CallRail is pushing AI voice from reactive to contextual with a new upgrade to its HubSpot integration—one that lets its Voice Assist platform access CRM data before a conversation even begins.

The result: an AI voice assistant that doesn’t just answer calls, but recognizes who’s calling, recalls past interactions, and responds accordingly. It’s a small technical shift with big implications for how businesses handle inbound leads—especially in a world where customers expect continuity, not repetition.

From “Hello” to Context in Seconds

At the center of the update is real-time CRM access.

When a call comes in, CallRail’s Voice Assist checks for a match inside HubSpot. If it finds one, the assistant can immediately tailor the interaction—skipping basic questions, referencing past conversations, and delivering a more personalized greeting.

In practical terms, that means:

• Returning callers are recognized instantly
• Conversations pick up where they left off
• Known details don’t need to be repeated

If no match exists, the system defaults to a standard intake flow, capturing new lead data without interrupting the experience.

It’s a straightforward feature on paper, but one that addresses a persistent pain point in AI-driven customer interactions: the lack of memory.

Fixing AI’s “Memory Problem”

Despite rapid advances in conversational AI, most voice assistants still operate in isolation. Each call is treated as a fresh interaction, forcing customers to reintroduce themselves and restate their needs.

That disconnect—what CallRail describes as the “memory gap”—does more than annoy users. It slows down conversations, erodes trust, and ultimately hurts conversion rates.

By integrating CRM context at the start of every call, CallRail is attempting to close that gap. The assistant doesn’t just respond; it continues a relationship.

That aligns with a broader trend across MarTech: the push to make AI systems stateful, not stateless—aware of history, context, and customer identity across touchpoints.

More Than Just Personalization

While personalization is the headline feature, the integration also tightens the feedback loop between conversations and CRM data.

With the update, Voice Assist can automatically log:

• Caller intake details
• Conversation summaries
• Updated lead information

directly into HubSpot.

That reduces manual data entry and ensures records stay current—an ongoing challenge for sales and marketing teams relying on CRM accuracy to drive decisions.

It also reinforces the idea that AI voice isn’t just a front-end experience layer. It’s becoming a data collection and enrichment engine in its own right.

Why This Matters for B2C Marketers

The timing of this update reflects rising expectations from customers—and increasing pressure on businesses.

As CRM adoption grows, so does the expectation that companies will actually use that data in real time. Customers don’t just want to be stored in a database; they want to be recognized.

Failing to meet that expectation can have measurable consequences:

• Lower engagement during calls
• Increased drop-offs
• Reduced conversion rates

CallRail’s approach tackles that problem directly, especially for high-intent inbound calls where context can make or break the interaction.

AI Voice Moves Into Revenue Territory

There’s also a bigger shift at play.

AI voice assistants are evolving from cost-saving tools—handling overflow calls or after-hours support—into revenue-generating assets. By improving conversion rates and customer experience, they’re starting to influence the top line, not just operational efficiency.

CallRail is leaning into that narrative. Its updated Voice Assist is positioned not just as a support tool, but as a way to turn more conversations into conversions without adding headcount.

That’s particularly relevant for small and mid-sized businesses, where missed calls or inconsistent follow-ups can directly impact revenue.

Competitive Context

CallRail isn’t alone in this space. CRM-native AI and conversational platforms are racing to embed deeper context into customer interactions.

What differentiates this move is the timing of the data access—before the conversation begins, not midstream. That subtle distinction makes the interaction feel more natural, avoiding the awkward “let me look that up” moment that often breaks immersion in AI conversations.

It’s a step toward making AI voice feel less like a bot and more like a well-informed human agent.

The Bottom Line

With this HubSpot integration upgrade, CallRail is addressing one of the most noticeable gaps in AI voice: the inability to remember.

By bringing CRM context into the first second of a call, it’s turning voice interactions into continuous, relationship-driven experiences rather than isolated transactions.

For marketers, that could mean fewer dropped conversations, stronger customer trust, and ultimately, better conversion outcomes—all without adding strain on human teams.

As AI voice becomes more embedded in the customer journey, the winners won’t just be the ones who can talk—they’ll be the ones who can remember.

