Network of Giving is beginning its rollout in Seattle, Washington, and Edmonton, Alberta, introducing a community-focused loyalty platform that connects local shopping with merchant-funded contributions to nonprofits. The model allows participating businesses to decide how much they contribute, while consumers direct those contributions to local causes without paying additional fees or giving up existing rewards.

Network of Giving is launching in Seattle and Edmonton, marking the first two communities in its planned rollout across the United States and Canada. The platform connects local merchants, consumers and nonprofit organizations through a loyalty-style network designed to turn everyday purchases into ongoing community contributions.

The model combines elements of customer loyalty, merchant-funded marketing and charitable giving. Participating businesses determine the contributions they want to make to local nonprofits, while consumers choose where those contributions go. According to the company’s announcement, membership is free for consumers, with no additional payments, roundups or top-ups required.

The platform is also designed to work alongside existing loyalty programs. Consumers retain their current points, cashback and other rewards rather than replacing them with a new system. For merchants, Network of Giving positions participation as a way to connect marketing expenditure with measurable results while supporting community organizations.

The company says 100% of contributions are passed through under an independently accountable framework. However, the announcement does not identify the independent oversight body, explain the framework’s verification process or provide details on how contributions are tracked and reported. Those disclosures will be important for businesses, nonprofits and consumers assessing the model’s transparency.

The Seattle and Edmonton launches will expand in stages as more businesses, consumers and community organizations join. Network effects are central to this approach: more participating merchants can create more opportunities for consumer engagement, while a larger consumer base may make participation more attractive to additional businesses and nonprofits.

The concept also reflects a broader opportunity at the intersection of loyalty marketing and corporate community investment. Retailers and local service providers often compete on customer relationships, convenience and value. A program that links purchases with locally directed contributions could give participating businesses another way to differentiate themselves, provided customers can understand the benefit and trust how funds are handled.

Yet the platform’s effectiveness will depend on execution. Merchants will need clear reporting on participation and marketing outcomes, nonprofits will need visibility into the contributions they receive, and consumers will need a straightforward way to select causes and understand the rules. The company has not yet disclosed adoption figures, participating merchant names, contribution volumes or measured business results from the initial markets.

For now, the launch establishes Network of Giving’s intended model rather than demonstrating its impact at scale. Its progress in Seattle and Edmonton should help show whether a shared loyalty network can generate sustained local participation while giving merchants a measurable reason to fund community support.

Market Landscape

Network of Giving enters a market where loyalty programs are increasingly being used to build relationships beyond discounts and points. Its distinguishing feature is the combination of merchant-funded contributions, consumer choice and local nonprofit support within a shared network.

Research published in the Journal of Retailing and Consumer Services in 2025 found that loyalty programs combining customer rewards with charitable benefits can strengthen perceptions of corporate social responsibility, particularly when customers can choose the charity receiving support. This provides relevant context for Network of Giving’s consumer-directed model.

The commercial opportunity is to give local businesses another way to differentiate themselves while linking marketing activity with community support. But the model must demonstrate that its contribution reporting is credible and that participation delivers meaningful value for merchants, nonprofits and consumers.

Strategic Outlook

The Seattle and Edmonton rollout will test whether a shared giving network can attract enough merchants and consumers to sustain recurring community contributions. The platform’s longer-term prospects will depend on participation, transparent reporting and evidence that businesses receive measurable marketing value.

For local businesses, the key questions are how contributions are calculated, what performance metrics are provided and whether participation leads to stronger customer engagement or repeat visits. Nonprofits will need clear information about eligibility, contribution allocation and payment timing.

Network of Giving says contributions are passed through in full and independently accountable. Its website also describes independent auditing and oversight through the Network of Giving Foundation.

Publishing details of that oversight, alongside market-level participation and contribution results, could help establish trust as the network expands.

Top Insights

• Network of Giving is rolling out in Seattle and Edmonton, connecting local merchants, consumers and nonprofits through a shared, community-focused loyalty platform.

• Businesses set their own contribution commitments, while consumers choose recipient nonprofits without paying extra or sacrificing existing loyalty rewards.

• The platform aims to connect marketing expenditure with measurable outcomes, although no initial performance figures or contribution totals have been disclosed.

• Research suggests charitable loyalty programs can strengthen corporate social responsibility perceptions when consumers have a choice over which charities benefit.

• Future growth will depend on merchant adoption, consumer participation, transparent contribution reporting and evidence of tangible community and business outcomes.

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Hightouch has acquired Strata, an AI-powered platform that connects marketing tools without code, in a move aimed at expanding the integrations available to its agentic marketing platform. The deal is intended to help marketing teams give AI agents access to more customer and campaign data while reducing reliance on engineering teams to build and maintain connections between systems.

Hightouch has acquired Strata, an AI platform designed to connect marketing tools without code, as the company looks to strengthen the data integration capabilities behind its agentic marketing platform.

The acquisition addresses a persistent challenge for enterprise marketing teams: customer data is spread across customer relationship management systems, analytics platforms, advertising tools and other applications, often requiring technical support to connect and maintain. As marketers adopt AI agents to support campaign planning and execution, those agents need access to relevant, reliable data across the marketing stack.

Strata is designed to simplify that process. Rather than requiring users to configure every integration manually, teams describe what data they want to move and where it needs to go. An AI agent then builds the connection and maintains it as underlying systems change. The approach could reduce some of the technical work associated with onboarding marketing tools and adapting integrations as businesses update their technology stacks.

