So companies are pouring money into AI, but most of it isn’t working—and the reason isn’t AI.

It’s the data underneath it.

The Problem Enterprise Marketing Teams Have

Here’s where enterprise marketing teams have a problem most vendors aren’t talking about.

At scale, your marketing stack isn’t one system. It’s 12. Leads flow in from paid campaigns, content syndication, webinars, online forms, tradeshows and telemarketing— and every one of those sources feeds into a MAP that connects to multiple CRM instances, a unified data warehouse, analytics platforms, consent management systems and increasingly, AI models sitting on top of all of it making real-time decisions.

The Real Cost of Bad Data

The moment a bad record enters that stack, it doesn’t land in one place. It propagates. It hits the MAP and gets segmented. It moves to the CRM and gets routed. It flows into the data warehouse and gets stored. It surfaces in the analytics layer and gets reported on. The AI scoring model reads it and generates a recommendation. By the time anyone notices the record was garbage, it’s already inside every downstream system simultaneously—distorting segments, skewing scores, inflating pipeline forecasts and poisoning the training data for the next model run.

This is the real cost of bad data at enterprise scale. It’s not the cost of a single bad lead. It’s the cost of a bad lead at rest inside a 10-to-15 system stack, compounding silently across every tool that touches it.

The math on data quality was already damning before AI entered the picture. B2B contact data decays roughly 30% per year. One study tracking 1,200+ business contacts found 70% experienced at least one data change within 12 months (e.g., job title changes, phone numbers, email addresses, company moves), and 94% of organizations suspect their customer and prospect data is inaccurate. The average enterprise CRM carries a 25% critical error rate on contact records.

What AI Has Changed

The Sirius Decisions “1-10-100 rule” has been cited for years: $1 to verify a record at entry, $10 to clean it later, $100 if you ignore it. But that framework was built for a world where bad data landed in a CRM and stayed there. In a modern enterprise stack where a single record syncs in real time across a MAP, two CRM instances, a unified data store, an analytics platform and a consent layer, the multiplier isn’t 100x. It’s 100x per system.

Bad data costs the average organization $12.9 million annually, per Gartner. MIT Sloan puts the revenue impact at 15–25%. Those figures predate the era when every one of those corrupted records also feeds an AI model making autonomous decisions.

AI changes the stakes in a specific way that enterprise demand gen teams need to understand.

When a bad record sits in your CRM, a human sales rep might catch it. They call the number, it’s wrong, they update it. Slow and frustrating, but self-correcting at some level. When a bad record feeds an AI lead scoring model, there’s no human in the loop to catch the error. The model scores it, routes it and acts on it—confidently, at speed and at scale. The AI doesn’t know the contact changed jobs eight months ago. It doesn’t know the email domain bounced. It reads what’s there and optimizes accordingly.

The Formula for AI Value

This is the core problem. AI doesn’t correct for bad data. It amplifies it.

Forrester put it directly in 2024: “Data quality is now the primary factor limiting B2B GenAI adoption.” Not the models. Not the compute. Not the talent. The data. Gartner predicts that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data. And 59% of organizations don’t even measure data quality, so they can’t assess the foundation they’re building on.

A Sales Hacker survey of 250 Sales Operations Managers found 41% of predictive lead scoring initiatives failed. In most cases the algorithm wasn’t the problem. The CRM data was.

The investment pattern makes this worse. US B2B marketing data spending growth is tracking at 0.5% (eMarketer)—essentially flat—while AI tool spending is growing at 36% year over year. Enterprise marketing teams are wiring increasingly sophisticated AI into increasingly unreliable data infrastructure and wondering why the ROI projections don’t materialize.

BCG’s 10-20-70 framework is instructive here: successful AI transformation allocates 10% of resources to algorithms, 20% to technology and 70% to people and processes—which includes data governance, data quality and data readiness. The companies actually extracting value from AI spend 50–70% of their implementation budget on data preparation before a model ever runs. Most enterprise teams have this ratio inverted.

Why All Roads Lead Back to The Data

There’s a structural fix, and the best enterprise marketing teams are already doing it: validate data at the point of entry before it touches anything downstream.

The logic is simple. If a bad record never enters the stack, it can’t propagate through it. It can’t corrupt the MAP segments, the CRM routing, the analytics reports, the AI training data or the consent records. The cost stays at $1 instead of compounding to $100 per system. The validation gate isn’t a nice-to-have layer. At enterprise scale, it’s load-bearing infrastructure for everything downstream that depends on clean signals to function.

The question enterprise demand gen and marketing ops leaders need to ask isn’t “which AI vendor should we buy?” It’s “what’s the state of the data every system in our stack is reading from?”

Only 37% of organizations say they’ve been able to improve data quality even as AI investment surges, per Wavestone’s 2024 Data and AI Leadership Survey. The teams that close that gap—that treat data infrastructure as a prerequisite rather than a cleanup task—are the ones that will actually get the ROI everyone else is still projecting on slide 14 of the QBR.

The AI isn’t broken. The plumbing is. And at enterprise scale, fixing it later costs a lot more than fixing it first.

Jason Gladu, COO of Convertr, is a lead generation and demand gen expert with a track record of scaling B2B businesses and building innovative intent model.

The post Your AI Stack Has a Data Problem. And It’s Bigger Than One Bad Lead. appeared first on Demand Gen Report.

• Signal stacking reveals hidden intent.
• It’s time to redefine account coverage metrics to role-based participation and stage-fit tracking.