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Ndovesha AI is betting big on a future where marketing teams don’t just use AI—they delegate to it.

The company has announced an expanded version of its all-in-one AI agent platform, designed to help businesses generate marketing assets, automate content production, and accelerate digital growth using specialized, task-focused AI agents. The pitch is simple: replace fragmented tools and manual workflows with a unified system that actually produces finished marketing outputs.

It’s a crowded category, but Ndovesha AI is leaning into a clear differentiator—execution over assistance.

From Prompts to Production

Most generative AI tools still operate as assistants. They generate text, suggest ideas, or create drafts, leaving users to stitch together the final output across multiple platforms.

Ndovesha AI is aiming to collapse that process.

Its platform bundles a wide range of AI-powered agents into a single workspace capable of producing:

• Social media visuals, carousels, and video content
• Ad creatives and marketing captions
• Flyers, logos, and branded assets
• Landing pages and full websites
• Presentations and blog articles

The idea isn’t just speed—it’s completeness. Instead of moving from prompt to draft to design tool to publishing platform, users can generate ready-to-use assets in one environment.

That shift reflects a broader industry move toward agentic AI—systems that don’t just assist with tasks but execute them end to end.

The Rise of Specialized AI Agents

At the core of Ndovesha AI’s platform is a growing library of purpose-built agents, each focused on a specific marketing function.

These include tools like a prompt generator, AI ad creator, carousel generator, video content agent, and website builder, among others. While individually these capabilities aren’t new, packaging them into a coordinated system is where the company sees its edge.

This modular approach mirrors a wider trend in AI development: breaking down workflows into discrete, automatable units that can be orchestrated together. For marketing teams, that could mean faster campaign execution with fewer handoffs between tools and teams.

Targeting Speed—and Cost Pressure

The timing of this expansion is no coincidence.

Businesses across sectors are under pressure to produce more content across more channels, often with limited budgets and leaner teams. At the same time, expectations for quality and personalization continue to rise.

AI-powered content generation has emerged as a solution, but many tools still require significant human oversight and integration effort.

Ndovesha AI is positioning itself as a more practical alternative—one that reduces both production time and creative costs by automating the full lifecycle of asset creation.

That value proposition is particularly relevant for startups and SMEs, though the platform is also targeting agencies and enterprise teams.

Competing in a Crowded AI Stack

Ndovesha AI enters a competitive landscape filled with generative AI platforms, design automation tools, and marketing suites—all vying to become the central hub for content creation.

What sets this approach apart is its emphasis on consolidation. Instead of excelling at one function, the platform aims to cover many—design, copy, web development, and campaign assets—under a single interface.

That breadth can be a double-edged sword. While it simplifies workflows, it also raises expectations around quality and depth in each category, where specialized tools often still have an advantage.

Still, as AI models improve and workflows become more standardized, all-in-one platforms are gaining traction—especially among teams looking to reduce tool sprawl.

A Play for Emerging and Global Markets

Ndovesha AI is also positioning itself with a geographic lens, aiming to serve businesses across Africa and other growth markets.

In regions where access to design, development, and marketing resources can be uneven, an integrated AI platform could level the playing field—giving smaller teams the ability to launch and scale digital campaigns quickly.

That aligns with a broader democratization trend in AI, where advanced capabilities are becoming more accessible beyond traditional tech hubs.

The Bigger Picture: Agentic AI Goes Mainstream

Industry analysts have been pointing to autonomous and agentic AI as the next major wave of productivity tools. The shift is from tools that assist humans to systems that act on their behalf.

Ndovesha AI’s platform is a clear example of that transition in the marketing domain.

Instead of asking, “How can AI help me create this asset?” the question becomes, “Which agent should handle this task?”

It’s a subtle but important shift—one that could redefine how marketing teams operate over the next few years.

The Bottom Line

Ndovesha AI is stepping into the agentic AI race with an ambitious goal: to turn marketing production into a largely automated, AI-driven process.

By combining multiple specialized agents into a single platform, it’s aiming to streamline workflows, cut costs, and help businesses move faster from idea to execution.

