Most attractions and destinations still treat AI chat as a support tool. Something that answers hours and ticketing questions so the phone line rings less. That view misses what is actually happening in these conversations. Every question a visitor asks an AI agent is a signal about what they want, when they want it, and what would get them to spend more. Organizations that treat that signal as a customer service log are sitting on data that marketing teams would pay for.

A good example of this comes from Franklin Park Conservatory and Botanical Gardens in Columbus, one of the founding members of the Agentic City program launched by Satisfi Labs this year with Experience Columbus. Over a three-month period, Franklin Park’s AI agent fielded thousands of guest conversations, and the patterns inside those conversations tell a much bigger story than “we answered some questions.”

Start with timing. A large share of the conversations occurred at 10 pm, 11 pm, and later hours, when no staff member is on site to answer the phone. People were asking about hours, ticket availability, membership eligibility, and event registration well after the gates closed. For an attraction, that is a staffing problem solved without adding staff. For a marketer, it is something else entirely. It is a record of intent that would have evaporated by morning.

Also Read: HubSpot’s Aja Frost on Marketing in the Age of AI Search

That intent shows up most clearly around events. When Franklin Park hosted a Gabby’s Dollhouse Meet ‘n’ Greet, the agent absorbed a massive spike in volume, easily a third of all conversations during that window. Guests asked how to register, why a second ticketing step wasn’t appearing, and whether the event was sold out. The agent guided families through a process at scale, without additional hires. But it also surfaced something the events team hadn’t had visibility into before. Teams could see exactly where the ticketing flow was breaking down for real guests, in real time, and in their own words. What began as a customer service interaction ultimately became a source of insight for both the events and marketing teams.

The same pattern revealed something traditional analytics tools rarely catch. Guests asked repeatedly about Museums for All and social services-based discount programs, including questions about specific managed care plans and out-of-state eligibility. These are not casual browsers. They are people actively trying to visit the Conservatory, and the questions show how conversational AI can help visitors navigate access programs and find information that might otherwise be difficult to obtain. That is an audience insight with implications for outreach, programming, and community partnerships that extend well beyond admissions.

Then there is the revenue conversation hiding inside the customer service conversation. Guests asked about weddings, birthday parties, graduation parties, and barn rentals, often with specific dates, venues, and budgets in mind, and frequently at night when the events team was unreachable. Others asked whether the special exhibit changes seasonally, when a specific show is running, and how much the butterfly larvae in the gift shop cost. None of that would show up in a sales report. It shows up only in the conversation itself, and only if someone is positioned to capture it.

Also Read: The Agency-Led E-commerce Model Is Changing

This is the shift marketers need to make. Unlike traditional FAQ tools, AI-powered conversations do more than reduce support volume. They’re giving marketers direct visibility into audience intent. A guest asking about wedding venues at 10 pm is a lead. A guest’s question about whether an exhibit rotates seasonally is a programming signal. A guest asking about a discount program is an equity and outreach signal. A spike in confused questions about a single event is a UX problem marketing can fix before the next campaign.

Every one of these signals already exists inside the conversations attractions are having with visitors right now. Marketers have spent years trying to infer customer intent from clicks, page views, and conversion data. Increasingly, customers are telling us exactly what they want. The organizations that learn to listen will have an advantage that no dashboard can provide.

The post Your AI Chatbot Knows More Than Your Marketing Team appeared first on MartechView.

Artificial intelligence is rapidly emerging as the next frontier for digital advertising. Reports that OpenAI’s advertising pilot generated more than $100 million in revenue within six weeks have only intensified interest from marketers eager to secure an early advantage.

But while the commercial opportunity is undeniable, consumer trust will ultimately determine whether AI advertising succeeds.

Our survey of 1,000 large language model (LLM) users in the United States found that consumers are open to advertising inside AI assistants—but only if those experiences remain transparent, relevant, and respectful of personal boundaries.

