As iGaming and sports betting operators face intensifying pressure from rising acquisition costs, tightening regulation, and margin erosion, the industry’s long-standing reliance on cash-heavy bonuses is starting to look unsustainable. Optimove believes it has a better answer.

The player engagement vendor today unveiled Optimove Loyalty, a new no-code product designed to help operators shift away from bonus-led retention toward gamified, non-monetary incentives. Built for marketing teams—not engineering backlogs—the platform enables operators to design, deploy, and optimize loyalty experiences in real time while protecting margins and extending player lifetime value.

The launch reinforces Optimove’s broader push around Positionless Marketing, where marketers are empowered to act independently, without waiting on technical resources to execute and iterate on engagement strategies.

A response to bonus fatigue and margin pressure

Bonusing has long been the default growth lever in iGaming. But escalating competition has turned it into an arms race—one that’s increasingly expensive and, in some markets, increasingly regulated.

Optimove Loyalty is positioned as a direct response to that dynamic. Instead of trying to “out-bonus” competitors, operators can redirect engagement and retention efforts toward gamified loyalty mechanics that rely on intrinsic motivation rather than cash incentives.

According to Optimove, this shift allows brands to protect margins while still keeping players engaged—an increasingly difficult balance as profit pressure mounts and regulatory scrutiny around bonusing intensifies.

“Gamified loyalty creates progression and recognition that keep players coming back, without having to escalate bonus spend,” said Shai Frank, SVP of Product and GM Americas at Optimove.

Built for today’s regulatory and competitive realities

The timing of the launch is notable. Regulators across multiple markets are paying closer attention to how bonuses are used, particularly around responsible gaming and player protection. At the same time, switching costs between platforms are falling, making differentiation harder to sustain.

Optimove Loyalty is designed to address both challenges.

By focusing on non-monetary rewards and recognition, the platform offers operators a more future-proof engagement model—one less exposed to regulatory tightening. At the same time, customizable gamified experiences give brands new ways to stand out in markets where product features and odds are increasingly commoditized.

Rather than interrupting players with offers, Optimove says its approach taps into intrinsic motivators such as progress, achievement, and discovery—earning attention instead of buying it.

What Optimove Loyalty includes

Optimove Loyalty provides a modular set of gamification components that marketers can configure without code, tailoring experiences to different player segments and behaviors.

Key components include:

Badges
Operators can create branded, tiered, rare, and collectible badges that players earn for milestones, missions, or specific behaviors. For example, a badge might be awarded for trying multiple casino games or reaching a wagering threshold.

Virtual Currencies
Brands can define their own loyalty currencies—naming them, assigning value, and determining how they’re earned or redeemed. Multiple currencies can coexist, including rare or time-limited options, to guide player behavior. Examples include diamonds, coins, or chips, each serving a different strategic purpose.

Missions
Missions are designed to guide player behavior through structured challenges. Optimove Loyalty supports three mission types:

• Simple: One-off actions, such as depositing $50 to earn rewards

• Progressive: Multi-step journeys, like making five qualifying deposits to earn a badge

• Accumulative: Time-bound challenges, such as depositing a set amount within a month

These missions allow marketers to encourage specific actions—from deeper engagement to product discovery—without defaulting to cash incentives.

Widgets and Live Games
Ready-made, designable widgets and live game experiences can be embedded directly into apps and digital touchpoints. Examples include in-app player profiles or interactive loyalty elements that feel native to the betting or gaming experience.

Positionless marketing in practice

A defining feature of Optimove Loyalty is its no-code design. Marketing teams can build, adjust, and optimize loyalty programs on the fly—without waiting for engineering resources or release cycles.

That autonomy aligns with Optimove’s Positionless Marketing philosophy, which argues that speed and independence are now competitive advantages. In fast-moving iGaming markets, the ability to test and iterate engagement strategies in real time can make the difference between retention and churn.

“Optimove Loyalty powers marketers to move at the speed of the market,” Frank said, “building and tuning gamified loyalty experiences without relying on engineering cycles.”

Part of a broader gamification strategy

Optimove Loyalty is currently available in closed beta and forms part of the company’s broader Optimove Gamify suite, which also includes minigames and promotion optimization capabilities.

Together, these tools signal Optimove’s belief that the next phase of player engagement will be driven less by escalating incentives and more by intelligent design—where data, personalization, and game mechanics work together to sustain long-term value.

The bigger picture for iGaming engagement

As iGaming operators confront rising costs and regulatory headwinds, loyalty is shifting from a nice-to-have to a strategic necessity. Platforms like Optimove Loyalty reflect a broader industry realization: retention can no longer depend solely on cash.

By giving marketers the tools to build engaging, gamified experiences that reward behavior rather than spending, Optimove is betting that the future of player engagement will be smarter, leaner, and more sustainable.

For an industry under pressure to do more with less, that’s a message likely to resonate.

