Carolyn Healey
Santa Barbara, California, United States
18K followers
500+ connections
Articles by Carolyn
-
Training Your AI to Think Ethically: How to Ensure Your AI-Driven Brand Builds Trust
Training Your AI to Think Ethically: How to Ensure Your AI-Driven Brand Builds Trust
Artificial Intelligence (AI) is not just transforming business operations—it’s reshaping brand reputations. Today, as a…
23 Comments -
Finding the Right AI Tool to Improve Marketing: A Breakdown of the Top 3 Enterprise AI Platforms
Finding the Right AI Tool to Improve Marketing: A Breakdown of the Top 3 Enterprise AI Platforms
Artificial intelligence is revolutionizing business operations, but with so many options, it can be overwhelming to…
13 Comments -
Case Study: Leveraging a Fractional CMO to Drive Growth and Improve Marketing Performance
Case Study: Leveraging a Fractional CMO to Drive Growth and Improve Marketing Performance
Company: Confidential SaaS Provider for Small Businesses Industry: SaaS (Small Business Management Software) Employees:…
6 Comments -
Is Your Website Chatbot-Ready?
Is Your Website Chatbot-Ready?
As artificial intelligence becomes a key intermediary between businesses and their customers, B2B companies face a…
1 Comment -
Maximizing Email Marketing Success with AI Tools: A Step-by-Step Guide
Maximizing Email Marketing Success with AI Tools: A Step-by-Step Guide
In today's digital landscape, email marketing remains a powerful tool for businesses to connect with their audience…
-
Unlocking Success: The Power of a Fractional CMO in Your Business
Unlocking Success: The Power of a Fractional CMO in Your Business
What is a Fractional CMO? In today's fast-paced business landscape, having a strong marketing strategy is crucial for…
-
Improve Marketing Performance in 2025 With a Marketing Strategy Audit
Improve Marketing Performance in 2025 With a Marketing Strategy Audit
Every business encounters obstacles on its journey to success. Perhaps your once-effective marketing efforts are no…
-
AI Marketing Tools: How Custom GPTs Can Fit into Your Marketing Org Chart
AI Marketing Tools: How Custom GPTs Can Fit into Your Marketing Org Chart
In today’s fast-paced marketing world, efficiency and creativity must go hand-in-hand to stay competitive. One of the…
9 Comments
Activity
18K followers
-
Carolyn Healey shared thisYour AI policy isn’t a compliance document. It’s the difference between AI that scales and AI that creates risk. Most CXOs are still getting it wrong. AI adoption is widespread: nearly 90% of organizations now use AI (McKinsey, 2025). But only ~43% have governance policies, and just 1 in 4 have operationalized them (PEX Network, 2025; AuditBoard, 2025). This is an important execution gap. Here’s what separates AI policies that work from the ones that sit in SharePoint: 1/ Start With an AI Inventory, Not a Mission Statement → You can’t govern what you haven’t catalogued → Include internal tools, embedded vendor AI, and shadow AI Bottom line: If it touches your data, it’s your risk. 2/ Define Acceptable Use in Plain Language → Employees are already using AI, often more than leaders realize (McKinsey, 2025) → Clearly define what’s allowed, restricted, and requires approval Bottom line: Ambiguity creates liability. 3/ Assign Cross-Functional Ownership, Not Just IT → AI governance must span legal, HR, procurement, and operations → Only 28% of CEOs actively oversee AI governance (McKinsey, 2025) Bottom line: If ownership isn’t explicit, it won’t happen. 4/ Build a Risk Tiering Framework → Define tiers: assistive, human-reviewed, autonomous decisions → Apply stricter controls to high-impact use cases (e.g., hiring, credit) Bottom line: Uniform governance leads to uneven risk. 5/ Govern Vendors as Rigorously as Internal Systems → AI is being embedded across your SaaS stack → Require risk classification, audit rights, and incident reporting Bottom line: Your biggest exposure is often third-party AI. 6/ Build Continuous Monitoring — Not Annual Reviews → Models drift, data changes, and regulations evolve → Organizations with governance platforms are 3.4x more effective (Gartner, 2025) Bottom line: Governance must be operational, not static. 7/ Treat Agentic AI as a Separate Category → Agents act autonomously with speed and scale → 40% of enterprise apps will include AI agents by 2026 (Gartner, 2025) Bottom line: Policies for tools won’t work for agents. 8/ Bake in Regulatory Alignment From Day One → Global AI regulation is accelerating rapidly → Governance tech will reduce compliance costs ~20% by 2028 (Gartner, 2026) Bottom line: Compliance must be built in — not bolted on. 9/ Make Governance a Living System With a Named Owner → Assign executive ownership with board visibility → Only 1% of companies report full AI maturity (McKinsey, 2025) Bottom line: The gap isn’t adoption; it’s governance depth. The companies getting this right aren’t slowing AI down. They’re building the infrastructure that lets it scale with fewer incidents and more confidence. Save this for future reference.