Founded in 2024 by Kelsey Mullaney, Julian Mclain and Ilya Brin, Strata was established by a team whose founders met while working as early employees at Iterable. Mullaney, Strata’s CEO, will join Hightouch to help lead revenue programs and go-to-market strategy.

Hightouch co-founder and CTO Joshua Curl said the acquisition supports the company’s aim of helping marketers use customer data without depending on engineering teams for every new campaign or connection. Mullaney similarly positioned the deal as a way to bring data, AI and marketing execution together for more users.

Hightouch expects the acquisition to simplify customer onboarding and give customers greater flexibility as their technology stacks evolve. However, the announcement does not disclose the financial terms of the deal, the number of Strata customers affected, or a detailed timeline for integrating Strata’s technology into Hightouch’s platform.

Those details will matter to customers assessing how quickly the acquisition translates into usable capabilities. Integration tools can reduce the time required to connect applications, but their value also depends on data quality, access controls, monitoring, error handling and the ability to maintain connections when third-party APIs or schemas change.

For marketing teams experimenting with AI agents, the acquisition highlights an important operational issue: an agent’s usefulness depends not only on its reasoning and execution capabilities, but also on whether it can access the right data across business systems. Expanding integration coverage could help Hightouch address that dependency, although the announcement does not specify which new connectors will be available or how Strata’s functionality will be packaged.

The deal also puts greater emphasis on the infrastructure behind AI-driven marketing. As platforms move toward agents that can act on customer data and execute workflows, integration and governance become central to determining how safely and reliably those agents can operate.

Market Landscape

Marketing technology is moving from disconnected applications toward platforms that can coordinate data, decisions and execution across multiple systems. AI agents increase the pressure on integration infrastructure because they need timely, relevant context to perform tasks reliably.

Gartner reported in October 2025 that 81% of surveyed marketing technology leaders were piloting or had implemented AI-agent initiatives, while half of those surveyed said their organizations lacked the technical and data-stack readiness required for deployment. The findings point to a gap between interest in agentic AI and the infrastructure needed to support it.

Hightouch’s acquisition of Strata targets part of that gap. Rather than relying exclusively on manually developed connectors, the combined company aims to use AI to build and maintain connections for a wider range of marketing tools. The strategy may help Hightouch respond to specialized customer requirements, though its practical advantage will depend on connector reliability, security and the breadth of supported applications.

Strategic Outlook

The acquisition gives Hightouch a potential route to expand integration coverage while bringing Strata’s founder into its commercial organization. For enterprise buyers, the important test will be whether this translates into faster implementation, less maintenance work and dependable access to customer data.

Organizations evaluating agentic marketing platforms should look beyond the number of integrations advertised. They should assess data permissions, audit trails, failure recovery, monitoring and governance controls, particularly when agents can trigger actions that affect customers or campaigns.

Financial terms, a product-integration timetable and specific new connector commitments have not been disclosed. Until Hightouch provides those details, the acquisition is best understood as a strategic investment in marketing-stack connectivity rather than evidence that customers will immediately receive a defined set of new capabilities.

Top Insights

• Hightouch acquired Strata to broaden marketing integrations, giving AI agents access to more data across the tools businesses use to manage customer relationships and campaigns.

• Strata uses AI to build and maintain software connections from user instructions, potentially reducing the engineering work associated with custom integrations.

• Strata CEO Kelsey Mullaney will join Hightouch to help lead revenue programs and go-to-market strategy following the acquisition.

• The deal targets a key barrier to agentic AI adoption: fragmented marketing data and insufficient technical readiness across enterprise technology stacks.

• Financial terms, rollout timing and specific connector additions remain undisclosed, leaving customers to await details on the acquisition’s practical impact.

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Industrial and building-products manufacturers often need to explain complex technical products to audiences that rely on specialist publications, practical expertise and trusted industry sources. GreenHouse Digital + PR has received two 2026 Golden Trumpet Awards from the Publicity Club of Chicago for campaigns serving GF Building Flow Solutions Americas and Oatey Co. The awards recognise two distinct approaches to B2B communications: long-term trade media relations and expert-led technical content marketing, with reported results spanning media placements, backlinks and website referral traffic.

GreenHouse Digital + PR Wins Recognition for Building-Industry Marketing

GreenHouse Digital + PR, an agency specialising in construction, industrial, HVAC, plumbing and maintenance, repair and operations (MRO) markets, received a Gold Trumpet Award in Media Relations and a Silver Trumpet Award in Content Marketing at the Publicity Club of Chicago's 68th Annual Golden Trumpet Awards on October 6, 2026.

The Gold award recognises the agency's 25-year media relations partnership with GF Building Flow Solutions Americas. The Silver award honours its technical thought leadership programme for Oatey Co., a manufacturer serving plumbing and building professionals.

The recognition highlights how specialist B2B communications agencies can use earned media and technical expertise to reach audiences in industries where product selection, installation requirements and practical performance matter. The campaign figures supplied by GreenHouse also illustrate how public relations programmes can be assessed using different measures, from editorial placements and estimated media impressions to backlinks and referral visits.

Long-Term Media Relations Drives Trade Publication Coverage

GreenHouse's relationship with GF Building Flow Solutions and its predecessor brands, including Uponor and Wirsbo, spans 25 years. The agency's work connects company executives, engineers and technical specialists with journalists covering plumbing, heating, ventilation and air conditioning, and the wider building sector.

According to GreenHouse, its 2025 programme generated 501 publicity placements and 202 million measurable impressions across trade and consumer media. The campaign included 92 feature placements, 30 bylined articles and 10 trade-media cover stories or mentions.

