Most enterprises have no problem handing off customer service to an outside partner. Billing disputes, tech support, and returns are all fair game when it comes to outsourcing. Yet, the moment the conversation shifts to revenue-generating functions like sales, leaders become more cautious. Sales is often perceived to be too close to the brand, too strategic, and too important to trust to anyone outside the building.

That logic is costing companies growth.

A recent IDC InfoBrief, “From Efficiency to Excellence: Driving Enterprise Value Through Customer Experience Partnerships,” sponsored by TELUS Digital, draws on survey data from 287 enterprise decision-makers on their business priorities and CX partnership strategies. The results show a striking imbalance in modern outsourcing. 

The Trust Gap Is the Real Bottleneck

Organizations are comfortable delegating customer friction, but they remain tethered to an outdated belief that revenue-generating functions must stay strictly in-house to maintain control. That mindset is holding enterprises back. 

According to IDC’s InfoBrief, customer analytics is now the most outsourced function to CX partners, cited by 27% of respondents. Inbound B2B sales trail at 17%, inbound B2C sales at 9%, and outbound sales functions rank even lower.

The disparity is telling. Enterprises are willing to hand over the data and insight layer of the customer relationship, but they still hesitate at the transactional moment. Modern CX outsourcing has evolved from transactional labor to high-fidelity brand extension. The barrier isn’t a lack of partner capability. It’s a legacy mindset that treats sales as ‘core identity’ and service as a ‘utility.’ When enterprises mistake proximity to the office for quality of the outcome, they inadvertently limit their own scale.

Same Tools, Different Silos

Consider this: the same AI and predictive analytics that enterprises deploy to resolve support tickets are perfectly suited for identifying upsell triggers, forecasting churn, and surfacing expansion opportunities.

The IDC data shows that 34% of enterprises rank improving operational efficiency as their top priority over the next 12 to 24 months, with 31% prioritizing improved customer experience and 21% focused on revenue growth. Most companies chase these with separate budgets and separate teams. But think about what actually happens in a single customer interaction: an AI tool resolves an issue faster, and that’s efficiency. The customer walks away satisfied, and that’s experience. And because the system flagged a cross-sell opportunity during that same conversation, the agent closes an expansion, and that’s revenue. One interaction, one platform, three outcomes. The only reason companies don’t see it that way is that they’ve organized themselves not to.

Technology and talent are commercially agnostic. Only internal silos prevent a service tool from becoming a revenue engine. CX partners, by nature, sit outside organizational walls. The best among them have spent years perfecting the science of hiring, training, and scaling the specific skill sets required for consultative, high-conversion interactions.

When Procurement Undermines Growth Strategy

The most significant insight from the IDC InfoBrief concerns how enterprises buy CX partnerships. Contract pricing and flexibility are the top vendor selection factors at 28%, and 72% of enterprises expect 10% to 19% cost savings from their CX partner. Cost discipline matters, of course. But when pricing is commoditized, partners are forced into a defensive posture, focusing on baseline service level agreements (SLAs) rather than proactive growth.

In a revenue partnership, the conversation must shift from cost per head to return on investment. In a service-only model, success is often measured by how quickly you can get off the phone. In a revenue partnership, success is measured by conversion rates, pipeline contribution, and account expansion. 

Enterprise leaders need to think beyond the cost of a partner, considering how effectively they can accelerate outcomes and shorten the sales cycle. IDC’s data shows that 22% of enterprises already view revenue growth as a quantifiable outcome of their CX partnerships. That number should be much higher. You cannot expect a vendor to drive strategic growth if the contract is designed only to manage tactical costs. A true revenue partnership transforms the CX provider from a defensive cost center into an offensive growth engine that pays for itself.

Agentic AI Changes the Math

The emergence of agentic AI accelerates all of this. According to IDC, 32% of enterprises have already deployed agentic AI use cases in their outsourcing operations, and only 6% report no interest in the technology. This is no longer about chatbots deflecting routine inquiries. Agentic AI analyzes behavior in real time to trigger next-best-action recommendations, ensuring expansion opportunities aren’t missed during a service interaction. It moves CX from manual task execution to autonomous revenue orchestration.

For CX partners already managing both the data layer and the customer interaction, agentic AI can be the connective tissue that turns a support conversation into a qualified pipeline event.

The Full-Lifecycle Imperative

The enterprises that will pull ahead are the ones that stop treating acquisition, retention, and expansion as separate workstreams managed by separate vendors. Deploying a single partner across the full customer lifecycle ensures data continuity, with the sales team informed by support data and the support team staying aligned with the original value proposition. IDC’s data reinforces this: 25% of enterprises say the highest satisfaction driver is alignment of service delivery with their business. A fragmented customer journey is a silent revenue killer. A unified lifecycle is a force multiplier

Time to Rethink the CX Partner Relationship

The CX outsourcing market has matured well beyond transactional staffing models. The partners, the technology, and the talent models exist today to drive measurable revenue impact. What hasn’t caught up is the enterprise mindset. The risk isn’t in outsourcing sales, but in the cost of not scaling it.

For leaders still treating their CX partner as a cost center, the IDC InfoBrief sponsored by TELUS Digital offers a clear direction: the same partnership you’re using to manage friction could be the engine that drives your next phase of growth.

