Ticketing platform vivenu is expanding beyond transaction management with the launch of vivenu Engage, a customer relationship and marketing solution designed to help event organizers activate ticketing data in real time. The new offering integrates audience segmentation, campaign execution, customer insights, and marketing attribution directly into vivenu's ticketing infrastructure, eliminating the need for manual data exports and third-party workflow management. The move reflects a growing trend across MarTech and customer data platforms as businesses seek to unlock first-party data for more personalized customer engagement.

As third-party data becomes less reliable and customer acquisition costs continue to rise, organizations across industries are searching for ways to maximize the value of first-party customer information. In the live entertainment sector, ticketing systems generate some of the richest behavioral data available, yet much of that information remains underutilized after a transaction is completed.

vivenu is attempting to change that dynamic with the launch of vivenu Engage, a new marketing and customer relationship layer embedded directly within its enterprise ticketing platform.

The company positions the product as a bridge between ticketing operations and customer engagement, enabling event organizers to turn purchase behavior into actionable marketing intelligence without relying on external systems or manual data management processes.

The launch comes at a time when marketing teams are grappling with increasingly fragmented technology stacks. According to Forrester, 78% of B2C marketing executives still operate across siloed technology environments, creating challenges around customer data accessibility, campaign orchestration, and audience segmentation. At the same time, customer databases can experience significant annual data decay, reducing the effectiveness of marketing initiatives over time.

Ticketing data presents a unique opportunity because it captures highly valuable behavioral signals including purchase frequency, attendance history, spending patterns, promotional activity, geographic information, and customer loyalty indicators. However, these insights often remain trapped within ticketing systems and disconnected from broader marketing workflows.

vivenu Engage seeks to address this challenge by bringing customer segmentation, campaign management, and performance measurement directly into the platform where ticket transactions occur.

The product's core functionality centers on real-time audience segmentation. Organizers can create dynamic customer groups based on factors such as ticket purchases, total spend, attendance records, donations, promotional activity, geographic location, and email engagement. Unlike traditional static lists, segments automatically update as customer behavior changes, allowing marketing teams to target audiences based on current actions rather than historical snapshots.

This capability aligns with a broader shift toward real-time customer data activation that has become increasingly important across modern MarTech ecosystems. Customer Data Platforms (CDPs) and marketing automation vendors such as Salesforce, Adobe, HubSpot, and Microsoft have emphasized the importance of continuously updated customer profiles for personalization and campaign optimization.

Where vivenu differentiates itself is by embedding those capabilities directly into the ticketing workflow rather than requiring organizations to synchronize data across multiple platforms.

Another notable feature is the inclusion of embedded customer insights within the segmentation process. As audiences are created, organizers can immediately view metrics such as customer lifetime value, average basket size, spending distributions, and audience overlap. This reduces the need for separate analytics tools and enables faster decision-making around campaign strategy.

The platform also includes a native email marketing environment, allowing organizers to create, manage, and distribute campaigns from within the same system used to sell tickets. Marketing performance metrics, including delivery rates, open rates, and bounce tracking, are captured directly inside the platform.

Perhaps more significant from a customer experience perspective is the introduction of automated entitlements and personalized rewards. The system can automatically apply discounts, waive service fees, or unlock benefits when customers meet predefined criteria such as repeat attendance or high-value purchasing behavior. These actions occur without requiring promo codes or manual customer list management.

The launch reflects increasing demand for personalization across consumer-facing industries. Research from McKinsey & Company suggests that organizations with strong personalization strategies can achieve revenue increases of 10% to 15%, while top-performing companies often generate substantially higher revenue growth than their peers.

For live entertainment organizations, personalization has become particularly important as venues, festivals, sports organizations, and entertainment brands compete for audience attention and repeat attendance. Access to real-time customer insights can help organizations tailor offers, improve loyalty programs, and strengthen long-term customer relationships.

The platform also introduces functionality called Secret Shops, allowing organizers to restrict access to exclusive inventory for selected audience groups such as VIP customers, season ticket holders, or repeat attendees. This capability reflects growing interest in loyalty-driven commerce models that reward customer engagement with exclusive experiences.

From a compliance standpoint, vivenu Engage incorporates built-in consent management tools designed to support GDPR requirements. Consent collection occurs across multiple customer touchpoints, including online checkout, account registration, and point-of-sale interactions, with audit trails maintained automatically.

Importantly, vivenu is not positioning Engage as a replacement for existing enterprise marketing ecosystems. Instead, the platform maintains integrations with external systems including HubSpot, Salesforce, and other marketing technologies through its API infrastructure. This ecosystem-first approach acknowledges that many organizations already operate complex technology stacks and prefer solutions that complement rather than replace existing investments.

The introduction of marketing attribution capabilities further strengthens the platform's value proposition. By connecting marketing activity directly to ticket sales and revenue outcomes, organizers gain clearer visibility into campaign effectiveness and return on investment.

As first-party data strategies become increasingly central to marketing operations, platforms that combine transactional systems with customer engagement capabilities are gaining momentum. vivenu Engage reflects this convergence, turning ticketing infrastructure into a source of actionable customer intelligence rather than simply a transaction-processing system.

For event organizers, the ability to move from customer insight to campaign execution without leaving the platform could help simplify marketing operations, improve personalization efforts, and unlock greater value from every ticket sold.

Market Landscape

The convergence of ticketing, customer data, and marketing technology is creating new opportunities across the live entertainment industry. As privacy regulations limit third-party data availability and customer acquisition costs continue rising, organizations are increasingly investing in first-party data activation strategies.

According to Forrester, marketing teams continue to face significant challenges from fragmented technology ecosystems, while McKinsey research highlights personalization as one of the strongest drivers of revenue growth. This has accelerated demand for platforms that combine transactional data, customer intelligence, campaign execution, and analytics within unified environments.

The launch of vivenu Engage places the company within a broader movement toward integrated customer engagement platforms, where operational systems evolve into strategic sources of customer insight and revenue optimization.

