For home-service businesses, generating a lead is only the beginning. A marketing campaign can produce calls, but the real business outcome depends on whether someone answers, qualifies the inquiry, follows up and ultimately books the job. Cactus is now attempting to connect those stages with an expanded marketing-management service built around its existing AI call handling and appointment-booking technology.

The company says its new offering will combine marketing management with its existing services for answering calls, responding to web forms, following up with customers and booking appointments. The expansion is being developed through Cactus's relationship with Upfront Plumbing Drains Heating and Air, where customer conversations and appointment outcomes will inform marketing decisions.

The underlying idea is straightforward: campaign performance becomes more useful when marketers can see what happened after a lead responded.

From Lead Generation to Revenue Outcomes

Traditional marketing reporting often stops at metrics such as impressions, clicks, forms or phone calls. For a home-service contractor, those numbers can obscure important differences between inquiries.

A campaign that generates 100 calls for services a contractor does not provide is fundamentally different from one that produces 20 qualified prospects who never receive a timely response. Cactus is positioning its expanded service around that distinction.

Its AI system can analyze customer conversations and connect those interactions with appointment and follow-up information. Those signals can then inform marketing decisions, including campaign messaging, targeting and customer reactivation.

The model effectively creates a feedback loop between acquisition and operations: marketing generates an inquiry, AI handles the initial interaction, the business records what happened to the customer, and those outcomes can influence future marketing activity.

That approach is becoming more relevant as marketing technology shifts from reporting toward automated decision support. Gartner reported in 2026 that 81% of marketing leaders are evaluating AI-driven automation based on time savings, while the research organization argues that marketers need to move beyond productivity use cases toward AI-supported strategic decision-making.

Cactus Builds on Its Upfront Relationship

Cactus says its expanded marketing service builds on an existing engagement with Upfront Plumbing Drains Heating and Air.

A July 2026 Cactus case study reported that its AI system handled 878 calls for Upfront and attributed 40 invoices totaling $95,690 in invoiced work to those calls. Those figures come from Cactus's published case study and should therefore be viewed as company-reported results rather than an independent performance benchmark.

The new marketing-management relationship gives Cactus another layer of data to work with. Instead of treating phone activity and campaign activity as separate records, the company can examine which sources generated inquiries, what callers wanted, whether those inquiries matched available services and what happened after the conversation.

For contractors, that creates a potentially more useful measurement framework than call volume alone.

The Rise of Closed-Loop Marketing Automation

Cactus's strategy fits into a larger movement toward marketing systems that connect customer acquisition with downstream business outcomes.

Marketing automation platforms traditionally handle lead nurturing, segmentation, campaign execution and reporting. Increasingly, AI is being added to the decision layer, allowing systems to identify patterns, recommend actions and automate portions of customer journeys.

Gartner's September 2026 research on B2B marketing automation platforms highlights data ingestion and activation, workflow and journey orchestration, measurement, unified audience activation and generative and agentic AI among the capabilities enterprises are evaluating.

Cactus is applying a similar principle to a narrower vertical: home services. Rather than building a general-purpose marketing automation platform, it is connecting marketing activity directly to calls, appointments, estimates and customer reactivation.

That specialization could matter in industries where the path from lead to revenue is highly dependent on human conversations and fast response times.

What It Means for Home-Service Marketing Teams

The broader opportunity is to make customer conversations part of the marketing intelligence layer.

Marketing teams could use recurring questions from callers to identify gaps in campaign messaging. Service mismatches could inform targeting. Missed follow-ups could reveal operational problems rather than marketing problems. Completed jobs could provide stronger evidence about which acquisition sources generate valuable customers.

The approach also changes the role of AI. Instead of simply answering a phone or generating marketing copy, the system becomes part of a continuous acquisition-and-conversion workflow.

The challenge will be attribution accuracy. Connecting a campaign to a call is relatively straightforward; connecting that call to a booked appointment, completed job, revenue outcome and repeat customer requires consistent data across systems.

For Cactus, the expansion represents a move from AI-powered communications toward a broader marketing operating layer for home-service companies.

Market Landscape

Small businesses continue to face customer-acquisition pressure. The Federal Reserve's 2026 Report on Employer Firms identified reaching customers and growing sales as the most commonly reported operational challenge among surveyed employer firms.

That creates an opening for vertical AI platforms that combine marketing, communications and operational workflows rather than requiring smaller businesses to assemble multiple specialized tools.

Cactus's model sits between conventional marketing agencies, marketing-automation software and AI customer-service platforms. Its differentiation is the attempt to connect advertising and lead generation directly to the phone conversation and booking process.

Strategic Outlook

The next stage of AI marketing for service businesses may be less about generating more leads and more about understanding which leads become revenue.

Cactus's expansion illustrates how AI systems can move across traditionally separate functions: acquisition, communication, qualification, booking and reactivation. If those systems can reliably connect customer conversations with downstream outcomes, they could give smaller service businesses a more complete view of marketing performance without requiring a large analytics operation.