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As the creator economy races toward an estimated $500 billion valuation by the end of the decade, Fanvue is staking a clear claim on where it believes the next phase of growth will come from: AI-first creator businesses. The London-based, AI-powered creator monetization platform has raised a $22 million Series A round, backing its ambition to define what it calls the “Creator AI Economy.”

The funding comes at a pivotal moment for Fanvue. The company says it has already crossed a $100 million annualized revenue run rate, supports more than 250,000 creators, and attracts over 17 million monthly active users. Those numbers put it in rarefied territory for a platform that launched just three years ago—and suggest investors aren’t just buying into a vision, but into real traction.

From creator tools to AI-native businesses

Unlike legacy creator platforms that primarily monetize attention through ads or subscriptions, Fanvue is positioning itself as an AI-native operating system for creators. Its pitch is straightforward: creators shouldn’t have to scale linearly with time, nor should they depend on platform algorithms or advertising economics to earn a living.

According to Fanvue, more than 93% of creators on its platform are already using at least one proprietary AI tool, including AI-driven analytics, voice, and content capabilities. These tools are designed to help creators understand fan behavior, automate and personalize content, and ultimately monetize more efficiently.

This heavy adoption rate is central to Fanvue’s argument that it isn’t reacting to AI trends—it’s being built around them. Rather than bolting AI features onto an existing platform, Fanvue is framing AI as the engine that allows creators to scale their businesses faster and with greater ownership.

Defining a new category, not chasing incumbents

Fanvue’s leadership is careful to avoid positioning the company as a direct competitor to established creator platforms. Instead, it’s defining a new category altogether: the Creator AI Economy. The idea is that the next generation of creators—spanning influencers, athletes, and digital entrepreneurs—will rely on AI to expand earnings, deepen fan relationships, and unlock new revenue streams beyond traditional ads or sponsorships.

That positioning appears to have resonated with investors. The Series A round was led by Inner Circle, a fund backed by more than 50 exited founders, financiers, and cultural figures across sports and entertainment. Inner Circle’s broader portfolio includes high-profile names such as Revolut, Anthropic, and xAI, signaling confidence in Fanvue’s technology-led approach.

Other backers include Moonbug founder René Rechtman, founders of UK unicorn Marshmallow, and general partners from leading European venture firms—an investor lineup that blends consumer, fintech, and deep-tech experience.

“AI is redefining the creator economy,” said James Cox, co-founder of Inner Circle. “Fanvue isn’t reacting to that shift; they are pioneering it. The team is building a category-defining platform that enables creators globally to monetize their audiences at scale.”

Growth metrics that back the narrative

Beyond funding headlines, Fanvue’s growth metrics help explain the momentum. The company reports 450% year-over-year revenue growth and has nearly tripled its workforce in the past 12 months, growing from 42 to 115 employees. Its operations are anchored in London’s Canary Wharf, where the leadership team and core product functions are based.

The platform has also been recognized externally, earning the title of Fastest Growing Company in Europe at the International Business Awards (Stevies). These signals matter in a crowded creator tech landscape, where many platforms struggle to translate hype into sustainable revenue.

AI, athletes, and mainstream visibility

Fanvue is also leaning into cultural relevance as part of its expansion strategy. The company recently announced the signing of Alisha Lehmann, the Swiss professional footballer and one of the world’s most-followed athletes on Instagram, with more than 16 million followers.

For Fanvue, the partnership is more than a celebrity endorsement. It underscores the platform’s belief that athletes and mainstream creators will increasingly seek direct-to-fan monetization models—powered by AI—rather than relying solely on sponsorships or social platforms.

“Announcing two major milestones in the same week—the Series A and Alisha—reinforces our vision that AI will enable the next generation of athletes and creators to build real businesses,” said Will Monange, co-founder and CEO of Fanvue.

Built by creators, for creators

Fanvue’s origin story plays directly into its product philosophy. Co-founder Joel Morris, a former YouTuber, launched the platform in 2022 after experiencing firsthand the limitations of existing creator platforms. Alongside co-founders Will Monange and Harry Fitzgerald, Morris built Fanvue around three core principles: Fan Connection, Creator Freedom, and Business Ownership.

Those values are reflected in the platform’s emphasis on direct monetization, control over content and data, and AI tools that help creators grow without surrendering ownership to platforms or advertisers.

According to COO Harry Fitzgerald, AI fundamentally changes the economics of creator businesses. “Thanks to AI, creators on Fanvue can scale products, content, and connections in ways that weren’t possible before,” he said. “We’re shifting creators away from reliance on advertising and toward direct monetization.”

What the funding unlocks next

Fanvue says the $22 million Series A will be used to accelerate global expansion, hire top-tier talent, and further invest in AI capabilities across the platform. With more than 20,000 new creators joining in the past month alone—and additional high-profile creator announcements expected in early 2026—the company is signaling that growth is far from slowing.

In a creator economy increasingly shaped by AI, regulation, and shifting platform dynamics, Fanvue is betting that creators want more than reach—they want leverage. If its AI-first approach continues to deliver at scale, Fanvue may prove that the future of creator monetization isn’t just social. It’s intelligent, direct, and increasingly automated.

