Luxury Retailer - Case Study

How we reached over 1,000,000$ in revenue from online sales.

Before UAWC

Revenue from online sales


ECommerce conversion rate



Revenue from online sales


ECommerce conversion rate


Hero Data
Case Image

At UAWC, we are always excited about testing our expertise in new directions.

With this client, we had the opportunity to work with an old, reputable, established brand.

Read this case study to learn how we made their business even better.



Increase online sales

While the client's products were extremely popular in offline stores and boutiques, their online presence was lacking. We needed to change that.


Scale digital channels

We needed to expand and optimize the client's digital marketing to help them carve out their piece of the online market.


ECommerce tracking

We needed to make sure the attribution was set up correctly, the metrics were relevant, and the KPIs were consistent across all platforms.

Advertising channels


The Road to Profits

Luxury retailer case study

The Challenge

Despite the client's long history and their significant investments in digital advertising, they had several critical issues.

First of all, their tracking and reports were not well-configurated. This made it hard accurately evaluate the performance of their campaigns.

Aside from that, their online purchases were on the decline.

We needed to fix all that.

The Strategy

Right away, we needed to fix the client's tracking and reports.

We needed to improve brand awareness. While the brand was well-known to fashion enthusiasts in the offline world, we needed to put their name in the minds of a wider online audience.

We also needed to bring relevant, convertible traffic to the client's website in order to increase purchases and revenue.

For that, we used the following channels:

  • Google Ads;
  • Pinterest Ads;
  • Bing Ads;
  • Facebook Ads;
  • Reddit Ads.

The Results

Luxury retailer case study

Google Ads

On Google Ads, we used the following:

  • SKAG structure for Search Campaigns.
  • Separate Smart Shopping Campaigns for different product categories based on the product price.
  • DSA (dynamic search ads) Campaign for TOP Pages.
  • Display Remarketing Campaign structured based on URL condition (male, female products, sale page, etc).
  • Video campaign for Brand Awareness purposes.
  • Tested different destination URLs based on GA insights.
  • Experiments with bidding strategies. Target ROAS performed the best.

Bing Ads

On Bing, we implemented the following:

  • SKAG structure for Search campaigns.
  • Separate Shopping campaigns for different product categories based on the product price.
  • Tested different destination URLs.
  • Performed additional keyword research for Bing purposes.
  • Utilized all ad extension types.

Facebook Ads

Here's a list of our experiments on Facebook:

  • Website Conversion campaign and Catalog Sales campaign with cross-exclusions of the Audiences to avoid overlapping.
  • Tested different variations of the warm audiences as well as cold audiences based on interests and lookalike audiences.
  • Optimization for Purchase events performed better compared to add-to-cart optimization.
  • The best-performing audience turned out to be Men, Age 35+, and lookalikes based on customers' email lists.

Pinterest Ads

And here is what we did on Pinterest:

  • Website Conversion campaign. Targeting based on keywords and related interests.
  • Catalog Sales campaign. Targeting based on Lookalikes and Warm segments performed the best.
  • The segment that performed best was Men&Women, Age 35+.

Reddit Ads

And on Reddit, we used the following:

  • Website conversion campaign. Targeting Cold and Warm audiences based on Interests with exclusions to avoid overlapping.
  • The best-performing ad set turned out to be Subreddits.
  • The segment that performed the best was Men, Age 35+.

As a result, we achieved:

  • 39% increase in revenue from online sales - from $782,178.28 to $1,087,227.81.
  • 22% increase in revenue from Facebook Ads - from $2,230.69 to $ 2,721,44.
  • 37% increase in traffic, from 116,293 to 159321 website visitors.
  • 37% increase in new users, from 101,874 to 147,252.
  • 28% increase in average time on page, from 00:03:25 to 00:03:32.
  • 8% increase in conversion rate, from 0.99% to 1.07%.
  • 23% increase in transactions, from 2,152 to 2,324.

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