-
Carolyn Healey shared thisThe most advanced agentic AI deployments don't just have agents doing the work. They have agents watching the agents. This is the governance model most CXOs aren't talking about yet. 79% of companies report agents already in use. Yet 75% of tech leaders say governance is their primary deployment challenge (PwC, 2025). Three in four executives are deploying agents they can't fully govern. Most are relying on one method to catch errors: human review at the end of the chain. That's a bottleneck with a compliance label on it. Here's the framework that separates confidence from exposure: 1/ The single-agent assumption is already obsolete → 66% of enterprise implementations now use multi-agent architectures (Landbase, 2025) → Each agent handoff is a new failure point and a new opportunity for compounding error Reality: Your risk doesn't scale linearly with agents. It compounds. 2/ Human-in-the-loop alone won't scale → Human review works at 10 agent actions per day. It breaks at 10,000 → Agentic AI could automate $2.9 trillion in US economic value by 2030 (McKinsey, 2025) and that volume cannot wait for human sign-off at every node Reality: Reserve human judgment for high-stakes decisions. Deploy AI to govern the routine. 3/ Reviewer agents operate on a different mandate → Worker agents optimize for task completion. Reviewer agents optimize for accuracy, compliance, and risk → Separation of function between agents is the same principle as separation of duties in finance Reality: It works for the same reason: independent verification catches what execution misses. 4/ Audit trails make multi-agent systems defensible → Every agent action must be logged: not just outcomes, but reasoning chains and handoff states → Without this, your response to a board or regulator asking "what happened?" is silence Reality: You can't explain what you can't trace. 5/ Governance is now a competitive moat → 73% of executives agree that how they use AI agents will determine competitive advantage in the next 12 months (PwC, 2025) → Only 35% have broadly adopted agents and fewer than half have restructured work around them (PwC, 2025) Reality: The CXOs who treat AI governance as a brake pedal will be passed by those who treat it as an accelerator. The model that scales isn't human oversight of every agent output. It's agents checking agents, with humans governing the system, not inspecting each transaction. That's not a loss of control. That's what control looks like at enterprise scale. Save this for future reference.
-
Carolyn Healey shared thisThe AI advantage is not the most technical team. It is the team that thinks clearly and adapts quickly. Workers with AI skills now command a 56% wage premium over peers without them (PwC, 2025). Not just engineers or data scientists. It’s who combines AI fluency with judgment, creativity and adaptability. The next winning workforce has these traits: 1/ AI Fluency, Not AI Expertise → Literacy first: understand what AI can and cannot do → Basic prompting: know how to direct a model toward a useful output → Understand how AI agents may automate multi-step workflows Reality: The bar is now “Can you apply AI judgment in your domain?” 2/ Critical Thinking Is a Competitive Moat → AI generates answers. Humans still have to evaluate them. → Knowing which output is right requires deep contextual judgment. → The ability to interrogate AI outputs is the skill most organizations underestimate. Reality: Analytical thinking remains the most sought-after core skill among employers. 3/ Creativity Is Accelerating → AI can accelerate execution. Creative direction becomes the scarce input. → The organizations seeing the highest returns are not just automating tasks. They are reimagining them. → Creativity is now a strategic differentiator. Reality: Creative thinking and resilience are among the top rising skills globally through 2030, alongside AI and big data fluency (World Economic Forum, 2025). 4/ Adaptability Is the New Tenure → What created value 2 years ago may not be enough to create value now. → The half-life of specific technical skills keeps shrinking. → Adaptability is the core competency of this era. Reality: The most valuable person in your organization may be the fastest learner. 5/ Domain Knowledge Multiplies AI Value → AI without domain context produces generic output. → Deep expertise + AI fluency is where disproportionate value is created. → Your experience becomes more valuable when you know how to apply it through AI. Reality: Contextual expertise directing AI is gaining value. 6/ Technical Skills Are Necessary But Not Sufficient → Tools matter. Judgment matters more. → Technical capability without strategic direction creates activity, not advantage. → The question is now “Can our leaders think with AI?” Reality: The most valuable skill profiles combine technical capability with human skills AI cannot replicate, like creative thinking and resilience. 7/ Continuous Learning Is Not Optional → Employers expect 39% of core job skills to change by 2030 (World Economic Forum, 2025). → The curve has already started. → Organizations building learning infrastructure now are creating compounding advantage. Reality: AI skills can quickly become outdated without systems that help the workforce keep learning. The winners will not be the companies that simply hire more technical talent. They will be the companies that build teams capable of learning, questioning, adapting, and applying AI with judgment.