Top Insights

•  vivenu Engage combines customer segmentation, campaign execution, marketing attribution, and audience analytics directly within the ticketing platform, eliminating manual data exports and disconnected workflows.
• The platform enables real-time audience targeting using ticket purchases, attendance history, spending patterns, donations, geographic data, and engagement signals.
• Automated entitlements and personalized rewards allow organizers to deliver discounts, fee waivers, and exclusive experiences based on customer behavior without manual intervention.
• Native marketing attribution connects customer outreach directly to ticket sales and revenue outcomes, providing clearer visibility into campaign effectiveness and ROI.
• The launch reflects growing demand for first-party data activation and personalization strategies across entertainment, sports, festivals, venues, and live event businesses.

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JustAI, an AI-native marketing platform focused on personalization, experimentation, and autonomous decision-making, has secured $17 million in Series A funding as enterprises increasingly seek ways to simplify complex marketing technology stacks and scale AI-driven customer engagement. The funding round, led by Base10 Partners with participation from Y Combinator and Peak XV Partners, underscores growing investor confidence in agentic AI platforms designed to automate core marketing functions traditionally managed through multiple disconnected tools.

The race to build AI-powered marketing infrastructure is accelerating, and JustAI is positioning itself at the center of a growing shift toward agentic marketing.

The company announced a $17 million Series A funding round led by Base10 Partners, with additional backing from Y Combinator, Peak XV Partners, and a group of strategic investors that includes operators from Anthropic, Chime, Notion, HubSpot, Eppo, and Vapi. The investment arrives as marketing organizations face mounting pressure to deliver higher levels of personalization, customer engagement, and measurable business impact without increasing operational complexity.

At the heart of JustAI's strategy is the belief that modern marketing teams are overwhelmed by fragmented technology ecosystems. According to the 2025 Marketing Technology Landscape, organizations now have access to more than 15,000 martech products. While these tools promise improved efficiency and customer insights, many enterprises continue to struggle with disconnected workflows, siloed data, and growing operational burdens.

The challenge has become even more pronounced as AI adoption expands across marketing departments. Gartner's 2026 CMO Spend Survey found that chief marketing officers now allocate 15.3% of their marketing budgets to artificial intelligence initiatives. Yet only 30% report being prepared to scale AI capabilities effectively across their organizations.

JustAI aims to address that gap by consolidating multiple marketing functions into a unified agentic platform. Rather than relying on traditional campaign management systems built around workflows, audience segments, and manual optimization, the company uses coordinated AI agents that can generate campaigns, optimize decisions, analyze performance, and continuously improve outcomes.

The platform is structured around four specialized AI agents that work together across the marketing lifecycle.

The Strategy Agent analyzes audiences, customer segments, and product touchpoints to identify growth opportunities. The Creative Agent transforms those insights into personalized messaging across channels including email, web experiences, and in-app communications. The Decisioning Agent continuously optimizes customer interactions based on business objectives such as engagement, retention, and revenue growth while remaining within marketer-defined parameters. Supporting the system is a Data Agent that measures campaign performance, identifies patterns, and feeds new insights back into the platform.

This architecture reflects a broader industry movement toward autonomous marketing systems. Rather than using AI solely as a productivity assistant, vendors are increasingly developing platforms capable of making recommendations and executing decisions independently.

Major technology companies including Google, Microsoft, Salesforce, and Adobe have introduced AI agents across their enterprise software portfolios. However, many of these offerings remain focused on task automation and workflow assistance. JustAI is pursuing a more ambitious vision centered on end-to-end marketing orchestration and decision-making.

The company describes its platform as a replacement for deterministic campaign logic and static segmentation models. Instead of requiring marketers to manually define customer journeys, establish testing frameworks, and manage campaign optimization, JustAI's agents adapt continuously based on user behavior and business outcomes.

That approach appears to be resonating with customers. JustAI reports that annual recurring revenue increased fivefold over the past year and that its platform influenced more than $100 million in customer revenue during the same period.

The company has already attracted enterprise customers including online learning platform Coursera. According to marketing leadership at Coursera, capabilities delivered through JustAI would previously have required substantial engineering resources and development support, highlighting the growing role AI platforms can play in reducing technical barriers for marketing teams.

The founding team brings a combination of marketing and machine learning expertise that reflects the platform's dual focus on growth and AI infrastructure.

Founder and CEO Neha Mittal previously held growth and retention leadership roles at Twitter and Pinterest, where she experienced firsthand the challenges associated with scaling personalized customer engagement through legacy marketing systems. Co-founder Jeff Hara contributes expertise in machine learning and recommendation systems, providing the technical foundation for the company's decisioning and optimization capabilities.

The funding announcement also reflects increasing investor interest in agentic AI applications beyond productivity software. While much of the AI investment cycle has focused on foundational models and generative AI tools, attention is shifting toward domain-specific platforms capable of automating business processes and delivering measurable operational outcomes.

For enterprise marketing teams, the emergence of agentic platforms could significantly alter how campaigns are planned, executed, and optimized. Instead of coordinating multiple point solutions for customer data management, experimentation, campaign orchestration, analytics, and personalization, organizations may increasingly adopt unified systems that combine these capabilities under a single AI-driven framework.

JustAI plans to use the new capital to expand its engineering and go-to-market operations while advancing its underlying agentic infrastructure. The company also intends to broaden its market reach beyond consumer-focused organizations and into e-commerce and B2B marketing environments.

As enterprises continue evaluating how AI fits into their marketing technology strategies, platforms that combine decisioning, personalization, experimentation, and measurement into cohesive systems are likely to attract growing attention. JustAI's latest funding round suggests investors believe autonomous marketing may become one of the next major categories in enterprise MarTech.

Market Landscape

The marketing technology industry is undergoing rapid transformation as organizations seek to simplify increasingly complex digital ecosystems. According to Gartner, AI spending now represents a growing share of marketing budgets, while IDC projects global AI investment to exceed hundreds of billions of dollars over the coming years. At the same time, enterprises are looking beyond isolated AI tools and toward integrated platforms capable of automating workflows, improving customer experiences, and delivering measurable business outcomes.

Agentic marketing platforms are emerging as a new category within MarTech, combining AI-powered decisioning, experimentation, personalization, analytics, and campaign orchestration. Vendors across the ecosystem are increasingly competing to provide autonomous systems that can operate across customer journeys with minimal manual intervention. This trend is expected to reshape how marketing teams approach customer engagement, lifecycle management, and revenue optimization throughout the remainder of the decade.

