As brands invest more heavily in physical experiences, sports venues and immersive activations, experiential agencies are increasingly competing on the ability to connect architecture, technology, storytelling and fabrication. ASTOUND Group is responding by adding two senior creative leaders, appointing Ross Guntert as Executive Creative Director, Head of Design, and Ivan Druzic as Executive Creative Director, Experiential.

ASTOUND Group has appointed Ross Guntert and Ivan Druzic to executive creative roles as the experiential design and fabrication company expands its work across sports venues, events, retail and brand environments in North America.

Guntert becomes Executive Creative Director, Head of Design, while Druzic takes the position of Executive Creative Director, Experiential. The appointments add senior creative expertise at a time when ASTOUND is expanding its venue portfolio with projects involving teams and clients across Major League Baseball (MLB) and the National Hockey League (NHL) in the United States and Canada.

The move also continues a broader leadership investment at the company. ASTOUND previously added a Head of Sports and Chief Financial Officer, strengthening commercial and operational capabilities alongside its creative expansion.

For the experiential marketing industry, the appointments reflect a larger shift. Brands are no longer treating physical experiences simply as event marketing. Stadiums, arenas, pop-ups, retail environments, trade-show installations and immersive activations increasingly operate as extensions of a brand's broader customer experience strategy.

That requires creative teams to work across architecture, digital experiences, data, content and fabrication rather than treating each discipline as a separate project.

Architecture Meets Experiential Marketing

Guntert brings nearly two decades of experience spanning architecture, fabrication and experiential design. Before joining ASTOUND, he served as Northwest Region Design Director at Gensler, where he helped oversee design across six offices.

His previous work included contributions to NVIDIA's flagship headquarters in Santa Clara, while his career has also included work with Brooklyn-based design practice Snarkitecture.

At ASTOUND, Guntert will oversee the creative standard across architectural environments, exhibitions, live events and integrated brand experiences.

His remit includes developing creative systems and scalable workflows that can maintain design quality across ASTOUND's teams and offices. He will also play a role in integrating artificial intelligence into the company's creative practice, potentially using AI to accelerate ideation, iteration and communication of design concepts.

That is increasingly relevant across creative industries.

Generative AI is changing how agencies and design studios explore concepts, produce variations and communicate early-stage ideas. The more difficult challenge is incorporating AI into professional workflows without compromising brand consistency, design quality or intellectual property controls.

For an organization that combines design with physical fabrication, AI's role may be less about generating finished assets and more about speeding the transition from an initial concept to a viable built environment.

Druzic Brings Brand Storytelling Into the Experience

Druzic joins ASTOUND with more than two decades of experience across live events, experiential marketing, digital, advertising and sales promotion.

His previous work includes campaigns and experiences for brands such as Budweiser, Pepsi, Loblaws, Google and Xbox. At Mosaic North America, he eventually became Vice President, Creative Operations.

One of his notable projects was Sensorium, an immersive dining experience developed for Stella Artois. The project used multiple sensory elements to translate a brand identity into a physical environment.

That experience provides a useful indication of Druzic's remit at ASTOUND. His role will focus on developing experiential concepts across the company's clients and industry verticals, with an emphasis on turning brand strategy and consumer insight into physical experiences.

The distinction between experiential marketing and traditional advertising has become increasingly fluid. A stadium activation, immersive installation or branded pop-up can generate social content, customer data and digital engagement alongside the physical interaction.

For marketers, that makes experiential environments part of a broader omnichannel customer journey.

Sports Venues Become Marketing Platforms

ASTOUND's expanding sports-venue business adds another dimension to the appointments.

Professional stadiums and arenas are increasingly designed as multifunctional entertainment and commercial environments. Sponsorship activations, premium hospitality, retail, digital displays, fan engagement technologies and branded spaces all contribute to the experience.

MLB and NHL venues are particularly attractive environments for marketers because they combine recurring audiences with high-value physical interactions.

For agencies, however, these projects also create greater operational complexity. A successful venue experience has to work at architectural scale while remaining commercially useful and recognizable to fans.

That is where Guntert's architectural background and Druzic's experiential expertise could complement each other.

The Convergence of Physical and Digital Experiences

ASTOUND says the two executives will work across the company's full portfolio, including stadium and arena environments, trade-show exhibits, immersive activations, retail pop-ups, mobile tours and architectural projects.

The common thread is the convergence of physical and digital touchpoints.

This is increasingly relevant to enterprise marketing teams. Salesforce, Adobe and other major marketing technology providers have pushed toward connected customer experiences in which physical interactions can feed into broader digital journeys.

The experiential industry is moving in the same direction, although its core asset remains physical space.

For ASTOUND, combining strategy, design and fabrication under one organization can reduce the distance between concept and execution. That becomes especially valuable when a project needs to translate a digital brand identity into something customers can physically enter, touch or experience.

Why the Appointments Matter

The appointments are ultimately less about adding two creative titles than about how experiential agencies are evolving.

Brands increasingly expect agencies to deliver experiences that are visually distinctive, technologically integrated and commercially measurable. At the same time, venue owners and sports organizations need environments that serve multiple audiences and revenue objectives.

ASTOUND is positioning its expanded creative leadership around that intersection.

Guntert brings architectural systems thinking and design leadership, while Druzic adds experiential storytelling and brand activation expertise. Together, their roles suggest that ASTOUND sees the future of experiential marketing as a discipline that sits between architecture, advertising, entertainment, technology and customer experience.

The challenge will be turning that breadth into measurable business outcomes. As experiential budgets become more closely scrutinized, agencies will increasingly need to demonstrate not only whether an experience generated attention, but whether it contributed to engagement, customer relationships, revenue or brand value.

Market Landscape

Experiential marketing is becoming more integrated with broader brand and customer-experience strategies. Brands are using physical environments alongside social media, digital advertising, CRM and content to create connected customer journeys.

The global events and experiential marketing ecosystem is also benefiting from renewed investment in live entertainment, sports and in-person brand engagement. Research from PQ Media has consistently identified experiential marketing as a major component of the broader alternative marketing sector, while agencies increasingly combine physical experiences with digital technology and data.

ASTOUND's positioning is differentiated by its combination of strategy, architecture, experiential design and fabrication. Competitors across the broader experience economy include agencies and production companies such as Jack Morton, GMR, George P. Johnson and Imagination, although their service mixes and market positioning differ.

The competitive advantage increasingly lies in the ability to manage complex projects from concept through physical execution while maintaining a consistent brand experience.

Strategic Outlook

The next phase of experiential marketing will likely be defined by greater convergence between physical environments, AI-assisted creative development, digital engagement and measurable customer outcomes.

AI could accelerate concept development and personalization, while connected physical environments can create new opportunities for data collection and customer interaction. Sports venues, retail spaces and immersive activations are particularly well positioned to benefit from this convergence.

ASTOUND's new creative leadership suggests the company is preparing for that environment by combining architectural rigor with experiential storytelling and AI-enabled creative workflows.

For enterprise marketers, the implication is clear: physical experiences are increasingly becoming another layer of the connected marketing stack rather than an isolated event channel.

