First party data should not lie idle
Posted: Thu Jan 30, 2025 3:52 am
The real power of machine learning is in the data. Whoever has the most data always wins, right? Not quite.
The quality of the data is also crucial. It is no surprise that CRM integration tools such as 'Customer Match' are being touted so heavily by the big tech companies. The ad companies know the relevance and value of direct customer data.
Because what we ultimately want in online marketing are new users who are as similar as possible to our actual existing customers. In terms of behavior, interests, demographics. The hypothesis behind this is that these lookalike users tend to buy to the same extent as their targeting mirror images.
What we are looking for in online marketing are statistical twins of our customers
We want to find these users using statistical models in macedonia phone number data machine learning . We want to recognize patterns and put the puzzle together by recognizing a potential customer as such. If user X has the same characteristics as existing customer Y, there is a greater probability that we can convince this user to buy our product as well.
Only first-party data offers us this information value. Data from users who have already interacted with your own brand . Collected, for example, via website tracking, email subscribers, app engagements or digital checkout systems.
From a machine learning perspective, as described, it is about using data as input that reflects our hypothesis as closely as possible. However, the further the data is from the customer (third-party data), the more our hypothesis becomes blurred. The model becomes less accurate. It becomes unusable for making business decisions based on it.
“The answers are all out there. We just need to ask the right questions” – Oscar Wilde (writer)
At the end of the day, it's about being able to ask the right questions with the data you have. We ask about the value of individual variables and information and draw conclusions about the actions of new user groups based on conversions that have already taken place.
Owned Machine Learning therefore follows the approach of building and training our own effective models, which we can use flexibly at any time in conjunction with our own data. This gives us the opportunity to make better, faster and more well-founded decisions. In addition, target groups and projects can be prioritized in a more targeted manner.
The quality of the data is also crucial. It is no surprise that CRM integration tools such as 'Customer Match' are being touted so heavily by the big tech companies. The ad companies know the relevance and value of direct customer data.
Because what we ultimately want in online marketing are new users who are as similar as possible to our actual existing customers. In terms of behavior, interests, demographics. The hypothesis behind this is that these lookalike users tend to buy to the same extent as their targeting mirror images.
What we are looking for in online marketing are statistical twins of our customers
We want to find these users using statistical models in macedonia phone number data machine learning . We want to recognize patterns and put the puzzle together by recognizing a potential customer as such. If user X has the same characteristics as existing customer Y, there is a greater probability that we can convince this user to buy our product as well.
Only first-party data offers us this information value. Data from users who have already interacted with your own brand . Collected, for example, via website tracking, email subscribers, app engagements or digital checkout systems.
From a machine learning perspective, as described, it is about using data as input that reflects our hypothesis as closely as possible. However, the further the data is from the customer (third-party data), the more our hypothesis becomes blurred. The model becomes less accurate. It becomes unusable for making business decisions based on it.
“The answers are all out there. We just need to ask the right questions” – Oscar Wilde (writer)
At the end of the day, it's about being able to ask the right questions with the data you have. We ask about the value of individual variables and information and draw conclusions about the actions of new user groups based on conversions that have already taken place.
Owned Machine Learning therefore follows the approach of building and training our own effective models, which we can use flexibly at any time in conjunction with our own data. This gives us the opportunity to make better, faster and more well-founded decisions. In addition, target groups and projects can be prioritized in a more targeted manner.