A missed call can mean a missed repair appointment for a home services company. Invoca is betting that combining lead management, AI agents and revenue analytics can close that gap.

The company has launched a data and AI solution for home services that connects marketing campaigns, websites, AI voice and messaging agents, customer service representatives and field-service systems in a single revenue workflow. The goal is to move leads from search, Google Local Services Ads (LSAs), AI discovery, websites and affiliates through qualification and scheduling to a completed job.

Home services marketing has an unusually short conversion window. Someone searching for emergency plumbing, HVAC repair or restoration services is often looking for a provider immediately, making response time as important as the initial lead acquisition.

Invoca's new solution is designed around that constraint. Its AI agents can respond to calls, texts and digital leads around the clock, qualify prospects, determine whether a service is available in the customer's area and either schedule an appointment or route the opportunity to a human customer service representative.

That makes the announcement broader than another AI chatbot launch. Invoca is attempting to connect marketing automation, conversational AI, CRM data and field-service scheduling into one revenue execution layer.

The platform integrates with systems including ServiceTitan, Salesforce Field Service and HubSpot. Its AI agents can book, reschedule and cancel appointments while updating field-service teams, reducing the manual coordination that can occur between marketing, call centers and operations.

For home services companies, that integration is important because the value of a lead depends on what happens after acquisition. A campaign may generate thousands of calls or website inquiries, but revenue ultimately depends on whether those prospects can be contacted, qualified and converted into scheduled work.

Invoca says its benchmarks show that increasing call-answer, lead and conversion rates by five percentage points each can produce nearly 40% more conversions from the same advertising spend. The company does not provide the underlying benchmark methodology in the announcement, so the figure should be treated as a vendor-reported performance claim rather than an independent industry benchmark.

The solution also targets Google Local Services Ads, where response speed can influence the economics of lead generation. Invoca says its voice AI agents can answer calls immediately, including under Google's newer 20-second billable-call rules. That potentially reduces wasted spend on callers who abandon before reaching a human representative.

The LSA connection illustrates why AI agents are increasingly moving beyond customer support. For performance marketers, an AI agent that answers a lead immediately can become part of the acquisition funnel itself.

The same principle applies to CRM data. Invoca says its agents can record conversations and qualification information in ServiceTitan, Salesforce and HubSpot. Instead of leaving call transcripts or qualification details in a separate AI system, the information can become part of the customer record used by sales and service teams.

The platform also attempts to connect the top and bottom of the funnel. Invoca says marketers can link impressions and clicks from connected TV, search, social and AI discovery with leads, booked jobs and revenue.

That is strategically significant as marketers increasingly face pressure to connect media spending with actual business outcomes. Salesforce, Adobe and other enterprise marketing platforms have spent years building cross-channel customer and analytics infrastructure, while specialized revenue platforms such as Invoca are targeting specific conversion-heavy workflows.

For home services organizations, the potential advantage is narrower but more operational: fewer handoffs between lead generation, AI engagement, human sales teams and scheduling systems.

The bigger question is whether AI agents can handle enough real-world complexity to improve conversion without creating new customer-service problems. Emergency repairs, service-area constraints, pricing questions and scheduling conflicts can require context that automated systems may not always have.

If Invoca can reliably coordinate those interactions while escalating complex cases to humans, its approach could shift AI from a marketing assistant into an active component of revenue operations.

Market Landscape

AI-powered lead response is becoming an important layer of marketing technology as companies try to reduce the gap between lead generation and sales engagement.

Google, Salesforce and other major technology ecosystems increasingly connect advertising, customer data, conversational interfaces and automation. The difference with Invoca is its focus on phone-heavy, appointment-driven businesses, where a lead's commercial value can depend on minutes rather than days.

The home services market is particularly suitable for this model because consumers frequently arrive with high purchase intent. Search, AI assistants, LSAs and websites can generate demand, but the operational system handling the lead determines whether that demand becomes revenue.

That makes the integration between AI agents and field-service platforms potentially more important than the conversational interface itself.

Strategic Outlook

The next stage of AI marketing automation may be less about generating another message and more about completing the transaction that follows it.

For home services brands, that could mean an AI system that receives a lead, verifies serviceability, answers basic questions, books the appointment, updates the CRM and hands the customer to a human when necessary.

The winning architecture will likely be one where AI is connected to operational systems rather than operating as a standalone layer. Invoca's integration with field-service and CRM platforms reflects that direction.

For enterprise marketing teams, the implication is straightforward: lead-generation performance increasingly needs to be measured through downstream revenue, not simply clicks, calls or form submissions.

Top Insights

• Invoca connects marketing, AI agents, CRM systems and field-service scheduling, targeting the operational gap between lead generation and booked jobs.
• AI voice and messaging agents can respond around the clock, qualify prospects and schedule appointments, potentially reducing revenue lost to delayed lead follow-up.
• Integrations with ServiceTitan, Salesforce Field Service and HubSpot move conversational AI closer to core revenue and customer operations.
• The solution links advertising interactions with leads, bookings and revenue, addressing growing pressure on marketers to prove downstream business impact.
• Home services provide a strong use case for AI revenue automation because high-intent leads often require immediate responses and appointment scheduling.

