Glossary
Our market and solution use many technical concepts, but our goal is also to democratize and explicate these notions to all.
A first-party cookie is a cookie managed by the website owner to track users' activities and remember preferences. It is opposed to third-party cookies which are not owned by the website but partners (advertising partners, for example). Ad blockers and browsers are blocking more and more third-party cookies, that's why first-party cookie is a great workaround to keep a continuity on tracking users.
It indicates the IP address of a given domain. 'A' for address, it is the most fundamental DNS record type (Domain Name System = website domain). By adding Commanders Act on your A-record, it allows Commanders Act to launch tags as first-party cookies, avoiding ad blockers and limitations regarding third-party cookies.
A client-side cookie (HTTP cookie, browser cookie) is a small piece of data that a server sends to a user's web browser. These cookies are stored on the browser.
A server-side cookie (session) is a unique Session Identifier stored on the browser and cookie information are stored on the server. The Session Identifier is used to match the request with the data stored on the server.
Intelligent Tracking Prevention is a feature on browser Safari (Apple) to restrict the use of cookies to track consumers.
With public cloud, companies don't have to purchase, manage and maintain heavy and expensive infrastructures. All of this is managed by third-party providers, and companies only pay for what they consume.
An ETL (Extract - Transform - Load) is a system that can extract the data from a source, transform the data (normalization, cleaning...) and load, insert the data to a destination.
A normalized data layer refers to ready-to-use data: after collection and normalization, the data quality is ensured, data is clean and ready to be sent to destinations.
From collected data, it is possible to enrich the data layer with computed data: create new properties from existing properties to create a sum, count, average, ratio... This allows to create high value properties to send to destinations for a better segmentation or personalization.
For example, it is possible to count the total value of purchases per user or the average basket value per user.
When an event is collected, it is possible to enrich it from stored data to add more information on the event before to send it to destinations. For example, a purchase event can be enriched with data coming from CRM, like hashed email address or any other relevant information. It can also be enriched with information coming from the product catalog, from the product ID we can add product details, color, weight, materials...
The goal of Identity resolution is to create a unified customer profile, whatever devices (desktop, mobile) or channels used (online and offline). To achieve this goal, we built a real-time proprietary reconciliation algorithm to create the complete view of customers.