Why Legal Lead Scoring Works Better With Explanations (Full Transcript)

A legal tech approach that replaces opaque lead scores with recommendations, confidence levels, clear reasoning, and feedback to build trust.
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[00:00:00] Speaker 1: A lot of other companies out there that do lead scoring for various industries, you put data in and this box just spits out a score, right? Yeah. You don't really know what happened. In our learnings, when we were building this product, we learned that that's not what, like, that's not going to give lawyers the warm and fuzzy, you know? Yeah. So first of all, we decided to not give a score, we give more of a recommendation. The agents will give you like a plan, like, hey, you should chase this lead hard or you should refer this out or something like that, not necessarily a score. But then we give them a confidence level on that rating and we also give them all of the reasons why. We give them summaries as to the thinking behind why this determination was made and then we allow them to give feedback to help train it, like, hey, this is, I expected to see this but I got this and that really gives our customers that confidence and that comfort using it and I think that's a big part of figuring this all out.

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Arow Summary
Speaker explains why their lead-qualification product for lawyers avoids opaque, one-number lead scores. Instead, it provides actionable recommendations (e.g., pursue aggressively or refer out), includes a confidence level, and explains the reasons and summaries behind each recommendation. The system also supports user feedback to improve the model, which builds customer trust and comfort.
Arow Title
Transparent Lead Recommendations for Lawyers
Arow Keywords
lead scoring Remove
legal tech Remove
recommendation system Remove
explainability Remove
confidence level Remove
model transparency Remove
user feedback Remove
agent workflow Remove
trust Remove
lead qualification Remove
Arow Key Takeaways
  • Opaque lead scores can undermine trust, especially for lawyers.
  • Replacing a numeric score with actionable recommendations better fits user needs.
  • Providing confidence levels and clear reasoning improves explainability.
  • Summaries of the model’s thinking help users validate decisions.
  • Built-in feedback loops allow customers to correct outputs and improve the system over time.
Arow Sentiments
Positive: Tone is constructive and optimistic, emphasizing learning, customer comfort, and building confidence through transparency and feedback.
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