Deloitte has been recognised as a Leader in the 2026 Gartner Magic Quadrant for Global Digital Marketing Agencies and was recognised across all seven use cases in Gartner’s accompanying Critical Capabilities report, according to the company’s announcement. The assessment places a spotlight on the growing convergence of marketing strategy, creative services, customer experience engineering, data and artificial intelligence.

Deloitte attributes its position to its “Growth Agency” model, which combines brand strategy, marketing services and customer experience capabilities with advanced AI and agentic technologies. Its approach reflects a wider shift in enterprise marketing: agencies are increasingly expected to connect campaign execution with digital commerce, content operations, performance measurement and the technology systems that support customer journeys.

Gartner’s recognition is an analyst assessment, not an endorsement or a guarantee of client outcomes. For marketing leaders, the practical question is how effectively an agency can translate integrated capabilities into measurable improvements in customer experience, operating efficiency and profitable growth.

Seven Use Cases Covered in Gartner’s Assessment

The accompanying Critical Capabilities report examines how agencies serve different marketing needs, rather than relying solely on overall market positioning. Deloitte was recognised across all seven use cases:

• Global Brand/Marketing Campaign Execution: Coordinating brand strategy and campaigns across markets.

• Omnichannel Content Generation: Producing and managing content across customer touchpoints.

• AI-Driven Marketing Innovation: Applying AI to marketing processes, services and operating models.

• Data-Driven Performance Marketing: Using data and measurement to optimise campaign performance.

• B2B Marketing: Supporting business-to-business marketing strategy and execution.

• Digital Commerce and CX Overhaul: Modernising digital buying journeys and customer experiences.

• Website and Digital Product Design: Designing digital properties and products around user needs.

Deloitte says its Converge Marketing OS assets and tools—including CreativEdge, ConvergeCONSUMER, MSuite, Navigate and AI Advantage for Marketing—support its approach. The company positions these capabilities as part of a broader model connecting creative work, technology, data and operating processes. The announcement does not provide independent comparative performance figures for these tools.

Market Landscape

Gartner’s 2026 Magic Quadrant evaluates 10 global digital marketing agencies, including Deloitte Digital, Accenture Song, dentsu, DEPT, Digitas, GUT Network, Havas CX, Monks, Omnicom Precision Marketing and VML. The report’s stated purpose is to help chief marketing officers assess agency providers as AI reshapes the market and organisations scrutinise agency spending and returns.

The market is moving beyond traditional campaign production toward integrated services that combine brand strategy, creative development, marketing technology, customer experience and commerce. Agencies are increasingly expected to help clients coordinate these disciplines while demonstrating how their work contributes to business growth.

Deloitte’s recognition across all seven use cases suggests broad coverage within Gartner’s evaluation framework. It does not mean the company received the highest score in every use case, nor that it will be the right partner for every organisation. Buyers should review the detailed capabilities and cautions in Gartner’s research against their specific requirements.

Strategic Outlook

The recognition highlights a wider change in agency selection. As AI tools make content creation and campaign execution faster, differentiation may increasingly depend on how effectively an agency combines those tools with proprietary data, enterprise technology, governance and operational expertise.

Deloitte’s Growth Agency model aims to connect strategy and execution across campaigns, content supply chains, digital commerce and omnichannel experiences. Its longer-term test will be whether this integrated approach produces measurable gains in campaign effectiveness, speed to market, customer experience and profitable growth.

For marketing leaders, the assessment offers a starting point for agency evaluation—not a substitute for it. Organisations should establish clear outcome metrics, scrutinise AI governance and data practices, and determine how proposed capabilities fit their existing marketing technology stack before committing to a partner.

Top Insights

• Deloitte earns Leader recognition: Gartner’s 2026 assessment places Deloitte Digital among the Leaders in global digital marketing agencies, giving CMOs a reference point for agency evaluation.

• Coverage spans seven use cases: Deloitte was recognised across campaign execution, omnichannel content, AI innovation, performance marketing, B2B, commerce and digital product design.

• Integrated services are gaining importance: Deloitte’s Growth Agency model combines creative, technology and customer experience capabilities, reflecting demand for coordinated marketing operations.

• AI is changing agency expectations: The assessment comes as marketing leaders look beyond content generation toward AI-enabled execution, data integration and measurable business outcomes.

• Buyers still need independent validation: Gartner’s designation is not an endorsement; organisations should compare detailed capabilities, governance, costs and proven results against their own requirements.v

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Webflow has launched a connected app for Google Gemini, allowing teams to build web pages, manage content, analyse performance and audit websites through AI-assisted workflows. The integration connects Gemini to Webflow through the platform’s Model Context Protocol (MCP) server, extending access to Webflow capabilities from within Google’s AI assistant.