The post Your CX Partner Is a Revenue Engine. Treat It Like One. appeared first on MartechView.

Companies have spent years and significant resources collecting more data than ever before. Yet the bottleneck is rarely acquisition — it’s communication. The most consequential insights routinely get buried under dashboards, spreadsheets, and competing reports, leaving decision-makers with more noise than signal. The result is a paradox: organisations drowning in data, starved of clarity.

The High Cost of Information Overload

The modern workplace is awash in dashboards and reports that were designed to improve decisions but often do the opposite. When employees must sift through increasingly complex datasets simply to identify what matters, data becomes a burden rather than an asset — adding to workload and accelerating burnout without delivering commensurate value.

The consequences of data without context can be severe. In 2018, Zillow launched Zillow Offers, an AI-powered instant homebuying service with an ambition to generate $20 billion in annual revenue within five years. The platform ingested vast amounts of seller data to make near-instant property offers — but by the third quarter of 2021, it had lost $421 million and was shut down. Chief executive Rich Barton attributed the failure to the AI’s inability to accurately predict listing prices. The volume of data was not the problem; the absence of contextual judgment was.

Also Read: Three Myths That Are Keeping Brands Away From AI

The Shift to Meaningful Data Communication

The shift required is not technological but editorial — from collecting data to communicating it. Research supports the urgency: 84% of communication specialists report that C-suite executives have asked them to provide greater clarity on corporate strategy, suggesting that even senior leadership feels the weight of information overload. The solution is not another dashboard. It is a discipline: interpreting data correctly, stripping out what is peripheral, and building a narrative that connects numbers to decisions.

3 Strategies to Improve How You Communicate Data

To shift from information overload to actionable insights, you must adopt a strategic approach that concentrates on narratives, purpose-driven visualization and a methodical system. These three strategies can help you transform your data communication from a jumbled mess to a message that resonates and drives results.

Tell a Story

Narrative structure makes data digestible and memorable in ways that raw numbers cannot. The approach is straightforward: identify the business challenge the data speaks to, walk the audience through what the findings reveal, and close with a clear recommendation and its likely impact. Data presented as a story is harder to ignore and easier to act on than a table of figures — even when the underlying numbers are identical.

Visualize With a Purpose

Visualisation is not decoration — it is an analytical tool. Every chart or graphic should answer a specific business question; if it does not, it should not be there. The most effective visuals are almost always the simplest ones: the goal is clarity, not sophistication. Well-designed dashboards surface patterns and anomalies that would remain invisible in raw data, which is where their real value lies.

Focus On Minimum Viable Metrics

Tracking too many metrics is functionally equivalent to tracking none — attention gets diluted and the signal disappears into the noise. The discipline here is ruthless prioritisation: identify the handful of KPIs that are genuinely tied to strategic outcomes, and make everything else secondary. Misaligned metrics are a well-documented failure mode. When a measure diverges from the actual goal, teams will optimise for the measure rather than the outcome — a dynamic economists call Goodhart’s Law. The call centre example illustrates it plainly: reward agents for call volume and you will get short calls, not satisfied customers.

Also Read: The Organizations That Survive Disruption Never Had to Recover From It

Closing

The competitive advantage in data no longer belongs to the organisation that collects the most — it belongs to the one that communicates it most clearly. Narrative, purposeful visualisation, and disciplined metric selection are not soft skills peripheral to analysis. They are the mechanism by which analysis becomes decision, and decision becomes outcome.

The post More Data Does Not Always Mean Better Communication appeared first on MartechView.

In a recent discussion with a marketing analyst, she noted that while brands are aware of AI tools and view them positively, adoption remains slow.

The primary reason is that brands often believe their data is not ready for AI tools.

This is surprising, as brands typically require minimal preparation to use AI. Many are held back by common misconceptions, which can be addressed as follows:

Myth #1: Not Enough Data

Many brands hesitate to adopt AI because they believe they lack sufficient data. This perception is understandable, as AI marketing often emphasizes processing large data sets.

However, brands do not need large data sets to benefit from AI-powered insights. Small and mid-sized businesses often have enough customer records to leverage AI for audience targeting or customer acquisition modeling.

Ideally, a brand should have 20,000 customer records to import into an AI system, though 10,000 records can still yield meaningful results. Brands with only a few hundred records may not benefit significantly, but established companies with extensive customer profiles are well positioned to begin.

Myth #2: Disorganized Data

Even when brands have sufficient data, they may hesitate due to concerns about data organization. However, many current AI tools can organize data for brands or agencies and return it in a usable format.

AI tools can enrich datasets and connect customer profiles, which is a fundamental capability. Brands may use AI solely for data organization before exploring more advanced features, allowing for a gradual approach to adoption.

Also Read: The Organizations That Survive Disruption Never Had to Recover From It

Myth #3: Cannot Afford New Staff or Technology

A major barrier to AI adoption is the anticipated demands of integrating new tools, including concerns about staffing, training, and infrastructure.

This is based in reality. Brands have spent the past two decades navigating the integrations of buying tools and data and customer management platforms so that they can take advantage of this data-driven age. 

