Facebook Conversion API (further as FB CAPI) is a way to avoid losing data due cookies blocking, browser loading errors, ad blockers, iOS 14+ changes and other effects that have impact on your Facebook campaign performance. Soon the most widely used web browser Google Chrome is going to limit 3rd party cookies which will affect measured data via Facebook pixel even more. Use FB CAPI to mitigate these issues and get even more benefits:
FB is losing up to 15 - 20 % of signals today, and this will worsen with Google chrome limitation of 3rd party cookies.
By using FB CAPI together with the FB pixel you can reduce your cost per incremental conversion by 7–27% as per official case studies by Facebook.
Data from Conversion API is more resilient than the pixel by browser (loading errors, connectivity issues and ad blockers).
Level Up your measurement by exploiting benefits of Offline FB CAPI, which can help you to measure offline return to your ad spend, show ads to people based on actions they take offline and more.
Optimize your campaigns on better data such as on offline conversions, margins or customer scoring.
We find out your needs and expectations regarding FB CAPI
We agree on cloud provider that suits the best (such as Google Cloud Platform, Amazon Web Services)
We agree on events that you would like to be measured server-side
Implementation of FB CAPI and testing
DNS A record creation on web hosting level (necessary step done by the client regardless implementation type)
Monitoring
Implementation Packages does not cover subsequent infrastructure costs which differs based on number of hits on your website and cloud provider selected. Monthly infrastructure cost is usually around $100 (medium sites, SMBs) – $250 (larger sites with heavy traffic). Infrastructure costs are paid directly to the cloud provider (Google, Amazon).
Implementation price is per website. If you have a larger number of identical websites (eg language/country versions), please contact us for a custom quote.
Facebook Conversion API (FB CAPI) provides you with the option to directly connect your marketing data with Meta systems. Which may help you to optimize ads targeting, descrease cost per action and and have your campaign outcomes measure out properly.
There are currently three options for FB CAPI implementation:
It usually depends on flexibility / capacity of your IT team and your needs. If you have an Amazon Web Services account and you want to test Facebook conversion API and have it quickly ready, it may be a good start to try Facebook Conversion API Gateway which is a self-serve option in Event Manager.
Event Match Quality (EMQ) is an indicator of effective matching of your customer information send from your server with event instances in your Facebook account.
Facebook calculate the score out of ten based on the quality match. In Event Manager you can then see one of the four results of it: Poor, Ok, Good, Great.
Official Facebook recommendation is to aim at Good or Great score.
Advanced matching parameters helps you to reach higher Event Match Quality. Here are a few examples of parameters that may increase Event Match Quality score:
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Obratem se ozveme a domluvíme se s čím vám můžeme pomoci.
Bezplatný data-driven atribuční model (DDA) je dostupný v GA4 od ledna 2022 pro všechny uživatele – na rozdíl od původní verze Google (Universal) Analytics, kde byl model DDA dostupný pouze zákazníkům s placenou verzí GA360.
DDA je algoritmický atribuční model, který kvantifikuje hodnotu každého touchpointu v rámci cesty zákazníka (jako je kliknutí na kampaň).
Výhody DDA modelu v GA4 oproti původním pohledům:
Dostupný DDA model vylučuje téměř všechny přímé návštěvy – neuděluje jim tak podstatnou váhu. Ať už se nám takové nastavení líbí, nebo ne, doporučujeme ho vždy – stejně tak jako jakýkoliv jiný atribuční model – ověřit pomocí marketingových experimentů.
Google BigQuery je efektivní a vysoce škálovatelné cloudové datové úložiště optimalizované pro vysoký výkon i v rámci práce s objemnými datovými soubory. Z GA4 můžete exportovat všechna svá data na úrovni událostí do BigQuery a následně zde zpracovávat další analytické nebo data science iniciativy.
Příklady použití mohou být:
Pro tento účel budete potřebovat nastavený a spravovaný účet Google Cloud. S tímto sestavením a následným představení daného rozhraní a celé infrastruktury vám rádi pomůžeme!
Napište nám. Obratem se vám ozveme a domluvíme se na dalších krocích.
Napište nám. Obratem se vám ozveme a domluvíme se na dalších krocích.
Napište nám. Obratem se vám ozveme a domluvíme se na dalších krocích.
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One of the new and exciting features of GA4 is called “predictive metrics” – with these you can learn about your customers and their shopping behavior. These are added automatically to your data and are based on Google’s machine learning expertise.
GA4 currently includes the following predictive metrics:
Google BigQuery is a cost-effective and highly-scalable cloud data warehouse optimized for high performance on very large data sets. With GA4 you can export all your event-level data to BigQuery for additional analytics or data science initiatives.
Example use cases might be
Example use cases might be
You will need to have a Google Cloud account set up and maintained for this purpose. That is why we are here and can solve all the infrastructure for you and with you!
Since 1/2022 GA4 has made a data-driven attribution model available to all users – unlike in Universal Analytics where the DDA model was only available to GA360 customers.
DDA is an algorithmic attribution model that quantifies the value of each touchpoint in the user journey (such as campaign click) and it does so by smart modelling behind the scenes without human bias that is always present in rule-based models.
The DDA model in GA4 is also better than the one on GA360 as it takes into account up to 50 touchpoints in the user journey vs. the GA360 only took 4 touchpoints.
It is also possible to set the default attribution model to DDA (or another model) from the old Last Non-Direct Click. And then all your reports and data exported to BigQuery will use the newly selected model as default.
DDA in Google Analytics 4 excludes almost all direct visits from receiving credit – you may or may not like that, but as it’s been always the case with attribution we recommend validating any model using marketing experiments.
GA4 is built for a world where more and more users opt out of cookie consent and other methods for data collection.
Google uses machine learning modeling to fill in the data gaps – some of these are already in the current GA4 others will be deployed in the future.
For example, modelling conversions allows GA4 to properly attribute conversions without user identification – this is crucial for optimized advertising campaigns and automated bidding. This covers situations such as some browsers limiting the time window for first-party cookies, conversions for unconsented users, Apple’s App Tracking Transparency (ATT) impacts, cross-device user behaviour, and others.