A new industry report from Omnisend suggests the secret behind high-performing ecommerce marketing agencies isn’t just bigger audiences—it’s extracting more value from the customers brands already have.

Analyzing campaigns run by 717 agencies managing nearly 3,000 small and mid-sized ecommerce brands, the company found that the top 10% of agencies generate $170,000 in annual revenue per client, averaging $16.70 per subscriber.

The research reveals that these standout agencies don’t rely on a single growth tactic. Instead, they combine several practices—particularly SMS marketing, systematic A/B testing, and automation—to continuously improve how customer audiences are used.

The result is a compounding revenue engine rather than one-off marketing campaigns.

The Playbook Behind Top-Performing Agencies

According to the report, agencies ranked in the top 10% by subscriber revenue consistently apply a mix of strategies designed to increase engagement and conversion rates.

Among the strongest performance indicators:

• Agencies using SMS marketing see 202% higher revenue per client on average.
• Agencies running regular A/B testing are linked to 192% higher revenue.
• Agencies applying audience segmentation see a smaller but consistent 8% revenue uplift.

Individually, none of these tactics are groundbreaking. Combined, however, they form a disciplined approach to customer lifecycle marketing.

Instead of treating campaigns as isolated events, successful agencies continually test, refine, and personalize their messaging.

“When you combine channels, test what actually works, and tailor messages to different customers, every decision becomes more informed,” said Marty Bauer, ecommerce expert at Omnisend.

Over time, Bauer argues, this iterative process creates a level of customer understanding that’s difficult for competitors to replicate.

Automation: The Revenue Engine Working Behind the Scenes

If testing and personalization improve campaign performance, automation is what turns those improvements into long-term revenue.

The report found that automated messages generate $5.96 per send on average, compared with $0.67 per standard campaign email—a nearly ninefold increase in revenue efficiency.

Because of that difference, automation accounts for 45% of total email revenue among the top-performing agencies analyzed.

These agencies also move quickly to establish automation systems when onboarding new ecommerce clients.

On average, they:

• Launch their first automation within eight days of starting work with a brand
• Maintain 5.3 automation workflows per client

These workflows typically include common ecommerce automations such as welcome sequences, browse abandonment reminders, cart recovery emails, and post-purchase follow-ups.

While those flows are widely known across ecommerce marketing, the key differentiator appears to be execution speed and consistency.

Email Is No Longer Just a Broadcast Channel

Many brands still treat email marketing as a simple broadcasting tool—something used primarily to announce promotions or product launches.

But the agencies generating the strongest results approach it differently.

“Many brands still treat email like a megaphone—something you turn on when you have something to say,” Bauer said.

The best-performing agencies instead treat email and SMS as an always-on revenue system that responds to customer behavior in real time.

Triggered messages based on actions such as browsing products, abandoning a cart, or signing up for a newsletter create more relevant interactions with customers.

And relevance, in marketing, typically translates into higher conversions.

Behavioral Marketing Beats Calendar Marketing

Another key finding in the report is the shift from calendar-based marketing to behavior-driven messaging.

Traditional marketing calendars often revolve around sales periods, product launches, and promotional cycles. While those events still matter, the most effective agencies combine them with behavior-triggered messages.

Examples include:

• Welcome messages when a user subscribes
• Browse abandonment reminders after product views
• Cart recovery emails after incomplete purchases
• Re-engagement campaigns for inactive customers

By reacting to real customer behavior instead of fixed schedules, agencies can reach shoppers at moments when they’re already considering a purchase.

This approach improves both timing and relevance—two of the most important drivers of conversion.

Why SMS Is Emerging as a High-Impact Channel

One of the most striking statistics in the report is the 202% revenue increase linked to agencies using SMS marketing.

SMS has been gaining traction in ecommerce marketing over the past several years because of its exceptionally high engagement rates.

While email open rates vary widely depending on industry and campaign type, text messages are typically opened within minutes.

For brands, that immediacy makes SMS an effective complement to email, particularly for time-sensitive promotions, abandoned cart reminders, and order updates.

However, successful agencies rarely treat SMS as a standalone channel. Instead, they integrate it with email marketing workflows to create coordinated campaigns.

Lessons for Marketing Agencies

According to Bauer, the biggest difference between average and top-performing agencies isn’t simply the tactics they deploy—it’s how they think about marketing systems.

“Best-performing agencies treat email and SMS as systems to improve over time, not tasks to complete,” Bauer said.

That mindset influences how agencies prioritize their work, measure success, and collaborate with clients.

The report highlights several practical steps agencies can adopt to improve performance:

Launch automation early.
Delays in setting up welcome flows, browse reminders, and cart recovery sequences mean lost revenue opportunities.

Use campaigns as learning tools.
Testing subject lines, timing, and offers generates insights that can be applied to both campaigns and automated workflows.

Prioritize behavioral triggers.
Messages triggered by customer actions tend to outperform scheduled campaigns because they arrive at the right moment.