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Marketing leaders increasingly recognize that distinctive creative can drive business impact, yet many organizations remain reluctant to approve work that challenges established norms. A new study from FleishmanHillard and Contagious argues that this gap between belief and behavior is becoming a competitive problem.

Called The Chaos Advantage, the study analyzes recent Cannes Lions and Effie winners alongside a survey of 1,000 senior marketing and communications leaders across North America, EMEA and APAC. It finds that 88% of respondents believe bold creative drives impact, while most say their organizations still produce predominantly safe work.

The central argument of The Chaos Advantage is deliberately provocative: in an uncertain market, avoiding risk may itself be a form of risk.

According to the study, 87% of marketing leaders agree that uncertain environments create opportunities for bold work. Yet two-thirds say their organization's risk-management processes prevent action more often than they enable it. Meanwhile, 58% say missed opportunities caused by caution occur more frequently than negative consequences from taking bold action.

That disconnect has direct competitive implications. Forty-two percent of respondents say they have already watched competitors capture market share while their own organizations hesitated.

The research also challenges the assumption that conservative creative provides greater protection from backlash. FleishmanHillard and Contagious report that brands pursuing safe creative experience public backlash at roughly the same rate as brands taking bolder approaches. Their analysis further claims that uninspired creative is associated with a 37% decline in brand conversion and a 14% reduction in long-term ROI. Those findings come from the study's proprietary analysis and should therefore be distinguished from independent effectiveness research.

There is growing external evidence that creative quality deserves more attention in marketing investment decisions. NIQ's 2026 CMO Outlook found that 60% of surveyed CMOs considered creative a significant driver of marketing ROI, behind audience targeting and reach at 78% and campaign structure and execution at 69%. NIQ also says its research has found that strong creative can generate up to a 50% sales lift.

Research from LIONS points in a similar direction, although it also shows why organizations struggle to act on the insight. Its 2025 State of Creativity found that only 13% of companies considered themselves creative risk-friendly, while 29% described themselves as highly risk-averse. The same study found that 57% of brands struggled to react quickly to cultural moments.

The implication is not that marketers should become reckless. Instead, the more useful interpretation is that organizations need better systems for distinguishing manageable creative risk from uncontrolled business risk.

That is where FleishmanHillard's response becomes relevant to the technology side of the story. Alongside the research, the agency is promoting its Crisis, Issues and Risk Solutions Suite, including Risk ID, an expert-guided AI platform designed to identify, prioritize and mitigate potential risks before campaigns launch.

The concept reflects a broader evolution in marketing operations: AI is increasingly being positioned not only as a content-generation tool but also as infrastructure for decision-making, governance and risk assessment.

For enterprise marketing teams, that could be more consequential than simply producing more creative variations. If technology can help organizations identify potential reputational or operational risks before launch, risk management could become less of a veto mechanism and more of an enabler of experimentation.

The broader challenge remains measurement. Bold creative is valuable only when it is distinctive, relevant and connected to a business objective. Kantar's research has similarly found that creative quality and effectiveness are closely connected, while its 2026 Cannes analysis emphasizes that organizations need infrastructure, processes and measurement systems capable of scaling effective creativity.

That makes The Chaos Advantage less an argument for "taking more risks" than an argument for modernizing how marketing organizations evaluate them.

Market Landscape

The tension between creative ambition and risk management is becoming more pronounced as marketers operate across fragmented media, faster cultural cycles and AI-generated content.

Nielsen's 2024 global marketing research found that 70% of marketers planned to prioritize performance marketing over brand-building initiatives, even though long-term and full-funnel ROI remained among their leading objectives. Only 38% said they measured traditional and digital marketing together.

That measurement gap matters. If organizations evaluate campaigns primarily through short-term performance indicators, unconventional brand-building work can be harder to defend internally.

At the same time, the growth of AI is increasing the volume of creative output while making differentiation more difficult. Enterprise marketers therefore face a paradox: technology makes it easier to produce content, but stronger creative judgment may become more important as audiences encounter an increasing amount of interchangeable material.

Strategic Outlook

The emerging model is likely to combine creative experimentation with structured risk intelligence.

Marketing organizations will still need governance, but the goal may shift from preventing unusual ideas to understanding their potential consequences quickly enough to make informed decisions.

For MarTech teams, this creates an opportunity for AI-powered risk assessment, creative testing, brand-safety monitoring and marketing analytics to work together rather than operate as separate functions.

The competitive advantage may ultimately belong to organizations that can move quickly without becoming careless. In that environment, caution is not automatically the safer strategy—and boldness is not automatically the riskier one.

Top Insights

• FleishmanHillard and Contagious identify a gap between marketers' belief in bold creative and organizations' continued preference for safer, more predictable work.
• Two-thirds of surveyed leaders say risk-management processes block action, suggesting governance can become a competitive constraint when uncertainty accelerates decision cycles.
• AI-powered risk assessment could help enterprises distinguish manageable creative experimentation from genuine reputational, regulatory or operational exposure.
• External research from NIQ and LIONS reinforces the importance of creative quality while highlighting persistent organizational barriers to creative risk-taking.
• The emerging opportunity is not unrestricted boldness but faster, evidence-based decision-making that lets brands experiment without abandoning marketing governance.