Consumers Are Highly Engaged With AI 

AI assistants are already deeply embedded in everyday life.

Nearly two-thirds (63%) of respondents currently use only the free versions of AI assistants, while 31% have access to premium services. Of those paying for premium access, only 22% fund subscriptions themselves, with the remainder receiving access through employers or educational institutions.

Engagement is equally strong. More than half (56%) use AI assistants every day or several times a day. ChatGPT remains the most widely used platform, with 61% of respondents reporting they had used it during the past six months, ahead of Gemini (41%), Copilot (25%), Claude (13%), and Grok (11%).

For marketers, that represents a rapidly growing audience with unusually high levels of attention and intent.

Early Opportunity Comes With High Expectations 

History suggests that early adopters often benefit from new advertising channels.

Brands such as Duolingo and Ryanair established strong positions on TikTok before the platform became saturated, benefiting from lower competition and higher organic reach.

AI assistants may offer a similar opportunity, but under very different conditions.

Our research found that 82% of respondents consider advertising inside AI assistants to be at least as trustworthy as Google Search ads. At the same time, only one-third said AI advertising would be more intrusive than ads on social media platforms.

That combination of trust and engagement creates an attractive environment for marketers—but only if it is handled carefully.

Also Read: How Brands Turned the World Cup Into a Cultural Play

Trust Is the Real Competitive Advantage

Consumers are less concerned about seeing advertisements than they are about whether advertising will compromise the quality and objectivity of AI-generated responses.

One-third of respondents said they worry AI answers could become biased in favor of advertisers. Another 32% cited privacy and data concerns, while others questioned whether sponsored content would always be clearly distinguishable from unbiased recommendations.

Respondents also identified situations where advertising should remain off limits. Confidential work discussions (17%), mental health conversations (16%), medical topics (14%), and legal or financial discussions (12%) emerged as the contexts where advertising would feel most inappropriate.

For brands, these findings reinforce that AI advertising cannot simply replicate existing approaches to search or social media.

Context Matters More Than Personalization

The temptation for advertisers is obvious. AI assistants understand users far more deeply than traditional digital platforms, creating unprecedented opportunities for personalized marketing.

Consumers, however, appear cautious about that trade-off.

More than half of respondents said they would reconsider using memory and personalization features if advertising became more prominent. While 27% said they would disable personalization altogether, another 25% said they would keep memory features enabled but opt out of advertising based on those interactions.

Rather than building increasingly detailed behavioral profiles, marketers may achieve better long-term results by focusing on the context of individual prompts.

The most effective AI advertising may not depend on understanding who users are, but on understanding what they are trying to accomplish in that specific moment.

Also Read: The Agency-Led E-commerce Model Is Changing

A New Advertising Model

OpenAI has said that any future advertising will remain clearly separated from AI-generated responses and excluded from sensitive conversations.

Those commitments will be critical.

Consumers appear willing to embrace advertising in AI assistants—but only as long as they believe those platforms remain trustworthy.

For marketers, the opportunity is significant. The responsibility to preserve that trust may prove even greater.

The post AI Ads Will Win Only If They Earn Consumer Trust appeared first on MartechView.

Loyalty is often framed in terms of points, perks, and post-purchase engagement. Those elements matter, but they don’t tell the full story. In B2B commerce, loyalty is shaped much earlier, when a buyer decides whether and how to complete a transaction.

The payment experience is a defining moment that influences how buyers evaluate the overall relationship and decide whether to make another purchase. It is where convenience, trust, and operational fit either come together or begin to break down. For marketers focused on growth and retention, this is an area that deserves far more attention.

Payments Are Where Experience Becomes Real

We spend a lot of time optimizing the front end of the customer journey. We invest in messaging, personalization and digital engagement. But if the payment experience introduces friction, those upstream gains are quickly undermined.

