Evaluate the effectiveness of different lead scoring automation strategies for a B2B SaaS company selling project management software.  Consider various data points, including website activity (page visits, time spent, downloads), email engagement (opens, clicks, replies), and CRM interactions (form submissions, demo requests).  Compare and contrast three distinct lead scoring models: a simple point-based system, a predictive model using machine learning, and a tiered system based on firmographic data.  For each model, outline the implementation process, including data sources, scoring algorithms, and integration with existing CRM and marketing automation tools.  Analyze the potential benefits and drawbacks of each model, focusing on accuracy, cost-effectiveness, and scalability.  Finally, propose a comprehensive evaluation plan to measure the ROI of each lead scoring strategy, including key performance indicators (KPIs) such as conversion rates, sales cycle length, and customer lifetime value.  The output should be a comparative analysis structured as a table, detailing the strengths and weaknesses of each model, implementation steps, and proposed KPIs for measuring their effectiveness.  The analysis must focus on improving conversion rates and lead qualification.