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.

Yuno, the global payments infrastructure company, on Sunday introduced Payments Concierge, an autonomous AI agent designed to monitor, troubleshoot, and optimize a merchant’s payment operations around the clock.

The announcement, made at the HumanX conference, represents a departure from conventional payment dashboards and alert systems. Rather than notifying a payments team that something has gone wrong, Payments Concierge is built to detect problems and act on them in real time — adjusting routing rules, toggling payment providers on or off, and reordering checkout options to favor the method most likely to succeed.

The need for such automation, Yuno says, reflects the compounding costs of inaction. A single card issuer going offline can silently decline thousands of transactions before a team identifies the source. A misconfigured routing rule can quietly erode revenue for weeks. And diagnosing what happened — pulling raw data, running performance analyses, assembling reports — can consume hours of manual work.

Payments Concierge addresses each of those pain points. The tool surfaces interchange and scheme fees at the transaction level, giving merchants a granular view of what each payment actually costs and enabling routing decisions that balance approval rates against expense. Reporting tasks that previously took hours, the company says, can now be completed with a single prompt, whether the output needed is a detailed data breakdown or a board-ready summary.

The agent is accessible through WhatsApp, Telegram, WeChat, and Slack, and all automated actions operate within a merchant’s preconfigured security permissions.

Also Read: Your Rebrand Is Failing. Have You Tried Listening?

“Payment operations today are mostly reactive, with teams finding out something broke after the revenue is already lost,” said Juan Pablo Ortega, chief executive and co-founder of Yuno. “Payments Concierge catches issues humans can’t see, optimizes costs humans can’t track, and executes changes in real time.”

Yuno’s platform connects more than 1,000 payment methods and fraud tools through a unified API. Its clients include McDonald’s, Uber, GoFundMe, and Rappi.

Ortega is scheduled to speak at HumanX on Monday, leading a roundtable titled “When AI Becomes the Buyer” and joining a panel on building global financial infrastructure.

The post Yuno Unveils AI Agent to Automate Payment Operations appeared first on MartechView.

Sprinklr is rolling out its Spring ’26 Release — a broad update to its AI-powered customer experience platform that touches service, marketing, insights, and platform governance. The throughline across nearly every new capability is the same: giving enterprise teams more visibility into what their AI is deciding, and more confidence that it is deciding correctly.

For a company that has staked its identity on AI-native customer experience management, the update reflects a maturing conversation in the market. Deploying AI agents is no longer the hard part. Trusting them at scale is.

The Trust Problem in AI-Powered Service

The most significant addition in this release is Autonomous Evaluation, a new capability within Sprinklr Service that provides explainable logs and test-backed validation for AI agents operating autonomously in contact center environments.

The logic is straightforward and overdue. As AI agents take on more customer interactions without human intervention, the organizations deploying them have limited visibility into why a particular decision was made, what went wrong in a failed interaction, or whether agent behavior is drifting from intended parameters. Autonomous Evaluation is designed to make that behavior legible — providing the audit trails and test infrastructure that allow teams to understand, refine, and scale agents with some degree of confidence.

Alongside this, Agent Copilot has been upgraded to operate proactively, surfacing real-time guidance during live customer interactions rather than waiting to be consulted. The focus is on core service metrics: first call resolution and average handle time — two indicators that contact center leaders actually manage to.

Operational improvements round out the service updates, including automatic shift bidding for agent scheduling and unified KPI dashboards that aim to shorten the distance between insight and action.

Also Read: Is AI Reshaping Ad Agencies for Good?

Listening With Less Noise

On the insights side, Sprinklr is addressing a problem that has quietly undermined social listening for years: the signal-to-noise ratio.

AI Topics now use generative AI enrichments to filter out irrelevant mentions and surface only the conversations that are genuinely meaningful to a brand. The distinction between a brand being mentioned and a brand being discussed in a way that matters is one most listening tools have struggled to make reliably. The update is an attempt to close that gap.

Customer profiles are also being consolidated across channels, giving teams a single, coherent view of each customer rather than fragmented data spread across disconnected dashboards. Global Web Survey support has been expanded with one-click localization, stronger governance controls, and intelligent sampling — improvements aimed at producing cleaner, more representative feedback at scale.

Action Plans, previously limited to parts of the platform, now extend across the full Insights suite, allowing teams to assign tasks, set owners, and track progress on insights without leaving Sprinklr.

