Overview

To improve product uptake across our advisor and institutional base, I led the development of the LPL Product Recommendation Engine, a predictive solution that scores and surfaces which advisors or institutions are most likely to adopt specific products. The engine enables hyper-targeted outreach, streamlines resource allocation, and shortens the path from awareness to conversion.

Problem

With hundreds of product offerings, our distribution teams were overwhelmed by the challenge of knowing who to target, when to engage, and what to pitch. The result:

• Generic communications that didn’t resonate with advisors’ needs
• Long, inefficient sales cycles and marketing campaigns with institutional clients
• Missed opportunities to grow product adoption in strategic segments (e.g., HNW-focused advisors)

There was no system in place to:

• Match the right product to the right advisor/institution at the right time
• Quantify likelihood to buy or integrate
• Personalize product enablement at scale

Solution

I led the design and execution of the Product Recommendation Engine, leveraging behavioral, firmographic, and product alignment data to infer purchase propensity.

Key Capabilities:

• Dual Propensity Scoring:
  • Advisor-Level: Based on book of business, prior product behavior, client segmentation (e.g., HNW, retirement-focused), and channel preferences
  • Institutional-Level: Considers firm size, integration readiness, existing tech stack, and prior partnership patterns
• Product–Persona Mapping: Aligns product features to specific advisor and institutional personas (e.g., “Model Portfolios for HNW advisors” or “Automation tools for RIAs with back-office constraints”)
• Dynamic Target Lists: Reps receive priority lists of high-likelihood targets by product, ranked and refreshed regularly
• Embedded in GTM Workflows: Scores flow directly into Salesforce and sales dashboards to guide outreach and campaign planning

Strategic Impact

• Brought personalization and precision to product distribution strategy
• Gave product marketers and sales leaders clear visibility into who is most likely to convert
• Strengthened collaboration between product, analytics, and distribution teams
• Enabled experimentation with tailored messaging by persona and score band

Results

• 40% increase in product adoption rate among advisors in the top-scoring decile
• Reduced time-to-conversion for institutional pilots by 22%
• Improved campaign ROI by shifting focus to high-intent segments
• Now used to inform quarterly GTM planning, enablement targeting, and sales team prioritization