-
Carolyn Healey shared thisThe difference between AI experimentation and AI performance is operational design. MIT’s NANDA Institute found that 95% of generative AI pilots fail to deliver measurable P&L impact. Not because of the models. Because of how companies build and deploy AI. The ones breaking out of pilot purgatory are running a different system. Mid-market companies have a unique edge: fewer layers of bureaucracy & faster decision cycles let them move from idea to impact quicker. Here’s the 7-point framework winners are using: 1/ Hybrid Org Design, Not a Centralized AI Team → One AI leader (Ops, RevOps, or IT) owns direction and governance → Embedded power users in Marketing, Sales, and CX drive execution → External partners support setup and early use cases Takeaway: Centralized in direction, distributed in execution is what scales. 2/ Use-Case First, Not Platform First → Start with: “Where can we save time or increase revenue within 90 days?” → Not: “Let’s build an AI platform” → Common entry points: content, sales personalization, support automation Takeaway: If you start with a platform, you end up with a science project. 3/ A 3-Layer Stack, Consumed, Not Custom-Built → Layer 1: Models (OpenAI, Anthropic, Google via API) with no custom training, no hosting → Layer 2: Orchestration (Zapier, Make) is the control layer that connects AI to your CRM, CMS, and workflows. → Layer 3: Data (CRM, warehouse, lightweight RAG systems) is where most mid-market companies struggle due to messy or inaccessible data Takeaway: Mid-market advantage = speed. Heavy infrastructure kills it. 4/ Prompt Playbooks, Not Pilots → Winners don’t run 6-month pilots hoping to scale → They build prompt libraries, workflows, and repeatable use cases → Playbooks spread. Pilots stall → Example: A sales personalization playbook used by every SDR vs one isolated AI pilot in RevOps Takeaway: Designed for adoption, not experimentation. 5/ Governance That Enables Speed, Not Friction → Define a small set of approved tools/models → Establish clear guardrails: what data can and cannot be used → Require lightweight vendor review (security, data handling, compliance) → Assign ownership (who is accountable for AI usage across teams) → Skip heavy frameworks early, as they slow momentum before value is proven Takeaway: Most AI initiatives fail because governance either doesn’t exist or shows up too heavy, too early. 6/ Small Bets → Proven ROI → Then Scale → Initial budgets: $10K–$100K → Spend on tools, not headcount → Expand only after measurable returns Takeaway: AI budgets grow on proof. 7/ Measure Efficiency First, Then Tie to Revenue → Early wins: time saved (content, research, reporting) → Then: pipeline, conversion, CX metrics → If AI doesn’t connect to revenue within ~6 months, it stalls Takeaway: The gap between “we use AI” and “AI is a capability” is execution + measurement. Most companies will adopt AI. Very few will operationalize it. That’s where the advantage is now.