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Managing marketing across dozens of locations creates a familiar problem for franchises, nonprofits and distributed organizations: local teams need autonomy, while central leaders need visibility and control.

Constant Contact is addressing that gap with Constant Contact Teams, a multi-account marketing command center that combines organization-wide reporting, centralized brand governance and AI-powered recommendations. The company says the product is designed for franchises, nonprofits, associations, educational institutions and other organizations managing marketing across multiple locations or teams.

Constant Contact Teams is less a new campaign tool than an attempt to solve the management layer sitting above distributed marketing operations.

Instead of forcing leaders to move between individual accounts, Teams introduces a central Hub with separate Workspaces where local teams can continue executing campaigns. Leaders can view aggregated campaign and contact performance, compare workspaces and drill down into individual accounts without losing the organization-wide view.

The platform also adds centralized governance. Brand controls, approval workflows and locked templates are intended to let headquarters establish boundaries while allowing local teams to create and execute campaigns independently.

That balance is important for multi-location businesses. A franchise, for example, may need every location to follow corporate branding while still allowing individual operators to communicate with customers based on local conditions. A fully centralized model can slow those teams down; a completely decentralized one can create inconsistent messaging and make performance difficult to compare.

Teams puts reporting and governance into the same operating layer.

Its roll-up reporting aggregates campaign and contact performance across accounts, while a performance leaderboard allows managers to compare individual workspaces. An AI Insights layer then surfaces trends, pending approvals and suggested areas of attention across the network.

The AI component fits into Constant Contact's broader strategy of embedding artificial intelligence throughout its marketing platform. Earlier in 2026, the company launched integrations with both ChatGPT and Claude, allowing users to create and work with marketing campaigns through conversational AI.

Teams applies that strategy to a different problem: organizational oversight rather than content generation.

The distinction matters. Gartner has identified AI as a force changing marketing workflows, including budgeting, resource management, analytics and talent optimization. The research firm argues that organizations need to rethink marketing operations as AI moves from simply assisting employees toward performing parts of workflows itself.

Constant Contact is effectively applying that concept to distributed marketing management. Instead of asking a regional marketing manager to inspect dozens of accounts and determine which ones require intervention, the system attempts to surface those issues centrally.

The product also reflects the growing importance of digital channels to marketing organizations. Gartner's 2025 CMO Spend Survey found that digital channels represented 61.1% of total marketing spend, with paid search alone accounting for 13.9% of total digital spending.

As more marketing activity moves into measurable digital channels, the management challenge becomes less about access to tools and more about coordinating execution across teams.

Constant Contact says an early customer, Lift-All, reduced the time required for organization-wide reporting from roughly half a day to less than an hour. That is a customer-reported result rather than an independently validated benchmark, but it illustrates the operational problem Teams is targeting.

The competitive landscape is broader than email marketing. Enterprise marketing platforms from Salesforce, Adobe and other vendors already offer centralized governance, analytics and multi-team workflows. Constant Contact's differentiation is its focus on smaller and midsized distributed organizations that may need enterprise-style control without adopting a full enterprise MarTech stack.

Market Landscape

Multi-account marketing is becoming an important product category as franchises, nonprofits and distributed businesses run increasingly sophisticated digital programs.

Constant Contact has been building Teams functionality before this formal launch. Its product documentation shows that the platform already supported hub-and-workspace structures and organization-level reporting during its beta program, while recent updates added contact reporting across workspaces.

The new release packages those capabilities into a more centralized operating model.

The competitive question is whether smaller organizations want a dedicated multi-location marketing command center or can assemble similar capabilities through their existing CRM, email, analytics and collaboration tools. Vendors such as Salesforce and Adobe have strong enterprise ecosystems, but those platforms can introduce greater complexity and cost.

Constant Contact is positioning Teams in the space between a collection of independent marketing accounts and a heavyweight enterprise marketing stack.

Strategic Outlook

The larger trend is toward centralized intelligence with decentralized execution.

For multi-location organizations, the ideal marketing architecture is unlikely to be complete centralization. Local teams still need the ability to respond to their audiences. The challenge is giving corporate marketing leaders enough visibility to enforce standards, identify underperforming locations and distribute successful campaigns.

AI can make that model more practical by turning aggregated data into prioritized recommendations rather than simply another reporting dashboard.

For Constant Contact, Teams also expands the platform's role from an email and digital marketing tool into a broader marketing operations platform for distributed organizations. If AI Insights can reliably identify where intervention is needed, the product could become valuable not just for campaign execution but for ongoing network-level marketing management.

Top Insights

• Constant Contact Teams centralizes performance, governance and AI recommendations while allowing local teams to retain control over day-to-day marketing execution.
• Roll-up reporting and workspace comparisons address a major operational problem for franchises and nonprofits managing dozens of separate marketing accounts.
• AI Insights moves the platform beyond reporting by attempting to identify trends, pending approvals and performance issues requiring management attention.
• Constant Contact is targeting the gap between standalone marketing tools and complex enterprise MarTech platforms from vendors such as Salesforce and Adobe.
• The product reflects a broader shift toward centralized marketing intelligence combined with decentralized execution across distributed organizations.