The key test will be whether the closed-loop model improves measurable business outcomes—not simply whether an AI system can handle more calls.

Top Insights

• Cactus is connecting marketing management with AI call handling, appointment booking and follow-up to create a closed-loop customer-acquisition workflow.
• Upfront's reported results show how call data can become a foundation for evaluating marketing outcomes beyond lead volume and response rates.
• The strategy reflects marketing's shift toward AI decision support, where customer interactions can inform targeting, messaging and reactivation.
• Vertical AI platforms such as Cactus are combining marketing automation with operational workflows for industries dependent on rapid lead response.
• Attribution remains critical: connecting campaigns, conversations, appointments and revenue requires reliable data across the customer journey.

Get in touch with our MarTech Experts.

Adswerve is expanding its Adobe consulting business from customer analytics into customer data infrastructure, announcing a new Adobe Real-Time Customer Data Platform (Real-Time CDP) specialization as enterprises look to connect fragmented customer data with increasingly automated marketing workflows.

The recognition formalizes Adswerve's expertise around Adobe Real-Time CDP after three years working with Adobe Customer Journey Analytics. The company says it has already deployed Real-Time CDP for several enterprise clients, positioning the specialization as an extension of its existing analytics work rather than a new capability built solely around the certification.

Adobe Real-Time CDP is designed to combine known and anonymous data from multiple enterprise sources into unified customer profiles that can be segmented and activated across channels. The platform includes identity management, data governance and audience capabilities, allowing organizations to connect information from sources such as websites, applications, CRM systems and offline interactions.

From Analytics to Activation

The strategic significance for Adswerve is the connection between measurement and execution.

Customer Journey Analytics gives marketing teams a way to analyze customer interactions and understand how people move across channels. Real-Time CDP adds the data and audience layer required to turn those insights into segments that can be activated. Adobe's architecture allows Customer Journey Analytics audiences to be published to Real-Time CDP for downstream activation and journey execution.

That creates a more connected workflow than the traditional model in which analytics, customer data and campaign activation operate in separate systems.

Adswerve CEO Tom Zawacki said the company views Real-Time CDP as a foundation for predictive marketing, particularly when combined with Customer Journey Analytics and Adobe Journey Optimizer. Adobe describes Journey Optimizer as its cross-channel journey-management platform, supporting personalized engagement across email, web, mobile and other channels.

For enterprise marketing teams, the distinction matters. Predictive marketing requires more than an analytics model identifying a likely outcome; organizations also need reliable customer identity, accessible data and an activation layer capable of putting those insights into market-facing workflows.

Adobe's CDP Push Meets the Agentic AI Shift

The announcement also comes as Adobe is increasingly connecting its customer data infrastructure to agentic AI.

Adobe introduced CX Enterprise Coworker in April 2026, describing it as an agentic layer designed to work across Real-Time CDP, Customer Journey Analytics and Journey Optimizer. The system can use enterprise data and defined goals to orchestrate customer-experience workflows while retaining organizational controls and human oversight.

Adobe's current documentation shows that Coworker can work with Real-Time CDP to create, explore and optimize audiences, while Customer Journey Analytics can be queried using natural-language prompts and used for deeper analysis.

That changes the competitive landscape for CDP services firms. The value is increasingly moving beyond implementation of a data platform toward helping enterprises operationalize data, analytics, AI and activation as a connected system.

What the Specialization Means for Enterprise Marketers

Adswerve's expansion puts it into a broader competitive market that includes Adobe's own professional-services ecosystem as well as consultancies and systems integrators supporting platforms from Salesforce, Oracle and other enterprise technology providers.

Gartner's August 2026 research describes the CDP market as increasingly divided among standalone, composable, platform and embedded architectures, arguing that CDP decisions are becoming broader architectural choices affecting data ownership and AI readiness.

That makes implementation expertise increasingly important. Enterprises adopting a CDP must resolve identity, governance, data quality, integrations and activation requirements before sophisticated personalization or agentic workflows can deliver consistent results.

Adswerve's Real-Time CDP specialization therefore reflects a wider industry transition: marketing analytics is becoming less isolated from customer data management, while CDPs are evolving from audience databases into infrastructure for AI-assisted decision-making and customer engagement.

Market Landscape

The CDP market is shifting toward platforms that combine customer data, analytics, activation and AI. Adobe's Experience Platform already brings Real-Time CDP, Customer Journey Analytics and Journey Optimizer into a connected ecosystem, while its newer CX Enterprise Coworker layer is designed to use those systems within agentic workflows.

Gartner's 2026 market research similarly describes CDP buying as an architectural decision rather than simply a software selection exercise. The major consideration for enterprises is increasingly how customer data infrastructure supports downstream marketing, analytics and AI requirements.

The result is a competitive environment in which consulting partners can differentiate through implementation experience, industry expertise and the ability to connect multiple components of an enterprise MarTech stack.

Strategic Outlook

The next phase of CDP adoption is likely to be shaped by the convergence of unified customer profiles, predictive analytics and agentic marketing workflows.

