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Predict Purchasing Power with Infutor’s Credit Scoring Model

Marketing budgets are increasingly tight and the cost of traditional credit data remains high. It’s a formula that makes it increasingly difficult for marketers and data scientists to understand consumers’ purchasing power.

To bridge the gap, Infutor’s proprietary GeoCredit Score highly correlates to a prospect’s traditional credit score without violation of Fair Credit Reporting Act (FCRA) regulations. This credit scoring model enables you to send highly-targeted invitation-to-apply offers or build predictive models.

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100

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100

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100

Forecast purchasing behavior by predicting a consumer’s creditworthiness

Infutor’s GeoCredit Score is now available as a standalone onsite file or in real-time as part of the identities within our Identity Graph with approximately 20% more coverage than competitive credit scoring models. This proprietary data provides access to a consumer’s creditworthiness without the need to purchase traditional credit data, and is shown to produce up to a 40% lift over other similar credit scoring models.

When combined with the depth, coverage and integrity of Infutor’s Identity Graph, your marketing engine transforms into a powerful lead generation, analytics and cross-sell tool.

GeoCredit Competitive Advantages

  • 1,500 proprietary demographic, psychographic, attitudinal, econometric and summarized credit attributes are utilized to build the GeoCredit Score database
  • Compiled from more than 100 sources
  • Less costly than traditional credit scoring models
  • Strong predictive indicator for invitation-to-apply offers and data modeling
  • Applied at the ZIP+4 level, with a universe of 300 million geographies
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GeoCredit Score

Traditional Credit Score Equivalent

  • A  820 – high
  • B  800 – 819
  • C  780 – 799
  • D  760 – 779
  • E  740 – 759
  • F  720 – 739
  • G  700 – 719
  • H  680 – 699
  • I  660 – 679
  • J  640 – 659
  • K  620 – 639
  • L  600 – 619
  • M  580 – 599
  • N  560 – 579
  • O  540 – 559
  • P  520 – 539
  • Q  500 – 519
  • R  480 – 499
  • S  460 – 479
  • T  low – 459

Forecast consumer purchasing power by predicting creditworthiness

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