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Brand tracking has long given marketers measures such as awareness, consideration and preference. Quantilope is betting that a more specific measure of what consumers remember in buying situations can provide a stronger link between brand health and commercial performance.

The automated research technology company has launched Mental Availability Norms, a benchmarking framework designed to help brands compare their performance against competitors across categories and markets.

Built using nearly 8,000 data points covering thousands of brands in 35 countries, the norms span North America, Europe, APAC and Latin America and six industries: non-food CPG, food and beverages, fashion, durables, retail and services. The methodology is based on research from the Ehrenberg-Bass Institute for Marketing Science (EBI).

Mental Availability measures how readily a brand comes to mind when consumers enter a buying situation. Instead of asking simply whether someone recognizes a brand, the approach examines the situations or Category Entry Points (CEPs) associated with it.

EBI describes CEPs as the thoughts that occur as category buyers move toward a purchase. They form the building blocks of Mental Availability because they help determine which brands are mentally accessible in particular buying situations.

Quantilope's new benchmark focuses on three metrics: Mental Market Share, Mental Penetration and Network Size. Mental Market Share measures a brand's proportion of category-entry-point associations relative to competitors. Mental Penetration measures the share of category buyers who connect a brand with at least one CEP, while Network Size measures the average number of CEPs associated with a brand.

The practical objective is to give marketers a reference point for determining whether a score represents strong or weak performance within a particular competitive environment.

For example, Quantilope says brands in the bottom 40% for Mental Penetration are struggling to reach non-buyers. A marketer might respond by shifting media investment toward broader-reach campaigns rather than concentrating spending primarily on frequency.

That recommendation reflects the underlying EBI philosophy: building Mental Availability requires brands to be connected with a broad range of relevant buying situations and to remain easy to retrieve from memory. EBI also emphasizes broad-reach media and distinctive brand assets as part of that growth strategy.

The new norms could therefore change how brand-health tracking is used. Rather than treating a tracking score as an isolated KPI, marketing teams can compare it with category norms, establish realistic targets and connect changes in Mental Availability to commercial outcomes.

Quantilope says its own 2024 analysis of more than 100 brands found a correlation of r = 0.83 between Mental Market Share and sales data, with an R² of 0.69. In statistical terms, the company's analysis found that the survey metric explained roughly 69% of the variation in sales data. That is a quantilope analysis rather than an independent industry meta-analysis, so the finding should be interpreted accordingly.

The benchmark also produces some intriguing geographic differences. Quantilope reports that North American and European markets tend to have lower and more evenly distributed Mental Market Share scores, while APAC and LATAM can show substantially higher scores, which the company attributes partly to category maturity and first-mover advantages.

Another finding is that the strongest 20% of brands can encounter what Quantilope calls a "growth ceiling." At that point, simply taking mental share from established competitors may become less productive than expanding the number of category buyers or purchase situations associated with the category.

The significance extends beyond consumer packaged goods. Quantilope says clients in services, durables, retail and some B2B categories are applying Mental Availability and CEP analysis, consistent with EBI's broader work on how B2B brands grow through mental and physical availability.

For enterprise marketing teams, that makes the launch less about another brand-awareness dashboard and more about standardizing how brand memory is measured across markets. The real test will be whether these norms improve marketing planning and investment decisions rather than simply adding another benchmark to already crowded marketing analytics stacks.

Market Landscape

Mental Availability sits between traditional brand awareness research and broader marketing effectiveness measurement. Quantilope already offers automated tracking around Mental Market Share, Mental Penetration and Network Size, while the new norms add external category and market context.

That distinction matters. A brand can know whether its Mental Market Share is rising without necessarily knowing whether the result is strong relative to competitors. Normative benchmarks provide the missing reference point.

The approach also differs from conventional brand-health studies that emphasize awareness, consideration or purchase intent. Mental Availability asks a more behaviorally specific question: when a buying situation occurs, which brands can consumers retrieve from memory?

EBI has developed Category Entry Point research as a way of identifying those buying situations and assessing a brand against competitors.

Strategic Outlook

The launch reflects a broader movement toward evidence-based marketing measurement. As CMOs face greater pressure to justify media and brand-building investments, metrics that can connect consumer psychology with business outcomes become more valuable.

For MarTech and marketing analytics teams, the opportunity is to incorporate Mental Availability into a wider measurement architecture rather than treat it as a replacement for sales, attribution or customer data.

That could make the benchmark particularly useful for multinational organizations. Comparing the same brand across regions can reveal whether weak performance reflects execution problems, category maturity or fundamentally different competitive dynamics.

Top Insights

• Quantilope's Mental Availability Norms give marketers external benchmarks for judging brand salience rather than relying solely on year-over-year tracking changes.
• Nearly 8,000 data points across 35 countries create a broader reference framework for comparing brand performance across regions, industries and competitors.
• Mental Penetration focuses attention on non-buyers, potentially helping media teams identify when broader reach should take priority over frequency.
• Quantilope's reported 69% sales-variance finding strengthens the case for Mental Market Share, although the analysis remains company-generated rather than independently validated.
• Extending Mental Availability into services, retail and B2B could make Category Entry Point analysis relevant well beyond traditional CPG brand marketing.

