AI-Powered Predictive Modeling in Retail

Improve revenue impacting KPIs like conversion rate, average order size, and retention rate using predictive AI.

In the rapidly evolving retail industry, one thing remains constant: the need to understand and anticipate customer behavior. Predictive modeling, powered by AI, has become a game-changer in this space, offering retailers the tools they need to enhance customer experiences, drive sales, and optimize operational efficiency. By leveraging AI-based predictive models, retail businesses can stay ahead of the curve and meet the demands of today’s dynamic market.

Today’s retailer knows that recommendation systems are pivotal in guiding customers toward the most relevant content. These systems leverage advanced data science techniques like collaborative filtering to provide a more personalized customer experience, improving consumer happiness and your bottom line.

The Role of Predictive AI in Retail

In the retail world, predictive modeling often includes predicting customer preferences, shopping patterns, and demand trends, which in turn enable retailers to tailor their offerings and strategies more effectively. With advances in AI, these models have become even more powerful, allowing for highly personalized and real-time decision-making that impacts both customer experience and bottom-line performance.

AI Powered Recommendations: Yieldmo & Kumo AI

Yieldmo is a digital advertising platform that helps brands invent creative experiences through tech and AI, using bespoke ad formats, proprietary attention signals, predictive format selection, and privacy-safe premium inventory curation. Yieldmo believes all ads should be human-centered, tailored, and provoke users' emotions and actions. Yieldmo helps brands deliver the best ad for every impression opportunity, merging creative and media for proven results.

With the digital advertising and retail industries moving away from third-party cookies due to increasing privacy concerns and regulatory changes, companies are seeking alternative methods to reach their target audiences. Major web browsers, such as Google Chrome and Apple Safari, are phasing out support for third-party cookies to enhance user privacy and data protection. This shift requires advertisers and digital platforms to adopt new strategies and technologies for audience targeting as ad buys are increasingly likely to rely on ID-independent targeting solutions.

Yieldmo chose to partner with Kumo AI to improve audience targeting and enhance ad performance. Offering a solution that could seamlessly integrate with Yieldmo’s existing infrastructure, which included Snowflake as its central data warehouse, various data processing pipelines, and multiple ad-serving platforms, Kumo provided the necessary tools to improve ad relevance and effectiveness while navigating the transition away from third-party cookie-based audience targeting.

This solution would ultimately need to take the form of an “audience recommendation system” wherein Yieldmo could input a series of known users, and output a recommended set of targetable assets (URLs, placements) that were not dependent on cookie-based targeting.

Kumo AI’s Impact on Yieldmo

Kumo AI provided Yieldmo with a comprehensive solution to address its ad targeting challenges:

• Introduced graph neural networks (GNNs) and link prediction models to improve recommendation systems
• Mapped relationships between users and URLs, increasing targeting accuracy by 20%
• Used graph transformer architecture and embeddings for deeper understanding of user behavior
• Improved downstream model performance by 5–10% through better input data

The Benefits of AI in Retail Predictive Modeling

The impact of predictive modeling in retail extends beyond personalized recommendations. AI-based models can drive improvements across the entire retail value chain, from optimizing inventory management to anticipating demand and reducing waste. For example, by analyzing purchasing patterns, retailers can forecast demand spikes, ensuring they stock the right products at the right time. This is particularly valuable in categories where product availability can fluctuate depending on seasonality or local preferences.

Common AI-Powered Use Cases for Retailers

• Marketing campaign optimization
• Segmentation
• Content recommendation
• Website personalization
• Churn prediction
• Fraud detection
• Forecasting
• Price optimization

In addition, Kumo AI’s ability to automate and optimize various processes reduces the burden on data science teams, freeing them up to focus on higher-value activities.

For retailers looking to enhance their customer experience and improve operational efficiency, AI-powered predictive modeling is no longer a luxury—it’s a necessity.

Conclusion: The Future of Retail Lies in AI

As retail continues to evolve, businesses must embrace advanced technologies like AI to remain competitive. Predictive modeling, powered by sophisticated AI tools, allows retailers to make sense of massive amounts of data, predict customer behavior with greater accuracy, and deliver highly personalized shopping experiences.

With companies like Yieldmo leading the way, it’s clear that the future of retail will be shaped by businesses that leverage AI to not only meet but exceed customer expectations.

Kumo AI’s partnership with Yieldmo serves as a blueprint for how AI can transform retail, offering significant improvements in personalization, scalability, and cost efficiency.

By tapping into the potential of AI-driven predictive models, retailers can unlock new opportunities for growth and innovation, setting themselves apart in an increasingly competitive marketplace.

Learn how AI can unlock revenue growth by visiting Kumo AI at Tech for Retail booth: B104

Jure Leskovec, Chief Scientist, Kumo AI