Dynamic copy adapts based on variables: who sees the ad, when, via which channel, on which device and in what context. The basic structure of the ad remains the same, but specific elements vary.

Examples of variables:

• Location: "Delivery in Manchester within 2 hours" vs. "Delivery in Birmingham within 2 hours"
• Segment: "For small businesses" vs. "For enterprise teams"
• Behaviour: someone who has already viewed a product receives a different message than a new visitor
• Stage in the journey: awareness vs. retargeting vs. customer retention

Until recently, this required manually writing each variation. AI makes it possible to generate those variations based on a template and a dataset of variables.

The approach works in three steps:

1. Create the template: write the basic structure of the ad with placeholders for elements that vary. For example: "[Segment], discover how [Benefit] works for [User scenario]."

2. Define the variable matrix: compile a list of all relevant combinations. Which segments, locations, product categories or behavioural variations exist?

3. AI fills in and generates: AI completes the template with each combination of variables and generates a complete copy variation. With ten segments, five locations and three tones, you have 150 unique variations without writing each one manually.

Google Ads has built-in functionality for dynamic ads: keyword insertion, location insertion and ad customisers. Meta offers Advantage+ and dynamic ad sets where copy can vary per audience. LinkedIn offers limited dynamic options, but more possibilities are available via the API.

For more advanced personalisation, where AI actively generates copy based on real-time data, external tools and API integrations are needed. This requires technical implementation, but the scale benefits can be considerable.

The risk of dynamic copy at scale: you lose oversight. When hundreds of variants are in circulation, it is difficult to assess what is live and whether all variants are of sufficient quality.

Recommendations for quality assurance:

• Use a review layer before variants go live, especially for new combinations
• Set up automated filters for prohibited terms or brand-specific exclusions
• Monitor performance per variant and deactivate poorly performing variants quickly
• Conduct periodic manual checks on a sample of live variants

Not all personalisation performs better than a good generic ad. Over-personalisation can feel uncomfortable for users: if an ad knows too precisely what someone has done, it can create resistance.

Additionally, personalisation is only effective when the data is correct: an incorrect location or wrong segment produces an ad that backfires. Data quality is a prerequisite, not an afterthought.

The most powerful applications combine AI-generated copy with dynamic delivery. You build a solid base campaign with human-written core messaging, and use AI to generate variations for specific segments or contexts. This keeps brand identity intact while delivering relevance at scale.

AI-generated dynamic advertising copy makes personalisation at scale possible without the manual burden of writing every variation. But quality control, data quality and strategic decisions about which personalisation is worthwhile remain human responsibilities.

Mach8 helps organisations set up scalable content production and AI-driven personalisation. View our content production service or get in touch.