Get in touch with our MarTech Experts.

For years, SEO was often sold to startups as a monthly agency retainer. But as search becomes more fragmented across Google, AI Overviews, ChatGPT and Perplexity, some young companies are moving toward a different model: hiring specialist SEO talent directly.

Seotal, a platform connecting US startups and small businesses with freelance SEO professionals across India and Southeast Asia, says it is seeing growing demand for experts who can work on specific problems rather than broad, bundled SEO programs.

The shift reflects a larger change in search marketing. Startups increasingly need technical SEO, content strategy, local search optimization and answer engine optimization (AEO) or generative engine optimization (GEO) at different stages of growth. A single agency retainer may not always provide that flexibility.

The traditional SEO agency model was built around predictable retainers, account management and teams of generalists. For startups operating under tighter budgets, that structure can become difficult to justify when the immediate requirement is a specific skill.

Seotal says its model addresses that gap by matching companies with vetted freelance specialists. The company reports that a mid-level SEO professional in India typically costs between $1,100 and $2,300 per month through its network, with clients saving about 60% compared with US rates.

Those figures are Seotal's own pricing comparisons, rather than an independent industry benchmark, but the underlying shift is significant. Companies are becoming more comfortable assembling distributed marketing teams around specialized expertise instead of outsourcing an entire function to one agency.

AI is accelerating that transition.

Google has expanded AI Overviews to more than 200 countries and territories and more than 40 languages. The company says AI Overviews are driving more than 10% growth in Search usage for the types of queries where they appear in major markets including the US and India.

Google's AI Mode also changes the nature of search queries. Early testers have been asking queries two to three times longer than traditional searches, according to Google, while the system uses AI to break complex questions into multiple searches across the web.

That creates demand for expertise beyond conventional keyword rankings. SEO professionals increasingly need to understand structured content, topical authority, entity relationships, technical accessibility and how information is interpreted by generative search systems.

Seotal says startups are responding by hiring AEO/GEO specialists alongside technical SEOs, local search experts and content strategists. Instead of paying for an agency's entire service portfolio, a company can bring in the required specialist when the business problem changes.

The model also promises more direct communication. Founders can work with the person responsible for the SEO strategy rather than communicating through an account manager. That can matter for early-stage businesses where search decisions are closely connected to product positioning, content and customer acquisition.

Seotal says it provides candidate profiles within 72 hours and typically gets specialists onboarded within two weeks. Its network currently includes more than 50 freelance SEO professionals across India, the Philippines, Vietnam, Indonesia, Malaysia and the UAE.

The company also reports a 96% client retention rate, although that figure is not independently verified.

The broader market suggests that specialization is becoming more important. McKinsey has estimated that generative AI could create productivity value equivalent to 5% to 15% of total marketing spending, potentially changing how marketing organizations allocate work between internal teams, agencies and specialized external talent.

That does not mean agencies are disappearing. Larger brands still need integrated strategy, creative production, media buying and complex enterprise execution. For startups, however, the economics can favor a smaller internal team supplemented by highly specialized freelancers.

Market Landscape

The emerging freelance SEO model is less about replacing agencies outright than unbundling what agencies traditionally sell.

An agency can provide breadth, process and continuity. Freelancers can provide deeper specialization and potentially lower fixed costs. The trade-off is that companies must manage quality, coordination, strategic consistency and continuity themselves.

AI search makes that decision more complicated. Gartner's 2026 research found that only about one-third of surveyed consumers considered GenAI chatbots as effective as search engines for learning new information, reinforcing the need for marketers to optimize for both AI-driven and traditional search.

For startups, the implication is clear: AEO and GEO should complement SEO rather than replace it.

Strategic Outlook

The most likely outcome is a hybrid search-marketing workforce. Startups may retain a core marketing or growth team while bringing in freelance specialists for technical audits, content systems, international SEO, local search, link strategy or AI-search visibility.

Platforms such as Seotal are betting that this creates a new layer between traditional agency outsourcing and full-time hiring.

The competitive advantage will ultimately depend less on whether talent is employed by an agency or independently and more on whether the specialist can connect search visibility to measurable business outcomes. As Google, ChatGPT, Perplexity and other AI interfaces reshape discovery, that distinction is becoming increasingly important.

Top Insights

• Startups are increasingly unbundling SEO services, hiring specialists directly when technical, content, local or AI-search requirements change.
• AI Overviews and AI Mode are expanding search behavior, increasing demand for SEO professionals who understand AEO, GEO and conversational discovery.
• Freelance specialists can reduce fixed agency costs, but startups assume greater responsibility for strategic coordination, quality control and long-term continuity.
• Seotal's network illustrates the globalization of SEO talent, connecting US businesses with specialists across India and Southeast Asia.
• Traditional SEO remains important because AI search complements rather than completely replaces conventional search, making hybrid optimization increasingly necessary.

Get in touch with our MarTech Experts.

Amazon brands are facing a more complicated competitive environment as marketplace visibility increasingly depends on retail execution, content quality, advertising and the ability to surface products through AI-driven shopping experiences.

