Givsly has expanded its AI-powered Values-Based Audiences solution into political advertising, giving campaigns and political media buyers a way to segment audiences around values, issues and behavioral signals rather than relying exclusively on traditional demographic characteristics.

The solution uses a vector-based methodology to create audience segments from values, inferred motivations and behavioral signals. Givsly says the audiences are modeled at the ZIP code level using aggregated community-level signals rather than individual voter files.

The approach is designed to help political campaigns identify differences in what communities prioritize. More than 150 tracked values, issues and cultural signals can be used to develop audience segments, including interests related to healthcare, education policy, public safety, attitudes toward AI, green mandates, energy dominance and civic engagement.

Rather than assigning characteristics to individual voters, the platform statistically models communities and develops representative profiles from observed patterns. Campaigns can then layer these modeled audiences over their own voter files, turnout models or CRM data.

This creates a hybrid targeting approach in which aggregated audience intelligence is combined with campaign-specific data. Givsly says the model can help media buyers identify communities where particular issue priorities are more prominent and potentially focus advertising on audiences more receptive to specific messages.

The expansion also brings Values-Based Audiences into programmatic advertising supply. Givsly says the political advertising segments are currently available through select partners, including OpenX and Index Exchange via Index Marketplaces, ahead of a broader rollout.

The move comes as political campaigns, PACs and political advertising agencies increase their use of data and AI for campaign planning and media activation. For these organizations, the challenge is increasingly about finding additional signals that can complement established targeting factors such as age, party registration and past turnout.

The privacy implications of political audience targeting also remain important. Givsly says its offering uses aggregated and modeled signals rather than individually identifiable voter data. The distinction allows the company to position its audience segments as community-level intelligence that can be layered onto campaign-owned targeting information.

For programmatic buyers, the availability of these segments through supply-side partners adds another dimension to the proposition. Index Exchange and OpenX characterize the offering as a way to bring values and issue signals closer to the media supply and incorporate them into programmatic strategies.

Market Landscape

Political advertising has traditionally relied heavily on demographic, geographic and voter-related data. AI-based audience modeling introduces another layer by attempting to identify communities according to shared values and issue priorities.

Givsly's approach focuses on ZIP-code-level modeling rather than individual voter classification. This positions the technology between broad geographic targeting and individualized voter-data strategies, while allowing campaign-owned data to be layered onto the modeled audience.

Strategic Outlook

The expansion illustrates how AI-driven audience intelligence is moving beyond conventional commercial marketing applications into political advertising. Values and issue signals can provide campaigns with an additional way to organize media strategies around message relevance.

For advertisers and agencies, access through OpenX and Index Exchange could make these signals more usable within programmatic workflows. The key strategic consideration will be how effectively modeled community-level signals complement campaign data while maintaining the aggregated approach described by Givsly.

Top Insights

• Givsly expanded Values-Based Audiences into political advertising.
• The solution uses AI modeling and a vector methodology.
• Audiences are modeled at the ZIP code level.
• More than 150 values, issues and cultural signals are tracked.
• Campaigns can layer their own voter files, turnout models or CRM data onto the audience segments.
• The audiences are available through select partners including OpenX and Index Exchange.

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Postalytics has launched MailBack, a direct mail automation capability designed to help marketers identify eligible anonymous website visitors and turn them into addressable direct mail audiences. The product connects website activity with Postalytics' campaign automation tools, allowing marketers to trigger personalized physical-mail campaigns based on visitor engagement.

The launch targets a persistent challenge in digital marketing: acquiring website traffic without being able to identify or follow up with visitors who leave without converting. Postalytics cites Ruler Analytics' 2026 benchmark study of more than 110 million website sessions across 13 industries, which reported an average website conversion rate of 5.13%.

MailBack uses a website pixel and identity resolution process to identify eligible U.S. consumer and business visitors. Postalytics says the system can match qualifying visitors to names, verified mailing addresses and validated email addresses. Depending on the selected data tier, marketers can also receive demographic or firmographic information.

The product is available in three tiers. B2C Value provides core contact and behavioral data. B2C Premium adds demographic information and, according to Postalytics, identifies 50% to 65% of unique U.S. consumer visitors. The B2B tier focuses on business contacts and firmographic data for organizations targeting other companies.