Whether it can compete with more established players will depend on how well it balances breadth with quality. But the direction is clear—AI in marketing is moving beyond assistance and into full-scale execution.

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Cloudinary is doubling down on video automation with a fresh set of AI-powered upgrades to MediaFlows, its no-code workflow engine. The new capabilities aim to tackle one of the most stubborn bottlenecks in digital marketing: the slow, manual grind of video post-production.

With features like automated subtitles, metadata generation, and chapter creation, the company is positioning MediaFlows as more than a workflow tool—it’s becoming an end-to-end video optimization engine built for scale.

Automating the Parts No One Wants to Do

Video may dominate engagement metrics, but producing it efficiently—especially at scale—remains a challenge.

According to Wyzowl, 63% of consumers prefer learning about products through video. That demand is pushing brands to produce more content, faster, and for more channels. But behind every polished video is a stack of manual tasks: captioning, tagging, localization, and formatting for discoverability.

Cloudinary’s latest update targets exactly those pain points.

MediaFlows now uses AI to automate key post-production workflows that traditionally require specialist tools and teams, including:

• Generating and translating subtitles
• Creating titles, descriptions, and metadata tags
• Adding chapter markers for easier navigation

The goal is straightforward: reduce time-to-publish while improving accessibility and search performance.

Built for a Multi-Platform, Multi-Language World

One of the standout features is automated subtitle generation and translation.

As brands expand into global markets, localization has become essential—not optional. Yet translating video content has historically been resource-intensive and slow. By automating subtitle extraction and translation, Cloudinary is making it easier to adapt content for different regions without duplicating effort.

This also ties directly into accessibility requirements, which increasingly mandate subtitled or captioned content across industries.

In parallel, AI-generated metadata helps videos perform better not just in traditional search engines, but also in emerging discovery environments—particularly LLM-powered or “agentic” search systems.

That’s a subtle but important shift. Video SEO is no longer just about keywords; it’s about structured, machine-readable context that AI systems can interpret.

Smarter Navigation for Long-Form Content

Another addition—automated chapter generation—addresses a growing trend in long-form video.

From product demos to explainer content, longer videos are becoming more common in B2B and e-commerce. But without proper structure, they can be difficult to navigate.

By automatically inserting chapter markers, MediaFlows improves the viewing experience while also making content more usable across platforms that support segmented playback.

It’s a small feature with outsized impact, particularly for brands investing in educational or high-intent video content.

No-Code Meets Natural Language

Cloudinary is also leaning into usability.

Teams can build custom workflows using MediaFlows’ no-code interface or simply describe what they want using a natural language workflow agent. That lowers the barrier for marketing and content teams who may not have technical expertise but still need to manage complex video pipelines.

This aligns with a broader industry push toward democratizing AI—moving advanced capabilities out of engineering teams and into the hands of everyday users.

Why This Matters for Marketers

The bigger story here isn’t just automation—it’s operationalization.

Most brands already know video drives engagement, conversions, and trust. The challenge has been scaling production without ballooning costs or timelines.

Cloudinary’s approach reframes video not as a creative bottleneck, but as a process that can be optimized and automated like any other part of the marketing stack.

For industries like e-commerce, media, and enterprise tech—where speed, localization, and compliance all matter—this could be a meaningful shift.

Same-day publishing across regions, for example, becomes far more achievable when subtitles, metadata, and structure are handled automatically.

Competitive Context

Cloudinary isn’t alone in bringing AI into video workflows. A range of platforms—from video editing tools to marketing suites—are adding automation features.

What differentiates MediaFlows is its focus on orchestration rather than creation. Instead of generating video content itself, it optimizes and prepares that content for distribution, discovery, and compliance.

That makes it a natural fit for teams already producing video but struggling to scale post-production efficiently.

The Bottom Line

With these MediaFlows updates, Cloudinary is targeting a critical gap in the video pipeline: everything that happens after the edit.

By automating subtitles, metadata, and navigation, it’s helping brands move faster while improving accessibility and search performance across both traditional and AI-driven channels.

