Machine learning (ML)
Also known as: ML, machine learning in sales
In short
Machine learning (ML) is a field of artificial intelligence in which software learns patterns from examples instead of following rules a person wrote by hand. Given enough past data, it can predict outcomes, group similar items, or recommend the next action.
In B2B go-to-market work, ML is the method behind lead scoring, account ranking, lookalike audiences, and churn prediction. This page covers the field itself. A machine learning model is the specific trained artifact that field produces.
How machine learning works
Most GTM use cases follow the same loop: collect labeled history, train, test, then apply to new records.
Supervised learning
The system learns from examples with known outcomes, such as accounts that closed-won versus closed-lost. It then predicts the outcome for accounts it has not seen. Lead scoring and churn prediction usually work this way.
Unsupervised learning
There is no known outcome. The system finds structure on its own, for example clustering customers into segments that share traits you had not defined.
Generative models
Large language models are also built with machine learning. They generate text rather than predict a score, which is why they are used for drafting and summarizing instead of ranking.
Where GTM teams use machine learning
- Lead and account scoring: Learn which traits and behaviors separated past wins from losses, then rank new records.
- Lookalike discovery: Find companies that resemble your best customers across many attributes at once.
- Forecasting: Estimate pipeline conversion or churn risk from historical deal patterns.
- Data matching: Detect duplicates and link records that describe the same company or person.
Machine learning vs. rules
A rule says "score 10 points if the company has over 200 employees." A learned model works out which attributes matter and how much from your own history. Rules are easy to explain and need no data. ML adapts to patterns people miss but needs enough clean examples.
How to start with machine learning in GTM
1. Pick one decision
Choose a concrete question, such as which inbound leads should get a call today. Vague goals produce vague models.
2. Check your data
You need consistent outcomes recorded in the CRM. Say a team has two years of closed deals but half are missing a loss reason. Fix that first, or the model learns noise.
3. Compare against a simple baseline
Test the model against your current rules on held-out deals. If it does not beat them, keep the rules.
4. Monitor and retrain
Markets shift. Review performance regularly and retrain when accuracy drops.
Common mistakes
- Too little data: A handful of deals cannot support a reliable model.
- Biased history: If reps only worked certain segments, the model learns that bias as if it were fit.
- Black-box trust: Reps ignore scores they cannot understand. Show the top reasons behind each score.
Machine learning FAQs
What is machine learning in sales and marketing?
Machine learning in sales and marketing means software that learns from past deals, campaigns, and customer behavior to predict outcomes. Common uses include scoring leads, ranking target accounts, finding lookalike companies, and estimating churn risk without manually written scoring rules.
Is machine learning the same as artificial intelligence?
No. Artificial intelligence is the broad goal of software performing tasks that normally need human judgment. Machine learning is one way to achieve it, by learning from data. Rule-based systems can also be called AI without using machine learning.
Do you need a data scientist to use machine learning for lead scoring?
Not always. Many CRM and data tools include built-in ML features. You still need clean, consistently labeled outcome data and someone who can judge whether the scores beat your existing rules before the team relies on them.
What are the limits of machine learning for GTM teams?
Models depend on the quality and volume of historical data, can repeat past biases, and struggle with new markets that have no history. Their scores also need explanations, or reps tend to ignore them.