You just finished an incredible demo. Your primary contact loved the presentation, shared your deck internally, and your pipeline looks perfectly healthy. Then, out of nowhere, the deal stalls. The problem is not your product or your pitch. Instead, the problem is that your strategy relied on a single champion.

At the B2B Marketing Exchange (B2BMX) 2026 powered by Advertising Week, industry leaders tackled this exact scenario in a session titled Champion to Consensus: Practical Buying Group Coverage That Improves Conversion. The panel, moderated by Demand.com’s Rick Robinson that featured his colleague Terry Arnold, Gorilla Logic’s Whitney Goldstein and Delinea’s John Johansen, dismantled traditional, single-lead approaches that create massive pipeline friction.

The four experts explained how B2B decisions are increasingly made not by individuals but, rather, by committees. To help teams adapt, the panel examined three persistent myths about buying group engagement and offered practical prescriptions to fix them.

Does One Engaged Contact Equal Account Momentum?

Although many sales and marketing teams falsely believe that a highly engaged champion means the entire account is ready to buy, the reality is quite different. Two-thirds of B2B buying committees now consist of six or more stakeholders.

The prescription for mitigating this risk is proactive engagement. Teams must define required roles early in the process and run targeted plays to handle objections before they become deal-killing vetoes. Marketing and sales must work together to build narratives that resonate across the entire organization — from executive leadership down to daily tactical users.

Arnold highlighted how marketing teams can structure this approach effectively by grouping stakeholders and mapping content to these clusters. By doing so, teams can ensure no critical voice is left in the dark.

“We create role-based clusters, which basically start to look at specific levels,” he explained. “We break that out so that, in marketing, we are engaging each of those different roles in a way which is relevant to the issues that they’re trying to solve.”

Does High Intent Equal Account Readiness?

Intent data has become a staple for revenue teams, leading to the assumption that high intent automatically equals account readiness. However, intent is almost always persona-specific, the panel members observed. Just because one mid-level manager is researching a problem does not mean the broader buying group acknowledges the issue or has budget allocated to fix it.

To combat this, teams must validate intent with role breadth and stage fit. A single signal is just a starting point. More important is for marketers to look for signal stacking — evidence that multiple people within the same account are researching related topics. Furthermore, as search behaviors shift, teams must optimize their content to leverage Large Language Models (LLMs) and Answer Engine Optimization (AEO) to identify hidden intent.

Goldstein stressed the importance of questioning initial data and demanding verifiable proof of momentum, stating, “I look at intent as a clue, not necessarily the conclusion. What I’m trying to do is make our pipeline predictable. And so, we really do need to distinguish what is the signal versus what is the noise.” Signal stacking, she said, is a great strategy for doing so.

How Should Teams Measure Account Coverage?

For years, marketing success was graded on lead volume. And many teams still believe that account coverage can be measured with Marketing Qualified Leads (MQLs), clicks, or a handful of hot prospects. But here’s the reality: Coverage is actually about role participation, depth of engagement and stage fit. A high volume of leads means nothing if they all sit in the same department and ignore the financial or operational stakeholders.

The prescription is a fundamental shift in tracking. Revenue teams must track exactly who is engaged, identify which key roles are missing, and define the precise next action to bridge that gap. An operations narrative that hasn’t been communicated represents a massive structural risk to the deal.

Measuring coverage means looking at the balance of engagement across the entire account. Johansen shared a practical way that his team enforces this mindset through systemic tracking.

“The answer for me is how many people you can get attached to the opportunity in your CRM,” Johansen said. “Starting to work on that behavior and being able to say who are you engaged with [and] add them here. And it also gives us that signal to ask, ‘OK, well, where are we lacking coverage?”

How Can Revenue Teams Shift from Champion to Consensus?

The transition from champion to consensus requires a complete shift in how revenue teams operate. Arnold pointed out that building structural coverage demands tailored narratives for every layer of the business. “If teams fail to communicate value to the tactical, operational, and executive levels simultaneously, deals will inevitably stall at the finish line,” he said.

Goldstein reinforced that marketing must build a foundation of credibility and proof. Relying on anonymous website traffic or isolated clicks is no longer enough to forecast accurately.

“Teams must demand verified engagement across multiple first-party touchpoints, ensuring that content drives measurable outcomes and answers the diverse questions of the entire buying collective,” she offered.

Johansen summarized the cultural shift required between sales and marketing, saying, “Both departments must share ownership of the pipeline and stop treating committee penetration as solely a sales responsibility.”

But the most important takeaway from the session, which the entire panel echoed, serves as a warning for modern revenue teams: Don’t confuse activity with coverage. True pipeline health only exists when the entire buying group moves forward together.

The post B2B Buying Decisions Demand Consensus, Not Champions: Lessons From B2BMX 2026 appeared first on Demand Gen Report.

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• RTIC Outdoors achieved 11% B2B growth using Anteriad Growth Accelerator’s data-driven marketing.
• Incrementality testing and channel orchestration drove measurable revenue impact.

Expanding sales to a B2B customer requires a different marketing approach than selling to consumers. When RTIC Outdoors wanted to focus on expanding their nascent B2B custom shop, they sought outside marketing support.

RTIC Outdoors, a leading outdoor lifestyle brand known for high-performance coolers, insulated drinkware, and gear built to last, operates a B2B custom shop that provides branded gear for businesses, institutions, and enterprise organizations—bringing its “overbuilt, not overpriced” philosophy to corporate and promotional use.

