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Frequently Asked Questions

  1. 1. How is Infer different than traditional lead scoring?

    Many marketing automation systems support lead scoring. So how is Infer different?

    • Infer adds thousands of signals mined from across the web and your website
    • Infer applies machine learning to weigh each signal appropriately
    • Infer is able to automatically adjust to new signal and market dynamics
    • Infer can accurately determine “fit” on the first touch

    For more information on what sets Infer apart, click here.
  2. 2. What external signals does Infer track?

    Our system pulls in several thousands of external signals, going well beyond what most organizations track in Salesforce.com or other CRM and marketing automation tools.

    Broadly speaking, Infer gets these signals from three sources: crawling the web, purchasing data and inferring signals from raw data sources.

    For more specific examples of the external data we use In Infer’s models, click here.
  3. 3. Does predictive scoring work if I’ve got bad data?

    Our platform was designed to handle bad data. All that we really need to build the model is historical outcomes (which prospects converted and which prospects went on to become customers). We don’t require super clean customer profile data.

    To learn more about what data we need, click here.
  4. 4. Does predictive scoring work if I’m trying to enter a new market?

    The short answer is yes. Predictive scoring can be extremely helpful in breaking into new markets.

    To find out more about the value predictive lead scoring can offer to someone pushing into a new market, click here.
  5. 5. Which is more important when scoring leads, fit or behavior?

    While both ingredients provide valuable data points, the first question to ask about any lead is fit — does it look like your existing customers? If the prospect is not a fit for your product, it doesn’t matter how engaged they are; they’re not likely to buy.

    To learn more on how a fit-first approach increases the quality of your pipeline, click here.