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ClickHouse is making a decisive move to cement its role as foundational infrastructure for the AI era. The real-time analytics and data warehousing company has closed a $400 million Series D round, one of the largest recent financings in modern data infrastructure, signaling strong investor conviction that performance-driven data platforms will sit at the center of production AI systems.

The round was led by Dragoneer Investment Group, with participation from a heavyweight roster that includes Bessemer Venture Partners, GIC, Index Ventures, Khosla Ventures, Lightspeed Venture Partners, T. Rowe Price–advised accounts, and WCM Investment Management. The scale and composition of the investor group underscore a broader belief: as AI moves from experimentation into production, data infrastructure—not models—becomes the bottleneck.

Growth fueled by production-grade workloads

ClickHouse’s funding arrives on the back of rapid, sustained growth. The company now serves more than 3,000 customers on ClickHouse Cloud, its fully managed service, with annual recurring revenue growing more than 250% year over year. Over the past quarter alone, organizations such as Capital One, Polymarket, Airwallex, Lovable, and Decagon have either adopted ClickHouse or expanded existing deployments.

These customers join a base that already includes data- and AI-intensive brands like Meta, Tesla, Sony, and Cursor. Notably, ClickHouse isn’t just replacing legacy analytics systems—it’s enabling new, real-time use cases that were previously impractical due to cost or latency constraints.

Unlike many data platforms that primarily serve internal BI teams, ClickHouse is frequently embedded directly into customer-facing products. That distinction matters. In always-on systems—fraud detection, real-time personalization, observability, AI-driven decisioning—performance and reliability are not nice-to-haves; they define the end-user experience.

“ClickHouse was built to deliver exceptional performance and cost efficiency for the most demanding data workloads, and this momentum validates that strategy,” said CEO Aaron Katz. “As we look toward the future, we’re expanding into unified transactional and analytical workloads and adding LLM observability, so developers can build and run AI applications on the best possible technical foundation.”

Why Dragoneer is leaning in

Dragoneer is known for its selective, research-heavy approach and long-term partnerships with category-defining companies. Founded by Marc Stad in 2012, the firm has backed several of the most influential data and infrastructure platforms of the past decade, as well as foundational AI companies.

For Dragoneer, ClickHouse stood out because it sits closest to production—a critical advantage as AI systems scale. AI-driven applications generate far higher query volumes, demand tighter latency, and require continuous evaluation of outputs. As models become more capable, the performance burden shifts decisively to the data layer.

“Major platform shifts ultimately reward the infrastructure companies that sit closest to production,” said Christian Jensen, Partner at Dragoneer. “As models improve, data infrastructure becomes the bottleneck. ClickHouse delivers the performance, efficiency, and reliability required for AI systems operating at scale.”

That assessment reflects a broader industry trend. As enterprises move beyond pilots and proofs of concept, they are prioritizing platforms that can support always-on, data-intensive workloads without spiraling costs.

Enter LLM observability, via Langfuse

One of the most consequential moves tied to the funding is ClickHouse’s acquisition of Langfuse, an open-source LLM observability platform. While traditional observability focuses on system health and performance metrics, LLM observability tackles a newer, more complex problem: evaluating the quality, safety, and behavior of non-deterministic AI outputs in production.

As generative AI becomes embedded in workflows—from customer support to financial analysis—the ability to understand why a model produced a given output is rapidly becoming table stakes. Langfuse has emerged as a leading project in this space, ending 2025 with more than 20,000 GitHub stars and over 26 million SDK installs per month.

“We built Langfuse on ClickHouse because LLM observability is fundamentally a data problem,” said Marc Klingen, CEO of Langfuse. “Together, we can deliver faster ingestion, deeper evaluation, and a much shorter path from a production issue to a measurable improvement.”

The acquisition positions ClickHouse to offer a differentiated observability stack—one that spans traditional analytics, system observability, and now AI behavior monitoring. For teams deploying LLMs in production, that convergence could significantly reduce operational risk.

A unified data stack: Postgres meets ClickHouse

ClickHouse is also pushing into another strategic frontier: unifying transactional and analytical workloads. The company announced a native, enterprise-grade Postgres service deeply integrated with ClickHouse, aimed squarely at modern AI applications that need both real-time transactions and high-speed analytics.

The service includes scalable Postgres backed by NVMe storage, native change data capture (CDC), and tight synchronization with ClickHouse—enabling up to 100x faster analytics on transactional data. A unified query layer, powered by a native Postgres extension, allows developers to build applications that span transactions and analytics without managing separate systems.

The Postgres service is built in partnership with Ubicloud, an open-source cloud company led by veterans from Citus Data, Heroku, and Microsoft.

“Postgres and ClickHouse naturally complement each other for AI applications,” said Umur Cubukcu, Co-CEO and Co-Founder of Ubicloud. “Together, we’re removing complexity and delivering a production-grade stack where transactions and analytics work as one.”

