It’s a question every marketing leader should ask. A recent discussion highlighted a critical point: while marketing teams work hard, their initiatives aren't always perfectly aligned with overarching business objectives. Often, product and tech departments are taking the lead on key projects, like system overhauls for personalized communication, sometimes without clear marketing input from the start.
“The only metric that matters in customer service is resolution. The Zendesk Resolution Platform is not just making service faster – it is making Agentic AI actually work for service, solving every issue with less effort and better outcomes,” said Tom Eggemeier, CEO of Zendesk. “Our network of AI agents built with service at the heart works like a well-trained search and rescue team, ensuring every interaction leads to a resolution. And as the only large service software provider offering outcome-based pricing, we make sure customers only pay for problems that are resolved – not for interactions or failed attempts. Resolutions are the future of customer service, and Zendesk is leading this revolution.”
Legacy players such as mParticle, ActionIQ, and Lytics have faced major shakeups, with acquisitions marking the end of an era for traditional CDPs. Outdated architectures, sluggish onboarding cycles, and questionable ROI have resulted in these platforms struggling to keep up with the evolving demands of modern data management.
As startups scale, effective sales implementation becomes the difference between stagnation and sustainable growth. After analyzing hundreds of sales organizations across startups, I’ve distilled the key pieces of advice that founders and leaders should keep in mind.
There’s light at the other end of the tunnel, however. We’re starting to see which GenAI use cases are best positioned to survive the next few years. And in the retail industry, there are five that have the potential to usher in a new era of e-commerce. We’ll explain how in this piece as well as make the case for why GenAI fits naturally into the broader arc of modern e-commerce innovation.
For nearly a decade, the marketing world has fantasized about the inevitable convergence of adtech and martech. Analysts promised a revolution: a frictionless union of these ecosystems that would forever change how brands connect with consumers.
Christopher O’Donnell believes the fundamental problems with CRM—incomplete data, complex workflows, siloed work products and the fear of leads falling through the cracks—can finally be solved through AI. Founder of Day.ai and former Chief Product Officer of HubSpot, Christopher explains how his team is building a system that automatically captures the full context of customer relationships while giving users transparency and control. He shares lessons from building HubSpot’s CRM and why he’s taking a deliberate approach to product development despite the pressure to scale quickly in the AI era.
The value of AI in CRM is real. Customer interactions produce huge volumes of high-quality, well-governed data; unleashing AI on this data lets organizations summarize and surface key insights, predict the best actions to take, and start pursuing goals autonomously. The result? Increased productivity and effectiveness for the front office, which enables organizations to deliver on the promise of better customer relationships, better customer retention, and increased revenue.
Growing your business doesn’t always have to involve complicated strategies or large investments. In fact, one of the easiest and most effective ways to increase your client base is through referrals.
B2B tech stacks are becoming increasingly complex, with too many point solutions — some rarely used — and mounting SaaS subscription fees. Even worse, many stacks aren’t fulfilling their intended purpose. Only 17% of companies feel their tech stacks meet all their needs, a recent Webflow report reveals.
New and promising tools are emerging almost daily and deserve a closer look. After all, shiny objects often shine for a reason. Here are 13 B2B marketing tools that have caught my attention.
When it comes to measuring customer experience (CX), the three most popular tools are CSAT, NPS, and CES.
Each of these metrics provides insight into how your customers view your company—but each has its own strengths and its own niche application.
How you integrate these metrics into your customer satisfaction surveys will depend on the kind of business you’re in, the way in which you’re looking to grow, and the points and CX processes you’re aiming to improve upon.
We asked the respondents who said improving CX was a priority how they planned to do it, and almost a quarter said they are going to “create cross-functional teams aligned around customer journeys.” Cutting the sample by broad industry groupings backs up what we’ve heard from clients — firms in sectors like telecom, hospitality, utilities, and banking, which sell ongoing service-based “products,” are more likely to embrace cross-functional journey teams than industries like retail, where transactional channels can still dominate.
Low-code development is revolutionizing digital transformation; The accessibility of creating apps beyond the expertise of conventional coders has profoundly accelerated innovation, enabling rapid response to evolving market demands.
"Start with the human to create the connection first before you move to content, data, support, or arguments.... If you start with 'here is what we do, here is how we do it, and here's why we do it,' you're marketing wrong," he says.
"You start with the why, then you go to the how; the what is last."
Key Takeaways:
Traditional marketing cycles are slow, manual, and miss key windows for growth.
CDPs and legacy platforms aren’t the answer. They create more silos and lag time instead of speeding you up.
The GrowthLoop Compound Marketing Engine uses agentic AI trained on your enterprise data cloud to run campaigns, optimize in real time, and continuously learn.
As technology and AI make the actual execution of performance advertising easier, the value of creative strategy becomes even more important.
But there’s a glaring juxtaposition in the term “creative strategy.”
"When done properly, win-loss analysis can deliver deep and surprising insights that help build the roadmap to winning more and the success that every marketing and sales organization strives for." - Stu Perlmeter
Once again, security risks dominate the software supply chain. JFrog emphasizes this in the recently published Software Supply Chain State of the Union 2025 report. A quartet of problems presents itself: vulnerabilities, malicious packages, exposed secrets and misconfigurations or human error.
AI expands beyond chat. Generative AI is now capable of taking calls, conducting research, and simulating personalities, pushing its utility far past simple chatbot interactions.
Customer service is in AI’s sights. Sales and support roles are among the most impacted, with AI agents proving to be more patient, knowledgeable, and consistent than some human counterparts.
Real-world use cases show promise. Klarna’s CEO reported equal customer satisfaction scores between AI agents and human agents, prompting a reduction in outsourced human support.
Human-like AI is already here. From AI-generated podcasts to voice bots conducting interviews, the technology is becoming eerily convincing — and potentially more effective in some communication scenarios.
JIT manufacturing thrives on prevision and repeatability. JIT marketing, by contrast, tends to undercut the creative and strategic processes that make campaigns effective – like brainstorming, testing, and incorporating feedback.
With marketers under pressure to do more with less, some have turned to no-code email builders as a way to cut costs. However, from an accessibility perspective, these platforms can carry substantial legal risks for email marketers that shouldn’t be overlooked.
AI is everywhere – whether we realise it or not. A recent Gallup poll found that while nearly all Americans use products with AI features, 64% don’t realise they do. The rapid rise of AI has created enormous pressure for organisations to integrate it into their operations, fearing they will fall behind competitors who are racing to leverage the technology’s potential.
On its own however, AI is not the silver bullet for organisational transformation. It is a powerful tool, but its success depends on leadership, culture and human qualities. When AI adoption fails, it is often not due to the technology itself, but rather to leaders neglecting the human side of transformation.
