AWS for Industries

Accelerate Marketing campaign planning by 3x with Treasure Data AI Agents powered by Amazon Bedrock

Marketing teams face significant challenges when planning and executing campaigns across multiple channels. Traditional campaign development requires months of coordination between systems and teams for hypothesis creation, audience analysis, journey mapping, content development, activation, and measurement. This lengthy process causes brands to miss critical moments when customers are ready to engage.

Treasure Data’s customer data platform (CDP) serves major brands globally, managing customer profiles that represent a significant portion of the internet-connected population. Working with Amazon Web Services, Inc. (AWS), the company leveraged Amazon Bedrock to create AI-powered solutions for marketing teams. Amazon Bedrock provides fully managed access to high-performing foundation models for building generative AI applications, allowing organizations to deploy AI agents that understand natural language instructions and interact with various systems autonomously.

This blog explores how Treasure Data’s AI-powered offerings, built on Amazon Bedrock, transform campaign creation from a months-long process into hours or days. These solutions enable marketing and CX teams to respond quickly to market opportunities and deliver personalized experiences at scale while maintaining the security and governance standards required by enterprise customers.

Building AI agents on a foundation of trusted data

The true power of AI lies in combining advanced foundation models with high-quality customer data. The integration between Treasure Data’s platform and Amazon Bedrock enables marketers to analyze customer data quickly, generate targeted audience segments, create detailed personas, and make data-driven decisions without requiring technical expertise. This combination reduces campaign creation time dramatically while improving targeting accuracy and campaign performance.

Collaborative development with AWS

Treasure Data worked closely with AWS to identify key bottlenecks in traditional campaign planning and execution processes. Rather than simply adding a chat interface to existing tools, the collaboration focused on redesigning fundamental workflows to maximize AI effectiveness.

The partnership emphasized finding the right balance between human expertise and AI capabilities. Marketing professionals retain strategic oversight while AI agents handle time-intensive analytical tasks. This approach required building agents that could process complex data relationships and provide actionable insights grounded in actual customer behavior.

The collaboration resulted in a multi-agent framework built on Amazon Bedrock that addresses specific marketing challenges while maintaining the security and compliance standards required by enterprise customers.

How Amazon Bedrock Powers This Innovation

Treasure Data selected Amazon Bedrock as the foundation for its AI agents because it enables rapid deployment without sacrificing control or security. Amazon Bedrock simplifies model selection, allowing teams to access advanced foundation models without requiring specialized data science expertise.

The fully managed platform enables quick deployment to production environments without building custom infrastructure from scratch. Customer data remains private and secure within the shared responsibility model between AWS and customers. AWS secures the underlying infrastructure while customers maintain control over their content and access controls.

The combination of Treasure Data’s customer data expertise and AI foundation models, provided by Amazon Bedrock, enables organizations to scale AI initiatives while maintaining security and governance standards.

Meet Treasure Data’s Specialized AI Agents

Treasure Data has developed several purpose-built AI agents, powered by Amazon Bedrock, to address specific marketing challenges. Each agent targets critical pain points in the campaign planning and execution process.

The Audience Agent enables marketers to discover and create high-value audience segments from behavioral signals quickly without needing SQL or advanced data skills. Data analysis and audience segmentation become faster and more accurate as the agent identifies patterns in customer behavior automatically. Figure 1 shows Audience Agent, which retrieves customer data based on queries. For example, when asked ‘I’d like to understand my most loyal customers,’ it identifies relevant attributes and presents the results.

Figure shows Audience Agent console answering question about most loyal customers.Figure 1: Audience Agent Console

The Deep Research & Analysis Agent compresses hypothesis-building processes from months to less than a week. Instead of spending extensive time on manual analysis and marketing, customer teams can generate high-quality hypotheses grounded in behavioral signals that inform strategy, testing, and execution decisions. Treasure Data’s Deep Insight Platform provides “Question Management” capability, which lets users pose questions for multiple analyses such as Churn Rate Trend and Email Performance Analysis as shown in Figure 2.

Figure shows Treasure Data’s Deep Insight Agent with various analysis such as Trend Analysis, Comprehensive Analysis

Figure 2: Treasure Data Deep Insight Platform

Available as part of Treasure Data’s CDP Trade-Up program, the Migration Agent accelerates transitions from existing customer data platforms by up to 60%. It extracts queries, segments, and transformation logic from current systems and automatically generates SQL, pipelines, and orchestration workflows. This agent helps organizations preserve their segments, workflows, and business logic when moving data, avoiding the need to start from scratch.

These agents use retrieval augmented generation (RAG), combining data processing capabilities, with the power of Amazon Bedrock inference, to provide accurate and data-grounded responses. This ensures AI suggestions reflect actual customer behavior rather than generic recommendations.

Introducing Treasure Data’s AI Agent Foundry

While the pre-built agents address common marketing challenges, Treasure Data customers expressed the need to create customized agents tailored to their unique business requirements and industry-specific use cases. The AI Agent Foundry emerged as the solution to this demand.

The AI Agent Foundry serves as the foundation for building custom AI agents tailored to specific business needs. Marketing, customer experience, and data teams can create, refine, and deploy agents without deep technical knowledge. High-impact use cases could include journey orchestration, data health monitoring, and campaign optimization specific to their organization.