This is not a new concept. In consumer markets, we’ve seen that streamlined checkout experiences reduce abandonment and increase conversion rates. The same dynamic is now playing out in B2B, but with higher stakes.

Business purchases are more complex. They involve larger order values, multiple stakeholders, and defined procurement processes. Buyers are not only asking, “Do I want this?” They are asking, “Can I complete this purchase in a way that works for my business?” If the answer is no, the path to an alternative supplier is short.

Also Read: The End of the Predictable B2B Buyer Journey

B2B Buyers Expect Flexibility, Not Compromise

One of the biggest shifts in recent years is the expectation among B2B buyers for the same seamless experience they get as consumers. Buyers now expect the same level of ease and optionality they experience in their personal lives, but with their organizations’ operational requirements layered in, which includes payment.

Options like net terms enable purchasing ease, allow buyers to manage cash flow, align with internal approval cycles and make repeat purchasing more predictable. In fact, our research shows more than 80% of B2B buyers research payment methods before purchasing. 

When flexible payment options are missing or hard to access, it creates friction that extends beyond a single transaction. It signals that doing business with a supplier may require extra effort each time. Over time, that friction adds up, creating the kind of operational frustration that can push fleets to reconsider suppliers altogether.

The Payment Experience Is a Loyalty Engine

Loyalty in B2B is not built through a single interaction. It is reinforced through consistent, reliable experiences across the relationship. Payments play a central role in that consistency.

When buyers can move from order to invoice to reconciliation without delays or manual workarounds, it builds confidence that they chose the right supplier. When they can access credit quickly, see clear terms, and manage payments across channels, it creates a sense of control that reinforces how they manage their day-to-day efforts.

These factors influence the entire experience.  More importantly, they influence behavior. Buyers who can purchase easily and predictably are more likely to return, expand their spend, and stay loyal.

This is where payments shift from being a functional requirement to a strategic lever.

Also Read: Can B2B Brands Adapt to Volatility with Long-Tail Thinking?

Why This Matters for Marketing Leaders

Marketing leaders are accountable for outcomes that extend beyond acquisition. Retention, lifetime value, and share of wallet are central to measuring success. This requires looking beyond campaigns and across the entire customer journey. Payments are one of the most underutilized touchpoints in that journey.

If a brand positions itself as easy to do business with, the payment experience needs to reinforce that. If the message is about partnership and flexibility, buyers need to see that reflected in how they pay. A clean payments experience is a direct way to deliver on the promises made earlier in the funnel.

In many cases, this means closer collaboration between marketing, finance, and payments teams. It also means treating payment data and behavior as signals that can inform segmentation, personalization, and lifecycle strategies.

Moving From Friction to Flow

Improving the payment experience does not require marketers to become payments experts. But it does require a shift in perspective.

Start by viewing the checkout and invoicing journey through the lens of a business buyer. Where are the delays? Where do processes feel rigid or unclear?

Then evaluate whether your current payment options align with how your key customer segments operate. Do they support different purchasing models, approval workflows, and regional requirements?

Finally, consider how to make the experience more consistent across channels. B2B buyers move between digital, sales-assisted, and in-person interactions. The payment experience should feel connected across all of them and remove complexity for the buyer. 

Loyalty Is Earned in the Moments That Matter

Loyalty programs and post-purchase engagement will always have a role to play. But they cannot compensate for a poor experience. In B2B, loyalty is built through reliability, predictability and ease. It is reinforced every time a buyer completes a transaction without friction.

That is why the payment experience matters so much. It is where expectations are tested, trust is either strengthened or weakened, and where long-term relationships begin.

The post Payment Experience Is the Foundation of B2B Loyalty appeared first on MartechView.

A category manager pulls a report and sees that a hero SKU is missing an ingredient keyword that nobody caught, causing the listing that ranked No. 3 to drop to No. 8. Meanwhile, a competitor who adjusted their bids overnight took the top sponsored placement before anyone could react. 