Top Insights

• ASTOUND's new creative leadership combines architecture and experiential expertise as brands increasingly connect physical environments with broader digital customer experience strategies.
• Ross Guntert brings architectural design systems and AI-enabled creative development to ASTOUND as the company expands its sports and venue portfolio.
• Ivan Druzic adds more than two decades of experiential and advertising experience, strengthening ASTOUND's ability to translate brands into immersive environments.
• MLB and NHL venue projects highlight growing opportunities for experiential agencies as stadiums evolve into entertainment, sponsorship and customer engagement platforms.
• AI integration could accelerate experiential concept development, but agencies will still need to demonstrate measurable business outcomes from increasingly complex physical experiences.

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Automotive service departments are increasingly moving routine inspections into digital workflows, but technicians still need to stop working to photograph vehicles, document problems and update inspection records. CardinaleWay Hyundai & Genesis of Mountain View is testing a different approach by putting those tasks into a hands-free workflow using Meta AI glasses and UpdatePromise's Symphony platform.

CardinaleWay Hyundai & Genesis of Mountain View has become the first dealership to integrate Meta AI glasses into its digital multi-point inspection (MPI) workflow through UpdatePromise's Symphony platform, creating a hands-free method for documenting vehicle inspections.

The integration connects Meta's wearable technology with an established digital inspection process. Built using the Meta Wearables Device Access SDK Toolkit, the system allows technicians to interact with the inspection workflow through voice while capturing photos and videos during the inspection.

Those visual records are incorporated directly into the digital MPI rather than requiring technicians to repeatedly stop, handle another device and manually document findings.

The change may appear incremental, but it highlights a larger direction for enterprise technology: wearable AI is moving beyond consumer experimentation and into operational workflows where employees need access to information without interrupting physical tasks.

For automotive dealerships, the service bay is a particularly practical environment for that model.

How Meta AI Glasses Fit Into the Inspection Workflow

A traditional digital vehicle inspection still requires technicians to interact with a phone, tablet or workstation while moving around a vehicle. That creates a practical trade-off between documenting work thoroughly and keeping attention on the vehicle.

With the CardinaleWay implementation, technicians can complete an MPI through hands-free, voice-enabled interactions while capturing inspection images and video through Meta AI glasses.

The resulting information becomes part of the customer's digital service experience.

Customers can review inspection findings, see supporting visual evidence, communicate with their service advisor and approve recommended repairs digitally.

That creates a connected chain between technician activity, service-advisor communication and customer approval.

The technology is therefore not simply an AI wearable deployment. Its significance lies in how the wearable is connected to an existing dealership workflow.

Why Wearable AI Matters for Automotive Service

The broader enterprise opportunity for AI glasses is hands-free access to software during physical work.

Technicians, warehouse employees, field-service engineers and healthcare workers often operate in environments where repeatedly reaching for a screen is inefficient. Wearables can potentially bring instructions, documentation and data capture directly into the worker's field of activity.

Meta has been expanding its wearable technology ecosystem through products such as Ray-Ban Meta smart glasses, while its developer tools allow third-party applications to interact with wearable capabilities.

The CardinaleWay deployment illustrates a different layer of that ecosystem: using wearable hardware as an interface to existing business software.

That distinction could become important as enterprise AI adoption matures. Instead of asking workers to move between separate AI applications, companies can embed AI-enabled interfaces inside processes employees already understand.

From Inspection Documentation to Customer Transparency

For dealerships, the commercial value of digital inspections is closely connected to transparency.

A service recommendation supported by photographs or video gives customers more information than a technician's written description alone. Adding hands-free capture could make it easier to collect visual evidence consistently during the inspection process.

The workflow could also reduce administrative friction for technicians and service advisors. Rather than capturing media first and organizing it later, documentation can become part of the inspection itself.

However, the effectiveness of the approach will depend on implementation details.

Wearable cameras introduce questions around privacy, consent, data storage, device management and workplace policies. Dealerships will also need to ensure that captured content is correctly associated with the appropriate vehicle and repair order.

These considerations become increasingly important as AI-enabled devices move from pilot programs into everyday operations.

The Emerging Enterprise Wearables Market

The dealership's deployment arrives as technology companies increasingly position wearable devices as a new interface for AI.

Meta, Google and other technology companies are exploring AI-powered wearables that can understand context, respond to voice commands and interact with digital services. Meanwhile, enterprise software providers are looking for ways to integrate AI into frontline operations rather than restricting it to desktop applications.

The automotive industry offers an especially useful testing ground because service technicians already perform highly physical, information-intensive work.

A technician may need to identify a component, document damage, photograph a repair issue, consult service information and communicate the result to a customer. A wearable interface can potentially connect those activities without requiring constant interaction with a conventional screen.

CardinaleWay's implementation with UpdatePromise is an early example of that model.

What It Means for Enterprise Technology Teams

The larger lesson is that AI adoption does not necessarily require replacing existing software systems.

In this case, Meta's wearable technology is being integrated into an existing digital multi-point inspection workflow rather than introduced as an independent application.

That model could prove attractive to enterprises evaluating emerging AI hardware. Instead of rebuilding operational processes around a new device, companies can use wearables as an interface layer over existing platforms.

For dealerships, the immediate benefits are centered on inspection documentation, workflow efficiency and customer communication. Longer term, similar integrations could expand into technician guidance, service documentation, parts identification and other frontline applications.

The critical question will be whether these devices can deliver measurable productivity improvements while maintaining privacy, reliability and operational controls.

If they can, AI glasses could evolve from a consumer technology experiment into a practical enterprise interface for workers who spend their days away from desks.

Market Landscape

The automotive service industry is becoming increasingly digital, with dealerships using inspection platforms, CRM systems, customer communication tools and service-management software to connect technicians with advisors and vehicle owners.

At the same time, AI wearables are emerging as a new interface category. Meta's smart-glasses strategy has helped move camera- and voice-enabled eyewear into mainstream consumer use, while enterprise technology companies are exploring similar hands-free interfaces for frontline workers.

The CardinaleWay deployment is notable because it connects wearable hardware directly to an operational dealership workflow. Rather than treating AI glasses as a standalone gadget, the implementation positions them as an input and documentation layer within an existing service platform.

That integration model could be more important than the hardware itself as enterprises evaluate AI wearables.

Strategic Outlook

The next stage of automotive AI may not be defined solely by generative AI assistants or predictive analytics. Increasingly, the opportunity lies in connecting AI to the physical environments where employees perform their jobs.

For service departments, wearable devices could eventually support richer inspection documentation, real-time technical assistance and faster communication between technicians and customers.

The CardinaleWay deployment provides an early example of that direction. Its success will ultimately be measured not by the novelty of using AI glasses, but by whether hands-free workflows improve inspection quality, technician productivity and customer confidence without adding operational complexity.

Top Insights

• CardinaleWay's deployment connects Meta AI glasses with UpdatePromise Symphony, bringing hands-free visual documentation directly into an established automotive service workflow.
• Technicians can capture inspection photos and videos while working, potentially reducing device switching and improving the consistency of digital multi-point inspection documentation.
• Customers benefit from a connected service experience where digital inspection findings, visual evidence, repair recommendations and approvals are available through one workflow.
• The deployment demonstrates how AI wearables can function as enterprise software interfaces rather than standalone consumer devices, particularly in frontline operational environments.
• Privacy, data governance, device reliability and measurable productivity gains will determine whether wearable AI can scale across dealership service departments.