For enterprise marketing organizations, the practical challenge will be turning those capabilities into governed processes that marketers can trust. Adswerve's expansion into Real-Time CDP illustrates how services firms are responding by extending their role from analytics implementation toward the broader customer-data and activation lifecycle.

The success of that model will ultimately depend on data quality, integration depth, measurable marketing outcomes and the ability to maintain human control as more campaign decisions become AI-assisted.

Top Insights

• Adswerve's Adobe Real-Time CDP specialization expands its role from journey analytics toward customer data, activation and predictive marketing infrastructure.
• Adobe's integration of Real-Time CDP, Customer Journey Analytics and Journey Optimizer creates a connected foundation for personalized enterprise marketing.
• Adobe's CX Enterprise Coworker signals a shift toward agentic workflows that can use customer data and analytics while retaining approval controls.
• Gartner's 2026 CDP research highlights architecture, data ownership and AI readiness as increasingly important considerations for enterprise buyers.
• Marketing technology consultancies are increasingly competing on their ability to connect data, analytics, activation and AI rather than implementing isolated applications.

Get in touch with our MarTech Experts.

Small businesses are gaining access to a class of AI marketing tools designed to do more than summarize campaign data. Nas.com has opened access to its specialized AI marketing agents, targeting small and medium-sized businesses that need more marketing capacity without building large in-house teams.

The platform connects business data and marketing systems, then uses specialized agents to analyze performance, identify opportunities and prepare actions for human approval. Nas.com says deployments are tailored to each customer's systems and priorities, with engineers helping configure agent responsibilities and approval controls.

Turning Marketing Data Into Action

The timing reflects a persistent challenge for smaller companies. The U.S. Small Business Administration's Office of Advocacy says small businesses represent 99.9% of U.S. businesses, with more than 36 million small firms counted in its 2026 figures.

At the same time, marketing remains a capacity problem. The Federal Reserve Banks' 2026 Report on Employer Firms found that 57% of surveyed firms identified reaching customers and growing sales as an operational challenge in 2025, making it the most commonly reported operational issue.

Nas.com is attempting to address that gap by connecting advertising, CRM and analytics information through what it calls a Marketing Brain. The system is intended to provide a shared data layer for its agents, allowing them to examine campaign performance alongside downstream lead and customer outcomes rather than treating advertising metrics in isolation.

The company is initially organizing its offering around three areas: paid advertising, organic marketing and business data.

Paid advertising agents can examine audiences, creative, search terms, campaign performance and CRM outcomes before proposing changes to targeting, keywords, bids or creative testing. Organic-channel agents analyze brand context, audience conversations and historical content performance while supporting content ideation and reputation monitoring.

The data-focused capability is potentially the most important piece for businesses with fragmented marketing stacks. By connecting advertising, analytics and CRM information, Nas.com aims to help teams trace leads from acquisition through sales-qualified opportunities and customers.

The Emerging AI Agent Competition

Nas.com's approach arrives as larger technology companies are also moving AI agents toward business workflows. Meta recently expanded its Muse AI agent for small businesses with connections to applications including Shopify, QuickBooks, Stripe and Canva.

OpenAI has also introduced its "dots" agents for business and enterprise users, reflecting a broader industry shift toward AI systems that can pursue tasks across software rather than simply answer questions.

The distinction is important. Nas.com is not positioning its agents as general-purpose assistants. Its product is built around marketing-specific workflows, connected business context and human approval. Its current deployment model also involves engineers configuring the system rather than asking a small business to assemble an autonomous marketing stack alone.

That specialization could be useful for lean marketing teams, but it also creates a higher bar for accuracy, permissions and data integration. An agent changing an advertising bid or recommending a budget shift operates much closer to a business decision than a conventional content-generation tool.

What It Means for Small Marketing Teams

The larger implication is that AI marketing software is moving from insight generation toward operational execution.

For small businesses, that could mean having systems continuously monitor campaigns, connect marketing activity with CRM results and surface actions that would otherwise wait for a weekly report. Human approval remains an important control point, particularly when agents can influence advertising spend or customer-facing communications.

Nas.com's model also illustrates how the marketing stack may evolve. Instead of adding another dashboard, businesses may increasingly deploy specialized agents on top of existing platforms such as Google Ads, Meta and CRM systems.

The competitive question will be whether these agents can consistently produce useful decisions from messy business data—and whether those decisions translate into measurable customer acquisition and revenue outcomes.

Market Landscape

AI marketing is moving toward a more action-oriented model. Meta is extending agent capabilities into small-business workflows, while OpenAI is pushing autonomous agents toward enterprise applications.

Nas.com takes a narrower approach by combining marketing-specific agents with implementation support and a shared marketing data layer. Its website says the company works with more than 1,000 companies and has deployed more than 2,600 agents, although these are company-reported figures rather than independently audited market measurements.

For smaller businesses, the emerging choice is therefore less about whether to use AI and more about where AI should sit in the marketing workflow: as an assistant, an analytics layer, or an agent capable of proposing and executing actions.