Improve systems incrementally.
Revenue growth rarely comes from a single breakthrough tactic. Instead, it builds over time as agencies add channels, refine automations, and optimize targeting.

A Data-Driven Look at Agency Performance

The report’s findings are based on anonymized campaign performance data from agencies operating on the Omnisend platform.

Researchers examined marketing activity across 2,990 ecommerce brands, focusing on campaign results, automation performance, and revenue generated through email and SMS marketing.

To ensure meaningful comparisons, agencies were ranked using revenue generated per subscriber rather than total revenue. This approach removes the influence of brand size, allowing smaller and larger clients to be evaluated on equal footing.

Agencies in the top 10% by subscriber revenue were classified as top performers and used as the benchmark throughout the study.

Outlier data points were removed to prevent extreme results from skewing the analysis, and all data was aggregated and anonymized.

The Bigger Martech Trend

The insights from the report reinforce a broader shift happening across marketing technology.

As customer acquisition costs rise and privacy regulations limit targeting options, brands are increasingly focusing on customer lifetime value and retention rather than pure audience growth.

That shift places greater importance on systems like automation, segmentation, and lifecycle marketing—the same strategies highlighted in Omnisend’s analysis.

For ecommerce brands and the agencies supporting them, the message is clear: growth doesn’t necessarily come from reaching more people.

Often, it comes from understanding the customers you already have—and communicating with them more intelligently.

Get in touch with our MarTech Experts.

As AI answer engines reshape how consumers discover products online, creator commerce platform MagicLinks is introducing a new tool aimed at helping brands stay visible in an increasingly AI-mediated marketplace.

The company today unveiled AI Shelf, an intelligence platform designed to help brands optimize creator content—particularly on YouTube—so it can be discovered, indexed, and cited by AI search systems such as ChatGPT, Perplexity AI, and Google Gemini.

The launch reflects a broader shift underway in digital commerce. Instead of scrolling through search results, consumers increasingly rely on AI assistants to generate a single synthesized recommendation.

For brands, that changes the rules of visibility.

The Shift From Search Rankings to AI Citations

The scale of the shift is already substantial. ChatGPT alone now serves roughly 800 million weekly users, while analysts at Gartner project that 25% of traditional search volume could migrate to AI assistants by 2026.

Meanwhile, Google’s own evolution toward AI summaries is altering user behavior. Nearly 65% of Google searches now end without a click, as consumers get answers directly from AI-generated overviews rather than navigating to websites.

That means brands must compete not just for search rankings—but to become the sources AI engines choose when generating answers.

“The AI shelf is the most important retail real estate of the next decade,” said Brian Nickerson, CEO and founder of MagicLinks. “Most brands don’t yet know how to own it.”

Why Creator Content Matters More in the AI Era

MagicLinks’ approach focuses heavily on creator-driven video content, particularly on YouTube.

That’s not accidental. According to the company’s analysis, YouTube has become the most cited source across major large language models, referenced roughly 200 times more often than any other video platform and even surpassing Reddit as an AI citation source.

The trend aligns with the explosive growth of the creator economy, now valued at roughly $250 billion globally and expanding at about 23% annually.

For AI systems trained on massive datasets, authoritative creator content—especially long-form product reviews and tutorials—often provides the contextual signals needed to generate product recommendations.

MagicLinks says AI Shelf is designed to help brands ensure their creator partnerships generate those signals consistently.

Inside the AI Shelf Platform

AI Shelf combines several layers of analytics and optimization designed to align creator marketing campaigns with how AI engines interpret and rank information.

The system includes three primary intelligence capabilities.

Commerce Intelligence
This module analyzes creator videos to ensure they meet standards that help them perform well in both search and AI answer engines. The system evaluates factors including brand safety language, SEO metadata, FTC disclosure compliance, revenue optimization, and Answer Engine Optimization (AEO) scoring.

Match Intelligence®
MagicLinks’ creator-matching system identifies and activates creators likely to produce high-impact content at scale. The goal is to create consistent signals across videos and channels so AI engines recognize a brand as a category authority.

Discovery Intelligence
This component measures a brand’s visibility across AI search platforms and benchmarks performance against competitors. It also identifies which queries trigger brand mentions—and where gaps exist.

Together, the system attempts to solve a problem many marketers are only beginning to recognize: visibility inside AI-generated answers.

The Long Tail of Creator Commerce

MagicLinks’ pitch is backed by a decade of campaign data.

The company says it has helped drive more than $12.3 billion in gross merchandise value through creator commerce programs over the past ten years.

Analysis of hundreds of thousands of YouTube videos reveals that creator content has a long revenue tail. According to MagicLinks, nearly 20% of revenue generated by YouTube videos occurs more than 12 months after publication.

That contrasts sharply with short-form social media posts, which typically drive engagement for only a few days or weeks.

The company also found that always-on creator campaigns produce 122% more growth over one year compared with one-time influencer activations.

Those findings mirror broader industry research from Influencer Marketing Hub, which estimates brands earn roughly $5.78 for every $1 spent on influencer marketing.