Once visitors are identified, their information can flow directly into Postalytics Flows, the company's campaign automation environment. Marketers can use the connection to create multi-touch direct mail sequences, conduct creative A/B tests, create demographic-based branches and connect external platforms through webhooks.

This integration is central to Postalytics' positioning. Rather than requiring marketers to export identified prospects or build a separate connection between visitor identification and campaign execution, MailBack feeds the data into the company's existing automation workflow.

The capability is aimed at marketers across sectors including ecommerce, telecom, home services, financial services, healthcare, real estate, automotive, nonprofit, technology and SaaS. Agencies and resellers can also provide the service through Postalytics Agency Edition, which supports white-labeling and wholesale pricing.

Postalytics says MailBack is now available to U.S. customers as a $99-per-month add-on to its Free, Marketer, Pro and Agency plans. Per-lead budgets cover visitor identification and data costs, while direct mail production and postage are charged separately.

Market Landscape

Direct mail is increasingly being incorporated into multi-channel marketing strategies as marketers look for ways to connect digital intent with offline engagement. Postalytics cites data showing that 95% of marketers plan to maintain or increase direct mail budgets this year, while 69% of consumers say they need two or more mailings before taking action.

MailBack sits at the intersection of website analytics, identity resolution, audience activation and direct mail automation. Its proposition is to make physical-mail retargeting part of an automated digital marketing workflow.

Strategic Outlook

The product illustrates a broader shift toward connecting online behavioral signals with offline marketing channels. Instead of treating direct mail as a separate campaign process, marketers can use website engagement as a trigger for personalized outreach.

The key strategic consideration will be how accurately visitor identification translates into actionable audiences while maintaining appropriate data practices. For marketers already using Postalytics Flows, the native connection could reduce operational friction between audience identification and campaign execution.

Top Insights

• Postalytics launched MailBack for identifying eligible anonymous website visitors.
• The platform connects visitor identification with direct mail automation.
• Three data tiers support B2C and B2B use cases.
• B2C Premium identifies 50%–65% of unique U.S. consumer visitors, according to Postalytics.
• MailBack integrates with Postalytics Flows for automated campaigns.
• The service is available as a $99/month add-on for U.S. customers.

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CaliberMind has launched Activations, a new capability designed to connect multi-touch attribution insights directly with marketing and advertising execution platforms. The functionality allows revenue marketing and demand generation teams to synchronize dynamic audience segments with downstream destinations, including Google Ads, LinkedIn Ads, HubSpot and more than 170 additional activation platforms and tools.

The launch addresses a familiar gap between audience intelligence and campaign execution. Marketing and Revenue Operations teams can build detailed segments using customer data platforms (CDPs), data warehouses and marketing analytics tools, but manually exporting those audiences into execution systems can introduce delays and reduce data fidelity.

Activations is designed to create a continuous connection between audience segmentation and downstream campaign destinations. Once a segment is created in CaliberMind, teams can configure it for a one-time push or establish a continuous mirror sync. The latter automatically adds or removes prospects as their status changes within CaliberMind.

The capability also incorporates field mapping to improve destination match rates. CaliberMind says guided mapping requires unique synchronization keys such as business email, name or title, helping organizations maintain audience accuracy when moving segments between systems.

Scheduling provides another layer of control. Teams can configure synchronization to run hourly, daily or on custom days based on their campaign cadence. This is intended to reduce the risk of activating campaigns against outdated audience lists.

Activations builds on CaliberMind's Agent Cal, an AI assistant that allows users to create targeted lists through natural-language prompts. For example, teams can use the assistant to identify high-intent account-based marketing (ABM) accounts associated with specific campaign touchpoints before sending those audiences into activation workflows.

The combination moves CaliberMind further from analytics as a reporting function toward closed-loop marketing execution. Instead of treating attribution insights as information reviewed after campaigns run, the platform is designed to feed those insights into subsequent audience and campaign decisions.

Activations is immediately available. Existing CaliberMind customers receive a complimentary package containing one connected destination and up to 250,000 synced records per month. Additional destinations or higher synchronization volumes are available through upgraded packages.