As video consumption continues to rise—and as discovery shifts toward AI-powered systems—the ability to produce optimized content at scale could become a defining competitive advantage.

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NIQ is sharpening its focus on localized growth with the launch of Precision Solutions in the U.S., a new platform designed to help brands and retailers move beyond one-size-fits-all strategies.

The premise is simple: broad, market-level decision-making no longer cuts it in a fragmented retail landscape. Shopper behavior now varies dramatically by store, neighborhood, and region—and companies that fail to adapt risk wasting spend and missing high-value opportunities.

Precision Solutions aims to close that gap by bringing together data, analytics, and execution into a single, localized decision engine.

From Mass Strategy to Micro Decisions

For years, retail growth strategies have leaned heavily on aggregated data—national trends, category performance, and generalized shopper insights.

That model is increasingly outdated.

Today’s consumers behave differently not just by demographic segment, but by geography. What sells in one store—or even one zip code—may underperform just a few miles away. Yet many organizations still lack the tools to act on that level of granularity.

NIQ’s new platform is built to change that.

By combining retail measurement data, consumer panel insights, and AI-driven analytics, Precision Solutions helps organizations pinpoint where growth is actually happening—and where it isn’t.

A Unified View of Growth Opportunities

One of the key selling points is integration.

Historically, brands have had to piece together insights from separate tools: one for sales data, another for shopper behavior, and yet another for analytics. That fragmentation slows decision-making and makes it harder to connect actions with outcomes.

Precision Solutions consolidates those capabilities into a single platform, allowing teams to:

• Identify high-potential stores and local markets
• Pinpoint valuable shopper segments
• Allocate spend more effectively
• Test strategies in real-world conditions
• Measure impact with clearer attribution

The goal is not just better insights, but faster and more confident execution.

AI Adds a Predictive Layer

Beyond data aggregation, NIQ is layering in AI-enabled analytics to move from hindsight to foresight.

The platform can simulate potential outcomes, helping teams understand which strategies are likely to perform before committing resources. It also aims to isolate true performance signals—cutting through noise to identify what’s actually driving growth.

That’s particularly valuable in retail environments where multiple variables—pricing, promotions, assortment, media—interact in complex ways.

Instead of guessing which lever to pull, teams can test and refine strategies with measurable feedback loops.

Why Localization Is Becoming Critical

The launch comes at a time when localization is emerging as a competitive differentiator.

Rising media costs and tighter margins are forcing brands to be more selective with their investments. At the same time, retailers are demanding more tailored strategies that reflect local demand patterns.

The result is a shift from “do more everywhere” to “do the right things in the right places.”

NIQ’s Precision Solutions is built around that philosophy—prioritizing depth over breadth in growth strategies.

Implications for Brands and Retailers

For brands, the platform offers a way to align trade spend, marketing, and assortment decisions with actual local demand—potentially improving ROI and reducing wasted investment.

For retailers, it provides more granular insights into store-level performance, enabling better merchandising and promotional strategies.

In both cases, the emphasis is on measurable outcomes. The ability to test strategies in-market and quickly assess results could shorten decision cycles and reduce risk.

Competitive Context

NIQ operates in a space where data providers and analytics platforms are increasingly converging.

What sets Precision Solutions apart is its focus on unifying multiple data streams—retail measurement, consumer panels, and AI analytics—into a single workflow. That integration could appeal to organizations looking to simplify their tech stack while gaining deeper insights.

At the same time, the success of such platforms depends heavily on data quality and usability. Delivering actionable insights at the local level requires not just granular data, but also clear, intuitive tools for decision-makers.

The Bottom Line

With Precision Solutions, NIQ is making a clear bet: the future of retail growth lies in localization powered by data and AI.

By helping brands and retailers identify, test, and scale strategies at a granular level, the platform aims to turn fragmented shopper behavior from a challenge into an opportunity.

In a market where efficiency and accountability are under constant scrutiny, precision may prove to be the new competitive edge.

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Bloomreach is tightening its grip on the Shopify ecosystem with a new embedded app that aims to make advanced personalization both powerful and painless.