The programme combined proactive media outreach, executive commentary, product announcements, case studies and trade-show media relations. In specialist markets, this type of sustained engagement can help manufacturers explain technical developments and maintain relationships with publications serving professional buyers.

However, publicity volume and estimated impressions measure exposure rather than business impact. Demonstrating commercial value requires additional evidence, such as qualified website visits, sales enquiries, distributor engagement or attributed revenue.

Oatey Campaign Uses Technical Experts to Build Editorial Authority

The Oatey programme takes a different approach, using the manufacturer's technical applications managers, product managers and engineers as sources for educational articles aimed at plumbing and building professionals.

GreenHouse reports that the 2025 programme secured 70 bylined feature placements, exceeding its target of 50. The articles generated an estimated 129,000 views across publications with a combined audience of 9.73 million. The agency also reports 120 backlinks from earned coverage and more than 2,000 referral visits to Oatey's website.

The content addresses practical subjects such as product selection, installation, maintenance, drainage and job-site troubleshooting. Rather than relying exclusively on product announcements, the programme turns technical expertise into material intended to help professionals solve real-world problems.

That strategy aligns with a wider challenge in B2B marketing. The Content Marketing Institute's B2B Content Marketing Benchmarks, Budgets, and Trends: Outlook for 2025 found that 56% of surveyed B2B marketers cited difficulty attributing return on investment to content marketing.

Backlinks and referral visits offer useful evidence of distribution and engagement, but connecting them to qualified leads and revenue requires additional measurement.

Market Landscape

The two Golden Trumpet-winning campaigns illustrate complementary models for specialist B2B communications. Media relations aims to earn editorial coverage through ongoing journalist relationships, timely announcements and expert access. Technical content marketing builds on subject-matter expertise to produce useful articles that can reach professional audiences through trade publications and search.

For manufacturers operating in construction, HVAC and plumbing markets, these channels can support awareness among contractors, distributors, specifiers and other professional buyers. Their effectiveness depends on reaching the right audience, providing credible information and connecting exposure to business objectives.

The distinction between potential reach and demonstrated impact is important. The 202 million impressions reported for GF Building Flow Solutions represent a measure of media exposure, while Oatey's referral visits represent observed traffic. Neither metric alone establishes incremental revenue or a direct effect on purchasing decisions.

Strategic Outlook

For industrial marketers, the campaigns offer a practical reminder that technical expertise can be a communications asset when it is translated into accessible, useful information. Combining sustained media relationships with expert-led content may help manufacturers maintain visibility across specialist publications and relevant professional audiences.

The next step for programmes like these is to connect communications performance more closely with business outcomes. Tracking referral quality, enquiries, distributor engagement, assisted conversions and sales opportunities can help marketing teams understand which placements contribute value beyond exposure.

GreenHouse's awards recognise two established communications approaches rather than a new marketing technology. Their relevance lies in showing how specialist PR and content programmes can be measured using a mix of editorial, audience and website-performance indicators.

Top Insights

• Industry recognition: GreenHouse Digital + PR won two 2026 Golden Trumpets, recognising media relations and technical content marketing for building-products manufacturers.

• Sustained media relations: The GF Building Flow Solutions programme reported 501 publicity hits and 202 million measurable impressions, demonstrating the scale of its reported media exposure.

• Expert-led content: Oatey's programme secured 70 bylined features, turning technical knowledge into educational content for plumbing and building professionals.

• Referral performance: The Oatey campaign generated 120 backlinks and more than 2,000 referral visits, providing measurable evidence of content distribution and website traffic.

• Measurement challenge: B2B marketers still struggle to attribute content ROI, making lead quality, conversion tracking and revenue measurement important complements to publicity metrics.

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Search engine optimisation has traditionally revolved around rankings, clicks and organic traffic. But as consumers increasingly turn to AI assistants for product recommendations, business comparisons and information, marketers face a new question: does their brand appear in the answers people receive? WordPress SEO plugin provider Rank Math expects AI visibility to become a standard part of SEO reporting in 2027, reflecting a shift toward measuring brand mentions, citations and recommendations across AI-powered search experiences.

Rank Math Bets on AI Visibility as the Next SEO Standard

Rank Math is predicting that AI visibility will become a routine component of search engine optimisation reporting in 2027, as marketers expand their focus beyond traditional search rankings. The company's position reflects a broader industry shift: businesses increasingly want to understand whether AI systems mention their products, cite their websites or recommend them when users ask questions.

The prediction comes as search platforms begin providing more visibility into generative AI experiences. Google introduced dedicated performance reporting for generative AI features in Search Console in June 2026, covering AI Overviews, AI Mode and generative AI features in Discover. Google completed the worldwide rollout on August 31, giving website owners a separate view of performance associated with these experiences.

That development gives marketers a more direct way to assess some AI-related search exposure within Google's ecosystem. However, it does not provide a complete picture of how brands appear across independent AI assistants such as ChatGPT, Perplexity or Claude.

Consumer Behaviour Is Changing Search Measurement

The potential importance of AI visibility extends beyond platform reporting. A June 2026 Pew Research Center survey found that 42% of US adults who use AI chatbots reported using them to search for information, making information retrieval the most commonly reported use in the study. The survey covered 5,119 US adults and was conducted in February 2026.

For marketers, this introduces a measurement gap. A company may rank prominently in Google Search but rarely appear in AI-generated recommendations. Another business might be cited frequently in AI responses without that exposure translating directly into conventional organic clicks.

Rank Math COO Uzair Nazeer argues that rankings and traffic remain essential but no longer tell the entire story. The company expects marketers to track whether brands appear in AI answers alongside the SEO metrics they already use.