Top Insights

•  JustAI raised $17 million in Series A funding to expand its agentic marketing platform that combines personalization, experimentation, analytics, and AI-driven decisioning in a unified system.
• The platform uses four coordinated AI agents focused on strategy, creative generation, decision optimization, and performance measurement to automate complex marketing workflows.
• Investor participation from Base10 Partners, Y Combinator, Peak XV Partners, and AI industry operators highlights growing confidence in agentic enterprise software categories.
• JustAI reported 5X annual recurring revenue growth, signaling increasing enterprise demand for autonomous marketing technologies that reduce operational complexity.
• The company plans to expand beyond consumer businesses into e-commerce and B2B marketing environments, broadening the reach of agentic AI across the marketing ecosystem.

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MoEngage has acquired AI decisioning startup Aampe in a move that signals the next phase of customer engagement technology. The deal combines MoEngage's customer engagement and marketing automation capabilities with Aampe's autonomous AI decisioning infrastructure, creating a platform designed to help brands deliver individualized customer interactions at scale. Financial terms of the acquisition were not disclosed.

As artificial intelligence continues reshaping enterprise marketing, personalization remains one of the industry's most persistent challenges. While marketing platforms have become increasingly sophisticated, many brands still rely on audience segments, predefined customer journeys, and rule-based automation systems that struggle to adapt to individual customer behavior in real time.

MoEngage's acquisition of San Francisco-based Aampe aims to address that limitation through what both companies describe as agentic decisioning—a model where dedicated AI agents continuously learn and optimize engagement strategies for individual consumers.

Unlike traditional marketing automation systems that group customers into segments, Aampe provisions an autonomous AI agent for every user. These agents determine what message should be delivered, when it should be sent, which channel should be used, and how frequently engagement should occur. The system continuously refines its decisions based on customer responses and behavioral signals.

The acquisition represents a significant expansion of MoEngage's AI strategy. The company already offers Merlin AI, a suite of AI-powered tools that assist marketers with campaign creation, customer journey orchestration, content generation, and analytics. With Aampe's technology integrated into the platform, MoEngage moves beyond AI-assisted execution toward autonomous decision-making at the individual customer level.

The move comes as enterprise marketing teams face growing pressure to increase personalization while managing larger volumes of customer data across multiple channels. According to Gartner research, organizations continue to prioritize AI investments that improve customer experience and marketing efficiency, particularly as digital engagement channels proliferate.

A key differentiator in Aampe's architecture is its per-user agent model. Rather than creating predictive models around customer segments, each consumer receives a dedicated agent that develops a persistent understanding of behavioral patterns, preferences, engagement timing, and content relevance.

This approach contrasts with many existing customer engagement platforms from vendors such as Salesforce, Adobe, and other marketing cloud providers, which often rely on segmentation frameworks combined with machine learning recommendations. While those systems have increasingly incorporated AI capabilities, many still require marketers to design campaigns, build journeys, and define optimization rules manually.

Aampe's technology introduces a more autonomous layer where AI agents continuously make engagement decisions without requiring constant campaign adjustments from marketing teams.

For enterprise marketers, the practical implication is a potential reduction in operational complexity. Traditional personalization programs often involve managing hundreds of audience segments, maintaining numerous workflows, and conducting ongoing A/B testing. As customer bases grow, those requirements typically increase staffing demands and platform complexity.

By allowing autonomous agents to manage optimization decisions, marketing teams could theoretically focus more on strategy, creative development, and business objectives while AI systems handle execution and adaptation.

The acquisition also strengthens MoEngage's position within the increasingly competitive customer engagement platform market. As AI becomes a primary differentiator across MarTech, vendors are racing to integrate generative AI, predictive analytics, and autonomous decision-making capabilities into their offerings.

The concept of agentic marketing has gained momentum across the technology sector, particularly as advances in large language models and reinforcement learning enable AI systems to perform increasingly complex tasks with limited human intervention. Major technology providers including Google, Microsoft, Amazon, and Salesforce have all introduced AI agent initiatives aimed at automating business workflows.

MoEngage's acquisition suggests customer engagement may become one of the most significant application areas for agent-based AI systems.

The companies point to several production deployments as evidence that the model can scale. Aampe reports that its infrastructure currently manages hundreds of millions of AI agents and processes more than 200 billion decisions each week across consumer-facing brands.

Among the notable implementations is Taxfix, a European digital tax platform that replaced portions of its rule-based CRM infrastructure with Aampe's agentic system. According to company-reported results, the deployment generated measurable improvements in engagement performance and revenue outcomes compared with traditional customer lifecycle programs.

The technology has also been used by consumer platforms including Swiggy, Grab, and ZenBusiness, providing further validation that individualized decisioning can operate across large user bases and high-volume engagement environments.

An important aspect of the acquisition is MoEngage's "Start Anywhere" strategy. Existing customers can access Aampe's decisioning capabilities without replacing current engagement infrastructure, while brands already using other marketing automation platforms can integrate Aampe's per-user agents independently.

That flexibility may reduce barriers to adoption for organizations that are hesitant to undertake large-scale platform migrations.

Looking ahead, the acquisition highlights a broader industry shift from AI-assisted marketing toward AI-directed marketing. Instead of helping marketers create campaigns more efficiently, next-generation platforms are increasingly focused on enabling AI systems to make decisions autonomously while operating within human-defined goals and governance frameworks.

As enterprise marketing stacks evolve, the combination of customer data platforms, AI-driven orchestration, predictive analytics, and autonomous decisioning could redefine how brands engage consumers. For MoEngage, acquiring Aampe represents a strategic bet that the future of personalization will be built around individual AI agents rather than audience segments.

Market Landscape

The customer engagement platform market is entering a new phase driven by agentic AI, first-party data strategies, and autonomous marketing workflows. IDC projects worldwide AI spending will continue growing at double-digit rates through the decade, while McKinsey estimates organizations successfully deploying AI-driven personalization can achieve revenue improvements of 5–15%.