While it may seem that AI requires similar integration efforts, much of the process can occur within a single application. This eliminates the need for additional internal technology stacks or extensive engineering, as the necessary infrastructure is managed externally.

With app-based AI tools, training and staffing requirements are minimized. While some workflow adjustments are necessary, these tools typically do not require hiring new staff or extensive training.

Fearless Adoption

These three myths are understandable, but if they persist, they will hinder brands from embracing AI-powered advertising. Most brands are already equipped to begin using AI tools, even on a trial basis. Overcoming these misconceptions will encourage adoption and drive further innovation in marketing.

The post Three Myths That Are Keeping Brands Away From AI appeared first on MartechView.

While devices are ever-present, most users do not welcome intrusive messages at inconvenient times. In the Shopping category on iOS, the average push notification opt-in rate is just 33.2%, so two-thirds of users opt out. As a result, most intended recipients never receive these messages.

Interruption-based messaging has declined in the U.S. Although smartphones remain largely unchanged, user attention has shifted. Mobile users increasingly dislike frequent interruptions, and the volume of push notifications has become a significant annoyance. For example, receiving notifications during an important conversation can be disruptive.

Attention Has Quietly Moved Elsewhere

Users remain engaged, but their attention has shifted. According to the Pew Research Center’s Mobile Fact Sheet, approximately 91% of U.S. adults own a smartphone. The user base is stable, but people are increasingly resistant to interruptions that disrupt their focus.

Audiences now use mobile devices with clear intent, such as when they actively open an app. Push notifications fall outside this context, as they attempt to regain attention users have already shifted elsewhere. As attention becomes more session-driven, the gap between message delivery and user readiness widens.

This rejection is often passive. Messages may be absorbed, ignored, or filtered out, reducing the effectiveness of interruption-based communication.

The Moment Push Notifications Lost Their Edge

Push notifications were designed to capture attention through urgent alerts such as buzzes, banners, or badges. Initially, this approach was effective, with notifications treated like incoming calls. However, as users have become more familiar with smartphones and exposed to frequent advertising, the impact of these messages has diminished.

Now, users rapidly filter through numerous notifications, often without reading them. Messages that follow familiar promotional patterns are quickly dismissed before they are even processed.

A key issue is the language in push notifications, which often relies on exaggeration and urgency rather than relevance. Over 70% of brand messages use hype-driven language that audiences increasingly ignore. As this tone becomes predictable, notifications lose their impact and relevance.

Retail’s Shift to Behavioral Triggers

Retail brands continue to prioritize mobile engagement, but they are adopting new methods that align with changing user behavior.

Starbucks relaunched its rewards program to include personalized offers and challenges based on purchase frequency and past activity, keeping customers engaged through its app.

Albert Heijn, a Dutch supermarket chain, has also achieved measurable results by shifting to personalized in-app engagement. After implementing behavior-driven messaging, the company reported a 16% conversion rate within its loyalty program, demonstrating the impact of timely and relevant communication.

Inside Behavioral Trigger Systems

Modern systems send messages based on user behavior, such as repeated product browsing, cart abandonment, or incomplete actions. Timing is critical; messages sent during active sessions receive more attention than those delivered hours later. These systems prioritize real-time interaction over traditional broadcasting.

Why a Behavioral Notification Model Works

The move to behavioral triggers aligns with how people use mobile devices. Interruptions cause friction by forcing context-switching, while in-session messaging feels like a natural extension of the user’s current activity.

Raj De Datta, co-founder and CEO of Bloomreach, said, “Agency remains with the consumer when technology is designed to respond to their intent, stay transparent in its decisioning, and keep humans in control of outcomes. When it drifts from that, it stops being helpful and starts becoming opaque.”

In-session messaging is central to modern user experience. Approximately 69% of people value micro interactions that guide them through a website or app. A seamless user journey drives engagement and loyalty.

What Changed in the Results

As retail apps move away from broadcast push, organizations are shifting focus from traditional metrics like delivery volume and open rates to in-session metrics such as engagement and conversion throughout the user journey.

The same shift is happening in engagement systems. Brian Wisniach, content brand manager at OneSignal, points out, “Notifications are expected to be less about summoning users back into an app and more about solving something instantly on the surface. A food delivery update, a fraud alert, a sports score — all now deliver standalone value without demanding another tap.”

In engagement strategies, appearance and timing of messages are now more important than quantity.

What This Signals About Mobile Engagement

Push notifications remain relevant, but their role is evolving. Interruption is less effective than before. Better results occur when users are already engaged with the software. Retail’s adoption of behavioral triggers reflects a broader trend toward sending fewer, more timely messages based on user signals.

The post Push Notifications Are Broken. Here Is What Comes After Them appeared first on MartechView.

Each week, I speak with business leaders who have invested significantly in AI and CRM, yet still see customers waiting on hold, repeating information, and leaving frustrated. Our latest research quantifies this: poor customer service in EMEA costs consumers 9.2 hours annually, more than a full working day. While AI can address these issues, applying it to fragmented systems only worsens the experience. The real solution is to rethink the systems supporting service, not just the technology layered on top.

Frontline customer service agents often lack the necessary tools and information, with only a third of organizations reporting their agents are truly empowered, according to ServiceNow’s annual CX Shift report. Despite investments in technologies like AI, many businesses undermine these efforts by failing to unify their organizations within a single CRM system.

