What Are Parameters?
In artificial intelligence and machine learning, parameters are the internal variables, weights, and coefficients that an AI model learns and adjusts during training. Think of them as the "settings" that allow an AI system to make accurate predictions or decisions. When an AI model processes data – like user behaviour, ad performance, or audience demographics – it uses parameters to transform that data into actionable insights.
Parameters are different from inputs. Inputs are the data you feed into the model (like user age or browsing history), while parameters are what the model learns and refines to interpret that input effectively.
How Parameters Work in Ad Tech
In advertising and media buying, AI models use parameters to:
- Predict user behaviour: Parameters help models learn which users are most likely to click an ad or convert
- Optimize bid strategies: Programmatic systems adjust parameters to find the best price for each impression
- Personalize content: Parameters enable AI to tailor ad messaging and creative to different audience segments
- Improve targeting: Models learn parameter values that distinguish high-value audiences from low-value ones
For example, a machine learning model predicting ad conversion might have parameters that represent the importance of factors like time of day, device type, or user location. During training, the model adjusts these parameters thousands of times to minimize prediction errors.
Parameters vs. Hyperparameters
It's worth understanding the distinction:
- Parameters: Learned automatically by the AI model during training (e.g., weights in a neural network)
- Hyperparameters: Settings you choose before training to control how the model learns (e.g., learning rate, number of layers)
Hyperparameters are like the recipe instructions, while parameters are the actual ingredients and their quantities that change as the model trains.
Why Parameters Matter for Marketers
Understanding parameters helps you:
- Trust AI recommendations: Knowing that models are learning specific, measurable parameters makes their outputs feel less like a "black box"
- Evaluate model quality: Models with well-tuned parameters deliver better ROI and more accurate audience predictions
- Debug performance issues: If campaign performance drops, parameter drift (when parameters shift unexpectedly) could be the cause
- Make informed decisions: When your ad platform shows why it recommended a particular targeting strategy, it's referencing learned parameters
Practical Example
Imagine you're using an AI-powered ad platform to target e-commerce customers. The model has learned the following parameters through training:
- Users who viewed product pages have a 40% higher conversion probability (high parameter weight)
- Users from mobile devices have a 20% lower conversion rate (lower parameter weight)
- Users who engaged with email campaigns have a 35% higher lifetime value (high parameter weight)
These learned parameters guide the system to bid higher for desktop users and those with email engagement history, optimizing your budget automatically.
The Training Process
During training, an AI system starts with random or default parameters, then:
- Makes predictions using current parameters
- Compares predictions to actual outcomes
- Calculates how wrong those predictions were (the "error")
- Adjusts parameters slightly to reduce that error
- Repeats hundreds or thousands of times
This iterative refinement is why fresh data and ongoing model updates matter – they allow parameters to adapt to changing market conditions.
Key Takeaway
Parameters are the "learnable" elements of AI systems. They're the values the model discovers through exposure to data, allowing it to make better predictions and optimize advertising performance. For marketers, recognizing that parameters are being continuously refined helps explain why AI-powered campaigns improve over time – the system is literally learning what works best for your specific audience and goals.