Finding the best prospects among 8.5 million providers

The challenge

The company sells to healthcare providers through a field team of more than 50 reps. Its prospect list was short and its sales history thin, and a rep’s time was too valuable to spend on weak leads.

The approach

1. Built the provider universe. Every US provider by National Provider Identifier, individuals and organizations, joined with 18 government datasets and a proprietary dataset of each provider’s purchases in dollars, updated every quarter.
2. Trained the models on real purchases. Four machine learning models (XGBoost) learned from those purchases, for individual providers and for organizations: one for whether a provider looks like the providers who buy, and one for the volume it would buy.
3. Scored every prospect twice. A propensity score and a volume score, each a rank from 1 to 100 against every provider. A volume score of 90 means 90 percent of providers would buy less.
4. Put the scores into everyday work. Both scores in Nockpoint for analysis, and in Salesforce on every prospect, where a rep sees them while working the account.
5. Kept the data current. Each quarter, Nockpoint brings in the newest purchases as a delta and the scores refresh, well ahead of the government’s yearly data.
6. Loaded deep, assigned narrow. The top fifth of scored prospects went into Salesforce. A territory assignment gave each rep a focused list, and any prospect a rep met was already there, with its scores.

The results

• About ten times as many qualified leads in front of the sales team as before.
• 8.5 million providers screened: 6.7 million individuals and 1.8 million organizations.
• The top fifth of scored prospects in Salesforce, with both scores on every record.
• The best leads shared fairly across more than 50 reps, by geography first.
• Scores refreshed every quarter from the newest purchase data, well ahead of the government’s yearly release.