Marketing to the Gen Z audience: Image Rebranding with Generative AI

Sphere’s Marketing Copy generates fast, intelligent copy for moments in life when words don’t come easy. It effortlessly curates captivating social media, advertising, and slogan copies, giving you curated ideas in the blink of an eye.

Rebranding is a great way to reinvent household names. But sometimes, things don’t go as planned. Take Tiffany & Co.’s controversial 2021 marketing campaign for example.

The brand is traditionally associated with classic jewelry pieces, and not usually referred to as “trendy”. In an effort to shift away from that perception and rebrand themselves as edgy and cool to appeal to the younger generation, they launched a campaign with a tagline “Not Your Mother’s Tiffany” — but in doing so, seems to have excluded its original (and older) clientele, resulting in some backlash.

But we totally get it. One of the toughest challenges brands face is having to reinvent themselves to appeal to different generations. The marketing campaigns that worked for the Baby Boomers might not achieve their desired results when it comes to Millennials, so there’s a constant need to understand what each generation responds well to.

Who’s to say if Tiffany’s campaign worked or not? It’s certainly turned heads and generated some buzz, but whether it’s done more harm than good to the brand’s image remains to be seen.

With these lessons in mind, we used Sphere’s Marketing Copy app to create a range of effective and personalized copy for Gen Z consumers, using the popular brand Marks & Spencer as a case study.

Known for being a leading British retailer that offers a wide range of clothes, homeware, and food, some consumers tend to remain loyal to the brand because they associate it with quality. We explore how its retail section can continue to appeal to a younger audience that’s become notorious for brand hopping.

Here’s what our generative AI came up with:

Request a Sphere demo and get an endless flow of natural, targeted copy through machine learning.