AI Advertising Trend

Introduction

Marketers are constantly working to ensure that the right products are presented to consumers at the right time. The advancements in artificial intelligence (AI) have revolutionized the advertising industry, making personalization a necessity for marketers and customers alike. AI-powered product recommendation advertising has played a crucial role in guiding consumers through their purchase journey.

Machine learning algorithms have the ability to analyze vast amounts of historical data and predict the products that users are most likely to be interested in. This has led to a reduction in irrelevant ads and a more personalized advertising experience for consumers. However, the potential of AI in advertising goes beyond current personalized product recommendation ads. There is an opportunity to create hyper-relevant, contextual ads that have a greater influence on consumers.

Understanding How Machine Learning Powers Personalization

Marketers have long been overwhelmed by data, but with the advent of AI, they now question whether they have enough data, the right data, and if they are utilizing their data to its fullest potential. The democratization of AI through ad tech platforms has changed the game. AI can process large amounts of data, allowing marketers to understand users at an individual level and serve highly relevant ads. In the realm of AI-powered advertising, no amount of data is too much.

Machine learning models are trained to identify patterns within vast historical data sets, including online shopping behavior and even offline point-of-sale data. These models then predict the best outcome, which in this case is the product that each user is most likely to purchase next. The resulting ads are tailored to each individual recipient and are optimized to generate the desired outcome, whether it be a click or a purchase. The feedback generated from each outcome improves the performance of the model.

The beauty of AI lies in its automation, but it is crucial for marketers to understand key elements of this process:

1. AI Benefits Advertisers and Consumers:
2. AI allows marketers to maximize the value of their data and their budgets.
3. Machine learning models can be optimized to meet specific objectives, resulting in ads that provide the most value for ad spend.
4. Hyper-relevant ads offer true 1:1 offers, content, and ads.

5. AI-powered product recommendation ads help consumers discover new products that they are most likely to want to buy.

6. Understanding Hyper-Relevance: From Product Recommendations to Design:

7. Personalized ads offer products that are likely to interest users, but hyper-relevant ads take context into account.
8. By using machine learning to analyze a user’s past ad interactions and browsing history, ads can be customized based on factors such as color, format, and channel.

9. AI has the capability to serve truly unique ads to every user.

10. AI is Only as Good as Your Data:

11. Accurate data is essential for effective AI.
12. High volumes of data can lead to poor-quality data slipping through the cracks, which can have a significant impact when AI is involved.
13. It is important for marketers to address any data quality concerns and work with trusted partners to ensure the accuracy of their data.

AI-Powered Product Recommendation Advertising: Where We Are Now

Paid display advertising is currently the most widely used global ad tactic, according to research from The State of Ad Tech 2019 report. Product recommendation ads play a crucial role in guiding consumers through their buyer journey and bringing them closer to making a purchase. However, the effectiveness of product recommendation advertising depends on the sophistication of the recommendation model and the power of the underlying machine learning algorithms.

Recommender systems have evolved significantly over the years. Early recommendation models relied on explicit feedback from users, such as product rankings and reported preferences. However, recent advancements in AI and computing power have allowed recommendation models to shift towards implicit feedback, which takes into account user engagement and purchase behavior. Additionally, product recommendations have become more granular, extending beyond product categories to the SKU level.

At Criteo, our AI Lab is dedicated to researching and testing recommendation models to achieve hyper-relevance. The focus of these models is on the customer’s path to purchase, rather than the products themselves. The aim is to predict what users want to buy before they even realize it themselves. Evolved recommendation models not only benefit advertisers by helping them meet their goals and add value to the consumer’s digital experience, but they also improve user engagement and satisfaction. According to Accenture Interactive, 91% of consumers are more likely to shop with brands that recognize them across channels and provide relevant offers and recommendations.

What’s Next for Recommendation Engines? Factoring in Causality to Improve Efficiency

As marketers look towards the future, they are eager to know what’s next in AI-powered advertising. At Criteo’s AI Lab, we believe that the next generation of recommendation systems will go beyond correlations and incorporate the causal effects of product recommendations on users.

Current recommendation engines rely on correlations between user data and product recommendations. However, future recommendation systems will not only consider historical user data but also take into account the impact of each product recommendation on user behavior. This data will enable algorithms to update future recommendations in real-time, maximizing their impact on consumers. Marketers will gain a deeper understanding of how their products influence consumer behavior through ads.

Although our causal recommendation model is still in the testing phase, we anticipate that it will revolutionize product recommendation advertising. Recommendations based on causation will enhance the efficiency of ad budgets and provide valuable insights into user behavior. Ultimately, this will drive more sales and reduce the proportion of irrelevant advertising that reaches users. For consumers, hyper-relevant ads generated through this new model will be more diverse, surprising, and inspiring, guiding them towards new products they want to purchase.

Unleashing Your Data’s Potential

AI has the power to activate data for companies of all sizes. With an ad platform powered by advanced AI, businesses can compete with larger companies when it comes to personalization and the customer experience. Criteo is committed to investing $23 million over three years in our AI Lab to help companies unleash the potential of their data. Our AI Lab, combined with Criteo Research and Machine Learning Platform Engineering teams, strives to integrate state-of-the-art AI research into our production systems, driving innovation in the industry as a whole.

The future of marketing lies in leveraging shopper data to create hyper-relevant ads, enhance user experiences, and foster stronger customer relationships. To learn more about the research and publications from our AI Lab, visit ailab.criteo.com, and stay tuned for more updates on our research in causal recommendation.