Podean, in partnership with Amazon data platform Jungle Scout, has launched The Marketplace Index™ 2026, its annual benchmark of Amazon brand performance. The fourth edition evaluates more than 3,500 brands across six major Amazon markets: Australia, Brazil, Germany, Mexico, the UK and the US.

The report adds AI Readiness as a new scoring pillar alongside Retail, Content, Customer and Media, reflecting a shift in how consumers discover products on marketplaces.

The introduction of AI Readiness is arguably the most significant change in this year's index. Amazon's shopping ecosystem is increasingly being shaped by AI-powered discovery and shopping assistants, creating a new layer of competition beyond traditional search rankings and sponsored placements.

The Marketplace Index attempts to measure that shift alongside more established components of Amazon performance. Jungle Scout supplies Amazon marketplace data covering categories, brands, products and keywords, while Podean uses those signals to benchmark performance across markets and categories.

For enterprise ecommerce and marketplace teams, that creates a framework for comparing performance beyond headline sales figures. A brand may have strong retail execution but weak advertising coverage, for example, or compelling product content without the infrastructure needed to perform well in emerging AI-assisted discovery.

The Australian findings illustrate the differences.

Beauty and Health and Household & Personal Care emerged as the strongest-performing category groups in the Australian market, with Skin Care, Body Care, Hair Care and Makeup among the leading categories. Grocery categories such as Pantry Staples and Wine, Beer & Spirits scored lower.

Content was Australia's strongest area of improvement, with scores increasing by 10 percentage points year over year. Podean attributes the improvement to stronger product titles, richer image stacks and greater adoption of Amazon A+ Content. Yet significant gaps remain: about one in five brands has no active Amazon Brand Store.

Advertising remains a more difficult problem. The report identifies Media as Australia's weakest scoring pillar, with limited ad placements and intense category competition contributing to the gap.

The distinction between brand and category search is particularly relevant. While 84% of Australian brands in the index run sponsored advertising against brand terms, only 56% target category keywords. That leaves a potentially important discovery gap for shoppers who have not yet decided which brand to buy.

AI Readiness presents another emerging weakness. The report says product descriptions frequently fail to answer shopper questions, while almost nine in 10 brands do not use Amazon's Premium Q&A module.

That points to a broader change in ecommerce optimization. As AI systems increasingly interpret product information and answer consumer questions, marketplace content may need to become more structured, comprehensive and useful rather than simply optimized for conventional keyword searches.

For marketers, this moves Amazon optimization closer to the broader AEO and GEO conversation. Product descriptions, structured information, customer questions and contextual relevance can increasingly influence whether products are surfaced by both marketplace search and AI-mediated discovery.

The index's six-market scope also highlights another strategic issue: there is no single global Amazon playbook. Performance standards vary by market, category and competitive intensity. Brands expanding internationally therefore need localized marketplace strategies while maintaining consistent measurement across regions.

Market Landscape

Amazon remains a central battleground for brands competing across ecommerce, retail media and digital discovery. The marketplace is simultaneously becoming a media channel, a product-information environment and an increasingly important source of first-party shopping signals.

The Marketplace Index's five-pillar framework reflects that convergence. Retail establishes marketplace fundamentals; Content affects product presentation and discoverability; Customer measures consumer-facing performance; Media captures advertising activity; and AI Readiness introduces the emerging layer of machine-mediated product discovery.

The partnership between Podean and Jungle Scout is significant because it combines marketplace strategy expertise with Amazon-focused data. For brands, the practical value lies less in the ranking itself than in identifying specific gaps relative to category competitors.

Strategic Outlook

AI-powered shopping could make product information quality an increasingly important competitive variable on Amazon. Brands that treat AI readiness as an extension of ecommerce content strategy may have an advantage as shopping assistants become more capable of comparing products and answering purchase questions.

The bigger opportunity is integration. Retail, content, media and AI optimization are often managed by separate teams, but marketplace performance increasingly depends on how those functions work together.

For enterprise MarTech and ecommerce teams, that means Amazon strategy is moving toward a more unified operating model in which marketplace analytics, retail media, content management and AI visibility are measured together.

Top Insights

• Podean's Marketplace Index adds AI Readiness to Amazon benchmarking, reflecting the growing importance of AI-mediated product discovery for ecommerce brands.
• Australian brands improved content performance by 10 percentage points, but weak Brand Store and A+ adoption indicate substantial optimization opportunities.
• Only 56% of Australian brands target category keywords with sponsored ads, potentially limiting discovery among shoppers without established brand preferences.
• Nearly nine in 10 Australian brands do not use Amazon Premium Q&A, highlighting a significant opportunity as AI-assisted shopping becomes more conversational.
• The six-market benchmark shows Amazon performance varies substantially by geography, making localized marketplace strategies increasingly important for global ecommerce teams.

Get in touch with our MarTech Experts.

Glimmer is adding managed services to its enterprise creator platform, aiming to address a less visible problem in influencer marketing: the operational complexity of managing thousands of creators, contracts, payments and budgets across markets.