The company’s latest release—Loomi AI for Shopify—connects Shopify stores directly to Bloomreach’s marketing, search, and merchandising tools, all powered by its Loomi AI engine. The pitch: give merchants real-time, AI-driven personalization across every customer touchpoint without requiring code, integrations, or IT overhead.

In a market where personalization often comes with complexity, Bloomreach is betting that simplicity will win.

Personalization Without the Plumbing

For many Shopify merchants, personalization remains a trade-off. Sophisticated capabilities typically require stitching together multiple tools, managing data pipelines, and relying on technical teams.

Loomi AI for Shopify aims to remove that friction.

By embedding directly into Shopify, the app creates a unified layer where customer, product, and commerce data flow together in real time. That means merchants can activate personalization across channels—search, email, SMS, and onsite experiences—without exporting data or building custom integrations.

The result is less time managing infrastructure and more time optimizing customer experiences.

Real-Time Context Becomes the Differentiator

What sets this release apart is its emphasis on real-time intelligence.

Instead of relying solely on historical data, Loomi AI uses live behavioral signals—what customers are browsing, clicking, and buying in the moment—to adjust experiences dynamically.

That enables:

• Personalized search and browsing: Delivering relevant product results without manual rules
• Triggered marketing campaigns: Using live data to power email, SMS, and omnichannel outreach
• On-site personalization: Tailoring experiences from the first interaction, even for anonymous users

This shift toward real-time decisioning reflects a broader industry trend. Static segmentation is giving way to continuous, context-aware personalization that evolves with each interaction.

A Unified Approach to Merchandising and Marketing

Another key feature is synchronization.

Loomi AI connects merchandising and campaign execution into a single workflow. During high-stakes moments—like product launches or seasonal campaigns—teams can align onsite experiences with marketing efforts in real time.

That coordination is often easier said than done. In many organizations, merchandising and marketing operate in silos, leading to inconsistent customer experiences.

By unifying these functions, Bloomreach is aiming to reduce those gaps and create a more cohesive journey from discovery to purchase.

Global Reach, Local Relevance

The app also taps into Shopify Markets data to deliver localized personalization at scale.

For merchants selling internationally, that means adapting search results, recommendations, and campaigns based on region and language—without managing separate systems for each market.

Localization has become a critical growth lever in ecommerce, but it’s notoriously complex to execute. Automating that process could give merchants a meaningful edge, especially as cross-border commerce expands.

Smarter Promotions, Not Bigger Discounts

One of the more nuanced capabilities is AI-driven promotion targeting.

Rather than applying blanket discounts, Loomi AI identifies which shoppers actually need an incentive to convert—and targets them specifically. The goal is to protect margins while still driving incremental revenue.

It’s a subtle shift, but an important one. As customer acquisition costs rise, indiscriminate discounting is becoming less sustainable. Precision, not volume, is the new priority.

Why This Matters Now

The launch comes at a time when Shopify merchants are under increasing pressure to differentiate.

With more brands competing for attention—and new channels like AI-driven discovery platforms entering the mix—delivering consistent, personalized experiences is no longer optional.

Bloomreach is positioning Loomi AI as a way to meet that demand without adding operational complexity. The mention of emerging channels like AI assistants and conversational platforms hints at where this is heading: personalization that extends beyond traditional ecommerce touchpoints.

Competitive Context

Bloomreach isn’t alone in bringing AI personalization to Shopify. A growing number of apps and platforms are offering similar capabilities, from recommendation engines to marketing automation tools.

What differentiates Loomi AI for Shopify is its breadth and integration. Instead of focusing on a single function, it connects search, marketing, and merchandising under one AI-driven layer.

That all-in-one approach could appeal to merchants looking to consolidate tools and reduce fragmentation in their tech stack.

The Bottom Line

With Loomi AI for Shopify, Bloomreach is aiming to make enterprise-grade personalization accessible to everyday merchants.

By combining real-time data, AI decisioning, and a no-code interface, the platform promises to deliver consistent, context-aware experiences across every channel—without the usual complexity.

As ecommerce continues to evolve—and as customer journeys become more fragmented—the ability to unify data and act on it instantly may prove to be a defining advantage.

