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.

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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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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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Most retailers have a good idea of what personalization ought to look like. They recognize that customers expect individualized experiences that reflect their preferences, context, and timing, and they understand what delivering on those expectations would mean for their bottom line. But too often, retailers encounter operational barriers that prevent them from achieving personalization at scale. And those barriers typically stem from a breakdown between strategy formulation and practical implementation, or from the clash between organizational complexity and technological reality. 

While investments in AI and customer data platforms continue to grow rapidly, disconnected teams, slow activation cycles, and limited real-time decisioning often mean those strategies don’t translate into consistent customer experiences. Customer data teams develop sophisticated segmentation models, while loyalty teams operate their own programs with distinct customer tiers. Technology groups build impressive data platforms that merchandising teams rarely access effectively. When every retail department is pursuing personalization within its own domain, fragmentation becomes inevitable. 

Moving Beyond Static Segmentation Models 

Let’s start with one of the most obvious barriers to true one-to-one personalization: customer segmentation. Traditional engagement approaches group customers into demographic segments or purchase-based categories, creating broad buckets that miss individual behaviors and context-dependent needs. A customer casually browsing winter coats during a weekday evening has entirely different requirements than someone urgently checking the same products during an unexpected winter storm. Age, income, and historical purchases provide no insight into these contextual differences. 

There is an antidote for this broad-brush problem: predictive, behavior-driven engagement that responds to individual actions, context, and timing rather than fixed customer groups. This approach tracks subtle behavioral signals, such as the pause before abandoning a cart, product-comparison patterns, and timing between browsing sessions, which reveal intent more accurately than a demographic profile. 

Leading pet retailers, like Petco, demonstrate this by tracking pet life-stage transitions rather than segmenting customers by pet type or spending levels. A customer gradually shifting from puppy to adult dog products triggers different marketing or recommendation sequences than someone researching products for multiple pet types simultaneously.  

This kind of predictive engagement reacts to in-the-moment customer behavior rather than assumed characteristics, but it’s only possible with access to rich customer data and AI-powered models to make sense of it. 

Breaking Down Data Silos for Connected Execution 

This is why connected data is so important. When customer, loyalty, promotions, and retail media data remain siloed, execution breaks down. Connecting these systems enables faster decisions, more relevant interactions, and better control over promotional spend. Traditionally, most retailers maintain separate platforms for loyalty programs, marketing campaigns, promotional offers, and commerce transactions. These disconnected systems prevent a comprehensive understanding of customers and limit personalization capabilities to narrow functional areas. 

Unified data architecture connects every customer touchpoint into coherent insights into individual customers. Transaction histories merge with browsing behaviors. Loyalty interactions integrate with promotional sensitivity. Marketing responsiveness correlates with customer service data. This holistic view enables real personalization that reflects the complete customer relationship. 

The impacts of connected data go well beyond customer experience improvements. Connected systems enable retailers to optimize promotional dollars by understanding which offers drive incremental spending rather than cannibalize existing purchases. Loyalty program investments can be redirected toward tactics and incentives that genuinely influence buying patterns. Retail media campaigns can target audiences based on demonstrated (and attributable) purchase behaviors. 

Retailers can also link customer data platforms to operational systems, enabling promotional campaigns that automatically trigger inventory adjustments or staffing recommendations in response to predicted demand spikes. Connected data enables a retail environment where every customer-facing action coordinates with backend operations to ensure consistent experiences. 

Execution Hinges on Organizational Alignment 

Consistency and coordination are also required to achieve advanced personalization. Success depends on aligning marketing, loyalty, data, and technology teams around shared goals and execution models. The most sophisticated algorithms and platforms fail without organizational structures supporting integrated execution across functional boundaries. 

Instead of separate departments managing acquisition, conversion, and retention through isolated systems, successful personalization comes from cross-functional teams responsible for the whole customer relationship. These teams share data, insights, accountability, and, most importantly, execution responsibility for the entire customer lifecycle. When teams align around customer value creation rather than metrics that govern their individual function areas, the retail brand can get the most out of its technology investments and personalization strategies. 

Leading retailers also establish clear decision-making protocols for real-time personalization. When AI systems identify optimal promotional timing for individual customers, operational teams and systems must be ready to respond. Inventory management has to make micro-adjustments based on demand predictions, just as HR and customer service must staff up based on traffic forecasts. These coordinated responses require organizational commitment beyond technological capability. 