The company’s B2B custom shop had a compelling product portfolio, and with direct-to-consumer sales plateauing, they wanted to turn their focus on growing B2B.  However, they needed help identifying prospects and creating sufficient reach and differentiation. Additionally, marketing success was measured in clicks and impressions, not revenue.

Why Partnership with Anteriad

To help with this growth effort, RTIC searched for a solution that could expand reach, communicate differentiation, and prove real business impact before increasing investment. It led company leaders to partner with Anteriad to launch Anteriad Growth Accelerator, an acquisition strategy using precision audience data, coordinated multi-channel messaging, and incrementality measurement that tied marketing directly to revenue.

Anteriad empowers B2B marketers to get in front of their next customer faster through full-funnel marketing programs powered by responsible AI and deep human expertise. Their global data and Anteriad Marketing Cloud drive advanced analytics engagements, managed service multichannel brand and demand campaigns, programmatic audiences, and BDR-as-a-service programs.

How the Accelerator Works

Anteriad Growth Accelerator is an integrated email and programmatic acquisition solution purpose-built for B2B marketers, including consumer and DTC brands expanding into business channels. The Anteriad Growth Accelerator strategy has four characteristics:

• Channel orchestration. Pairing targeted, orchestrated outreach to surround buyers across channels.
• Incrementality testing. Channel by channel and combined campaigns tested side-by-side to isolate performance drivers.
• Advanced match-back measurement. Employee-level and site-level match-back analysis tied sales and quotes directly to marketing exposure.
• Holdout methodology. Control groups were maintained to measure true lift versus baseline performance.

“RTIC was able to quickly launch and scale their promising B2B business with Anteriad Growth Accelerator, a dynamic digital marketing campaign, to drive measurable growth. We provided the data, strategy and execution to set in place a profitable new revenue opportunity for the company”, said Scott Tinkoff, Executive Vice President of Audience Identification & Activation at Anteriad.

“With a combination of intelligent channel orchestration, testing and measurement, our Growth Accelerator delivers powerful results quickly. RTIC’s journey from a DTC-first brand to a scaled B2B operation is a path we’re seeing more consumer companies pursue, and one where Growth Accelerator is purpose-built to accelerate.”

Pilot Program Results

The engagement launched as a three-month pilot, enabling RTIC to quickly understand what worked and why. Just two months into the three-month pilot, Anteriad Growth Accelerator delivered clear, measurable outcomes with $242,000 in new orders, $611,000 in quotes generated and $532,000 in additional site-level sales identified beyond employee-level matches.

The combined channel campaigns outperformed single-channel efforts, validating the orchestration strategy early. As RTIC continued the program over six months, performance scaled:

• $2.7 million in incremental revenue delivered
• 11% growth in the B2B business
• 7x incremental ROAS, consistently driven during active months
• $8.5 million in new B2B quotes generated and progressing through the pipeline

RTIC moved from slight decline to sustained B2B growth powered by accountable marketing.

“We’re producing strong returns on spend, but more importantly, it’s efficient growth. That’s what allows us to reinvest and keep compounding,” said Brent Wiltz General Manager, Custom Shop, RTIC Outdoors.

The post RTIC Outdoors’ Journey to Sustained B2B Growth Powered by Anteriad appeared first on Demand Gen Report.

Buyers are not just researching differently; they are evaluating and shortlisting differently and increasingly deciding before sales reps ever enter the conversation. Sales teams are losing early-stage influence. Pricing power is shifting. Vendor differentiation is happening algorithmically, and the top of the funnel is tightening rapidly.

The implications for Chief Revenue Officers (CRO) and Chief Marketing Officers (CMO) are profound. A recent multi-industry survey confirms what many commercial leaders have sensed anecdotally: traditional sales motions are being displaced by an AI-accelerated, increasingly self-service buying process.

The Rise of AI Usage

In fact, 60% of buyers use AI moderately or extensively when researching potential solutions and 43% of buyers say AI has saved 30% or more of their time in discovery and qualification.

CROs and CMOs that adapt quickly will shape buying journeys in their favor; those that do not risk being excluded before conversations ever begin. Following are the four commercial imperatives that demand immediate action and what CMOs and CROs need to do to meet them.

Engineer Your Digital Footprint for AI Discovery

 If AI can’t interpret you clearly, it won’t recommend you. The test executives can use to determine how they are faring with this is to look at when AI summarizes your category, does your perspective shape the answer?

 Here’s what CMOs and CROs need to do:

• Restructure websites, case studies, pricing pages, and technical documentation so AI systems can easily ingest, analyze, and synthesize them – including proprietary research, named frameworks, and proof points.
• Make positioning explicit and declarative. Remove ambiguity in how you describe your category, differentiation, and ideal customer profile.
• Strengthen authority signals through consistent thought leadership, backlinks, and AEO driven formatting to increase AI citation likelihood.
• Conduct quarterly AI mystery shopping to assess how generative engines describe you versus competitors and close narrative gaps immediately.

Win the First Five Minutes

In an AI-accelerated buying cycle, speed and substance determine inclusion. The new standard means that the first touch must advance the buyer’s thinking.