-
Carolyn Healey shared thisAI leaders don’t pick the “best” model. They built the right model into the right workflow. That’s the shift most enterprise AI strategies are still missing. Right now, too many AI decisions are driven by vendor proximity, not strategic fit. The same way companies chose cloud providers a decade ago. But AI doesn’t behave like cloud. The major platforms - ChatGPT, Claude, Gemini, and Copilot - are not interchangeable. They have fundamentally different strengths, failure modes, and roles inside an organization. And the companies pulling ahead understand this: They’re not choosing a platform. They’re designing a system. Here’s how that system actually breaks down: ChatGPT (OpenAI) Best for breadth, adaptability, and innovation velocity → Strong general reasoning across use cases → Deep ecosystem (custom GPTs, APIs, integrations) → Ideal for marketing, strategy, and cross-functional work Where it wins: Versatility at scale Where it struggles: Governance and consistency in large orgs Claude (Anthropic) Best for precision, reasoning depth, and regulated environments → Excels at long-form analysis and complex documents → Strong safety architecture by design → Ideal for legal, compliance, and financial workflows Where it wins: High-stakes, high-accuracy work Where it struggles: Less breadth across general workflows Gemini (Google) Best for multimodal workflows inside Google ecosystems → Deep integration with Docs, Sheets, Gmail, Drive → Large context window for complex inputs → Strong across text, image, and data workflows Where it wins: Google-native organizations Where it struggles: Value drops outside that ecosystem Microsoft Copilot Best for embedded productivity at scale → Lives inside Word, Excel, Outlook, Teams → Grounded in company data via Microsoft Graph → Built for enterprise compliance environments Where it wins: Workflow integration Where it struggles: User preference vs. standalone tools Here’s the part most CXOs are just starting to realize: This is no longer a single-vendor decision. Enterprises are moving toward multi-model environments, where different tools are assigned to different workflows, not forced into a one-size-fits-all solution (Gartner, 2026). And adoption is accelerating fast. The majority of enterprises already have AI in production in some form (IDC, 2026). But deployment doesn’t equal advantage. Strategy does. The companies pulling ahead are doing three things differently: → Assigning models to specific workflows → Building governance around usage, not just access → Treating AI like a portfolio; not a platform The AI race isn’t about who has the best model anymore. It’s about who designs the best system. And most organizations are still thinking one layer too shallow. So here’s the real question: Are you choosing a tool or are you architecting how work gets done? Save this for future reference.
-
Carolyn Healey shared thisThe CMOs gaining boardroom influence aren’t asking for bigger budgets. They’re rebuilding marketing into something the CFO actually trusts. Gartner’s 2025 CMO Spend Survey says it all: budgets are stuck at 7.7% of revenue, and 59% of CMOs say it’s not enough. The money isn’t coming. The mandate is. The advantage now isn’t better campaigns. It’s better systems. This isn’t a trend list. It’s an operating model shift. Here are the 9 pillars separating CMOs gaining influence from those losing it: 1/ AI-Powered Personalization As Infrastructure → Personalization drives 5–15% revenue lift (McKinsey) → Most teams adopted AI but didn’t rebuild workflows around it Reality: This isn’t a tactic. It’s how revenue gets generated. 2/ First-Party Data as a Strategic Asset → Third-party tracking is disappearing across browsers → Server-side tracking recovers critical lost conversion data Reality: If your data is broken, your budget decisions are too. 3/ Brand as a Growth Engine → 84% of companies are stuck in a brand “doom loop” (Gartner) → Companies that escape it are 2x more likely to hit growth targets Reality: Brand isn’t a cost center; it’s what makes performance work. 4/ Customer Experience = Margin Protection → CX is now the #1 priority for B2C CMOs (Forrester) → Loyalty is weakening in a more price-sensitive market Reality: When price becomes the lever, you’ve already lost margin. 5/ Measurement That Earns Trust → Only 32% of execs trust CMOs to drive strategy (Gartner) → Measurement remains a top priority but largely unsolved Reality: If you can’t prove incrementality, you can’t defend spend. 6/ Agentic AI as the Operating Layer → Most organizations are piloting AI agents → Very few have restructured how work actually gets done Reality: AI isn’t replacing marketers. It’s replacing the operating model. 7/ Content That Wins in AI-Mediated Discovery → Search is shifting toward AI-generated answers → Traditional click-driven funnels are fragmenting Reality: If your brand isn’t cited by AI, it doesn’t exist in the buying journey. 8/ Cross-Functional Ownership of Revenue → Marketing, sales, and CX are increasingly integrated → CMOs who lead across functions outperform expectations Reality: If marketing isn’t shaping revenue strategy, it’s reporting on it. 9/ Talent That Makes AI Better → CMOs are reducing headcount and simplifying roles → AI is reducing reliance on external agencies Reality: You’re not building a team. You’re building a system with humans at the controls. You don’t get a bigger seat at the table by doing marketing better. You get it by making marketing indispensable to how the business runs. Need help with your marketing & AI strategy? Book a strategy call: https://lnkd.in/gEY5pN7z Save this for future reference.