Why Content Optimization Matters

The performance gap between optimized and non-optimized creator content can be dramatic.

In one campaign cited by MagicLinks involving a major national retailer, two creators produced nearly identical videos with comparable view counts and content quality scores.

Yet their sales results differed dramatically.

One creator generated $52,060 in revenue, while the other produced just $1,261.

According to MagicLinks, the difference came down entirely to optimization signals:

• Keyword-rich video titles
• Clear brand visibility in thumbnails
• Structured descriptions with calls to action
• Metadata designed for AI discovery

AI Shelf is designed to surface those optimization signals before campaigns launch, allowing brands to adjust content strategy before spending advertising dollars.

A New Marketing Discipline: AEO

The platform also reflects the growing importance of Answer Engine Optimization (AEO)—a marketing strategy focused on making content discoverable and quotable by AI systems.

Instead of optimizing solely for search rankings, AEO aims to ensure content is structured in ways that AI assistants can easily interpret and cite.

According to Jennifer Piña, co-founder and VP of Brand Strategy and Revenue at MagicLinks, brands that move early in this area are already seeing measurable results.

“Early AEO adopters are capturing 3.4 times more AI-driven traffic than competitors who wait,” Piña said.

The Race for the “AI Shelf”

The concept behind the platform—owning the “AI shelf”—reflects a shift in how digital retail visibility may work in the future.

Traditionally, ecommerce competition centered around:

• Search engine rankings
• Marketplace listings
• Sponsored product placements

In an AI-driven discovery environment, however, product recommendations increasingly come from conversational responses rather than search result pages.

That means brands must focus on becoming authoritative sources within the datasets AI models rely on.

For MagicLinks, creator content—especially long-form YouTube videos—may be one of the most effective ways to achieve that authority.

The Future of AI-Driven Commerce Discovery

As AI assistants continue to evolve into shopping advisors, the marketing ecosystem around them is rapidly emerging.

Platforms are beginning to develop tools focused on:

• AI search visibility tracking
• Content optimization for LLM citation
• Data signals that influence AI answers

AI Shelf positions MagicLinks squarely within that new category.

Whether brands fully embrace the concept of “AI shelf space” remains to be seen. But one thing is becoming clear: as AI assistants increasingly decide which products consumers see first, the battle for visibility is moving far beyond traditional search results.

Get in touch with our MarTech Experts.

Market research firm Savanta is stepping deeper into AI-driven insights with the launch of Virtual Personas by Savanta, a platform designed to simulate consumer interviews and focus groups in seconds.

The new system allows marketing and research teams to interact with AI-generated personas representing different audience segments—testing messaging, probing motivations, and modeling consumer reactions without waiting for traditional research cycles.

The move reflects growing demand for faster, more flexible research tools as businesses look to make decisions in days rather than weeks.

“Virtual Personas by Savanta is the natural evolution of our investment in consumer data,” said Christine Petersen, CEO of Savanta. “Brands can maintain an always-on view of their audiences rather than relying on periodic snapshots.”

AI-Powered Focus Groups—Without the Logistics

Traditional consumer research—focus groups, interviews, and surveys—remains one of the most reliable ways to understand audience behavior. But it’s also slow, expensive, and often difficult to scale.

Savanta’s new platform attempts to close that gap by allowing teams to run simulated research sessions with AI-generated personas modeled on real-world consumer data.

Users can conduct:

• One-on-one interviews with specific audience personas
• Multi-person focus groups simulating group dynamics
• Messaging tests for campaigns and creative assets
• Hypothesis testing on consumer behavior and motivations

The system is designed to complement traditional research rather than replace it, giving teams a fast way to explore ideas before committing to more formal studies.

For example, marketers might test messaging concepts with virtual personas first, then validate promising insights through real-world focus groups.

Free Access to Accelerate Research

Savanta is launching the platform with a free access tier, an unusual move in a market where AI-powered insights tools are often priced for enterprise buyers.

The free version includes:

• Eight pre-defined personas
• Coverage across four key audience segments
• Support for both interviews and focus groups

The company says the goal is to democratize access to research insights, particularly for teams under pressure to move faster while managing tighter budgets.

Paid tiers add deeper customization, allowing brands to create personas tailored to their own audience data and explore creative assets—including video-based simulations.

Built on Behavioral Science, Not Just AI

While AI-generated personas have become increasingly common in research tools, Savanta emphasizes that its system is grounded in established psychological frameworks.

The platform incorporates two major behavioral science models:

• The Big Five personality traits (also known as the OCEAN model)
• The Schwartz Theory of Basic Human Values

Combining personality traits with motivational drivers allows the platform to simulate more realistic decision-making behavior—such as hesitation, skepticism, or emotional responses to pricing and messaging.

According to Savanta, this approach helps recreate the kinds of friction and uncertainty that influence real consumer choices.

Data-Driven Personas With Real-World Validation

The AI personas are trained using more than a decade of Savanta’s proprietary consumer research data across multiple industries.

