Google Ads Case Study: Luxury Fashion Retailer #googleadscribe

Overview:

Many luxury fashion retailers make the ultimate fashion faux pas: neglecting Google Ads. Getting the most out of Google Ads campaigns requires a keen eye for detail and a willingness to dig deep into the data.

I’m going to share the secret to scaling internal brands while growing partner brands. I’ll reveal how I generated nearly $170k for one satisfied client using Google Ads.

Keep reading to find out the behind-the-scenes issues and the methods I used to fix them including:

  • Incorrect Primary and Secondary Conversion
  • Product Segmentation
  • No Experimentation
  • GMC Product Data and Promotions

 

By correcting these problems, I was able to turn an underperforming account into a

high-flying success story.

Fashion.Luxury.GoogleAds

Client Results

Check out these impressive stats: from April 12th to October 14th, we delivered $170k in revenue, with a 4.8 return on ad spend and 400 purchases to boot - all while keeping ad spend under $35k.  

The account was a bit of a mess when I took it over. Conversion tracking was way off, and bad data was ruining everything.

Fashion.Luxury.GoogleAds

Problem 1: Incorrect Primary and Secondary Conversion

So, let's dive into the results. The figures weren't quite adding up. The conversion tracking was way off, basically not tracking sales accurately.

Fashion.Luxury.GoogleAds
Fashion.Luxury.GoogleAds

So, we switched it up. We made Google Shopping app purchases the primary conversion.

And also added carts and checkout starts as primary conversions. This helped Google get more window shoppers and, in turn, affected the overall results of the account.

Fashion.Luxury.GoogleAds
Fashion.Luxury.GoogleAds

But that's not all - even page views (as seen above) were being counted as conversions, which totally inflated the conversion value and revenue. It messed with the account's performance and the cost-per-acquisition of new customers. So, switching that over was priority number one.

Problem 2: Product Segmentation

The client was focused on pushing their own internal brands, and for good reason - those profit margins are hard to resist. Plus, when the client is in control of their own brand, they can do so much more with marketing, pricing, and everything in between. They were even considering revamping their website to give their internal brands more love, but we had a better idea.

Fashion.Luxury.GoogleAds

We tested out a few different campaign types - a Performance Max (Pmax) campaign with segmented products was the clear winner. Before, they had just one campaign with all their products lumped together, not even sorted by brand type.

Fashion.Luxury.GoogleAds

By breaking it down and testing Pmax, we managed to score a 7x Return On Ad Spend (ROAS) and generate $28k in revenue while only spending $3,000. This was a big win – it allowed us to push those higher-margin products and really drive the client's results.

Fashion.Luxury.GoogleAds

We even used customer lists as a signal to target people who had already bought from them, so we could upsell them on their internal brands.

Problem 3: No Experimentation

Next, we wanted to explore ways to scale the account beyond just the internal brands. To do this, we conducted various tests, including running Discovery campaigns to create demand for certain product lines that previously didn't exist. Discovery campaigns, which display ads on the Google app, Gmail, and YouTube homepage, are useful for generating demand in short sprints. Later, we could retarget with Pmax, making it more likely to scale outside.

Fashion.Luxury.GoogleAds

Discovery ads help advertisers reach people who are ready to discover and engage with their brand. Discovery ads are displayed automatically on YouTube Home and Watch Next feeds, Discover and the Gmail Promotions and Social Tabs that use a single campaign.

Fashion.Luxury.GoogleAds

Fashion.Luxury.GoogleAds
Fashion.Luxury.GoogleAds

We also tested shopping campaigns (above), which helped us to bid on lower-funnel terms and pick up cheaper sales, ultimately expanding the account. Running these different campaign types was highly beneficial.

Fashion.Luxury.GoogleAds

Fashion.Luxury.GoogleAds

Then, we wanted to conduct some experiments on the Pmax side. Specifically, we tested the best sellers without assets, meaning no images, pictures, or videos, to see how they would perform. We also tested newer items, as well as bestsellers with assets.

Fashion.Luxury.GoogleAds
Fashion.Luxury.GoogleAds

The results showed that assets outperformed even best sellers without assets, which is not surprising given the visual nature of the fashion and beauty niche. The feeling and emotion are missing. It's essential to have a creative approach to nurture the funnel, rather than just competing on price. We also found that lifestyle imagery, target customer usage, and beneficial copy all performed well in testing signals around bestsellers and competitors in the marketplace.

Fashion.Luxury.GoogleAds

Additionally, we tested newer items with no audience signal testing to see how they would perform in the market, as there was no existing data on them. These experiments were all valuable in helping the account grow and avoid flatlining.

 

Fashion.Luxury.GoogleAds

Diving into the nitty-gritty of Campaign experiments, notice that some experiments, like PMax with new assets, show us that Performance Max outperforms standard shopping. We also run phrase-match-type experiments to A/B test and ensure our account is always improving and not stagnating.

Problem 4: GMC Product Data and Promotions

Moving on to Google Merchant Center optimization, we make sure to keep promotions fresh and relevant. This ensures the account is getting the proper amount of conversion clicks. For instance, we recently implemented a fall sale heading into Q4, capitalizing on the promotional mindset of customers.

Fashion.Luxury.GoogleAds

This is crucial to ensuring our products are up-to-date and appealing to customers. Additionally, we closely monitor Google Merchant Center to avoid unnecessary product disapprovals. Machine learning can sometimes flag products incorrectly, so we stay on top of it to prevent this from happening. By appealing and addressing these issues, we maintain a high-performing account.

Final Word

By meticulously analysing Google Analytics and Google Ads conversion tracking issues, we were able to identify and address pesky discrepancies. Next, we dived into product segmentation, optimising ROAS with Performance Max. We didn't stop there, though. We experimented with Shopping, Discovery, and Asset groups, taking this account to new heights. Finally, some TLC was given to Google Merchant Center optimisation and promotions, resulting in a healthy boost to overall performance. Remember: a well-oiled advertising machine requires regular tune-ups. Stay curious, stay vigilant, and keep optimising.

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About the Author

Ted is the founder of TGQ Marketing a PPC, Analytics and CRO agency focused on client results.