The announcement expands Webflow’s integrations with AI tools and gives marketing teams another way to manage digital experiences without switching between applications. Users can ask Gemini to create responsive pages, update content in Webflow’s content management system (CMS), review analytics, evaluate A/B tests and investigate deployment issues, subject to their existing Webflow plans and permissions.

Available from launch at no additional installation cost, the connector does not currently consume Webflow AI credits. The companies say existing roles, permissions and activity-log attribution remain in place, making governance an important part of the integration as AI agents gain access to more website operations.

What the Gemini connector can do

Webflow’s connector covers several stages of the digital experience lifecycle, combining content creation with analytics and site maintenance.

• Build pages and interactions: Generate responsive pages and reusable components based on existing brand styles, then add interactive elements such as hover, scroll and click effects.

• Manage CMS content: Create and update content collections, organise records and work with structured fields through natural-language requests.

• Analyse website performance: Query Webflow Analyze for page traffic, engagement and goal data, helping teams investigate changes in performance.

• Review experiments: Access Webflow Optimize experiment results to identify leading variations and check statistical significance. The current integration reads experiment data; it does not replace the full experiment-management workflow.

• Measure AI visibility: Use AEO Analytics to examine tracked prompts, AI crawler activity and how answer engines respond to queries about a brand.

• Audit and troubleshoot: Review SEO, accessibility, broken links and content quality, and manage Webflow Cloud deployments through supported workflows.

The integration is built on Webflow’s Model Context Protocol server, which exposes supported platform functions to compatible AI tools. Webflow says its MCP integrations respect existing user roles and permissions. Its announcement also describes activity-log attribution, allowing organisations to retain oversight of AI-assisted work.

Availability caveat: Webflow’s launch documentation says the Gemini connector is available in English in the United States and currently connects using a personal Google Account. Support for Gemini Enterprise is in development. Some features also require specific Webflow plans or add-ons.

Market Landscape

The integration reflects a broader move from AI assistants that primarily answer questions toward agents that can interact with business software and carry out tasks. For marketing teams, that shift brings website management, content operations and performance analysis closer together in a conversational interface.

Webflow already supports MCP-based access from tools including Claude and ChatGPT. Adding Gemini broadens the range of assistants teams can use, while the ability to query analytics, experiment results and AI visibility data creates a more connected workflow.

A notable part of the announcement is the inclusion of Answer Engine Optimization (AEO) Analytics. As brands assess how their content appears in AI-generated answers, marketers increasingly need to understand which pages AI crawlers request and how answer engines respond to tracked prompts. Webflow’s tools bring some of those signals into the same workflow used to manage a website.

However, crawler activity is not proof that a page was cited in an AI-generated answer, and a change in AI visibility does not automatically translate into traffic or revenue. Teams need to interpret these metrics alongside referral visits, conversions and other business indicators.

Strategic Outlook

The connector could reduce the operational distance between identifying a website problem and acting on it. A marketer might use Gemini to investigate a drop in traffic, compare the result with active experiments, review AI crawler activity and then request a content or page update.

The value will depend on how well the agent understands the site, whether its recommendations are accurate and how much human review is required. Organisations should also test permission boundaries, publishing controls and rollback procedures before allowing AI agents to make consequential changes.

For Webflow, the integration strengthens its position as a platform where teams can build and manage digital experiences through both visual tools and AI assistants. For marketing leaders, the more important question is whether these workflows improve publishing speed, site quality and conversion performance without introducing new governance risks.

Top Insights

• Gemini gains direct Webflow access: The connector lets marketing teams build pages, update CMS content and analyse site performance through an existing AI interface.

• Analytics joins content operations: Access to traffic, experiment results and AEO Analytics helps teams investigate website performance and identify potential improvements within one workflow.

• MCP connects AI to software: Webflow’s Model Context Protocol server exposes supported platform capabilities to compatible assistants, extending AI beyond conversational answers into operational tasks.

• Governance remains part of the integration: Existing roles and permissions continue to apply, giving organisations a basis for controlling which site data and actions users can access.

• Availability and plan requirements matter: The initial Gemini connector is limited to English in the US with personal Google Accounts, while feature access depends on Webflow entitlements.

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Syndigo has been named a Leader in Gartner’s inaugural Magic Quadrant for Product Information Management (PIM) Solutions, according to an announcement from the product experience and data management provider. The October 12, 2026, report evaluates vendors in a market focused on helping organisations manage, enrich and distribute accurate product information across commerce channels, retail systems and other business applications.