Traditional marketing automation platforms have focused on segmentation, journey orchestration, and campaign management. Emerging agentic marketing platforms are shifting toward autonomous decision-making that continuously optimizes customer interactions at the individual level. This evolution is expected to influence customer engagement, loyalty marketing, lifecycle marketing, and retention strategies across retail, fintech, food delivery, subscription services, and digital commerce sectors.

Top Insights

• MoEngage acquired Aampe to add autonomous AI decisioning capabilities that personalize messaging, timing, frequency, and channel selection for individual consumers.

• Aampe's per-user AI agent model moves beyond traditional segmentation, allowing customer engagement systems to continuously learn and optimize at scale.

• The combined platform positions MoEngage within the growing agentic marketing category, where AI systems increasingly manage complex engagement decisions autonomously.

• Existing deployments at Swiggy, Taxfix, Grab, and ZenBusiness demonstrate real-world applications of individualized AI decisioning across large customer bases.

• The acquisition reflects a broader MarTech trend toward AI-powered customer engagement platforms that combine automation, analytics, orchestration, and autonomous optimization.

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As artificial intelligence takes on a larger role in marketing, advertising, and customer engagement, the quality of the data feeding those systems is becoming a decisive factor in performance. IPinfo has been recognized as a Data & Identity "One to Watch" in Snowflake's Modern Marketing Data Stack 2026: Governing the Agentic Enterprise report, highlighting the growing importance of internet intelligence, IP data enrichment, and real-time network context in AI-powered marketing operations.

IPinfo has been named a Data & Identity "One to Watch" in Snowflake's The Modern Marketing Data Stack 2026 report, a recognition that underscores the expanding role of internet intelligence within modern marketing and advertising technology ecosystems.

The announcement was made during Cannes Lions 2026, where industry leaders gathered to discuss the next phase of AI-driven marketing transformation. Snowflake's annual report examines how organizations are evolving from fragmented technology stacks toward governed, AI-enabled infrastructures capable of supporting increasingly autonomous decision-making.

Now in its fifth year, the report draws on insights from more than 11,500 Snowflake customers and ecosystem partners across 13 categories, highlighting how enterprises are bringing applications closer to their data while maintaining governance, privacy, and trust.

For marketers, one challenge continues to grow in importance: understanding the quality and context of the data used to power AI systems.

As organizations adopt agentic AI, automated audience targeting, predictive analytics, and real-time campaign optimization, enrichment data has become a foundational component of decision-making. Without accurate contextual signals, even the most advanced AI models can produce inaccurate recommendations, ineffective targeting strategies, or flawed business outcomes.

IPinfo's recognition reflects the increasing value of internet intelligence in addressing that challenge.

The company provides IP-based data enrichment capabilities that help organizations understand the context behind internet traffic, devices, networks, and digital interactions. Its datasets are available directly through Snowflake Marketplace, allowing enterprises to integrate internet intelligence into marketing, analytics, fraud prevention, and AI workflows without introducing additional complexity into their data environments.

Unlike traditional IP databases that often rely heavily on static internet registries, IPinfo emphasizes a measurement-based approach to data collection.

The company's proprietary ProbeNet platform continuously measures and validates internet behavior, providing updated insights into geolocation, network characteristics, proxy usage, and traffic patterns. This approach is designed to improve accuracy as internet infrastructure evolves and user behaviors change.

The distinction is becoming increasingly relevant in AI-powered environments.

Modern marketing systems rely on contextual signals to support audience segmentation, geographic targeting, fraud detection, compliance verification, and customer experience personalization. As these functions become more automated, the reliability of enrichment data directly affects campaign performance and operational effectiveness.

For example, accurate IP intelligence can help advertisers improve audience targeting, identify potentially fraudulent traffic, validate geographic attributes, and strengthen identity frameworks used across digital marketing channels.

The technology also plays an important role in compliance and security initiatives.

Organizations operating in highly regulated industries such as financial services, healthcare, retail, and telecommunications often require additional context around customer interactions to meet governance requirements and reduce risk. Verified internet intelligence can provide valuable signals that support these objectives while enhancing the effectiveness of AI-driven decisioning systems.

The broader market trend aligns with growing investments in data quality and AI governance.

According to Gartner, data quality remains one of the most important factors influencing enterprise AI success. Organizations that improve the accuracy, consistency, and trustworthiness of data assets are significantly more likely to generate meaningful returns from AI initiatives.

Similarly, IDC has identified trusted data ecosystems as a critical foundation for digital transformation and intelligent automation strategies.

The rise of agentic marketing is further increasing demand for contextual data.

Unlike traditional marketing automation systems that follow predefined workflows, agentic AI platforms continuously analyze information, generate recommendations, and execute actions with limited human intervention. These systems require high-quality enrichment data to make informed decisions and adapt to changing market conditions.

As a result, internet intelligence is becoming an increasingly important layer within enterprise MarTech and AdTech stacks.

The recognition from Snowflake also highlights how data enrichment providers are evolving from niche infrastructure vendors into strategic contributors to AI-driven business operations. As organizations seek to operationalize AI across customer acquisition, personalization, advertising optimization, and revenue generation initiatives, access to accurate contextual data becomes increasingly valuable.

Competition in this space continues to expand as technology ecosystems including Google, Microsoft, Amazon, and Salesforce invest in data intelligence, identity solutions, and AI-powered analytics capabilities.

For enterprise marketers, the implications are significant.

As AI increasingly drives customer engagement, audience targeting, measurement, and campaign execution, the focus is shifting from simply collecting data to ensuring that data accurately reflects real-world behavior. Internet intelligence platforms like IPinfo are becoming part of the infrastructure that enables organizations to make faster, more reliable decisions at scale.

Snowflake's recognition of IPinfo reflects a growing industry consensus that trusted enrichment data is no longer optional. In an era of AI-driven marketing and automated decision-making, data quality, context, and validation are becoming essential competitive advantages.

Market Landscape

The data enrichment and identity intelligence market is experiencing rapid growth as enterprises expand investments in AI-powered marketing, analytics, and customer engagement platforms. Gartner continues to identify data quality and governance as foundational requirements for successful AI adoption, while IDC highlights trusted data ecosystems as a key driver of enterprise digital transformation.