That hybrid model—blending synthetic data with historical research—aims to produce more reliable insights than AI systems trained solely on generalized datasets.

Savanta says internal testing shows:

• 99% factual recall accuracy
• 84% logical inference accuracy

Those metrics are designed to address one of the biggest concerns surrounding AI-driven research tools: trust.

“Synthetic data without emotional intelligence is just noise,” said Dr Nick Baker, Savanta’s chief research officer.

“Virtual Personas by Savanta is not a chatbot—it’s a psychological simulation that models how consumers feel, not just what they say.”

Transparency Built Into Every Insight

One distinguishing feature of the platform is its built-in trust scoring system.

Each response generated by a virtual persona includes a confidence score showing how reliable the answer is based on underlying data and reasoning models.

The system also allows users to:

• View the datasets informing responses
• Understand why a persona produced a specific answer
• Benchmark outputs against established research standards

Savanta says this transparency is critical if AI-driven insights are to be trusted in real-world business decisions.

Filling the Gap Between Research Studies

The company positions Virtual Personas primarily as a research accelerator, helping teams explore ideas quickly before launching traditional studies.

That workflow might look like this:

1. Use virtual personas to test multiple concepts quickly
2. Identify the most promising options
3. Validate results through real consumer research

By narrowing options early, companies can reduce the cost and complexity of formal research.

According to Savanta, the goal isn’t simply generating more data—but reducing time to decision.

A Growing Category: AI for Market Research

Savanta’s launch highlights the rapid transformation underway in the insights industry.

Market research firms are increasingly experimenting with AI tools that can:

• Simulate customer segments
• Predict consumer reactions
• Accelerate concept testing

These systems are particularly attractive for marketing teams working on tight deadlines, where traditional research timelines may be too slow.

However, many researchers remain cautious about relying entirely on synthetic data.

Savanta’s strategy reflects that caution by positioning Virtual Personas as a supplement to traditional research, rather than a replacement.

The Future of Always-On Consumer Insights

If tools like Virtual Personas gain traction, they could significantly change how marketing teams approach consumer research.

Instead of commissioning studies at key moments—product launches, brand repositioning, campaign planning—companies could maintain an always-on research environment where audience insights are continuously available.

For brands navigating fast-moving markets, that shift could prove valuable.

As Petersen puts it, the real opportunity isn’t just faster research—it’s keeping a constant conversation with customers, even when they’re not in the room.

Get in touch with our MarTech Experts.

As global companies increasingly rely on multilingual virtual meetings, the quality of AI translation has become a mission-critical factor for collaboration tools. A new benchmark study suggests DeepL may currently hold the edge.

The AI language technology firm revealed results from an independent evaluation conducted by research and language intelligence company Slator, comparing real-time translation and caption performance across major collaboration platforms.

The study found DeepL Voice outperformed built-in caption translation systems in Google Meet, Microsoft Teams, and Zoom, delivering stronger translation quality and more stable live captions during meetings.

For enterprises increasingly conducting cross-border negotiations, strategy sessions, and customer calls in multiple languages, those improvements could have real-world business implications.

AI Translation Is Becoming Core Infrastructure

As international teams collaborate more frequently through video conferencing platforms, real-time translation has shifted from a convenience feature to a foundational communication layer.

Even small translation errors—or unstable captions that constantly rewrite themselves on screen—can slow meetings, cause confusion, or derail discussions in high-stakes scenarios.

“Language AI is becoming the core infrastructure for how global businesses operate,” said Jarek Kutylowski, CEO of DeepL.

“In that context, accuracy and stability aren’t features—they’re requirements.”

The benchmark suggests DeepL’s voice translation system currently performs better than native caption tools included in mainstream meeting platforms.

Translation Quality: DeepL Leads the Pack

The Slator evaluation assessed translation quality using blind reviews conducted by 28 professional linguists across 14 language combinations.

Seven translations were evaluated into English, and seven from English, covering a wide range of multilingual meeting scenarios.

The results showed DeepL achieving significantly higher quality scores:

• 96.4 / 100 for DeepL Voice in Zoom
• 96.3 / 100 for DeepL Voice in Microsoft Teams

By comparison, competing caption translation systems scored between 87 and 89 across the tested platforms.

In practical terms, DeepL Voice also generated fully accurate translated segments 79% of the time, nearly double the 42% success rate seen in other tools evaluated.

Another key metric involved critical translation errors—mistakes that could meaningfully alter meaning during a conversation.

DeepL Voice reduced major or critical errors by 76% on average compared with the competing systems.

Caption Stability: The Often Overlooked Factor

While translation accuracy is the most visible benchmark, Slator’s research highlights another critical aspect of real-time communication: caption stability.

In many AI captioning systems, subtitles constantly update as speech recognition models refine their output. That behavior can cause words or phrases to flicker or rewrite themselves repeatedly.

For meeting participants reading captions in real time, that instability can disrupt comprehension.

