Understanding predictive vs. causal analytics in marketing

Not all marketing analytics are created equal. Some tell you what’s likely to happen. Others prove what actually happened and why. If you’re relying on just one, you’re missing half the picture.

This is where predictive analytics vs. causal analytics come into play. While predictive analytics in marketing forecasts future trends based on historical data, causal analytics dives deeper to understand the causal effect of marketing actions and outcomes. 

This guide breaks down the difference between causal and predictive analytics to show you how you can leverage the two effectively.

What is causal analytics in marketing?

Causal analytics is the method of studying the cause-and-effect relationships between marketing actions and outcomes. It uses techniques such as: 

• Experimental designs like A/B testing to determine causality in marketing by isolating confounding variables and controlling for external/environmental factors 
• Bayesian methods to enhance causal inference methods by integrating prior knowledge and current evidence, providing a more nuanced understanding of causal marketing and relationships

As a result, causal analytics help you identify the most effective unified marketing strategies and allocate resources by focusing on the drivers behind business outcomes.

How does causal analytics work?

Causal analytics relies on controlled experiments and advanced statistical modeling techniques. A typical causal marketing analytics approach includes the following steps:

• Run a matched market test or geo test: Based on your market research, launch your campaign in one set of markets (test group) while keeping another similar set “dark” (control group).
• Measure lift using incrementality testing: Compare performance between the test and control groups to isolate the true effect of your campaign and establish causal relationships between your campaign and the outcome.
• Control for outside factors: Use statistical techniques to account for independent variables like seasonality, promotions, or competitor activity.
• Use prior data smartly: Tools like Bayesian MMM incorporate historical data and update predictions as new results come in.

What is predictive analytics in marketing?

Predictive analytics in marketing is the practice of using historical data and machine learning models to forecast future outcomes, like sales, conversions, or customer behavior. It enables proactive decision-making by: 

• Anticipating changes in consumer shopping behavior and market dynamics
• Providing probabilistic forecasts that allow marketers to assess uncertainty 

How does predictive marketing analytics work?

Predictive marketing analytics works by feeding large sets of historical data into statistical models or machine learning algorithms to forecast future outcomes. In marketing, two core components drive this process: 

1. Historical sales data
2. Machine learning algorithm

1. Historical sales data 

Historical sales data is the backbone of predictive analytics, enabling businesses to make a sales forecast based on past patterns. Some examples of the data collection include: 

• Sales volumes
• Revenue figures
• Customer purchase behavior
• Seasonality in marketing 

By analyzing this information, you can identify patterns and trends that are likely to repeat. 

For instance: 

• If a product has shown a consistent increase in sales during the holiday season for the past few years, it’s safe to predict a similar uptick in the upcoming holiday season.
• Analyzing sales dips can help in adjusting stock levels or marketing planning to mitigate potential losses.

Keen’s MMM platform helps this process, allowing for the assimilation of vast datasets and precise analysis to inform decision-making in the real world. 

2. Machine learning algorithms

Machine learning algorithms analyze your historical data to uncover patterns that may not be apparent through traditional analysis. ML-powered MMM can make accurate predictions about future trends and behaviors, being trained on large data sets. 

The machine learning algorithms can adapt and learn from new data, providing you with immediate and personalized recommendations for improving their strategies.

Once trained, they can:

• Predict how changes in budget or marketing channel mix might impact future performance
• Estimate the marketing ROI of a campaign before launch
• Provide real-time recommendations based on new incoming data

Predictive vs. causal analytics in marketing mix modeling (MMM): Key differences

Both predictive and causal methods are used in marketing mix modeling (MMM). But predictive analytics does not equal causal relationships. 

Here are the important differences between causal and predictive analytics: 

Aspect	Casual analytics in MMM	Predictive analytics in MMM
Goal	Prove why something happened and identify which tactics actually caused marketing incrementality.	Forecast what’s likely to happen in the future based on past patterns.
Method	Uses experiments (like matched market test) and statistical techniques to control for factors and isolate the effect of each marketing activity.	Uses machine learning and builds models using historical sales and marketing data to find patterns and relationships. These models are then used for scenario-based planning.
Use case	Understand which channels truly drive incremental sales.	Estimate future sales under different budget scenarios.
Example	Did TV spend directly cause a 10% sales lift in Q1?	What will Q3 sales look like if we increase paid social spend by 20%?

The interplay of predictive causal analytics: Why marketers need both

The debate between causal analytics vs predictive analytics isn’t about choosing one over the other. They answer different questions, but give you a complete picture when used together.

• Causal marketing analytics lays the foundation by providing information about cause-and-effect relationships, helping you understand how your actions directly impact outcomes. 
• Predictive analytics complements causal insights by forecasting future trends, allowing you to anticipate changes and adjust your marketing channel strategy accordingly. 
• Integration of both approaches offers a holistic understanding of marketing dynamics, empowering you to make informed decisions that drive sustainable growth. 

Used together, they’re far more powerful than either approach alone. For example:

• Run a causal test to prove that a new campaign drives sales.
• Then use predictive models to estimate how scaling that campaign will impact future quarters.

Leverage predictive analytics using causal methods with Keen’s dual-approach software

Predictive and causal analytics offer invaluable information, with predictive analytics forecasting future trends and causal marketing unraveling the reasons behind marketing outcomes.

Keen’s marketing measurement platform effectively integrates both approaches, providing you with a comprehensive view of your data. You get a dynamic causal machine learning environment where you can forecast future outcomes along with measuring your past performance.

The causal forecasting capability ensures that your marketing strategies are agile, adapting to market dynamics and consumer behaviors quickly with: 

• Data integration: Keen’s platform seamlessly integrates your existing data, using data points like weekly revenue, marketing investments, and external market factors, with convenient software integrations to your data provider(s).
• Bayesian modeling: Keen’s Bayesian modeling approach helps predict and refine future marketing efforts based on historical and real-time data.
• Strategic resource allocation: Embracing Keen’s predictive capabilities allows for strategic allocation of budgets, optimizing both current marketing campaigns and planning future ones with greater accuracy.

Want to learn which approach to choose between causal analytics vs predictive analytics?  Request a demo with our team today.