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Supergoop! is rethinking how it scales growth—and it’s starting with media consolidation.

The sunscreen brand has named January Digital as its media agency of record, bringing, for the first time, its direct-to-consumer, retail, and marketplace media efforts under a single partner. The move signals a broader shift toward unified, full-funnel strategies as brands look to connect fragmented customer journeys across channels.

The partnership kicked off with a campaign centered on Supergoop!’s flagship product, Unseen Sunscreen SPF 50—a product that has helped redefine sunscreen as an everyday essential rather than an occasional purchase.

From Channel Silos to Unified Strategy

Historically, many consumer brands have managed media across DTC, retail, and marketplaces in silos—often with different agencies, budgets, and measurement frameworks.

That model is increasingly difficult to sustain.

Consumers move fluidly between channels, discovering products on social, researching via search, and purchasing through retailers or marketplaces. Without a unified strategy, brands risk inconsistent messaging, inefficient spend, and limited visibility into what’s actually driving performance.

Supergoop!’s decision to consolidate media under one agency reflects a growing recognition that full-funnel coordination isn’t just a nice-to-have—it’s essential for scale.

A Campaign Built Around Cultural Momentum

The initial campaign focuses on Unseen Sunscreen SPF 50, arguably the brand’s most recognizable product.

Positioned as invisible, wearable, and suitable for daily use, the product has played a key role in shifting consumer perception of SPF—from a beach-day necessity to a daily skincare staple.

The campaign leans into that positioning, emphasizing ease of use and performance to reinforce habitual adoption. It also ties into Supergoop!’s broader brand evolution, including its expansion into sports through partnerships like the PGA TOUR.

In that context, Unseen Sunscreen serves as both a hero product and a bridge between lifestyle and performance use cases.

Full-Funnel, Multi-Channel Execution

January Digital is activating a comprehensive media mix that spans:

• Paid social and search
• Connected TV (CTV) and video
• Marketplace channels like Amazon
• Retail media networks, including Target Roundel and Nordstrom

This kind of cross-channel orchestration is becoming standard for brands aiming to capture demand wherever it emerges. But executing it effectively requires more than just presence—it requires alignment.

That’s where consolidation can make a difference. A single agency overseeing the full funnel can optimize spend holistically, rather than channel by channel.

Measurement Takes Center Stage

Beyond media execution, measurement is a key driver behind the partnership.

As brands invest across more channels, proving ROI becomes more complex. Attribution models often struggle to connect the dots between awareness, consideration, and conversion—especially when purchases happen off-site in retail or marketplace environments.

By centralizing media strategy, Supergoop! aims to bring more rigor and consistency to how performance is tracked and evaluated.

That focus on measurement reflects a broader industry trend: marketing leaders are under increasing pressure to tie spend directly to business outcomes.

Why This Matters for CPG Brands

Supergoop!’s move highlights a shift happening across consumer brands, particularly in categories like beauty and personal care.

The traditional divide between brand marketing and performance marketing is fading. Instead, companies are adopting integrated approaches that balance storytelling with measurable impact across every touchpoint.

Retail media networks, in particular, are playing a growing role in this mix, offering closed-loop attribution that connects ad exposure directly to sales. Combining those channels with DTC and marketplace efforts creates both opportunity—and complexity.

Consolidation is one way to manage that complexity.

The Agency Angle

For January Digital, the win reinforces its positioning as a partner for full-funnel, data-driven growth strategies.

Agencies are increasingly expected to go beyond channel execution, offering strategic guidance across the entire customer journey. That includes not just media planning, but also measurement frameworks and cross-channel optimization.

Winning AOR roles like this suggests demand is shifting toward agencies that can operate at that level.

The Bottom Line

Supergoop!’s decision to unify its media under January Digital reflects a broader evolution in how brands approach growth.

As customer journeys become more fragmented, the need for coordinated, full-funnel strategies is becoming unavoidable. Consolidating media efforts is one way to bring clarity, efficiency, and measurable impact to that complexity.

For Supergoop!, the goal is clear: turn strong brand affinity into sustained, scalable growth across every channel where customers shop.