To measure the issue, Slator conducted frame-level analysis of captions as they appeared on screen, evaluating flicker, oscillation, and rewrite frequency.

Here again, DeepL’s system performed strongly.

The platform recorded caption stability scores of:

• 88.6 / 100 for DeepL Voice in Zoom
• 85.8 / 100 for DeepL Voice in Teams

DeepL Voice also reduced caption churn—the constant rewriting of subtitles—by:

• 37.6% compared with Microsoft Teams
• 54.7% compared with Zoom

For meeting participants relying on captions to follow multilingual conversations, that stability could make discussions easier to track in real time.

Linguists Overwhelmingly Preferred DeepL

Beyond numerical scoring, the study also gathered subjective evaluations from professional linguists.

Across all blind comparisons, 96% of linguists preferred DeepL Voice over competing caption translation tools.

According to Alex Edwards, the evaluation focused not just on accuracy but on how translations behave during real-world reading.

“We didn’t just want to know if the words were right at the end,” Edwards said. “We wanted to see how captions behave while someone is trying to read them.”

That meant evaluating readability, linguistic fluency, and visual stability simultaneously.

Why Caption Behavior Matters in Meetings

Slator’s findings suggest that caption stability may be as important as translation accuracy in real-time settings.

Even when translations are technically correct, frequent updates and rewrites can break a reader’s concentration.

That disruption is especially problematic during:

• Customer negotiations
• Sales presentations
• Executive strategy discussions
• Cross-border team collaboration

In these scenarios, participants often rely on captions to keep pace with conversations happening in unfamiliar languages.

When subtitles constantly shift or flicker, comprehension can suffer—even if the final translation is correct.

How the Benchmark Was Conducted

The evaluation used a combination of human and automated testing methods.

Key elements of the methodology included:

• 28 professional linguists conducting blind evaluations
• 14 language pair combinations
• Analysis of real-time caption rendering behavior
• Comparison of standard out-of-the-box platform settings

The report compared native translation features in Google Meet, Microsoft Teams, and Zoom with DeepL Voice integrations for Teams and Zoom.

Slator emphasized that it maintained full editorial control over the evaluation process and findings, despite the study being commissioned by DeepL.

A Competitive Market for Language AI

DeepL has built a reputation in recent years for high-quality machine translation, often competing with platforms from major tech companies.

Real-time meeting translation is becoming one of the most competitive segments of the AI productivity market, as businesses increasingly expect language barriers to disappear inside digital collaboration tools.

For enterprise teams operating across continents, reliable AI translation can mean faster decision-making and smoother collaboration.

What’s Next for DeepL Voice

The benchmark results arrive ahead of a broader product update planned by DeepL.

The company says it will unveil major upgrades to DeepL Voice on April 16, 2026, including expanded capabilities for translation automation and cross-platform collaboration.

If adoption continues to grow, tools like DeepL Voice could become a standard layer across enterprise meeting platforms—quietly translating conversations in real time while teams focus on the discussion itself.

For global organizations, that could make language barriers increasingly invisible.

Get in touch with our MarTech Experts.

Marketing teams today have no shortage of dashboards. What they lack, increasingly, is clarity.

That’s the challenge Lifesight hopes to address with the launch of Mia, a new AI-powered marketing intelligence agent designed to analyze performance data, generate strategic recommendations, and help ecommerce and retail brands act faster on insights.

Built on Lifesight Unified Measurement OS, Mia enables marketing teams to interact directly with performance data across online and offline channels, transforming raw metrics into actionable decisions.

The goal is simple but ambitious: reduce the time marketers spend buried in spreadsheets and dashboards—and help them focus on strategies that actually drive revenue.

AI Agents Are Entering the Marketing Stack

The launch of Mia reflects a broader shift across enterprise software toward agentic AI—systems capable of analyzing complex data, generating recommendations, and assisting with decision-making workflows.

For retail and ecommerce brands, that evolution comes at a critical moment.

Marketing ecosystems are becoming increasingly fragmented. Teams must now manage performance across:

• Retail media networks
• Marketplaces
• Social media platforms
• Paid search channels
• Owned customer data platforms

At the same time, privacy regulations and signal loss have weakened traditional attribution models, making it harder to determine which marketing investments actually drive growth.

AI-powered agents are emerging as a potential solution to this complexity.

According to Gartner, task-specific AI agents are rapidly entering enterprise software. The research firm projects that 40% of enterprise applications will incorporate AI agents by 2026, up from less than 5% in 2025.

Adoption at the executive level is also accelerating. Research from eMarketer suggests that nearly three-quarters of U.S. C-level executives expect AI agents to play a role in their organizations.

In other words, the dashboard era may be giving way to something more proactive.

Turning Marketing Data Into Strategic Decisions

Mia operates as an AI intelligence layer on top of Lifesight’s unified measurement infrastructure, allowing marketing teams to move beyond static reporting and toward AI-assisted decision-making.

Instead of simply displaying campaign metrics, the platform analyzes performance data and recommends actions based on measurable business outcomes.

