From buzzword to bottom line: Mastering machine learning for measurable growth

The buzz around Artificial Intelligence (AI) and Machine Learning (ML) is overwhelming, confusing, and has made skeptics out of all of us. We jumped right from the Web3 hype to the AI hype and did a phenomenal job of what we do best as an industry — we market ourselves. Well done, adtech.

So, how do we decipher what’s valuable? How can advertisers cut through the noise? Start at the end. To leverage the transformative power of ML is to focus on what matters most: delivering real business results. Once we anchor on what business results to drive, we can prioritize the changes required in the approach to data, measurement, and identifying value at every point in the customer journey. This focus allows us to decipher the immense potential of ML and learn how to harness its power for measurable business growth.  

First, let’s define the buzzwords

Terms like AI and ML often seem interchangeable, but there are important differences:

• Artificial intelligence: Think of it as the user experience (UX) and interface (UI). Artificial intelligence is what users interact with and is an overarching term that encapsulates the broad spectrum of advanced technologies enabling machines to mimic human intelligence.
  
  
• Machine learning: Think of it as the brain or the decision-making processor. Machine learning is the underlying mechanism that enables AI to learn from data and improve over time without being explicitly programmed.
  
  
• Operational machine learning: Think of it as expertise or input from a specialist. Operational machine learning delves deep into the complexities of specialized tasks, leveraging massive amounts of data and deploying autonomous decision-making that learns in real-time. 

Advertising with machine learning

With two-thirds of digital ad spending flowing to the three Big Tech platforms — Google, Meta, and Amazon — what gives them the edge? It's not merely about the volume of data they possess; it's their capability to deploy state-of-the-art ML engines that prioritize business goals and drive outcomes. These platforms have set the benchmark — their customer experiences, deeply integrated with ML-driven insights, result in advertising that is relevant for customers and ultimately achieves measurable outcomes for advertisers.

However, while these walled gardens have harnessed ML for years, players like Moloco are leveraging operational machine learning on the open internet to drive incremental results.

Advertising is increasingly shifting towards performance-driven marketing, with machine learning being the driving force behind this shift. The true challenge for businesses is to supply ML with real-time data and clear objectives to tap into its growth potential. While ML is a game-changer, marketers are still in the driver's seat. They must be on point, feeding the ML models with their first-party, real-time data and setting clear targets for the machine learning engine to operate at its best.

Reimagining advertising with machine learning

Gone are the days of rigid targeting based on cohorts. Today's ML-driven strategies dynamically adapt to customer behavior, catering to individuals’ ever-changing preferences and behaviors. A core aspect of this transformative technology is its ability to ingest, learn from, and ultimately deliver real business outcomes using first-party and real-time data.

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Diverse advertising objectives are leveraging ML, from serving the right streaming ad to promotional posts on a website to driving app installs and in-app actions. And in the competitive advertising space, where capturing customer attention is paramount, the power of real-time data cannot be understated. It's the difference between staying ahead of the curve and playing catch-up.

Let's look at the mechanics — ML has elevated how we drive performance by automating tasks that were once manually done. Now, everything is honed in at the individual level, in real-time:

• Target using first-party data, enriched with real-time customer behavior and contextual signals, to ensure precise and relevant ad delivery.
  
  
• Bid with advanced optimization, ensuring to pay just the right price to win the impression while maximizing ROI.
  
  
• Measure by tapping into real-time campaign insights, providing marketers with an in-depth understanding of their customer's behaviors that can be leveraged by their entire organization.

To do all the above, machine learning needs real-time data to solve real-time problems. Ben Kruger, CMO of Event Tickets Center and former Googler stated, "The role of a performance marketer is now 100% about giving the right signals, data, and assets to the machine. At this point, an account that is managed by a human intervention will lose out to one that is leveraging AI and automation."

Unlocking ML's potential: Data as the catalyst

How often have you encountered relevant and accurate ads for products on YouTube or Instagram immediately after searching for them elsewhere? In contrast, how often have you been barraged with banner ads for items you've already purchased or even something completely irrelevant? This stark dichotomy in customer experience is pivotal, distinguishing mere ad viewership from serving relevant, helpful ads.

The secret behind this success is two-fold. Firstly, there's an unwavering emphasis on outcomes. This isn't about splashing ads across screens, hoping for visibility. Instead, it's about ensuring each advertising dollar is translated into tangible, quantifiable ROI. Secondly, central to this strategy is real-time data. Platforms like YouTube and Instagram have fully grasped ML and understand that the efficacy of ML is intrinsically tied to the relevancy and immediacy of the data it's trained on. At its core, ML thrives on data.

Simply put, the richer and more real-time the data, the more precise and powerful the ML-driven predictions become. As customers navigate the digital landscape, their interactions — from merely browsing products to actions like downloading an app — serve as digital footprints that signal their preferences, behaviors, and purchasing intent. Advertisers need to fuel the ML engine with these customer touchpoints (first-party data) to unleash its full potential.

Optimize for the customer journey

By digitally mapping the customers' touchpoints, businesses lay the groundwork for machine learning success. This harnessing of ML provides unparalleled insights and actionable intelligence, enhancing decision-making and strategies. Driven by first-party data and powered by ML, this approach also ensures that every marketing dollar spent is optimally allocated and achieves measurable, sustained growth.

As businesses lean further into ML-driven marketing, choosing platforms that leverage businesses’ real-time data, deliver measurable outcomes, and align with clear objectives becomes paramount. Everyone says they do ML, so discernment is important. To help understand the capabilities under the hood, these are the set of criteria with questions to gauge who is delivering real value with ML: 

• Are they relying on traditional methods, like asking for manual inputs such as keywords and audience lists, or have they advanced into more automated solutions?
• Do they require a first-party, real-time data integration?
• Do they offer transparent reporting and insights?‍

The bottom line

ML is transforming advertising, and to navigate this tool successfully is through understanding and proactive engagement. Utilizing ML as a collaborative solution partner can be part of the pathway forward. The evolving advertising landscape involves a deeper alignment with ML, prioritizing clear goal-setting, and integrating real-time data — all aimed toward achieving measurable growth. It's important to recognize that this is a dynamic field, and the full implications and potential of ML are still areas of exploration and refinement.

Are you prepared to harness the potential of ML in your advertising strategies? Connect with us on how you can master machine learning for measurable outcomes.