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Yiren Digital is moving its artificial intelligence strategy beyond individual financial-services use cases, building a shared enterprise AI architecture that allows models, agents and workflows developed for one function to be reused across the organization. The approach could help the company reduce duplicated AI development while bringing automated decision-making into areas ranging from marketing and customer operations to risk management and compliance.

Yiren Digital Ltd. (NYSE: YRD), a China-based financial technology company, said it is expanding its enterprise AI capabilities across core business functions as it works toward what it describes as an AI-native, multi-industry operating platform.

Rather than developing separate AI systems for every department, the company has established a common framework intended to standardize models, AI agents, workflows and governance. The goal is straightforward: an AI capability developed for one business process should be reusable in another without rebuilding the underlying technology from scratch.

That architectural shift reflects a broader change in enterprise AI. Companies are increasingly moving from experimental generative AI projects toward shared infrastructure that can support multiple departments and use cases. McKinsey's 2025 global survey found that 88% of respondents said their organizations regularly use AI in at least one business function, but most organizations were still working to scale AI beyond individual use cases.

For Yiren Digital, financial services provide the initial testing ground.

From Financial AI Projects to Shared Enterprise Infrastructure

Yiren Digital said its credit and insurance businesses have been used to develop and validate AI capabilities in production. Those deployments have given the company an environment in which models, decision frameworks and workflows can be tested against operational requirements before being standardized for wider use.

The company's architecture combines proprietary large language models, multi-agent infrastructure, workflow execution and centralized governance.

Its technology stack includes the Zhiyu and Yizhi large language models, MagiCube 2.0 multi-agent platform, XuanJi workflow execution engine and ZhiNao orchestration layer.

The components are designed to work as reusable building blocks rather than isolated applications.

That distinction matters for enterprise technology teams. A company can build a highly capable AI model, but the model itself is only one part of deploying AI at scale. Production systems also require data access, workflow integration, permissions, monitoring, risk controls and governance.

Yiren Digital's strategy is effectively to treat those layers as shared infrastructure.

AI Moves Into Marketing, Risk and Customer Operations

The company said recent production deployments span customer operations, capital operations, marketing, risk management and asset recovery.

Marketing is particularly relevant because AI adoption is moving beyond content generation into campaign decision-making, customer segmentation, service automation and predictive analytics. Enterprise marketing teams increasingly need AI systems that can connect models with customer data, workflows and downstream business processes rather than operate as standalone assistants.

For financial-services organizations, the same architecture can support more sensitive applications.

Fraud detection, credit decisioning, compliance and risk management require systems that can explain or validate decisions and operate within defined controls. Yiren Digital said its architecture emphasizes reusable fraud detection models, validation frameworks and decision layers that can be combined with partner systems.

That approach also points toward a more modular model for enterprise AI procurement. Instead of adopting a single black-box platform, organizations could potentially assemble AI capabilities around existing infrastructure.

The Rise of Multi-Agent Enterprise AI

One of the more significant elements of Yiren Digital's announcement is its focus on multi-agent infrastructure.

Traditional enterprise AI implementations often center on individual models or applications. Multi-agent systems take a different approach by allowing specialized AI agents to perform different tasks and coordinate through a broader workflow.

Yiren Digital's MagiCube 2.0 platform and ZhiNao orchestration layer are intended to provide that coordination layer, while XuanJi handles workflow execution.

The concept is increasingly relevant as enterprises explore autonomous AI workflows. Microsoft, Salesforce, Google and other major technology providers are developing agent-oriented enterprise platforms designed to move AI from answering questions toward performing business tasks.

For Yiren Digital, the proposed model is particularly significant in financial services, where autonomous workflows could potentially connect fraud detection, customer service, risk assessment and operational decisions.

However, autonomy also increases governance requirements. An AI agent that can execute a workflow creates a different risk profile from a chatbot that simply generates information. Enterprises therefore need controls around data access, model behavior, approvals, auditability and human intervention.

Why the Reusable AI Model Matters

Yiren Digital's core proposition is not simply that it has deployed AI across several departments. The more important development is the attempt to create a repeatable enterprise AI operating model.

Reusability could reduce the time and cost associated with deploying AI into additional functions. Models and workflows can be adapted instead of rebuilt, while governance structures can remain consistent across applications.

That could create operating leverage if the architecture performs as intended.

The challenge, however, is that AI portability is rarely frictionless. Different business units often have different data structures, regulatory requirements, workflows and risk tolerances. A model developed for credit operations cannot necessarily be transferred directly into marketing or compliance without additional testing and controls.

Yiren Digital's production deployments therefore represent an important validation step, but the larger test will be whether its shared architecture can continue to perform as the company expands into new AI-enabled verticals.

For enterprise technology leaders, the strategy highlights an increasingly important question: Should AI be purchased as a collection of applications, or built as reusable organizational infrastructure?

Yiren Digital is clearly pursuing the latter.

Market Landscape

Enterprise AI is shifting from isolated pilots toward shared platforms capable of supporting multiple business functions. Gartner has predicted that agentic AI will become a significant component of enterprise software, while technology vendors including Microsoft, Google, Amazon and Salesforce are embedding AI agents and orchestration capabilities into their enterprise ecosystems.

The competitive landscape is therefore moving beyond large language models themselves. Increasingly, the differentiators are agent orchestration, workflow execution, enterprise data access, governance, security and integration.

Yiren Digital's architecture follows this broader direction but applies it initially to financial technology and insurance. Its emphasis on reusable models and centralized governance could be particularly relevant in regulated industries where AI deployment needs to be repeatable and controlled.

Strategic Outlook

Yiren Digital's next phase will depend on whether its internal AI infrastructure can become a genuine platform rather than simply a collection of connected technologies.

If models, agents and workflows can be reused across credit, insurance, marketing, customer operations, compliance and risk without extensive redevelopment, the company could gain meaningful operational leverage from its AI investments.

The longer-term opportunity is broader. Financial institutions are increasingly looking for AI systems that can execute business processes rather than simply generate text. That creates demand for enterprise architectures combining LLMs, AI agents, workflow automation, data infrastructure and governance.

Yiren Digital's strategy places it squarely within that transition.

Top Insights

• Yiren Digital is consolidating AI models, agents and workflows into shared infrastructure, potentially reducing duplicated development across financial and enterprise business functions.
• Production deployments in credit and insurance provide operational testing grounds before Yiren Digital expands standardized AI capabilities across marketing, risk and customer operations.
• MagiCube 2.0 and ZhiNao position multi-agent orchestration as a core layer for coordinating increasingly autonomous enterprise workflows and financial-services processes.
• Reusable fraud detection and decision components could help financial institutions integrate AI capabilities without adopting rigid, black-box technology architectures.
• Enterprise AI scalability will depend not only on model performance but also on governance, workflow integration, data access and regulatory controls.

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Digital advertising measurement has long depended on a patchwork of platform dashboards, agency reports, verification scores and post-campaign reconciliation. FouAnalytics is now proposing a more contractual approach: make measurement evidence part of the definition of whether digital media should be billable in the first place.

FouAnalytics, an independent analytics and verification platform for digital advertising, websites and mobile applications, has released public Master Services Agreement (MSA) boilerplate designed to help advertisers and procurement teams establish measurement requirements before digital-media budgets are committed.