The recognition comes as product data becomes increasingly important to digital commerce and AI-powered product discovery. Brands and retailers need consistent information about products, attributes, specifications and regulatory requirements to support online listings, retail partnerships and emerging AI shopping experiences. Syndigo says its Product Experience Cloud is designed to address these needs, with recent additions including its Synapse AI platform and AI-assisted product classification.

Gartner’s recognition is an analyst assessment, not an endorsement or a guarantee of business results. The announcement does not disclose comparative scores or establish that Syndigo is the best fit for every organisation evaluating PIM software.

Why product information management is gaining strategic importance

Product information management software helps organisations create and maintain an approved, consistent version of product content for use across sales and commerce channels. That can include product descriptions, specifications, images, identifiers, classifications and compliance details.

Gartner’s new market assessment identifies growing demand for structured, enriched product data across digital channels and AI-driven commerce. Its report describes how product content can be fragmented across master data management (MDM), enterprise resource planning (ERP), product lifecycle management (PLM), supplier systems and other repositories.

PIM systems address that fragmentation by giving product, commerce and marketing teams a controlled way to manage and distribute information. For brands selling through multiple retailers and marketplaces, consistent data can help reduce listing errors, speed up product launches and maintain accurate representations across channels.

AI adds another dimension. Product discovery systems and shopping assistants need structured attributes and dependable product descriptions to interpret queries and compare options. Incomplete or inconsistent information can make products harder to discover or recommend, even when they meet a buyer’s requirements.

Syndigo’s AI direction

Syndigo’s Product Experience Cloud combines product information management with capabilities for managing and distributing product content. The company says recent additions include its Synapse AI platform and AI-assisted product classification, with a roadmap that includes multistep AI agents and a conversational shopping assistant.

Automated classification can help assign products to appropriate categories and organise attributes, potentially reducing manual work when businesses manage large catalogues. AI agents could eventually coordinate more complex tasks across product data workflows, while conversational shopping tools could use product information to help customers discover suitable items.

However, a roadmap is not the same as a generally available capability. Organisations evaluating these features should establish which functions are currently available, how human review works, and how the system handles inaccurate, incomplete or conflicting source data.

The Gartner recognition provides a new reference point for buyers assessing the PIM market, but it should be considered alongside product demonstrations, integration requirements, implementation costs, governance capabilities and customer references.

Market Landscape

Gartner’s inaugural Magic Quadrant for Product Information Management Solutions includes vendors such as Akeneo, Salsify, Stibo Systems, Inriver, Pimcore and Syndigo, among others. The report evaluates providers using two broad dimensions: Ability to Execute and Completeness of Vision.

The market includes products with different emphases, from product content management and syndication to broader product experience and data management. Buyers should compare vendors against their actual needs, including catalogue complexity, retail distribution requirements, regulatory obligations, globalisation and the maturity of their existing data architecture.

Syndigo’s Leader placement is notable because the category is being formally evaluated by Gartner for the first time. It does not, by itself, establish that Syndigo outperforms every competitor for all use cases. Gartner explicitly states that its research represents analyst opinions and is not an endorsement of any vendor.

Strategic Outlook

The growing role of AI in commerce makes product data quality a strategic concern rather than a back-office task. As brands prepare information for search engines, marketplaces, retail systems and AI assistants, PIM platforms may become increasingly important infrastructure for product discovery and digital shelf management.

Syndigo’s challenge will be to demonstrate that its platform can deliver measurable improvements in data accuracy, speed to market and product discoverability while managing the complexity of enterprise integrations. Its AI capabilities will also need to show how automation improves workflows without compromising data quality or governance.

For buyers, the practical takeaway is to assess PIM as part of a wider commerce data strategy. Gartner’s evaluation can help build a shortlist, but final decisions should depend on fit, implementation evidence and independently validated outcomes.

Top Insights

• Gartner formally recognises the PIM market: Syndigo’s Leader placement gives buyers a new analyst reference point for evaluating product information management vendors and their enterprise capabilities.

• Product data supports AI discovery: Structured and accurate product content helps search tools, commerce platforms and AI assistants interpret products and match them with buyer needs.

• Syndigo is extending AI workflows: Synapse AI and AI-assisted classification expand the company’s automation focus, while multistep agents and conversational shopping remain part of its stated roadmap.

• Data fragmentation remains a core problem: PIM platforms help consolidate product content scattered across ERP, MDM, PLM and supplier systems, improving consistency across commerce channels.

• Recognition is not a purchasing verdict: Buyers should assess vendor fit, integration demands, governance, implementation costs and proven outcomes rather than rely on a Magic Quadrant position alone.