Measuring Integrated Impact 

Traditional metrics evaluate personalization initiatives in isolation, missing cumulative impact across connected customer experiences. Comprehensive measurement frameworks track changes in customer lifetime value, cross-category expansion, improvements in engagement depth, and the strengthening of long-term loyalty. These metrics reveal whether personalization creates genuine value or simply redistributes existing spending patterns. 

Unified systems enable measurement that spans entire customer relationships rather than individual promotional responses. Machine learning algorithms continuously improve based on actual customer behavior, feeding insights back into operational systems for ongoing optimization. The entire ecosystem becomes more intelligent through connected measurement and response cycles. 

Realizing the Personalization Vision 

Retailers finally have access to the predictive AI tools and advanced technology platforms to achieve true one-to-one personalization. But bridging the personalization gap demands more: a commitment to execution, organizational alignment around customer value creation, connected data systems, and operational processes that respond to insights in real time.  

Retailers that find a balance between strategic vision and operational execution create competitive advantages that technology investments alone cannot provide. Only then will their personalization strategies come to fruition, and only then will they be able to implement them consistently at scale.

The post From AI Vision to Retail Personalization at Scale appeared first on MartechView.

After years of inflation, supply chain chaos, and pandemic-era price spikes, consumers have seen nearly every reason for their shopping carts to become more expensive. With overall U.S. prices climbing almost 25% since 2020, the last thing consumers need is another hit to their wallets. 

Yet, tariffs are adding pressure to brands, which are facing tough choices about whether to eat the cost or pass it downstream. This is especially fraught for those selling on platforms like Amazon, Target, and Walmart, where a few dollars can mean the difference between winning the Buy Box or disappearing from view.

This tension raises a central question for brands: Are shoppers becoming conditioned to constant price hikes, or have cost increases become so routine that consumers barely notice? 

The quiet dangers at the value end

If the past few years have taught brands anything, it’s that consumers can adapt quickly to moving price targets. During the pandemic, for example, online shoppers were inundated with “out of stock” messages, conditioning them to grab items when available, even if prices were higher than usual.

Meanwhile, dynamic pricing has become more routine. On Amazon and Walmart, algorithm-driven price changes happen so frequently that most shoppers rarely expect the same item to hold steady for long. When cart totals creep up by a few dollars, many people chalk it up to the usual algorithmic shuffle. 

But that doesn’t mean consumers don’t care. They care deeply — and they know they have alternatives. When online sellers raise prices faster than what’s justifiable to shoppers, these consumers are more willing to defect to retailer-owned alternatives, like those by Amazon Basics or Walmart’s Great Value. Nearly six in 10 consumers say they’re now buying more private label products than a year ago, pushing sales in the U.S. to a record $271 billion in 2024.

For brands, that’s the real warning sign. Small increases may be tolerated in the moment. Still, price perception at the value end is less about loyalty and more about the algorithm rewarding the lowest-cost option, making even “tiny” increases risky.

High stakes for high-ticket items

Big-ticket categories are where price sensitivity turns into outright resistance. Electronics, appliances, and branded footwear are particularly vulnerable, both due to consumer psychology and supply chain dynamics: 100% of U.S. imports of electric toasters and alarm clocks, and around 90% of microwaves, LED bulbs, keyboards, and baby strollers, come from China. Similarly, almost all shoes sold in the U.S. are imported.

When a laptop suddenly costs more than expected, or branded sneakers cross a familiar threshold, shoppers are jolted into comparison mode. Carts sit idle, wishlists grow longer, and some may abandon the purchase altogether. One report found that 25% of shoppers are holding off on big-ticket items over $500, while nearly 20% who plan to buy major items are waiting for a sale. 

What else to watch for (and how to respond)

Every brand is navigating the fallout from the tariff. Some are absorbing the added costs, which protects conversion in the short term but squeezes margins thin. Others are passing increases directly to consumers, a move that risks higher cart abandonment and accelerated trade-downs to cheaper alternatives. So what’s the smarter path? 