The Digital Media Governance, Verification, Transparency, and Brand Safety MSA Boilerplate is positioned as a contractual framework for advertiser-agency and advertiser-vendor relationships. It emphasizes directly observed, placement-level evidence rather than treating platform reporting, invoices or aggregate verification scores as conclusive proof of delivery.

The proposal arrives as digital advertising becomes increasingly automated and fragmented across search, social, programmatic, connected TV, retail media, mobile applications and other channels. According to the IAB/PwC Internet Advertising Revenue Report, U.S. digital advertising revenue reached nearly $300 billion in 2025, up 13.9% year over year.

At that scale, the question is no longer simply whether advertisers can measure campaigns. It is whether they can establish what evidence should count when determining media quality, performance and payment.

Turning Measurement Into a Contractual Requirement

The FouAnalytics framework takes a measurement-first approach to billability. Under the proposed model, a placement becomes billable only when the required measurement fields and sufficient measurement data specified in an advertiser-approved campaign Measurement Plan are available.

That distinction is significant.

A platform may report an impression, for example, without independently demonstrating that an advertisement actually rendered in the intended environment, was viewable, reached a human user or satisfied the advertiser's quality requirements.

The boilerplate therefore treats platform dashboards, ad-server events, exchange records, publisher reports, invoices and placement reports as supporting evidence rather than definitive proof.

FouAnalytics CEO Dr. Augustine Fou argues that advertisers should be able to inspect and reconcile evidence surrounding their media investments rather than relying exclusively on aggregated reporting.

The framework also requires agencies and vendors to disclose known measurement limitations before campaigns launch. Those limitations can involve browsers, devices, privacy controls, consent requirements, CTV environments, publishers and individual advertising platforms.

That creates an important shift in procurement logic: measurement limitations become something to negotiate before purchase, rather than something to discover after a campaign has finished.

From Verification Scores to Placement-Level Evidence

Another central feature is the distinction between independent verification and directly observed evidence.

The MSA states that a source should not automatically be considered directly measured simply because it is described as independent, third-party, certified or accredited. Instead, the proposed standard calls for underlying evidence where technically available, including rendering information, screenshots, actual environment evidence, placement attributes, device and time information, supply-source details and other analytical fields.

This puts the approach somewhat apart from conventional ad verification platforms such as DoubleVerify and Integral Ad Science, which provide advertisers with fraud detection, viewability, brand-safety and media-quality measurement. Gartner describes both as platforms used to analyze and verify digital advertising quality, including viewability, fraud and brand safety.

The distinction is not necessarily that one model replaces another. Rather, FouAnalytics is attempting to move the conversation from “What score did the verification provider give this campaign?” to “What underlying evidence supports this particular media event?”

That could make the model particularly relevant to enterprise procurement, where marketing, finance, legal and compliance teams increasingly need defensible evidence for large media expenditures.

Measurement Gaps Become Procurement Decisions

The framework also introduces a defined distinction between measurement discrepancies and measurement gaps.

A variance of more than three percentage points between FouAnalytics and comparable third-party reporting would trigger an audit and technical investigation. The discrepancy itself would not automatically make media non-billable. Instead, billability would remain tied to whether the placement has the required measurement evidence.

That nuance matters because discrepancies are inevitable in complex advertising ecosystems. Different vendors can use different methodologies, sampling techniques, timestamps, identity signals and measurement boundaries.

The proposed framework therefore does not treat every reporting difference as fraud. It establishes the discrepancy as a reason to investigate while keeping the underlying evidence standard separate.

Extending Governance Across the Media Stack

The MSA is designed to cover display, video, mobile web, mobile apps, social, search, native, programmatic, CTV, OTT, audio and retail media.

For walled gardens where conventional in-ad measurement cannot be deployed, the template proposes FouAnalytics ClickTrackers, where technically supported and permitted, to compare platform-reported clicks with directly observed clicks and post-click arrivals.

On advertiser-owned websites, the framework also supports post-click measurement to compare traffic quality and engagement across campaigns, publishers, platforms and channels, subject to privacy, consent, security and technical requirements.

Brand safety is addressed through contextual evidence as well. The framework calls for page, URL, application or CTV-level information and, where technically available, contextual text surrounding designated keywords. It also explicitly recognizes that the presence of a keyword alone does not necessarily constitute a brand-safety violation.

Why Enterprise Marketers Should Pay Attention

The broader issue is control.

AI-driven advertising systems are increasingly making decisions about targeting, placement, optimization and creative delivery on behalf of marketers. Gartner warned in 2026 that AI is moving more advertising decision-making into platform algorithms while increasing challenges around control, transparency and cross-platform measurement.

That makes measurement infrastructure more important, not less.

For enterprise marketing teams, a measurement-first MSA could provide a way to align marketing, procurement, finance and legal teams around a common evidence standard. It could also make media contracts more explicit about what happens when measurement is unavailable, incomplete or inconsistent.

The approach will not eliminate every measurement problem. Privacy restrictions, walled gardens, CTV fragmentation and technical limitations can make direct observation difficult or impossible in some environments. A contractual requirement cannot manufacture data that a platform does not expose.

Its significance lies elsewhere: it attempts to make measurement capability, evidence access and remediation rights part of the commercial architecture of digital advertising.

If that model gains adoption, advertisers could increasingly evaluate media suppliers not only on price and performance, but also on whether their delivery can be independently evidenced.

Market Landscape

Digital advertising measurement is evolving from campaign reporting toward broader media governance and accountability infrastructure. Traditional verification providers focus heavily on fraud, viewability, brand safety and contextual quality, while platforms such as Google, Microsoft, Amazon and Meta increasingly automate campaign execution and optimization inside their own ecosystems.

At the same time, enterprise measurement platforms such as Measured approach the problem from another direction, combining media mix modeling and incrementality testing to evaluate business impact across channels. Gartner identifies measurement and proving advertising value as increasingly challenging amid technological disruption, privacy concerns and changing consumer behavior.

FouAnalytics is targeting a narrower but potentially important layer: evidence of media delivery itself.

That positioning makes the MSA less of a conventional analytics product announcement and more of a proposed governance framework. Its success will depend on whether advertisers, agencies and media suppliers are willing and technically able to adopt measurement requirements at the contractual level.

Strategic Outlook

The larger trend is toward greater separation between platform-reported activity, independently measured delivery and actual business outcomes. Enterprise marketers will increasingly need all three.

Platform data remains essential for campaign operations. Verification can provide quality and risk signals. Independent measurement can strengthen auditability. And incrementality, attribution and marketing mix modeling can help determine whether the resulting exposure actually contributed to business growth.

The challenge will be integrating these layers without creating excessive operational complexity.

FouAnalytics' public MSA is therefore notable less because it introduces another measurement dashboard and more because it asks advertisers to reconsider where measurement belongs: not only inside reporting systems, but inside the commercial agreement itself.