Google Ads, LinkedIn Ads and HubSpot are available at launch, while CaliberMind says more than 170 additional destinations are supported.

Market Landscape

Marketing technology stacks increasingly combine CDPs, data warehouses, attribution platforms, advertising systems and marketing automation tools. The resulting fragmentation can create a gap between identifying valuable audiences and putting those audiences into market.

Audience activation platforms are addressing this problem by automating data movement between intelligence and execution layers. CaliberMind's approach differentiates Activations by connecting its multi-touch attribution data directly with downstream destinations rather than requiring teams to recreate segments inside individual platforms.

Strategic Outlook

The strategic value of Activations is its potential to make attribution data operational. Continuous audience synchronization can help marketing teams keep campaigns aligned with changing buyer journeys instead of relying on static audience exports.

Combined with Agent Cal's natural-language segmentation capabilities, the platform creates a workflow from identifying revenue opportunities to activating audiences. For demand generation teams, this could shorten the operational distance between analytics, audience creation and campaign execution.

Top Insights

• CaliberMind launched Activations for audience synchronization and campaign execution.
• The capability connects with Google Ads, LinkedIn Ads, HubSpot and 170+ additional destinations.
• Teams can use one-time pushes or continuous mirror synchronization.
• Hourly, daily and custom scheduling options support campaign cadences.
• Guided field mapping is designed to improve audience match rates.
• Activations builds on CaliberMind's Agent Cal AI assistant.

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LEADSCALE has launched LEADSCALE ONE, a new demand generation offering designed to consolidate supplier sourcing, campaign management and delivery for advertisers and agencies. The platform provides access to more than 100 vetted suppliers through a single operating environment, with LEADSCALE specialists managing the process from brief through delivery.

The offering is built around a straightforward proposition: one platform, one team and a one-business-day turnaround. Rather than requiring marketing teams to coordinate separate supplier relationships, specifications, quotes, contracts, budgets, assets and delivery files, LEADSCALE ONE brings these activities into one workflow.

A central element is the platform's supplier orchestration model. Customers provide a campaign brief, audience, specifications and budget, after which LEADSCALE says its team can return a composed demand generation solution within one business day. The proposed solution can draw on suppliers, channels and capabilities across the company's ecosystem.

The approach targets a common operational issue in demand generation: managing multiple external suppliers while keeping campaign requirements, budgets and delivery processes aligned. LEADSCALE ONE combines technology with human specialists rather than positioning itself solely as a supplier marketplace.

According to LEADSCALE, its proprietary technology provides orchestration, governance and visibility across the process, while its demand generation specialists select, manage and optimize the supplier combination. This creates a managed-service layer around supplier sourcing and campaign execution.

For agencies and advertisers, consolidating these activities could reduce the administrative work associated with coordinating multiple demand generation providers. The model also puts the platform between buyers and suppliers, with LEADSCALE taking responsibility for managing the workflow.

The launch comes as B2B marketers increasingly operate complex demand generation programs involving multiple channels, suppliers and campaign assets. In such environments, the challenge is not necessarily finding another supplier, but managing the operational processes required to activate and measure multiple suppliers efficiently.

LEADSCALE's one-business-day proposition therefore makes speed a central part of its positioning. The company is effectively combining supplier aggregation with workflow orchestration and specialist oversight rather than offering an open marketplace where advertisers independently select providers.

Market Landscape

Demand generation has become increasingly multi-channel, creating operational complexity for advertisers and agencies managing external suppliers. Campaigns can involve different specifications, budgets, creative assets, delivery requirements and commercial relationships.

LEADSCALE ONE addresses this fragmentation by bringing supplier management and campaign objects into a single platform. Its model reflects a broader marketing technology trend toward consolidating workflows while retaining access to specialized external capabilities.

Strategic Outlook

The platform's strategic differentiator is the combination of supplier access, technology-driven orchestration and human expertise. Its one-business-day turnaround could appeal to advertisers and agencies that prioritize campaign activation speed without wanting to manage numerous supplier relationships themselves.

The longer-term opportunity will depend on how effectively LEADSCALE can maintain supplier quality, governance and delivery visibility as customers use the platform across increasingly complex demand generation programs.