Strategic Outlook

The next stage of AI marketing will likely be defined by how well specialized agents connect data, reasoning and execution.

For enterprise and SMB marketing leaders, Nas.com's launch highlights a broader shift toward hybrid teams in which people retain decision authority while AI systems continuously monitor channels, investigate performance and prepare actions. The technology's long-term value will depend on data quality, integration, transparency and measurable business impact—not simply the number of agents available.

Top Insights

• Nas.com is shifting AI marketing from reporting toward execution, giving small teams specialized agents for advertising, organic channels and business data.
• The platform connects CRM, analytics and advertising information, addressing a common problem for businesses operating fragmented marketing technology stacks.
• Meta and OpenAI are also expanding business-oriented AI agents, increasing competition around autonomous workflows and software integrations.
• Human approval remains central to Nas.com's model, providing an operational control layer when agents recommend changes affecting advertising and customer engagement.
• Rising customer-acquisition challenges among small businesses create a practical market for AI systems that can continuously monitor and act on marketing signals.

Get in touch with our MarTech Experts.

The latest enterprise-planning technology landscape for consumer goods is shifting from dashboards and retrospective analytics toward faster, AI-assisted decision-making. The Promotion Optimization Institute’s (POI) 2026 Consumer Goods Enterprise Planning and Retail Execution Vendor Panorama puts that shift into sharper focus, examining how vendors are applying generative AI, agentic AI, advanced analytics and connected planning to pricing, promotions and retail execution.

Now in its 11th year, the Panorama evaluates 20 technology providers across enterprise planning, Revenue Growth Management (RGM), Trade Promotion Management and Optimization (TPMx), Retail Execution, Integrated Business Planning (IBP), data management, analytics and AI. The participating vendors include Salesforce, SAP, Anaplan, o9 Solutions, Instacart, Aforza, Buynomics, UpClear, Visualfabriq and XTEL, among others.

Why the POI 2026 Panorama Matters

The report arrives as consumer goods companies face a practical problem with AI adoption: technology experimentation is moving faster than the operational systems required to turn recommendations into action.

POI's 2026 State of the Industry research, based on 146 consumer goods manufacturers, found that 61% struggle to execute promotions as planned, while 75% report constraints in retail execution. Another 81% still depend on manual or semi-manual compliance processes. At the same time, 62% have not yet adopted generative AI.

Those figures suggest that the next phase of enterprise AI in consumer goods will not simply be about generating content or summarizing reports. The bigger opportunity is connecting data, planning and execution so that teams can identify an issue, evaluate scenarios and act within the same workflow.

The Panorama also highlights a growing divide between AI capabilities that are available in production and those that remain emerging. That distinction is increasingly important as enterprises evaluate agentic AI systems that can perform multi-step tasks rather than simply return an answer to a prompt.

From Analytics to Decision Velocity

POI describes “decision velocity” as a central theme for commercial transformation. In practice, this means reducing the time between detecting a pricing, promotion, assortment or execution issue and taking an informed action.

Agentic AI could accelerate that process by coordinating analytical tasks, surfacing exceptions and recommending next steps. But the technology still depends on reliable enterprise data, clearly defined business rules and human oversight.

The competitive landscape therefore extends beyond standalone AI tools. Vendors such as Salesforce and SAP are approaching the opportunity from broad enterprise platforms, while specialized providers focus on revenue growth management, trade promotion optimization, retail execution or planning. The differentiation increasingly comes from how well those capabilities connect across the commercial organization.

The 2026 Panorama expands its scope beyond software vendors with Commercial Transformation Partner and Data & AI Partner categories, recognizing systems integrators, consultants, managed-service providers and data specialists. Baringa, BCG, Periscope by McKinsey and NTT DATA are among the participating partners.

What It Means for Enterprise Marketing and Commercial Teams

For enterprise marketing, sales and revenue teams, the significance extends beyond trade promotion software. Modern consumer businesses increasingly need a connected view of pricing, promotions, customer demand, eCommerce activity and supply-chain constraints.

POI's research found that 88% of surveyed manufacturers expect eCommerce activation to be essential within the next 12 to 24 months. That places greater pressure on organizations to connect digital commerce signals with broader commercial planning.

The result is a market moving toward integrated decision infrastructure rather than isolated applications. Enterprise teams evaluating these platforms will need to look beyond AI feature lists and assess implementation requirements, data readiness, workflow integration, adoption and measurable business outcomes.

As generative AI evolves into more autonomous systems, the strongest enterprise use cases are likely to be those embedded directly into established planning and execution processes. For consumer goods companies, AI's value may ultimately depend less on how sophisticated the model is than on how quickly the organization can turn its recommendations into coordinated action.

Market Landscape

The POI Panorama reflects a broader movement across retail and consumer goods technology. Gartner has identified AI as an emerging force across retail business processes and execution, while its research on retail execution management points to software that connects promotional agreements, execution tracking and actionable insights.