Top Insights

• FouAnalytics is turning media measurement into a contractual requirement, potentially giving enterprise procurement teams stronger leverage over campaign billability and evidence standards.
• The MSA separates platform-reported impressions from observed rendering, creating a clearer distinction between delivery claims and independently supported media evidence.
• The framework could complement traditional ad verification platforms by emphasizing underlying placement-level evidence rather than relying exclusively on scores or aggregate classifications.
• AI-driven advertising increases the importance of measurement governance as automated platforms assume greater control over targeting, placement and campaign optimization.
• Advertisers may increasingly demand pre-campaign disclosure of measurement limitations, especially across walled gardens, CTV, privacy-restricted environments and fragmented media supply chains.

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Event marketers are under growing pressure to create experiences that do more than put a logo on another promotional item. The Sweet Printer is responding with a broader portfolio of 25+ live personalization activations, extending its original cookie-printing concept into edible printing, laser engraving, UV printing, apparel customization, personalized gifting and other event technologies.

The San Diego-based experiential marketing company says the strategy is built around a simple premise: when guests participate in creating an item and watch it being produced in real time, the promotional product becomes part of the event rather than just another piece of swag.

The traditional event giveaway has a problem. Branded pens, bags, shirts and other promotional products can put a company's logo in front of attendees, but they rarely give people a reason to stop, interact or share the experience.

Live personalization takes a different approach.

Instead of handing attendees a finished product, brands can invite them to help create it. A photograph can become a printed cookie, a name can be transformed into an edible design, a luggage tag can be engraved in front of its owner, or apparel can be customized while an attendee waits.

That is the model behind The Sweet Printer's expansion into more than 25 interactive brand activations.

The San Diego-based company began with live cookie printing but has expanded into a broader experiential marketing and event technology portfolio covering edible printing, 3D sugar creations, laser engraving, UV printing, apparel personalization, branded food experiences, corporate gifting and photo-to-product experiences.

The company is targeting corporate events, trade shows, conferences, incentive travel programs, product launches, hospitality events and other large-scale marketing activations.

The underlying technology is not necessarily the most important part of the proposition. The experience created around it is.

A conventional promotional product is generally manufactured before an event and distributed to attendees. Live personalization turns production into entertainment. Guests provide an input, watch a machine or specialist create the product, and leave with something made specifically for them.

That changes the role of merchandise within an event marketing strategy.

Instead of being an endpoint, the product becomes a reason to engage with the brand.

For trade-show marketers, that distinction can be significant. Exhibitors compete for attention in crowded environments where attendees can move between dozens or hundreds of booths. An activation that creates a visible process — printing, engraving, decorating or producing something edible — can provide a natural focal point for foot traffic.

It can also create opportunities for social content.

A guest photographing a personalized product is effectively documenting an experience rather than simply showing a branded giveaway. That can extend the reach of an activation beyond the physical venue, although the amount of organic sharing will ultimately depend on the appeal of the experience and the audience.

The Sweet Printer's portfolio reflects the broader convergence of experiential marketing, personalization and event technology.

Its offerings include live cookie printing, candy and sugar printing, 3D engraving, luggage tag personalization, customized travel accessories, golf ball printing, beauty-product personalization, apparel customization and corporate gifting.

The company says it works with brands, agencies, event producers, hotels, management companies and corporate meeting planners to incorporate these experiences into existing event formats.

That flexibility is important because most enterprise event teams are not looking to create an entirely new production environment for every campaign. They need activations that can fit into a trade-show booth, conference space, VIP lounge, hotel environment or incentive travel program without disrupting the broader event.

Personalization can also help brands address a long-standing problem with promotional merchandise: relevance.

A generic branded item may be useful, but it does not necessarily feel personal. A product created around a guest's name, photograph, destination or preferences has a different perceived value.

The approach is particularly interesting as artificial intelligence and digital personalization become increasingly common in marketing.

AI can generate content, personalize recommendations and automate digital customer experiences at massive scale. Live personalization offers the physical counterpart: technology is used to create an individualized object in front of the customer.

That combination may become more relevant as event marketers look for experiences that counterbalance increasingly digital customer journeys.

The Sweet Printer's expansion also illustrates how specialized event technology companies are moving beyond a single product category.

Rather than positioning itself solely as a food-printing provider, the company is building a portfolio that crosses food, fashion, travel, beauty, sports and corporate gifting. That gives agencies and event planners more options for matching an activation to a particular audience or campaign.

The challenge, however, is scalability.

An activation that works well for a small VIP gathering may require very different workflows at a conference with thousands of attendees. Production speed, equipment reliability, staffing, customization limits and venue logistics all become important when personalization moves from a novelty into a high-volume marketing operation.

The Sweet Printer says its workflows are designed to accommodate both intimate VIP groups and larger corporate events.

That operational layer could ultimately be as important as the personalization technology itself.

The company's philosophy can be summarized by the idea that the moment an item becomes personalized is the activation. It represents a broader shift in experiential marketing: brands are increasingly competing not simply on what they give attendees, but on what attendees get to do.

As marketers search for alternatives to passive promotional merchandise, live creation offers a way to combine entertainment, personalization and physical brand interaction.

For The Sweet Printer, the expansion into more than 25 activations suggests that live personalization is becoming less of a single novelty and more of a broader experiential marketing platform.

Market Landscape

Experiential marketing is increasingly focused on participation rather than passive brand exposure. Trade shows, conferences and corporate events give marketers a physical environment where personalization and interactive technology can complement digital campaigns.

The broader marketing technology ecosystem is moving in a similar direction. Platforms from Salesforce, Adobe and other enterprise vendors increasingly use customer data and AI to personalize digital interactions. Live event activations bring that concept into the physical world.

The Sweet Printer's model occupies a specialized position between promotional products, event technology and experiential marketing. Its differentiation comes from making the personalization process itself part of the customer experience.

The competitive advantage will depend less on the novelty of individual printing technologies and more on execution: throughput, customization, reliability, visual appeal and the ability to integrate activations into complex corporate events.

Strategic Outlook

The next generation of experiential marketing is likely to combine digital intelligence with tangible experiences.

AI can help brands personalize messaging and identify audiences, while live production technologies can turn those personalized inputs into physical products. The result is a marketing interaction that can be both data-informed and human-centered.

For event marketers, the opportunity is to move from branded merchandise to participatory brand experiences.

The Sweet Printer's expanding portfolio is one example of that transition, particularly as brands seek experiences that generate engagement before, during and after an event.

Top Insights

• The Sweet Printer is expanding beyond cookie printing into 25+ live personalization experiences spanning food, apparel, gifting and event technology.
• Live production turns promotional merchandise into an interactive attraction, potentially helping trade-show exhibitors generate booth traffic and attendee engagement.
• Personalized products can create stronger physical connections than generic giveaways while giving guests an experience worth photographing and sharing.
• The company's model reflects a broader experiential marketing shift toward participation, personalization and technology-enabled physical brand interactions.
• AI and digital personalization may increase demand for tangible experiences that give customers a human, physical interaction with brands.

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B2B buyers are increasingly using generative AI tools to research software, compare vendors and narrow their choices before visiting a company's website. That shift is creating a new marketing problem: brands must understand not only where they rank in traditional search, but how AI systems describe and recommend them.

Veloz, an AI-native Answer Engine Optimization (AEO) agency, has become a verified partner of Profound, an AI visibility platform. The partnership places Profound at the center of Veloz's AEO services, with the agency using the platform to monitor AI visibility, identify opportunities and connect changes in brand representation to pipeline and conversion outcomes.