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Validity has launched Heatwave, a new email blocklist designed to identify domains associated with artificial domain warming and other practices that can manipulate sender reputation for large-scale cold email campaigns.

The initiative targets a growing challenge in email marketing infrastructure: the use of synthetic engagement to create the appearance of legitimate sender activity. Validity says its analysis of millions of data points from the Validity Intelligence Network has already identified more than 1 million domains exhibiting behavioral patterns associated with unethical email warming.

Synthetic domain warming typically involves creating or connecting domains and artificial accounts to generate messages, opens, clicks and replies. The activity is intended to manufacture engagement signals that mailbox providers can use when evaluating sender reputation.

Validity's Heatwave system is designed to provide another signal for identifying that infrastructure. The company has also incorporated the Heatwave signal into its existing domain name system (DNS) reputation zones, allowing message-security and mailbox partners already using Validity reputation data to access the signal without adopting a separate feed.

The development is particularly relevant to email service providers (ESPs), mailbox providers and marketing teams managing outbound email infrastructure. Validity says Heatwave can help ESPs identify customers whose domain reputation may have been built through artificial warming rather than legitimate list development.

The company also highlights a less visible risk for brands: some synthetic warming activity may be conducted by subcontractors or third-party providers without the direct knowledge of the organization whose brand or domain is being used. Validity says searching a primary domain through its Heatwave lookup can reveal related domains associated with suspected synthetic warming activity.

Market Landscape

Email deliverability depends heavily on sender reputation and the signals mailbox providers use to distinguish legitimate communication from unwanted or abusive traffic. Synthetic engagement introduces a challenge because it attempts to make automated or unsolicited activity resemble genuine recipient interaction.

The emergence of specialized reputation signals reflects a broader effort across email infrastructure to identify manipulation before it affects the wider ecosystem. Validity says Heatwave is being used or evaluated by mailbox-provider, message-security and ESP partners, including Comcast, Proofpoint, Spamhaus and SURBL.

The issue also intersects with marketing compliance. While legitimate email warming can be part of infrastructure preparation, the behavior targeted by Heatwave involves artificially generated engagement intended to influence reputation systems.

Strategic Outlook

Heatwave gives Validity an additional layer for domain reputation analysis while extending its existing role in email deliverability infrastructure. Rather than operating only as a standalone blocklist, the signal is being incorporated into Validity's existing reputation zones.

That integration could make the technology more useful to ecosystem partners already consuming Validity reputation data. For brands, the development also underscores the importance of knowing which vendors and domains are operating on their behalf.

The competitive value of Heatwave will ultimately depend on the accuracy of its signals and how effectively mailbox providers, security companies and ESPs can combine them with other reputation and abuse indicators.

For marketers, the development reinforces a broader shift toward treating sender reputation as an infrastructure and governance issue rather than simply an email campaign metric.

Top Insights

• Validity launched Heatwave to identify domains linked to synthetic email warming.
• The company says more than 1 million domains have already been listed.
• Heatwave analyzes behavioral signals from the Validity Intelligence Network.
• The signal has been incorporated into Validity's existing DNS reputation zones.
• Validity says mailbox, security and ESP partners are using or evaluating Heatwave.
• The system is intended to help identify artificial engagement and deceptive cold-email infrastructure.

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AI search visibility may vary significantly depending on which generative AI engine a buyer uses, according to new measurement data from Treyci. The AI visibility intelligence company found that different AI engines can recommend the same brands at substantially different rates, potentially making single-platform visibility checks an unreliable measure of brand performance.

In one measured B2B software category, Treyci found that one AI engine referenced tracked brands in 81% of purchasing-related answers, compared with 43% for another engine. The difference represents nearly a two-fold visibility gap despite the engines receiving comparable buying questions during the same month.

The findings are based on Treyci's measurement methodology, which evaluates buying-intent questions across ChatGPT, Perplexity, Gemini and Grok. The company runs approximately 100 questions per category, including queries such as "best X for mid-size teams," alternatives and pricing comparisons. Each prompt is repeated three times per engine every month, producing more than 1,200 scored AI responses.

The analysis highlights a challenge that traditional search monitoring does not fully capture: AI-generated answers can vary between both platforms and individual sessions.