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Aurora Mobile’s enterprise AI platform, GPTBots.ai, is partnering with Japanese enterprise IT services company AI Storm and its subsidiary Nihon Telesystem (NTS) to test AI-assisted contact center operations, using 38 years of frontline experience to improve agent training, real-time customer support and call quality reviews.

The initiative focuses on a persistent challenge for customer experience (CX) teams: experienced agents often rely on judgment and problem-solving techniques that are difficult to transfer consistently to new employees. By turning that operational knowledge into reusable AI-supported workflows, the companies aim to improve service consistency while keeping human agents responsible for customer interactions.

The collaboration is beginning with live operational validation at NTS. The companies have not yet disclosed quantified results, deployment scale or a timeline for broader rollout, so its impact on productivity and customer satisfaction remains to be demonstrated.

How GPTBots.ai will be used at NTS

The deployment centres on a continuous cycle that connects agent training, live customer interactions and post-call evaluation. Rather than treating AI as a standalone chatbot, the companies plan to embed it in the operational processes contact center staff already use.

AI role-play for agent training. GPTBots.ai can simulate different customer types, inquiry scenarios and emotional tones, allowing new agents to practise responses and receive feedback. The goal is to make training based on veteran employees’ experience more repeatable and easier to refine.

Real-time assistance during calls. During customer conversations, the platform is intended to surface relevant business information and response suggestions using the customer’s question, conversation context and corporate knowledge. This could help agents locate accurate answers faster and reduce differences in service quality between experienced and less-experienced staff.

Automated post-call review. After calls, AI can summarise conversations, extract key information and support service-quality analysis. This may reduce repetitive documentation work while giving managers more structured information for coaching and operational improvement.

The three stages are designed to reinforce one another: training prepares agents, live assistance applies organisational knowledge in customer interactions, and post-call review captures information that can inform subsequent training and quality management.

Why the partnership matters

For NTS, which has operated contact centers for more than 38 years, the challenge is preserving institutional knowledge as employees change roles, new agents join and customer needs evolve. Converting experienced agents’ approaches into documented knowledge and AI-supported workflows could make that expertise more accessible across teams.

The arrangement also builds on an earlier partnership between Aurora Mobile and AI Storm announced in June 2026. That collaboration established a broader effort to test Aurora Mobile’s EngageLab and GPTBots.ai products within NTS’s business operations in Japan. The latest announcement details a more specific application focused on contact center training, live-call assistance and quality evaluation.

The commercial significance will depend on implementation results. AI suggestions must be accurate, relevant to the customer’s situation and consistent with company policies. Call summaries and quality evaluations also need review processes that account for transcription errors, missing context and potentially misleading AI-generated assessments.

Neither company has yet published independent performance data showing reductions in training time, average handling time or operating costs. The project should therefore be viewed as an operational validation effort, not proof that the technology has already delivered measurable improvements.

Strategic Outlook

The NTS deployment illustrates a human-led approach to enterprise AI: automate repetitive tasks and make organisational knowledge easier to access, while retaining people in the customer interaction and decision-making process.

For contact center leaders, the most useful measures of success will include new-agent proficiency, response accuracy, escalation rates, customer satisfaction, after-call work and quality-assurance consistency. Businesses will also need clear policies governing customer data, call recording, access to knowledge sources and human review of AI-generated recommendations.

If the initiative produces measurable improvements, it could provide a model for applying AI to other service operations where experience is difficult to transfer and consistent responses matter. For now, the partnership gives GPTBots.ai a live Japanese contact center environment in which to test that proposition.

Top Insights

• Veteran knowledge becomes reusable: GPTBots.ai aims to capture NTS’s 38 years of contact center experience in AI-supported training and workflows, helping service leaders spread expertise across teams.

• AI supports live agents: Real-time information and response cues could help contact center staff answer inquiries more consistently, provided suggestions remain accurate and relevant to each conversation.

• Training and quality connect: AI role-play and post-call analysis create a feedback loop, potentially helping managers identify recurring knowledge gaps and refine agent coaching.

• Japan is the deployment market: AI Storm’s local business presence and NTS’s operating experience provide a setting to validate enterprise AI within Japanese customer service operations.

• Outcomes remain unproven: No quantified productivity or customer satisfaction results have been announced, so buyers should look for evidence on accuracy, efficiency and service quality before scaling.

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Adstra is expanding access to its Conexa identity resolution platform through Snowflake Marketplace, giving enterprise teams a way to assess, resolve and enrich customer identities without moving data outside their Snowflake environment. The offering combines a free Identity Health Assessment with a Conexa Native App available through Snowflake Marketplace Capacity Drawdown (MCD), allowing eligible customers to apply existing Snowflake commitments toward the service.