The Foundry includes built-in security features, permission controls, auditability, and access management that meet enterprise governance requirements. Organizations can experiment with AI capabilities and deploy agents while maintaining data security, privacy and regulatory compliance. This approach enables customers to build agents that address their specific market dynamics and business processes.

Practical applications driving results

The specialized agents address several critical marketing use cases through their integration with Amazon Bedrock. Decision support helps marketers evaluate multiple factors simultaneously when determining campaign targeting, messaging, and channel selection. The AI provides recommendations based on comprehensive data analysis rather than intuition alone.

Multiple team members can collaborate with AI agents simultaneously, democratizing access to customer insights across marketing organizations. This capability eliminates bottlenecks caused by limited technical expertise on marketing teams.

The agents continuously learn from customer interactions and campaign performance, enabling organizations to refine their approach and achieve better results through rapid iteration and optimization.

Real-world impact: Nobitel case study

Nobitel Co., Ltd., a leader in health and sports services, operates Dr. Stretch, a specialized stretching chain with 240+ locations across Japan. The company faced challenges with its marketing operations, where manual campaign planning and data silos prevented non-technical teams from accessing customer insights and delivering timely personalized offers.

To address these challenges, Nobitel implemented Treasure Data’s AI Agent Foundry, built on AWS AI/ML services including Amazon Bedrock. This implementation transformed their marketing operations, enabling non-technical marketers to execute personalized campaigns without advanced data skills. The results included 3x faster campaign planning and 20% improvement in store efficiency. Learn more about Nobitel’s transformation in their case study.

The future of AI-powered marketing

AI agents represent the beginning of a transformation that will reshape marketing and customer experience operations. Future developments will see agents testing messaging variations, generating creative content, orchestrating multi-channel campaigns, and optimizing spending in real-time across devices and regions.

Marketing and CX professionals will evolve from campaign executors to strategic orchestrators. The critical question becomes whether data infrastructure can support numerous autonomous campaigns running simultaneously with precision and control.

This future requires robust data foundations, sophisticated AI capabilities, and governance frameworks that ensure trust and compliance at scale. Organizations building this infrastructure today position themselves to capitalize on autonomous marketing and CX operations.

Transforming marketing through AI and data

Treasure Data’s specialized AI agents and AI Agent Foundry, powered by Amazon Bedrock, represent a fundamental shift in how marketing, CX, and data teams drive value from customer data. By combining trusted data with advanced foundation models, teams can analyze data, create segments, generate personas, and make strategic decisions in hours rather than months.

This transformation democratizes access to customer insights and automates complex analytical tasks. Marketing teams can respond faster to market opportunities and achieve better results through rapid iteration. The solution demonstrates that effective marketing requires both intelligent agents and the robust data infrastructure that makes them truly powerful.

Security and compliance remain a shared responsibility between AWS and customers. AWS provides a secure, compliant foundation through Amazon Bedrock, while customers maintain control over their data and access policies. This approach enables organizations to innovate with AI while meeting their governance requirements.

Conclusion

Treasure Data’s AI Agent Foundry and pre-built AI agents, powered by Amazon Bedrock, transform marketing campaign creation from a months-long process to just hours or days. These AI solutions enable marketers to quickly analyze data, create segments, generate personas, and make data-driven decisions without deep technical expertise. By democratizing access to customer insights and automating complex analytical tasks, all powered by Amazon Bedrock’s foundation models, marketing teams can now respond faster to market opportunities and achieve better results through rapid iteration.

Treasure Data – AWS partner spotlight

Treasure Data, an AWS partner, Treasure Data is the Intelligent Customer Data Platform purpose-built for enterprise scale. Trusted by Yum! Brands, Stellantis, AXA, and over 80 Global 2000 companies, Treasure Data is where trust, performance, and AI-first architecture converge to drive revenue with hyper-personalized customer experiences, lower marketing costs, and reduce risk. Treasure Data provides both out-of-the-box agents and the AI Agent Foundry that enables data-driven teams or partners to utilize, create, and deploy AI Agents on the Treasure Data platform and across their workflows while leveraging their data within the trusted Treasure Data environment.

Suggested reading

Treasure Data on AWS Marketplace

Treasure Data Partner Profile

Nobitel case study

Ronak Shah

Ronak Shah

Ronak Shah is a Principal Partner Solution Architect with the AWS Industry Vertical team based in the New York City area. He works with AWS Partners in the Retail and CPG verticals to co-innovate on AWS. He is interested in finding new trends in retail and building innovative solutions in the areas of digital commerce, supply chain, customer experience and marketing technology. Outside of work, he volunteers at scouting and local debate competitions.

Hiroshi Nakamura

Hiroshi Nakamura

Hiroshi Nakamura is an accomplished technology leader with extensive experience in software engineering and system architecture. Currently serving as CTO and VP of Engineering at Treasure Data since October 2014, Hiroshi has been instrumental in designing and developing a cloud-based Data Management platform capable of handling immense data volumes. An active open-source developer since April 1999, contributions include significant enhancements to Ruby and JRuby. Hiroshi holds a Master's degree in Science and Engineering from Waseda University.

Pranjal Gururani

Pranjal Gururani

Pranjal Gururani is a Solutions Architect at AWS based out of Seattle. Pranjal works with various customers to architect cloud solutions that address their business challenges. He enjoys hiking, kayaking, skydiving, and spending time with family during his spare time.