This move reflects a growing industry push toward simplification. As AI applications proliferate, teams are increasingly wary of stitching together fragmented data stacks that add latency, cost, and operational risk.

Global expansion and ecosystem momentum

Alongside product expansion and acquisitions, ClickHouse continues to grow its global footprint. Over the past year, the company entered the Japanese market through a partnership with Japan Cloud and deepened its relationship with Microsoft Azure, including work around OneLake.

ClickHouse has also invested heavily in community and ecosystem development, hosting user events across San Francisco, New York, Amsterdam, Sydney, and Bangalore. These events have attracted more than 1,000 attendees and featured speakers from OpenAI, Tesla, Capital One, Ramp, and Canva—signaling broad adoption across industries.

On the product side, ClickHouse has expanded support for modern data lake formats, including Apache Iceberg and Delta Lake, and strengthened compatibility with widely used data catalogs. Full-text search capabilities have been enhanced to support observability and AI monitoring use cases, while lightweight updates have been introduced to meet the demands of AI-driven applications.

According to recent benchmarks, ClickHouse continues to outperform leading cloud data warehouses on price-performance—a critical differentiator as AI workloads drive data volumes sharply higher.

The bigger picture

ClickHouse’s $400 million raise is about more than scale—it’s about positioning. As AI applications become mainstream, the winners won’t just be model providers. They’ll be the infrastructure platforms that quietly, reliably power production systems at scale.

By combining real-time analytics, a unified transactional-analytical stack, and LLM observability under one roof, ClickHouse is betting that the future of AI runs on data platforms built for speed, efficiency, and continuous evaluation. For enterprises moving AI from prototype to production, that bet may prove well-timed.

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Alorica is staking its claim as one of the CX industry’s most pragmatic AI innovators. The digitally powered customer experience (CX) services leader has been named an Innovative Organization winner in the 2026 BIG Innovation Awards, a global program recognizing companies that turn innovation into measurable, real-world business outcomes.

The award highlights Alorica’s ability to operationalize artificial intelligence at scale across enterprise CX environments—an area where many vendors promise transformation but struggle to move beyond pilots. At the center of Alorica’s recognition are Alorica IQ, its digital innovation practice, and evoAI, its enterprise-grade conversational AI platform. Together, they represent a platform-driven approach grounded in what Alorica calls a “human-in-command” design philosophy.

In practical terms, that means AI is deployed not to replace people, but to augment agents, streamline workflows, and improve customer outcomes—without breaking existing systems or frontline trust.

From AI ambition to operational impact

Enterprise CX leaders are under intense pressure to modernize operations with AI while maintaining service quality, compliance, and employee engagement. Alorica’s BIG Innovation Award reflects its ability to navigate that balancing act.

“This recognition validates our belief that innovation only matters when it delivers real business outcomes,” said Max Schwendner, Co-CEO of Alorica. “Through Alorica IQ and evoAI, we help brands convert AI investment into operational value—where human expertise and intelligent automation work together to build trust, efficiency, and long-term growth.”

That framing aligns closely with how the CX market is evolving. As generative AI adoption accelerates, buyers are increasingly skeptical of standalone tools and black-box automation. What they want instead are platforms that integrate with existing ecosystems, scale globally, and deliver clear ROI.

Alorica IQ: Turning applied AI into a repeatable system

Alorica IQ functions as the company’s innovation engine, designed to move AI from experimentation to enterprise-wide execution. Unlike traditional innovation labs that operate at the edge of the organization, Alorica IQ blends platform engineering, frontline agent insight, and large-scale deployment capabilities.

This model allows Alorica to operationalize applied AI across client programs in a repeatable way—turning point solutions into standardized, scalable capabilities. For global brands managing millions of customer interactions, that repeatability is critical.

Alorica IQ’s role is less about flashy demos and more about building production-ready systems that can be governed, secured, and continuously improved. In an era where AI risk management is top of mind for CMOs and CIOs alike, that approach is resonating.

evoAI: Conversational AI designed with agents, not around them

At the core of Alorica’s AI stack is evoAI, a conversational AI platform built specifically for enterprise CX use cases. Rather than forcing organizations to redesign their operations around AI, evoAI is designed to integrate seamlessly into existing technology environments.

The platform supports more than 100 languages and dialects and delivers context-aware, emotionally intelligent interactions across both voice and digital channels. Importantly, evoAI was built in partnership with frontline CX agents—a design choice that directly addresses one of the biggest barriers to AI adoption in contact centers: agent resistance.

By enhancing workflows instead of disrupting them, evoAI improves adoption, boosts agent confidence, and drives performance gains without eroding trust.

That human-centric design is increasingly important as regulators, unions, and employees scrutinize how AI is deployed in customer-facing roles.

Measurable results across industries

What ultimately sets Alorica apart in a crowded CX technology landscape is performance data. Across multiple enterprise deployments, evoAI has demonstrated the ability to automate up to 50% of customer interactions and reduce average handle time by as much as 40%.