Search marketing has historically revolved around a relatively familiar sequence: a buyer enters a query, search engines return results, and businesses compete for visibility on the results page.

Generative AI is disrupting that model.

A buyer researching B2B software can now ask ChatGPT, Claude, Gemini or Perplexity which products are best for a particular use case and receive a synthesized recommendation rather than a conventional list of links.

That changes what it means for a brand to be visible.

A company can have strong organic rankings and still be poorly represented in AI-generated answers. Conversely, a competitor may appear prominently in an AI recommendation despite not occupying the top position for traditional search queries.

Veloz is building its Answer Engine Optimization practice around that emerging gap.

The AI-native AEO agency has become a verified Profound partner, adopting Profound's AI visibility platform as core infrastructure for its client work across the United States and Europe, the Middle East and Africa.

The partnership is aimed primarily at B2B software companies attempting to influence how AI systems understand, evaluate and recommend their products.

At the center of the offering is measurement.

Veloz uses Profound to track how clients appear across AI-generated answers, identify prompts and topics where visibility matters, and monitor changes after content and technical updates. The agency says it also connects those visibility changes to business metrics such as lead generation, demo requests, pipeline and conversions.

That last part is important.

AEO can easily become another reporting category if marketers measure mentions without establishing whether those mentions influence commercial outcomes. Traditional SEO already has a mature set of metrics around rankings, impressions, clicks and organic conversions. AI search introduces less established measures such as citations, recommendation frequency, brand sentiment and share of voice.

Veloz is attempting to connect those signals to revenue-oriented metrics rather than treating AI visibility as an isolated vanity metric.

The partnership also includes Profound FactCheck, which Veloz uses to identify cases where AI engines inaccurately represent a brand. The agency then traces potential sources of those inaccuracies and applies content or technical changes intended to provide AI systems with clearer context.

That addresses one of the more difficult problems in generative search: marketers do not directly control the answer generated by an AI engine.

An AI system may synthesize information from a company's website, third-party publications, reviews, directories and other sources. If those sources contain conflicting or incomplete information, the resulting answer can differ from the company's preferred positioning.

For B2B software companies, the implications can be significant.

A buyer asking an AI assistant to compare several vendors may receive a recommendation based on factors such as product capabilities, pricing, integrations, security, customer sentiment or perceived market leadership. The brand's own marketing website is only one potential input.

That makes AI brand visibility a broader information-management problem rather than simply another form of keyword optimization.

Veloz's approach combines human marketers with technical specialists and AI agents. The agency says its team includes agent engineers, including a former machine learning engineer at Nextdoor and a neuroscience researcher from the University of Cambridge. Founder Brian Colivet previously held go-to-market and AI deployment roles at Meta and Personio.

The company offers the work as a managed service or alongside internal marketing teams.

This hybrid model is becoming increasingly common across enterprise MarTech.

AI can monitor large numbers of prompts and identify patterns faster than a human team, while marketers and technical specialists can determine which changes are appropriate for a company's positioning, content architecture and commercial goals.

The distinction is particularly important in AEO because simply producing more content does not guarantee that an AI system will cite or recommend a brand.

Technical accessibility, entity consistency, authoritative third-party references and the quality of source material can all influence how AI systems construct answers. Marketers therefore need to think beyond conventional on-page optimization.

Veloz's use of Profound also highlights the emerging competition around AI visibility platforms.

The market is attracting SEO companies, enterprise software providers and specialized AI-search platforms attempting to help businesses measure how they appear in ChatGPT, Gemini, Perplexity and other generative interfaces.

Profound's role in this partnership is primarily measurement and intelligence, while Veloz provides strategy and execution.

That division resembles the broader MarTech ecosystem, where technology platforms provide data and workflow infrastructure while agencies translate those capabilities into business programs.

For B2B companies, the timing is significant.

The research phase of a software purchase can involve dozens of queries, comparisons and follow-up questions. If AI systems increasingly influence that process, a brand's ability to shape its representation before a buyer reaches a vendor website could become a competitive marketing advantage.

The challenge is that AI search remains dynamic.

Models change, retrieval systems evolve and answers can vary according to prompts, context, geography and available sources. A strategy that improves visibility today may not produce identical results as AI search systems change.

That makes continuous monitoring more important than a one-time optimization project.

Veloz's partnership with Profound is built around that ongoing model: monitor AI representation, identify gaps, make improvements, measure outcomes and repeat.

The larger shift is from search engine optimization toward answer engine visibility.

SEO is unlikely to disappear. AI systems still rely heavily on web content, authoritative sources and technical infrastructure. But B2B marketers now have another discovery environment to manage—one in which the search result may be a synthesized answer rather than a ranked page.

For software companies competing in crowded categories, being technically discoverable may no longer be enough.

They also need to be understandable, accurately represented and consistently recommended when buyers ask AI systems which solution to choose.

Market Landscape

The emergence of AI search is creating a new layer in the B2B MarTech stack.

Google remains a dominant source of commercial discovery, while Microsoft, OpenAI and other technology companies are embedding generative AI into search and productivity experiences. Platforms such as ChatGPT, Gemini, Claude and Perplexity can increasingly influence how buyers discover and evaluate software.

This has created demand for Answer Engine Optimization, Generative Engine Optimization and AI visibility platforms.

Traditional SEO tools remain focused primarily on rankings, backlinks, organic traffic and search performance. AI visibility platforms instead attempt to measure how brands appear inside generated answers, including mentions, citations, competitive share of voice and sentiment.

Profound is competing within that emerging category, while Veloz is positioning itself as an execution partner that turns AI visibility data into content, technical and strategic actions.

The competitive question is moving from "Who ranks first?" toward "Which companies does the AI recommend, and why?"

Strategic Outlook

AEO is likely to become increasingly integrated with traditional SEO rather than replacing it.

AI systems still depend on websites, structured information, authoritative sources and third-party content. Strong technical SEO and high-quality content can therefore provide part of the foundation for AI visibility.

The next generation of enterprise search strategies will likely combine organic rankings, AI citations, brand sentiment, entity consistency and commercial outcomes.

For B2B marketers, the most valuable platforms will be those that can connect these new visibility metrics to tangible results such as qualified leads, pipeline and revenue.

Top Insights

• Veloz's Profound partnership targets B2B software brands seeking greater visibility and narrative control across ChatGPT, Gemini, Claude and Perplexity.
• Profound provides AI visibility measurement while Veloz combines strategy, technical expertise and execution to improve how AI systems represent client brands.
• Profound FactCheck helps identify inaccurate AI-generated brand descriptions, allowing marketers to trace information sources and improve contextual signals.
• The partnership measures AEO success through demos, pipeline and conversions, moving AI visibility beyond simple mentions and citation counts.
• The emergence of AI recommendations creates a new MarTech discipline where brands must optimize both traditional search rankings and machine-generated answers.

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Jewelry suppliers have traditionally had a limited view of how shoppers interact with their products online. Website analytics can show what happens on an individual retailer's storefront, but they rarely reveal broader demand patterns across an entire network. GemFind Digital Solutions is attempting to close that gap with Global Pixel Analytics, a new capability within JewelCloud 2.0 that aggregates anonymized product engagement data across participating jewelry retailers.

