With changes in privacy and our increasingly digital habits, it becomes critical for businesses to redefine their approach to attribution. Improvements in technologies make it easier and more effective to adopt more stable attribution models.
Data-driven attribution can yield results with precision that other models don't using machine learning to analyze how different touchpoints throughout the customer's purchase journey affect conversion results, and the model distributes conversion credit accordingly.
Privacy Sandbox is a project to find an alternative way to develop alternatives to third-party cookies focused on privacy. In this way, it will be possible to provide companies with the information to show relevant content, but without having to follow it around the web.
Through cookies it is possible to show a message or a product to a potentially interested public by tracking the following behaviors interest-based and navigation.
This new approach is possible through strategies that aim to safeguard privacy:
- Instead of monitoring people on the web to find out what each of them might be interested in, users can be divided into large groups with similar interests.
- Instead of measuring how people react to ads in a way that can reveal their identity, you can remain anonymous by limiting the amount of data about them that is shared.
- Instead of collecting audience information while showing ads, companies can store this data on people's devices so they stay private.
Measurement of campaigns with the new guidelines
There is a decreasing in the conversions observability after the GDPR application. Without cookie using, the tracking is not able to measure that an event, an addition to the cart or a conversion has occurred.
Using observable user paths, the templates can fill in the missing attribution paths. This creates a more complete and accurate view of advertising spend and results, all while respecting the user's consent choices.
To take advantage of the algorithms and artificial intelligence of marketing channels, it is important to be able to correctly measure all the events that are generated on the platform. Smart bidding campaigns are automated through machine learning which requires a minimum number of signals to be able to optimize the algorithm well. The more signals you give the machine learning model, the more precise the focus on the correct audience will be.
Defining new strategies
Consumers' propensity for privacy and regulation are changing and finding an alternative to today's web tracking practice is critically important to help smart bidding work right.
Take advantage of solutions that use automation and machine learning to help you detect trends and define conversion patterns when there are gaps in available data.
Use your data wisely
The Customer Data Platform will become the beating heart (for some companies it already is) of marketing activities. Gaining knowledge about the behavior and historicizing the events of their customers is the extra weapon that will allow companies to scale their marketing campaigns.
The use of data has become an art where human ingenuity plays a fundamental role: knowledge of users and their behaviours allow you to create strategies and creativity of greater impact, while respecting the sensitivity of users.
Algorithms and machine learning therefore make it possible to achieve fine-tuning and the best configuration in delivery, where a completely manual planning would be unable to manage the thousands of variables involved.
The customers clustering, and the collection of events will almost certainly become the key to a new type of marketing campaign strategy.
Furthermore, the data can also be used to create loyalty programs that are more relevant to the purchasing behavior (predictive or current) of its customers.