The Engagement Platform for Your Sustainable Growth.

1st party data is data collected by the company itself on its own platforms and in exchange with prospects and customers. For example, from web analytics or product feed.
3rd party data, on the other hand, is provided by a third party for a fee. The collection of 3rd party data is often done on your own platform by integrating a tracking pixel.

Cookieless literally means "without cookies". ODOSCOPE does not require cookies or cookie detection to display user-specific personalization in real time. ODOSCOPE works immediately even for unknown and new users.

In martech sector, however, the term is often used to promote technologies that use "only 1st party cookies" instead of also 3rd party cookies. However, these solutions are still based on cookies.
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ODOSCOPE only sets a session cookie to keep a visit in the respective A/B test group: ODOSCOPE group vs. control group in case of a return visit. The display of any personalization is independent of this.

A look-alike audience refers to a groups of similar users that are dynamically determined from a database based on the data points and behavior of the current user. A look-alike audience is intended to make statistically probable statements about the current user's interests and behavior so that personalized content can be served based on an individual look-alike audience.

In contrast to dynamically identified look-alike audiences, there are predefined segments into which users are divided according to their behavior and for which certain rule-based playout is also predefined.

Personalization is the term for the individual approach and presentation of content, products or messages to individual users, customers and people. ODOSCOPE understands personalization as individual, dynamic and flexible. That is why ODOSCOPE does not use rules and predefined segments that are not individual, but treats all users in the respective segment in exactly the same way.

The most powerful lever for uplift in ecommerce is the search or product list like on category overview pages. Many products on many pages appear here. Users look to these product lists or category pages for inspiration. It is therefore essential to show relevant products immediately on a category page, otherwise a user will leave the store after a few seconds.
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ODOSCOPE's customers often achieve sales and purchase uplifts of more than 10% by displaying personalized product lists sorted by individual relevance (and often more than 20% on mobile devices. Here the prominent display of the most relevant products on the small screen is even more critical.

Data activation is the use of data points to determine a (personalization) decision such as a specific selection, sorting of products, news, images, search results, recommendations, etc. according to individual relevance. ODOSCOPE works with real-time data activation, which adapts flexibly and dynamically to the user's current situation with every click.

A data lakehouse is a central platform for multiple data sources and their metadata. But unlike a traditional data warehouse (DWH), which is designed for SQL queries and traditional analytics, data is imported directly into the data lakehouse without a potentially error-prone and time-consuming extract, transform, load (ETL) process. A data lakehouse can store very large volumes of structured, semi-structured, and unstructured data in the same system, enabling machine learning applications and making data teams more efficient by eliminating the need to query and join data across multiple systems.

Situationalization refers to a user's current situation. It matters a lot whether someone is on the subway during rush hour surfing on a cell phone, at work on a desktop PC during the day, or at home on a tablet on the sofa on a Sunday evening. Each situation is different. So is user and customer behavior. How different that behavior is, and whether it differs more by time of day, device type, geography, or many other data points and combinations thereof, is contained in the shop's historical data and is determined for each user and click by real-time analysis.