The system evaluates marketing effectiveness using a unified framework that blends:

• Causal marketing mix modeling
• Incrementality testing
• Causal attribution analysis

By combining these methodologies, Mia aims to provide a more reliable view of marketing impact across both online and offline channels.

What Mia Can Do

At launch, Mia offers several capabilities designed to streamline marketing analysis and strategic planning.

AI-Driven Performance Analysis
The platform continuously analyzes campaign data across multiple channels using Lifesight’s unified measurement framework to identify performance patterns and growth opportunities.

Scenario-Based Strategy Recommendations
Rather than offering a single optimization suggestion, Mia generates multiple strategic scenarios. These may include aggressive growth strategies, balanced optimization approaches, or more conservative investment options depending on a company’s objectives.

Transparent Decision Logic
Unlike some black-box AI systems, Mia provides visibility into the assumptions and data signals behind each recommendation. Marketing teams can review the reasoning behind suggested actions before implementing them.

Automated Insight Generation
Users can query Mia directly about campaign performance, channel effectiveness, or budget allocation. The agent then generates insights without requiring manual reporting or dashboard analysis.

Reduced Manual Analysis
By automating repetitive analysis tasks, the platform aims to free marketing teams from hours of reporting work each week, allowing them to focus on campaign optimization and growth strategies.

Why Measurement Matters More Than Ever

Unified measurement has become a priority for many marketing teams navigating a world of privacy restrictions, declining third-party cookies, and increasingly fragmented customer journeys.

Traditional attribution models often struggle to capture the full picture of marketing performance, especially across online and offline channels.

Lifesight’s approach attempts to solve this by combining multiple measurement methods into a single system, then layering AI intelligence on top to interpret the results.

“For years, marketing teams have been buried in dashboards but still struggling to answer a simple question: what actually drove growth,” said Tobin Thomas, co-founder and CEO of Lifesight.

“Agentic AI changes that dynamic. Mia analyzes unified measurement data and surfaces clear, actionable recommendations so teams can move from reporting on performance to actively improving it.”

AI Marketing Intelligence Meets Retail

The company plans to showcase Mia publicly at Shoptalk 2026, one of the largest retail and ecommerce industry conferences.

At the event, Lifesight will demonstrate how unified measurement combined with agentic AI can help brands understand which marketing investments are genuinely driving revenue.

For retailers navigating increasingly complex advertising ecosystems—spanning retail media networks, marketplaces, and social platforms—tools that simplify performance analysis could quickly become essential.

The Rise of AI Decision Engines

Mia represents a growing category of AI-driven marketing tools that act less like software dashboards and more like decision engines.

Instead of asking marketers to interpret dozens of reports, these systems analyze data automatically and propose specific actions.

If the trend continues, marketing teams may soon rely on AI agents not just for content generation or analytics—but for strategic guidance as well.

For Lifesight, Mia is an early step toward that vision: a marketing intelligence layer that helps teams spend less time analyzing data and more time acting on it.

Get in touch with our MarTech Experts.

Marketing teams experimenting with AI tools often run into the same problem: the technology can generate content quickly, but the surrounding workflow—planning, approvals, publishing, and execution—remains messy and manual.

That’s the gap Haley Marketing aims to address with the launch of Rogue Active Intelligence, a new company focused on practical AI and automation for marketing operations.

Its first product, RogIQ, is designed to help marketing teams manage the entire digital marketing lifecycle—from strategy and planning to content production and distribution—inside a single AI-assisted workflow.

The platform’s goal isn’t simply faster content generation. Instead, it focuses on orchestrating the entire marketing process while keeping human decision-making at the center.

Beyond AI Content Generators

Over the past two years, generative AI tools have flooded the marketing stack. Most of them focus on a narrow function—writing blog posts, generating social captions, or assisting with SEO.

But marketing teams rarely operate in those isolated silos.

Producing a campaign typically involves multiple steps:

• Strategic planning
• Content ideation
• Draft creation
• Internal reviews
• Client approvals
• Publishing across platforms
• Performance monitoring

RogIQ attempts to unify those steps within a single system rather than forcing teams to stitch together multiple tools.

“RogIQ was built to make the future of human-guided AI marketing practical,” said Victoria Kenward, co-CEO of Haley Marketing.

The platform’s design emphasizes human-guided automation, meaning AI assists with tasks but strategic decisions remain with marketing professionals.

A Workflow Engine for Digital Marketing

At launch, RogIQ focuses primarily on core digital marketing use cases.

The platform supports:

• Marketing strategy development
• Content planning and editorial calendars
• Blog creation and management
• Social media content production
• SEO optimization workflows
• Scheduled publishing to websites and social channels
• Workflow management for review and approvals

The idea is to help teams move away from fragmented processes that rely on spreadsheets, messaging apps, and multiple software tools to manage campaigns.

Instead, RogIQ acts as a centralized workflow engine where AI assists with planning, creation, and execution.

Solving the AI Adoption Dilemma

Many marketing teams face a balancing act when adopting AI.