The announcement targets a common challenge in customer data management: enterprises may have consolidated large volumes of customer information but still struggle to match records, identify households and build profiles that marketing teams can use. By combining identity assessment, enrichment and procurement within Snowflake, Adstra aims to reduce the steps between evaluating data quality and activating customer data.

How Conexa’s Snowflake integration works

The offering is structured around three stages, beginning with measurement rather than a full platform commitment.

1. Assess identity data. The free Conexa Identity Health Assessment evaluates match rates, identifies unique individuals and households, and highlights gaps in existing records. This gives data and marketing teams a baseline for understanding how effectively their first-party data can support audience targeting, suppression, personalisation and measurement.

2. Resolve and enrich records. The Conexa Native App links records to help build more complete customer and household identities. The resulting profiles can support audience activation, campaign measurement and customer relationship management. Adstra says the app runs inside the customer’s Snowflake account, an approach designed to reduce the need to transfer customer data into a separate vendor environment.

Snowflake’s Native App architecture supports applications that run within a customer’s account, using its existing governance controls. For identity resolution, this can allow customer records to be matched against a provider’s identity data without moving the raw customer dataset out of Snowflake. The precise data flows, permissions and outputs still depend on the application’s implementation and configuration.

3. Purchase through existing commitments. Marketplace Capacity Drawdown lets eligible Snowflake customers use a designated portion of their committed capacity for supported Marketplace purchases. That can simplify purchasing by consolidating eligible third-party software spending within the Snowflake Marketplace, although participation and regional eligibility requirements apply.

This purchasing option is an important part of the announcement. Instead of treating identity resolution as a separate procurement exercise, customers may be able to buy Conexa through an existing commercial relationship with Snowflake. That could reduce administrative friction, though it does not by itself guarantee faster implementation or better business outcomes.

What the announcement means for customer data platforms

Identity resolution is an important layer in the customer data stack because records collected across websites, CRM systems, transactions and other touchpoints may not consistently identify the same person or household. Incomplete identity matching can weaken audience coverage, personalisation and campaign measurement, even when the underlying data is already centralised.

Adstra’s approach reflects a broader shift toward bringing data applications closer to where enterprise information is stored. Snowflake’s customer data platform ecosystem already includes tools for profile enrichment, identity resolution and governance, making marketplace integration a route for specialist providers to reach customers within existing workflows.

The announcement does not include independent performance benchmarks, quantified improvements in match rates or evidence of reduced deployment times. Those outcomes will depend on the quality of customers’ source data, identity coverage, configuration and use case. The free assessment may help teams establish a starting point, but buyers should evaluate actual match quality and incremental coverage before committing to a broader deployment.

Strategic Outlook

For marketing operations, data engineering and customer analytics leaders, the main question is whether the new workflow makes identity data more actionable without creating additional governance or cost burdens. A native deployment may reduce data movement, while Marketplace Capacity Drawdown can simplify payment for eligible customers. Neither replaces the need for due diligence on data provenance, consent, access permissions, retention and downstream use.

The practical test will be whether Conexa produces meaningful incremental identity matches and improves activation or measurement across the channels a business uses. Teams should also compare total costs, including Snowflake compute consumption and the commercial terms of the identity service, against the benefits achieved.

If the assessment-to-activation process proves effective, the model could appeal to organisations that have invested in centralising customer data but still lack a reliable identity layer. For now, the announcement establishes a more direct purchasing and deployment path, rather than demonstrating quantified marketing gains.

Top Insights

• Identity assessment comes first: Adstra offers a free Conexa Identity Health Assessment, helping marketers and data teams identify matching gaps before investing in identity resolution.

• Resolution runs within Snowflake: The Conexa Native App processes identity data in the customer’s Snowflake environment, supporting workflows designed to limit unnecessary movement of customer records.

• Marketplace purchasing may reduce friction: Capacity Drawdown lets eligible customers apply existing Snowflake commitments to supported purchases, potentially simplifying procurement without guaranteeing faster implementation.

• Identity quality affects activation: More complete person- and household-level records can support audience targeting, personalisation and measurement, but results depend on source data and match quality.

• Business impact remains to be measured: Adstra has not supplied independent outcome benchmarks in this announcement, leaving buyers to validate incremental matches, deployment costs and campaign improvements.

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Instagram brand engagement rates rose nearly 70% worldwide year over year in the third quarter of 2026, even as median impressions per post fell sharply, according to Emplifi’s latest Social Media Benchmarks report. The data points to a changing social media marketing landscape in which engagement efficiency, content sharing and paid campaign performance may matter more than raw reach alone. The report also records higher paid social click-through rates across markets, while highlighting continued pressure on organic visibility across Instagram, Facebook and TikTok.