As organizations move toward agentic marketing and real-time decision-making, contextual enrichment data is becoming increasingly important for audience targeting, fraud prevention, compliance, and personalization. This trend is accelerating demand for internet intelligence platforms capable of delivering accurate, continuously updated network and behavioral insights.

Top Insights

• IPinfo was recognized as a Data & Identity "One to Watch" in Snowflake's 2026 Modern Marketing Data Stack report.
• AI-powered marketing systems increasingly depend on high-quality enrichment data to support targeting, personalization, and automated decision-making.
• IPinfo's measurement-based approach provides continuously validated internet intelligence rather than relying solely on static registry information.
• Accurate IP and network context can improve audience targeting, fraud detection, compliance initiatives, and identity intelligence strategies.
• Internet intelligence is becoming a critical data layer for agentic marketing, AI-driven analytics, and real-time customer engagement.

Get in touch with our MarTech Experts

As enterprises accelerate investments in AI-powered marketing, customer data platforms, and personalization technologies, privacy governance is emerging as a foundational requirement rather than a compliance afterthought. Transcend has been recognized as a "One to Watch" in the Privacy & Consent category of Snowflake's Modern Marketing Data Stack 2026: Governing the Agentic Enterprise report, highlighting the growing importance of real-time data permissions and consent management in the era of agentic marketing.

Transcend has been named a "One to Watch" in Snowflake's The Modern Marketing Data Stack 2026: Governing the Agentic Enterprise report, earning recognition for its approach to privacy, consent management, and data governance within modern marketing ecosystems.

The announcement, made during Cannes Lions 2026, reflects a broader industry trend: as marketing organizations adopt artificial intelligence, customer data platforms (CDPs), and autonomous decision-making systems, managing customer permissions has become a strategic business requirement rather than solely a legal obligation.

Snowflake's annual Modern Marketing Data Stack report, now in its fifth edition, draws on insights from more than 11,500 customers and ecosystem partners. The report examines how organizations are shifting away from fragmented marketing technology stacks toward governed, AI-enabled infrastructures capable of supporting increasingly sophisticated marketing operations.

Among the report's key themes is the growing need for trusted data foundations.

Marketing teams today are under pressure to deliver highly personalized customer experiences while simultaneously complying with increasingly complex privacy regulations. As organizations collect and activate first-party data across digital channels, ensuring that customer permissions are accurately enforced has become essential to maintaining trust and reducing compliance risks.

Transcend's platform addresses this challenge by embedding data-use permissions directly into enterprise systems that process customer information.

Rather than relying on disconnected consent databases or manual compliance workflows, the platform creates a real-time source of truth for consent preferences, data permissions, and business rules. This enables organizations to determine whether customer data can be used for a particular purpose before activation occurs.

The importance of this capability is increasing as organizations move toward agentic marketing models.

Agentic marketing leverages AI systems that can analyze customer information, make decisions, and execute actions with minimal human intervention. While these technologies promise greater efficiency and personalization, they also introduce new governance challenges. AI systems can only operate responsibly if they have access to accurate permission frameworks that define how customer data can be used.

Without those controls, organizations risk exposing themselves to regulatory penalties, reputational damage, and declining consumer trust.

According to Gartner, privacy and data governance remain among the most significant barriers to enterprise AI adoption. Organizations that establish clear governance frameworks are more likely to successfully operationalize AI while maintaining compliance with evolving regulatory requirements.

Transcend's collaboration with Snowflake is designed to address these concerns by integrating consent management directly within governed data environments.

The combined approach allows marketing and data teams to establish a single framework for managing customer permissions across MarTech and AdTech ecosystems. Instead of maintaining separate privacy controls across multiple platforms, organizations can apply consistent rules throughout the customer data lifecycle.

This model is becoming increasingly important as first-party data strategies gain momentum.

The decline of third-party cookies, growing privacy regulations, and changing consumer expectations have pushed organizations toward first-party data collection and activation. However, unlocking value from first-party data requires confidence that customer information is being used appropriately and transparently.

Joint customers across industries including retail, financial services, healthcare, and media are using the platform to support identity resolution, consent enforcement, and customer data activation initiatives. These capabilities help organizations accelerate personalization, customer engagement, and AI-driven marketing programs while maintaining governance standards.

The recognition also highlights a broader evolution in privacy technology.

Historically, privacy management tools focused primarily on regulatory compliance and responding to consumer requests. Today's platforms are increasingly becoming operational components of enterprise data infrastructure, helping organizations automate governance decisions and support real-time data activation.

This shift is particularly relevant as AI adoption expands.

Enterprise marketing teams are increasingly seeking technologies that allow them to deploy AI-powered customer experiences without creating additional compliance burdens. Automated data decisioning platforms help bridge this gap by enabling AI systems to operate within predefined governance frameworks.

Competition in this category continues to intensify as major technology providers including Salesforce, Adobe, Microsoft, and Google expand investments in privacy, consent management, and data governance capabilities.

For enterprise marketers, the challenge is increasingly clear: AI initiatives cannot succeed without trusted customer data, and trusted customer data requires effective governance.

Snowflake's recognition of Transcend underscores the growing industry consensus that privacy, consent, and data governance are no longer separate functions operating on the sidelines of marketing technology. Instead, they are becoming integral components of the infrastructure that powers AI-driven customer engagement, personalization, and agentic decision-making.

As organizations continue investing in AI-enabled marketing strategies, the ability to operationalize privacy at scale may become one of the most important competitive differentiators in the modern marketing stack.

Market Landscape

The privacy technology and consent management market is expanding rapidly as enterprises increase investments in AI, customer data platforms, and first-party data strategies. Gartner identifies data governance and privacy management as critical enablers of responsible AI adoption, while IDC continues to highlight trusted data ecosystems as essential components of digital transformation initiatives.

The shift toward first-party data, combined with stricter privacy regulations and growing consumer awareness around data usage, is driving demand for platforms that can automate consent enforcement and data governance. As agentic AI becomes more prevalent, privacy technologies are increasingly evolving from compliance tools into operational infrastructure supporting enterprise marketing and customer engagement.