 Here’s what CMOs and CROs need to do:

• Redesign lead management to achieve best-in-class response times (five minutes or less) – with real-time measurement and accountability.
• Ensure the first human interaction adds insight, not friction. AI-sourced leads expect expertise, not qualification scripts.
• Equip SDRs with AI tools to instantly contextualize the buyer, personalize outreach, pre-qualify intelligently, and route precisely.
• Elevate frontline technical fluency through training and revised coverage models; introduce SMEs earlier where it materially accelerates trust and deal velocity.

Remove Friction from the Buying Experience

 If buyers can research faster, they expect to purchase faster. The commercial reality is that speed and simplicity are increasingly competitive advantages.

 Here’s what CMOs and CROs need to do:

• Compress internal decision cycles – streamline pricing approvals, legal review, and contract negotiation.
• Simplify packaging, terms, and onboarding to reduce perceived risk and time-to-value.
• Deploy interactive ROI models, configurators, and AI-driven demo walkthroughs – now table stakes in competitive categories.
• Audit every stage of the buying journey for latency, redundancy, and unnecessary internal complexity.

Make Pricing AI-Resilient and Strategically Defensible

AI is increasingly interpreting and comparing your pricing model before a rep ever engages.  The question executives should ask themselves to determine how they are faring with this is if AI is reinforcing your premium (or undermining it) when it explains your pricing to a buyer.

 Here’s what CMOs and CROs need to do:

• Clarify competitive advantage and differentiation in ways that are explicit, structured, and machine interpretable.
• Simplify pricing architecture to ensure it is benchmark-aligned, value-backed, and easy to explain – both by sellers and by AI systems.
•  Evaluate outcome-based or hybrid pricing structures where they reinforce strategic positioning.
• Align pricing tightly to your core value drivers; ambiguity will be exposed and commoditized.

AI has abruptly and fundamentally reshaped the B2B buying journey. Buyers research more, shortlist differently, evaluate faster, expect transparency, and require higher-value interactions from reps. Sales organizations that respond proactively, redesigning content, tools, pricing, and capabilities, will thrive. Those that do not will increasingly lose deals before conversations ever begin.

Michael Smith is the Senior Managing Director – Technology, Media & Telecom Practice Leader at Blue Ridge Partners. Michael has over 35 years of experience helping companies accelerate revenue growth and develop winning sales strategies. Previously, Michael worked at McKinsey & Company and in multiple corporate executive operating roles running businesses and sales teams. Michael received his MBA from Stanford University and lives in the Boston, Massachusetts area.

The post The 4 Actions CMOs & CROs Must Take to Catch Up to the AI-Augmented Buyer appeared first on Demand Gen Report.

• Target specific decision-makers and hidden stakeholders instead of relying on broad account-level outreach. 
• Eliminate sales latency by synchronizing marketing and sales efforts to deliver personalized educational content at the precise moment of need. 

Account-based marketing (ABM) requires precision, intelligence, and a deep understanding of human behavior. At B2B Marketing Exchange (B2BMX) 2026, powered by Advertising Week, LiveRamp’s Ed Vander Bush and Influ2’s Doug Madey discussed the importance of focusing  marketing efforts on the right individuals. By treating key buyers as the main characters of your outreach, B2B marketers can drive unparalleled revenue growth and foster meaningful business connections.

Understanding the buyer’s journey forms the foundation of any successful account-based marketing strategy, the two stated.. As buyers move from initial awareness into deep consideration, their needs and expectations shift dramatically. Marketers must map these stages meticulously to ensure they provide relevant information exactly when the prospect requires it.

Buyer Journey and Personas

Personas play an equally critical role in this progression. Targeting an entire account often dilutes the message, whereas focusing on specific individuals allows you to tailor your communication to the distinct pain points of the decision-makers. This contact-level approach transforms generic outreach into valuable, consultative engagement.

Reflecting on this need for precise targeting, Vander Bush noted, “It really comes down to understanding not only the account, but the person on the other side of the process.”

Challenges in the Buying Process

Modern B2B purchasing decisions rarely happen in a vacuum, presenting significant challenges for sales and marketing teams. Buyers conduct extensive independent research, meaning that by the time they finally reach out for a meeting, they have often already finalized their decision. This dynamic forces revenue teams to engage prospects much earlier in the evaluation phase.

Another major hurdle is the presence of hidden stakeholders who influence the final decision but remain invisible within your CRM. When sales representatives execute unexpected outreach without understanding the full buying group, they risk alienating these unseen decision-makers. Navigating this complexity requires gathering deeper contact-level insights.

“Having been recently on the buying side, I was surprised by hidden stakeholders within my own company ” noted Vander Bush. “Colleagues who brought real, important perspectives to the table that become completely invisible if we were to try to reduce the buying journey to the account level.”

Latency and Orchestration in Sales

Latency in the sales process occurs when representatives react to outdated signals and reach out too late. Relying solely on account-level intent data often creates a lag, causing sellers to miss the critical window of opportunity when the prospect is actively seeking solutions. Eliminating this delay is essential for maintaining momentum in the deal cycle.

Vander Bush emphasized the importance of this alignment, stating, “Too often, we align around the real problem too late, waiting for a sufficient ‘score’ rather than orchestrating marketing and sales together around the signals that reveal what real people are trying to achieve as they move through the customer journey.”

To address the latency problem, revenue teams must master the orchestration of relevant materials. Serving the right content at the precise moment requires marketing and sales to operate in total alignment, moving beyond siloed activities. This synchronized effort ensures that the buyer receives continuous value without aggressive, premature sales pitches.