Addressing this challenge requires a fundamental shift in CRM usage toward a connected enterprise, where departments share information effectively and CRM functions as a system of action across the business. Moving away from siloed approaches reduces customer frustration and repetition. Every customer interaction, whether proactive or reactive, can either build or erode loyalty.

The Cost of Disconnection

The research surveyed 17,335 executives, service representatives, and consumers across EMEA. It found that 47% of customers would switch brands after a single poor or slow service experience. Even in sectors where quick resolution is critical, such as banking and telecoms, issues can take three to four days to resolve.

A critical gap remains between insight and action, as businesses struggle to translate AI investments into improved customer relationships. Too often, organizations layer AI onto fragmented CRM systems, expecting a transformation that does not materialize. Siloed workflows and disparate data only accelerate negative customer experiences.

Half of the surveyed executives report deploying end-to-end CRM platforms that support the entire customer lifecycle. However, what is needed is an AI-driven service powered by CRM data, enabling seamless, efficient customer escalation.

Connected Enterprise Is the Foundation, Not Just a Goal

Notably, 76% of EMEA organizations expect progress in the next three years to result from a connected enterprise approach, compared to 51% who expect it from AI adoption alone. A truly connected CRM is increasingly viewed as the operational foundation for enhanced customer experience.

Traditional CRM systems, which most organizations have used, were designed primarily for the front office. They capture customer requests but often do not manage fulfillment, resolution, or back-office actions, which are handled by separate systems. This fragmentation forces customers to repeat themselves, as no single system provides a complete view.

Current expectations require a new approach. Sellers, service agents, field technicians, and back-office teams should operate on a unified AI-powered platform, sharing a single customer record in real time. Customer issues raised through chat should be tracked, escalated, fulfilled, and resolved without requiring the customer to repeat information.

When CRM is implemented in this manner, businesses experience stronger customer loyalty, increased agent productivity, and improved cost efficiency.

The Agent Experience Is Part of the Customer Experience

It is essential that agents have access to the right tools and data, which requires rethinking how information is shared across the organization. In most organizations, agents are hindered by fragmented systems and disconnected tools. The research found that 79% of EMEA service agents must log into three to five separate systems to resolve customer queries, and inconsistent data across these systems remains a significant challenge.

Service agents prefer to focus on complex customer issues where human assistance is essential, rather than compensating for fragmented systems. By adopting integrated systems, agents are freed from switching between platforms, administrative tasks, and searching for information across departments. Instead of investing in automation without a strategy, organizations should reimagine CRM as a connected engine that unites teams, data, and processes, enhancing the service agents provide.

When organizations implement CRM effectively, the benefits extend beyond improved service interactions. A connected, service-led CRM strategy saves time for both customers and employees, transforming fragmented touchpoints into cohesive journeys that foster trust and long-term loyalty. Customers are more likely to deepen relationships when they feel recognized and understood at every stage. Internally, teams gain shared data visibility, enabling faster decisions, more effective AI use, and meaningful human engagement where it matters most.

AI Delivers When the Plumbing Is Right

However, few organizations have integrated customer data across touchpoints to create a unified view. Only 43% of EMEA organizations surveyed have achieved this, and just 23% have broken down silos to enable seamless AI strategies that connect departments.

This highlights the need for an approach that connects data across cloud environments, departmental tools, and legacy systems, providing AI with a complete customer view before taking action. In this model, AI agents act autonomously, coordinate across departments, and operate continuously without waiting for human input.

This is the difference between AI that resolves a billing dispute at 2:00 AM without human intervention and AI that simply accelerates the same flawed experience customers already face.

Service as a Growth Engine

Organizations that implement connected CRM effectively are discovering what the cost-center perspective overlooks: when service has full visibility into the customer relationship, it becomes a revenue driver.

In practice, this allows service agents to identify upsell and cross-sell opportunities in real time. They can create quotes, manage renewals, and escalate commercial opportunities on the same platform used for service requests. Service and sales become integrated, enabling the agent who resolves an issue to also strengthen the customer relationship.

A connected, service-led CRM strategy not only reduces the nine hours EMEA consumers lose annually to resolution friction but also builds trust and encourages customers to strengthen their relationships rather than switch brands.

Curbing Repetition, for Good

When repetition is the issue, the solution can be straightforward. By establishing a connected enterprise where customer information is shared across all channels and applications, agents are empowered to deliver effective service. A single interface with a comprehensive information streamlines work. In this model, service becomes a growth driver by enhancing productivity, insight, and competitive advantage across the organization.

Investing in AI to accelerate flawed processes will not yield results. Business leaders should instead reimagine CRM as a system of action across all channels, ensuring agents can deliver, and customers no longer waste valuable time with each interaction.

The post Is Your CRM Making Your Customer Service Worse? appeared first on MartechView.

Not long ago, I was having coffee with a fellow CMO at an industry event. Like most marketing conversations these days, we ended up talking about AI. At one point, she paused and said something disarmingly honest.

“Every time leadership talks about AI, they talk about efficiency. But all my team hears is that we’re becoming replaceable.”