Glimmer announced Creator+ at Advertising Week New York as a managed service for global and regional creator campaigns. The offering combines Glimmer's software with producers who handle campaign strategy, creator sourcing, negotiations, onboarding, workflow management and brand-safety processes.

The creator economy has matured beyond the problem of simply finding influencers. For large companies, the harder challenge is increasingly the infrastructure required to manage creator relationships at scale.

Glimmer's Creator+ is designed around that problem. Rather than functioning solely as a creator discovery or campaign measurement platform, it combines software with human-managed campaign execution. The company says brands can use the service to onboard creators, execute electronic contracts, manage communications, process payments and monitor campaign spending from a single system.

That financial layer is a significant part of Glimmer's pitch to enterprise marketers. Budgets can be assigned by business unit and automatically enforced, while real-time spending data and forecasting are intended to give marketing, procurement, finance and creative teams a shared view of creator expenditure.

The platform supports payouts across 190 countries and includes 1099-K tax reporting, according to Glimmer. It can also synchronize spending information with enterprise resource planning systems including Oracle and NetSuite, potentially making creator activity easier to incorporate into existing financial workflows.

Glimmer says Creator+ has been tested privately for two years and has been used by Fortune 500 companies across technology, advertising and financial services. In one deployment, more than 3,000 creators were reportedly onboarded, contracted and paid within six weeks across multiple markets.

The offering also draws on Glimmer Select, a network of 326 executive producers. Those producers can manage creator sourcing, negotiation, campaign workflows and brand-safety checks, giving the platform a managed-service component that distinguishes it from creator software focused primarily on discovery, activation or measurement.

That distinction matters as creator marketing moves deeper into enterprise procurement. Platforms such as Salesforce and Adobe have helped large organizations centralize customer and marketing workflows, but creator campaigns often remain fragmented across agencies, specialist platforms, spreadsheets and payment systems.

Glimmer is effectively positioning Creator+ as infrastructure connecting those fragmented processes. Its creator workflows sit alongside the company's broader content-production platform, which Glimmer says includes more than 50,000 vetted production teams worldwide.

The approach could appeal to enterprises that want to expand influencer programs without building equally large internal operations teams. It also reflects a broader shift in marketing technology: creator campaigns are increasingly being treated as measurable, finance-governed marketing programs rather than isolated social-media activations.

Market Landscape

The timing reflects the rapid expansion of creator marketing. Goldman Sachs has projected that the creator economy could grow from roughly $250 billion to $480 billion by 2027.

That growth creates an operational challenge for enterprise marketers. More creators and markets mean more contracts, tax requirements, payments, approvals, usage rights and brand-safety decisions.

Most creator platforms compete around discovery, campaign management, audience intelligence or measurement. Creator+ takes a different route by combining those operational workflows with managed services and financial controls.

For enterprise marketing teams, the more important competitive question may therefore be whether creator infrastructure can become part of the broader MarTech stack. Integrations with ERP systems and existing content platforms are particularly relevant because creator spending ultimately has to reconcile with procurement and finance systems.

Strategic Outlook

Creator+ points toward a creator-marketing model in which technology handles governance and visibility while specialized teams manage execution. That hybrid approach could become increasingly attractive to multinational companies that need local creator expertise without maintaining large regional operations.

The challenge for Glimmer will be proving that its managed-service model can scale economically while maintaining consistent governance across markets. As creator budgets grow, enterprises are likely to demand the same controls they already expect from other major marketing channels: auditable spending, standardized workflows, measurable outcomes and integration with existing enterprise systems.

Top Insights

• Glimmer Creator+ combines creator software with managed services, targeting enterprises struggling with global contracting, payments, governance and campaign execution.
• Real-time creator-spend visibility connects marketing activity with procurement and finance, potentially making influencer budgets easier to govern at enterprise scale.
• Support for 190 countries and ERP integrations positions Glimmer around the operational complexity of multinational creator marketing programs.
• The managed-service model could help Fortune 5000 companies expand creator programs without building large internal teams across every regional market.
• Creator+ reflects a broader shift toward treating influencer marketing as governed enterprise infrastructure rather than an isolated social-media campaign.

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Marketers are becoming more confident in AI-generated content without becoming willing to surrender editorial control, according to new research from Ahrefs. The company's 2026 State of AI in Content Marketing Survey found that perceptions of AI content quality are improving, AI adoption has reached 93% among respondents, and productivity gains are widespread. Yet human review is becoming more important, pointing to a maturing AI-assisted content workflow rather than fully autonomous content production.

The debate over AI-generated content is shifting from whether marketers should use it to how much control humans should retain over the output.

New research from Ahrefs suggests marketers are becoming more comfortable with AI-generated content while simultaneously increasing their quality-control processes.

In Ahrefs' 2026 State of AI in Content Marketing Survey, 24% of respondents said AI-generated content is higher quality than human-written content, up from 14% in 2025. Meanwhile, the share saying human-written content is better fell from 65% to 57%.

That represents a significant change in perception, but it does not translate into unconditional trust.

Among AI users, 89% manually check AI-generated content, compared with 80% in the previous survey. Two-thirds—66%—said they trust AI-written content only when a human reviews it first, while just 2.7% said they generally trust AI content without review.

