Many CRMs hold a treasure trove of "dark data"—unstructured, unanalyzed information scattered across notes, emails, and support tickets. "The Dark Data Lead Score" is a strategy that leverages natural language processing (NLP) and other AI techniques to extract valuable insights from this unstructured data, adding new dimensions to lead scoring and uncovering hidden lead potential that traditional methods miss.
This innovative approach goes beyond explicit fields in the CRM. The dark data lead score process involves:
Ingesting Unstructured Data: Pulling in text from sales overseas data call notes, email exchanges, support ticket descriptions, chat transcripts, and even social media mentions within the CRM.
NLP Analysis: Using AI to analyze this text for keywords, sentiment, stated needs, pain points, and specific product mentions. For example, an NLP model might detect a repeated mention of "scaling challenges" or "budget constraints" in call notes from a lead in Rajshahi.
Sentiment Analysis: Identifying positive or negative sentiment expressed by the lead across interactions.
Intent Identification: Recognizing implicit intent signals within the text (e.g., "looking for alternatives," "evaluating options," "need a solution by Q3").
Score Contribution: Assigning points or weighting factors to these extracted insights, which then contribute to the overall lead score.
Actionable Insights: Providing sales reps with a summary of these insights, even flagging specific phrases or details extracted from past conversations.
By implementing "The Dark Data Lead Score," businesses can unlock a deeper understanding of their leads, identifying those with high potential based on nuanced, qualitative signals that would otherwise remain buried. This leads to more precise lead qualification, more personalized outreach, and ultimately, higher conversion rates by turning overlooked data into actionable intelligence for lead generation.