For two decades, online forms have largely followed the same formula: pick a template, drag fields into place, tweak logic, repeat. Now Jotform wants to replace that workflow with something closer to chatting with an assistant.

The company today unveiled Jotform AI, a conversational system designed to build and manage forms using natural language prompts. Instead of manually configuring fields, conditional logic, and integrations, users can simply describe what they want—by typing or speaking—and the platform generates the form automatically.

The launch marks a significant strategic shift for the company, which is repositioning its platform beyond form building toward a broader AI-driven productivity and data management layer.

“Jotform AI represents the next stage of our evolution,” said Aytekin Tank, founder and CEO of Jotform. “With this launch, Jotform shifts from a traditional form and productivity tool to an intelligent data management platform that executes at the request of a prompt.”

From Form Builder to AI Assistant

Traditional form builders—even modern ones—still rely heavily on graphical interfaces and manual configuration. Jotform AI flips that model by introducing a conversational interface that generates and modifies forms dynamically.

Users can prompt the system to:

• Generate complete forms from scratch
• Create or modify form fields
• Build conditional logic and workflows
• Structure layouts automatically
• Analyze submission data
• Suggest third-party integrations

The experience is powered by an AI assistant called AI Form Copilot, which works alongside the existing builder interface. Instead of digging through menus or configuration panels, users can instruct the system conversationally to refine questions, add logic, or restructure workflows.

The goal is straightforward: eliminate the learning curve that typically comes with form automation tools.

For many organizations—especially small teams or nontechnical users—that complexity has historically been a barrier. HR departments building onboarding forms, marketing teams collecting lead data, and educators managing submissions often rely on templates because customization can be time-consuming.

With Jotform AI, the company is betting that describing a form will prove far faster than building one.

Built for Busy Teams

While enterprise automation platforms often target developers or operations teams, Jotform’s pitch focuses on accessibility.

The company says Jotform AI is particularly useful for:

• Small and medium-sized businesses
• HR teams managing employee workflows
• Marketing teams handling lead capture
• Educators collecting student information

Another practical shift: the AI interface works across both desktop and mobile. That means users can generate or modify forms without navigating the full builder interface—a move that reflects how increasingly mobile many workplace workflows have become.

In theory, that could enable someone to create a lead capture form during a meeting or adjust a survey while traveling.

Riding the Generative AI Wave

Jotform’s announcement arrives amid a broader transformation in productivity software driven by generative AI. Platforms across the SaaS landscape—from document tools to CRM systems—are rapidly integrating conversational interfaces designed to reduce friction and automate repetitive work.

Behind the scenes, Jotform’s new AI capabilities are powered in part by technology from OpenAI, enabling multimodal inputs and conversational automation across the company’s ecosystem.

But Jotform’s AI push didn’t appear overnight. The company has spent the past year layering AI features into its platform, building the foundation for today’s launch.

A Year of AI Momentum

Before introducing Jotform AI, the company rolled out a series of AI-powered capabilities designed to automate customer interactions and workflows.

Among them were Jotform AI Agents, conversational agents that organizations can deploy across customer support channels, lead qualification flows, and internal operations.

The adoption numbers suggest significant traction:

• 750,000 AI Agents created by users
• 200 million agent sessions conducted
• 22 million messages sent through the system

Meanwhile, the earlier release of AI Form Copilot—now a core component of Jotform AI—has also seen strong usage. Since its debut, users have run more than 450,000 sessions, sending over 180,000 prompts to generate or refine forms.

According to Tank, those metrics helped validate the company’s belief that AI can dramatically accelerate everyday workflows.

“Generative AI has opened up incredible possibilities for software tools,” he said. “Our data shows users are building forms more efficiently and deploying autonomous customer service at high rates.”

In other words, the conversational interface isn’t just a flashy feature—it’s becoming the primary way users interact with the platform.

Expanding the AI Layer Across the Platform

Today’s launch focuses primarily on conversational form creation and editing. But the company says this is only the beginning.

Jotform plans to expand the AI layer across its broader product suite, including:

• Jotform Tables for structured data management
• Jotform Workflows for automation pipelines
• Jotform Sign for e-signature management
• Jotform Apps for building custom apps

The idea is to allow users to orchestrate entire workflows—data collection, processing, approvals, and automation—through conversational prompts.

If successful, that approach would effectively turn Jotform into an AI-powered operations layer rather than just a form tool.

Competing in the AI Productivity Race

Jotform’s pivot mirrors a broader shift across enterprise software vendors that are embedding AI directly into their user interfaces.

Many productivity platforms now offer AI-assisted generation, but they often stop at producing templates or drafts. Jotform’s approach goes further by enabling end-to-end form creation, automation, and integration through conversation.

That distinction could matter.

Forms remain a critical entry point for structured data across industries—from marketing lead capture to customer onboarding and employee workflows. Automating that process with AI could significantly reduce setup time and operational overhead.

For businesses increasingly adopting automation-first workflows, the appeal is obvious: fewer clicks, faster deployment, and less reliance on technical teams.

The Bigger Picture

The launch also arrives at a milestone moment for Jotform. The company recently celebrated its 20th anniversary, a rare longevity for a SaaS platform in the rapidly evolving productivity software space.

Two decades ago, web forms were mostly static tools used to collect basic information. Today, they power complex workflows tied to CRM systems, marketing automation, and customer support platforms.

By embedding AI directly into form creation, Jotform is positioning itself for the next evolution of that ecosystem—one where automation is driven less by configuration and more by conversation.

