Today, I’m incredibly excited to share something that’s been a long time in the making: we’re launching a major upgrade to our Analyst Agent, a product that’s going to change the way marketing teams run analyses on their existing marketing initiatives.

If you’ve ever waited days—or weeks—for a data science team to answer questions like “What are the characteristics of our highest performing customers?” or, “Which email variations resonated most with customers from a specific acquisition channel?”, you know how frustrating it can be. We built the Analyst Agent to solve that exact problem. And now, it’s better, faster, and smarter than ever.

Ask a Question, Get the Why—Instantly

With the new Analyst Agent, you don’t need to be technical to understand what’s driving your performance with Auxia. Just ask a question in plain English:

• “Which cohort responded best to the onboarding email we refreshed last week?”
• “Did our upsell nudges in the app outperform control for high-income users?”
• “What are the characteristics of creative that perform well? What performs poorly?”

Our AI Decision Agent is already making hundreds of millions of decisions every day—deciding the optimal action, content, incentive, surface, and frequency to drive our customers’ objectives. Now, with Analyst Agent, your team can actually understand why those decisions worked—and how to make them even better.

Saying Goodbye to the “Black Box”

Let’s be real: marketers have been stuck in the dark for too long. AI systems can often perform better than rules-based logic, but without visibility into what’s driving results, teams are left guessing. That’s the “black box” problem—and we’re breaking it wide open.

So what’s new with our latest release? Here are the three big breakthroughs we’ve delivered:

• Move As Fast As Your Ideas: Weeks of analyst work? Gone. With a new, chat-based interface, the Analyst Agent delivers answers at the speed your campaigns move. That means faster experiments, faster learnings, and faster revenue impact.
• Intelligence Without the Overhead: The Analyst Agent is built for the way marketers think and doesn’t require a technical background. Simply ask a question—about customer cohorts, regions, or content variants—and the agent adapts to surface what matters most. It’s powerful enough to handle complex questions, but intuitive enough for anyone to use.
• Enterprise-Grade Security and Reliability by Design: Connect securely to your existing data with the same compliance, privacy, and performance standards you expect from any mission-critical tool. Auxia does not share data across companies, ensuring your critical insights and data remain private and protected by default.

From Insight to Advantage: How Analyst Agent Compounds Value

Unlike traditional analytics tools, the Analyst Agent is designed to uncover deeper, campaign-level insights. It’s not just reading data—it’s learning from it.

What makes the Analyst Agent unique is that it connects directly to Auxia’s proprietary treatment framework and decisioning data. This means the agent operates on top of proprietary data and intelligence that already understands who saw what, when, and why–unlocking real-time insights that a generic BI tool can’t replicate.

Because every Auxia-powered experience is already structured for measurement from the start, the Analyst Agent can automatically isolate causal effects, compare treatment variants, and synthesize learnings without manual setup. The result: precise, actionable insight—without SQL, delays, ambiguity, or guesswork.

The best part? The more you use it, the better it gets. Every interaction with the Analyst agent adds to a growing body of institutional knowledge for your organization. It logs your questions, tests, and outcomes into a structured knowledge base, remembering prior decisions and identifying crucial patterns. Over time, it starts to surface the right insights before you even ask—reducing repetitive work, accelerating learning, and making your team smarter with every session.

With the Analyst Agent, growth intelligence doesn’t just scale—it compounds.

Using the Analyst Agent to Drive Smarter Decisions

The Analyst Agent isn’t just a tool you query—it’s a partner in your thinking.

From the moment you engage, it guides you through a collaborative exploration process designed to unlock insight, even when you're not sure what to ask. It can:

• Propose instructions for exploration based on the analyses it can run
• Ask clarifying questions to refine the scope of your inquiry
• Adapt in real time based on your responses, feedback, and hypotheses

Whether you're pressure-testing a campaign strategy or chasing an unexpected spike in conversions, the agent helps frame the right questions and drives you toward the “why”.

What’s Next

The enhanced Analyst Agent is rolling out to select customers now, and we’ll be expanding access throughout the summer. If you're already using our AI Decision Agent, you're going to love what this unlocks. And if you're new to Auxia, this is the perfect time to explore what Agentic Customer Journey Orchestration can really do for your organization.

