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What is PingOne Protect?

Protect your accounts from takeovers, fraudulent registrations, and the fatigue associated with multifactor authentication by utilizing PingOne Protect's extensive capabilities. This advanced solution evaluates multiple attack vectors, assigns risk levels, and delivers insightful data, facilitating the deployment of countermeasures that effectively block potential dangers while allowing legitimate users to authenticate with ease. By employing intelligence-based policies, PingOne Protect synthesizes results from various risk factors to create a holistic risk score. This score is associated with specific policies that determine the amount of friction experienced by users during interactions, including methods such as CAPTCHA, password resets, selfie verifications, and push notifications. To maximize the accuracy of the predictors, consolidate them, incorporate signals from outside sources, and apply overrides when necessary. The predictors include bot detection, IP velocity, user velocity, unusual velocity patterns, discrepancies in user location, IP reputation assessments, use of anonymous networks, behavioral risks, user-based risk models, detection of new devices, identification of suspicious devices, and custom or third-party predictors, all of which contribute to a robust risk management strategy. By implementing these comprehensive strategies, organizations can greatly enhance their defenses against a wide range of cyber threats and maintain user trust in their security measures. This proactive approach not only secures sensitive information but also improves the overall user experience by minimizing disruptions.

What is Fit Predictor?

A simple survey with two or three questions can effectively pinpoint the ideal size for users. Fit Predictor harnesses customers' historical purchasing data to formulate a customized Fit Profile that adapts as shopping behavior evolves. This innovative tool provides personalized sizing recommendations for every user, even those without a Fit Profile. In mere seconds, Fit Predictor determines the most appropriate size for a customer by analyzing existing data, thereby removing the necessity for manual measurements. For newcomers who lack previous shopping history, the service can predict the best fit by comparing the customer’s size with those of other brands. At present, Fit Predictor is used by over 40 million individual users each month and is available in ten countries, including the United States. It serves a diverse range of over 7,000 brands, enjoying a robust user community. Significantly, this cutting-edge technology has proven effective in boosting conversion rates while also reducing return rates, rendering it an indispensable tool for retailers. As Fit Predictor continues to evolve, it aspires to enhance the personalization of the shopping experience even further, ensuring that every customer finds their perfect fit seamlessly. Ultimately, the goal is to create a more satisfying and efficient shopping journey for all users.

Media

Media

Integrations Supported

Abstract Security
Knak
Motileo
Streamline AI
Youverse

Integrations Supported

API Availability

API Availability

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Online Training

Company Facts

Organization Name

Ping Identity

Date Founded

2002

Company Location

United States

Company Website

www.pingidentity.com/en/platform/capabilities/threat-protection/pingone-protect.html

Company Facts

Organization Name

Secret Sauce

Date Founded

2010

Company Location

United States

Company Website

secretsaucepartners.com/fitpredictor

Categories and Features

Fraud Detection

Not specified

Categories and Features

Size Chart

Not specified

Size Recommendation

Not specified

Virtual Fitting

Not specified

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