If the shift sticks, the drag-and-drop interface that once defined form builders may soon feel as dated as HTML tables.

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In an industry where timing can make or break a deal, PostcardMania is betting that automation—and a well-timed postcard—can give insurance agents a competitive edge.

The marketing technology firm, which reports more than 128,000 clients and $100+ million in annual revenue, has launched Insurance X‑Date Mailers, a new automated direct mail platform designed to help insurance agencies reach homeowners just before their policies expire.

For insurance professionals, that timing window—known as an “X-date”—is one of the most valuable opportunities to acquire new customers. It’s the period when homeowners are actively evaluating coverage, comparing rates, and deciding whether to renew or switch providers.

PostcardMania’s new system automates outreach during that critical decision-making phase, allowing agencies to deliver personalized postcards to prospects automatically without adding manual marketing work.

Targeting Homeowners at the Moment They’re Ready to Switch

In the insurance world, the X-date refers to a policy’s expiration or renewal date. Agents have long used this data to identify prospects likely to be in the market for new coverage.

The logic is straightforward: homeowners nearing renewal are already evaluating options.

Industry research supports the strategy. Homeowners who experienced premium increases were significantly more likely to shop around—29% compared with 21% among those without rate hikes. Meanwhile, 32.4% of consumers said they contacted another insurer for a quote within the past year.

That kind of active shopping behavior creates a narrow but valuable window for insurers looking to win new policyholders.

PostcardMania’s platform aims to capitalize on that moment by automatically sending targeted postcards ahead of a homeowner’s renewal date.

Automation Meets Direct Mail

Unlike traditional direct mail campaigns that require manual list building and periodic mail drops, Insurance X-Date Mailers operate continuously once configured.

The system uses proprietary data technology to compile homeowner and policy-timing information daily. When a qualifying homeowner enters the renewal window within an agent’s service area, the platform automatically triggers a postcard campaign.

Agents can refine targeting using property-level data such as:

• Property type
• Home value
• Year built
• Square footage
• Geographic location

Delivery timing is also customizable. Agencies can schedule postcards to arrive 30, 60, or even 90 days before a policy renewal, depending on their marketing strategy.

Once targeting and timing are set, the platform handles the rest—continuously identifying eligible prospects, printing postcards, and sending them automatically.

In other words, the campaign runs in the background while agents focus on closing policies rather than managing marketing logistics.

Part of a Broader Push Toward Trigger-Based Marketing

Insurance X-Date Mailers are the latest addition to PostcardMania’s portfolio of automation-driven direct mail products designed around real-life consumer triggers.

Other programs in the company’s lineup include:

• New to Town Mailers – targeting recent movers entering a local market
• Birthday Mailers – automated birthday greetings sent daily
• Medicare Mailers – outreach timed to individuals turning 65
• Website to Mailbox – postcards retargeting anonymous website visitors

The common thread is event-driven marketing. Instead of broadcasting the same message to a broad audience, these programs respond to specific consumer moments—moving to a new home, reaching a birthday milestone, aging into Medicare eligibility, or visiting a website.

For marketers, that approach increases relevance while reducing wasted outreach.

Direct Mail’s Unexpected Comeback

PostcardMania’s push into automated direct mail also reflects a broader shift happening in the marketing technology landscape.

While digital advertising dominates most marketing budgets, physical mail has quietly retained a powerful advantage: credibility.

Consumer research shows that 70% of people feel positive about direct mail, while 71% say physical mail feels more authentic than digital communications.

That authenticity has renewed interest in direct mail as a complement to digital channels, especially when combined with data-driven automation.

Within PostcardMania’s own business, the trend is already visible. Revenue from its automated “daily mailer” programs increased 18% in 2025 compared with 2024, signaling growing demand for marketing tools that blend automation with tangible outreach.

Timing, Personalization, and Automation

For founder and CEO Joy Gendusa, the new product reflects a broader evolution in marketing technology.

“Insurance X-Date Mailers are a great example of where marketing is headed—toward more responsive, personalized automation that helps businesses connect with people at the right moment,” Gendusa said.

That philosophy mirrors wider trends across the martech industry, where automation platforms increasingly rely on behavioral signals and real-world triggers rather than static campaign schedules.

In this model, the marketing system becomes proactive—detecting opportunities and launching campaigns automatically.

Why Insurance Agencies Are the Ideal Use Case

Insurance agencies represent a particularly strong fit for this type of trigger-based marketing.

Unlike many consumer services, insurance policies operate on predictable renewal cycles. That makes it easier to identify prospects who may soon be evaluating alternatives.

Historically, acquiring X-date data and executing campaigns around it required manual effort or third-party list providers. Automation changes that equation by continuously identifying prospects and launching campaigns without human intervention.

For smaller agencies and independent agents, the appeal is obvious: consistent lead generation without the complexity of running ongoing marketing campaigns.

The Future of Automated Direct Mail

PostcardMania’s new offering highlights an emerging trend in marketing technology: automation is moving beyond digital channels and into physical media.

For years, marketers have used automation for email campaigns, digital ads, and customer journey workflows. Now, those same principles—triggered events, behavioral targeting, and personalization—are being applied to print marketing.

The result is something that looks surprisingly modern for a decades-old medium: direct mail that behaves more like programmatic advertising.

As marketing teams search for ways to cut through digital noise and privacy restrictions, hybrid approaches that combine automation with physical media may become increasingly attractive.

For insurance agents chasing the next policyholder, that could mean the difference between landing in a crowded inbox—or arriving in a mailbox at exactly the right moment.

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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.

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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.

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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.

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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.

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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.

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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.

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