Qualifyed.AI - Lookalike Audiences

In the digital age, businesses face unprecedented challenges in understanding and reaching their target audiences. The traditional methods of customer analysis, reliance on cookie data, and generalized marketing approaches are becoming increasingly ineffective due to changing privacy regulations and user opt-outs. This is where advanced AI solutions like Qualified.AI come into play, offering sophisticated tools for lead scoring, lookalike audience identification, and data-driven campaign management. In this comprehensive guide, we will explore how this company's technology analyzes over 3,000 data points to provide actionable insights, maintain effectiveness in the face of privacy changes, and operate within strict security and data protection standards.

Pricing and Plans

The service is offered in two primary packages - AI Audiences Pro for $1,162 per month, which includes Acxiom Shopping Intent AI Customer Persona ML Trained Model Predictive Lookalike Audiences TransUnion Demographic Data AI Audience Insights. For an additional $822 monthly fee, customers can upgrade to the AI Audience Pro + ML Lead Scoring package, which adds Predictive Lead Scoring Data Append and Salesforce Integration.

The company's technology works by analyzing over 3,000 data points to score leads, combining CRM data with offline attributes through an Identity Graph system that integrates with Salesforce and Hubspot. This system processes 1st party Personally Identifying Information (PII) across 3,400 attributes to train machine learning models capable of targeting high-converting prospects. The resulting audience is pre-scored for 96% of US adults before being ingested into ad accounts via LiveRamp.

CEO Alex Herndon managed $5M monthly media buys for Fortune 500 clients using the company's technology, achieving a 1,375% return on advertising spend after iOS 14.5 privacy changes rendered Facebook's lookalike audiences nearly worthless. The process begins by ingesting CRM data of current and past customers to train the model, with Identity Graph solutions combining multiple data sources to create a unified lead view. This system automatically appends critical data like mailing addresses to create custom predictive lead scores, which are then pushed into Salesforce or Hubspot accounts on a scheduled basis.

The company emphasizes that their solutions provide more comprehensive lead information than traditional cookie-based tracking methods, which only consider time on site, number of site visits, recency, etc. All data infrastructure is located in the U.S., and the company uses third-party scripts to load cookies that track ad performance, including which ad was clicked and which keyword triggered the ad.

AI Predictive Lead Scoring

The company's predictive lead scoring mechanism works by analyzing over 3,000 data points, combining first-party CRM data with offline attributes through their proprietary Identity Graph system, which interfaces with major CRM platforms like Salesforce and Hubspot. The Identity Graph consolidates multiple data sources including email addresses, mobile numbers, and physical addresses into a unified lead profile, automatically appending crucial information like mailing addresses to create detailed customer personas.

The process begins with the ingestion of current and past customer data, which is used to train the machine learning model. Through this model, the system is capable of scoring new leads based on their probability to convert, resulting in custom predictive lead scores that can be automatically synchronized with Salesforce or Hubspot accounts. Compared to traditional cookie-based tracking methods, which typically only consider factors like time on site and number of visits, this approach provides a more comprehensive view of potential customers, allowing businesses to make more informed targeting decisions.

Lookalike Audiences Technology

The foundation of Qualified.AI's lookalike audience technology begins with the ingestion of 1st-party Personally Identifying Information (PII) data of US Consumers, covering 3,400 attributes in total. This comprehensive dataset forms the basis for training the company's machine learning models to identify patterns of high-converting prospects.

The process combines multiple data sources including email addresses, mobile numbers, and physical addresses through their proprietary Identity Graph system. This framework generates a unified lead profile that automatically appends critical information like mailing addresses, creating detailed customer personas. Each step of the process is designed to produce the most accurate model possible, with offline data attributes providing robust targeting capabilities immune to changes in third-party cookie policies.

After processing, the pre-scored audience is ingested into the company's ad account via LiveRamp as a hashed email list. This allows clients to target highly specific groups of potential customers with a known likelihood to convert. The system operates continuously, monitoring and improving the quality of sales data to maintain the accuracy of the predictive models.

Qualifyed.AI stands as a pioneer in making this sophisticated technology accessible to small and medium businesses, having managed multimillion-dollar media campaigns for Fortune 500 clients with this technology. The company emphasizes the resilience of its approach in today's changing privacy landscape, where traditional methods based on cookie data have become less effective due to user opt-outs and technical limitations.

Ad Performance during Privacy Changes

The company's predictive lead scoring and lookalike audiences technology has proven particularly resilient to changes in the privacy landscape. Even as iOS 14.5 updated its privacy settings, rendering Facebook's lookalike audiences nearly ineffective, Qualifyed.AI maintained robust performance. CEO Alex Herndon managed $5M in monthly media buys for Fortune 500 clients using the company's technology, achieving a 1,375% return on advertising spend during this challenging period for traditional targeting methods.

Unlike Facebook's approach, which relies heavily on both cookie and pixel data, Qualifyed.AI builds its models on comprehensive first-party Personally Identifying Information (PII) across 3,400 attributes, making it less vulnerable to changes in third-party tracking capabilities. The system processes this data through their proprietary Identity Graph solution, creating a unified lead profile that automatically appends critical information like mailing addresses - capabilities that remain effective even as 96% of US users have opted out of tracking and Safari and Firefox default to blocking all third-party cookies.

Security and Data Practices

The company employs robust security measures to prevent automated activity, including CAPTCHA across all applications. This service evaluates IP addresses, visit duration, and mouse movements to detect potential bot activity, using third-party services that provide spam score results without accessing evaluated information. This anti-bot assessment helps protect both the company's applications and the broader internet community from credential stuffing attacks and spam.

All data processing occurs within U.S.-based infrastructure, with third-party subprocessors assisting in running applications and providing services. For business functions like managing newsletter subscriptions and customer surveys, the company uses authorized third-party processors. Data sharing practices are limited to user-directed integrations with third-party services, and the company may share hashed email addresses with ad companies for exclusion purposes. The firm complies with legal requirements by responding to government requests only when compelled by legal process or in emergency situations, ensuring that affected users are notified unless prohibited by law.

Customer data rights include the right to know, right of access, right to correction, right to erasure ("right to be forgotten"), right to complain, and right to restrict processing, all subject to applicable legal limitations. The company maintains strict data protection standards while balancing customer privacy with operational needs, regularly inspecting and improving the quality of sales data to ensure the accuracy of their predictive models.