Market Landscape

Emplifi’s findings point to a growing measurement challenge for social media marketers: engagement rates can rise even when the number of people seeing brand content falls. Instagram’s 68% worldwide increase in engagement rate per impression coincided with a 48% decline in median impressions per post. Median interactions remained broadly stable.

The distinction matters when comparing performance across platforms. Facebook recorded a 14% worldwide decline in interactions per post, while TikTok interactions fell 38%. Instagram, by contrast, maintained relatively stable interaction volumes. These differences suggest that social media strategies need platform-specific benchmarks rather than a single engagement target.

Emplifi’s report analysed 77,038 Instagram profiles, 167,493 Facebook pages and 1,301 TikTok profiles worldwide. Its findings are based on median performance, which helps limit the influence of unusually large accounts or viral posts.

Strategic Outlook

For marketing leaders, the next step is to connect engagement metrics with business outcomes. Shares and saves can indicate content resonance, while click-through rates, conversions and customer acquisition costs help determine commercial value.

Emplifi’s figures also suggest that brands should assess paid and organic performance together. Worldwide paid social CTR increased 25% year over year to 1.24%, while the US rate reached 2.05%, up 30%. However, the US cost per thousand impressions rose 15% to $6.96, making campaign efficiency an important consideration as holiday advertising budgets are planned.

The strategic priority is not simply to maximise engagement rates. It is to understand which content earns attention, which interactions expand discovery and which campaigns deliver measurable business results.

Top Insights

• Instagram engagement rises: The worldwide engagement rate per impression reached 1.52%, up 68% year over year, giving social media teams a new benchmark for evaluating content response.

• Reach remains under pressure: Median Instagram impressions fell 48% worldwide, reinforcing the need for marketers to track audience exposure alongside engagement and conversion metrics.

• Shares gain strategic importance: Shares accounted for 12.4% of interactions among the highest-reach brand posts, making shareable content a priority for social media teams.

• Paid social CTR improves: Worldwide click-through rates rose 25% to 1.24%, while US CTR reached 2.05%, offering advertisers stronger click-through performance to assess against rising costs.

• Platform strategies must differ: Facebook and TikTok recorded falling interactions, while Instagram remained comparatively stable, underscoring the value of platform-specific content and measurement strategies.

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DriveCentric has introduced a customer data platform (CDP) designed to unify customer records, vehicle purchases, service history and communications across automotive dealership groups. The platform aims to address fragmented data across customer relationship management (CRM), dealer management systems (DMS) and third-party applications, giving dealerships a more complete view of customer relationships across multiple locations. The launch also lays the groundwork for AI-driven engagement and reporting, reflecting a broader shift in automotive retail technology toward connected customer intelligence rather than isolated lead management.

Automotive dealership groups increasingly operate across multiple locations, but their customer information often remains divided by store, department and software system. A buyer might purchase a vehicle at one dealership, arrange maintenance at another and contact a third location online, leaving employees without a consolidated view of that relationship.

DriveCentric’s new CDP is designed to address this problem by creating a shared data foundation across dealership locations, or “rooftops.” The platform brings together customer identity, vehicle and transaction records, service history, communications and other interactions into an enterprise customer profile.

The initial release is entering an Early Access phase, during which DriveCentric plans to work with dealership groups to assess data visibility, record quality and customer engagement. The company has not announced a general availability date.

Connecting CRM, DMS and Customer Records

A customer data platform consolidates information from multiple sources to create more unified customer profiles. In automotive retail, that can mean connecting a dealership’s CRM with its DMS, which typically manages operational records such as vehicle transactions and service activity, alongside other customer interaction systems.

DriveCentric says its CDP builds on integrations already maintained within its platform. The first major component, Enterprise Customer Profile, is designed to combine customer identity, deal history, service records, communications and activity across locations while preserving permissions and store-level attribution.

That last requirement is particularly important for dealer groups. A shared customer record can improve visibility across the business, but individual stores may still need controls over who can access information and how transactions or customer relationships are attributed.

Identity resolution is another central component. DriveCentric is developing technology to determine when records held across different systems or locations refer to the same person, helping reconcile duplicate or fragmented profiles. The effectiveness of this process will depend on matching accuracy, data quality and the ability to handle conflicting records without incorrectly merging customers.

Matt Leone, DriveCentric’s chief executive officer, said dealership technology needs to recognise customers across their entire relationship with a group rather than treating each lead or store record as an isolated interaction.

Building a Data Foundation for Automotive AI

The announcement also positions the CDP as infrastructure for future AI and automation capabilities. DriveCentric plans to expand the data foundation to support engagement, reporting and AI across its platform.

