In this module, you will build a clear mental model of how leads progress through a CRM—from initial capture to sales qualification. Through short videos, focused readings, and guided reflection, you will examine why accurate lead stages matter for sales execution, forecasting, and AI-supported decision-making. By the end of the module, you will understand lead tracking as a structured system that drives visibility, timing, and credibility in B2B sales workflows.

In this module, you will move from conceptual understanding to hands-on execution. Through demonstrations, guided reflection, and applied practice, you will create and manage lead records in a sandbox CRM—entering leads, documenting sales context, and scheduling follow-ups that mirror real B2B sales workflows. The focus is on disciplined CRM habits that signal ownership, support negotiation readiness engagement readiness, and maintain clean data for forecasting and AI-driven insights.

You will examine how conversational-AI plugins provide real-time cues and sentiment insights to support better interpretation of buyer signals during discovery calls.

You will apply an AI meeting assistant to analyze a call recording and extract meaningful indicators of deal progress to support informed decision-making.

You will design CRM workflows using AI-predicted churn signals to trigger timely, personalized follow-ups with clear entry and exit logic.

You will analyze engagement data to identify drop-offs and refine CRM sequences to improve response rates and overall performance.

This module will help you use a conversational AI assistant during live calls to spot objection cues, especially pricing, right as they happen and respond confidently in the moment. You’ll practice turning AI prompts into action by reframing to ROI/value and securing a clear next step before the call ends.

This module will let you turn your call data into proof—exporting outcomes, building a simple chart, and translating the numbers into a retro-ready story. You’ll quantify next-step lift, check the call-length tradeoff, and leave with a clear recommendation your team can act on.

You will design a dashboard that visualizes win-rate by discount band, negotiation length, and AI sentiment to support sales decision-making.

You will analyze dashboard insights to identify key predictors of win-rate and recommend concrete sales process improvements.

In this project, you will design an AI-powered sales workflow for a B2B software sales team. Your work will show how CRM processes, AI-supported selling activities, and analytics function as one connected system. You will create a workflow that improves data quality, supports timely follow-up, guides AI use during calls, and measures business impact through a clear performance framework. As part of this, you will define how AI insights are used during sales interactions and evaluate their impact by comparing call outcomes across AI-assisted and non-AI-assisted scenarios. You will also design analytics that help identify patterns in performance, including isolating key factors that influence win rates using dashboard data. You will create automated follow-up sequences and define how their effectiveness can be tested using A/B-style comparisons to determine which approaches improve engagement and deal progression. In addition, you will build a performance measurement framework that connects activity, AI usage, and outcomes, enabling continuous optimization of the sales workflow. This project reflects a real sales operations challenge where teams must not only implement systems but also measure what drives results. It focuses on integrating CRM, AI tools, and analytics into a continuous improvement loop where insights are tested, validated, and used to refine sales execution.