• Diversify your product offerings with intent: Cover both premium and entry-level SKUs so when shoppers trade down, they stay with your brand instead of defecting to a competitor. That could mean smaller pack sizes, mid-tier “value” versions, or bundles that deliver more perceived bang for their buck.
  
• Stay agile with pricing: Marketplace prices shift by the hour, not the quarter, which means manual monitoring often can’t keep up. Analytics solutions that report on price sensitivity can provide brands with an always-on view of competitor moves and shopper reactions, enabling them to adjust pricing accordingly before lost sales appear in the P&L.
  
• Get surgical with promotions: Blanket discounts drain profitability. The more effective approach is to utilize software to identify the categories and SKUs where sticker shock is most pronounced and deploy promotions accordingly. That way, you defend your share in vulnerable spots while preserving margin everywhere else.
  
• Forecast tariffs into your inventory plan: Tariffs increase landed costs, which alter the calculations for what to stock, how much to stock, and when to stock it. Brands that can see pricing, demand, and inventory signals together are better positioned to avoid overstocks on costly items and keep high-demand products flowing. AI-powered retail agents can surface those signals in one command center, provide recommendations, and make trade-offs easier to manage.

Tariffs won’t be the last disruption, and consumer responses will continue to swing between quiet acceptance and open resistance. That’s why brands need AI tools that can distinguish between them, ultimately empowering them to adapt pricing, promotions, and inventory in real-time. 

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The pace of change in business has never been faster. Customers expect more choices, employees want greater flexibility, and shareholders demand growth under increasingly volatile conditions. To remain competitive, companies are reshaping their operations, strategies, and offerings in ways that were unimaginable only a few years ago. At the center of this transformation is artificial intelligence. 

No longer a speculative concept or a distant promise, AI is emerging as a practical and essential tool for decision-making, efficiency, and innovation. It is not simply a matter of adding another software system to the stack. Companies need to redefine how organizations and their demands operate, how they interact with stakeholders, and how they design for the future.

Meeting the expectations of modern customers has proven to be one of the most pressing drivers of change. Buyers today want more than convenience; they want personalization, speed, and transparency in every interaction. They expect companies to know their preferences, anticipate their needs, and deliver with minimal friction. Static offerings, once sufficient, no longer resonate in an environment where consumers are accustomed to highly tailored experiences in their digital lives. 

Also Read: The Future of Targeting Isn’t Audience or Contextual. It’s Quality.

Businesses are responding by deploying AI systems that analyze patterns in behavior, segment audiences with unprecedented precision, and generate real-time insights that guide both marketing strategies and operational adjustments. Retailers can now create truly individualized shopping experiences, financial institutions can proactively recommend better options for customers, and healthcare providers can anticipate patient needs before they arise. The unifying thread is that companies are not relying on guesswork; they are continuously learning and adapting through data, allowing them to anticipate demand rather than merely react to it.

This transformation is not confined to the marketplace. Within organizations, employees are seeking the same level of modernization. They want tools that reduce repetitive work and allow them to focus on the higher-value contributions that matter most. AI-driven automation is becoming the bridge to meet this demand. In many organizations, generative AI is already performing functions such as drafting reports, summarizing meetings, and handling routine customer inquiries. This shift is not about displacing employees. Instead, it is about elevating them. By removing low-value tasks from their workload, companies create space for employees to concentrate on strategic thinking, creative problem-solving, and relationship-building. 

Far from diminishing the human role in the workplace, AI has the potential to enhance it, enabling teams to operate at a higher level of efficiency and effectiveness. However, realizing this vision requires deliberate investment in reskilling and upskilling. Employees must be equipped not only to work alongside AI but to supervise, refine, and improve these systems. The companies that are investing in training are ensuring that the uniquely human qualities of judgment, ethics, and creativity remain at the center of innovation.

Also Read: Edition 3: Sharp Minds Decoding Tech’s Next Wave

Beyond customers and employees, the operational backbone of businesses is also undergoing a profound shift. Volatility in supply chains, geopolitical uncertainty, regulatory changes, and environmental challenges have created an environment where agility is no longer optional. AI provides the speed and accuracy necessary to navigate these disruptions. 

Manufacturers are utilizing predictive analytics to identify potential supply shortages and switch to alternative suppliers before a crisis arises. Logistics companies are leveraging AI to optimize delivery routes, reduce costs, and lower their environmental footprint. In healthcare, predictive modeling is enabling hospitals and health systems to anticipate demand for resources, from staffing to medications, allowing them to respond with agility rather than scrambling in the moment. 

