Managing high-volume ad campaigns across multiple platforms often feels like an endless loop of repetitive tasks. Team members spend hours writing similar ad variations, manually excluding negative keywords, and digging through scattered reports to figure out why a campaign's Cost Per Lead (CPL) suddenly spiked.
When our team at InterCode took on the challenge of transforming a complex marketing platform, our goal was clear: eliminate repetitive manual workflows without sacrificing human oversight.
Here is how we designed AI-driven automation workflows that handle copywriting, negative keyword classification, and natural-language data analysis at scale.
1. Automated Ad Transformation (Goodbye, Manual Copywriting)
One of the biggest time sinks in multi-campaign management is turning special offers, product updates, or local promotions into platform-ready ad copy.
The Challenge:
Marketing teams manually wrote and adapted ad variants for each channel (Search, Social, Display) whenever a new promo launched.
The AI Solution:
We built an automated pipeline using LLM integrations that (which we implemented as part of our Street Digital Media project) that:
Ingests structured offer data directly from internal management systems.
Transforms promos into optimized ad copy tailored to the specific character limits and formatting requirements of target platforms.
Pushes approved copy directly to campaign management systems, drastically reducing launch times from hours to seconds.
2. Context-Aware Negative Keyword Classification
Broad negative keyword lists often block relevant traffic or fail to stop budget waste because they lack real-time context.
The Challenge:
Static negative keyword lists don't account for dynamic variables like changing inventory, localized pricing, or shifting service availability.
The AI Solution:
Instead of relying on rigid, pre-made keyword lists, we implemented contextual AI classification:
Real-time context checks: The system analyzes contextual parameters (e.g., current pricing, unit mix, geographic availability).
Smart exclusion: AI automatically flags and excludes negative search queries that don't match the active product state.
Budget protection: By preventing irrelevant clicks before they happen, ad spend is automatically preserved for high-intent users.
3. Conversational Data Analysis with Natural Language
Data without quick accessibility creates bottlenecks. Marketers often wait on analysts to query databases or build custom dashboards just to answer basic performance questions.
The Challenge:
Dashboards can be overwhelming, and finding specific insights (like root causes for performance drops) takes time.
The AI Solution:
We integrated an internal LLM chatbot connected to unified reporting data:
Ask in plain English: Team members can ask direct questions like "Why did CPL spike last week?" or "Which channel brought the highest return this month?"
Instant, data-backed answers: The system queries pre-processed data and returns clear, conversational explanations alongside actionable recommendations.
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