Personalization and Content Strategy

A robust content strategy relies heavily on personalization to drive internal consensus within target accounts. Vander Bush highlighted a case study involving a global brand where an internal champion recognized the immense value of a data clean room. However, this champion struggled to secure the necessary buy-in from skeptical colleagues across different departments.

Instead of aggressively pushing for more sales meetings, the team deployed a highly personalized content strategy. They distributed targeted educational videos designed specifically to provide thought leadership of those hesitant internal stakeholders. This strategic distribution of content naturally multi-threaded the account, elevating the conversation and ultimately leading to increased attendance at pivotal sales meetings.

“This wasn’t a time for a traditional multithreading strategy,” stated Vander Bush. “This was the time to put really well-timed, relevant educational content in front of those contacts our champion was trying to sell internally. That effort led to those contacts leaning in and asking to join the next call, because as their knowledge grew, so did their understanding of the potential business value of a clean room.”

Attribution and Campaign Impact

Proving the impact of targeted campaigns remains a persistent attribution problem for B2B marketers. Standard dashboards often fail to capture how specific marketing touches influence individual buyers over time. Accurately measuring this engagement is vital for demonstrating how ABM moves prospects further down the sales funnel. This is where taking a contact-level approach to your ABM program helps, because you can’t tailor the next message if you don’t know who engaged with what. There’s no context on which topic landed—or whether anything changed.

Such engagement can be measured at various points of the customer journey, even while performing different functions. For example, personalized messaging can serve as a powerful tool to overcome stubborn gatekeepers who block access to decision makers. By deploying the right level of personalization, you can bypass these barriers and deliver compelling arguments directly to the people holding the budget. This level of precision transforms stalled opportunities into active deals. Engagement that speaks directly to the attribution and impact of the campaign.

Vander Bush illustrated this impact with a real-world example: “I worked with a seller trying to close an end-of-quarter deal who was being blocked from everyone who needed to sign. Coming into that conversation, we were able to show how those stakeholders were already engaging, which opened the door to collaborating. We spent 20 minutes over Slack and hammered out a plan to deliver that personalized messaging to the right people.”

Collaboration Provides Results

The synergy between marketing and sales dictates the overall success of any account-based strategy, noted Vander Bush. Delivering personalized, relevant content requires both teams to share insights seamlessly and understand the buyer’s point of view.

When revenue teams collaborate effectively, they empower internal champions to advocate for their solutions with confidence.

“Understanding the buyer’s perspective fundamentally changes how you approach content creation and sales outreach,” he said. “Showing true value and relevance to stakeholders transforms your brand from a vendor into a trusted strategic partner. Delivering with empathy and specialized knowledge drive these engagements forward.”

The post Orchestrate ABM Around Main Characters, Not Extras: Lessons from B2BMX appeared first on Demand Gen Report.

What used to be considered the final stage of the funnel— advocacy— has quietly become one of the primary drivers of discovery, trust, and pipeline growth.

Why Advocacy Matters Now

Two structural shifts are making advocacy central in B2B marketing:

Hidden buyers wield decisive influence. Buying committees increasingly include various stakeholders, including finance, legal, procurement, and operations, who rarely meet sales reps but influence vendor selection.

Many stakeholders say thought leadership is more persuasive than product sheets, and over 79% are more likely to champion a vendor with consistent, high-quality ideas. What’s more, over 40% of deals stall due to internal misalignment, which is often driven by these silent influencers.

Discovery is shifting toward AI + third-party proof. AI-driven search is changing how B2B buyers research and shortlist vendors. According to G2’s 2025 Buyer Behavior Report, leads that originate through AI-powered search convert approximately 40% better than those from traditional search engines, primarily because buyers encounter credible, third-party content earlier in the journey.

This evolution highlights a larger truth: advocacy must exist where research actually happens. The most persuasive proof points are increasingly discovered on neutral ground. Buyers are looking at review platforms, community discussions, and practitioner-authored content long before they reach a brand’s website.

Three Advocacy Engines B2B Organizations Can Scale

1. Customer Communities That Deliver Value (and Reduce Cost)

Communities should not be treated as engagement tools alone. When structured effectively, they generate long-term ROI across support, adoption, upsell, and advocacy.

Cisco’s partnership with Khoros is a good example: over their first year, engineers published 47% more content internally, community interactions drove over 1 million annual views, and the program delivered approximately $54.2 million in case deflection savings. Mature community programs are shifting focus from vanity metrics (posts, users) to business outcomes (deflection, retention, engagement).

Tactical moves:

• Stimulate “how-we-fixed-it” threads contributed by customers and internal experts
• Surface accepted answers, highlight best practices, and integrate these into onboarding, product documentation, and training
• Track deflection rates, time-to-first-answer, usage lift, and expansion signals

2. Peer Proof on Platforms Buyers Trust

Trust increasingly forms outside of brand-owned channels. Decision-makers rely on independent, experience-based sources. Review platforms, practitioner communities, and peer content often guide vendor selection.

Demand Gen Report’s 2024 B2B Buyer’s Survey highlights that discerning buyers increasingly rely on peer reviews and in-depth research as trusted guidance in purchase decisions. These findings point to a consistent pattern: credibility is earned on neutral ground. Buyers place greater weight on what peers and practitioners say about a solution than on what the vendor claims about itself.

