How Machine Learning and First

In the past, ad systems relied on basic heuristics, which can be effective for making immediate judgments, but often result in inaccurate conclusions. To really optimize for what advertisers and marketers care about — which is delivering custom marketing campaigns on the open internet that result in a hard ROI on their ad dollars — you need first-party data and a sophisticated machine learning (ML) platform that can optimize for return on ad spend (ROAS). Under the cover of a modern ML-based platform, there are many different ML models doing everything from predicting conversion likelihood to determining the best price to bid for an individual ad request.

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Activating your first-party data is more important than ever given the seismic privacy changes happening in the industry, including Apple's ATT and Google's Privacy Sandbox, which are making it incredibly challenging for traditional ad tech systems to adapt. ML-based approaches, however, have a distinct, almost magical ability to adapt to these changes faster and more holistically than what a vigilant technical team can do.

Developing first-party data sets isn't as easy as it sounds, however. Marketers need to be wary of the quality of the data that goes into machine learning models. These models have the capability to drive accurate and effective results; however, it can have an equal and opposite effect if brands are relying on static third-party data. To mitigate this, businesses need to invest in building and growing first-party datasets that ensure ads are being targeted more precisely and accurately to a relevant audience.

In performance marketing, it's critical to have confidence in the quality of data being used.

There is a famous saying in the machine learning world – garbage in, garbage out. Marketers should be reassured that there is no fraudulent data in their system and have the ability to remove such data – ensuring the model is being fed with quality inputs.

ML models make use of quality data that is a mix of contextual and behavioral signals that can help infer an individual's intent or interest in a particular ad. In general, if that data can help increase engagement for an ad, it is useful.

There are many types of useful data, and quality is largely determined by accuracy — for example exact location versus an inferred metro area; consistency, which requires having the same data available for every user or ad request; and timeliness, which relates to how often the data is refreshed.

The imminent depreciation of third-party cookies and improved privacy for device IDs mean that marketers and advertisers will be challenged to target consumers in a meaningful way. The good news is they have access to first-party data, which can be turned into gold if harnessed and used correctly.

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First off, it is important to understand what constitutes personally identifiable information (PII) in context of individual users. There are both intuitive and non-obvious ways that data can be PII, so it really requires a lot of thought and an overall strategy. Keep in mind that PII is not just how your product/service uses a piece of customer data, but the downstream potential for it to be combined with other data to identify individuals.

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Building a strong first-party dataset starts with having a system for collecting the data around your user journey and engagement activities in your products or services, including how customers shop, the brands they prefer to purchase, their site journey, pages visited, items clicked and navigation sequence, and organizing it into user profiles, segments, and audiences. Like what product managers need to build great products, marketers need to have a thorough understanding of their users, the user journey, and, ultimately, the value users derive from a product or service.

The next step is to integrate the data with other business systems (CRM or data warehouse) so you can gather insights through a mix of analytics, Mobile Measurement Partner (MMP), or business intelligence tools.

With the proliferation of cloud data warehouses, this doesn't have to be a massive initial effort since these platforms can scale to manage more complex use cases as your data grows.

In the past, marketers had to rely on human intelligence and manual optimization such as daily budget adjustments or pub throttling. With the advent of machine learning, those tactics no longer add value and quite often actually have a negative impact. It is extremely important to "let the machines do the work" and minimize any extraneous human interaction or data throttling.

In addition to human error, there are other factors contributing to the need for modern ML, including an explosion in the amount of data available, particularly as mobile device growth and usage are now at a peak; the sophistication in tools and systems for supporting large scale data processing in the cloud; and the sophistication in ML algorithms, particularly neural network-based ML.

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It's important to note that not all machine learning systems are created equally. To successfully leverage the power of the technology and achieve performance marketing goals such as ROAS, CPI, CPA, or revenue, the ML platform should include the following:

Another important aspect of machine learning is it allows marketers to develop privacy-safe approaches to doing relevant ad targeting, which is critical in today's privacy-first environment.

Machine learning can be used to construct more advanced behavioral cohorts that make it impossible to accidentally reveal PII information. ML models for targeting can also be run "on the edge" so that sensitive information never leaves a user's mobile device.

This is an exciting and innovative time for the industry. Advanced ML-based solutions are enabling advertisers of all sizes to develop privacy-safe or privacy-first approaches that deliver relevant ads, generate ROI, and accelerate their business.