Predictive Analytics For Leads Generation And Engagement Recommendations

What is this patent about?

’964 is related to the field of predictive analytics and, more specifically, to systems that generate sales leads and engagement recommendations. The background involves the challenge of efficiently moving potential customers through a sales funnel. Businesses need to identify and engage leads effectively, but this process can be costly and time-consuming. The patent addresses the need for an automated system that can improve lead generation and scoring.

The underlying idea behind ’964 is to leverage machine learning and data analysis to identify promising sales leads and suggest optimal engagement strategies. The system analyzes a combination of data from public sources (like websites and social media) and internal client data (CRM, marketing automation systems) to determine similarities between potential customers and existing successful clients. By understanding the characteristics and behaviors of current customers, the system can predict which new leads are most likely to convert and recommend personalized engagement tactics.

The claims of ’964 focus on a computer-implemented method for generating sales leads with engagement recommendations. The method involves determining similarities between the fitness, engagement, and intent characteristics of target clients and existing clients. It also includes crawling web pages and categorizing them using a trained classifier to collect unstructured text information. Finally, it generates a feature matrix for the target client and compares it with the feature matrix of existing clients to generate engagement recommendations.

In practice, the system uses smart web crawlers to gather data from various online sources, categorizing web pages based on their content and link structure. This allows the system to extract relevant information about potential leads, such as their industry, contact details, and online behavior. The system then uses machine learning models to analyze this data and identify leads that are similar to the client's existing customers. The engagement recommendations are tailored to these similarities, suggesting specific actions that the sales team can take to increase the likelihood of conversion.

’964 differentiates itself from prior approaches by combining data from diverse sources and using machine learning to personalize engagement recommendations. Unlike traditional lead generation methods that rely on generic marketing campaigns, this system provides targeted insights and actionable advice for each lead. The use of a trained classifier to categorize web pages and extract relevant information from unstructured text also allows the system to efficiently gather and analyze data from a wide range of sources, improving the accuracy and effectiveness of lead generation.