructured data sources such as news articles, social media tweets, and industry reports. Previous research has focused mainly on structured or semi-structured data in internal CRM databases and overlooked the complexity and inherent noise inherent in external, unstructured data environments. Such oversight limits the strength and adaptability of existing enrichment methods. This study attempts to bridge the current knowledge gap by scientifically comparing different NLP pipelines in large-scale benchmarking, determining their effectiveness, accuracy, and flexibility in handling a broad range of external textual data benchmarks.

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