Creative automation and ad creative at scale

What creative automation is.

Creative automation uses templates, data and AI to produce many versions of an ad without making each one by hand. A single concept can become different sizes, languages, products, prices and messages, all from one controlled source.

There are a few common forms. Feed-based ads pull product names, images and prices from a catalog. Template systems swap approved text and images into a set layout. Generative AI can draft new copy or images for review. Dynamic creative optimization, usually called DCO, assembles ad parts while the campaign runs, depending on the audience and what's performing.

Most programs use a mix. We choose based on what you need to produce and how much control your brand or category requires.

When it makes sense.

Creative automation is most useful when you have many products, markets, languages or audiences, or when a small team can't keep up with what the platforms need. Ad platforms ask for many sizes and formats, and audiences get tired of seeing the same ad.

It's a poor fit when you have only a couple of products and a single message, or when each ad has to be a one-off piece of craft. It won't fix a weak idea either. Automation makes more versions of whatever you give it, so we make sure the concept is worth repeating first.

How we plan production for scale.

Volume starts with decisions made before production. For CAMPIA, a Montréal student residence, we wrote a production brief that covered format, size, duration, copy and language for each of 22 campaigns, which came to roughly 105 exports and 24 copy sets.

One simple rule kept the cost down: no text baked into the image and no lip sync. Language could then live in the voice-over, subtitles and end card, so four language versions became an edit rather than four shoots.

For AQPER, the format kit came from the two-minute hero film and Innergex's own film, so seven funders were served without seven productions.

We apply the same thinking to automated systems. We separate the parts that change from those that don't, so a new product, language or message can flow through the system without starting over.

Keeping quality and brand control.

More versions create more chances for something to go wrong. We build the checks into the system.

Testing what you produce.

A large number of variants is only useful if you can learn from them. If everything changes at once, you can't tell what worked. We group variants around clear questions, such as which hook, offer or format performs better.

We also watch for false winners. A variant with few impressions can look great by chance, so we wait for enough data before moving budget. Winning ideas then feed the next round, along with what we learned from those that lost.

How we measure it.

We look at the campaign outcome first, such as qualified leads or sales. We then compare production time and cost per asset, along with how quickly new creative can go live. Faster production is only a win if the ads still do their job.