If you’re curious to see it in action, fill out this form for a demo.

Today, we’re excited to announce Auxia’s official launch in Japan as we expand the reach of our Agentic Journey Orchestration Platform. Across countless conversations with marketing and product teams, we’ve consistently heard a similar theme: teams want to deliver more relevant, personalized experiences—but face real constraints around time, resources, and complexity.

We believe AI can shift this paradigm. Auxia enables teams to move faster, experiment more intelligently, and scale personalization in ways that were previously out of reach. With this launch, we’re thrilled to bring our platform to one of the most sophisticated and quality-driven markets in the world—and to support Japanese organizations in shaping the next generation of customer experiences.

Why Japan?

Japan is globally recognized for its exceptional standards in quality, precision, and customer experience—values that deeply resonate with us at Auxia. From the beginning, Japan has been a key market in our global vision. Today, we’re proud to introduce our Agentic Journey Orchestration Platform more broadly to the region. We've already partnered with several forward-looking enterprises, and we see tremendous potential to support Japanese companies as they navigate the global shift toward more intelligent, AI-powered customer engagement.

Local Team and Leadership

Leading the Auxia’s Japanese operations is Hirotaka Yoshitsugu, who has been appointed as CEO of Auxia Japan K.K.

Yoshitsugu brings over 20 years of experience in building platform ecosystems. After managing i-mode partnerships at NTT Docomo, he helped launch the Tokyo office of AdMob. Following Google’s acquisition of AdMob, he joined the U.S. headquarters, where he built the international expansion of Google Play from the ground up. Later, he returned to Japan to lead Google Play’s partner business locally.

His deep roots in Japan’s tech industry make him an essential leader for Auxia’s expansion in the region.

Bringing Agentic Journey Orchestration to Japan

Today, Auxia powers customer experiences across a wide range of industries—including finance, retail, media & entertainment, and telecommunications. But our platform offers far more than simple automation.

At its core, Auxia helps teams reimagine what’s possible by removing the barriers that traditionally slow personalization down. Our platform:

• Activates all your first-party data, eliminating the need for months of engineering work by automatically handling the infrastructure required to deploy machine learning models into production.
• Enables rapid experimentation at scale—testing hundreds of hypotheses in parallel without relying on rigid, rules-based journeys or time-consuming manual A/B tests.
• Delivers the right message, action, offer, timing, and frequency for each individual customer. Just define your goal, set the guardrails, and Auxia intelligently handles the rest.
• Continuously adapts and optimizes in real time, learning from every interaction to improve outcomes—without manual tuning.
• Surfaces granular behavioral insights, highlighting subtle differences across customer segments that are difficult to identify with traditional tools.

And perhaps most importantly, Auxia puts all of this power directly in the hands of marketers—no technical expertise required.

What's Next

In the coming months, we’ll be establishing a physical presence in Japan and deepening our partnerships across a wide range of industries. As part of this commitment, we’re actively hiring for key roles in customer success, solutions engineering, research, and more.

Our priority is to build a team rooted in local expertise—professionals who deeply understand the unique dynamics of the Japanese market and share our ambition to transform how customer experiences are designed and delivered. By investing in talent, infrastructure, and long-term collaboration, we’re laying the foundation to support our customers in Japan with the precision, responsiveness, and care they deserve.

Looking Ahead: Building for Long-Term Impact in Japan

To all enterprises and organizations in Japan looking to harness AI to elevate your customer journeys — we invite you to join us in building the next layer of intelligent infrastructure.

We’re excited to collaborate with you and help shape the future of your business, together.

The rise of Agentic Journey Orchestration and AI-driven decisioning is transforming the way marketing and product teams work — not just incrementally, but fundamentally. These systems go far beyond traditional automation by embedding intelligence directly into user journeys, enabling real-time decision-making and hyper-personalized experiences at scale.

