Machine learning model
In short
A machine learning model is a mathematical program trained on past examples to recognize patterns and apply them to new cases. Instead of following hand-written rules, it learns the relationship between inputs and outcomes from data.
In go-to-market work, models rank leads, predict churn, cluster customers, and find lookalike companies. The model is only as good as the data and the outcome definition used to train it.
How a model is built
Training data
You supply past records with known outcomes, such as accounts that did or did not become customers.
Features
Features are the inputs the model sees: industry, employee count, tools used, recent hiring, engagement. Better features usually matter more than a fancier algorithm.
Training and testing
The model learns from part of the data and is checked on a held-out part it has not seen. This shows whether it generalizes or just memorized.
Common types in GTM
- Classification: Predicts a category, such as will convert or will not.
- Regression: Predicts a number, such as expected deal value.
- Clustering: Groups similar accounts without predefined labels.
- Ranking: Orders accounts or leads by likelihood or priority.
Example
Say a team has 2,000 past opportunities, each marked won or lost, with company traits attached. A classification model learns which traits tend to appear in wins. It can then score new accounts so reps start with the most similar ones. The numbers here are illustrative only.
Common mistakes
- Too little data: A few dozen deals rarely support a reliable model. Rules may serve better.
- Biased history: If reps only worked certain segments, the model learns that habit, not true demand.
- Data leakage: Including information only known after the sale makes test results look unrealistically good.
- No monitoring: Markets shift, so retrain and recheck performance regularly.
Machine learning model FAQs
What is a machine learning model?
A machine learning model is a program trained on historical data to find patterns and make predictions on new data. It learns the link between inputs and known outcomes rather than following rules a person wrote by hand.
How is a machine learning model different from a rule-based system?
A rule-based system applies logic a person wrote, such as scoring 10 points for a certain title. A machine learning model derives its logic from past data. Rules are easier to explain; models can capture patterns people miss but need enough quality data.
How much data do you need to train a machine learning model?
It depends on the task and the number of signals used. Models predicting rare outcomes need many examples, and a handful of deals is usually too few. If data is thin, start with simple rules and collect outcomes.
How do GTM teams use machine learning models?
GTM teams use them to score leads and accounts, predict churn or expansion, group customers into segments, and find companies similar to their best customers. Results should be validated against real outcomes before reps rely on them.