McKinsey research similarly describes agentic AI as particularly relevant to retail because the industry involves high volumes of data and recurring decisions around pricing, assortment and promotions. Its 2026 analysis suggests agentic AI could allow retail merchants to reclaim substantial time from repetitive work and redirect it toward strategic decisions.

The competitive implication is significant: enterprise planning, retail execution, analytics and AI are increasingly converging. Vendors that can connect those layers may compete for a larger share of the enterprise commercial technology stack.

Strategic Outlook

The POI 2026 Panorama indicates that AI adoption in consumer goods is moving toward a more operational phase. The next question for manufacturers is not simply whether a vendor offers generative or agentic AI, but whether that technology can operate reliably against enterprise data and within established commercial workflows.

For technology buyers, that makes production readiness, integration and organizational adoption as important as the underlying AI capabilities.

Top Insights

• POI's 2026 research shows persistent execution gaps, creating demand for platforms that connect planning, analytics and retail workflows.
• Agentic AI is moving into enterprise planning discussions as manufacturers seek faster responses to pricing, promotion and execution signals.
• The Panorama's expanded partner coverage reflects the growing importance of data governance, implementation expertise and AI enablement.
• Salesforce, SAP and specialized planning vendors illustrate a market increasingly defined by connected commercial decision infrastructure.
• Rising eCommerce importance is pushing consumer goods organizations to integrate digital activation with broader revenue and retail planning.

Get in touch with our MarTech Experts.

Managing campaigns across multiple social networks can leave creators, agencies and businesses juggling separate service providers and dashboards. NiceSMMPanel is expanding its multi-platform social media services in 2026, bringing services for more than a dozen networks into a centralized ordering and account-management platform.

NiceSMMPanel has expanded its social media marketing service catalogue to cover platforms including Instagram, TikTok, YouTube, Facebook, Telegram, X, Discord, Twitch, Spotify, LinkedIn, Reddit, Pinterest, Vimeo and Quora.

The platform is designed around a centralized dashboard where users can browse services, submit orders, manage account balances and monitor order status. Service availability varies by network and can include followers, subscribers, likes, views, reactions, shares, comments, members and livestream views.

The model addresses an operational problem faced by agencies and resellers managing campaigns across multiple social platforms: maintaining separate accounts, ordering processes and service relationships for each network.

Rather than switching between providers, users can select a social network within the NiceSMMPanel dashboard, choose a service, enter the relevant target link, specify a quantity and submit an order.

The company says standard services generally operate using publicly accessible social media links rather than requiring customers to provide account passwords or login credentials. Requirements differ depending on the service and whether the target is an account, page, group, post, video or other piece of content.

That distinction is important because different social networks structure engagement around different objects. An Instagram campaign might target a profile, Reel, Story or post, while a YouTube service may be associated with a channel or video. Facebook services can similarly target pages, profiles, groups or individual pieces of content.

NiceSMMPanel organizes these options according to the requirements of each network, giving users service-specific information before an order is submitted.

The platform also targets agencies and social media resellers through API functionality. According to the company, supported API integrations allow external systems to retrieve available services, submit orders and monitor order information.

For agencies handling multiple campaigns, an API can turn a manually operated ordering process into an integrated workflow. Resellers can similarly connect supported services to their own systems rather than managing every transaction directly through a dashboard.

The broader social media marketing technology market, however, is increasingly focused on genuine audience development, content performance, creator analytics and community management. Platforms such as Sprout Social, Hootsuite, Later and Buffer emphasize publishing, analytics, engagement management and workflow collaboration rather than supplying engagement services.

That creates an important distinction for businesses evaluating platforms such as NiceSMMPanel. Purchased or delivered engagement metrics are not equivalent to organic audience growth, customer engagement or business outcomes. Social networks also maintain policies governing artificial or inauthentic engagement, making platform rules and campaign objectives important considerations when using third-party services.

For agencies and resellers, the centralized model can nevertheless address a practical workflow requirement: consolidating access to different social media services and automating repetitive ordering tasks.

The expansion reflects a fragmented social media ecosystem in which businesses increasingly operate across multiple networks simultaneously. Whether centralized service procurement ultimately improves marketing performance depends on how those services are incorporated into broader content, audience and measurement strategies.

Market Landscape

Social media management has evolved into several distinct technology categories, including publishing platforms, social listening tools, creator-management systems, analytics platforms and marketing automation services.

NiceSMMPanel operates in a different segment focused on accessing social media marketing services through a centralized ordering infrastructure.

The distinction matters for enterprise marketers. Social publishing and analytics platforms generally measure activity and audience response, while third-party service marketplaces can provide specific account or content-level services. Businesses therefore need to evaluate each service against network policies, campaign objectives and the quality of the resulting audience activity.

Strategic Outlook

The expansion of multi-platform service catalogues reflects the operational complexity of managing social campaigns across an increasingly fragmented network landscape.