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Innovid is sharpening its pitch to performance-focused marketers with a new round of measurement upgrades aimed at answering a deceptively simple question: what’s actually driving results?

In its latest “Feature Beat” update, the company is rolling out enhancements designed to connect media exposure to real business outcomes—moving beyond surface-level metrics toward deeper attribution, clearer sales impact, and more actionable insights.

It’s a timely shift. As ad channels multiply and budgets face tighter scrutiny, marketers are under pressure to prove not just engagement, but effectiveness.

From Metrics to Meaning

For years, digital advertising has leaned heavily on proxy metrics—clicks, impressions, view-through rates. Useful, but often disconnected from actual business performance.

Innovid’s latest updates aim to close that gap.

The platform now incorporates enhanced purchase measurement, allowing marketers to track both online and offline conversions while using control groups to isolate incremental impact. In other words, not just whether a campaign correlates with sales—but whether it caused them.

That distinction matters more than ever as brands look to justify spend across increasingly complex media ecosystems.

Attribution Gets More Granular

Attribution is another area getting a significant upgrade.

Innovid is expanding visibility into how specific variables—campaigns, audiences, creative versions, and targeting strategies—contribute to outcomes. The data is sourced directly from ad inventory, giving marketers a clearer view of where ads actually ran.

This level of detail enables:

• Source-level transparency across publishers, apps, and properties
• Better identification of high-performing channels and tactics
• More precise optimization during campaigns, not just after

For media buyers and planners, that kind of granularity can help untangle overlapping signals across platforms—a long-standing challenge in multi-channel advertising.

Shedding Light on Opaque Environments

One of the more notable aspects of the update is its focus on transparency.

Certain environments—particularly connected TV (CTV) and programmatic ecosystems—have historically been harder to analyze at a granular level. Innovid’s enhancements aim to bring more clarity to those spaces by showing exactly where ads appeared.

That visibility could be especially valuable as brands shift more budget into CTV and other premium digital channels, where measurement has often lagged behind spend.

Benchmarks Add Context to Performance

Data alone isn’t always enough. Without context, it’s difficult to know whether performance is strong, average, or underwhelming.

To address that, Innovid is introducing vertical benchmarking capabilities.

Marketers can now compare campaign performance across industries, formats, and objectives, gaining a clearer sense of where they stand. The platform also provides insights into reach and frequency efficiency, helping teams understand how their optimizations impact audience exposure over time.

This kind of contextualization is increasingly important as teams move toward continuous optimization rather than post-campaign analysis.

Why This Matters Now

The broader trend is clear: measurement is becoming a competitive differentiator.

As third-party signals decline and privacy regulations reshape data access, marketers are looking for more reliable ways to connect media spend to outcomes. At the same time, finance teams are demanding stronger accountability from marketing investments.

Innovid’s updates reflect that shift, emphasizing:

• Outcome-based measurement over engagement metrics
• Incrementality over correlation
• Transparency over black-box reporting

It’s part of a wider industry move toward more rigorous, evidence-based marketing.

The Bigger Picture

These enhancements also highlight how ad tech platforms are evolving.

Measurement is no longer a standalone function—it’s becoming deeply integrated into campaign execution. The ability to analyze, optimize, and validate performance in near real time is quickly becoming table stakes.

For Innovid, strengthening its measurement capabilities could help differentiate it in a crowded ad tech landscape where many platforms offer similar activation tools but vary widely in analytics depth.

The Bottom Line

With its latest updates, Innovid is pushing measurement closer to what marketers actually need: a clear line between media investment and business outcomes.

By combining purchase impact analysis, granular attribution, and contextual benchmarks, the platform aims to give teams the confidence—and the evidence—to make smarter decisions faster.

In a fragmented, performance-driven advertising landscape, that clarity isn’t just helpful. It’s essential.

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Hightouch is making a strong bid to define the next phase of marketing automation—one driven not by prompts, but by autonomous AI agents.