Marketing and Social Updates

For marketing teams, the release adds access to TikTok’s Commercial Music Library, enabling compliant video content creation without the licensing friction that has complicated branded content on the platform. Integration with Canva’s Digital Asset Management system streamlines how creative assets move into Sprinklr workflows while maintaining brand governance.

Performance analytics have also been upgraded. Automated root-cause analysis now flags and explains sudden shifts in campaign results, and a unified dashboard compares pre- and post-boost metrics in a single view. LinkedIn seller profile tracking gives social selling teams visibility into performance data that was previously difficult to surface in one place.

Also Read: CES 2026: The Year “Physical AI” Claimed the Real World

Platform and Governance

At the infrastructure level, Sprinklr is introducing bulk testing and AI telemetry within AI+ Studio, allowing enterprises to evaluate AI performance across large volumes of interactions rather than sampling spot-checks. Additional updates include unified integration management through the Sprinklr Marketplace, automated data ingestion via CRON-based scheduling, and an updated compliance framework under DRP 2.0.

“As AI Agents resolve more customer issues autonomously, we’re giving teams the transparent, test-backed validation they need to trust and scale them,” said Karthik Suri, Chief Product and Corporate Strategy Officer at Sprinklr. “These advancements help brands turn automation into measurable impact — and ultimately into more seamless, personalized moments of customer delight.”

The Spring ’26 Release is available now.

The post Sprinklr’s Spring Release Puts AI Agents on Trial appeared first on MartechView.

The slow death of the cookie has been anticipated for years. The operational reality of advertising without it has arrived faster than most campaigns were built to handle.

MINT Square, the programmatic advertising specialist, is announcing a strategic partnership with Digiseg, a provider of ID-independent audience targeting, designed to give advertisers a scalable alternative to identifier-dependent targeting — one that works even in browsers and environments where traditional signals are unavailable or restricted.

The partnership takes effect in early 2026 and will be available to MINT Square clients across all markets.

The Problem It’s Solving

Addressability across the open web has become increasingly inconsistent. Cookies are blocked or deprecated across major browsers. Personal identifiers are increasingly restricted in regulatory environments. For advertisers trying to plan and execute campaigns with any reliability, the result is a patchwork of signal availability that traditional targeting infrastructure was not designed to navigate.

The MINT Square and Digiseg partnership sidesteps that problem by anchoring audience targeting in household-level characteristics rather than individual identifiers. Segments are built from real-world attributes — income bands, homeownership status, life stage — derived from household data rather than cookies, device IDs, or any form of personal tracking.

Those segments are accessible through Adform and The Trade Desk, integrating directly into existing advertiser workflows without requiring new infrastructure.

Also Read: AI Isn’t Killing PR. Bad Measurement Is.

Privacy-Safe by Architecture, Not by Workaround

The distinction Digiseg draws is architectural. Its audience segments are not constructed by anonymizing personal data after the fact — they are built from the ground up without personal identifiers. Digiseg’s Insights Platform extends this further, offering campaign performance analysis and audience understanding without touching personal data at any stage.

For advertisers operating under tightening privacy regulations across multiple jurisdictions, that is a meaningful difference. Compliance is not a retroactive adjustment; it is the baseline condition of how the targeting works.

“ID-independent targeting is now a commercial necessity,” said Christoph Berg, CEO of MINT Square. “This collaboration allows our clients to plan and activate with confidence across all major browsers, while benefiting from lower costs and a simplified route to market.”

Part of a Broader Ecosystem Play

Digiseg joins MINT Square’s newly launched membership-based offering, a tiered access model designed to lower the barriers to entry for advertisers and agencies navigating the programmatic landscape. The structure introduces three membership tiers, each providing access to a suite of targeting, contextual, and data capabilities — with Digiseg’s audiences now included as a core component.

For MINT Square, the partnership is one piece of a larger push to build a comprehensive ecosystem of premium publishers and targeting partners. Additional collaborations are expected to be announced throughout the year.

“MINT Square’s deep knowledge of the programmatic ecosystem and strong activation capabilities across markets make it a natural partner,” said Ulrik Krag Morell, CEO of Digiseg. “Together, we can make it easier for advertisers to adopt ID-independent targeting at scale, without compromising on reach or effectiveness.”

The post MINT Square Bets on Cookieless Targeting With Digiseg appeared first on MartechView.

Customer feedback has a timing problem. By the time a survey reaches someone’s inbox, the experience it’s asking about is already days old — and response rates reflect that. SurveyMonkey is addressing that gap directly with a new capability that automates SMS survey invites triggered by real-time events inside Salesforce.