On one hand, automation promises major efficiency gains. On the other, excessive reliance on AI-generated output can dilute brand voice, reduce quality control, or weaken strategic thinking.

RogIQ is designed to address that tension.

Rather than replacing human marketers, the system focuses on reducing repetitive tasks, such as formatting, drafting, scheduling, and workflow coordination.

That approach frees teams to spend more time on higher-value activities like strategy, creative development, and client engagement.

A New Venture Focused on AI Marketing Infrastructure

The platform’s launch also marks the debut of Rogue Active Intelligence, a new venture created by Haley Marketing to develop AI-powered marketing technology.

According to David Searns, the initiative reflects the company’s broader push to help businesses navigate the rapidly changing marketing landscape.

“Haley Marketing has always believed that better marketing comes from combining smart strategy with great execution,” Searns said.

“With Rogue Active Intelligence and RogIQ, we’re creating a scalable way for marketing teams to apply AI and automation to both.”

The new venture signals a growing trend among agencies and marketing firms: building proprietary technology platforms rather than relying solely on third-party tools.

The Rise of AI-Orchestrated Marketing Operations

RogIQ enters a crowded—but still evolving—market of AI-powered marketing tools.

While generative AI platforms have attracted enormous attention, the next frontier in marketing technology may lie in AI orchestration—systems that coordinate workflows, tools, and teams rather than simply generating content.

For marketing departments juggling multiple campaigns across channels, workflow efficiency can be just as important as creative output.

By focusing on the operational side of marketing, RogIQ aims to position itself as an infrastructure layer for modern digital marketing teams.

What Comes Next for RogIQ

At launch, the platform is centered on core marketing execution tasks such as content planning, SEO, and publishing workflows.

However, the company says RogIQ will expand into additional marketing functions over time as the platform evolves.

Potential areas for future development could include deeper analytics integration, campaign optimization tools, and expanded automation capabilities across digital channels.

For now, RogIQ’s pitch is straightforward: help marketing teams combine human insight with AI efficiency—without losing control of the process.

In an era when AI can generate endless content in seconds, that balance may prove just as valuable as speed itself.

Get in touch with our MarTech Experts.

Two privacy-focused technology companies are joining forces to challenge the dominance of Big Tech in search and web browsing.

MASQ Network and Timpi have announced a full product merger that combines decentralized connectivity, private browsing, and independent search infrastructure into a single integrated platform.

The unified product will launch under the MASQ brand, bringing together MASQ’s privacy browser and distributed VPN technology with Timpi’s independent search index—an alternative to the centralized search infrastructure dominated by major technology companies.

The companies say the result is a browsing experience designed to function without user tracking, ad profiling, or centralized data collection.

A New Attempt to Break Big Tech’s Search Monopoly

The modern internet largely runs on infrastructure controlled by a handful of technology giants. Most web searches and browsing activity ultimately flow through ecosystems dominated by companies like Google, Microsoft, and Apple, whose services are deeply tied to advertising-driven data collection.

MASQ and Timpi are positioning their merger as an alternative to that model.

Rather than routing user activity through centralized data systems, the combined platform relies on decentralized networking and an independent search index that operates outside traditional Big Tech infrastructure.

The aim is to offer private browsing and search capabilities without relying on surveillance-based advertising systems that track user behavior.

“Consumers don’t adopt infrastructure—they adopt products,” said Aaron Friedlander, founder of MASQ.

“This merger lets us package private browsing, independent search, and secure connectivity into one experience that everyday users can actually use.”

How the Combined Platform Works

The integrated MASQ platform merges three core components:

Private Browser
MASQ’s browser provides privacy-focused web access designed to minimize data collection and tracking.

Decentralized Network and VPN
The platform routes internet traffic through a distributed peer-to-peer network rather than centralized servers, adding an extra layer of privacy and resilience.

Independent Search Index
Timpi contributes the search engine infrastructure, which has been under development for three years and operates independently of the major search providers.

Together, these elements create a browsing environment where users can search and navigate the web without feeding data into the advertising ecosystems that dominate most online services.

Timpi Search Enters Public Beta

Alongside the merger announcement, Timpi Search is now available to the public in open beta.

Users can access the search engine directly through the Timpi website without creating an account, marking the first time the company’s independent search index has been broadly accessible.

Previously, the platform had been limited to community testing.

The beta version can currently be used through:

• The Timpi website
• A standalone browser extension
• The MASQ Browser environment

The release signals Timpi’s shift from experimental infrastructure toward a publicly available search product.

Building Infrastructure Outside Centralized Platforms

The merger also reflects a growing movement toward decentralized internet infrastructure.

Advocates argue that much of today’s digital ecosystem is controlled by centralized platforms that influence search visibility, advertising economics, and data ownership.

By contrast, Timpi’s architecture aims to create a community-driven index that can operate outside those centralized control points.

“The internet today runs through a handful of chokepoints,” said Gareth Evans, co-CEO of Timpi.