Those efficiency gains translate directly into cost savings, but the impact goes beyond operations. Alorica reports quantifiable improvements in CSAT, engagement, and conversion across verticals including telecom, retail, healthcare, and financial services.

For brands navigating margin pressure and rising customer expectations, those results underscore why AI is becoming a strategic CX lever rather than a back-office optimization tool.

A broader signal to the CX and MarTech market

Alorica’s BIG Innovation Award lands at a moment when CX, MarTech, and AI are converging. Marketing leaders are increasingly accountable for post-acquisition experiences, while CX teams are being asked to contribute directly to growth, loyalty, and lifetime value.

In that context, platforms like evoAI blur traditional lines—connecting conversational AI, data, and human expertise into a single operational layer that impacts both marketing and service outcomes.

“Innovation has always been part of Alorica’s DNA, but we focus on technology that drives scalable growth for both Alorica and our clients,” said Mike Clifton, Co-CEO of Alorica. “Our solutions are built to be trusted, secure, and enterprise-ready, helping brands modernize operations and create more meaningful, efficient customer interactions.”

Recognition builds on a growing awards track record

The 2026 BIG Innovation Award adds to an expanding list of industry accolades for Alorica and evoAI. The platform is now a seven-time award winner, with previous recognition including a Bronze Stevie for Technology Excellence, a Gold Globee Disruptor Award, the AI Breakthrough Award for Conversational AI Innovation, and multiple BIG and TMC honors.

While awards alone don’t define market leadership, the consistency of recognition points to a clear narrative: Alorica is executing where many AI initiatives stall—at the intersection of scale, trust, and measurable business impact.

For enterprise brands evaluating how to bring AI into CX without sacrificing human connection, Alorica’s latest win reinforces a growing consensus in the market: the future of customer experience isn’t human versus AI—it’s human plus AI, deployed with purpose.

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Shippo, the e-commerce shipping platform used by brands, marketplaces, and developers, is placing a bigger bet on growth as competition in logistics tech intensifies. The company has appointed Brad Ramsey as its new Chief Marketing Officer, tasking the veteran marketer with leading Shippo’s global marketing and sales organizations as it expands beyond its SMB roots into the midmarket and platform-driven commerce ecosystem.

Ramsey will report directly to Shippo CEO and co-founder Laura Behrens Wu, and his mandate is clear: sharpen Shippo’s go-to-market execution, evolve the brand for a broader audience, and help the company scale as shipping becomes a strategic lever—not just a backend function—for modern e-commerce businesses.

A seasoned operator for a crowded market

Ramsey brings more than two decades of sales and marketing leadership experience, with a résumé that reads like a tour of modern tech marketing. Before joining Shippo, he served as CMO at Pirate Ship, where he helped expand the brand’s reach into small and mid-sized businesses—an audience increasingly hungry for cost-efficient, easy-to-use shipping solutions amid rising carrier rates and margin pressure.

Earlier roles at Facebook, Google, Twitter, and Quizlet saw Ramsey build and scale global marketing teams across both B2C and B2B environments. That mix matters. Today’s e-commerce infrastructure companies are no longer selling to a single persona. They must appeal simultaneously to merchants, developers, platform partners, and increasingly, operations and finance leaders who scrutinize shipping costs as closely as ad spend.

Shippo’s leadership clearly sees Ramsey’s background as well suited to that complexity.

“Brad brings a rare combination of brand leadership, operational rigor, and deep domain experience with scaling marketing organizations at some of the world’s most innovative technology companies,” said Behrens Wu. “As we continue to invest in new capabilities and expand our solutions to solve challenges faced by larger organizations, Brad’s leadership will be critical to our next phase of growth.”

Why this hire matters now

Shipping has quietly become one of the most competitive layers of the MarTech and commerce tech stack. Platforms like Shopify and Amazon continue to tighten their grip on fulfillment, while independent tools—from Shippo to ShipStation and EasyPost—race to differentiate on integrations, automation, and analytics.

For Shippo, the challenge is no longer awareness alone. It’s positioning. As merchants scale, they expect shipping platforms to behave more like infrastructure partners than point tools. That means clearer value propositions, stronger ecosystem messaging, and tighter alignment between product, marketing, and sales.

Ramsey’s role spans all of that. According to the company, he will focus on scaling cross-segment go-to-market execution, refining Shippo’s brand strategy, and supporting platform growth as the company deepens its reach across e-commerce brands, marketplaces, and technology partners.

In practical terms, that likely translates into more vertical-specific messaging, clearer differentiation against both embedded platform solutions and standalone rivals, and a stronger narrative around how shipping data and automation can drive growth—not just savings.

From growth marketing to brand maturity

One notable aspect of Ramsey’s appointment is the emphasis on both brand and growth. In recent years, many B2B platforms leaned heavily into performance marketing. But rising acquisition costs and longer buying cycles have forced a rethink. Brand, trust, and perceived reliability now play a larger role, especially as platforms move upmarket.