The technology gives suppliers a network-level view of product impressions, clicks and engagement, potentially turning fragmented e-commerce activity into a broader source of merchandising, inventory and marketing intelligence.

The jewelry industry has been steadily moving toward digital commerce, but its data infrastructure remains fragmented.

A jewelry brand may know which products perform well on its own website. A supplier may receive sales reports from individual retailers. A manufacturer may track inventory and product syndication separately. What is harder to determine is how shoppers across a wider retail ecosystem are interacting with products before those interactions become purchases.

GemFind's JewelCloud 2.0 is designed to address part of that problem.

The company has introduced Global Pixel Analytics, a new feature that uses JewelCloud 2.0's proprietary pixel technology to anonymously track product engagement across participating retailer websites. GemFind says the system aggregates millions of interactions involving diamonds and fine jewelry and turns them into business intelligence for suppliers and brands.

The distinction from conventional web analytics is important.

Tools such as Google Analytics generally provide insight into activity on an individual website or digital property. JewelCloud's approach is based on the network itself. By collecting anonymized engagement signals from participating retailers, GemFind aims to show suppliers how products perform across multiple storefronts rather than limiting analysis to a single site.

That creates a potentially valuable new data layer for jewelry merchandising.

Suppliers can see which products attract attention, which generate stronger click-through rates and how demand differs across geographic markets. They can also examine product popularity over time, category performance, retailer engagement and the effectiveness of marketing campaigns, according to GemFind.

For a supplier managing hundreds or thousands of products, those signals could help answer questions that conventional sales reporting cannot.

A product receiving strong shopper engagement across several retailers, for example, could represent an emerging demand trend even before sales data fully reflects it. Conversely, a product receiving relatively little attention may prompt a supplier to reconsider its positioning, pricing, merchandising or promotional support.

That could make cross-retailer shopping intelligence particularly relevant to inventory planning.

Inventory decisions in jewelry can be complicated by the value and variety of products involved. Diamonds, gemstones and finished jewelry can carry significant inventory costs, while consumer preferences can shift across styles, categories and price points.

Having additional demand signals could allow suppliers to complement historical sales data with real-time or near-real-time shopper engagement.

The technology also introduces a competitive intelligence dimension.

Suppliers can potentially compare product engagement across participating retailers and identify geographic or category-level patterns. Instead of asking only how a product performed on one retailer's website, they can examine how shoppers interacted with products throughout the connected JewelCloud ecosystem.

That network effect is central to GemFind's proposition.

JewelCloud 2.0 already connects jewelry manufacturers, brands and retailers through product syndication, inventory synchronization, e-commerce integrations and purchase order automation. GemFind is now adding analytics to that infrastructure, creating a feedback loop between product distribution and consumer behavior.

The broader trend is familiar across digital commerce.

Retailers and consumer brands increasingly want to move from descriptive analytics — what happened — toward predictive and prescriptive intelligence that helps determine what to do next.

In the jewelry sector, that could mean identifying collections gaining momentum, adjusting assortments, allocating marketing budgets or deciding where to prioritize new product launches.

However, the usefulness of the model depends heavily on the breadth and quality of the participating network.

Cross-network intelligence becomes more valuable as more retailers contribute data and as the participating businesses represent a diverse range of markets, customer segments and product categories. Suppliers will also need to understand how representative network-level engagement is of the wider jewelry market.

There is another important consideration: privacy.

GemFind says Global Pixel Analytics aggregates anonymized shopping activity and does not collect or share personally identifiable consumer information. That approach is significant because behavioral analytics increasingly operates under tighter expectations around consent, privacy and data governance.

For suppliers, the appeal is therefore not simply access to more data. It is access to aggregated behavioral signals without requiring direct access to individual shoppers.

The launch also demonstrates how specialized industry platforms are evolving beyond transactional infrastructure.

JewelCloud began as a way to connect participants in the jewelry supply chain. Its expanded capabilities increasingly resemble a vertical commerce operating layer, combining product information, inventory, integrations, automation and now consumer intelligence.

That puts the platform in an interesting position relative to broader commerce technologies from companies such as Salesforce, Adobe and Shopify. Those platforms provide powerful customer and commerce analytics, but GemFind is focusing its data model on the specific structure of the jewelry supply chain.

That specialization could be its strongest advantage.

Jewelry suppliers do not necessarily need another generic analytics dashboard. They need insight into diamonds, fine jewelry categories, retailer behavior, product engagement and inventory demand within the context of their industry.

Global Pixel Analytics is designed around those requirements.

The longer-term opportunity could be even broader. As more shopping interactions enter the JewelCloud ecosystem, the resulting dataset could potentially support increasingly sophisticated forecasting, product recommendations, assortment planning and AI-powered merchandising.

For now, GemFind is positioning Global Pixel Analytics as a new intelligence layer for jewelry commerce.

The technology does not eliminate the need for sales or inventory data, nor does a click necessarily indicate purchase intent. But by adding network-wide behavioral signals to existing commercial information, the platform could give jewelry suppliers a more complete view of how consumers discover and engage with products.

In an industry where product trends, inventory decisions and retailer relationships can have a significant impact on margins, that additional visibility could become an increasingly valuable competitive asset.

Market Landscape

E-commerce analytics has traditionally been built around individual websites, marketplaces or retailer accounts. The rise of connected commerce networks is changing that model by allowing aggregated intelligence to span multiple participants.

Large technology ecosystems such as Salesforce Commerce Cloud, Adobe Commerce and Shopify provide merchants with increasingly sophisticated analytics and customer data capabilities. Their strength comes from broad commerce infrastructure.

GemFind is taking a more vertical approach.

JewelCloud 2.0 connects jewelry manufacturers, suppliers, brands and independent retailers, giving GemFind access to a specialized network in which product data, inventory information and retailer activity are already interconnected.

Global Pixel Analytics extends that infrastructure into consumer behavior.

The strategic question will be whether network-level intelligence becomes more valuable than isolated store analytics as the JewelCloud ecosystem expands. More participating retailers could increase the statistical value of aggregated trends, while improved AI capabilities could eventually turn those signals into forecasting and merchandising recommendations.

Strategic Outlook

The next stage of digital commerce analytics is likely to focus on connecting behavioral data with operational decisions.

For jewelry suppliers, that could mean using shopper engagement signals alongside inventory, sales and retailer data to determine which products to promote, replenish or develop.

AI could eventually make this process more automated by identifying emerging patterns and recommending assortment or marketing changes. But the quality of those recommendations will depend on the representativeness of the underlying network data.

GemFind's strategy suggests that vertical commerce platforms may have an advantage when they combine specialized industry infrastructure with proprietary behavioral intelligence.

Top Insights

• GemFind's Global Pixel Analytics extends JewelCloud 2.0 from commerce infrastructure into cross-retailer shopping intelligence for jewelry suppliers and brands.
• The platform aggregates anonymized product engagement across participating retailers, giving suppliers visibility beyond the analytics available on individual storefronts.
• Network-wide engagement data could help jewelry companies identify emerging trends, optimize assortments and make inventory decisions using additional demand signals.
• JewelCloud's vertical focus differentiates its analytics approach from broader commerce platforms by concentrating data around jewelry products, suppliers and independent retailers.
• Privacy remains central to behavioral analytics, with GemFind stating that Global Pixel Analytics uses aggregated activity without collecting or sharing personally identifiable consumer information.