For agencies and resellers, centralized dashboards and API access can reduce repetitive administrative work and simplify service management. The longer-term marketing value, however, depends on whether activity generated through these services translates into authentic audience engagement, qualified traffic and measurable business outcomes.

As social platforms continue tightening controls around artificial engagement, marketers will also need to distinguish between numerical activity and genuine community or customer growth.

Top Insights

• NiceSMMPanel has expanded its centralized service catalogue across more than a dozen social networks, including Instagram, TikTok, YouTube, Facebook and LinkedIn.
• The platform provides dashboard-based ordering, account-balance management and order tracking for different types of social media services.
• API functionality is designed for agencies and resellers that need to automate service retrieval, order submission and order monitoring.
• Service requirements vary by network and target type, covering profiles, channels, pages, posts, videos, groups and other social media destinations.
• Businesses should distinguish purchased engagement metrics from organic audience growth and evaluate third-party services against individual platform policies.

Get in touch with our MarTech Experts.

AI shopping assistants are moving beyond customer-service chatbots and becoming part of the commerce interface itself. A new report from Lyro by Tidio examines evidence from Amazon, Walmart, Etsy, Salesforce and other retailers, arguing that the next challenge for ecommerce teams is not proving that shoppers interact with AI, but determining whether those interactions create incremental commercial value.

The report, AI Shopping Assistants for E-Commerce, was authored by Bart Turczynski, Head of Growth at Tidio, and draws on 39 published sources including retailer disclosures, industry research, market data and controlled experiments.

Its central argument is that retailers need to distinguish between sales associated with AI assistance and sales that occurred because of the intervention.

Amazon provides one of the largest examples cited in the report. The company said its AI shopping assistant was used by more than 300 million customers in 2025 and contributed nearly $12 billion in incremental annualized sales. Amazon also reported that customers using the assistant were 60% more likely to complete a purchase.

Those figures provide different types of evidence, however. The $12 billion figure is described by Amazon as incremental, while the purchase-completion figure represents an observed relationship between assistant usage and customer behavior. The report notes that Amazon's public earnings disclosure does not provide the experimental methodology behind its incremental-sales estimate.

That distinction is increasingly important as AI becomes embedded throughout ecommerce journeys.

An assistant can receive credit for an order even when a shopper would have purchased the same product through search, navigation or recommendations without AI assistance. In that scenario, attributed AI revenue increases without necessarily increasing total retailer revenue.

An Etsy experiment highlighted in the report illustrates the problem. A recommendation change increased recommendation clicks by 28.3%, but conversion increased by a statistically insignificant 0.22%, while gross merchandise value declined by a statistically insignificant 0.25%. Organic-search clicks simultaneously declined by a statistically significant 1.4%.

Etsy's causal mediation analysis found that the recommendation change itself produced a 0.50% conversion increase, while the resulting reduction in search activity offset 0.28 percentage points of that gain.

The findings suggest that engagement metrics can tell only part of the story. For ecommerce teams, the more meaningful measurement is whether an AI intervention creates additional purchases after accounting for behavior that would otherwise have occurred through existing channels.

That does not diminish the commercial signals emerging from retailers.

Walmart reports that shoppers using its Sparky assistant have an average order value approximately 35% higher than shoppers who do not use it. Magalu has reported conversion three times higher in its WhatsApp shopping experience than in its app. Salesforce has estimated that AI and agents influenced $262 billion in global online holiday sales in 2025, based on data covering more than 1.5 billion shoppers.

The report treats these figures as commercially significant but distinguishes them from randomized measurements of causal lift.

For smaller retailers, the immediate business case may also come from operational efficiency. UK mattress retailer MattressNextDay, a Tidio customer, reports that its assistant resolves 73% of customer conversations without human intervention and returns more than 400 hours per month to its support team.

Another Tidio customer, UK flooring retailer Renovate Direct, reports that its resolution rate increased from 42% to 69% after introducing intent-based ticket routing. Both figures are operator-reported rather than results from controlled experiments.

The report proposes proactive AI assistance as a particularly practical area for experimentation. Retailers can expose one group of comparable shoppers to an AI prompt while withholding it from another group, creating a holdout test without replacing existing search or recommendation infrastructure.

For marketers and ecommerce leaders, the implication is straightforward: AI shopping assistants should increasingly be evaluated as commerce interventions, not simply engagement tools.

Market Landscape

AI shopping is expanding across major ecommerce ecosystems, with retailers experimenting with conversational product discovery, personalized recommendations, shopping agents and AI-assisted customer service.

Amazon, Walmart and other large retailers have the resources to integrate AI across discovery and purchasing journeys. Smaller merchants face a different challenge: determining whether an assistant generates enough incremental revenue or operational savings to justify implementation costs.

This makes measurement methodology increasingly important. Click-through rates, conversation volume and AI-attributed revenue can demonstrate usage, but they do not necessarily establish causal business impact.

Strategic Outlook

The next phase of AI commerce will likely shift the conversation from “How many shoppers used the assistant?” to “What happened because the assistant was present?”