The result is a "trust but verify" model in which AI increasingly handles production work while humans retain responsibility for quality, accuracy and editorial judgment.

That distinction is becoming more important as marketers use AI for increasingly sophisticated tasks.

Ahrefs found that 93% of respondents now use AI to create or help create content, up from 87% in 2025. Among AI users, 85% said the technology allows them to produce content faster, including 49% who described the improvement as "much quicker."

The productivity impact is particularly pronounced among senior marketers. Ahrefs said 62% of upper-management respondents reported that AI made content production "much quicker," compared with the overall respondent group.

This suggests AI is becoming less of an experimental tool and more of an operating layer within content teams.

The most common applications remain relatively familiar: brainstorming, creating outlines, improving content and editing or rewriting. But adoption is moving deeper into the workflow.

Data analysis increased from 28% in 2025 to 60% in 2026, while image creation rose from 19% to 44%. Topic identification doubled from 23% to 46%, and using AI to create content from scratch increased from 44% to 55%.

That progression matters because it changes what marketers expect from AI. Instead of using generative AI primarily as a writing assistant, teams are increasingly using it for research, analysis, ideation and production.

Ahrefs' earlier research provides additional context. Its 2025 study of 879 marketers found that AI users published a median of 17 pieces of content per month compared with 12 among non-AI users—a 42% difference. It also found that 97% of companies edited or reviewed AI-generated content, while only 4% published "pure" AI-generated content.

The 2026 findings suggest that the market has moved another step forward. Human review is no longer simply a safety mechanism for early AI adoption; it is becoming part of the standard operating model for AI-assisted content.

That is particularly relevant for SEO and AI search.

Content teams are now producing material that must work across traditional search results, AI-generated answers and increasingly complex discovery environments. Ahrefs' own 2026 research has highlighted the growing importance of AI search and the changing behavior of users researching through AI systems.

In that environment, scaling content production without maintaining expertise, factual accuracy and editorial differentiation could create more content without creating more authority.

The survey's findings on AI tools also reveal a changing competitive landscape.

Claude was used by 83.2% of AI users surveyed, ahead of ChatGPT at 75.7% and Gemini at 57.9%. That represents a notable shift from Ahrefs' 2025 survey, when ChatGPT was the most commonly reported tool.

The numbers should not be interpreted as market-share figures. They represent tool usage among Ahrefs survey respondents, and respondents can use multiple AI systems. But the movement illustrates how quickly marketers' AI stacks are diversifying.

For MarTech vendors, that creates a different competitive requirement. Marketers increasingly need workflows that can accommodate multiple AI models rather than a single preferred assistant.

The bigger takeaway is that AI adoption is no longer synonymous with automation without supervision.

The emerging model looks more like AI-accelerated production combined with human-led quality assurance. AI expands the amount of work a marketing team can perform, while editors, subject-matter experts and strategists remain responsible for determining whether the output is accurate, useful and appropriate for the brand.

That could become the defining operating model for AI content marketing in 2026: not humans versus machines, but humans managing a much larger machine-assisted production system.

Market Landscape

AI has moved from experimentation into the mainstream of content operations. Ahrefs' 2026 survey found 93% of respondents using AI to create or assist with content, while 69% said the share of AI content they publish increased during the previous year. Among AI users, that figure reached 74%.

At the same time, AI-generated content is not synonymous with unedited content. Ahrefs found that 66% of marketers trust AI-written content only after human review, demonstrating that adoption and governance are developing together.

The market is consequently moving toward AI-assisted editorial systems, where AI handles increasingly large portions of research and production but human expertise remains part of the quality-control architecture.

Strategic Outlook

The most important implication of the Ahrefs research is that marketers appear to be moving beyond the simplistic question of whether AI content is "good" or "bad."

Instead, they are developing workflows around where AI is useful and where human judgment remains essential.

That distinction becomes especially important as AI takes on more analytical and strategic tasks. A model that generates an outline is relatively easy to supervise. An AI system identifying topics, interpreting performance data or producing content from scratch has greater influence over the final marketing strategy.

Human review therefore becomes more—not less—important as AI moves deeper into the workflow.

For enterprise marketing teams, the emerging requirement is likely to be AI governance embedded directly into content operations: subject-matter review, editorial standards, factual verification, brand controls, source validation and clear accountability.

The competitive advantage may ultimately come from combining AI's production speed with a company's proprietary expertise and editorial judgment.

Ahrefs' research supports that direction. AI is making content production faster, but marketers do not appear ready to outsource responsibility for what gets published.

Top Insights

• AI content is gaining credibility: 24% of surveyed marketers now rate AI content higher quality than human content, up from 14% in 2025.
• Human oversight is increasing: 89% of AI users manually review AI-generated content, showing that adoption is advancing alongside stronger governance.
• AI has become a production infrastructure layer: 93% of respondents use AI to create or assist with content, while 85% report faster production.
• AI use is becoming more sophisticated: Data analysis, image creation, topic identification and full content creation all recorded significant year-over-year increases.
• AI tool preferences are changing rapidly: Claude led surveyed AI users at 83.2%, ahead of ChatGPT and Gemini, illustrating increasingly diversified AI marketing stacks.

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