Predictive Models – Future Insights from Past Data

Using statistical algorithms and machine learning techniques to analyze historical and current data to make informed predictions about future events. 

Or “determining future performance based on current and historical data” (Investopedia).

That’s predictive analytics.

Predictive analytics models are the (often very sophisticated) tools used to perform this analysis.

Real-world applications of predictive analytics modeling can be found across various industries with many use cases. A few examples: 

• Healthcare – assessing patient risk
• Finance – reviewing creditworthiness
• Retail – predicting demand and sales and managing inventory
• Manufacturing – predicting when a machine will need maintenance (or replacement)
• Marketing – segmenting customers based on potential buying behavior, preferences, and customer value

Modeling is the secret weapon, the special sauce that ensures the predictive analytics is as close to spot-on as possible.

How predictive analytics modeling works in practice

Data. It all starts (and ends) with data. 

Predictive modeling starts with data collection — gathering relevant data. This data can come from historical records (think CRM), real-time feeds (social media, BI tools), structured data (spreadsheets), unstructured data (text, images, etc), and more.

Next up: Data cleansing. As the saying goes, “garbage in, garbage out” (or “bad data in, bad data out”). Your data must be squeaky clean. This step cannot be overlooked or rushed. Find missing values. Remove duplicates. Transform data into an analysis-ready format.

Now, choose the features (or variables) most relevant to the outcome you are trying to predict. This may also be where your team realizes you need to add more features to the model to improve its accuracy.

Pick your model! The model you choose depends on the problem you are attempting to solve. Note: We’ll dive into the various models in the next section.

Training is not just for athletes. Ensuring your model is “trained up” and ready to go is essential. Start this process with a small sample of the data. This is when the model learns to recognize patterns or relationships between the features and the outcome.

Time to test and validate. Feed in a different set of data from the one used during training. You are assessing the model’s accuracy — how well does it perform with new, unseen data?

It’s time to fully deploy your model in a real-world environment where it can start making predictions.

Predictive modeling is not a “set and forget” situation — it requires constant monitoring and updating. Some models degrade over time as data (and patterns) change.

To recap (or TL; DR), here is the 7-step modeling process: 

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

There is no one-size-fits-all model. There are different predictive models for various situations.

What are the various predictive models?

There is more than one way to crack an egg (model predictive analytics).

Below is a brief recap of the 6 most commonly used models.

1. Classification Model

This model categorizes or classifies data into predefined labels or classes. It can be binary (two categories) or multinomial (several categories). 

• Binary example: check an email and classify it as “spam” or “not spam.” 
• Mutilnomial example: categorize customer support tickets into various types such as “billing,” “technical support,” or “general inquiry.”

A classification model is beneficial when the output (the prediction) assigns each input data point to one of the discrete categories or classes. 

In marketing, this model is often used to predict customer behavior categories.

2. Regression Model

This model predicts a continuous outcome or numerical value based on one or more input features.

They are often used for predicting quantitative outcomes like stock prices, sales and revenue forecasts, customer lifetime value, etc.

In sales and marketing, regression models can be used to analyze customer behavior — identifying key factors that influence customer purchasing decisions.

3. Time Series Model

This model is a statistical technique for forecasting future values based on historical data, especially when the data is sequential and time-dependent. In time series forecasting, data points are collected at consistent intervals over time.

In the marketing and sales world, time series modeling can be effective in: 

• Understanding seasonal trends (When do most leads enter the pipeline? Which months/quarters see the most significant bumps? etc.).
• Predicting sales growth. The time series model can predict future sales volume by analyzing past sales data, reviewing market trends and economic indicators, and studying consumer behavior.
• New product launches, performing marketing campaign analysis, forecasting customer demand, and more.

4. Clustering Model

This model groups data points with similar characteristics. 

The two areas cluster modeling are used most often in marketing and sales are: 

• Customer Segmentation: Segmenting customers into distinct groups based on purchasing behavior, demographics, tech stack, and engagement levels. 
• Optimizing Sales Strategies: Sales teams can use clustering to identify which customer segments are most likely to respond to specific sales tactics or which products are often purchased together.

5. Anomaly & Outlier Detection

This model identifies unusual patterns (anomalies or outliers) in data sets. 

Outlier detection models can help uncover unusual sales patterns —sudden changes in sales that aren’t explained by typical trends or seasonal variations. 

This model is also used for market and competitive analysis. Anomalies in market data can provide early warnings about changes in the competitive landscape or shifts in market dynamics.

6. Decision Tree

This model uses a tree-like structure of decisions and their possible consequences. 

Simple, yet powerful.

In a decision tree, each internal node represents a “test” on an attribute, each branch represents the outcome of the test, and each leaf node represents a decision.

This model is often seen when performing churn analysis, helping identify critical factors contributing to customer churn and empowering businesses to take proactive measures to retain high-risk customers.

So which is the best predictive model? As with most things in business (and life), it depends. More than anything, the “it depends” is related to the problem you are trying to solve. And often, these models are used side-by-side, not simply as one-offs.

Demandbase runs on predictive models

Predictive models are a powerful and effective way to forecast future events (sales, marketing trends, etc.) based on historical and current data. 

Using the FIRE method, Demandbase customers use our B2B predictive analytics tools to set up models for scoring accounts based on company Fit, high Intent actions, journey stage to nurture the Relationship, and Engagement across your website, email, inbox, CRM, and marketing automation (FIRE).

Get on a path to predictive revenue today.