Tactical moves:

• Treat reviews as a structured, ongoing program rather than one-time requests.
• Keep third-party profiles current with recent customer quotes, screenshots, and implementation details.
• Reuse authentic peer feedback in enablement content—always linking back to its verified, external source.

3. Employee & SME Advocacy That Reaches Hidden Buyers

Thought leadership remains one of the few credible routes into parts of an organization where sales lacks access. Even small adjustments to employee-shared content can boost reach dramatically.

Tactical moves:

• Issue a monthly advocacy brief with three credible themes (customer story, data, contrarian insight)
• Provide lightweight framing rather than scripts, and encourage personalization
• Track reach, engagement, and account-level influence

Building an Advocacy System, Not Just Tactics

• Design mutual value exchange.
  • For customers: offer visibility, early access, roadmap influence, expert forums
  • For employees: offer recognition, guardrails, support, and training
• Make advocacy easy. Toolkit components might include business-case one-pagers, compliance/security FAQs, comparison visuals, and internal champion scripts.
• Break down silos. Let best community content feed into knowledge bases, reviews, case studies, and SME insights. Enable CSMs and product leaders to nominate strong customer stories.
• Measure what matters. Go beyond engagement metrics. Tie advocacy to account coverage (which ICP accounts have an engaged advocate?), toolkits downloaded, review velocity, and SME reach by role. Emphasize outcomes over activity counts.

What to Report (and Benchmark)

Case deflection & cost savings (modeled or actual)

Engaged-account coverage: percent of target accounts with at least one advocate touchpoint

Champion enablement metrics: downloads/shares of toolkits, internal referrals

Review velocity & depth: number of reviews per quarter, recency, qualitative depth

SME influence on hidden buyers: impressions, engagements, internal advocacy contributions

A 60-Day Advocacy Launch Plan

Final Thought

As buying journeys become more distributed and AI surfaces more third-party insight, the companies that grow fastest will be those that build credible ecosystems of proof. Advocacy is no longer simply about retention or customer satisfaction. It is a scalable demand engine that influences discovery, accelerates consensus inside buying groups, and reinforces trust at every stage of the decision process.

Cynthia Ortiz is a Marketing Program Coordinator for Televerde, a global revenue creation partner supporting marketing, sales, and customer success for B2B businesses around the world. A purpose-built company, Televerde believes in second-chance employment and strives to help disempowered people find their voice and reach their human potential.

The post The New Demand Engine: Why Peer Proof Is Reshaping the B2B Buying Journey appeared first on Demand Gen Report.

• Demandbase AI centralizes go-to-market execution by using proprietary Context Intelligence to filter out market noise and align account signals with pipeline goals.
• The launch features new tools like a conversational Site Customization Agent and an open-standard Model Context Protocol to seamlessly connect with major AI assistants like ChatGPT and Claude.

At its annual customer conference April 14, Demandbase touted the debut of Demandbase AI as setting the new standard for the artificial intelligence (AI) GTM era to help enterprises scale strategy into measurable pipeline.

The debut at GO London of this new AI-first experience presents a simplified, conversational interface for orchestrating go-to-market execution across the platform— and is anchored by several key innovations, including a Site Customization Agent, LLM integrations including ChatGPT and Claude, and new capabilities for proving Pipeline Influence.

With marketers drowning in GTM signals and struggling to turn insights into outcomes. Demandbase AI uses Context Intelligence— a proprietary layer that applies each company’s unique GTM context— to analyze account signals and patterns against pipeline goals, identifying the opportunities most likely to drive results.

What Demandbase AI Does

Instead of leaving teams to activate the strategy across every channel, Demandbase AI removes the overwhelm by coordinating programs and plays across marketing, sales, and advertising to drive pipeline, said Gabe Rogol, CEO of Demandbase.

“In the rush to adopt AI, the industry is seeing that more data and activity don’t lead to better outcomes,” said Rogol in a statement. “AI without context creates noise— it requires more oversight and misses what actually matters. Demandbase AI is moving the industry beyond insights and point solutions to a unified system that activates teams, focuses them on what matters, and helps them drive revenue more predictably.”

‘Delivering on AI’s Promise’

Demandbase AI brings together data, teams, and workflows across native and ecosystem integrations to form a continuous system that turns goals into outcomes, transforms signals into actionable insights, coordinates cross-channel activations and continuously adapts.

To bring this system into how teams work everyday, the company is introducing new capabilities that enable harmonious workflows across the entire go-to-market. From agent interoperability to a robust ecosystem that enables data and tool integrations, Demandbase is extending AI-driven intelligence directly into the solutions teams rely on:

• LLM Workflow Integration: Demandbase now delivers deep company, contact, technographic, and intent data through Model Context Protocol (MCP), an open standard that enables seamless data interoperability between Demandbase AI and major AI assistants like ChatGPT, Claude, CoPilot, and Gemini.
• Site Customization Agent: A conversational interface that enables marketers to quickly refine campaign-matched landing pages. By “reading” page and audience context, it will reduce production time from days to minutes while improving conversion and pipeline outcomes, with every recommendation grounded in account and buying group signals.
• Pipeline Influence measurement: Through Demandbase AI Chat—a chat-based interface that enables prompt-based insights—Pipeline Influence easily moves teams beyond fragmented metrics to show how programs are driving pipeline across the GTM, helping teams scale what’s working.