The distinction is important because AI visibility is not the same as website traffic or business performance. A brand mention may increase awareness without generating a click, while a citation can indicate that a system used a website as a source without proving that the user trusted or acted on the answer.

How Rank Math's AI Visibility Tool Works

Rank Math has begun incorporating AI visibility tracking into its SEO platform. According to the company's product documentation, the tool monitors how brands and products appear in AI-generated responses, tracking mentions, citations, sentiment and changes over time. It also supports competitor comparisons and provides access to AI response transcripts.

This gives SEO teams another layer of competitive intelligence. Instead of asking only where a webpage ranks for a keyword, marketers can investigate which brands AI systems recommend for a particular question, which sources those systems cite and how the responses change over time.

The product also places Rank Math in a growing market for AI search analytics. Traditional SEO platforms are expanding into generative engine optimisation (GEO), while specialist vendors are building tools to monitor AI mentions, citations and share of voice.

The challenge is measurement consistency. AI answers can vary according to prompts, location, model, retrieval sources and the time a query is made. Reliable reporting therefore requires repeatable testing and careful interpretation rather than treating a single AI response as a stable ranking.

Why AI Visibility Could Reshape SEO Reporting

Rank Math's forecast points to a likely expansion of the SEO reporting model rather than the replacement of existing metrics. Search rankings, impressions, click-through rates and organic traffic will continue to matter, but marketers may increasingly pair them with AI mentions, source citations, recommendation frequency and competitive share of voice.

For enterprise marketing teams, that could change how SEO performance is presented to leadership. Reporting may need to distinguish between conventional search exposure, visibility in AI-generated answers and the downstream business outcomes associated with each.

It also creates new questions for content strategy. Brands may need to strengthen factual accuracy, publish authoritative resources, improve the structure of important web pages and ensure that product information is consistent across trusted sources. These practices can support discoverability, although no particular content format guarantees inclusion in AI responses.

Rank Math's 2027 prediction remains a forecast, not an established industry standard. Its significance lies in the direction of travel: as AI-powered discovery becomes a measurable part of search, marketers will need better ways to assess where their brands appear, how they are represented and whether that exposure contributes to meaningful results.

Market Landscape

AI visibility tracking is emerging as a new layer of the SEO software market. Established platforms, specialist GEO providers and WordPress-focused tools are competing to help brands understand their presence across AI-generated answers.

Rank Math's advantage is its existing position in the WordPress SEO ecosystem and its effort to bring AI visibility measurement into a familiar optimisation workflow. Its competitors include specialist AI search monitoring tools and established SEO platforms expanding their analytics capabilities.

Google's Search Console reporting is an important development, but its scope differs from cross-platform AI monitoring. It measures visibility within Google's own search experiences, whereas tools such as Rank Math's AI Visibility aim to track brand representation and citations across AI platforms.

The next competitive challenge will be proving that these metrics are reliable, comparable and actionable. Marketers need to know not just whether their brands are mentioned, but whether the information is accurate, the audience is relevant and the resulting exposure supports business goals.

Strategic Outlook

SEO teams should begin treating AI visibility as a complementary measurement layer rather than abandoning conventional search reporting. A practical approach is to establish a baseline of relevant buyer questions, monitor brand mentions and citations across selected AI platforms, compare competitor visibility and connect findings with referral traffic and conversions where measurable.

Businesses should also recognise the limits of current measurement. AI answers are variable, and visibility scores from different vendors may use different prompts, sampling methods and scoring systems. Consistent methodology will be essential before these metrics can support meaningful comparisons over time.

Top Insights

• New SEO metric: Rank Math expects AI visibility to become standard in 2027, prompting SEO teams to measure brand mentions and citations alongside rankings and traffic.

• Google reporting expands: Search Console's generative AI reports provide additional visibility data, helping website owners assess performance in Google's AI-powered search experiences.

• Consumer behaviour shifts: Pew Research Center found that 42% of US adults who use AI chatbots use them to search for information, highlighting a growing discovery channel.

• Competitive intelligence: Rank Math's tool tracks AI mentions, citations and sentiment, giving marketers another way to compare brand representation against competitors.

• Measurement remains complex: Variable AI responses and differences between tracking tools mean enterprises must standardise methods before using AI visibility scores to judge SEO success.

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As marketing budgets span retail media, paid social, search and offline channels, enterprise teams face a persistent problem: different measurement systems can produce conflicting answers about what drives revenue. At Advertising Week New York, marketing measurement company Fospha unveiled a connected platform that brings marketing mix modeling, multi-touch attribution, incrementality testing and AI-powered automation into one system. The goal is to help brands move from analysing campaign performance to making faster, evidence-based budget decisions.

Fospha Connects Marketing Measurement With Budget Automation

Fospha's relaunch centres on integrating four capabilities that enterprise marketing teams often manage separately: multi-touch attribution (MTA), marketing mix modeling (MMM), incrementality testing and AI-powered automation. The company presented the connected system at Fospha & Friends, an event held during Advertising Week New York.

The platform is designed to give marketers a consistent measurement framework across digital and offline channels. Daily reporting supports operational decisions, monthly experiments help validate performance, and quarterly modeling informs broader budget planning. Fospha's approach is intended to reduce the discrepancies that emerge when attribution, experimentation and long-term forecasting operate independently.

The distinction matters because each measurement method answers a different question. MTA estimates how marketing touchpoints contribute to conversions, MMM evaluates the relationship between marketing investment and business outcomes over time, and incrementality testing examines whether advertising generates results that would not otherwise have occurred.

Combining these methods can provide a more complete picture of marketing effectiveness, provided their assumptions, data and measurement windows are properly calibrated.