The rationale is straightforward: AI-powered tools can only make useful recommendations when they have access to relevant and sufficiently accurate information. If a system sees only a recent sales enquiry but not the customer’s previous purchase, service history or communications with another location, its recommendations may lack important context.

Michael Affronti, DriveCentric’s chief product and business officer, said the company views unified customer data as a prerequisite for more informed intelligence across its platform.

For dealership groups, a consolidated profile could eventually support more coordinated follow-ups, customer retention initiatives and reporting across sales and service operations. These are potential applications, however, rather than demonstrated outcomes from the newly announced platform.

Dennis Gingrich, sales and finance director at The Niello Company, described cross-location customer visibility as a persistent challenge for dealership groups and endorsed DriveCentric’s direction.

What Dealer Groups Should Evaluate

DriveCentric’s move reflects a wider enterprise technology trend: AI investments increasingly depend on reliable data integration and identity management. In automotive retail, the challenge is not simply collecting more customer information, but connecting it accurately while respecting access permissions and the operating needs of individual dealerships.

During Early Access, the key questions will be how effectively the platform reconciles records, handles data governance and makes unified information useful to frontline teams. Integration coverage, implementation complexity and measurable improvements in customer engagement will also influence its value.

The CDP gives DriveCentric a foundation for extending its engagement platform beyond lead management and follow-up. Whether that translates into a meaningful competitive advantage will depend on execution, data quality and the results dealership groups can demonstrate as the platform develops.

Market Landscape

Customer data platforms have become an important part of enterprise marketing infrastructure because they help organisations connect fragmented records across channels and operational systems. DriveCentric is applying this model to automotive retail, where customer relationships frequently span vehicle sales, financing, maintenance and multiple dealership locations.

The platform enters a market that includes established CRM and dealership management vendors, alongside broader customer data and marketing technology providers. Its automotive focus could be an advantage if it can connect dealership-specific information without adding substantial integration work.

Unlike a conventional marketing CDP focused primarily on audience segmentation and campaign activation, DriveCentric’s stated priority is group-level customer intelligence across dealership operations. Its differentiation will depend on how effectively it combines cross-location identity resolution, permissions and operational data with practical engagement and AI applications.

Strategic Outlook

DriveCentric’s CDP launch reflects a shift toward treating unified customer data as a foundational capability for AI-enabled automotive retail. The company is initially concentrating on customer profiles and identity resolution, with engagement, reporting and AI applications planned for future development.

For dealership groups evaluating the platform, Early Access will be an opportunity to assess record-matching accuracy, data governance, integration coverage and the practical value of a shared customer view. The longer-term opportunity is to connect sales and service interactions across locations without losing store-level accountability.

The announcement establishes the platform’s intended direction, but its commercial impact will depend on implementation results and evidence that unified data improves customer engagement and operational decision-making.

Top Insights

• Unified dealership data: DriveCentric’s CDP combines customer, vehicle, transaction and service information, helping dealership groups develop a more complete view of customer relationships across locations.

• Identity resolution is central: Matching duplicate records across rooftops and systems could improve customer intelligence, provided the platform can maintain accuracy and prevent incorrect profile merges.

• AI depends on data quality: DriveCentric plans to use unified customer information to support future AI and automation capabilities, highlighting the importance of reliable enterprise data foundations.

• Governance remains essential: Enterprise Customer Profile is designed to preserve permissions and store-level attribution, addressing operational requirements when customer records are shared across dealership groups.

• Early Access will test execution: Participating dealership groups will help evaluate data visibility, quality and engagement, providing an initial indication of the platform’s practical value.

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Thryv has been named to Constellation Research’s 2026 ShortList for marketing automation platforms serving small and midsize businesses, placing its Thryv Growth Platform among eight selected vendors. The Nasdaq-listed software provider is positioning its AI-powered marketing tools around a practical challenge for small businesses: identifying which campaigns generate leads, which channels contribute to revenue and where additional marketing budget is likely to deliver value. The recognition highlights growing competition to bring marketing analytics, customer intelligence and automated optimisation capabilities to businesses with limited budgets and staff.

Small businesses often operate without the specialist marketing teams and analytics infrastructure available to larger enterprises. As AI becomes more integrated into marketing software, vendors are competing to make campaign measurement, customer insights and budget allocation accessible through simpler platforms.

Thryv’s inclusion in Constellation Research’s 2026 ShortList reflects this shift. The company says its Thryv Growth Platform combines AI-powered analytics, marketing insights and customer intelligence to help local service businesses assess their marketing performance and make more informed investment decisions.

According to Thryv, its tools can identify which campaigns are producing leads, which channels are contributing to revenue and where marketing budgets may be better allocated. The objective is to reduce reliance on fragmented reporting and guesswork when deciding how to attract new customers.