Holdout experiments provide one way to answer that question. Retailers can measure eligible sessions, exposure rates, incremental order-rate changes and average order value, then incorporate margin, returns and operating costs.

That approach turns AI shopping from an emerging technology experiment into a measurable commercial channel.

The Lyro report's broader message is that AI assistants have already become part of the ecommerce interface for major retailers. The unresolved question is how much of their reported commercial activity represents genuinely incremental value.

Top Insights

• Lyro's report argues that AI shopping assistants should be measured by incremental commercial contribution rather than attributed revenue or engagement alone.
• Amazon reports nearly $12 billion in incremental annualized sales from its shopping assistant, although the public disclosure does not detail the methodology behind the estimate.
• An Etsy experiment demonstrates how recommendation gains can be partly offset when AI-driven interactions displace existing organic-search behavior.
• Walmart, Magalu and Salesforce have reported significant commercial activity associated with AI-assisted shopping, although these measurements differ from randomized causal estimates.
• Proactive AI prompts can create practical holdout experiments, allowing retailers to compare comparable shopper groups and measure incremental order and revenue effects.

Get in touch with our MarTech Experts.

For sales and marketing teams expanding into new markets, knowing which businesses operate in a particular location can be as important as knowing who their decision-makers are. Structured local business data is increasingly becoming an input for lead generation, market research, location intelligence and customer acquisition, giving organizations a way to replace fragmented manual research with searchable datasets.

Local business information has traditionally been used for basic directory searches. Today, the same data is becoming part of broader sales and marketing workflows as organizations use business intelligence to identify prospects, segment markets and evaluate regional opportunities.

Business names, categories, locations, websites, operating hours, ratings and other publicly available information can provide a starting point for identifying companies that fit a defined ideal customer profile.

The challenge is scale.

A sales team entering a new market may need to identify thousands of businesses across multiple cities or industries. Researching those companies individually can consume substantial time before a salesperson has even qualified a prospect.

That has created demand for tools capable of collecting and organizing publicly available business information into structured datasets. Instead of manually reviewing individual listings, teams can search by geographic area, business category or keyword and use the resulting data for prospecting and analysis.

The development also fits into a wider convergence between business intelligence, CRM platforms, sales automation and AI. Structured data gives automated systems something consistent to analyze, while sales representatives can use prospect information to tailor outreach rather than starting with generic market lists.

For marketing agencies, local business datasets can serve a different purpose. Agencies can identify businesses within particular industries or geographic markets that may need services such as local SEO, digital advertising, website development or reputation management.

Location intelligence extends the use case beyond customer acquisition. Retailers can examine concentrations of businesses and commercial activity when evaluating potential locations. Franchise organizations can compare markets before expansion. Real estate professionals can study business density, while consultants can use local competitive information as an input into market assessments.

This makes local business data less of a simple prospecting resource and more of an operational intelligence layer.

Platforms such as Outscraper's Google Maps Scraper illustrate this shift. The service allows organizations to collect publicly available business information from Google Maps and organize it into structured datasets for use in prospecting, market research and competitive analysis.

The practical advantage is the ability to perform research at a larger scale. A company evaluating several cities, for example, can build datasets around particular industries and locations rather than manually opening individual listings and recording information.

Those datasets can then be analyzed alongside existing CRM records, marketing databases or internal business intelligence systems. The resulting workflow can help teams identify market gaps, discover potential prospects and compare local business activity.

There are, however, important considerations around data quality and responsible use. Business listings can change, information can become outdated and different sources may contain inconsistent records. Organizations using publicly available business information also need to consider applicable privacy, platform and data-use requirements before incorporating it into automated outreach or commercial workflows.

That makes data freshness, provenance and governance increasingly important alongside the volume of information collected.

The larger trend is clear: as sales and marketing organizations adopt more automation, the value of structured inputs increases. AI systems and automated workflows can accelerate analysis, but they still depend on the quality and relevance of the underlying data.

Local business information is therefore evolving from a directory-style resource into a component of modern market intelligence. For organizations operating across multiple regions, structured local data can shorten research cycles and provide a more systematic way to identify opportunities.

Market Landscape

The market for business intelligence and prospecting data spans B2B databases, CRM platforms, location intelligence, local SEO tools and data-enrichment services.

Providers increasingly combine geographic information with business attributes that allow sales and marketing teams to create more specific audience segments. CRM and sales-engagement platforms can then incorporate those records into broader acquisition workflows.

The emergence of AI adds another layer. AI can classify businesses, identify patterns across large datasets and help researchers turn raw business information into market insights. But automation also increases the importance of data accuracy because errors can propagate quickly across downstream systems.

Strategic Outlook

Local business data is becoming more valuable as organizations move toward automated, location-aware customer acquisition.

The strategic opportunity is not simply collecting more business records. It is connecting reliable local information with CRM data, market intelligence and marketing workflows so teams can identify opportunities faster and make decisions using a consistent dataset.

For vendors in this space, differentiation will increasingly depend on data freshness, geographic coverage, enrichment capabilities, integration options and transparent data practices rather than dataset size alone.