From Incrementality Tests to Day-to-Day Decisions

Fospha is positioning incrementality as an ongoing input into marketing decisions rather than an isolated experimental result. According to the company, test results calibrate forecasts, forecasts inform its budget planner, and automation can shift spending across channels while campaigns are running.

That creates a potential link between measurement and execution. Instead of waiting for a retrospective report to identify underperforming campaigns, marketing teams could use calibrated estimates to adjust allocations as new evidence emerges.

The company is also preparing its AI suite, comprising Analysis, Actions and Agents, for general availability in the fourth quarter of 2026, according to the announcement. The suite is intended to turn measurement insights into recommendations and operational actions.

However, automated budget allocation depends on more than AI capabilities. Data quality, reliable incrementality estimates, spending constraints and human oversight will determine whether automated decisions improve returns. Fospha's announcement describes the intended workflow, but does not independently establish the financial gains customers will achieve.

Why the Launch Matters Ahead of Holiday Trading

The launch comes as retailers prepare for a major seasonal spending period. Deloitte forecasts that US holiday retail sales will reach $1.70 trillion to $1.71 trillion between November 2026 and January 2027, with e-commerce sales projected at $316.1 billion to $318.9 billion.

During peak trading, delayed or inconsistent measurement can make it harder for brands to identify which channels are generating additional sales. A connected system could help marketing and finance teams work from a shared set of estimates and respond more quickly when campaign performance changes.

Fospha's proposition also reflects a wider shift in marketing technology: measurement platforms are increasingly expected to inform execution, rather than simply report results. Google Analytics has similarly introduced a measurement approach combining data-driven attribution, its Meridian marketing mix modeling framework and incrementality experiments to help advertisers evaluate media performance and explore budget scenarios.

The competitive question is whether Fospha can make these methods work together reliably at enterprise scale. Unified reporting alone is not enough; buyers will need evidence that its calibrated forecasts improve allocation decisions and that automated changes produce incremental business value.

Market Landscape

Marketing measurement is moving toward a multi-method model in which attribution, experimentation and marketing mix modeling complement rather than replace one another.

Gartner's research on marketing mix modeling identifies its value in evaluating marketing investment alongside external and business factors, while its 2026 guidance recommends combining attribution and testing to better demonstrate marketing's contribution.

Fospha's differentiator is its attempt to connect these approaches directly to budget planning and in-flight automation. That positions it against both specialist measurement providers and broader advertising analytics ecosystems, including Google's measurement tools. The announcement does not establish that Fospha outperforms those alternatives, and prospective customers will need to compare channel coverage, data integrations, model transparency, experimentation capabilities and implementation costs.

Strategic Outlook

For enterprise marketers, the value of unified measurement depends on whether the platform can translate different evidence sources into decisions that are both timely and statistically credible. Marketing leaders should assess how frequently models are recalibrated, how uncertainty is communicated, whether automated reallocations require approval, and how incremental revenue is distinguished from conversions that would have happened anyway.

Fospha's launch signals an important direction for marketing analytics: connecting measurement, planning and execution in a single operational workflow. Its commercial impact will ultimately depend on demonstrated improvements in budget efficiency, incremental revenue and decision-making speed.

Top Insights

• Unified measurement: Fospha combines attribution, MMM and incrementality testing, helping enterprise marketers reconcile performance signals across digital and offline channels.

• AI-powered execution: Its Analysis, Actions and Agents suite is designed to turn measurement findings into recommendations and operational decisions, subject to data quality and governance.

• Faster budget decisions: Connecting forecasts with in-flight budget allocation could help retail marketing teams react to changing campaign performance during peak trading.

• Competitive pressure: Google's combined attribution, Meridian MMM and incrementality capabilities illustrate how established platforms are also bringing measurement methods together.

• Proof remains essential: Enterprise buyers should validate incremental revenue, forecast accuracy and automation safeguards before treating a connected measurement system as a source of business advantage.

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As consumers increasingly use Reddit discussions, traditional search engines and AI-generated answers to research products, brands are exploring new ways to influence what buyers discover online. Nicely Network has been recognised by Expert Consumers as a leading Reddit marketing agency for Q4 2026, highlighting an approach that combines community discussions, editorial article placements and generative engine optimisation (GEO). The recognition reflects a growing marketing trend, although the agency's reported campaign results remain company-supplied figures rather than independently verified performance data.

Nicely Network Combines Reddit Marketing With Generative Engine Optimisation

Nicely Network's strategy centres on making brand-related discussions discoverable across Google Search, Reddit and AI-powered search experiences, including ChatGPT Search, Google AI Overviews and Perplexity. Rather than treating Reddit as an isolated social channel, the agency combines discussion-based marketing with third-party article placements to expand a brand's potential visibility across multiple discovery platforms.

According to the agency's media kit, Nicely Network has generated more than 15,000 AI citations, served over 300 brands and recorded more than 160 million views across ranked discussions. It also reports more than $150 million in tracked sales revenue. These figures are agency-reported metrics and should be assessed in the context of each campaign's attribution methodology.

The Expert Consumers recognition for Q4 2026 highlights the connection between community-driven product research and AI-assisted discovery. However, an industry award or ranking does not, by itself, establish that an agency outperforms competitors across every category or campaign objective.

Why Reddit Matters for AI Search Visibility

Reddit has become a relevant source of product recommendations, user experiences and comparisons for consumers researching purchases. Its discussion format also contains the conversational questions that people increasingly enter into AI assistants.