Shippo appears to be following that shift. Its customer base has expanded beyond scrappy startups to include more established e-commerce brands and platforms that demand stability, scale, and roadmap clarity. A CMO with experience at global consumer and enterprise brands signals an intent to mature how Shippo presents itself to the market.

Ramsey himself framed the move in terms of connection and enablement rather than pure acquisition.

“I’m very excited to join the team to help grow our brand globally and create stronger connections with merchants and partners,” he said. “Shippo’s mission of using simple, technology-based shipping solutions to help businesses thrive really resonates with me—and I’m excited to help accelerate its next phase of growth.”

The broader MarTech implication

Shippo’s move reflects a broader trend across MarTech and commerce infrastructure companies: leadership hires are increasingly about readiness for scale, not just momentum. As AI-driven optimization, real-time logistics data, and platform ecosystems reshape e-commerce operations, marketing leaders are expected to bridge product complexity with market clarity.

For MarTech leaders watching the space, the takeaway is straightforward. Shipping is no longer an operational afterthought—it’s a strategic growth channel. And companies that can articulate that story clearly, to the right audiences, stand to gain an edge as merchants rethink their tech stacks in 2025 and beyond.

With Ramsey at the helm of marketing, Shippo is signaling that it intends to compete not just on features, but on narrative, trust, and long-term platform value.

Get in touch with our MarTech Experts.

Inspired Thinking Group (ITG) is deepening its bet on AI-powered content operations. The company announced it will integrate Adobe Firefly’s generative AI services directly into Storyteq, its content marketing platform (CMP), giving enterprise marketers native access to Adobe’s creative AI models within their day-to-day content workflows.

The move positions Storyteq not just as a system of record for content, but as an execution layer where creation, personalization, governance, and performance optimization increasingly converge. At a time when brands are under pressure to produce more content across more channels—with fewer resources—this integration targets one of marketing’s most persistent pain points: scaling high-quality, on-brand content without slowing teams down.

What Adobe Firefly adds to Storyteq

Through Firefly Services, Storyteq users will be able to tap directly into Adobe’s generative and creative AI models, extending the platform’s existing AI capabilities. Rather than treating generative AI as a bolt-on creative tool, ITG is embedding Firefly into the operational fabric of content production—where briefs are written, assets are created, approved, localized, and activated.

According to ITG CEO Andrew Swinand, the goal is to make Storyteq a “foundational backbone” for AI-driven content management and automation.

“Adobe’s acknowledged strength in generative AI gives Storyteq users another great tool out of the box to help create better content faster, more cost-effectively and at scale,” Swinand said.

That framing matters. Many brands are experimenting with generative AI in isolation—design teams here, social teams there. ITG’s approach suggests a shift from experimentation to infrastructure, where generative AI becomes a governed, repeatable part of enterprise content operations.

Halo Intelligence meets Firefly

A key differentiator in ITG’s announcement is the pairing of Adobe Firefly with ITG’s proprietary Halo Intelligence. While Firefly handles generative and creative execution, Halo Intelligence focuses on analysis—using AI-driven insights to determine what content is most likely to engage specific audiences, in specific contexts.

By combining the two, Storyteq aims to close the loop between insight and output.

“Pairing Halo Intelligence with gen AI models such as Firefly means users are not only able to create more content for less, but also create content that is optimized and personalized to every customer interaction,” Swinand explained.

This speaks to a broader trend in MarTech: generative AI alone isn’t enough. Without intelligence layers that guide what to create and why, brands risk flooding channels with content that is fast—but ineffective. ITG is positioning Storyteq as a platform that helps marketers decide, generate, and optimize in one system, rather than stitching together disconnected tools.

Operational efficiency, not just creativity

Beyond content creation, ITG says the Firefly integration will drive efficiencies across the full content lifecycle, including briefing, workflows, approvals, and compliance. For global brands, these steps are often where scale breaks down—assets get delayed, governance becomes inconsistent, and localization introduces risk.

By embedding AI into these operational stages, ITG is targeting a less glamorous but more valuable outcome: speed with control. That’s especially relevant in regulated industries and large enterprises, where compliance and brand governance can’t be sacrificed for velocity.

The integration will also enhance ITG’s global creative services, suggesting Firefly-powered workflows won’t be limited to self-serve platform users but will extend into managed services and enterprise engagements.

A crowded market, but rising expectations

The CMP and DAM markets are becoming increasingly competitive as vendors race to layer generative AI into their platforms. Adobe, Salesforce, Sitecore, and a growing list of composable MarTech players are all making similar moves. The difference often comes down to depth of integration and practical usability, not AI claims alone.

Storyteq has already built a reputation around AI-driven content lifecycle management. Gartner previously noted that “ITG’s AI solutions lead the market” and described Storyteq as “uniquely powering the full content lifecycle.” Gartner has also named Storyteq a market leader in both content marketing platforms and DAM, citing its enterprise asset management capabilities.

