Using AI to Scale Personalized Ad Copy

"Broad targeting" is the current advice from Facebook and Google. Let the algorithm find your audience. But while targeting is broad, your creative should not be generic.

The paradox of modern advertising is: Target Broad, Speak Specific.

How do you speak specifically to 20 different personas without hiring 20 copywriters? Enter AI personalization at scale.

The Segment Matrix

Before opening ChatGPT, build a matrix.

• Verticals: Real Estate, SaaS, E-commerce, Local Service.
• Pain Points: "Too expensive," "Too hard to use," "Takes too long."
• Hooks: Question, Statement, Statistic, Story.

Prompt Engineering for Scale

Don't ask ChatGPT to "write an ad." Ask it to "populate the matrix."

Input Prompt:

"I have a product that helps people track expenses. Generate 3 distinct Facebook ad primary text options for the following personas:

1. The Busy Freelancer (Value: Time saving)
2. The Small Business Owner (Value: Tax compliance)
3. The College Student (Value: Budgeting/Saving)

For each, follow the AIDA framework (Attention, Interest, Desire, Action)."

Dynamic Creative Optimization (DCO)

Once you have these 50+ variations, you don't run them all as separate ads. You feed them into Meta's Advantage+ Creative or Google's PMax asset groups.

These platforms treat your copy as data inputs. They will match the "Small Business" copy with the user who shows small business intent signals, and the "Student" copy with the user browsing university sites.

The Result: Relevance at Scale

By feeding the algorithms high-quality, diverse inputs, you lower your CPMs and increase CTR. AI allows you to cover every angle of your product without the creative burnout.