Nicely Network cites 2026 Semrush data indicating that Reddit appeared in 11.29% of responses across ChatGPT Search, Perplexity and Google AI Mode. The figure is attributed to the agency's media kit; its methodology and precise measurement scope would need to be reviewed before drawing broader conclusions about Reddit's overall influence.

For marketers, the opportunity lies in understanding how search visibility extends beyond conventional blue-link rankings. A discussion may attract organic search traffic, influence purchasing decisions and potentially appear as a cited source in an AI-generated answer. These outcomes are related, but they are not interchangeable: visibility does not automatically translate into citations, qualified leads or sales.

Nicely Network says it develops Reddit discussions around high-intent keywords and relevant communities, alongside supporting editorial content. For brands, the strategic challenge is ensuring that participation provides useful, accurate information and complies with each platform's policies, disclosure requirements and community rules. Inauthentic engagement or undisclosed promotional activity can undermine trust.

Case Studies Connect Visibility With Commercial Outcomes

The agency's case studies illustrate how it measures results across different industries. In cybersecurity, Nicely Network reports that 10 discussions generated more than 250 citations for an antivirus brand, while its ChatGPT share of voice rose from 6% to 17%.

A direct-to-consumer furniture campaign reportedly generated $150,000 in tracked sales, 21,190 website clicks and one million organic discussion views. The agency also cites increases in brand mentions across AI search experiences. These examples suggest that Reddit campaigns can be evaluated against both discovery metrics and commercial outcomes, although the results are campaign-specific and do not establish typical performance.

The agency also uses AI-SEO article placements to complement Reddit activity. Its reported examples include articles receiving thousands of views and Google or AI retrievals. The combined approach is intended to give brands more opportunities to surface in traditional search results and AI-assisted research.

Measuring AI Visibility Beyond Rankings

Nicely Network tracks three main categories: AI search visibility, organic search performance and content engagement. Its metrics include AI mentions, citations, impressions, share of voice, Google rankings, referral clicks and sales attributed through tracking links.

This broader measurement model reflects an important shift in digital marketing analytics. Search rankings remain relevant, but marketing teams also want to know whether their brands appear in AI-generated recommendations and whether that exposure contributes to customer acquisition.

Research from Semrush offers independent context for the trend. In a November 2025 study analysing 248,000 Reddit posts cited by ChatGPT Search, Google AI Mode and Perplexity, the company found that Reddit was the most-cited domain on Perplexity and ranked among the top three on the other two platforms.

This supports Reddit's relevance to AI search visibility, but it does not independently validate Nicely Network's individual campaign results.

Market Landscape

The competitive field includes specialist Reddit marketing agencies, conventional SEO firms, digital PR providers and broader marketing platforms. Companies such as Salesforce, Adobe and Semrush operate in adjacent parts of the marketing technology ecosystem, but they are not direct equivalents to a specialist agency offering managed Reddit discussions and editorial placements.

Nicely Network's positioning rests on combining community content with third-party articles and measuring their appearance across AI search products. Its stated monthly engagement plans range from $5,000 to $15,000 for Starter engagements to $30,000–$60,000 for Leadership engagements, with enterprise programs available for larger portfolios. Those prices place the service in a substantial investment category, particularly for smaller businesses.

The broader market also calls for caution. Gartner reported in January 2026 that only about one-third of surveyed US consumers considered generative AI chatbots as effective as traditional search engines for learning new information. Its findings suggest that AI search is becoming part of a wider research journey, not necessarily replacing conventional search.

Strategic Outlook

For enterprise marketing teams, Reddit and AI search visibility may be worth testing as part of a wider organic discovery strategy. The most useful evaluation combines branded and non-branded visibility, referral traffic, qualified conversions, sales attribution and the quality of discussion generated.

Governance is equally important. Reddit explicitly prohibits spam, including repetitive promotional activity intended to inflate exposure or generate financial gain.

Brands should assess how an agency handles disclosure, authentic participation, community-specific rules and long-term reputation risks before committing to a campaign.

The central question is not simply whether a brand can appear in an AI answer. It is whether that visibility reaches relevant buyers, earns their trust and contributes measurable business value. Nicely Network's approach reflects an emerging direction in digital marketing, but its long-term effectiveness will depend on transparent measurement, credible content and sustained results beyond individual case studies.

Top Insights

• Reddit and AI search: Reddit discussions can appear in AI-generated answers, creating new discovery opportunities for brands, SEO teams and marketers targeting recommendation-driven purchases.

• Combined content strategy: Nicely Network pairs Reddit discussions with editorial placements, giving brands multiple content formats to pursue organic search visibility and AI citations.

• Campaign performance: The agency reports measurable results across cybersecurity, furniture and wellness campaigns, but enterprise buyers should validate attribution and repeatability before investing.

• Broader measurement: AI mentions, citations, referral traffic and tracked revenue complement conventional rankings, helping marketing leaders assess whether visibility contributes to business outcomes.

• Governance matters: Reddit's anti-spam rules make authentic engagement and transparent promotional practices essential for agencies and brands seeking sustainable community visibility.

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Rembrandt Charms is expanding its marketing resources for jewelry retailers, combining refreshed lifestyle imagery, seasonal campaign kits and merchandising guidance to help stores promote charm jewelry across physical and digital channels. The initiative pairs consumer-facing brand awareness efforts with ready-to-use creative assets, giving retail partners tools to market the category without having to develop every campaign element themselves.

Rembrandt Charms Connects Brand Marketing With Retail Execution

Jewelry brands depend on more than product availability to drive sales. Retailers also need compelling ways to present collections, connect products to gifting occasions and give customers reasons to return. Rembrandt Charms is addressing that challenge with an expanded retailer marketing program as part of a broader brand refresh.

