.png)
Proprietary Bidding System: Petron Bids
.png)
Smarter bidding.
Measured results.
How PetronGroup developed Petron Bids: a bidding and budgeting system designed to combine Facebook campaign data with Google Analytics insights and execute decisions through the Facebook API.
From accurate measurement and product feeds to a carefully designed set of operating rules, this case study explains the approach and presents the original year-on-year results across four market segments.
Campaign performance
Business outcomes
Conceptual representation of the system described in the original case study.
A strong system starts with solid foundations.
Before introducing automation, the original project focused on the essential prerequisites for dependable measurement and a smooth customer journey. Even sophisticated bidding logic cannot make up for missing events, incorrect product identifiers or an unreliable checkout.
Facebook Pixel & Conversions API
Validate the measurement setup and key customer actions: page load, product view, add to cart, initiate checkout, enter payment information and purchase. These events help connect advertising activity with meaningful outcomes.
Accurate product catalog
For catalogs populated by XML or CSV feeds, verify that products are correctly imported and that the product IDs in the feed match the identifiers sent by the pixel.
Checkout and usability
Ensure the website works reliably, the ordering process is straightforward and buyers have useful payment and delivery options. A user-friendly e-shop is part of successful campaign performance.
From platform reports to actionable rules.
The team spent dozens of hours per month refining bidding rules. A key challenge was that decisions based only on Facebook Ads data did not reflect the desired performance view from Google Analytics. The system therefore needed to consider both sources.
The challenge
Campaign adjustments had to respect the learning behavior described in the original workflow while responding to performance measurements across different timeframes.
After developing the rules, the team used the Facebook API to connect an external decision system with the advertising platform. That integration enabled bidding and budgeting adjustments informed by the team's analysis.
The resulting proprietary workflow was named PETRON BIDS.
Original operating rules
These are historical rules reported in this case study, not universal recommendations for every current Meta campaign.
Collect data
Review Facebook campaign signals alongside Google Analytics business performance.
Apply bidding logic
Evaluate results using the project's reporting windows and change constraints.
Adjust through the API
Use the integration to manage campaign bids and budgets based on the analysis.
Four market segments. Real reported numbers.
The figures below reproduce the numbers published in the original Petron Bids case study. All traffic, transaction and revenue-growth comparisons are year-on-year and refer to Facebook-attributed activity. PNO figures are kept as originally reported.
Furniture
Slovakia · Year-on-yearFashion
Slovakia · Year-on-yearGifts
Czech Republic · Year-on-yearFurniture
Czech Republic · Year-on-yearFacebook revenue growth, year on year
A visual comparison of the four reported segments. Bars use the same scale; figures are historical rather than forecasted.
Chart scale: the largest reported increase (+231%) is shown as the full-length bar. Values have not been aggregated or averaged.
Better decisions begin with better foundations.
The Petron Bids project brought together data quality, operational rules and an API-driven execution workflow. These examples show how the team approached Facebook campaign management using more than one source of information.
The practical lesson is not to copy a specific bid adjustment. It is to make sure measurement, product data, website experience and campaign decisions work together.
Reliable events, catalogs and identifiers form the basis of useful decisions.
Consider campaign data alongside business results and relevant time windows.
Use a consistent decision framework instead of reactive changes.
This case study presents the historical figures published by PetronGroup. It does not report a controlled experiment or establish that any one change independently caused the stated revenue growth.
Ready to explore a more data-driven approach?
Tell us about your business, current advertising setup and target markets. We can discuss a practical path forward based on your goals and the services that fit your needs.