Marketers are drowning in data. Clicks, impressions, conversions, and so on.
But raw numbers don’t tell you why something works or doesn’t.
One particular ad outperforms another six for the same campaign, but why? Is it the wording? The color scheme? The call to action?
Existing BI tools won't tell you, they were never designed to.
AI labeling adds context to your performance reports. It categorizes creative elements, messaging styles, and audience engagement patterns.
Instead of guessing why an ad performed well, you get a deeper understanding of why.
With AI labeling, marketers shift from reactive analysis to proactive optimization. Instead of adjusting campaigns after they fail, you can fine-tune them in real time, without the guesswork.
Marketing data is messy.
Social posts, ad campaigns, customer reviews, email marketing, and web analytics all contain their own data.
A typical marketing campaign can involve running ads on multiple platforms, posting on social media platforms, sending multiple email sequences and more.
Each platform requires specialist data tracking and configuration usually by a more 'technical' marketer, often gaps appear between 'first' and 'last' touch making accurate attribution difficult.
Each piece of content performs differently. Some ads convert well, others don't. Certain social posts engage your audience, while others fall flat.
Finding useful patterns in the performance data is painful and laborious. Traditional spreadsheets and manual tracking methods don't scale.
Marketers have access to so much information that finding meaningful patterns feels impossible.
Companies have spent millions investing in marketing activities and yet have no system in place to provide useful actionable insights as to what is working and what is not.
It's a huge technical mess, the bigger the organization, the bigger the mess.
Fortunately, with the advancement of AI, we now have a way to fix this mess - AI Labeling.
AI labeling is the process of categorizing and tagging data using machine learning algorithms to add structure and meaning.
It applies to various fields, such as healthcare, finance, and autonomous systems.
AI labeling enhances data processing by enabling machines to recognize patterns, classify content, and extract insights without or with little human intervention.
Poor labels lead to poor AI performance. If humans incorrectly label training data, the AI learns the wrong patterns.
That's why most AI labeling systems use a "human in the loop" approach - combining AI speed with human judgment to ensure accuracy.
In general, AI labeling can:
In marketing, we apply these labeling techniques to marketing assets.
Instead of just labeling "cat" or "dog," we label elements like "product feature," "value proposition," or "call to action." This helps track what resonates with audiences across different channels.
By implementing AI labeling, marketers (and other industries) can turn raw performance data into actionable intelligence, leading to smarter, faster decision-making with more optimized outcomes.
AI labeling helps marketers make smarter, faster decisions by breaking down all creative elements and messaging that were present on the content that helped to convince the customer or prospect to take action.
AI can help to spot what is present on the asset, but is not intelligent enough to know what is important. The labels are always decided by the marketer, not AI.
Marketers often tweak their campaigns based on intuition.
AI labeling removes the guesswork by tracking which elements, headlines, images, colors, calls to action, drive the most engagement.
When you look at your content think about what you are testing - value propositions, pain points, or any other types of messaging?
What creative elements do your think are likely to be affecting the performance of your content? Be it colors, styles, concepts or memes?
What is is about this content you created that you want to track?
Anything can now be labeled and analyzed.
AI labeling can answer any questions you have about what is really resonating best/worst across all your marketing channels.
Once the labels have been determined and applied, by connecting the associated metadata from the channel where it was published, you get a clear picture of the audience who interacted with your content.
A complete understanding of not just just what content was shown, but by whom.
Now you can categorize users based on how they respond to different ad styles.
This means marketers can tailor messaging to specific segments instead of using a one-size-fits-all approach.
If results indicate that younger audiences engage more with conversational tones and emojis, while professionals prefer formal messaging, you can refine their content accordingly.
Instead of relying on guesswork, teams get a clear understanding of what drives engagement and conversions.
Not only are the results and insights far superior to legacy systems and BI tools, the time required to get to these answers is vastly reduced.
No need to spend any time performing manual tasks, updating and maintaining multiple naming conventions or waiting for expensive data experts to come back to you with an answer.
The speed that AI labeling can work at is incredible, real time perfomance insights are available at the click of a button.
This means that if an ad underperforms in its first few days, AI labeling can highlight which elements are causing the drop, allowing marketers to pivot quickly rather than wasting ad spend on ineffective creatives.