It is the thing nobody says out loud in the strategy session, but everyone is thinking. According to recent Pew Research Center data, 52 percent of workers say they worry about the future impact of AI in the workplace. When leaders frame AI primarily as a productivity tool, it activates what researchers call FOBO — fear of becoming obsolete. The anxiety is real, and dismissing it does not make it go away.

But as our conversation continued, we kept arriving at the same conclusion: the leaders getting this right are not managing the fear. They are changing the frame.

The real shift in marketing is not about automation or headcount. It is about how teams turn information into insight, and insight into action.

The Real Challenge Marketing Leaders Face: Digital Complexity

At some point in our conversation, the CMO said something I have not stopped thinking about since.

“I don’t need more dashboards. I need help understanding what actually matters.”

That is where the real story begins. She was not struggling with a shortage of tools. Her team had access to more data and more platforms than ever before. What she was struggling with was complexity — the kind that accumulates silently until it becomes the job itself.

Modern marketers are navigating layered content ecosystems, constant algorithm updates, accessibility and compliance requirements, and more performance data than any team can meaningfully process. The challenge is not collection. It is comprehension — turning disparate information into decisions that actually move something. This is where the next phase of AI is beginning to reshape how marketing teams operate.

Marketing Technology Is Shifting From Tools To Intelligent Systems

From years of working in enterprise SaaS and through my work with the MACH Alliance, I have learned that the most effective digital systems rarely live inside a single platform. They are composable — different capabilities working in concert across systems, each doing what it does best.

We are seeing the same logic applied to AI. Rather than isolated tools, organizations are beginning to adopt agentic systems that work alongside teams—not replacing the workflow but removing the friction within it.

When I described this shift to the CMO I had been speaking with, she understood it immediately.

“So it’s not just about faster reports,” she said. “It’s about helping teams understand what to do next.”

Exactly. Instead of spending hours stitching together insights across platforms, marketers can focus on interpreting signals and making decisions. The system handles the assembly. The human handles the judgment.

The Winning Teams Will Elevate Human Work

Later in our conversation, she said something else that stayed with me.

“Honestly, I didn’t become a marketer to spend my days pulling reports.”

It’s something I hear often that I think requires an industry-wide perspective shift.

For many teams, a surprising amount of time is spent stitching together insights across systems, navigating dashboards, and translating data into something actionable, which can at times feel tedious or daunting, but is one of the most important aspects of our roles.

The key is to find ways to streamline these efforts. Data is the real power behind marketing. By leveraging AI tools to translate analytics data and campaign performance, marketing teams can better understand their audiences and the content that resonates most, enhancing their overall marketing strategies moving forward.

The organizations seeing the most success with AI understand this. They’re not simply introducing new technology. They’re rethinking how work happens inside their teams, using intelligent systems to remove friction so marketers can focus on the work that matters most.

In the end, the real value of these systems isn’t just speed. It’s their ability to help marketers speak the language of the CFO, turning raw data into meaningful insights that can inform future campaigns.

The Real Promise Of Agentic Marketing

When I think back to that conversation, what strikes me most is not the concern my colleague expressed about AI. It is the hope underneath it — that these systems might finally give her team the clarity they have been missing.

The organizations that succeed in the agentic era will not be the ones that automate the most work. They will be the ones who use automation to elevate the work that remains.

When leaders position AI as a collaborator rather than a threat, something shifts. Fear turns into curiosity. Curiosity turns into experimentation. Experimentation drives the kind of innovation that no efficiency mandate ever produced on its own.

The future of marketing will not be defined by AI replacing people. It will be defined by systems that give people the visibility, the insight, and the confidence to act — turning information into intelligence, and intelligence into decisions that move the business forward.

Clarity, in the end, is not a feature. It is the whole point.

The post Clarity Is Marketing’s Most Valuable Asset appeared first on MartechView.

Every industry has a word it reaches for when it does not know what else to say. In boardrooms today, that word is transformation. It gets invoked at strategy off-sites, written into annual reports, and handed to consultants as a mandate — often before anyone has agreed on what exactly needs to change, or why.

The results are predictable. Brands that once stood for something begin to feel like everything. Customer experiences that were once a competitive advantage become inconsistent and interchangeable. Revenue targets get hit in the short term. Brand equity quietly erodes over the long term.

The problem, more often than not, is not a lack of ambition. It is a lack of an anchor. Transformation without a clear sense of what must be preserved is not strategy — it is change for its own sake.

Giovanna B. Questioni has spent her career in that gap. A transformation expert with experience across luxury fashion, food, furniture, and the digital industries, she has led mergers and acquisitions, crisis interventions, and large-scale omnichannel overhauls across global markets. Her argument is not against change. It is for coherence.

The brands that survive disruption, Questioni argues, are the ones that understand the difference between what is negotiable and what is not. Design, quality, emotional resonance — these are not variables to be optimized in a transformation roadmap. They are the reason the brand exists. Everything else is in service of them.

That distinction shapes how she thinks about omnichannel strategy — an area where many brands have spent heavily and delivered inconsistently. The instinct is to treat every new channel as an opportunity. The discipline is to treat every channel as a responsibility to serve customers without breaking what they already trust.