The company’s updated lifestyle imagery presents charms as expressions of personal identity, covering themes such as relationships, travel, pets, hobbies and life milestones. The creative direction is intended to appeal both to customers discovering charm jewelry for the first time and to existing buyers who may want to add pieces to a collection.

For independent jewelers and other retail partners, the practical element is the availability of ready-to-use marketing materials. Rembrandt says its complimentary seasonal retail kits include lifestyle imagery, social media content, website banners, posters and counter cards. These assets can support campaigns across ecommerce and in-store environments without requiring each retailer to create a separate set of materials.

The expanded program also includes campaign playbooks, seasonal sell sheets and merchandising recommendations. These resources are designed to help retailers select product assortments, refresh displays and connect charm collections with relevant shopping occasions.

The approach reflects a familiar challenge in retail marketing: brands need consistent messaging, but individual stores must still adapt campaigns to their customers, inventory and local market. Providing reusable creative assets and merchandising guidance can reduce the workload for retailers while maintaining a recognizable brand presentation.

Turning Consumer Interest Into Retail Engagement

Rembrandt’s program connects two parts of the marketing process: generating consumer interest in charm jewelry and helping retail partners convert that interest into store visits and purchases.

Its consumer-facing campaigns encourage shoppers to explore the collection and find a retail partner. Retailers, meanwhile, receive materials intended to support the next steps, from presenting themed assortments to promoting products through their websites and social media accounts.

The company is also using themed charm trays to help retailers introduce or expand categories that reflect local customer interests. That could give stores a more structured way to organize merchandise around gifting themes, personal interests or occasions.

Rembrandt Charms President Eric Lux said the investment is intended to make it easier for retailers to introduce the category and keep it relevant throughout the year.

However, the announcement does not include sales-growth figures, campaign conversion rates or evidence of incremental consumer demand. The program establishes a set of marketing resources, but its commercial impact will depend on retailer adoption, product availability, campaign execution and how effectively the materials resonate with shoppers.

Holiday Campaign Support Arrives in November

The company is placing particular emphasis on seasonal retail execution ahead of the holiday shopping period. Its Holiday Playbook provides guidance for activating campaigns both in stores and online, while a corresponding holiday retail kit is scheduled to become available on November 3, 2026.

Retailers can also request customized marketing materials through their sales representatives, including creative options featuring their store logos. This customization could help individual jewelers maintain their own identities while using the brand’s broader campaign assets.

The timing gives participating retailers an opportunity to plan holiday merchandising and promotional activity in advance. The actual benefit will depend on how early retailers receive and deploy the materials and whether their assortments align with the themes featured in the campaigns.

Market Landscape

Retail brands increasingly support channel partners with centrally developed content, campaign templates and merchandising guidance. This model can help smaller retailers access creative resources that would otherwise require additional time, staff or agency expenditure.

For jewelry brands, seasonal campaigns also provide a framework for organizing products around gifting occasions and personal meaning. Charm jewelry lends itself to themes such as travel, family, hobbies and milestones, giving retailers multiple ways to present a collection to different customer groups.

Rembrandt’s strategy combines brand-level consumer awareness with retail-level execution tools. The model is designed to make the transition from discovering a product online to finding it through a retail partner more consistent.

Its effectiveness, however, will depend on whether retailers use the materials, adapt them appropriately and connect promotions with available inventory. Ready-made creative can reduce production work, but it does not replace local merchandising decisions, audience understanding or measurement of campaign results.

Strategic Outlook

Rembrandt Charms’ expanded retailer marketing support illustrates how brands can help retail partners bridge the gap between product storytelling and sales execution.

The initiative could be useful for jewelers seeking consistent seasonal content without building every asset from scratch. Customizable creative and themed merchandising resources also give retailers ways to connect a national brand campaign with local customer interests.

The next measure of success will be whether the program increases retailer participation, consumer engagement and sales of charm collections. Relevant indicators include asset usage, campaign engagement, product sell-through and traffic directed to participating retail partners.

For now, the announcement is primarily an expansion of marketing enablement rather than a new technology launch. Its significance lies in giving retailers a more coordinated set of tools to promote a product category throughout the year.

Top Insights

• Expanded retailer resources: Rembrandt Charms is providing complimentary creative assets and campaign guidance to help jewelers promote charm jewelry across stores and digital channels.

• Lifestyle-led positioning: Updated imagery connects charms with personal identity, relationships, travel, pets and milestones, giving retailers more ways to frame products for customers.

• Seasonal marketing support: Campaign playbooks, sell sheets and merchandising recommendations are intended to help retailers plan assortments and promotions around gifting occasions.

• Local customization: Retailers can request marketing materials incorporating their store logos, helping them adapt brand campaigns to their own businesses.

• Holiday activation: The holiday retail kit is scheduled for November 3, 2026, giving retailers a defined date for accessing the seasonal campaign materials.

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Sanas has introduced Supervised AI, a customer experience platform in which AI agents handle customer calls while a human supervisor monitors and can intervene in up to four conversations simultaneously. The system is designed to combine AI-driven call handling with human judgment, allowing supervisors to guide an agent, take over a conversation and return control without forcing customers to restart their interactions.

Sanas Proposes a Hybrid Model for AI-Powered Contact Centers

Sanas is positioning its new Supervised AI platform as an alternative to two established approaches to AI in customer service: using AI to assist a human agent during a call, or allowing an AI agent to manage the interaction autonomously until escalation becomes necessary.