AI Marketing Automation Moves Beyond Campaign Execution

Traditional marketing automation platforms have focused on tasks such as email campaigns, lead nurturing, contact management and follow-up communications. AI is expanding that role by helping businesses interpret performance data, identify patterns and make decisions about where to focus their efforts.

For a local service business, this could mean comparing lead generation across advertising channels, understanding which campaigns produce customer enquiries and adjusting spending based on reported performance. The commercial value depends on the quality of the underlying data and whether leads can be connected reliably to completed sales.

Thryv’s approach combines these capabilities within a broader growth platform designed for small and midsize businesses. Rather than requiring owners to assemble separate tools for customer management, marketing execution and performance analysis, the company is positioning its platform as a more integrated option.

That proposition is particularly relevant to service businesses operating with limited staff. Owners often manage customer acquisition alongside scheduling, customer communications and daily operations, leaving little time for detailed marketing analysis.

What the Constellation Recognition Signals

Constellation Research selected eight companies for its 2026 ShortList in the small and midsize business marketing automation category. The recognition provides market visibility for Thryv as it competes in a segment where ease of use, affordability and measurable business outcomes can be as important as advanced functionality.

Rees Johnson, Thryv’s chief product officer, said the company developed the Growth Platform to help businesses understand which technologies deliver value and reduce uncertainty around marketing investments.

The announcement does not disclose independent performance benchmarks demonstrating how much Thryv customers have improved lead conversion, reduced acquisition costs or increased marketing return on investment. The ShortList inclusion should therefore be understood as analyst recognition, rather than evidence of independently verified customer outcomes.

For prospective buyers, the distinction matters. AI-powered recommendations are only as useful as the data behind them, and marketing performance can be difficult to assess when phone enquiries, offline sales, website leads and advertising costs are tracked separately.

Competitive Positioning and Buyer Implications

Constellation Research’s B2B marketing automation ShortList also includes ActiveCampaign, Brevo, Constant Contact, HubSpot Marketing Hub, Ontraport, Salesforce Marketing Cloud Growth Edition and Zoho Marketing Plus. The analyst says its selection process considers customer references, partner conversations, vendor selection projects, market share and internal research, alongside the needs and resources of SMBs.

This places Thryv in a competitive category that spans email marketing, lead management, customer engagement and broader marketing automation. Its differentiation lies in connecting marketing visibility and lead intelligence with budget optimisation for smaller businesses, rather than competing solely on campaign-building features.

The platform's AI Lead Insights tools are designed to score leads, summarise interactions and recommend follow-up actions. Its broader growth offering also includes AI-powered websites and visibility tools.

For buyers, the key question is whether these features provide enough actionable insight to justify consolidating marketing activities within one platform. Businesses should evaluate lead quality, reporting accuracy, integrations, pricing and the ability to connect campaign spending with actual revenue.

Market Landscape

AI is pushing SMB marketing software beyond scheduled emails and basic campaign automation toward systems that can analyse customer signals and recommend actions. Constellation Research's 2026 B2B category reflects this shift, identifying AI-powered recommendations, campaign optimisation, generative content capabilities and integrations as relevant selection criteria.

The opportunity is significant for smaller businesses, but implementation remains important. Incomplete customer records or disconnected sales data can undermine automated recommendations, while owners still need transparency into how marketing budgets are allocated.

Strategic Outlook

Thryv’s ShortList placement strengthens its visibility in the SMB marketing automation market as the company positions its Growth Platform around AI-driven lead intelligence and marketing investment decisions. The broader opportunity is to make capabilities once associated with larger marketing departments more accessible to smaller operators.

The next measure of success will be practical performance: whether customers can identify stronger leads, reduce wasted spend and connect marketing activity to revenue. Recognition establishes market relevance, but transparent outcome data will be more useful to buyers assessing long-term value.

Top Insights

• AI-driven marketing analytics: Thryv brings campaign insights and budget optimisation together, helping small businesses identify lead-generating channels and make more informed spending decisions.

• Analyst recognition: Constellation Research selected Thryv among eight vendors, placing its SMB marketing automation offering alongside established email marketing and automation platforms.

• Lead intelligence: Thryv’s AI Lead Insights aims to prioritise prospective customers and recommend follow-up actions, potentially helping small teams manage enquiries more efficiently.

• Competition intensifies: ActiveCampaign, HubSpot, Salesforce Marketing Cloud and Zoho Marketing Plus compete in the broader automation market, giving buyers multiple options for campaign management and customer engagement.

• ROI remains the key test: Businesses should validate whether AI recommendations improve lead quality, conversion rates and attributable revenue rather than relying on platform recognition alone.

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