What is Data Labelling?
Data labelling is the process of adding descriptive tags, annotations, or classifications to raw data to create datasets that train artificial intelligence and machine learning models. In advertising, this involves humans (or automated systems) identifying and marking specific attributes, objects, or patterns within images, text, videos, or user behaviour data.
For example, a media buying agency might label thousands of images as "contains product", "professional setting", or "target demographic present" to train an AI model that automatically identifies ads likely to perform well with specific audiences.
Why Data Labelling Matters in Advertising
Accurate AI models are only as good as the data they're trained on. Without proper labelling:
- Poor targeting accuracy: AI models can't learn audience preferences if training data isn't clearly marked
- Wasted ad spend: Untrained or poorly-trained systems misidentify high-performing creative elements
- Brand safety risks: Models can't avoid unsuitable placements without understanding content context
- Slow optimization: Your campaigns can't improve if the AI doesn't understand what "success" looks like
Data labelling ensures your AI systems actually learn to recognize patterns that drive results.
Common Applications in Media Buying
Audience Segmentation: Labelling user data with demographic, behavioural, or psychographic attributes helps AI identify which users are most likely to convert.
Creative Optimization: Marking high-performing ads with attributes like colour, copy style, or imagery type trains models to generate or select similar creatives.
Brand Safety: Labelling content as "brand-safe", "controversial", or "suitable for children" trains systems to avoid inappropriate placements.
Sentiment Analysis: Tagging social media comments and customer feedback helps AI understand audience perception of your brand or campaigns.
The Labelling Process
Data labelling typically follows these steps:
- Define labelling criteria: Determine what attributes or classifications are important for your campaigns
- Create labelling guidelines: Establish clear rules so all human annotators work consistently
- Assign labels: Have trained annotators mark data according to guidelines
- Quality assurance: Check for consistency and accuracy across labelled data
- Feed into AI models: Use labelled datasets to train and improve machine learning systems
Manual vs. Automated Labelling
Manual labelling involves human annotators reviewing data and applying labels. It's more accurate for complex tasks but slower and more expensive.
Automated labelling uses existing AI models or rules to tag data quickly at scale. It's faster and cheaper but less accurate for nuanced decisions.
Many agencies use a hybrid approach: automated systems handle straightforward labelling, while humans review complex or edge-case data.
Challenges in Data Labelling
Consistency: Ensuring multiple annotators apply labels the same way
Scale: Labelling large datasets is time-consuming and costly
Subjectivity: Some attributes (like "engaging") are harder to define consistently than others
Bias: Biased labelling creates biased AI models that underperform for certain audiences
Getting Started
If you're investing in AI-driven advertising:
- Start with clear definitions of what you want to measure or predict
- Invest in quality control – garbage in, garbage out
- Consider outsourced labelling services if volume is high
- Regularly audit your labelled data for accuracy and bias
- Use feedback loops to improve labelling over time