What’s the outcome of using machine learning to model and plug gaps in data when users opt out of collection?
- Clients need larger volumes of data from customers who opted into data collection, to maintain accurate measurement and reporting.
- Conversions that are based on probabilities rather than actual purchases.
- Less accurate customer journeys that adhere to privacy regulations.
- A data set that represents a more accurate picture of website and app performance, while also remaining privacy safe.
Explanation:
Machine learning helps model and fill gaps in data when users opt out of data collection, resulting in a more accurate and comprehensive data set. This approach maintains privacy by using aggregated and anonymized data, ensuring that user behaviors are represented without identifying individuals. It allows advertisers to measure and optimize their campaigns effectively while adhering to privacy regulations.