AI Recommendation Engine for B2B Lead Generation
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Solution Overview
Problem
Business-to-business (B2B) companies face challenges in generating a predictable volume of high-quality leads due to the open-loop nature of conventional marketing strategies, advertising fraud, and outdated sales force automation systems that fail to capture potential opportunities effectively.
Innovation Solution
The development of methods and systems utilizing an AI recommendation engine to generate targeted advertising campaigns by analyzing historical data, determining correlations between experimental parameters and goal metrics, and optimizing Ideal Customer Profiles (ICPs) to identify high-conversion customer attributes, thereby generating and scoring test campaigns for improved lead quality and conversion rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional open-loop marketing strategies are used, then marketing activities can be executed, but lead quality and conversion rates remain low and unpredictable
Solution Approach 1:
The patent implements a closed-loop system where lead response data and conversion information are fed back into the AI recommendation engine. This feedback mechanism allows the system to continuously learn from actual lead behavior, refine Ideal Customer Profile (ICP) attributes, and improve future lead scoring accuracy, thereby ensuring consistent lead quality while maintaining high generation volume
Solution Approach 2:
The system dynamically adjusts ICP parameters and lead scoring weights based on accumulated feedback data. By changing the parameters that define ideal customers and the weights assigned to different lead attributes, the system optimizes lead quality consistency while preserving productivity through data-driven parameter refinement
2Productivity
If advertising fraud and data inaccuracies occur, then advertising campaigns can run, but data reliability and lead quality deteriorate
Solution Approach 1:
The AI recommendation engine incorporates feedback from lead responses and conversion outcomes to detect and correct data inaccuracies. By comparing expected vs. actual lead behavior and continuously refining ICP attributes based on verified conversion data, the system maintains measurement precision while allowing campaigns to execute
Solution Approach 2:
The system performs self-correction of data quality issues through automated feedback loops. The AI engine independently identifies patterns of data inaccuracy, adjusts ICP parameters accordingly, and improves lead scoring without external intervention, thereby maintaining both campaign productivity and data precision
3Ease of operation
If outdated sales force automation systems are used, then sales processes can be automated, but potential opportunities are missed and conversion rates decrease
Solution Approach 1:
The AI recommendation engine performs preliminary lead identification and scoring before sales automation engages. By pre-qualifying leads using refined ICP attributes and predictive analytics, the system ensures that only high-potential opportunities are passed to automated sales processes, thereby maximizing opportunity capture while maintaining ease of operation
Solution Approach 2:
The system dynamically adjusts lead scoring criteria and ICP attributes based on real-time feedback and changing market conditions. This dynamic adaptation allows the sales automation system to remain flexible and capture emerging opportunities that static outdated systems would miss, while preserving operational simplicity
Data Source
AI summary
Disclosed are methods and systems for generating targeted advertising campaigns for a business-to-business (B2B) company. The method comprises retrieving historical data on one or more historical experiments; determining, using a prediction engine, a prediction of a goal metric by finding a pattern in a historical metric that influences the goal metric, where the historical metric is included in the historical data; determining, using the prediction engine, correlations between one or more experimental parameters and the goal metric, based on the prediction of the goal metric; training, using the prediction engine, two or more experimental parameter models for the goal metric, based on the correlations between the one or more experimental parameters and the goal metric; generating, using a campaigns engine, one or more new experiments, each associated with the goal metric, and based on the two or more experimental parameter models; and generating, using the campaigns engine, a targeted advising campaign comprising a selected number of the new experiments.