“Every touchpoint — whether in retail, franchising, wholesale, e-commerce, or social commerce — must deliver a distinct yet cohesive experience. Physical stores thrive on human connection and sensory engagement, while digital platforms excel through simplicity, speed, and personalization.” 

A customer who buys online and returns in-store should not feel the friction of two separate systems. The seams should never show. And the sales assistant at that moment is not a workaround — they are an opportunity for the kind of personalization no digital platform has yet replicated.

What Questioni warns against is the version of transformation that becomes its own end. “Disruption is powerful, but it must always align with customer expectations and ROI,” she says. “Without this balance, transformation risks becoming an academic exercise — one that could dilute the brand’s reputation and desirability.” Bold innovation is not the enemy of brand integrity. Undisciplined innovation is.

The second argument she makes — and the one that challenges the most deeply held assumptions in corporate strategy — is about resilience. The dominant understanding is reactive: how fast can an organization absorb a shock and return to normal?

That framing, Questioni argues, is entirely the wrong one. By the time a company is managing recovery, it has already lost the most valuable thing: time. The organizations that emerge from disruption stronger are not the ones that responded fastest. They are the ones that had already built for it.

“Resilience isn’t just about bouncing back — it’s about building the future before it arrives. It’s the difference between surviving the unexpected and shaping it into opportunity.” 

A future-ready organization, in her view, has designed agility into its operating model before crisis arrives — and has given its teams the tools, authority, and mindset to move without waiting for direction from above. The question she puts to every leadership team is not how they responded last time. It is what they have already built for next time.

Which brings the conversation to the question that defeats most large-scale transformations before they ever reach the customer: execution. The strategy is rarely the problem. The problem is that a vision designed at the top must be delivered by teams moving at different speeds, across different functions and geographies.

Most organizations respond with governance frameworks. Questioni’s response cuts closer to what the problem actually is.

“Large-scale transformation operates like a symphony — an intricate performance where each element must harmonize under the guidance of a skilled conductor.” 

The conductor she has in mind is not ceremonial. It is a deeply operational C-level leader who holds the granular and the panoramic simultaneously — who understands how individual performance feeds collective outcomes, and who can align diverse functions without erasing what makes each of them effective.

The failure mode she sees most often is not a shortage of talent or resources. It is teams executing brilliantly within their own lanes while the overall composition falls apart. “True transformation isn’t about managing chaos,” she says. “It’s about precision, collaboration, and leadership that turns vision into reality.”

The conductor is not optional. Neither is the score. And the brands that understand the difference between noise and music — between change and transformation — are the ones that will still mean something when the disruption settles.

The post The Organizations That Survive Disruption Never Had to Recover From It appeared first on MartechView.

For nearly two decades, SEO was the digital marketer’s most reliable tool. Entire strategies and teams were built around a stable premise: rank well on Google, get seen. Visibility followed ranking, and traffic followed visibility. The system relied on Google’s shifting algorithms, but at least it was comprehensible.

The past two years have broken that logic entirely.

Rankings are holding — sometimes improving. But website traffic is declining. Dashboards look healthy. First-page positions remain. The clicks, however, are not there. In 2025, 60 percent of Google searches ended without a single click to any website.

The cause is not a mystery. AI-powered search engines now answer queries directly, absorbing the clicks that websites used to capture. We have entered a zero-click world, where AI synthesizes information and delivers it to users without ever requiring them to visit the source.

For marketers, the implication is significant: the goalposts have not just moved — they have been replaced entirely.

Ranking No Longer Leads to Reach

The old model of search rewarded technical precision. Keywords, backlinks, site architecture, and page speed sent signals that helped search engines determine relevance and authority. In today’s AI-mediated environment, relevance is no longer discovered — it is constructed.

AI chatbots do not surface results. They synthesize them. They draw from multiple sources, weigh credibility signals, and generate responses that may never require a user to visit the referenced content. Ranking highly on a search results page does not mean your brand will appear in an AI-generated answer. And if it does not appear in that answer, visibility effectively disappears — regardless of where the blue link sits.

The new definition of visibility is not where you rank. It is whether you are recognized as a source worth citing.

The End of Silos

Because AI changes how visibility is earned, it dissolves the boundaries that once separated public relations, SEO, and paid content. Historically, these functions operated independently because they influenced different outcomes. SEO drove rankings. PR generated coverage. Paid content drove traffic. Each was measured within its own channel.

Today, all of it is evaluated together — by the AI model deciding whose voice to include in an answer.

AI systems do not draw from a single source. They scan and combine articles, websites, forums, comments, and sponsored content across the entire web to construct a response. The implicit question these systems are asking is: Does this brand appear consistently, clearly, and credibly across multiple sources?

A PR placement is no longer just visibility in one publication — it becomes part of the dataset AI systems are trained on and referenced going forward. SEO content is no longer just about ranking — it is a structured repository of information for AI to extract. Paid placements do not just drive clicks — they shape how frequently and favorably a brand appears across the web.

The problem is consistency. A company might describe itself one way on its website, another way in media coverage, and appear differently still in paid placements. To a human reader, that is messy but navigable. To an AI model, it is noise, and noise weakens the signal. A brand that is difficult for AI to categorize is a brand more likely to be left out of the answer entirely.