The e-commerce advantage used to belong to whoever had the best data, but today, brands have more data than they can act on, and that edge has now shifted to execution speed. Retail algorithms are getting more sophisticated, retail media costs are rising, and competitor SKU counts keep increasing. Human teams operating on weekly review cycles can’t keep up, and no amount of improved dashboards or additional insights can help.

What these brands need is to adopt an agentic retail approach that moves them from analysis to action, with AI agents that work alongside human teams to execute at the speed that retail demands.

Also Read: Contextual Advertising: What It Is and Why It Matters

E-Commerce Doesn’t Have an Insight Problem. It Has an Execution Problem

CPG brands have more than enough data visibility, but not nearly enough time to act on it. In a recent CommerceIQ survey, 46% of CPG brands said their data isn’t actionable, and 42% said decisions take too long. Meanwhile, the tools brands have been relying on for the last decade aren’t built for the pace at which retail now operates. Dashboards show performance, while weekly reviews help teams plan what’s next. Neither was designed for immediate action, which is necessary to stay competitive in modern e-commerce.

Outsourcing also won’t help brands keep pace, since traditional media agencies can only prepare so many optimizations a day. Even with the additional help from agencies, there’s still a lag between finding a problem and fixing it. By the time a listing is updated, a competitor might have already swooped in. And according to the same survey, more than half of brands said agency costs are too high relative to results.

When a brand loses the Buy Box, a media campaign is outbid, or a stockout goes unnoticed, it might seem like a small issue. Multiply those missed optimizations across thousands of SKUs and a handful of marketplaces, and these seemingly minor problems add up to real revenue that’s regularly moving to competitors.

But how are these competitors moving so quickly? They’re no longer manually digging through their data to diagnose issues, only to wait for human teams to resolve them. They’re using AI agents to act in real time.

Agentic Retail Changes What Execution Looks Like

Agentic retail refers to the use of AI agents to analyze, decide, and execute across thousands of SKUs at once. With agentic retail, instead of teams reacting to their dashboards, they direct agents to act on what’s happening in real time.

These AI agents work across content, pricing, media, and availability 24/7 within the brand-defined guardrails. This looks like: 

• A content agent identifies and resolves PDP compliance and optimization gaps for SEO, AEO, and search visibility across every marketplace.
• A sales agent monitors real-time sales performance and recommends actions to close sales gaps before they impact quarterly results.
• A shelf agent monitors content, availability, assortment, and reviews to identify opportunities for optimization.
• A media agent optimizes retail media performance by leveraging dozens of signals at a scale that no manual process can match.

Each agent works continuously, across the full catalog, not just the top-performing SKUs.

This doesn’t take internal teams or agencies out of the equation; it just means brands can remove them from repetitive tasks and use their time more effectively on work that requires strategy and human judgment. 

Also Read: The CIO Who Says Governance Can Actually Speed Up AI

Agentic Retail Execution Is the New Competitive Advantage

The brands still pulling reports from their dashboards and increasing their agency retainers are solving a problem from the last decade. The ones investing in agentic retail execution are solving the one that will define this one.

It’s during the time between insight and execution that e-commerce performance is won or lost. The speed required to compete has outpaced what any manual process or agency can deliver. If brands can’t execute at the speed marketplaces operate, then their competitors will, taking the sale, the ranking, and the customer with them.

The post E-commerce Doesn’t Have a Data Problem. It Has a Speed One. appeared first on MartechView.

Refund and replacement claims, from “this serum gave me a rash” to “the palette arrived completely shattered,” are on the rise, each accompanied by photos and screenshots as proof. The influx is driven by a new returns fraud tactic, one where abusers are doctoring images using AI to make false claims seem more convincing.

Image manipulation with AI is now contributing to the $850 billion returns problem companies experience each year, with almost $77 billion of those returns being fraudulent. AI is helping bad actors polish their stories while revealing a new blind spot for many retailers who are unable to detect when AI is used in claims or to assess its impact on their post‑purchase margins.

