About the Project

A local plumbing store owner in the USA was referred to us, specializing in the sale of plumbing fixtures. At the start, there was no historical data available — a classic launch from scratch.

Tasks

  1. Implement Facebook Conversion API to improve data collection accuracy and optimize advertising campaigns.
  2. Build an effective advertising infrastructure.
  3. Integrate Salesforce CRM to track which leads actually convert into sales.
  4. Find and test effective advertising combinations.
  5. Scale successful campaigns while maintaining ROAS.
  6. Achieve a stable return on advertising investment.

Targeting and Creatives

Broad Audience: Women and men aged 25-54 living in Ludington — users of Facebook and Instagram.

Creative Formats: For each segment, we used various formats: videos, carousels, and static images.

Instagram and Facebook Feeds: Static product photos.

Stories: Short animated videos with prices and specifications.

Reels: Video reviews of products and their features.

Segmentation by Order Value:

Low Order Value: Focus on basic models and discounts. Creatives featured photos of inexpensive products with emphasis on price and promotions. Call-to-action: “2 for the price of 1,” “Only X days left,” “Sale,” “Save X%.”

Medium Order Value: Emphasis on characteristics and reviews. Creatives included products with parameter descriptions. Call-to-action: “Learn about characteristics,” “Compare models,” “Read reviews.”

High Order Value: Priority on brand, quality, warranties, and service. Creatives showcased ready-made interiors and detailed demonstrations. Call-to-action: “100% warranty,” “Delivery in 24 hours,” “Premium quality.”

Execution of Work

  1. Preparatory Stage
    • Individual advertising accounts for each project.
    • Implementation of Facebook Conversion API and setting goals, creating audiences in Google Ads for further retargeting.
    • Integration of Salesforce Small Business for eCommerce.
  2. Advertising Campaigns
    • Separate advertising campaigns by demand types.
    • Look-Alike audience (LAL) for buyers: 1% for low and medium order values, 1.5% for high order values.
    • Retargeting on abandoned carts.
  3. Testing Creatives
    • Groups with different formats: videos, carousels, and static images.
    • A/B tests of texts and calls-to-action.
  4. Reporting and Automation
    • Daily metric monitoring and data export to a table.
    • Automatic rules for budget management.

Results

  • Revenue: $14,258
  • Total Costs: $3,301
  • Average ROAS: 4.32
  • AOV: $259
  • Total Number of Purchases: 55
  • Cost per Purchase: $60

Conclusions

  1. A well-structured campaign and precise segmentation by demand types allowed us to quickly find effective combinations.
  2. A Look-Alike audience of 1% for low and medium order values, and 1.5% for high order values, enabled effective campaign scaling even without historical data.
  3. A comprehensive analytics system and daily metric monitoring allowed us to disable ineffective campaigns, saving the advertising budget.