Top Insights

• Transcend was recognized as a "One to Watch" in Snowflake's Privacy & Consent category for its real-time data governance and consent management capabilities.
• Enterprises are increasingly embedding privacy controls directly into customer data workflows to support AI-powered marketing initiatives.
• Agentic marketing systems require automated governance frameworks to ensure customer data is used appropriately and compliantly.
• First-party data strategies are driving demand for technologies that unify consent management, identity resolution, and data activation.
• Privacy and governance are becoming foundational components of modern AI-ready marketing technology stacks.

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As enterprise marketing teams increasingly rely on AI-driven decision-making, the ability to analyze and activate data without moving it across multiple systems is becoming a strategic advantage. Sigma has been recognized as a Leader in the Analytics & Measurement category of Snowflake’s Modern Marketing Data Stack 2026: Governing the Agentic Enterprise report, highlighting the growing importance of warehouse-native analytics and governed AI workflows in modern marketing operations.

Sigma has been named a Leader in the Analytics & Measurement category of Snowflake’s Modern Marketing Data Stack 2026 report, a recognition that reflects the evolving role of cloud data platforms in marketing analytics, measurement, and AI-powered decision-making.

The announcement, made during Cannes Lions 2026, underscores a broader shift occurring across the marketing technology landscape. As organizations move away from fragmented data environments and disconnected reporting tools, many are adopting architectures that bring analytics, AI, and operational workflows directly to governed data platforms.

Snowflake's annual Modern Marketing Data Stack report, now in its fifth year, draws insights from more than 11,500 customers and ecosystem partners across 13 technology categories. The report highlights how enterprises are increasingly deploying applications directly within cloud data environments rather than extracting and replicating data across multiple systems.

For marketing organizations, this approach addresses a longstanding challenge.

Traditional marketing analytics often relies on multiple reporting platforms, data exports, and business intelligence tools that create delays, governance risks, and inconsistencies in decision-making. As AI becomes embedded within marketing operations, those inefficiencies become even more problematic.

Sigma's platform aims to simplify that process by allowing marketing teams to analyze live data directly within Snowflake environments. Rather than moving information into separate analytics systems, users can create reports, dashboards, operational applications, and AI-powered workflows directly on top of data residing in the warehouse.

This warehouse-native approach is gaining traction as organizations seek to balance AI innovation with increasingly stringent governance requirements.

By operating directly within Snowflake, Sigma enables organizations to inherit existing security controls, audit capabilities, and access permissions already established within the data environment. This means marketing teams can access analytics and AI functionality without introducing additional governance complexity.

The significance of this model extends beyond reporting.

Modern marketing organizations are increasingly expected to support forecasting, attribution analysis, customer journey optimization, campaign measurement, and revenue intelligence initiatives. These use cases require access to large volumes of customer and operational data while maintaining compliance with privacy and governance standards.

According to Gartner, data governance and trust remain among the most critical factors influencing enterprise AI adoption. Organizations that establish strong governance frameworks are significantly better positioned to operationalize AI across business functions, including marketing, sales, and customer engagement.

Sigma's recognition reflects growing demand for platforms that bridge analytics and operational execution.

The company notes that many marketing teams are no longer using data warehouses solely as repositories for historical reporting. Instead, warehouses are becoming operational environments where decisions are made, workflows are executed, and AI applications are deployed.

This shift aligns closely with the emergence of agentic marketing.

Agentic systems use AI to automate analysis, recommend actions, and execute workflows with limited human intervention. However, these capabilities require direct access to trusted, governed data sources. Moving data into external systems can introduce latency, security concerns, and inconsistencies that reduce AI effectiveness.

Sigma's platform addresses this challenge through warehouse-native AI functionality, including natural language querying, workflow automation, application development, and writeback capabilities that operate within Snowflake's security framework.

The company has also expanded its collaboration with Snowflake through integrations with Snowflake Cortex and emerging AI development capabilities. These integrations are designed to help organizations build and deploy AI-powered applications while maintaining governance controls over enterprise data assets.

The partnership has produced notable industry recognition.

Sigma has been named Snowflake's Business Intelligence Data Cloud Product Partner of the Year four times and recently received the Snowflake CoCo Adoption Award for helping customers adopt Snowflake's coding agent and builder ecosystem.

The broader market opportunity continues to expand.

Research from IDC indicates that enterprise spending on AI-powered analytics, data intelligence, and business decision platforms is accelerating as organizations seek greater operational agility. Marketing departments are among the largest adopters of these technologies, driven by increasing pressure to improve campaign performance, customer acquisition efficiency, and revenue attribution.

Competition within the analytics market is also intensifying.

Major enterprise technology vendors including Salesforce, Adobe, Microsoft, and Google continue investing heavily in AI-powered analytics and customer intelligence capabilities.

Against this backdrop, warehouse-native analytics platforms are emerging as a compelling alternative for organizations seeking to reduce complexity while accelerating AI adoption.

Snowflake's recognition of Sigma highlights a growing consensus across the industry: the future of marketing analytics may depend less on moving data between systems and more on bringing analytics, AI, and operational workflows directly to where trusted data already resides.

For enterprise marketing teams, that shift could fundamentally change how insights are generated, decisions are made, and customer experiences are optimized in the age of AI.

Market Landscape

The analytics and measurement market is undergoing rapid transformation as organizations integrate AI into decision-making processes. Gartner identifies governed data environments as a critical requirement for scalable AI adoption, while IDC projects continued growth in enterprise spending on analytics, business intelligence, and AI-driven operational platforms.

Warehouse-native analytics is emerging as a key trend, enabling organizations to analyze, govern, and activate data directly within cloud platforms rather than relying on fragmented reporting ecosystems. This approach is particularly attractive to marketing teams seeking real-time insights, stronger governance, and faster execution across campaign operations and customer engagement initiatives.

Top Insights

•  Sigma was recognized as a Leader in Snowflake’s Analytics & Measurement category for enabling warehouse-native analytics and AI-powered marketing workflows.
• Marketing teams are increasingly shifting from fragmented reporting environments to governed analytics platforms that operate directly on live customer data.
• Warehouse-native architectures help organizations improve governance, security, and operational efficiency while supporting AI adoption.
• Agentic marketing initiatives require trusted, real-time data environments that can support automated analysis and workflow execution.
• AI-powered analytics platforms are becoming essential infrastructure for forecasting, attribution, customer intelligence, and revenue optimization.