Many brands are responding by tightening their policies, adding return fees, and asking customers to jump through more hoops to verify their claims. But this approach creates an even greater liability: losing loyal customers who become frustrated as they’re treated the same as abusers.

Also Read: The Death of Batch-and-Blast Email Marketing

AI Is Stress-Testing Retail Blind Spots

Fake proof is cheap and easy to produce with AI, allowing the same offenders to repeatedly send slightly different, altered photos and stories that look like real customer photos. 

With AI, post-purchase abuse can look like: 

• Generate or edit damage photos to add cracks, leaks, or broken packaging.
• Creating staged “stolen package” images that show an empty doorstep or an opened box with items missing.
• Producing fake drop‑off receipts as proof that an item was sent back when it never left the customer’s home
• Writing policy-aware emails and chat messages that align with current refund and replacement policies that make it hard to say “no.”

Meanwhile, customer service teams are now serving as fraud investigators. When a bad actor opens a ticket, attaches a photo, and explains what went wrong, reps can no longer simply follow the policy, which often states that a refund, replacement, or credit should be issued when the claim appears to meet the rules. Those rules were written for a world where proof was harder to fake, and few brands have a reliable way to tell when an image or story has been manipulated with AI.

Most retailers don’t have a consistent, cross‑team way to flag suspicious content and compare it to past behavior. Support teams see individual cases, often from what appear to be different customers. But in reality, the same person may be creating new profiles and repeating the same AI‑assisted claims, which is impossible to spot in a single ticket.

Rethink How You Respond to AI-Driven Return Fraud

The knee-jerk reaction for brands is to tighten return policies and train agents to say “no” more often. That feels like taking control, but it punishes loyal customers while determined abusers simply upgrade their AI tools and create more accounts to keep going.

Fraudsters will only find new and creative ways to use AI. Today, they’re generating fake photos, but tomorrow, they’ll use a new tactic. This is why retailers need to stop treating each claim in isolation and instead look for important patterns. How often does a customer report an issue? What kinds of issues do they report? How do their claims compare to those of other customers?

Use AI to Detect AI-Assisted Abuse

Retailers need the ability to connect orders, returns, claims, support tickets, credits, and even basic interaction patterns (like device and address history) into a single view of each customer. Then, flag behaviors that deserve a closer look, such as repeated “item not received” claims, frequent high‑value “damaged” reports, or clusters of accounts sharing the same details.

Update Your Policies Based on Behavior

Make policies more dynamic, rather than stricter across the board. Trusted customers, those with normal return behavior, can continue to enjoy fast, generous resolutions with minimal friction. Meanwhile, high‑risk customers can be routed into different flows, such as extra checks before issuing a refund, different return options, smaller credits, or, in some cases, blocked future claims.

Over time, that kind of behavioral playbook does more to protect both your margins and your best customers than another round of blanket crackdowns.

Also Read: Your PR Strategy Was Built for a Newsroom That No Longer Exists

Respond to AI Tactics With a Strong System of Defense

AI tactics in return fraud will continue to evolve, and they’re already forcing brands to confront how little they truly understand about post-purchase behaviors. But treating every customer like a suspect won’t fix retail’s returns fraud problem.

You can keep adding restrictions and hope your best customers tolerate the extra friction, or you can invest in technology that shows customer behavior end‑to‑end. Your team needs a defense system that can keep pace with these ever-evolving AI-driven return-fraud tactics. That way, claim resolution becomes less about the believability of a one-off story or supporting imagery and more about the identified patterns in how a customer shops, returns, and engages with support.

The post AI Is Supercharging Returns Fraud. Retailers Are Behind. appeared first on MartechView.

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.

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.

Before a customer logs in, loads a product page, or checks their order history, a cascade of invisible processes has already run. A database has retrieved its information. An API has connected the relevant systems. An automation has triggered the right response. A messaging system has delivered the update they needed. None of it is visible. All of it is felt.