The feature, now generally available on Salesforce AppExchange, allows customer experience and support teams to send personalized text message survey invites the moment a key event occurs — a support case closes, a transaction completes, a service interaction ends. No manual outreach. No delay.

Why SMS, and Why Now

The shift to mobile as the primary survey channel is not a forecast. It has already happened.

According to SurveyMonkey’s own 2025 data, 58 percent of global survey responses are now captured on mobile devices. Switching from a web link to an SMS invite has been shown to improve survey completion rates by 33 percent. Open rates on text messages consistently outperform email by a significant margin.

For teams whose feedback programs still rely primarily on email, those numbers represent a structural gap in how much signal they are actually capturing — and when.

Also Read: SEO Is Not Dead. But It No Longer Works Alone.

What the Integration Does

The SurveyMonkey-Salesforce integration already allowed teams to collect and act on customer feedback within their existing CRM workflows. The SMS capability extends that in four specific ways.

Automated outreach removes the manual step entirely. Teams configure workflows that trigger personalized SMS invites when defined Salesforce events occur, so feedback is requested while the experience is still immediate. Personalization is handled at scale by pulling Salesforce data fields — customer names, case numbers, transaction details — directly into the message text, without manual effort per send.

Responses map automatically back to the appropriate Salesforce objects, keeping customer records current and giving teams the data they need to route, escalate, or resolve issues based on what customers are actually reporting in real time.

“Customer insight is only useful if it’s captured in the moment,” said Cormac Kelly, vice president of customer success at SurveyMonkey. “This shift ensures feedback isn’t just a lagging report, but a real-time signal that allows teams to act while the experience is still top-of-mind.”

The post SurveyMonkey Can Now Text Your Customers for You appeared first on MartechView.

Forty-one percent of Americans are now shopping on TikTok Shop, up sharply from 30 percent a year ago, according to a new survey from ecommerce marketing platform Omnisend. The findings arrive as major brands accelerate their presence on the platform, raising a pointed question for retailers: Is TikTok Shop the next mainstream retail channel?

The data suggests it may already be.

Tariffs Are Not Slowing Chinese Marketplaces Down

Across every Chinese marketplace tracked in the survey, shopping activity increased over the past year — despite an ongoing tariff environment and rising consumer awareness of trade policy.

Temu usage climbed 9 percent, from 53 percent of respondents last year to 58 percent today. Shein saw a 23 percent increase, rising from 40 percent to 49 percent. AliExpress grew 38 percent, from 26 percent to 36 percent.

The growth is notable given that 46 percent of U.S. consumers now say they support tariffs — up 35 percent from last year, when only 34 percent said the same. Consumer sentiment and consumer behavior, it appears, are moving in opposite directions.

The explanation may lie in where price increases are actually landing. Seventy-four percent of respondents said they are spending more online this year than last. But the increases are being felt most acutely on Amazon, where 57 percent of shoppers reported higher prices, and on Walmart, where 48 percent said the same. By contrast, only 12 percent of TikTok Shop users reported price increases, along with 25 percent on Temu, 19 percent on Shein, and 12 percent on AliExpress.

“Price increases are finding their way to U.S. shoppers via stores with traditional business models like Amazon and Walmart,” said Mary Bauer, ecommerce expert at Omnisend. “However, since many Chinese sellers operate in a manufacturer-to-consumer model, even with tariffs, many are able to keep prices competitive. As shoppers seek value, stores like Temu and Shein continue to appeal to U.S. consumers.”

Also Read: The Invisible Infrastructure Behind Every Great App

Social Commerce Is Redrawing the Retail Map

The TikTok Shop numbers reflect something broader than platform preference. They reflect a structural shift in how discovery and purchase are collapsing into a single moment — one that traditional retail formats were not designed to capture.

“The blending of e-commerce and social media continues to redefine what we consider to be a modern storefront,” Bauer said. “Regardless of the channel, the customer journey remains at the forefront, meaning always-on brands have unique opportunities to attract shoppers.”

What Retailers Should Do Now

For brands trying to navigate this shift, Omnisend outlines several strategic adjustments worth considering.

Expanding to TikTok Shop and other Chinese marketplaces is worth evaluating for brands in a position to do so. The ability for a consumer to move from entertainment to purchase in a single scroll represents a meaningful conversion opportunity that traditional channels cannot replicate.

Email marketing remains one of the highest-ROI channels available. One in ten shoppers who click on a marketing email makes a purchase — a conversion rate that holds up against any social platform.