-
Carolyn Healey shared thisYour AI strategy isn't failing because the technology is wrong. It's failing because your leadership team isn't running the same play. → Your CTO sees a capability build. → Your CFO sees a cost reduction exercise. → Your COO sees an operational disruption. All three are looking at the same AI roadmap. All three are executing a different one. That's an alignment problem with a very expensive price tag. McKinsey's 2025 State of AI research shows 88% report using AI in at least one business function. But only about one-third have begun scaling it across the enterprise. The gap isn't in the models. It's in the leadership system behind them: 1/ AI Strategy Is Announced, Not Decoded → "AI-first" means automation to Operations, cost reduction to Finance, and a product feature to Marketing → Without shared interpretation, execution fractures at the seam Reality: If your C-suite can't articulate the AI strategy in the same words, you don't have alignment. 2/ Pilots Are Mistaken for Progress → 42% of companies abandoned most of their AI initiatives in 2025, more than double the prior year (S&P Global Market Intelligence) → The blockers are governance gaps, workflow rigidity, and absent ownership Reality: Pilot purgatory is what happens when no senior leader has actual skin in the game. 3/ Leadership Ownership Is the Strongest Predictor of AI Success → McKinsey's State of AI 2025 identifies senior leadership ownership as the single strongest predictor of high performance → Yet only 28% of organizations say the CEO takes direct responsibility for AI governance oversight Reality: AI treated as an IT upgrade will deliver IT-grade results. 4/ The Middle Layer Is Killing Execution → Frontline managers control which tools are "safe," how performance is measured, and what counts as progress → When they're excluded from the AI strategy, they become passive resistors Reality: Senior leaders approved the roadmap. Middle managers decide whether anyone actually uses it. 5/ Operating Models Are Being Ignored → Workflow redesign is the single highest-correlating factor with EBIT impact from gen AI → Yet only 21% of organizations using gen AI have fundamentally redesigned even some workflows (McKinsey) Reality: You cannot automate your way to transformation without rewiring how work actually flows. 6/ Measurement Is an Afterthought → Only 39% of respondents say AI has had any impact on enterprise-wide EBIT (McKinsey State of AI 2025) → Without KPIs tied to business outcomes, no one can distinguish a working AI program from a busy one Reality: Activity is not a strategy. Proving value is. Here's what separates the 6% of organizations McKinsey classifies as true AI high performers: They run a full-stack management playbook across strategy, talent, operating model, technology, data, and adoption. They don't bolt AI onto the org. They rewire the org around AI. Save for future reference.