That invisibility is the measure of a successful backend. When these systems work, customers experience so little friction that they never think about what is happening beneath the surface. When they fail — when a page loads slowly, a transaction stalls, or a notification arrives too late — trust in the platform erodes immediately. Users rarely articulate why an app feels unreliable. They simply stop using it.

For technology leaders focused on customer experience, understanding how backend infrastructure shapes what users actually feel — not just what they see — is becoming a strategic priority.

Databases: Speed Is the Product

A database stores everything that keeps an application running: customer profiles, order histories, product catalogs, login credentials and activity records. Every time a user interacts with a platform, the system is querying that database in real time.

The margin for error is narrow. Research has shown that a one-second delay in page load time reduces conversions by seven percent. At scale, that figure compounds quickly. A database that responds in milliseconds produces a customer experience that feels instant. One that lags results in a customer leaving.

Integrations: One Experience Across Many Systems

Most businesses today operate across a dozen or more software tools simultaneously — customer relationship management platforms, payment processors, marketing automation systems, analytics dashboards. Without integrations, these systems operate in isolation. With them, they share data in real time and present a coherent experience to the user.

The practical effect is visible in small moments. When a customer updates their phone number and sees the change immediately reflected in their support correspondence and email communications, that seamlessness is the product of well-built API integrations working behind the scenes. The same infrastructure enables personalization: research suggests that around 72% of recipients engage only with messages tailored to them, making the ability to link customer data to behavioral signals a direct driver of revenue.

Automations: Removing Friction Before Users Notice It

Automation handles the repetitive processes that keep operations running without human intervention — confirmation emails sent after purchases, support tickets assigned to the right team, profiles updated, and marketing messages scheduled. For users, these automations are invisible. What they experience instead is speed, consistency and the feeling that the system anticipated what they needed.

More sophisticated applications are emerging. AI-powered behavioral analysis can reveal why users abandon items in their shopping carts, identify friction points in checkout flows and suggest layout changes that improve conversion — all derived from patterns in backend data that no human analyst would have the capacity to review at scale.

Messaging Systems: Timing Is Everything

Customer expectations around communication have sharpened considerably. Research indicates that around 40% of users expect a response within an hour, and 79% within 24 hours. When a response arrives while a concern is still relevant — whether from a human agent or a well-designed chatbot — it builds confidence in the platform. When it arrives late, the moment has passed and the damage is done.

Real-time messaging infrastructure makes transparency possible at scale. Delivery updates, appointment reminders and account alerts arrive exactly when they are needed, without manual intervention. For smaller businesses operating with limited customer service resources, this infrastructure allows them to meet expectations that were previously achievable only by larger organizations.

The Backend Is the Brand

A polished interface can attract a user. It is the backend that keeps them. The systems that store, connect, automate and communicate are not technical details — they are the foundation on which every customer relationship is built. Users may never see them. But they feel them with every interaction, and they remember how the experience made them feel long after they have forgotten what the interface looked like.

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Retailers are tightening policies in 2026 as a response to snowballing returns that totaled nearly $850 billion last year, roughly 15.8% of annual sales. But while it’s true that brands have to do something to protect shrinking margins, the question is whether introducing shorter return windows, additional fees, and extra hoops for all customers is the right move.

Imagine a longtime shopper, one who’s spent thousands with your brand over the years, trying to return a pair of shoes that didn’t fit, only to be met with obstacle after obstacle. From their perspective, nothing changed — except that a brand they trusted suddenly stopped trusting them. 

Stricter returns can help deter abusers, but they can also drive loyal customers to spend with a competitor whose policies feel fairer. The retailers that see return policies as a CX advantage in 2026 won’t aim to have the most lenient or the strictest rules. Instead, they’ll strive for precision: cracking down on fraudsters while maintaining a frictionless return experience for customers who’ve demonstrated trust over time. ​

Blanket Policies Punish Your Best Shoppers

For years, retailers raced to make returns as quick and painless as possible. But as the gap between when a refund was issued and when a return was validated continued to grow, “no questions asked” began to turn into “no consequences.” 