“Our teams are under incredible pressure to both adopt AI and deliver real pipeline results,” said Ryan Oliver, Director of Enterprise Demand Generation Marketing, SAP Concur. “Demandbase is the first platform we’ve used that actually connects those two. It creates an AI-driven experience that works across our teams, keeps everyone aligned, and measures success based on the pipeline it generates. We’re reducing wasted spend and seeing better outcomes. Demandbase is truly delivering on the promise of AI and driving real business impact.”

To help the industry keep pace with the speed of AI innovation, Demandbase also launched a new AI GTM Certification program that will empower teams with the strategic framework and technical skills needed to master the AI GTM era.

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• Demand Gen Report’s 2026 Campaign Optimization Series focused on marketing strategy that build trust across external channels, as B2B buyers now conduct extensive independent research well before contacting vendors.
• Speakers discussed getting on buyers shortlists, why company websites are often not the first stop in the buying funnel and what an Agent Qualified Lead is

B2B marketing professionals continuously seek proven strategies to drive revenue generation and optimize their campaign life cycles.

During Demand Gen Report’s 2026 Campaign Optimization Series (#COSeries), industry leaders from Demandbase, NetLine, and Docket shared their expertise on navigating shifting buyer behaviors. Across three comprehensive webinars, these experts detailed how marketers can build dynamic campaigns, capture true intent, and leverage artificial intelligence to foster meaningful connections.

Here are five essential lessons learned from the series.

Influence Starts Long Before the Shortlist

B2B buyers now conduct extensive independent research well before they ever contact a vendor. They review peer insights, explore trusted third-party content, and define their exact requirements in private channels. This independent approach means marketing teams can no longer wait for buyers to raise their hands.

Instead, marketing professionals must focus heavily on shaping demand during these early research phases. Building influence requires a consistent presence across the broader digital landscape, delivering credible perspectives that help buyers validate their thinking. By the time a buyer forms a shortlist, their core decisions are largely finalized.

Josh Baez from NetLine highlighted this massive shift in buying behavior and the urgent need for marketers to adapt.

“The most important buying decisions are happening well before vendors even know that a deal exists,” said Baez. “Buyers are doing more research on their own. They’re involving more stakeholders, and they are delaying vendor interaction until much later in the process. In fact, 83% of buyers define their requirements before ever speaking to sales.”

To succeed, marketing content must do more than answer basic questions. Content must actively shape the questions buyers ask themselves, positioning your brand as a trusted authority. This proactive influence secures your place on the shortlist before the buyer even realizes they are ready to purchase.

Aligning Campaign Spend with Sales Priorities

Resource allocation remains a critical challenge for marketing teams striving to maximize their return on investment. Treating all target accounts equally drains budgets and dilutes the impact of personalized messaging. Marketers need a structured methodology to determine which prospects deserve the highest level of investment.

Hannah Jordan from Demandbase stressed that implementing a rigorous account scoring model provides the necessary framework to categorize accounts into specific tiers. This tiering system allows marketing operations to dictate exactly how much budget and effort goes toward engaging each prospect. High-value accounts receive premium, highly personalized outreach, while lower-tier accounts receive more automated, scalable communications.

Jordan emphasized the importance of using these tiers to synchronize marketing and sales efforts, stating “the tier is really going to determine the spend per account…we want to align our spend and our resources and make sure that we’re prioritizing the accounts that sales is also prioritizing.”

This deliberate alignment prevents wasted spend and ensures both departments work toward shared revenue goals. When marketing invests heavily in the exact accounts that sales wants to close, the entire campaign life cycle accelerates, resulting in stronger pipeline quality and demonstrable revenue growth.

Shifting to a Coverage-Centric Strategy

Relying solely on driving inbound traffic to a corporate website is an outdated approach to demand generation. Buyers spend the majority of their time consuming information on external platforms, industry publications, and peer networks. Marketers must expand their focus beyond their own digital properties to capture attention effectively.

A coverage-centric strategy solves this problem by ensuring your brand maintains a consistent presence wherever your buyers naturally gather. This means showing up across various channels, environments, and buying groups to build familiarity and trust over time. Coverage focuses on visibility in the places where actual buying decisions form.

Baez explained the fundamental difference between these two marketing philosophies during his session. “We move from a website centric model to a coverage centric instead of asking, how do we get buyers to come to us, we ask, how do we show up where buyers already are?” Baez said.

By prioritizing market coverage, brands integrate themselves into the buyer’s daily research habits. This consistent, multi-channel exposure ensures that when buyers finally decide to visit your website, they already view your company as a credible and familiar solution provider.

The Demise of the Traditional MQL

For over a decade, the Marketing Qualified Lead served as the ultimate metric for measuring campaign success and pipeline health. However, static forms and basic website click data fail to capture true buyer intent accurately. A massive gap now exists between raw website traffic and genuinely qualified sales pipeline.

Modern buyers utilize sophisticated tools, including large language models, to gather information before they ever navigate to a vendor’s site. Because they gather basic facts elsewhere, their visits to your website serve a much different purpose than they did years ago. Traditional lead scoring models simply cannot interpret this new pattern of behavior.

Lauren McHugh from Docket pointed out that marketing teams must abandon outdated measurement frameworks. McHugh explained, “The erosion of our traditional conversion signals like the MQL that metric has really been defined for B2B marketing over the past decade, and it really is no longer built for how buyers are behaving today.”

Marketers must rethink how they measure website conversion altogether. Instead of tracking passive clicks or document downloads, the focus must shift to capturing substantive engagement and measuring the depth of a prospect’s actual questions.