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As enterprise marketing teams move beyond experimentation and begin operationalizing artificial intelligence at scale, data infrastructure has become a critical competitive advantage. DAS42, a data and AI consulting firm focused on analytics and cloud modernization, has been recognized as a featured services partner in Snowflake’s fifth annual Modern Marketing Data Stack report, underscoring the growing importance of identity resolution, audience intelligence, and AI-powered campaign execution in modern marketing operations.

DAS42 has been named a featured services partner in Snowflake’s The Modern Marketing Data Stack 2026: Governing the Agentic Enterprise, a report that examines how organizations are transforming marketing operations through AI, governed data environments, and intelligent automation.

The recognition reflects a broader shift taking place across the marketing technology landscape. As enterprises deploy AI-powered marketing tools, customer data platforms (CDPs), predictive analytics systems, and agentic AI solutions, many organizations are discovering that success depends on the quality, accessibility, and governance of their underlying data infrastructure.

Snowflake’s annual report, now in its fifth edition, draws on insights from more than 11,500 customers and ecosystem partners across 13 categories. The findings highlight how enterprises are increasingly bringing applications directly to their data rather than moving data between disconnected systems, enabling faster execution while addressing growing concerns around privacy, governance, and trust.

DAS42 was recognized for its consulting work helping media, entertainment, and telecommunications organizations modernize customer data environments and prepare them for AI-driven marketing initiatives.

The company specializes in helping enterprises consolidate fragmented audience data, establish identity resolution frameworks, and deploy AI-powered analytics capabilities within Snowflake environments. These foundational capabilities are becoming increasingly important as marketing teams seek to improve personalization, customer acquisition, audience monetization, and advertising performance.

The challenge is familiar to many enterprise marketers.

Customer data often exists across CRM systems, advertising platforms, streaming services, customer support applications, loyalty programs, and analytics environments. Without a unified view of customer identity, organizations struggle to deliver personalized experiences, measure marketing effectiveness, and maximize the value of AI investments.

DAS42’s approach focuses on creating trusted customer data foundations before deploying advanced AI capabilities.

Within Snowflake environments, the company helps organizations build identity resolution and data enrichment frameworks that unify customer records and improve data quality. Once those foundations are established, enterprises can deploy AI-powered audience modeling, campaign optimization tools, and automation workflows designed to improve marketing performance.

One of the areas attracting significant industry attention is agentic marketing.

Unlike traditional marketing automation platforms that execute predefined workflows, agentic AI systems can analyze data, make decisions, and continuously optimize campaigns with limited human intervention. These technologies promise greater efficiency and responsiveness, but they require accurate customer identity data and governed analytics environments to function effectively.

DAS42’s work reflects the growing demand for infrastructure that supports these emerging capabilities.

By combining identity resolution, audience enrichment, lookalike modeling, and AI-driven campaign management, organizations can improve audience targeting while creating new revenue opportunities through advertising partnerships and addressable inventory monetization.

The trend aligns with broader developments across the marketing technology ecosystem.

Research from Gartner suggests that organizations with mature data management and governance frameworks are more likely to achieve measurable returns from AI investments. Similarly, IDC has identified unified data environments and AI-ready infrastructure as critical components of successful digital transformation initiatives.

For industries such as media, entertainment, and telecommunications, the stakes are particularly high.

These sectors manage vast amounts of customer and audience data while facing increasing pressure to improve engagement, optimize advertising revenue, and deliver personalized experiences across digital channels. AI-powered audience intelligence and campaign optimization are becoming strategic priorities as organizations seek new ways to drive growth and maximize customer value.

The recognition from Snowflake also highlights the expanding role of consulting and implementation partners within enterprise AI ecosystems.

While cloud platforms provide the infrastructure and technology foundation, many organizations rely on specialized service providers to design data architectures, implement identity frameworks, and operationalize AI initiatives. As a result, consulting partners are becoming increasingly influential in helping enterprises translate AI ambitions into measurable business outcomes.

Competition in this space continues to intensify as major technology ecosystems including Salesforce, Adobe, Microsoft, and Google expand investments in customer data, AI, and marketing intelligence capabilities.

Against this backdrop, organizations are increasingly focused on building marketing technology stacks that combine data governance, identity intelligence, automation, and AI-powered decision-making within a unified ecosystem.

Snowflake’s recognition of DAS42 reflects the growing importance of that approach. As enterprises accelerate investments in AI-driven customer engagement and agentic marketing systems, trusted customer data foundations are emerging as a prerequisite for success.

For marketing leaders, the message is becoming increasingly clear: before AI can transform customer experiences and campaign performance, organizations must first solve the challenge of creating connected, governed, and actionable customer data environments.

Market Landscape

The marketing technology industry is entering a new phase where AI adoption is increasingly dependent on strong data foundations. According to Gartner, organizations continue prioritizing data governance, identity resolution, and customer intelligence as key enablers of AI success. IDC research similarly highlights unified customer data ecosystems as a critical component of digital transformation and advanced analytics initiatives.

Media, telecommunications, and digital advertising companies are particularly focused on audience intelligence and identity resolution as they seek to improve personalization, optimize advertising revenue, and support AI-powered customer engagement strategies. This has accelerated demand for consulting partners capable of helping enterprises operationalize AI within governed cloud environments.

Top Insights

• DAS42 was recognized as a featured services partner in Snowflake’s Modern Marketing Data Stack report for its work in identity resolution and AI-powered marketing infrastructure.
• Enterprises are increasingly prioritizing unified customer data foundations to support personalization, audience monetization, and AI-driven campaign execution.
• Agentic marketing systems require trusted identity data and governed analytics environments to deliver accurate and scalable outcomes.
• Media, entertainment, and telecommunications organizations are investing heavily in AI-powered audience intelligence and advertising optimization technologies.
• Consulting and implementation partners are playing a growing role in helping enterprises operationalize AI initiatives within cloud-based marketing ecosystems.

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Industrial automation is entering a new phase where software, artificial intelligence, electrification, and digital intelligence are becoming as important as traditional machinery. At Automate 2026, Schneider Electric is using one of North America's largest automation events to demonstrate how open, software-defined automation architectures could help manufacturers modernize operations, improve flexibility, and prepare for increasingly autonomous industrial environments.