The line between normal shopping behavior and policy gaming has blurred. Many shoppers believe practices like wardrobing (wearing and then returning clothing) and bracketing (ordering multiple sizes or colors to try on) are acceptable.

Now, with margins under pressure, retailers are pushing back. In 2024, returns and claims cost retailers about $103 billion. Those are real losses, and stricter return policies are a knee-jerk reaction many retailers are already putting into place.

But blanket policies that tighten everything at once and are easy to roll out tend to punish loyal, low-risk customers while repeat abusers just find new ways around the rules. Not only that, but refunds slow down, exceptions become inconsistent, and customers shift future spending to brands that still feel easy to deal with. 

How Brands Can Protect CX Without Encouraging Abuse

The key to getting returns right in 2026 is using automated decisioning to maintain the same CX that loyal shoppers have come to expect while adapting quickly as abuse patterns evolve.

Risk-Based Routing

Instead of a blanket policy that treats repeat customers and repeat abusers the same way, risk-based routing segments shoppers by trust levels (trusted, standard, and high-risk) based on signals such as their return rate, claim history, support interactions, and ordering behavior. This moves loyal shoppers through the process quickly, while those exhibiting potential harm hit a speed bump.

Automated Approvals

With unified post-purchase visibility, patterns can be spotted in real-time. When a refund is initiated, the trusted customer is automatically approved, while the high-risk request triggers additional verification steps or manual review. The result is seamless CX for the customer you want to keep, without extending the same ease to likely abusers.

Personalization Through Automation

Not only could two shoppers be routed and approved differently, but they could also receive different offers. The loyal customer may receive a prepaid shipping label and an instant refund, while a repeat offender may be limited to store credit or required to cover their own return shipping. Therefore, leniency can no longer be assumed; it must be earned.

Putting Friction Where It Belongs

The customer journey doesn’t end when the package is marked as delivered. What happens next, whether your return policy feels effortless or frustrating, could be the moment a shopper decides if it’s worth coming back.

In 2026, protecting your margins won’t come from being the strictest, nor from staying the most lenient. It will come from using data to create trust-based experiences and being precise about where to place friction. Instead of building your customer experience around bad actors, provide loyal shoppers with the seamless CX they’re used to, and set guardrails against high-risk behaviors. This sends a clear message: trust is a two-way street, and we’ll still make returns easy for the customers who’ve earned ours.

The post Protecting Loyal Customers From Your Own Return Policies appeared first on MartechView.

When Target’s recommendation algorithm began identifying purchasing patterns consistent with pregnancy — prenatal vitamins, unscented lotion, cotton balls bought in bulk — the retailer did what any data-driven marketer would do. It acted on the insight, mailing a coupon book for cribs and baby clothes to the customer’s home address.

The problem was that the customer was 15 years old. Her father called the store to complain, accusing the chain of encouraging teenage pregnancy. He later called back to apologize. His daughter, it turned out, was pregnant — a fact he had not yet known. Target’s algorithm had figured it out before her family did.

That story, now a fixture in marketing case studies, captures the central tension of personalized marketing in a single episode: the same capability that makes recommendations feel helpful can, without warning, make them feel like a violation. The line between the two is not where most brands think it is.

The Infrastructure Behind the Insight

Modern recommendation systems can track consumer behavior down to mouse movements, dwell time and keystrokes. Search engines, email platforms and social media make it straightforward to monitor purchases, preferences and browsing habits in real time. What feels like casual scrolling — saving a destination photo, browsing furniture, pinning bathroom tile ideas on Pinterest — generates detailed behavioral profiles that brands and advertisers can access, often without the consumer’s awareness.