“We’re building the infrastructure that sits outside that—inspectable, decentralized, and owned by its community.”

A Different Business Model for Search

One of the biggest challenges facing alternative search platforms is revenue.

Most large search engines generate billions of dollars through targeted advertising fueled by user data collection.

MASQ and Timpi say their approach avoids selling user data while still supporting monetization through other channels.

The combined platform plans to generate revenue through:

• Search advertising that does not rely on user profiling
• Enterprise data and API services
• Consumer subscriptions for MASQ’s privacy services

Whether that model can compete with the massive scale of existing search ecosystems remains to be seen, but privacy-focused alternatives have steadily gained traction as users grow more concerned about online tracking.

What Comes Next

The fully integrated MASQ Browser with native Timpi Search is expected to launch in the coming months.

The companies also plan to expand distribution through MASQ’s partnership network while continuing to develop the underlying decentralized search infrastructure.

If successful, the combined platform could represent a new category of web tools—one that merges search, privacy, and connectivity into a decentralized alternative to the dominant Big Tech ecosystem.

For users increasingly wary of how their data fuels the modern internet, that proposition may be gaining relevance.

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AI assistants are quickly becoming the new interface for enterprise decision-making. Now FiscalNote wants to ensure policy and regulatory intelligence is part of that workflow.

The company announced that its PolicyNote MCP has been approved and listed in the OpenAI App Store, allowing developers, analysts, and enterprise teams to access structured policy and regulatory data directly inside ChatGPT.

The move effectively embeds FiscalNote’s policy intelligence infrastructure into one of the fastest-growing AI platforms. According to OpenAI, ChatGPT surpassed 700 million weekly active users earlier this year, making it a powerful distribution channel for enterprise data services.

With the listing, users can connect to the PolicyNote MCP server inside ChatGPT without additional integrations, allowing GPT-powered workflows to query real-time policy developments through natural language prompts.

Turning ChatGPT Into a Policy Intelligence Interface

Traditionally, policy monitoring tools have lived inside specialized enterprise platforms used by government affairs teams, legal departments, and regulatory analysts.

FiscalNote’s new integration shifts that model.

Instead of requiring users to access a separate application, PolicyNote MCP brings legislative and regulatory intelligence directly into conversational AI workflows.

Once installed, users can:

• Query legislative and regulatory developments worldwide
• Access verified information from government sources
• Monitor policy changes across jurisdictions
• Integrate structured policy data into research workflows

The integration also enables developers to build custom GPT-powered assistants focused on specific policy tasks using PolicyNote MCP tools.

For organizations managing regulatory compliance or government relations, that could mean faster analysis of policy developments without leaving their AI work environment.

From Software Platform to AI Infrastructure

The OpenAI App Store listing signals a broader strategic shift for FiscalNote.

Rather than operating solely as a destination software platform, the company is increasingly positioning its policy data as an intelligence infrastructure layer that powers AI-driven workflows.

“As AI agents become an increasingly important interface for how organizations operate and make decisions, the opportunity is expanding for trusted intelligence to be embedded directly into those workflows,” said Josh Resnik, CEO and president of FiscalNote.

By embedding its data inside AI environments, the company can potentially reach millions of users who may never purchase a full enterprise platform but still need reliable policy insights.

A New Distribution Channel for Enterprise Data

The OpenAI App Store serves as a global discovery layer for AI-integrated applications, allowing software vendors to distribute specialized tools directly inside ChatGPT.

For FiscalNote, that creates a new path to customer acquisition.

Instead of relying entirely on enterprise sales cycles, the company can reach developers, distributed teams, and analysts already building AI-driven workflows.

This approach could significantly expand FiscalNote’s addressable market by enabling product-led growth across new geographies and customer segments.

The model also introduces consumption-based revenue opportunities, where organizations pay for access to policy intelligence within AI workflows rather than licensing full software platforms.

Governance and Data Control

Despite the integration with ChatGPT, FiscalNote retains full control over its proprietary policy data.

Access to PolicyNote MCP remains a commercial offering, with users required to transact directly with FiscalNote to obtain the data.

The company emphasized that its legislative and regulatory datasets are only available within authorized user workflows and are not used to train AI models or for other platform-level purposes.

That distinction is likely to matter for government agencies, enterprises, and legal teams concerned about data governance when using AI-powered tools.

AI Agents and the Future of Enterprise Workflows

The integration also reflects a larger trend in enterprise software: the rise of AI agents as primary interfaces for accessing business intelligence.

Instead of navigating dashboards or complex data platforms, users are increasingly asking AI systems to retrieve and analyze information through conversational queries.

For companies like FiscalNote, embedding domain-specific intelligence into these environments could become a powerful distribution strategy.

By turning ChatGPT into a gateway for policy data, FiscalNote is effectively positioning its platform as part of the emerging infrastructure powering AI-native workflows.

If that strategy gains traction, the company may evolve from a policy software provider into a critical intelligence layer within the growing ecosystem of enterprise AI agents.

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