-
Carolyn Healey shared thisMost AI strategies are organized by org chart. That’s exactly why they fail. Job titles don’t produce outcomes. Workflows do. One stat should stop every CXO: Only 21% of organizations have redesigned even part of their workflows for AI (McKinsey State of AI, 2025) Think about that. Eight in ten enterprises are layering AI on top of the same broken processes and expecting different results. McKinsey calls this dynamic “AI theater”: adoption without operating model change. Only 6% of organizations report significant EBIT impact from AI (McKinsey State of AI, 2025) And they all made the same structural shift: They stopped asking “Which roles need AI tools?” And started asking “Which workflows need to be rebuilt?” 1/ Role-based thinking fragments the problem → “Give marketing an AI writing tool” → “Give finance an AI forecasting dashboard” → “Give HR an AI screening tool” Each role gets a tool. No one owns the outcome. Value lives in the handoffs between roles, exactly where role-based AI goes dark. 2/ Workflows expose what roles obscure → The insight that unlocks a deal doesn’t come from Sales alone → It touches CRM data, analyst input, legal review, executive context That’s four roles. One workflow. AI in workflows compounds. AI in roles adds features. 3/ Role-based deployment creates adoption theater → 88% of organizations are now using AI in at least one function (McKinsey, 2025) → But most remain in pilot or limited use. AI is widespread but not embedded deeply enough to transform how work actually happens. 4/ Workflows reveal where friction actually lives → Role-based AI asks: “What does this person do repeatedly?” → Workflow-based AI asks: “Where does value stall, data degrade, or decisions wait?” Different questions. Very different ROI. 5/ Agentic AI breaks role-based models → 62% of organizations are experimenting with AI agents (23% scaling, 39% piloting) (McKinsey, 2025) Agents operate across systems, functions, and data. They don’t follow reporting lines. Agentic AI cannot run on a role-based skeleton. The architecture is incompatible. 6/ Workflow-first design changes how you measure success → Role-based: “Did this team save time?” → Workflow-based: “Did cycle time compress? Did outcomes improve?” One measures activity. The other measures impact. 7/ The org chart will reorganize anyway Only ~34% of organizations are truly reimagining their business with AI, while the rest optimize existing processes (Deloitte State of AI, 2026) New roles are emerging: → AI operations → Human-AI interaction → Quality oversight At the workflow level, not the function level. The reframe: Your org chart maps accountability. Your workflows map value. AI belongs on the second map. The question for your next leadership offsite is “Which workflow, rebuilt with AI, changes our competitive position?” Need help with your AI strategy? Book a strategy call: https://lnkd.in/gEY5pN7z Save for future reference.
-
Carolyn Healey shared thisMove fast with AI and you create risk. Govern first and you create advantage. That difference shows up in breach costs, board-level risk and whether AI scales. Here’s the reality most executives won’t say out loud: → 88% of organizations deploy AI in at least one function. → Only 1% consider themselves AI-mature. (McKinsey, 2025) This isn’t a technology gap. It’s a governance gap. Shadow AI is where that gap becomes a liability. Inside most organizations today, shadow AI is already the default operating model: → 80%+ of employees are using unapproved AI tools (UpGuard, 2025) → 57% are entering sensitive company data into those tools (Menlo Security, 2025) → Only 18.5% are aware of any official company AI policy (ISACA, 2025) Your people aren’t being reckless. They’re being productive. The problem is they’re doing it outside any guardrails. The CXOs pulling ahead are converting shadow AI into governed advantage. Here’s how: 1/ Visibility before control → You cannot govern what you cannot see → Map every AI tool in use, sanctioned or not → Treat shadow AI as a signal, not a violation Shadow AI is often the clearest indicator of unmet business needs. 2/ Make approved tools the easiest choice → Procurement friction drives shadow behavior → Vet tools for security, compliance, and integration → Publish and regularly update an approved list When governance is easy, adoption follows. 3/ Ditch blanket bans. Tier the risk. → High-risk: customer-facing / sensitive data → Mid-risk: internal operations → Low-risk: individual productivity Blanket bans don’t stop shadow AI; they just push it underground. 4/ Bring AI inside your perimeter → Provide enterprise-grade, secure versions of the tools people already use → Keep data within your governance boundary → Enable productivity without sacrificing control Organizations with high shadow AI see breach costs run $670K higher on average (IBM/Programs.com, 2025). 5/ Put a name on accountability → AI governance belongs at the executive level → Not just IT or security → Assign a clear owner or formal oversight group Governance without ownership doesn’t stick. 6/ Train judgment, not compliance → Policies alone create checkbox behavior → Teach data risk, IP exposure and consequences → Deliver role-specific training Underinvestment in training is one of the biggest constraints on AI productivity (BCG). 7/ Match governance speed to innovation speed → The business won’t wait 90 days for approval → Create fast-track evaluation (≤10 business days) → Let business units surface tools; centralize risk review If “approved” is slower than “just use it,” governance fails. 8/ Treat governance as a capability, not a policy → Track adoption, visibility, and training → Report progress to leadership regularly → Continuously evolve as the AI landscape shifts The executives pulling ahead don't restrict AI. They’re creating conditions where AI can be trusted at scale by boards, regulators, customers & teams.
Experience
Education
-
-
-
-
-
-
-
-
-
Publications
-
Online Fraud is Growing. What Can Your Business Do?