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Building a targeted B2B prospect list often starts with a deceptively simple task: finding the right businesses. Once that research spans multiple cities, industries or territories, manually collecting company information can become a significant operational burden. Leadsscraper.io is expanding its lead generation platform with a Google Maps Data Scraper designed to automate the collection of publicly available business information for sales prospecting, market research and competitive analysis.

The launch comes as sales and marketing teams increasingly combine location intelligence, business data and automation to build more targeted pipelines and reduce repetitive research.

For businesses selling into local or geographically defined markets, knowing where potential customers operate can be just as important as knowing what industry they belong to.

A restaurant technology provider, for example, may want to identify restaurants across several cities. A marketing agency could search for businesses in a particular category that need digital services. A franchise operator might want to understand the concentration of competitors before entering a new territory.

Leadsscraper.io's new Google Maps Data Scraper is designed to automate that initial discovery process.

The platform allows users to search Google Maps using business categories, keywords, cities, postal codes and geographic areas, then export the resulting information into structured datasets. The company positions the tool for lead generation, market research, competitive intelligence and territory planning.

The underlying idea is not new: sales teams have long used directories and local business databases to identify potential accounts. What has changed is the scale at which companies expect that information to be collected and processed.

Manually opening business listings and copying information into spreadsheets becomes impractical when a prospecting project involves hundreds or thousands of companies. Automation can reduce that repetitive workload, leaving sales and marketing teams to spend more time on qualification, account research and outreach.

According to Leadsscraper.io, the scraper can collect publicly available information such as business names, categories, addresses, phone numbers, website URLs, publicly available email addresses, ratings, review counts, operating hours, geographic coordinates and social media links where available.

The breadth of information is important because business discovery is only the first step in a modern prospecting workflow.

A sales team may use business categories and locations to establish an initial list, then filter accounts according to its ideal customer profile. Website information can support additional research, while ratings, reviews and operating information can provide contextual signals about a potential account.

The resulting data can be exported to spreadsheets, CRM platforms, sales engagement systems, internal databases and business intelligence tools, according to the company.

That interoperability reflects a broader trend in sales technology.

Modern revenue teams increasingly operate through interconnected systems rather than isolated databases. CRM platforms store account information, marketing automation tools manage engagement, analytics systems measure performance and AI applications can assist with research and lead qualification.

Those systems all depend on structured information.

Location-based business data provides a particularly useful discovery layer for organizations whose prospects have physical locations. Unlike generic contact lists, geographic search can help teams build territory-specific prospect pools based on where companies actually operate.

For B2B lead generation, this can make prospecting more targeted.

A sales representative responsible for a particular region could identify businesses within selected industries and build an account list around that territory. A multi-location service provider could repeat the process across markets without rebuilding the research process manually.

Marketing agencies have a similar use case.

Local agencies can identify businesses within selected categories and geographic areas to support outbound campaigns or market research. A digital marketing company, for instance, could identify businesses in a city that match its target customer profile and subsequently evaluate their websites, advertising presence or local search performance.

The tool also has applications beyond direct sales.

Consultants and researchers can use structured local business information to understand market composition. Franchise organizations can investigate potential expansion areas. Companies planning new territories can use location data as an input into competitive and market analysis.

Local SEO teams could also use business information to study competitors and understand the businesses operating within a particular search market.

However, the growing availability of automated business data also creates an important distinction between data collection and qualified lead generation.

A list containing thousands of businesses does not necessarily represent thousands of viable opportunities. Sales teams still need to determine whether companies fit their ideal customer profile, whether the information is current, who the relevant decision-makers are and whether an outreach strategy is appropriate.

Data quality is another consideration.

Business listings can change. Companies move, close, rebrand or update their contact information. Duplicate listings and incomplete profiles can also affect datasets. For organizations using scraped information at scale, validation and data maintenance therefore become important parts of the workflow.

There are also compliance considerations. Organizations using publicly available business information for prospecting need to follow applicable privacy, data-use and communications regulations, as well as the terms governing the platforms from which information is collected.

The competitive market around sales intelligence is moving toward exactly this kind of workflow automation.

Platforms such as Salesforce and Microsoft increasingly incorporate AI into CRM and sales processes, while specialized providers focus on lead enrichment, intent data and prospect research. Leadsscraper.io is approaching the problem from the business-discovery side, using geographic and category-based information as the starting point.

That positioning could be particularly relevant for small businesses and agencies that may not require a large enterprise sales-intelligence platform but still need scalable prospect research.

The broader opportunity is turning local business information into usable commercial intelligence.

As companies expand into new markets and sales teams become more dependent on automation, the ability to quickly identify businesses that fit specific geographic and industry criteria can shorten the distance between market research and pipeline creation.

Leadsscraper.io's Google Maps Data Scraper is therefore less about simply collecting more business listings and more about making location-based prospect discovery a repeatable part of the sales and marketing workflow.

Whether that data translates into better revenue outcomes will ultimately depend on what businesses do with it after collection. The strongest use cases are likely to combine automated discovery with CRM enrichment, human qualification, personalized outreach and continuous data validation.

Market Landscape

The sales intelligence market is increasingly shifting from static contact databases toward dynamic, multi-source business intelligence.

CRM platforms, marketing automation systems and AI sales assistants are becoming more capable of identifying accounts, scoring opportunities and recommending outreach. Yet these systems still require reliable information about the companies they are expected to analyze.

Location intelligence adds another dimension.

For local service companies, agencies, franchise businesses and territory-based sales organizations, geography can be a core component of the ideal customer profile. A company may be attractive not simply because it belongs to a particular industry, but because it operates in a specific market with the right business characteristics.

Leadsscraper.io is targeting this discovery layer with its Google Maps Data Scraper.

The competitive advantage will depend on factors such as search flexibility, data coverage, accuracy, export capabilities, integration options and the ability to keep datasets useful as business information changes.

The wider market is likely to converge around data collection, enrichment and AI-assisted qualification, rather than treating prospect lists as static assets.

Strategic Outlook

The future of automated lead generation will likely involve several layers working together: business discovery, data enrichment, AI-assisted qualification, CRM integration and personalized engagement.

Google Maps-style location data can serve as the initial discovery layer, particularly for businesses with physical locations. AI can then help sales teams prioritize accounts and identify relevant signals, while CRM and marketing automation platforms manage follow-up.

For organizations adopting this model, the goal should not be maximizing the number of records collected. It should be creating smaller, more relevant and actionable prospect pools.

That distinction will become increasingly important as automated prospecting makes it easier to generate large volumes of business data.

Top Insights

• Leadsscraper.io's Google Maps Data Scraper automates business discovery, helping sales teams create location-specific prospect lists across industries, cities and geographic territories.
• Structured business data can support B2B lead generation, competitive research, franchise expansion and territory planning without relying entirely on manual research.
• Export capabilities allow collected information to move into CRM systems, spreadsheets, sales tools and business intelligence workflows for further qualification.
• Geographic prospecting is particularly useful for agencies, local service providers and businesses whose ideal customers are defined by physical location.
• Automated data collection increases efficiency, but businesses still need validation, qualification and compliant outreach processes to turn datasets into genuine opportunities.

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