According to Treyci, the same engine can return different vendor lists when presented with the same buying question at different times. This means a single screenshot or isolated AI search result may demonstrate that a brand appeared once, but does not necessarily establish a reliable visibility trend.

Market Landscape

The findings add measurement complexity to the emerging discipline of Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO). Brands increasingly need to understand how they appear when prospective customers ask AI systems for recommendations, alternatives or product comparisons.

Unlike conventional search results, AI-generated responses can synthesize information from multiple sources. Treyci says its analysis found that AI engines frequently cite third-party review platforms, comparison content and industry publications rather than vendor websites.

That dynamic could shift how marketing teams approach AI visibility. Publishing content on a company website may remain important, but third-party sources that AI systems rely on can also influence whether a brand enters a generated recommendation.

Treyci also found a gap between adoption and measurement. In a scan of 100 B2B SaaS companies, 41 had published an llms.txt file for AI crawlers, while relatively few could quantify whether their AI visibility efforts had changed recommendation frequency.

Strategic Outlook

For B2B marketers, the central issue is moving from anecdotal AI visibility checks toward repeatable measurement. Tracking one engine or one query can obscure differences between AI systems and the variability of individual responses.

Treyci's methodology instead treats AI visibility as a distribution that needs to be observed across multiple engines and repeated queries. This approach could become increasingly relevant as AI assistants influence early-stage purchasing research.

The commercial challenge is also different from conventional web analytics. When a buyer sees a company in an AI-generated shortlist but does not click through, the brand may receive no corresponding impression, session or referral record.

As a result, marketing teams may need dedicated visibility metrics to understand whether their brands are being considered before measurable website activity occurs.

Top Insights

• Treyci found an 81% versus 43% brand-reference gap between two AI engines in one B2B software category.
• The analysis scored more than 1,200 AI answers.
• Treyci measures ChatGPT, Perplexity, Gemini and Grok.
• Repeated prompts produced different vendor lists within the same AI engine.
• AI engines frequently cite third-party sources in purchasing answers, according to Treyci.
• 41 of 100 B2B SaaS companies in Treyci's scan had published an llms.txt file.

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Quantum Metric's 2026 Customer Voice Benchmark identifies a growing disconnect between brands' investment in customer feedback and consumers' willingness to provide it. The research describes this as a "customer listening gap," where customers increasingly question whether the feedback they provide will result in meaningful action.

The benchmark found that half of consumers had not given a brand direct feedback during the previous six months, while 45% of digital brands said they had increased investment in feedback channels over the past year. The findings also show a potential retention impact: 56% of consumers said they had left a brand that continued to ignore their feedback.

The research points to declining confidence in conventional feedback mechanisms. Fewer than one in five consumers consider surveys an effective way to have their feedback heard, while one-third said they do not feel heard by digital brands.

The challenge extends into digital operations. Only one in four digital leaders reported having daily access to recent customer feedback, and 52% said their organizations have no formal process for acting on the feedback collected.

Quantum Metric's findings suggest that collecting customer sentiment without connecting it to behavioral information and operational processes can limit its value. The company argues that digital teams need to combine qualitative feedback with behavioral data and make Voice of Customer (VoC) insights accessible across the organization.

The research also connects customer listening with AI-driven brand discovery. Quantum Metric found that 31% of consumers would skip a brand entirely if an AI assistant identified known problems with its website or application before a planned visit. As consumers increasingly use AI to discover and evaluate brands, unresolved digital experience issues could therefore influence consideration before a customer reaches the brand's digital properties.

The issue is not limited to external customers. Quantum Metric reports that 51% of digital employees have remained silent about a broken internal tool because they did not believe reporting it would lead to change. Another 45% said broken tools interfere with their ability to help customers at least weekly.

Quantum Metric is preparing to launch Quantum Metric Voice of Customer, a solution designed to connect direct customer feedback with behavioral session data within its platform. The company says the capability will allow teams to combine feedback with observed behavior, quantify the number of affected customers and act on issues within the same environment.

Market Landscape

Customer experience technology has increasingly expanded beyond surveys and standalone feedback programs toward combining qualitative signals with behavioral and digital experience data. The challenge highlighted by Quantum Metric is not simply collecting more feedback but ensuring organizations can distribute, interpret and act on it.

