Today, thanks to advanced ML-based technologies. Marketers and businesses of all sizes can create privacy-first or privacy-safe strategies that deliver relevant ads, produce ROI, and boost their business growth.
Ad systems in the past depended on fundamental heuristics, which can be useful for making snap decisions but often led to incorrect results. First-party data and an advanced Machine Learning (ML) platform that can maximize Return On Ad Spend (ROAS) are required to optimize for what advertisers and marketers care about truly. Many distinct ML models are operating under the hood of a modern ML-based platform, performing everything from estimating conversion potential to figuring out the best price to bid for a specific ad request.
Given the seismic privacy changes in the industry, which are making it exceedingly difficult for traditional MarTech platforms to adapt, activating first-party data is more crucial than ever. However, ML-based techniques clearly have a faster and more comprehensive ability to adapt to these changes than a vigilant technical team.
But it’s not as simple as it sounds to develop first-party data sets. Marketers should be cautious about the quality of the data used in ML models. These models have the potential to drive effective and accurate results. However, if brands rely on static third-party data, it can have the opposite effect. To mitigate this, businesses need to invest in developing and expanding first-party datasets that ensure ads are being targeted more precisely to relevant audiences.
Also Read: Increasing Business Resilience Through Data Maturity
The Key to Producing High-Quality First-Party Datasets
ML models use quality data that is a combination of contextual and behavioral cues to infer a person’s interest or intent in a certain advertisement. The data is generally helpful if it can raise the engagement level.
Having faith in the quality of the data being utilized in performance marketing is crucial. The system should be fed with high-quality inputs, so marketers should ensure that there is no fraudulent data in their system and that they have the ability to remove it.
Leveraging Sophisticated Machine Learning to Unlock the Power of Data
Previously, marketers had to rely on manual optimization methods like daily budget adjustments and human intelligence. With the development of Machine Learning, these strategies are no longer useful and often even have a detrimental effect. Automation of processes is crucial to reduce unnecessary human interaction and data throttling.
Also Read: How Businesses Can Build a Customer-Focused Strategy
In addition to human error, several other factors contribute to the need for modern ML. These include an explosion in the amount of data accessible, especially since the growth and usage of mobile devices are now at an all-time high, and the sophistication of ML algorithms, mainly neural network-based ML, and the sophistication of systems and tools for supporting large-scale data processing in the cloud.
Machine learning also offers marketers the opportunity to create privacy-safe methods for relevant ad targeting, which is essential in today’s privacy-first world.
Machine learning can be used to create more complex behavioural cohorts that prevent the unintentional disclosure of PII data. ML models for targeting can also be performed on the edge to ensure that sensitive data never leaves a user’s mobile device.
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