Understand How Einstein Scores Your Leads

Einstein Lead Scoring uses data science and machine learning to discover the patterns in your business’ lead conversion history, and to predict which current leads to prioritize. By using machine learning, Einstein Lead Scoring provides a simpler, faster, and more accurate solution than traditional rules-based lead scoring approaches.

The Scoring Model

Einstein analyzes your past converted leads, including custom fields and activity data, to determine your conversion patterns. It then identifies which of your current leads have the most in common with your prior converted leads. Based on this analysis, Einstein builds one or more scoring models for your organization.

During setup, Salesforce admins can choose to score all of your leads together, or group leads into segments based on field criteria. Einstein builds a separate scoring model for each lead segment. For each lead segment, admins can also choose to omit certain lead fields. When Einstein builds your scoring model, the omitted fields are ignored.

When you score all leads together without creating segments, and you don’t have enough lead conversion data to build your own predictive model, Einstein uses a global model. The global model uses anonymous data from many Salesforce customers. When you accumulate enough lead data, Einstein builds a scoring model with your data and uses the model with the better results.

Einstein models are refreshed every 10 days, or whenever the admin updates how Lead Scoring is configured. Lead scores are updated at least every six hours as needed.

Factors That Contribute to Scores

With each lead score, Einstein displays the lead’s field values that have the most significant positive and negative effects on its score. These fields are the lead’s top positives and top negatives. Sometimes, a lead’s score is due to a combination of several fields with only slight positive or negative effects, rather than a few very positive or very negative fields. In this case, Einstein doesn’t display top positives or top negatives for the lead.

When Scores Don’t Appear

Sometimes, a score doesn’t appear on a particular lead for any of these reasons.

• The scores haven’t been calculated yet because Einstein Lead Scoring was recently enabled. It can take around 24 hours before scores are available.
• The lead was added a few hours ago.
• The lead wasn’t modified in the 6 months before Einstein Lead Scoring was turned on.
• The lead has had no activities or updates in the last 90 days.
• The lead doesn’t meet the criteria for any lead segments.
• The lead’s Status value is unqualified or similar.
• The lead is in a lead segment that doesn't contain enough data to train a scoring model.

When Scores Don’t Change

Sometimes, a score doesn’t change on some leads any of the following reasons.

• A previously scored lead hasn’t been modified in 6 months.
• The lead was converted.
• Your admin chose to score all leads together in one group, but the amount of lead conversion data in your org falls below the minimum requirements for scoring leads. When there isn’t enough data, scores don’t update until more data becomes available.
• Your admin chose to score your leads in separate segments, and the lead is in a segment that falls below the minimum data requirements for scoring leads.