By integrating Adobe Firefly—one of the most widely recognized and enterprise-trusted generative AI offerings—ITG strengthens that positioning. It also reduces friction for marketing teams already invested in Adobe’s creative ecosystem, a practical advantage in large organizations where tool sprawl is a constant concern.

Why this matters for MarTech leaders

This announcement underscores a broader shift in MarTech strategy: AI is moving from feature to foundation. Marketers are no longer asking whether platforms support generative AI, but how deeply it is embedded into workflows, governance, and performance optimization.

For ITG, the Firefly integration signals an ambition to own the middle layer between creativity and execution—where content decisions, generation, and activation happen at scale. For enterprise marketers, it points to a future where AI doesn’t just accelerate production, but actively shapes what content gets made, for whom, and why.

As AI-driven content expectations continue to rise, platforms that can combine trusted creative models with operational intelligence may be best positioned to move from experimentation to sustained competitive advantage.

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TARA Media is taking a sharper stance against programmatic waste. The data-driven marketing agency has announced a strategic partnership with SWYM.ai that embeds algorithmic supply curation directly into TARA’s proprietary Media Buying Platform (MBP), giving advertisers far more control over where—and how—their media dollars are spent.

At its core, the integration is designed to tackle one of digital advertising’s most persistent problems: fragmented supply paths that dilute performance, inflate costs, and obscure accountability. By pairing TARA Media’s DSP and data infrastructure with SWYM.ai’s real-time supply shaping and decisioning engine, the two companies are aiming to make programmatic buying more intentional, transparent, and outcomes-driven.

Why supply curation is becoming non-negotiable

As digital advertising has expanded across websites, mobile apps, CTV, and emerging formats, the supply side has grown increasingly complex. Advertisers now navigate overlapping exchanges, resellers, and opaque auction dynamics—often bidding on impressions that add little value.

TARA Media’s MBP already positions itself as an all-in-one DSP that allows agencies and advertisers to activate campaigns, build data-driven audiences, and measure performance with a high degree of transparency. The SWYM.ai integration pushes that proposition further upstream, intervening before bids are placed.

Instead of reacting to inefficient supply after the fact, advertisers can now shape the bidstream in advance—filtering inventory based on quality, attention, pricing, and performance signals in real time.

In practical terms, that means fewer wasted impressions, tighter alignment with campaign KPIs, and more confidence that media budgets are flowing to publishers that actually deliver value.

How the TARA–SWYM.ai integration works

SWYM.ai’s technology is built around what it bluntly calls “Stop Wasting Your Money” algorithmic decisioning. Embedded directly into the MBP, it allows TARA Media users to:

• Curate and activate inventory across all of TARA’s supply partners from a single workflow

• Filter bid opportunities before bids are submitted, not after

• Build bespoke inventory packages aligned to specific campaign goals

• Prioritize high-attention placements rather than sheer reach

This approach is particularly relevant for medium and large-scale advertisers that need precision without the overhead typically associated with custom supply deals or holding company-level tooling.

“Our mission with the TARA Media MBP has always been to promote access to elite media buying tools for campaigns of all sizes,” said Jeff Kaplan, CEO of TARA Media. “By integrating SWYM.ai’s supply curation capabilities, we are giving our clients a level of control typically reserved for the largest holding companies.”

That last point is key. Supply path optimization and advanced curation have often been gated behind enterprise contracts, custom builds, or internal trading desks. TARA and SWYM.ai are positioning this integration as a way to democratize that level of control.

Key capabilities advertisers gain

The partnership introduces several capabilities that reflect where programmatic buying is headed:

Dynamic traffic and supply shaping
Inventory can be continuously built, curated, and activated based on live signals around quality, pricing, and performance—without jumping between platforms.

AI-driven decisioning tied to outcomes
The system evaluates supply paths and bid opportunities in real time, optimizing toward KPIs such as CPA, CTA, and ROAS rather than static rules or assumptions.

Contextual intelligence at scale
Instead of relying on outdated keyword lists or pre-approved site bundles, advertisers can activate contextual strategies using live content signals—an increasingly important capability in a privacy-first, post-cookie environment.

Collectively, these features point to a shift away from broad, exchange-level buying toward more surgical control of media access.

A broader signal for the AdTech market

This partnership reflects a wider industry trend: the growing recognition that where you buy matters as much as how you bid. As advertisers scrutinize media efficiency more closely—especially in CTV and open web environments—supply quality, attention metrics, and path transparency are moving from “nice to have” to core buying criteria.

SWYM.ai CEO and co-founder Ravi Patel framed the integration as a response to the realities of modern media buying.

“Advertisers work on immovable deadlines where every impression counts,” Patel said. “By embedding our decisioning engine into TARA Media’s MBP, buyers can continuously shape which inventory is accessed, how it’s priced, and how aggressively to bid, based on real performance and quality signals—at scale.”