Schneider Electric is taking center stage at Automate 2026 as manufacturers worldwide confront growing pressures to modernize production systems, improve operational efficiency, and adapt to rising energy and compute demands.

The company is using the event to highlight its vision for open software-defined automation (SDA), positioning the technology as a foundational component of next-generation industrial operations. The strategy combines automation, digital intelligence, electrification, and industrial software into a unified architecture designed to help organizations operate more efficiently while responding more quickly to changing business conditions.

The announcement comes at a pivotal moment for the industrial sector.

Manufacturers are balancing multiple challenges simultaneously, including aging infrastructure, labor shortages, supply chain disruptions, increasing sustainability requirements, and the growing impact of artificial intelligence on production environments. At the same time, renewed investment in domestic manufacturing and expanding data center infrastructure are creating additional demand for more agile and scalable industrial systems.

Against this backdrop, Schneider Electric is advocating for a shift away from traditional automation architectures toward software-centric operational models.

Software-defined automation separates automation software from proprietary hardware systems, allowing manufacturers greater flexibility when integrating equipment, upgrading facilities, and scaling operations. The approach is increasingly gaining traction across industrial sectors seeking to avoid vendor lock-in and improve interoperability across diverse production environments.

At Automate 2026, Schneider Electric is demonstrating how open automation frameworks can support adaptive operations capable of responding to real-time production conditions. The company argues that future industrial environments will require systems that continuously evolve rather than relying on static automation processes.

This vision aligns with broader Industry 4.0 trends reshaping global manufacturing.

According to Gartner, manufacturers continue increasing investments in intelligent automation, industrial AI, and digital transformation initiatives as they seek to improve resilience and competitiveness. IDC similarly projects strong growth in industrial software and automation spending as organizations modernize production infrastructure and embrace data-driven decision-making.

Schneider Electric's strategy centers on bringing together three major technology domains that have historically operated independently: automation, digital intelligence, and electrification.

The convergence of these technologies is becoming increasingly important as manufacturers seek greater visibility into operations while also managing energy consumption, sustainability goals, and production performance. Integrating these capabilities within a unified architecture enables organizations to optimize operational and energy efficiency simultaneously.

A significant focus of the company's presence at Automate is open ecosystem collaboration.

Schneider Electric is showcasing integrations with a broad network of technology partners, including AVEVA, AWS, HPE, Intel, Microsoft, NVIDIA, ETAP, Barbara, and Universal Automation. These partnerships reflect a growing industry shift toward open platforms that allow organizations to combine technologies from multiple vendors rather than relying on closed proprietary environments.

The collaborative approach is particularly relevant as industrial organizations adopt hybrid architectures that span edge computing, on-premises operations, cloud environments, and AI-powered analytics platforms.

One of the event's central themes is the role of digital twins and industrial AI in driving measurable business outcomes.

Digital twin technology enables manufacturers to create virtual representations of physical assets, processes, and facilities, allowing teams to simulate changes, identify bottlenecks, and optimize performance before implementing modifications in live environments. When combined with AI, these digital models can support predictive maintenance, operational forecasting, and autonomous decision-making.

Schneider Electric plans to further explore these opportunities through an executive session featuring NVIDIA, Fallas Automation, and Deloitte, focusing on how industrial AI and digital twins are delivering practical value across manufacturing operations today.

The company is also using Automate 2026 to highlight its EcoStruxure Automation Expert platform and the growing adoption of IEC 61499, an international standard designed to support distributed and interoperable automation systems. Through its Schmoooth Automators Challenge, Schneider Electric aims to encourage innovation around OT/IT convergence and open automation architectures among system integrators and engineering students.

The initiative reflects increasing demand for automation professionals capable of bridging operational technology and information technology environments.

Industry leaders increasingly view OT/IT convergence as a critical requirement for deploying AI-powered industrial applications, enabling data to flow seamlessly between production equipment, enterprise systems, analytics platforms, and cloud infrastructure.

The broader significance of Schneider Electric's Automate presence extends beyond product demonstrations.

As industrial organizations accelerate digital transformation initiatives, the conversation is shifting from simply automating tasks to creating intelligent, adaptive operations capable of continuous improvement. Software-defined automation, industrial AI, digital twins, and electrification are emerging as core technologies supporting that transition.

Competition in this market is intensifying as major automation providers including Rockwell Automation, Siemens, ABB, and Honeywell continue expanding software-driven industrial platforms.

For manufacturers, the challenge is no longer whether to digitize operations but how to build flexible architectures capable of adapting to future technologies and business requirements.

Schneider Electric's focus on open, software-defined automation suggests the industry may be moving toward a future where industrial systems operate less like isolated machines and more like intelligent, interconnected software ecosystems.

Market Landscape

The global industrial automation market is undergoing rapid transformation as manufacturers invest in Industry 4.0 technologies, AI-driven operations, digital twins, and software-defined architectures. According to IDC, industrial digital transformation spending continues to rise as organizations seek greater agility, efficiency, and resilience across production environments.

At the same time, open automation standards and interoperable platforms are gaining momentum as manufacturers seek to reduce complexity and improve system flexibility. Gartner identifies intelligent automation, industrial AI, and connected operations as key priorities for organizations modernizing production infrastructure.

Major industry players including Schneider Electric, Siemens, Rockwell Automation, ABB, and Honeywell are increasingly focusing on software-centric industrial platforms that combine automation, analytics, cloud connectivity, and operational intelligence.

Top Insights

• Schneider Electric is using Automate 2026 to promote open software-defined automation as a foundation for adaptive and autonomous industrial operations.
• The company is integrating automation, digital intelligence, and electrification into a unified architecture designed to improve operational flexibility and efficiency.
• Strategic partnerships with AWS, Microsoft, NVIDIA, Intel, AVEVA, and others highlight growing demand for open industrial ecosystems.
• Digital twins and industrial AI are becoming essential technologies for optimizing production performance and supporting data-driven decision-making.
• Manufacturers are increasingly adopting software-centric automation models to improve interoperability, scalability, and long-term operational resilience.

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