Redefining Strategy for the AI Era

If the goal is no longer to rank but to be referenced, the question becomes: how do you build a presence that AI systems will not ignore?

In 2026, an effective search strategy rests on three foundations.

The first is audience-driven visibility. AI systems weigh what real people say and think — customer reviews, forum discussions, and community conversations — more heavily than brand-produced marketing copy. The frequency with which AI Overviews and ChatGPT reference Reddit is not coincidental. If people are discussing your brand in credible, relevant spaces, those discussions become signals AI systems recognize and draw from. Marketers should actively encourage user-generated content and participate in the communities — forums, social channels, industry platforms — where their customers already gather. The goal is contribution, not promotion.

The second is strategic amplification. Search engines have long deprioritized paid content, but AI systems often make no distinction between organic and sponsored material. When placed thoughtfully on high-authority platforms, sponsored content can reinforce a brand’s presence in exactly the sources AI is most likely to reference. Paid and organic content are increasingly parts of the same ecosystem — and should be planned as such.

The third is clarity. AI models prioritize specificity. Users querying AI tools provide far more detail than they would in a traditional search — use cases, specifications, context. For content to be referenced, it must match that level of detail. Brands need to be explicit about what they do, how their products work, and what differentiates them — even when those details feel self-evident. If it is not clearly stated, it is less likely to be surfaced.

The New Definition of Success

The language of SEO has not yet caught up to the shift it is navigating. Rankings, keywords, and traffic remain the default metrics for measuring reach and success. But those metrics are growing increasingly detached from actual visibility in an AI-first environment.

The questions that matter now are different: Are you being cited? Are you being synthesized into answers? Are you present in the conversations and sources that shape those answers?

If the answer is no, it does not matter where you rank.

SEO, as it was understood for two decades, is less useful than it once was. But search is not disappearing — it is being redefined. The brands that understand that distinction and build for it deliberately will not simply adapt to the zero-click era. They will set the terms.

The post SEO Is Not Dead. But It No Longer Works Alone. appeared first on MartechView.

An e-commerce merchandiser’s day is a constant negotiation between forces that rarely agree: what customers are clicking and buying, what inventory exists and at what depth, what marketing is pushing this week, what the brand wants front and center, and what revenue and margin targets demand. Aesthetics. Creativity. Inspiration. Each pulls in a different direction. Merchandisers reconcile all of them — every day, across a vast catalog of products and collections.

Experienced merchandisers often have sharp instincts. But the forces shaping 24/7 ecommerce are dynamic, complex, and unrelenting. Under that pressure, there is rarely any confirmation that a merchandising decision outperformed what it replaced — or what else was possible. Teams won’t know for days or weeks, and by then, shoppers’ preferences, brand preferences, and market signals will have already shifted.

Every merchandising decision is a trade-off. Most are made without ever knowing what the alternative would have cost. That gap between decision and consequence is where revenue quietly slips away.

New Arrivals, Best Sellers, and Hidden Winners

Ask any senior merchandiser how they decide what goes where, and they’ll walk you through the logic: new arrivals get prominent placement to build velocity, best sellers stay visible because they convert, slow movers get deprioritized to protect real estate. It sounds rational. For many years, it worked well enough.

But in the fast-moving reality of modern e-commerce, “rational given my experience” and “optimized” are two very different things. Both matter. The gap between them, however, costs real revenue.

Consider new arrivals. Most teams give them a fixed window at the top — a week, two weeks, sometimes a month — regardless of how they are actually performing. The logic is understandable: new products need exposure to get a fair shot. But this approach treats every new arrival the same. A dud sits in a premium position just as long as a breakout hit. That is not a strategy. That is a schedule — and it burns valuable catalog real estate on products that have already signaled they won’t earn it.

Now consider best sellers. A product that ranks in your top ten overall is not necessarily a top performer in every collection. A bestseller in “Evening Wear” might underperform in “Casual,” where it occupies a prime position while stronger contextual fits remain buried. What works across a full catalog does not always work within a collection — and promoting it heavily in the wrong context wastes resources that could be driving real conversion elsewhere.

Then there are the hidden winners: products with strong conversion signals that never get the traffic to prove themselves because they’re sitting on page four, crowded out by slower-moving items consuming the placement and promotion they deserve. Without a system to surface them, they stay buried. Teams never know what they missed — or realize it only in hindsight, too late to act. And customers never find what they would have loved.

Short on Time, Not Signals

Most e-commerce teams measure what they did or conduct retrospectives on what happened days, weeks, or months ago — clicks, conversions, revenue per session, return rate. These are reasonable metrics. But very few teams systematically measure what they didn’t do: the product that could have been promoted but wasn’t, the placement that could have driven conversion but went to something weaker, the new arrival that earned its spot on day three but stayed there until day fourteen because the calendar said so.

Opportunity cost — the revenue left on the table by not surfacing the right product in the right place at the right time — is the most consequential metric almost no one is tracking. The reason is straightforward: you cannot measure what you cannot see. And most merchandising systems are not built to make the alternative visible.

This is not a data problem. Most brands have plenty of data, and AI is producing more of it every day. It is a decision intelligence problem. The data needed to make better calls already exists — it is simply not being assembled in a way that supports the actual decisions merchandisers need to make, in the moment they need to make them.