Top Insights

• Structured local business data is becoming an input for prospecting, market research, location intelligence and customer acquisition across multiple industries.
• Automation reduces the manual effort involved in researching businesses across cities, industries and geographic markets at scale.
• CRM, AI and sales-automation platforms increase the value of organized business datasets by turning raw records into actionable prospect and market segments.
• Retailers, franchise operators, consultants and real estate professionals can use location intelligence to evaluate regional competition and expansion opportunities.
• Data freshness, accuracy, provenance and responsible use remain critical as organizations incorporate publicly available business information into automated workflows.

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As enterprises move more customer data into cloud data platforms for analytics, personalization and AI, consent and usage policies can become a separate operational layer that slows activation. Transcend is addressing that gap with a Snowflake Native App that brings consent and preference data directly into a customer's Snowflake environment and turns those policies into controls applied to data at query time.

Transcend has launched Transcend Consent & Preferences Management as a Snowflake Native App on the Snowflake Marketplace, giving organizations a way to connect consent and preference information directly with customer data stored in their own Snowflake accounts.

The integration targets a problem that becomes more complicated as enterprises centralize first-party data. Knowing which customers have opted into a particular use is different from knowing which users or systems can technically access a dataset.

Transcend's application is designed to bridge those two layers. The company says consent and preference information can flow into Snowflake and be translated into governance controls, including row access policies and secure views, which Snowflake can then enforce when data is queried.

That architecture is significant for marketing and data teams because consent can directly affect whether customer information is usable for personalization, analytics and AI applications. Instead of maintaining a separate compliance pipeline that sits outside the data warehouse, organizations can apply policy within the environment where customer data is already being processed.

The product also introduces a coverage view intended to show data stewards and business users which datasets are governed and which still have policy gaps. Transcend says users can identify those gaps without opening a SQL editor or submitting a request to another technical team.

For enterprise marketing organizations, the distinction is increasingly important. First-party data has become a central input for customer segmentation, personalization and AI-driven experiences, but activation requires organizations to account for consent, preferences and permitted uses. A dataset can be technically available while still being inappropriate for a particular marketing or analytical purpose.

Snowflake provides the underlying data platform and governance capabilities, while Transcend supplies the consent and preference layer. According to Snowflake Vice President of Global ISV & Enterprise Technology Partnerships Rodrigo Rocha, the integration brings policy enforcement and data permissions into a single flow inside a customer's Snowflake account.

As a Snowflake Native App, Transcend can be installed and operated directly within a customer's Snowflake environment. This architecture is designed to reduce the need to move data between separate systems and can simplify deployment for organizations already using Snowflake as a central data platform.

TextNow is among the companies using the integration. Andrew Cotton, Head of Platform and Security Engineering at TextNow, said the combination reduced a task that would normally take weeks to a matter of hours. That is a customer-reported implementation experience rather than an independently verified benchmark.

The launch also extends Transcend's broader data-policy strategy. The company recently introduced Policy Engine, which it describes as runtime data-decision infrastructure for enforcing data-use policies.

The competitive landscape includes privacy management, consent management and data-governance platforms that increasingly need to integrate with enterprise data warehouses and AI infrastructure. The key shift is from simply documenting consent toward making consent an operational control that can influence whether data is actually used.

For marketing teams, that could make governed data activation less of a separate compliance exercise and more of an embedded component of the enterprise MarTech stack.

Market Landscape

The growth of first-party data strategies has increased the importance of connecting privacy governance with data activation.

Traditional consent-management systems often operate around collection preferences and regulatory compliance, while data platforms control storage, access and processing. Integrating these layers can reduce the gap between what customers have permitted and what downstream systems can technically use.

Snowflake's Native App model also reflects the broader trend toward applications running within enterprise data platforms rather than requiring organizations to create separate data pipelines.

For marketers, the result is a shift toward data infrastructure where governance can become part of segmentation, analytics, personalization and AI workflows.

Strategic Outlook

The next stage of enterprise data governance is increasingly about usage policy rather than access alone.

A user may have permission to access a dataset while still being restricted from using particular customer attributes for advertising, personalization or AI. Embedding those policies into the data environment can help organizations operationalize that distinction.

Transcend's Snowflake integration points toward a model in which consent becomes machine-readable policy that can influence data usage at runtime. As AI applications consume larger volumes of enterprise customer data, that type of control could become increasingly important to marketing and data teams.

Top Insights

• Transcend's Snowflake Native App connects consent and preference data directly to customer data stored inside an organization's Snowflake environment.
• The integration is designed to turn consent information into data-use controls, including row access policies and secure views enforced during queries.
• Business and data teams can reportedly identify governance gaps through a coverage view without relying on custom compliance pipelines or SQL workflows.
• TextNow says the integration reduced a normally weeks-long task to hours, although the result is a customer-reported implementation experience.
• The launch reflects a broader shift toward embedding privacy and consent controls directly into enterprise data and AI activation infrastructure.

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