By utilizing AI labeling, marketing teams can make more informed, data-backed decisions that lead to higher engagement, better targeting, and optimized ROI.
AI can process vast amounts of data at an incredible speed, but human oversight is key for accuracy.
Some nuances, like sarcasm in text or cultural differences in imagery, require human judgment. Using a hybrid approach ensures AI-generated insights remain relevant and actionable.
At AlphaGen we provide a Human in the Loop service for all our products that contain AI Labeling technology. Think of it as a Software AND a service.
This is because the accuracy of these labels is paramount to receiving the correct actionable insights for all our customers.
Interaction and input is encouraged though as ultimately the marketing team is in control of the the company strategy and we want to make sure that all required labels are applied.
How we work is as follows:
Specify the content you want to start with. Don't start too big, usually a single channel like LinkedIn Ads is a sensible place to start.
Look to work with a single channel to begin with, optimize for there and take your learnings to other channels next.
Define what success looks like and what insights you want to get from the implementation of labeling.
Before diving into labeling, define a structured set of categories that align with your business objectives. Think about how you want to analyze your data, whether it’s by messaging type, visual style, or call-to-action effectiveness.
A well-planned taxonomy ensures consistency and maximizes insights.
This must be built using a language everyone understands with clear definitions for each label. Document rules for consistent use.
AI can automate and speed up label application, but marketers should decide the labeling structure.
Marketers know what insights matter most, whether it’s tracking emotional tone, product positioning, or audience preferences.
AI should enhance strategy, not dictate it.
Marketing trends change quickly. What worked last quarter might not be relevant today.
Continuously refining your labeling framework ensures that you’re always capturing the most meaningful data.
During brainstorming sessions ideas might come up and new analysis required, this is easy to execute and answers can be gleaned in super quick time.
AI labeling is not a one time action, you can review and update at any time you want.
Provide some training and introduction into the labeling implementation, ensure everyone knows what the labels are for and how to best make the most of the new insights.
Encourage people to consider other labeling opportunities and make sure they are aware of this new super power available to them.
This new version of business insights is so far removed from the limited insights available previously it will take some time for some to realize just how instantly powerful the new analytics tool they have really is.
All new technologies come with some challenges no matter how small, here are a few to consider and how you can deal with them:
Problem: Different teams may label content inconsistently, making cross-platform analysis difficult.
Solution: Establish clear labeling guidelines and ensure AI models are trained on standardized data.
Problem: AI can misinterpret nuances like sarcasm, humor, or cultural differences.
Solution: Keep humans in the loop at all times to validate AI labels and refine them for better accuracy.
Problem: While AI speeds up the process, it lacks human intuition for subjective elements like emotional appeal and language nuance.
Solution: Implement a hybrid approach where AI handles bulk tagging and humans refine final outputs for accuracy and brand alignment.
Problem: Team members stick to old habits or resist using the new system.
Solution: Start with a small group on a single channel. Create initial reports for them to get them to an immediate 'aha' moment before they even have to engage with the solution.
AI labeling done correctly can deliver enormous improvements both on your content performance but also in your marketing operations.
All that time currently being wasted on manual analysis, both for your own content and your competitors (see here about how you can use AI labeling on your competitors LinkedIn ads), will be completely wiped out giving you more time to spend on more impactful strategic initiatives.
Each platform will determine the specific metrics you want to track but you can expect to see improvements across the board.
The list of potential improvements is enormous and dependent on each orgnanization but here are a few
Content Performance:
Cost Metrics:
Operations:
Start tracking these metrics and operational tasks before implementing AI labeling.
This gives you a clear baseline for measuring improvements.
By adding structure to marketing data, AI labeling allows teams to make smarter, faster, and more informed decisions.
The level of insights is a dream for strategic marketers, providing clear insights into what is working and what is not working , and why, without having to spend any time performing manual analysis.
Cutting waste from inefficient marketing practices has already started, most are not aware of the power of AI labeling so companies that move first will get the edge on the competition.
The goal isn't perfect data - it's better decisions.
Every properly labeled piece of content puts you one step closer to understanding what works for your audience.
Start small, stay consistent, and let the insights guide your marketing strategy.
So, which marketing channel would you like start with first?