The company has raised $150 million in a Series D round led by Growth Equity at Goldman Sachs Alternatives and Bain Capital Ventures, valuing the company at $2.75 billion. The round also drew participation from a roster of high-profile investors, including Iconiq Capital, Sapphire Ventures, Amplify Partners, Y Combinator, and TD7.

But beyond the funding headline, the bigger story is the company’s positioning: “agentic marketing” as a new category.

From AI Assistants to AI Operators

Over the past two years, marketers have experimented heavily with generative AI—mostly for content creation. The results, by many accounts, have been mixed.

Hightouch is taking aim at that gap.

Instead of tools that generate drafts or suggestions, its platform is designed to deploy AI agents that operate directly on enterprise data—identifying opportunities, creating campaigns, and executing them across channels with minimal human intervention.

It’s a shift from AI as a helper to AI as an operator.

According to the company, this approach has fueled rapid growth, with revenue increasing more than 100% annually over the past two years as enterprises look for more practical ways to apply AI.

Why Marketing Has Been Harder for AI

The pitch resonates because marketing has proven to be a tougher nut for AI to crack than other functions.

In software engineering, AI can work with structured code and well-defined systems. Marketing, by contrast, relies on:

• Brand voice and creative nuance
• Proprietary customer data
• Fragmented workflows across tools and channels

Most AI tools struggle to access—or understand—those layers of context. The result is often generic content that never makes it into production.

Hightouch’s answer is what it calls an “enterprise context layer,” combining customer data, brand guidelines, and campaign orchestration into a single foundation for AI agents.

Always-On Campaign Execution

Built on that foundation, Hightouch’s platform enables AI agents to:

• Analyze customer data to identify audience opportunities
• Generate on-brand creative assets
• Execute campaigns across advertising, email, SMS, and web
• Continuously optimize performance

The emphasis is on continuity. Instead of running campaigns in bursts, agents operate continuously—monitoring signals, adjusting strategies, and launching new initiatives as conditions change.

That “always-on” model reflects a broader shift in marketing toward real-time engagement and dynamic optimization.

Early Enterprise Traction

The company already counts major brands among its customers, including Domino's, PetSmart, DraftKings, Ramp, and WHOOP.

These organizations are using Hightouch to activate customer data and drive personalized marketing across channels—an area where traditional CDPs and marketing automation platforms have often fallen short.

The promise is improved speed, higher-quality output, and better campaign performance—though, as with any emerging category, results will vary depending on implementation.

A New Category—or a New Label?

Hightouch is explicitly framing this as the emergence of “agentic marketing.”

It’s a term gaining traction across the industry, referring to systems where AI agents can plan and execute tasks autonomously rather than simply responding to prompts.

The concept overlaps with existing categories—customer data platforms, marketing automation, and AI content tools—but aims to unify them into a single, execution-focused system.

Whether “agentic marketing” becomes a widely adopted category or just another buzzword will depend on how effectively platforms like Hightouch deliver measurable outcomes.

Why Investors Are Paying Attention

The funding round underscores strong investor interest in this direction.

Marketing is one of the largest enterprise functions, and one that remains relatively inefficient compared to areas like engineering or finance. If AI can meaningfully automate and optimize marketing workflows, the upside is significant.

Hightouch’s approach—building on top of existing data systems rather than replacing them—also aligns with enterprise preferences for interoperability and control.

That could give it an edge over more closed, all-in-one platforms.

The Road Ahead

The new capital will be used to expand Hightouch’s platform, particularly in areas like AI-driven decisioning, campaign orchestration, and cross-channel execution.

The ambition is clear: to become the system of record for agentic marketing.

That’s a tall order in a crowded MarTech landscape. But if the shift toward AI-driven execution continues—and early signals suggest it will—platforms that can combine data, context, and automation may have a real shot at reshaping how marketing operates.

The Bottom Line

Hightouch’s $150 million raise is more than a funding milestone—it’s a statement about where marketing technology is headed.

As enterprises move beyond AI experimentation, the focus is shifting to systems that can actually run marketing, not just assist with it.

If agentic marketing delivers on its promise, the role of marketers may evolve from executing campaigns to supervising intelligent systems that do it for them.

And that could change the MarTech stack as we know it.

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