The Rise of Agent Qualified Leads

Because buyers do their preliminary research elsewhere, the corporate website now functions as the endpoint of the buyer journey rather than the starting line. When buyers finally arrive at your site, they want immediate, specific answers to complex questions. Forcing these highly educated buyers to fill out static forms creates unnecessary friction.

Artificial intelligence provides a powerful solution through conversational engagement. AI-powered agents can conduct real-time discovery, answer sensitive questions, and provide personalized support without requiring human intervention. This interactive approach captures a much deeper level of intent than any traditional web form.

McHugh highlighted how this technological shift fundamentally changes the quality of leads passed to sales teams. Agent Qualified Leads (AQL) supply sales representatives with rich, contextual information about the buyer’s specific challenges and requirements. This intelligence allows sales teams to bypass generic introductory calls, moving directly into meaningful, solution-oriented discussions that drive faster revenue growth.

“AQL or an agent qualified lead really is substantive. In the context behind that conversation, the AQLs are moving through the pipeline faster and actually converting at higher rates because they’re arriving to those sales people,” McHugh said.

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For B2B marketers just getting started with buying groups, the first step is to identify the different stakeholder roles, from champion to procurement to technical implementation lead. Each stakeholder profile will then map to specific titles and roles at target companies, producing audience segments. But, before B2B marketers check the box and move on to “step two”— it’s important to note that the first step isn’t really a step at all, but an ongoing process that needs to be continuously managed and updated.

The reason is that audience segments can quickly get outdated. And when targeting a buying group, the network effect can amplify outdated audience segments quickly. Buying groups also mean different things in different companies, and B2B marketers need to pick up on those nuances and fine tune their approach to become more effective with each target account.

The Ever-Changing Buying Group

Companies are like organisms. There’s M&A, restructuring, and shifts in strategy that can affect buying groups.  At the individual level, people change due to promotions, internal transfers, job changes, maternity leave, reorgs, layoffs, contractors etc. We see consistent and meaningful contact and role changes occurring within months, not quarters or years, in active segments. On average the data degrades 2–5% per month. It doesn’t fail all at once, rather it erodes in layers with role relevance and buying group alignment deteriorating long before emails stop working—making continuous validation and refreshing essential.

Volatility is especially prevalent in high growth industries like tech, marketing and ops. 25-40% of buying group members can change within 6 months which leads to champions disappearing, buyers getting promoted or leaving. Decision-making authority starts shifting silently, so continuing to target the former decision maker leaves a gap in the current role owner.

Job title and employer change most frequently, and those shifts cascade into persona accuracy, buying group integrity, and activation performance—making continuous validation more important than record volume. Understanding that these job-related attributes change even when a contact stays at the same company is vital. Tracking internal movement is almost more important than company changes. As responsibilities shift, buying and decision making changes.

Managing the Buying Group System

Static buying group assumptions drive waste— assuming 6–12 month stability leads to mistargeted spend, missed influencers, and stalled deals. Roles persist, but people, influence, and intent change continuously.

To succeed, buying groups should be treated as living systems. Marketers should continuously monitor data and insights to understand patterns that can ensure they are updating and improving buying groups at the proper cadence.

• Buying group volatility: How frequently does the typical buying group composition change? (e.g., every 30, 60, or 90 days)
• Role stability: How often do individual titles/roles shift within buying groups?
• Core buying group composition: What are the most common titles consistently involved? What are the surprise titles?
• Industry variations: How does buying group size differ across industries (manufacturing vs. tech vs. finance, etc.)?
• Geographic patterns: Do buying group sizes vary by country/region?

Keeping tabs on these patterns can help marketers create a plan to update and refresh buying groups often enough to get ahead of changes.

Marketers should also continuously improve the accuracy of buying groups for top target accounts, because each organization is different. At one company, the CFO’s office holds the final decisions, while at another, a separate procurement group makes the final call. Each nuance about buying groups at a specific organization makes marketing and sales more accurate and more relevant.

Supporting The Buying Group Reality

Maintaining an accurate picture of each stakeholder requires access to high quality data that’s frequently updated, and the more types of data marketers have access to, the more accurate their understanding.

Even with accurate demographic and firmographic data, there can still be change within the buying group that affects marketing as a purchase process progresses. The influence paths for the role owner can shift mid-cycle due to budget owners changing, legal/procurement getting pulled in late, security suddenly becoming a blocker, or the exec sponsor disengaging. Buying groups expand and contract as deals mature.

This is where added insights such as intent data can be valuable. Intent is dynamic, not durable, so interest spikes and shifts by persona and topic need to be continually monitored as signals from last-quarter may be unreliable.

Focusing on buying groups can deliver great results, so putting a system in place is well worth the effort. Getting to know the nuances of a target account not only produces better marketing results, it enhances the connections that the sales team can make, creating stronger ties and shorter buying cycles.

Karie Burt, Chief Data and Privacy Officer, is a seasoned expert with over 20 years in B2B growth using data-driven solutions. Specializing in global data privacy, GDPR, and digital marketing strategies, particularly in Asia, she optimizes international data procurement and compliance. Karie advises on European Data Protection laws and cultural nuances for non-U.S. markets. Recognized by GRC World as a leading Woman in Privacy (2022), she champions responsible B2B marketing while respecting privacy rights. A member of the IAPP, her leadership extends to M&A activities. Karie enjoys international travel and her two unruly rescue dogs.

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