# Module 3 Quiz: Lead Qualification, AI Scoring & Intent Detection --- ## Question 1 You're implementing an AI lead scoring model. You have historical data showing that leads from companies with 200-500 employees, in the SaaS industry, who downloaded a pricing page have a 15% close rate. Leads from other segments have a 2% close rate. How should your AI scoring model weight these signals? A) All signals should be weighted equally to avoid bias B) The combination of company size (200-500), industry (SaaS), and pricing page download should receive a significantly higher score weight C) Only the pricing page download should be weighted — company size and industry don't matter D) Lead scoring should be random to avoid overfitting **Correct Answer: B** **Explanation:** AI lead scoring works by identifying patterns in your historical data that correlate with successful conversions. The combination of firmographic fit (200-500 employees, SaaS industry) and behavioral intent (pricing page download) is a strong buying signal. Your model should weight these high-value signals more heavily than generic actions like visiting a blog post. The more specific and data-driven your scoring, the better your sales team can prioritize. --- ## Question 2 A lead has visited your pricing page three times in the last week, downloaded your ROI calculator, and has a company size that matches your ICP. However, their email domain is a generic Gmail address rather than a company domain. What is the most likely interpretation? A) This is a hot lead — the behavioral signals are strong, so ignore the email domain B) This could be a competitor, a student, or someone researching on behalf of a company — verify their identity and company before prioritizing C) The lead is definitely fake — discard immediately D) The lead is only interested in free resources — deprioritize **Correct Answer: B** **Explanation:** While the behavioral signals (pricing page visits, ROI calculator download) are strong buying indicators, a generic email domain is a red flag that warrants verification. The lead could be a legitimate prospect using a personal email, a competitor doing research, or a student. The best approach is to engage with the lead but verify their company and role before investing significant sales resources. AI can flag this discrepancy automatically. --- ## Question 3 You're setting up data enrichment for your lead database. Which of the following data points is **most valuable** for B2B lead qualification? A) The lead's favorite color and hobbies B) Firmographic data (company size, industry, revenue), technographic data (tools they use), and trigger events (funding, hiring, leadership changes) C) The lead's social media follower count D) The lead's personal email preferences **Correct Answer: B** **Explanation:** B2B lead qualification relies on firmographic data (who they are as a company), technographic data (what tools they already use — indicating tech stack compatibility), and trigger events (signals that indicate buying intent like funding rounds or leadership changes). These data points help you understand fit, timing, and likelihood to buy. Personal details like hobbies or favorite colors are irrelevant for B2B qualification. --- ## Question 4 You're analyzing intent data for a prospect company. Which of the following behavioral patterns is the **strongest** signal of active buying intent? A) They followed your company on LinkedIn B) They visited your "Pricing" page, read your "Implementation Timeline" case study, and downloaded your "ROI Calculator" — all within 48 hours C) They opened one of your marketing emails D) They visited your homepage once three months ago **Correct Answer: B** **Explanation:** Intent data is about identifying patterns of behavior that indicate a prospect is actively evaluating solutions. Multiple high-intent actions (pricing page, implementation case study, ROI calculator) in a short timeframe (48 hours) strongly suggest the prospect is in an active buying cycle. A single homepage visit or social media follow is low-intent. The combination and recency of high-intent actions is what matters most. --- ## Question 5 Your AI lead scoring model has been running for 3 months. You notice that leads scored as "high priority" by the AI are closing at a 12% rate, while leads scored as "medium priority" are closing at 8%. However, your sales team has been ignoring the AI scores and working leads based on their own intuition. What should you do? A) Turn off the AI scoring — it's not working B) A/B test the AI scoring by having one sales team follow AI scores and another use intuition, then compare results after 30 days C) Force the sales team to follow AI scores immediately with no transition period D) Lower the AI scoring thresholds so more leads are marked as high priority **Correct Answer: B** **Explanation:** The AI scoring is actually performing well (12% vs 8% close rate), but the sales team's resistance to adoption is the problem. An A/B test approach lets you demonstrate the value of AI scoring with real data, build trust with the sales team, and identify any edge cases where human intuition outperforms the model. Forcing adoption without evidence creates resentment, while abandoning a working model wastes the investment.