What AI Merchandising Support Needs to Do

As AI becomes embedded across e-commerce, the goal should not be piling on more tools and dashboards. It should be deploying a genuinely intelligent merchandising approach — not “here are your top ten products” reports, not weekly performance summaries, but real-time decision support that reconciles competing constraints, surfaces what even the most experienced merchandiser cannot possibly see, and quickly tells you whether the arrangement you just published is performing better or worse than the one it replaced.

In practice, this means an AI model that knows when a new arrival has accumulated enough signal to be called a winner or a loser — and alerts a merchandiser, or automatically adjusts placement, rather than waiting on a fixed schedule. It means understanding that a product’s rank in one collection says almost nothing about how it should be positioned in another. It means flagging overexposed products that crowd out items with stronger contextual fit and surfacing quiet performers before the window to act on them closes.

The goal is not to replace the merchandiser. It is to handle what the merchandiser cannot: processing more variables simultaneously than any human can track, across more collections than any team can actively monitor.

The teams that will win in the next few years are not the ones with the most data or the most sophisticated tech stack. They are the ones that close the loop between decision and outcome fastest. E-commerce waits for no one. The catalog changes. The customer moves on. The question is whether your merchandising intelligence moves with it — or whether you’re still finding out what happened last week.

The post Merchandisers Are Drowning in Data and Still Flying Blind appeared first on MartechView.

There was a time when brands could simply list a product online and expect it to sell. Today, success requires a coordinated strategy across retail media, the digital shelf, pricing, and inventory. That strategy should be informed by data, but with thousands of SKUs across numerous marketplaces, brands not only have to interpret endless insights but also act on them. At scale, consistent execution becomes nearly impossible to manage manually.

In a recent survey, nearly half of business leaders said their data isn’t actionable, and more than 40% cited a lack of data accessibility or decision-making time as a core operational challenge. 

Teams don’t need more visibility — they need the ability to keep up with and act on the data they already have. It’s impossible for e-commerce teams to adjust every bid, fix every product description, and catch every stock issue themselves. To close that gap, brands need to rethink how the work gets done.

Why Today’s Execution Model Can’t Keep Up

Teams aren’t lacking data; they have too much of it. Dashboards have become more sophisticated, but seeing more doesn’t necessarily help teams do more.

Every optimization still requires someone to pull the report, identify the issue, decide what to do, and then execute. That delay between spotting a problem and fixing it is where performance starts to degrade. By the time a team corrects a content issue, the marketplace algorithms have already shifted.

What retailers are experiencing is a gap between insight and execution. The old approach was to do more: more reports, more meetings, more manual tweaks, more tools. But today, teams can spend most of their time looking for what’s broken and patching the same issues over and over.

The way the work used to get done won’t keep up with how fast modern e-commerce moves. With AI-powered discovery through Amazon’s Rufus and Walmart’s Sparky shaping how shoppers see and buy, the pace will only continue to accelerate.

Many brands have tried to address this by turning to agencies for support. In a recent survey, 67% of commerce teams said they rely on agencies, and about half said they spend 15% to 30% of their budget on agency fees alone. Yet 55% said those costs are too high for the results, and 40% said response times can’t keep up with the speed of algorithms.

While agencies can provide expertise, they don’t operate 24/7 — and the marketplaces retailers sell on never pause. Retailers can’t match that speed by adding more headcount or additional agency hours. 

Brands today need an algorithm to keep pace with the algorithm. They need a new execution model that can handle the volume and velocity of decisions in modern ecommerce.

What an Agentic Retail Strategy Looks Like

The shift happening now is toward agentic AI execution, where human teams are no longer tasked with doing the heavy lifting of weeding through data, reports, and manual workflows. Instead, they drive strategy and make final decisions while their agentic AI counterparts analyze large volumes of data and identify growth opportunities. 

This approach lets brands continuously extend execution across their entire catalog around the clock, rather than focusing only on top-performing SKUs. In practice, this looks like specialized agents that operate across the digital shelf:

• A content agent identifies and resolves product page issues across thousands of SKUs simultaneously, reducing the time required to update product detail pages from 35 minutes to 35 seconds.
• A media agent optimizes campaigns using dozens of signals at a scale no human team could match. 
• A sales agent monitors pricing and promo performance to flag gaps or opportunities.

To keep pace with the speed of modern ecommerce, retailers don’t have to add more tools on top of the same manual process; instead, they need to fundamentally change how the work is done. With agents handling the execution layer, the brand manager’s role changes. They spend less time pulling reports and making fixes and more time setting guardrails and deciding priorities.

The Real Shift Starts in Execution

Teams already know the old model isn’t working. The speed required to compete today has outgrown what humans can manage manually. There’s too much data to interpret and too many actions required to keep up with how quickly the digital shelf changes. 

Agent‑driven execution removes that bottleneck. According to a recent report, 82% of commerce leaders expect AI investment to increase in the next 12 to 18 months, and 71% are already familiar with or actively using AI agents. 

With the industry moving in this direction, retailers can’t afford to be left behind. The next era of e-commerce will belong to the companies that can execute at the same speed as the market.

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