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Improving Predictions

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Improving Predictions

How 'complete' your data is greatly impacts the ability of KIT's Predictions to present accurate information that reflects your specific dataset. The more accurate KITs Predictions are, the more they are able to guide informed, data-driven, and targeted strategy and outreach.

Below are tools in KIT that provide you with insight into how complete your data is and where to supplement your data points to improve Prediction quality. 

 

Data Completeness 

KIT's Data Completeness tool displays how complete your data is and areas you may want to supplement to increase Prediction accuracy. The Data Completeness tool can be found in Organization Settings under the 'Data Completeness' tab. Under this tab, you will see two sections:

  • Necessary Inputs - Data points that are required for our models to make predictions. The more complete these data points are within your dataset, the more we can rely on your organization's data to make nuanced predictions based on your unique data patterns. 
  • Good to Have's - Data points that are not required, but rather supplemental to the Necessary Inputs; allowing your predictions will be more precise and reflective of your unique dataset.

Within each section, there are various categories (e.g. Address Information) along with subsequent data points (e.g. Street). 

 

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Each data point shows you the percentage of your contacts that have 'complete' data in that particular category. To view the contacts with missing information, you can click on the number hyperlink; which will then bring up an exportable list of contacts missing that data point. 

 

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From this point forward, we recommend collecting this data and storing it in your CRM that is connected with KIT, to allow this information to sync into KIT.