Go To Market Strategy: Lead Scoring
Amplifying SaaS Sales Efficiency
As a growth specialist at Juniper Square, the pursuit of sales optimization led us to devise a top-of-funnel scoring system for prospects. The cornerstone of this project was the creation of a lead scoring model that would provide our sales team with the most qualified leads.
Our market research and sales data identified several pivot variables that became the foundation of our scoring model. By applying binary metrics to these variables, we could quickly assess a lead's sales potential. Here's a snapshot of the pivot variables that proved to be instrumental:
Service Fee: Prioritize future sorting by service fee.
US-Based: A marker for better alignment with our service offerings.
Active Investors: Targeting prospects aiming for a single fund for better engagement.
Enterprise Value Under Management (EUM): Filtering out entities over 1 billion, as they're not our ideal fit.
Deployment Date: Considering for future iterations to measure readiness.
Number of Funds: A critical inclusion to gauge scale and needs.
Fund Type and Size: Highlighting prospects with more than one fund, particularly closed-end funds.
Feature Adoption and Transaction Scores: Indicative of a prospect’s engagement and potential need for our services.
Waterfalls and Reporting Scores: These factors are excellent indicators of a lead's maturity and readiness.
Active Positions Count: To understand a lead's investment activity.
Salesperson's Success Rate: Using historical data to predict future success.
Here’s a screenshot of my V1 web scrapper binary scoring framework
By implementing this model, we created a statistical system that not only aligned our sales team with high-potential accounts but also enriched our CRM with valuable insights. Our researchers applied the model to score accounts, which directly correlated to target-market fit, scored on a scale of 1-10.
The model had a tangible impact. We increased our outbound sales demos by 25% over six months, proving the model's efficacy. This was more than a temporary boost; it led to evergreen adoption across our CRM systems. Additionally, we witnessed a 60% increase in lead capture and a 28% rise in lead quality, which translated into a higher return on investment.
The scoring model was a significant stride toward sales precision but it was just one part of the broader sales strategy. Enriching contact data and refining our sales approach was equally important to leverage the full potential of our account data.
In essence, our journey with the lead scoring model underscores the value of data-driven strategies in SaaS sales and marketing, affirming that the right approach can indeed translate into substantial growth and efficiency