Brands can also bridge the two worlds by converting social-referred web traffic into email subscribers through targeted signup prompts, creating a pipeline from social discovery to owned-channel loyalty.

Finally, in an environment where price sensitivity is elevated, promoting tangible value — free shipping, flexible returns, product quality guarantees — at every stage of the customer journey is no longer optional. It is the baseline expectation.

The post TikTok Shop Is America’s Fastest-Growing Storefront 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.

Most AI tools charge for access. HubSpot is now charging for outcomes.

Effective April 14, 2026, HubSpot is moving two of its Breeze AI agents to outcome-based pricing — a model that bills customers only when the agent delivers a defined result, not simply when it is used. The change applies to Customer Agent and Prospecting Agent, and it represents one of the more direct acknowledgments yet from a major software vendor that the current model of charging for AI potential has a credibility problem.

What Changes and How It’s Priced

Customer Agent, which HubSpot says resolves 65 percent of customer conversations and has cut resolution time by 39 percent across 8,000 customers, is moving from $1.00 per conversation to $0.50 per resolved conversation. The distinction is meaningful: under the old model, an unresolved conversation costs the same as a resolved one. Under the new model, customers pay only when the agent actually closes the issue.

Prospecting Agent is shifting from a recurring monthly charge per enrolled contact to $1 per qualified lead handed off to a sales team. Again, the trigger is outcome, not activity — customers pay when a prospect is qualified and passed to their team, not when outreach is simply initiated.

Also Read: SEO Is Not Dead. But It No Longer Works Alone.

Why HubSpot Believes Its Agents Can Back This Up

Outcome-based pricing is a credible offer only if the product consistently delivers outcomes. HubSpot’s argument is that its agents have a structural advantage over generic AI tools: access to the customer data already stored inside the HubSpot platform. That context — CRM history, conversation records, contact activity — is what generic tools lack and what HubSpot believes makes its agents meaningfully more accurate and effective in practice.

“Businesses are being asked to make big bets on AI right now. Too often, that means paying for potential rather than performance,” said Jon Dick, Chief Customer Officer at HubSpot. “Outcome-based pricing removes that risk. You pay when it works, full stop.”

The Broader Signal

The pricing shift reflects a tension that has been building across the enterprise software market since AI features became standard. Vendors have largely charged for AI access — seats, tokens, usage tiers — regardless of whether those capabilities produced measurable results for customers. For buyers under pressure to justify AI investment, that model is increasingly difficult to defend internally.

HubSpot is not the first company to experiment with outcome-based pricing for AI agents, but the move by a platform of its scale — and the specificity of the metrics it is tying pricing to — makes it worth watching. If the model holds, it puts meaningful pressure on competitors still charging for potential rather than performance.

The new pricing takes effect April 14, 2026.

The post HubSpot Now Charges Only When Its AI Actually Works appeared first on MartechView.

Sprinklr, an enterprise customer experience software company, and CreatorIQ, a creator marketing platform, announced Wednesday a partnership that links creator campaign data with corporate social media management systems, allowing brands to measure influencer, paid, and organic social performance from a single dashboard.

The deal addresses a persistent pain point for large marketing organizations: performance data from creator campaigns typically sits in a separate system from paid and owned social media metrics, making it difficult to assess the combined return on investment across channels.

Creator marketing has grown rapidly in recent years. Average brand investment in the category rose 171 percent year over year in 2025, according to figures cited by the companies, and more than half of marketers now use creator-produced content in both paid and organic campaigns.

Under the partnership, CreatorIQ’s proprietary data infrastructure — which the company says processes 123 million creator posts per day — will feed directly into Sprinklr’s social media reporting environment. The integration is designed to let brands evaluate creator performance alongside their broader social and paid media activity and draw clearer connections between creator spending and revenue outcomes.

“Integrating CreatorIQ data directly into Sprinklr gives brands one connected environment for strategy, execution, and measurement across creator, paid, and owned channels,” said Anish Chadda, vice president of product management at Sprinklr.

Also Read: The Real Retailer Readiness Gap Isn’t Price. It’s Content.

Tim Sovay, chief partnerships officer at CreatorIQ, said creator marketing data has historically been siloed from the broader systems enterprises use to manage social media and customer experience. “Together, we’re delivering the unified, enterprise-grade intelligence required for AI-driven marketing and full-funnel growth,” he said.

The integration will also allow brands to boost high-performing creator content directly through Sprinklr’s paid media tools, reducing the need to manually export data between platforms.

The post Sprinklr and CreatorIQ Unite Creator and Social Media Data appeared first on MartechView.