Lead Scoring

Determine which leads your team should focus on first

Lead Scoring Database Job

How it Works

This application isn’t interactive (sorry!), but we’ll explain why. The other apps shown here use ScienceOps to process input and make predictions in real-time. But ScienceOps can also be used in “batch mode,” which is what this example illustrates.

A data science team has built a model to rank which leads the sales team should contact first. They deploy this model to ScienceOps, where it is “called” every night by their CRM system (think Salesforce). Every morning when the sales team comes in to work, Salesforce shows the results, a rank ordered list of which leads are the hottest.

  • A data scientist develops a model to rank new sales leads
  • The model is deployed to ScienceOps as a "Batch Job"
  • Every night, the model ranks the latest leads for the sales team to contact
  • Each morning, the CRM system shows the ranked leads
83 lm.fit(df[features], df.converted)
85 rf = RandomForestClassifier()
86 rf.fit(df[features], df.converted)
87 ...
134  probs <- predict(logit, newdata=df, type="response")
135  grades <- cut(probs, 5, labels=c("F","D","C","B","A"),
136  ordered_result=TRUE)
137  lead_quality <- table(grades)
138  ...
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