Under the model described by Sanas, AI agents conduct customer conversations while a human supervisor oversees several calls from a single console. The supervisor can listen in, privately guide the AI, take control of a conversation or return it to the AI when appropriate.

The company says this approach is intended to avoid a common weakness in conventional escalation workflows: a customer who must repeat information after being transferred to a human agent who has not followed the interaction from the beginning.

Sanas says the supervisor remains present throughout the conversation, with alerts directing attention to calls that may need human judgment. These alerts can be triggered by falling customer sentiment, declining AI confidence or an approaching compliance step.

The distinction is operationally significant. Rather than treating human involvement as a fallback that begins only after an AI agent fails, Supervised AI makes human oversight part of the call-handling model. The approach could be relevant in industries where customer interactions involve sensitive information, complex decisions or regulatory requirements.

However, the platform’s effectiveness will depend on how accurately it identifies situations requiring intervention, how quickly supervisors can respond and whether one person can realistically oversee multiple complex conversations without missing important signals.

Human Intervention Without a Conventional Handoff

Sanas says each call begins with disclosure that the customer is speaking with AI and that a human supervisor is in command. Supervisors can then move between listening, guiding the AI and taking direct control.

The company’s stated design aims to keep the customer within the same conversation during these transitions. That could reduce friction compared with workflows in which an automated system transfers a caller to a separate queue.

The platform is designed for one supervisor to oversee up to four simultaneous conversations. This creates a potential productivity benefit, but it also introduces a capacity question: the number of calls a supervisor can effectively govern will vary with their complexity, customer needs and the frequency of intervention.

The announcement does not provide independent benchmarks for resolution rates, average handling time, customer satisfaction or supervisor workload. Enterprises will need operational testing to determine whether the model improves service quality as well as agent productivity.

Auditability and Deployment Flexibility

Sanas says every recommendation made to the supervisor cites its source document, while AI actions and human interventions are recorded in a complete audit trail. These capabilities could help organizations review decisions, investigate service problems and document how AI handled a particular interaction.

The platform is designed to run in a customer-controlled environment, including private cloud or on-premises deployments, using Sanas Fabric. The company says enterprises can change the underlying speech and language models. In the current release, the AI uses a disclosed Sanas voice.

This architecture may appeal to organizations with strict data-residency, security or technology-control requirements. Nevertheless, buyers should validate the specific deployment options, model-switching limitations, retention policies and security controls available in their intended configuration.

Enterprise Adoption Will Depend on Governance

Sanas says the platform builds on five years of work in speech AI and is intended for enterprise contact centers, including healthcare, banking, financial services and telecommunications.

The company also cites interest from UnitedHealthcare and TELUS Digital. UnitedHealthcare executive Marius Maree described the model as a way to combine AI scale with human accountability in sensitive member interactions. TELUS Digital’s Nick Williams said the company is evaluating the technology and identifying suitable use cases. These comments indicate interest, but they do not establish broad deployment or independently verified results.

For enterprises, the central question is whether supervised AI can balance automation with reliable human intervention. Buyers will need to evaluate disclosure practices, escalation accuracy, response times, audit records, privacy safeguards and the ability to handle vulnerable customers or high-risk decisions.

Sanas Supervised AI reflects a developing direction in customer experience technology: AI handles more of the conversation, while people retain responsibility for judgment and oversight. Its long-term value will depend on whether that arrangement improves customer outcomes without creating an unsustainable supervisory workload.

Market Landscape

Contact center technology is evolving across three broad models: conventional human-led service with AI assistance, autonomous AI agents and systems that combine AI-led conversations with ongoing human supervision.

Sanas is differentiating Supervised AI through the third approach. Its defining feature is not simply that human escalation remains possible, but that a supervisor can monitor multiple live interactions and intervene within the existing conversation.

That model addresses important enterprise concerns around accountability and continuity. However, it also creates a different staffing and operating model. Supervisors must be able to identify problems quickly, make informed decisions and manage competing demands across simultaneous calls.

For contact center buyers, relevant comparisons include voice quality, multilingual performance, integration with customer service platforms, escalation accuracy, compliance controls, deployment options and total cost per resolved interaction. Independent performance evidence will be important in determining whether supervised AI outperforms existing agent-assist or autonomous systems in particular use cases.

Strategic Outlook

Sanas Supervised AI could be relevant for organizations that want to automate routine customer conversations without removing human oversight from sensitive interactions. Healthcare support, financial services and telecommunications are potential evaluation areas, although suitability will depend on the specific use case and applicable requirements.

Before deployment, enterprises should establish clear intervention thresholds, define which decisions AI can make, test disclosure language and determine how supervisors handle simultaneous alerts. They should also measure customer satisfaction, first-contact resolution, escalation rates, compliance incidents and supervisor workload against an existing baseline.

The key test is whether the platform can deliver the efficiency of AI while preserving meaningful human accountability. The announcement outlines the operating model, but independent evidence of customer outcomes and productivity gains remains necessary.

Top Insights

• Human-supervised AI: Sanas lets AI agents handle customer calls while supervisors oversee up to four conversations, combining automation with ongoing human control.

• Continuous intervention: Supervisors can guide the AI, take over mid-conversation and return control without requiring customers to restart, according to the company.

• Auditability: Source-linked recommendations and recorded AI actions and human interventions are designed to support traceability and enterprise review.

• Deployment flexibility: Private-cloud and on-premises options may suit organizations with specific infrastructure and data-control requirements, subject to configuration and security validation.

• Performance remains unproven: Enterprises should test resolution rates, customer experience, intervention speed and supervisor workload before assuming the model improves contact center efficiency.

   

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