Rather than relying on static funnels or rule-based triggers, marketing teams can now deploy adaptive agents that continuously learn from customer behavior, optimizing touchpoints dynamically to increase a number of objectives, like engagement and conversion. Product teams, meanwhile, are using agentic orchestration to test and evolve features in real time, unlocking faster iteration cycles and more responsive user experiences.

These capabilities aren't theoretical. They’re driving measurable gains in campaign performance, customer retention, and product adoption across industries. By integrating decision intelligence and journey orchestration into their core processes, leading teams are shifting from reactive operations to proactive, context-aware engagement strategies.

Let’s explore some high-impact use cases where Agentic Journeys are delivering strategic value.

Retail

• Personalized recommendations to drive 2nd purchase based on real-time shopper behavior.
• Dynamic promotion optimization to increase conversion and margin.
• Customer journey orchestration for omnichannel engagement and loyalty.

In retail, AI-driven customer journeys are revolutionizing how businesses approach upselling, increasing margin with promotions, and dynamically engaging their customers. Instead of generic recommendations, AI analyzes vast customer data—including purchasing history, browsing behavior, and even sentiment—to deliver highly personalized and contextually relevant offers. This goes beyond simple "customers who bought X also bought Y" to understanding individual preferences and predicting needs.

Banking and Fintech

• Driving onboarding completion.
• Hyper-personalized financial product offers tailored to life stage and goals.
• Customer retention and upsell journeys using intent-driven insights.

Banks and fintechs use AI decisioning to intelligently recommend additional financial products — such as credit cards, loans, or investment accounts — tailored to each customer’s financial profile and timing, significantly boosting product penetration and wallet share.

Consumer SaaS

• In-product personalization to tailor features, nudges, and onboarding paths.
• Feature adoption journeys driven by user behavior and milestone tracking.
• Usage-based churn prediction and retention offers triggered in-session.

AI decisioning powers adaptive upgrade paths, timely feature unlocks, and plan optimization offers that feel intuitive to users — increasing conversion to paid tiers and expanding account value without friction.

B2B SaaS

• AI-powered onboarding flows that adapt based on team behavior and setup progress.
• Next-best-action recommendations predicted based on likelihood to expand.
• User journey orchestration for activation and retention across product touchpoints.

For B2B SaaS, AI decisioning identifies expansion signals and usage patterns that trigger well-timed upsell motions — whether it's new seats, feature tiers, or product modules — accelerating revenue growth within existing accounts.

eCommerce

• Personalized homepage, search, and cart experiences using contextual data.
• Smart bundling and cross-sell strategies based on past purchases and intent.
• Cart abandonment recovery journeys using behavioral and channel signals.

AI decisioning drives intelligent product bundling, personalized add-on suggestions, and dynamic checkout offers that elevate basket size and repeat purchase value — all tuned to individual shopper context.

Media & Entertainment

• Content personalization and recommendations based on viewing history and engagement.
• Subscription lifecycle orchestration with upsell and retention triggers.
• Predictive engagement modeling to surface trending or high-impact content.

Media platforms use AI decisioning to guide users toward higher-value subscription tiers, exclusive content packages, or event upsells, based on engagement depth and consumption preferences.

Hospitality and Leisure

• Personalized offers and upgrades using guest preferences and history
• Journey orchestration pre-, during-, and post-stay for loyalty and satisfaction.
• Churn prediction and proactive retention offers triggered mid-session or post-session.

Hotels, resorts, and leisure operators use AI decisioning to present compelling upgrade, amenity, and experience offers throughout the guest journey — increasing per-stay revenue while enhancing perceived value.

Unlocking the Future of Marketing: Agentic Journey Orchestration

A new layer of the marketing stack is reshaping how organizations operate and engage with their customers – Agentic Journey Orchestration. Also referred to as AI Decisioning, this article will provide an overview of Agentic Journey Orchestration, define how this approach differs from traditional, deterministic approaches to creating customer journeys, and touch on a few example use cases.

So what is Agentic Journey Orchestration?

Agentic Journey Orchestration is how marketing and product teams leverage traditional machine learning methods (ML) and recent advancements in LLMs to make real-time, personalized choices about how to interact with each individual customer in a highly personalized way.