That emphasis on continuous shaping is notable. It suggests a future where supply decisions are not static pre-campaign choices, but living systems that adapt as campaigns run.

What this means for marketers

For brands and agencies navigating tightening budgets and rising scrutiny, the TARA–SWYM.ai integration offers a clear promise: fewer wasted impressions, more control, and greater alignment between media spend and business outcomes.

More broadly, it signals where programmatic buying is headed. DSPs are no longer just execution engines; they are becoming decision layers that determine which parts of the digital ecosystem are worth accessing in the first place.

If that model takes hold, partnerships like this one may become less of a differentiator—and more of a baseline expectation.

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Amplitude is making a clear statement about where marketing analytics needs to go next. The digital analytics company has acquired InfiniGrow, an AI-powered marketing analytics startup, in a move designed to close a familiar—and costly—gap: insights that live in dashboards but never quite make it into real decisions.

For Amplitude, the acquisition reinforces a long-running strategy shift from descriptive analytics toward decision intelligence. The goal is no longer just to explain what happened, but to help marketers understand what to do next—and how those choices will affect revenue.

From dashboards to decisions

Marketing teams today are drowning in data but starving for clarity. Campaign performance, customer journeys, attribution models, and revenue metrics often live across disconnected tools. As a result, analysts surface insights, while budget and strategy decisions happen elsewhere—often based on intuition rather than evidence.

InfiniGrow was built to address exactly that problem. Its AI-driven platform focuses on measuring how marketing drives revenue, forecasting outcomes, and modeling tradeoffs before money is spent. By bringing InfiniGrow into the fold, Amplitude is aiming to make analytics directly actionable inside a single platform.

“InfiniGrow built AI to answer the hardest questions marketers face, and that’s exactly what Amplitude does,” said Spenser Skates, co-founder and CEO of Amplitude. “Together, we’re turning complex data into clear decisions teams can act on confidently.”

That framing matters. In a market crowded with analytics tools, Amplitude is positioning itself less as a reporting layer and more as a system for decision-making.

Why session-based analytics matter more than ever

Alongside the acquisition, Amplitude is doubling down on a core technical foundation: session-based analytics. While events have become the dominant currency of digital analytics, sessions remain critical for marketers trying to understand intent, journeys, and outcomes across channels.

Amplitude says it is making sessions “first-class” across the platform, preserving session context over time so marketing actions can be directly tied to business results. Without that continuity, insights can quickly become misleading—and optimization turns into guesswork.

This focus also reflects a broader industry realization. As privacy changes limit third-party data and attribution becomes harder, first-party session data is increasingly valuable. Accurate session context gives marketers a clearer view of how users move from engagement to conversion, and where marketing actually influences revenue.

InfiniGrow’s technology builds on that foundation rather than replacing it. Its AI models rely on clean, contextual customer journey data—precisely the kind of data Amplitude specializes in capturing.

What InfiniGrow adds to Amplitude

InfiniGrow brings advanced AI capabilities designed specifically for marketing decision-making, not just analysis. Key capabilities include:

• Revenue measurement: Clear visibility into how marketing efforts contribute to pipeline and revenue, not just clicks or conversions.

• Forecasting and what-if analysis: AI-driven scenario modeling that lets teams test assumptions, explore tradeoffs, and predict outcomes before committing budget.

• Optimization at planning time: Instead of optimizing only after campaigns run, marketers can iteratively plan and allocate spend based on forecasted impact.

This shifts analytics upstream in the marketing workflow. Rather than asking, “Did this work?” teams can ask, “What’s most likely to work next?”

“We built InfiniGrow to apply AI to the real decisions marketers face every day,” said Daniel Meler, co-founder and CEO of InfiniGrow. “Joining Amplitude allows us to scale that work and contribute to a broader AI analytics vision that empowers teams to act with confidence.”

A signal to the MarTech market

Amplitude’s move reflects a larger trend in MarTech: analytics platforms are racing to become decision engines. Vendors across the space are layering AI into dashboards, but many still struggle to connect insights directly to planning, budgeting, and execution.

By combining InfiniGrow’s AI-driven forecasting with its own session-based analytics foundation, Amplitude is betting that marketers want fewer tools—and more confidence. If successful, this could reduce reliance on separate planning, attribution, and modeling platforms.

It also raises the bar for competitors. Simply visualizing data is no longer enough. Marketers increasingly expect analytics to recommend actions, quantify risk, and show clear revenue impact.

What marketers should take away

For marketing leaders, the acquisition points to a practical shift: analytics should inform decisions before money is spent, not just explain results afterward. As budgets face tighter scrutiny, tools that can forecast impact and justify spend are becoming essential.

Amplitude’s challenge now is execution—integrating InfiniGrow’s capabilities seamlessly and proving that AI-driven forecasts can be trusted in real-world planning. If it succeeds, the company could move from being a product analytics leader to a central intelligence layer for modern marketing teams.

In a landscape where speed and confidence increasingly define competitive advantage, that’s a meaningful step forward.

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