Predictive models: Turning past data into future B2B growth

Predictive analytics uses statistical algorithms and machine learning to analyze historical and real-time data and forecast future outcomes.

Predictive models are the engines behind this process. They take complex data inputs and transform them into insights that help teams make smarter, faster decisions.

You’ll find predictive analytics models across industries:

• Healthcare: assessing patient risk
• Finance: evaluating creditworthiness
• Retail: forecasting demand and managing inventory
• Manufacturing: predicting equipment maintenance needs
• Marketing: identifying accounts most likely to buy

Predictive modeling is the “special sauce” that makes analytics actionable — ensuring insights are not just accurate, but usable by go-to-market teams.

How predictive analytics modeling works

Predictive modeling starts — and ends — with data.

First comes data collection. This includes historical data (CRM), real-time signals (intent, engagement), structured data (tables and fields), and unstructured data (text and activity patterns).

Next is data cleansing. In predictive analytics, quality matters. Missing values, duplicates, and inconsistent formats can all skew results. Clean data is essential to trustworthy predictions.

Then comes feature selection — choosing the variables most relevant to what you’re trying to predict. Often, this step reveals opportunities to enrich models with additional signals. Once features are selected, it’s time to choose a model. Different problems require different models.The model is then trained on a subset of data to identify patterns and relationships. After training, it’s tested and validated using unseen data to ensure accuracy.

Finally, the model is deployed into real-world workflows — where it can start influencing decisions.

Predictive modeling isn’t set-and-forget. Models must be monitored, updated, and retrained as data, markets, and buyer behavior evolve.

The predictive modeling lifecycle:

1. Data collection
2. Data cleansing
3. Feature selection
4. Model training
5. Testing and validation
6. Deployment
7. Monitoring and iteration

Common types of predictive models

There’s no one-size-fits-all approach. Most organizations use multiple models in parallel.

Classification models

Used to assign data to predefined categories — binary or multi-class.

Regression models

Designed to predict continuous numerical values like revenue, deal size, or customer lifetime value.

Time series models

Used when data is time-based and sequential, such as forecasting pipeline growth or seasonality.

Clustering models

Group accounts or customers based on shared characteristics to support segmentation and sales prioritization.

Anomaly and outlier detection

Identifies unusual patterns that fall outside expected norms, helping surface shifts in sales or market behavior.

Decision trees

Use a tree-like structure to map decisions and outcomes and are commonly used for churn analysis.

So which model is best? It depends on the question you’re trying to answer — and often, the most powerful insights come from using multiple models together.

How Demandbase uses predictive models

Demandbase is built on predictive intelligence.

Using our FIRE framework, customers deploy predictive models to score accounts based on Fit, Intent, Relationship, and Engagement across web, email, CRM, ads, and sales activity.

Predictive models don’t just forecast the future. When embedded into your GTM strategy, they help you create it.

Your next big deal is already in the data.