Most technology solutions today allow teams to send messages to their customers across channels by manually creating a cadence that follows rigid, pre-set rules for generic customer segments. Alternatively, Agentic Journey Orchestration constantly analyzes live data, behavioral signals, and historical information to determine the optimal touchpoint for each specific customer at that precise moment to drive the downstream behavior a business cares about (e.g. conversion, retention, etc). Whereas many solutions previously promised to deliver the “next best action”, AI-based systems can predict the a combination of the next best action, content, product, timing, frequency, and incentive to drive the desired goal.

AI vs. Traditional, Rules-Based Approaches

So how does this differ from how marketing and product teams orchestrated their product experiences and campaigns previously?

Before AI

Traditional, Rules-Based Decisioning is built on predefined rules and conditional logic, often requiring human adjustments. Picture a marketing team organizing a welcome email campaign. These systems require manual updates to modify logic or enhance performance, making them less adaptable to rapidly changing environments.

Companies building customer journeys often faced a number of challenges:

• Underutilized Data: A vast majority (over 68%) of rich customer data sits idle, unused for personalization, largely because most companies can’t affort the specialized data science and engineering resources needed to activate it.
• Manual & Inefficient Processes: Traditional rules-based journey creation is highly manual, cumbersome, and time-consuming, hindering agility and responsiveness.
• Scalability of Human Decision-Making: As enterprises grow, the limitations of human decision-making become apparent, making it increasingly difficult to coordinate efforts across teams and effectively test the numerous hypotheses required for truly intelligent, one-to-one customer experiences.

After AI

Agentic Journey Orchestration leverages sophisticated artificial intelligence techniques, primarily classical machine learning (ML) and large language models (LLMs), to make autonomous or semi-autonomous business choices, all laddering up to a company’s high level business objective.  

The ultimate goal is to deliver hyper-personalized, 1-1 experiences for each individual customer.

Here’s how it works:

• Agentic systems leverage all of your 1P data to make more impactful decisions for each customer. These systems have robust infrastructure that automates the complex data transformation work required to put ML models in production across your product or marketing experiences. For a typical team, creating this infrastructure typically requires several months of work from a well-staffed data science and engineering team.
• Teams simply define their goals, guardrails, and surfaces to engage their customers with across web / mobile product experiences or across any lifecycle channel (e.g. email, SMS, etc) of their choosing. What’s great about AI-based systems is that teams can create hundreds of variations of content for the models to choose from, as opposed to 4-5 with a typical A/B test.
• AI decides the optimal touchpoint and sequence for each customer and continuously optimizes the experience in an automated experimentation loop, analyzing vast and diverse datasets to inform and optimize decisions in real-time.

The main fundamental difference is that the experience with AI is incredibly tailored to each individual based on their data and previous interactions, but also extremely dynamic and adaptive. It continuously learns and refines strategies based on feedback and new data, allowing businesses to proactively respond to shifts in customer behavior. It can handle highly complex and unstructured data, making it uniquely suited for predicting intricate trends and behaviors that elude traditional rule-based systems.

The future of automated decision-making is a synergistic model where traditional enterprise marketing and product teams provides essential hypotheses, goals, guardrails, consistency, and governance, while AI Decisioning offers dynamic adaptability, personalization, and scale.

Example Use Cases

The combined power of AI Decisioning and Agentic Journey Orchestration is driving measurable impact across diverse industries:

• Retail - Drive 1st to 2nd Purchase: AI personalizes the specific product category, content, and format to drive a second purchase by analyzing purchasing history, in-session browsing behavior, and user preferences.
• Consumer SaaS - Improving activation and retention: Nudge your customers to adopt and frequently engage with features that causally improve retention and are specifically useful for their distinct needs
• E-Commerce - Win-back campaigns: AI personalizes the right message, timing, channel, and incentive to bring a customer back by automatically utilizing their past behavior, preferences, and interests.
• Fintech / Banking - Activation, upsell / cross sell, and referrals: Identify and serve the optimal touchpoint to drive a user to complete onboarding, understand what products are right for them, and automatically surface the right incentive amount to encourage them to refer their friends.
• Hospitality - Loyalty & Engagement: Personalize your customer's loyalty rewards and experience based on past bookings, preferences, and spending habits.

