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

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• 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.

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.

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.

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.

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.

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.

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+.

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+.