Service Objects Blog
See publicationGuarding against eCommerce fraud is a two-pronged effort: reducing online fraud itself and reducing revenue lost. For both of these issues, the key is implementing effective automated solutions.
-
Data Quality and Customer Experience
Service Objects Blog
See publicationWhile some organizations still have a break/fix mentality about customer support, the very best organizations now view their customer contact operations as the strategic voice of the customer – and leverage customer engagement as a strategic asset.
-
Data Quality and Compliance
Service Objects Blog
See publicationFor most people, regulatory compliance sounds about as exciting as doing your taxes. And this is actually a pretty good analogy, because compliance and taxes are both obligations that won’t go away if you ignore them.
-
Three Building Blocks to Global Data Protection Regulation (GDPR) Compliance
Service Objects Blog
See publicationFor most organizations, GDPR compliance pivots around three fundamental building blocks: consent management, data protection, and data quality.
-
The Cost of Incomplete Leads to Your Business
Service Objects
See publicationThe lifeblood of any marketing operation is its lead generation efforts. And sadly, many of these leads aren’t real - according to industry figures, as much as 25% of your contact data is bad from the start, and from there 70% of it goes bad every year as jobs, roles and contact information changes.
-
Why Data Quality is Key to the Sales and Marketing Relationship
Service Objects
See publicationBoth Sales and Marketing teams are linked to a common shared goal, and often frustrate each other when these goals don’t happen as planned. And very often, the culprit is data quality.
-
The Role of a Chief Data Officer
Service Objects Blog
See publicationNearly two-thirds of CIOs want to hire Chief Data Officers (CDO) over the next year. Why is this dramatic transformation taking place, and what does it mean for you and your organization?
-
The Importance of Data Accuracy in Machine Learning
Service Objects Blog
See publicationSince machine learning is fed by large amounts of data, its benefits can quickly fall apart when this data isn’t accurate.
-
Use the Net Promoter Score to Ensure You Get a Good Customer Experience
Service Objects Blog
See publicationBefore you buy a product or service from a company – particularly one you may need customer support from – be sure to do some research and find out their Net Promoter Score (NPS). NPS is a metric that captures a company’s customer feedback and provides a numeric value of its brand loyalty.
-
SupportIndustry.com Weekly Newsletter
Carolyn Healey
See publicationSupportIndustry.com provides senior-level service and support professionals with resources related to improving their customer service operations.
Recommendations received
2 people have recommended Carolyn
Join now to viewOther similar profiles
Explore more posts
-
flowres.io
Survey software or a Qual platform? If your project is meant to listen, not just measure; you do need more than a survey. This quick guide breaks down when to use each tool (and what you miss, if you choose wrong). Especially useful if you're buying research software for the first time or switching platforms. Read our latest article, make the right call 👇 https://lnkd.in/dR8taCKq #ResTech #QualitativeResearch #SurveySoftware #ConsumerInsights #MarketResearch #CustomerResearch #OnlineQual #ResearchOps #MRX #FocusGroups #DigitalDiaries #RemoteResearch #BuyersGuide
-
Backlink Indexing Tool LTD
Backlink Indexing Tool presents a comprehensive guide to the most effective link building strategies that drive real SEO results. In today's competitive digital landscape, successful link building requires a strategic approach that prioritizes quality over quantity, relevance over volume, and sustainable practices over quick fixes. The foundation of effective link building lies in understanding that search engines like Google use backlinks as trust signals and authority indicators. Backlink Indexing Tool emphasizes that high-quality links from relevant, authoritative sources carry significantly more weight than numerous low-quality connections. This principle guides every successful link building campaign. Creating linkable assets remains one of the most powerful strategies for earning natural backlinks. Backlink Indexing Tool highlights how original research, comprehensive guides, interactive tools, and data-driven content naturally attract links from other websites seeking valuable resources to reference. These assets serve as magnets for organic link acquisition. Digital PR and relationship building have become increasingly important in modern link building strategies. By fostering connections with industry influencers, journalists, and content creators, businesses can earn high-authority backlinks that boost domain credibility and search rankings. Backlink Indexing Tool demonstrates how consistent outreach and