Customer Challenge

A global financial services enterprise with $500B+ AUM recognized that consumer expectations were rising for hyper-personalized digital experiences. This led them to prioritize “one-to-one personalization” initiatives across the company, including a key initiative to enhance an investment and education platform within their portfolio of products.


The investment platform’s focus was to make investing more accessible for consumers. Despite a steady flow of site visitors engaging with the platform on a daily basis, a critical issue emerged: most users left the platform without creating an account. This significantly limited the institution’s ability to understand their audiences and introduce them to the broader ecosystem of products that drive their revenue. To address this, the enterprise partnered with Auxia to determine the most effective time, method, value proposition, and product surface to encourage account creation.

Solution

Auxia worked closely with the financial institution to identify the most impactful moments within the user journey where personalized interactions could drive new account sign ups. The team pinpointed three key locations within the platform to deploy personalized, dynamic treatments that change for each customer.


Using Auxia’s advanced machine learning infrastructure, they were able to leverage a dynamic set of user features—including referral sources, behavioral insights (such as the last five articles read), and demographic data (e.g. age brackets)—to tailor the right content, action, surface, and timing for each customer. This enabled the investment product to deliver highly relevant, context-aware experiences that incentivized users to complete the sign-up process, all in real-time.

Results

The collaboration between the financial services institution and Auxia yielded remarkable improvements:

• 50% boost in sign-up completion rate
• 22x increase in the number of experiments conducted per month
• 20x increase in click-through rates (CTR) for top-performing treatments

Additional details of the engagement included:

• 175+ content variations tested
• Achieved measurable impact in less than six weeks
• Millions of machine learning-driven decisions served
• Sub-75ms latency per decision, ensuring seamless user experience
• 40+ new treatments introduced per month to continuously optimize results

Expansion & Future Plans

Encouraged by these results, the financial institution has expanded its use of Auxia’s platform beyond initial touch points. The platform has since redesigned its entire homepage to leverage Auxia’s dynamic optimization capabilities, ensuring even greater personalization at scale.

Customer Challenge

A global language education platform sought to enhance its user experience, drive user engagement, and increase free-to-paid conversion by delivering more personalized learning journeys. Based on UX research, previous experiments, and their team’s own analysis, their team recognized that there was a strong correlation between users completing multiple lessons and upgrading to a paid plan.

However, they faced key challenges as they worked to refine their customer engagement, personalization, and growth strategies for the year:

• How do we determine which users should be further engaged versus those who already recognize the platform's value and are ready to consider a premium tier?
• If engagement is the right approach, what specific features or lessons should be surfaced to increase the chances of converting free users to paid subscribers?
• If an upsell is the best next step, which premium tier should be recommended to each user, and why?

Solution

Auxia’s decisioning capabilities were attractive to this team because it allowed them to reduce their team’s need to hire an expensive team of data scientists to better personalize their in-product experience. The language learning app was able to use Auxia to dynamically determine the right lesson, content, and action (e.g. engage, convert) for each individual customer.

• The initial launch included 20+ content variations that the AI models could test and optimize across.
• Over time, the system expanded to 50+ additional variations, continuously refining its recommendations.
• The initial integration was seamless, requiring less than a month to configure before deploying to their customers.
• Auxia’s system tested a spectrum of AI models, from basic bandits to sophisticated causal uplift models, ensuring the highest-impact recommendations.
• Auxia was also able to help the team determine the top predictive factors that influenced their product experience, including a user’s learning goal, their assessment score (0-100), and their last lesson completed.

Results

After integrating with just one surface, a content card on the home screen, during the initial phase of the engagement, the team was able to leverage Auxia’s decisioning models to deliver:

• +40% uplift in primary engagement metric
• +7% uplift in free-to-paid conversion
• +20% CTR for highest performing content cards
• 7-10x ROI

With Auxia’s AI decisioning system, the language education platform was able to intelligently guide users through their learning journey, optimizing both engagement and revenue while delivering a seamless user experience.