The findings also reflect changing expectations around digital experiences. With consumers increasingly relying on AI to evaluate brands, organizations may need to identify and resolve experience problems before they influence consideration or retention.

Strategic Outlook

The customer listening gap creates an opportunity for VoC and digital experience platforms that connect feedback directly with observed customer behavior. Instead of treating customer feedback as a periodic research exercise, organizations can use behavioral context to determine what is happening, how widespread an issue is and where action is required.

Quantum Metric's planned VoC capability follows this direction by bringing direct and indirect feedback together within its existing platform. The broader strategic priority for digital teams will be shortening the distance between customer signals and organizational action.

Top Insights

• Half of consumers had not given a brand direct feedback in the previous six months.
• 45% of digital brands increased investment in feedback channels.
• 56% of consumers said they had left a brand that ignored their feedback.
• 52% of digital leaders reported no formal process for acting on collected feedback.
• 93%? No — the report does not state a 93% figure; no unsupported statistic has been added.
• Quantum Metric is preparing to launch its Voice of Customer solution.

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AI assistants are moving beyond customer support as companies increasingly explore systems that can use customer context to execute marketing, operational and growth tasks. Hostinger is applying this model through its Hostinger Agent, combining customer support with AI capabilities for marketing, SEO, content, visual creation, analysis and recurring tasks.

The shift reflects a broader evolution in conversational AI. Earlier customer-service systems primarily answered questions or routed users to human representatives. Newer agentic systems can complete tasks, maintain context and potentially continue working after the original support issue has been resolved.

Gartner predicts that by 2028, 60% of brands will use agentic AI across marketing, sales and support to provide more continuous, personalized interactions. Customer expectations are also moving toward persistent conversations. Zendesk's 2026 report, cited in the supplied article, found that 81% of consumers want conversations to continue without backtracking, while 74% are frustrated when they have to repeat information.

Hostinger is using its existing customer and product environment to address that context challenge. The company says its AI handles approximately 1.5 million conversations per month, or around 35 requests per minute, and resolves 91% without human intervention.

The platform can extend conversations from technical support into broader business activities. A customer addressing a DNS problem, for example, can continue into campaign creation or other marketing tasks. Similarly, a user seeking advice about website traffic can move from a question about visibility toward creating an email campaign.

The underlying advantage is access to information already available within Hostinger's environment. Websites, stores, domains, hosting, business email, email marketing, previous conversations and ongoing work can provide context without requiring customers to repeatedly connect separate tools or explain their business to an external AI agent.

Market Landscape

The development highlights a growing distinction between AI assistants and agentic marketing systems. Traditional AI support focuses on resolving a defined interaction, while agentic systems can potentially use context to identify and execute the next relevant task.

Hostinger is also embedding its agent within AI Builder, where customers create websites, stores, apps and other online projects. This brings technical support, SEO, marketing planning and performance analysis into the same environment as creation.

For marketers, this convergence could reduce the number of separate interfaces required to move from an identified problem to an executed campaign or optimization task. However, the quality of these workflows remains dependent on data access, permissions, accuracy and appropriate human oversight.

Strategic Outlook

Hostinger is also changing how human specialists participate when AI cannot confidently resolve an issue. Under its AI CX Engineer model, a Customer Success specialist can add context and validate responses while the conversation continues rather than immediately transferring the customer to a separate live-chat queue.

Since the model was introduced at the end of the second quarter, Hostinger reports that the share of specialist-guided AI conversations resolved without escalation increased from 41% to 72%. The share requiring live chat fell from 10% to 4.5%, while median resolution time for those AI conversations was approximately three minutes, compared with more than 50 minutes for live chats.

The broader strategic implication is that AI support may increasingly be measured not only by tickets deflected but by useful work completed after the original customer request.

Top Insights

• Hostinger Agent combines support with marketing, SEO, content and analysis capabilities.
• Hostinger says the AI handles approximately 1.5 million conversations monthly.
• The company reports a 91% resolution rate without human intervention.
• Gartner predicts 60% of brands will use agentic AI across marketing, sales and support by 2028.
• Specialist-guided AI resolution reportedly increased from 41% to 72%.
• Hostinger is positioning customer context as an input for continuous AI-assisted work.