value-first approaches yield sustainable results. Link indexing plays a critical role in maximizing the impact of acquired backlinks. Non-indexed links provide no SEO benefit, making it essential to ensure search engines recognize and value your link building efforts. Backlink Indexing Tool accelerates this process, helping websites realize the full potential of their link acquisition campaigns. The future of link building continues to evolve with AI integration, user-generated content opportunities, and algorithm updates. Backlink Indexing Tool advocates for sustainable, white-hat practices that build long-term authority rather than pursuing short-term gains that risk penalties. #BacklinkIndexingTool #LinkBuilding #SEO #DigitalMarketing https://lnkd.in/gUUwm2W2
-
Growthner
Struggling to turn organic traffic into real SaaS signups? You're not alone. Choosing the right SEO partner can make all the difference. In our latest guide, we break down the 10 best SaaS-SEO consulting agencies for 2025 including what separates the winners from the wannabes, what pricing looks like, and how to pick an agency aligned with your growth stage. From technical SEO to AI-driven Generative Engine Optimization (GEO), we cover it all. Read the full list and find the partner that could transform your organic channel. https://lnkd.in/e_5VBc_X
-
Fuentes Zapata
As featured in Digital Journal 🔥 In The Remote Marketer's Toolkit, Fuentes Zapata distills years of experience into a comprehensive field guide that caters to both novice and seasoned marketers. By sharing proven techniques and templates, the book emphasizes the importance of foundational strategies, systematic approaches to marketing technology (MarTech), data integrity, and repeatable workflows. The goal is to empower marketers to produce results without adding unnecessary complexity. Check it out... https://lnkd.in/evcgQgTY
-
Joseph Braue
If 6Sense’s latest Buyer Experience research (and Kerry Cunningham) is right and buyers have prior experience with 80 percent of the vendors on their shortlist, then these are the implications for content marketing strategists: Buyers are smarter than you think they are: ---Your content should assume your buyers are aware of many of the available solutions out there. Add more comparative information to your assets: ---You need to consider putting comparative information in most mid-funnel and bottom-of-the-funnel assets, and even a lot of top-of-funnel pieces. Acknowledge competitive reality: ---Buyers have lots of choices and know of your competitors and are maybe even leaning to them. Admit there are multiple ways to solve buyer pain points, and suggest why your unique value proposition makes sense. Buyers are going to come to their own judgement about this anyway. Why not present your case? https://lnkd.in/eF-GEYZt #6Sense #ContentMarketing #INFUSE
3 Comments -
Poppy Marketing and Consulting
I built a simple, 5-point Local SEO checklist for Houston businesses that actually moves the needle. Each step includes before/after examples and clear next steps you can implement today. Which area costs you the most visibility — GBP, reviews, citations, schema, or location pages? Download the checklist and start fixing what matters: https://wix.to/AXpB2cN 🚀 #LocalSEO #HoustonBusiness #SmallBusinessMarketing
-
MSQ DX
Most CMS challenges aren’t platform problems. They’re architecture problems. In part 2 of Tom Robinson’s blog series, he explores the universal principles for scalable content models. Before your next build, ask yourself: - Are your content models designed to scale, or just to ship quickly? - Will your structure support future channels and experiences? - Are today’s shortcuts creating tomorrow’s technical debt? - Is your content model limiting flexibility? The decisions made in the first sprint often define long-term success. Read Part 2 to explore the principles behind resilient, future-ready content models 👉 https://lnkd.in/e8kry6pr #ContentModelling #DigitalArchitecture #CMS
1 Comment -
Dazlab
Most marketers use their CMS for publishing, but what about these underrated features? 🚀 Content Repurposing: Turn one post into multiple formats (blogs, emails, social posts) without starting from scratch. 🔄 Automated Workflows: Set up auto-approvals, scheduled updates, and streamlined handoffs to save hours. 🌎 Multilingual Content: Don’t copy-paste translations. Use built-in tools to create seamless global experiences. 🔎 Advanced Search & Filters: Quickly find past campaigns, media, and templates instead of digging through folders. 📊 Built-in Analytics: See what’s working inside your CMS, no extra tools needed. The best part? You don’t have to make do with what’s there. Your CMS can be tailored to work exactly how your team needs it: https://dazlab.global/ #cms #contentmanagement #software #softwaredevelopment #contentmarketing #techcommunity
Explore top content on LinkedIn
Find curated posts and insights for relevant topics all in one place.
View top content