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From insights to impact: The power of machine learning in advertising

Jimmy Morrow
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October 11, 2023

In a fast-changing digital world, marketers need transformative tools to maximize — and measure — profitability and Return on Investment (ROI). Machine Learning (ML) is increasingly pivotal in steering the course of advertising. Big Tech's success, from brand to performance to a blend of both, was made possible by their ability to develop and leverage advanced ML to drive measurable outcomes. And now, advanced ML is moving outside these Big Tech walled gardens, expanding its horizons into the vast open internet and unlocking new possibilities for advertisers.

Let's explore the applications of machine learning and how advertisers can take their performance to the next level.

A quick primer on machine learning

Machine learning is everywhere, fueling the breakthrough experiences reshaping our daily lives — from the generative AI in ChatGPT to the recommendation engine in Amazon to the generative fill in Adobe Photoshop.

At its core, Machine learning (ML) operates as the processor, or “brain,” behind Artificial Intelligence (AI). AI is seen as the user experience (UX), designed and directed to perform narrow tasks, while ML is the engine that understands our needs and determines how to meet them.

We go even further with operational machine learning, a specialized subset of ML. It deploys models that make autonomous decisions, impacting the business in real time. Operational machine learning drives tangible outcomes with speed, scalability, and adaptability. When change is constant in advertising — whether it's user behavior, policies, or macroeconomic headwinds — systems need to adapt to these changes on the fly. That's where operational machine learning can unlock immense value.

The vital role of machine learning in advertising

For advertisers, leveraging ML takes more than understanding advanced technology; it's about activating and connecting the potential to tangible advertising outcomes. Done right, ML enables:

  • Real-time decision-making: This allows a dynamic marketing approach in which campaigns operate on real-time data and adapt swiftly to consumer behavior and macroeconomic trends.

  • Outcome-based results: These optimize marketing strategies using first-party data inputs such as contextual signals, user behaviors, and campaign goals. With ML, campaigns consistently remain synchronized with changing goals to drive outcomes.

  • Optimized targeting: This ensures that targeting is grounded in real-time individual behavior rather than broad cohorts, allowing advertisers to engage users with dynamic pricing tailored to their unique ROI target, providing timely and effective engagement.

While the benefits of machine learning are clear, building an in-house ML solution is extremely hard for most organizations. It takes massive investment in terms of time, talent, and resources. That's why many marketers seek out dedicated ML-driven advertising platforms to deploy solutions quickly. However, advertisers need clear criteria to evaluate partners and solution providers based on their capabilities and fit with specific goals.

How to identify machine learning-powered solutions

The burning question is, how do you cut through the buzz around ML and determine if a solution would be truly effective? Here are three key considerations to keep in mind:

  • Real-time data integration: ML can leverage real-time data to solve real-time problems. A platform's inability or lack of demand for real-time data can indicate limitations in targeting and bidding capabilities.

  • Transparency in measurement: ML is exceptional at driving outcomes based on your campaign metrics. Real-time reporting and analytics are essential elements to help marketers understand performance and track ROI.

  • Compatibility with performance metrics: ML should leverage user data (i.e., behaviors, preferences) and advertiser goals (i.e., installs, in-app purchases, etc.) to continually optimize against key performance metrics that drive real business outcomes.

How Moloco drives growth using machine learning

At Moloco, operational machine learning is core in everything we do. Whether for a mobile app developer, a retail site, or a streaming platform, our vision remains the same: to harness the power of ML to drive growth and monetization for companies of all sizes. To date, we're serving customers in numerous businesses and verticals:

  • Moloco Cloud DSP: For mobile app performance marketers to quickly scale user acquisition and boost lifetime value through advanced prediction models.
  • Moloco Retail Media Platform: For retailers and marketplaces to build and grow their performance ads business.
  • Moloco Monetization for Streaming & OTT: For streaming media companies to build a scalable and profitable ads business that delivers results for their advertisers.

We've built our engine with a dedication to outcomes, and all of our solutions are predicated on the following: 

  • Autonomous adaptability: Systems designed to proactively adapt to the ever-changing advertising landscape, ensuring campaigns always remain relevant.

  • User relevance: Prioritizing the relevancy of ads to resonate with users ensures accurate targeting without compromising the user experience.

  • Scalable performance: Solutions that build for longevity and adaptability in catering to diverse campaign objectives.

  • Transparency and insights: Real-time reporting and close collaboration ensure that partners clearly understand performance metrics and are empowered to refine their strategies.

Ready to evolve your advertising strategy with operational machine learning? Contact us to explore solutions.

Jimmy Morrow

Director of Product Marketing, Moloco

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