Leverage AI to optimize prospecting, analyze CRM data, and improve sales performance
This training is designed for sales representatives, business developers, and CRM managers who want to integrate artificial intelligence into their sales processes. A basic understanding of sales and CRM is sufficient to participate.
Participants first explore the fundamentals of AI applied to sales—machine learning, natural language processing, and generative AI—before addressing concrete use cases: lead scoring, sales forecasting, offer personalization, and opportunity detection.
The training also covers the preparation and quality of CRM data, the creation of dashboards with key performance indicators (conversion rates, RFM segmentation, Pareto analysis), as well as machine learning concepts focused on churn prediction and lead scoring.
Generative AI also plays a central role: participants learn to use tools like ChatGPT to automate prospecting, produce sales content, and formulate effective prompts, while taking into account the tool’s limitations and the need for human oversight.
The training concludes with a mini-project—a prospecting assistant or scoring pipeline—applied to a real CRM file.
Is it for you ?
Sales representatives, business developers, sales managers. CRM or sales marketing managers.
Prerequisites
Basic knowledge of business and CRM
What You'll Walk Away With
- ✓ Prioritize leads effectively using scoring techniques and sales prediction models
- ✓ Prepare and structure CRM data for reliable and actionable analysis
- ✓ Build sales dashboards with key KPIs and methods such as Pareto and RFM segmentation
- ✓ Apply basic machine learning models for churn prediction and lead scoring
- ✓ Automate prospecting and generate sales content using generative AI tools
Training content
1 Introduction to AI in sales
- Key definitions and concepts (AI, ML, NLP, generative AI)
- Challenges and benefits for sales teams
- Overview of real-world applications (large corporations, SMEs, SaaS tools)
2 Concrete use cases for AI in sales
- Prospect scoring (prioritization)
- Sales forecasting (forecasting)
- Customization of offers according to customer segments
- Detection of opportunities via purchase history
- Exercise
3 Processing and preparation of sales data
- CRM data structure (extraction, field types, data quality)
- Data preparation for modeling (cleaning, encoding, normalization)
- Introduction to exploratory data analysis (EDA)
- Exercise
4 Visualization of sales performance indicators
- Creation of simple dashboards (Google Sheets or Python)
- Essential KPIs: conversion rate, average basket size, sales cycle
- Classic analyses: Pareto (80/20 rule), RFM segmentation (Recency, Frequency, Monetary)
- Exercise
5 Machine learning applied to sales
- Basic concepts: features, labels, training/testing
- Suitable models: logistic regression, decision trees
- Application to churn prediction or lead scoring
- Importance of interpretability (examples with SHAP or simple trees)
- Exercise
6 Automating prospecting tasks with generative AI
- Introduction to tools: ChatGPT, GPT for Sheets
- Automatic generation of sales content (emails, headlines, summaries)
- Best practices for writing effective sales prompts
- Limitations, biases, and human supervision
- Exercise
7 Mini-project summary: AI assistant or scoring pipeline
- Group or individual project
Option 1: Creation of a prospecting assistant with GPT (search + message)
Option 2: Prospect scoring pipeline from a CRM file + dashboard
- Presentation of results, discussion, feedback
📌 Practical information
Our training sessions are offered in Montreal or Quebec City, in person or in a virtual classroom. Dates and locations are specified when you select your session below. If you have any questions, check out our FAQ.