The infrastructure making this possible operates largely out of sight. Spy pixels, tracking cookies and browser fingerprinting have become standard tools in the personalization stack. Third-party data brokers collect, categorize and sell the behavioral data these technologies generate, frequently without consumers’ explicit knowledge or consent. The discomfort that results does not come from receiving a relevant advertisement. It comes from the realization of how comprehensively ordinary behavior was tracked, packaged and monetized.

The scale is significant. A Boston Consulting Group survey of 23,000 consumers found that while 75% are comfortable with personalized experiences, nearly 70% have had at least one experience they found invasive or inaccurate—and in many cases, they responded by unsubscribing or ending business with the company entirely. Separately, around 62% of consumers say they will remain loyal only to brands that personalize their experience, while almost 80% express concern about how companies collect their data. Both things are true simultaneously, and the gap between them is where trust is won or lost.

When Precision Becomes a Problem

The most common personalization failures fall into two categories: acting on misread data, and acting on data the consumer did not know you had.

The first is a technical problem. An algorithm that recommends a product to someone who just purchased it has simply made a mistake — it has failed the implicit promise that tracking behavior should, at minimum, benefit the person being tracked. The annoyance is mild but corrosive: it signals that the system is watching without understanding.

The second is more serious. A push notification that reads “We see you’re in the mall — stop in for 50% off” is not a helpful reminder. It is a demonstration of geolocation capability that many consumers did not realize they had consented to. The offer is irrelevant. What the message actually communicates is surveillance.

The same principle applies when brands venture into sensitive life stages — pregnancy, illness, divorce, bereavement, job loss — without being invited. Sending coupons for infant formula to someone experiencing infertility, or congratulating a couple on a pregnancy they have not announced, converts a data asset into a liability. The algorithm made a reasonable inference. The brand failed to ask whether it should act on it.

What Responsible Personalization Looks Like

The distinction between personalization and surveillance is not technical. It is one of consent and expectation. Consumers are comfortable with brands using data they have knowingly provided, in ways they can reasonably anticipate, to deliver experiences that serve them rather than expose them.

Macy’s offers a workable model. The retailer aggregates first-party behavioral data with real-time insights to personalize communications across its Star Rewards loyalty program, where members have actively opted in and understand the exchange. Fifty percent of messages to program members are now personalized, with more than 500 million custom offers sent since launch — a scale achieved without the invasive inference that has damaged other brands.

The principle scales down as well as up. A florist sending a birthday coupon featuring the recipient’s birth month flower is using a small, delightful piece of data to create a moment of genuine connection. The customer feels seen, not watched. That distinction — between being known and being monitored — is the one that personalization, at its best, is supposed to resolve.

First-party data, compiled from purchase history and direct customer engagement, is almost always preferable to third-party profiles purchased from brokers. It is more accurate, more current and carries none of the ethical ambiguity of data the consumer never knowingly generated. Before acting on any data point that touches sensitive territory — marital status, health, financial circumstances, family composition — brands should ask not only whether they have the information, but also whether the customer knows they have it, and whether acting on it will feel like service or exposure.

The Real Cost of Getting It Wrong

The Target story endures not because it is exceptional but because it is legible. Most personalization failures are quieter — a recommendation that misses, a notification that unsettles, a message that arrives at the wrong moment with the wrong assumption — but they accumulate in the same direction. Each one runs a small deficit against the trust that personalization is supposed to build.

The goal of personalization is to make customers feel understood. When it works, the transaction is invisible — the right offer at the right moment, and the customer reaches for it without thinking twice. When it fails, the mechanism becomes visible, and what the customer sees is an unhelpful brand. It is a system that has been watching them.

The capability to know more about customers than they know about themselves is not, by itself, a marketing strategy. Judgment about when to use it, and when to hold back, is what separates the brands that earn loyalty from the ones that learn, too late, what they should not have said.

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