In each of these cases, the advantage AI provides is not abstract. It is measurable in time saved, costs reduced, and risks avoided. Companies that once relied on weeks of manual analysis are now modeling scenarios in real time and making decisions within hours, if not minutes.

Perhaps the most compelling aspect of AI’s role in adaptation is its ability to enable entirely new business models. Organizations are no longer limited to selling products or services in traditional formats. Instead, AI is allowing them to redefine the value proposition altogether. For example, rather than simply selling equipment, firms are offering “uptime as a service,” where AI systems monitor machinery, predict failures, and schedule maintenance before problems arise. 

Also Read: Is the Future of Personalization About More Data, or Smarter Data?

This shifts the customer relationship from a transactional exchange to an ongoing partnership centered on outcomes. In industries such as pharmaceuticals, AI is accelerating drug discovery by rapidly analyzing vast datasets to identify promising compounds far more efficiently than conventional research methods. In media and entertainment, AI tools are helping creators design content that is both more engaging and more inclusive, aligning their outputs with the diverse needs of their audience. These examples point to a broader truth: innovation is no longer constrained by human capacity alone. The combination of human creativity and machine intelligence is generating once-unimaginable breakthroughs.

Still, with opportunity comes responsibility. Adapting to these new demands is not solely about technological capability. It is also about building and maintaining trust. Customers, employees, and regulators are watching closely, and they want assurance that AI is being used responsibly. That requires transparency in how AI systems function, fairness in their application, and accountability in monitoring their outcomes. 

Companies must invest in governance frameworks that identify and mitigate bias, ensure auditability, and safeguard security. They must also be candid with stakeholders about the limitations of AI, resisting the temptation to oversell its capabilities. Those who treat AI as a black box or pursue short-term gains at the expense of trust will find themselves at risk. Thoughtful leaders understand that the long-term viability of their organizations depends not just on what AI can do, but on how responsibly it is deployed.

For CEOs and business leaders, the challenge is to view AI not as a standalone initiative, but as an integral part of their corporate strategy. This means integrating AI into the core of business planning, aligning it across departments, and measuring success in terms of adaptability and resilience rather than short-term efficiency alone. 

Boards are beginning to ask more probing questions about how companies are using AI to safeguard competitiveness. Investors want to see clear strategies that strike a balance between opportunity and governance. Employees look to leadership for clarity about how AI will shape their roles and careers. In this environment, CEOs must bring coherence to the conversation, setting a vision that unites all stakeholders and establishes a clear path forward.

Also Read: The AI-Powered CDP: A New Era for Customer Data Management

Looking ahead, it is clear that we are at a turning point. Companies that adapt quickly to these new demands, empowered by AI, will define the next decade of business. Those who hesitate may find themselves struggling to compete in markets where agility is the ultimate differentiator. AI’s role is not to replace people or strip organizations of their values. Its role is to extend human capability, to bring clarity to complexity, and to help businesses deliver outcomes that are both meaningful and sustainable. 

The organizations that recognize this truth are not simply surviving change; they are shaping it. The responsibility of today’s leaders is to thoughtfully embrace these tools, invest in people, and pursue innovation with integrity. Companies that do so will not only thrive in this era of transformation, but they will also set the standard for what the future of business looks like.

The post AI and the New Rules of Business Adaptation appeared first on MartechView.

From dynamic content to targeted offers, personalization has become the gold standard for modern marketing. This is natural: consumers increasingly expect experiences tailored to engage them based on their preferences. But as data collection tools become more sophisticated to meet this demand, a critical question has emerged: Is the future of personalization really about collecting more data, or is it instead about using the right data more effectively?

For many marketing teams, the default approach has been volume. The more data, the better. Every pixel, clickstream, and third-party attribute is tracked in pursuit of deeper insights. But this data deluge often creates more noise than signal. Bloated customer profiles, misaligned segmentation, and underutilized dashboards can slow down execution and dilute personalization efforts. Worse, it introduces significant compliance and trust challenges in an era of growing consumer privacy orientation.

The smarter path forward lies in zeroing in on the right data—data that is accurate, intentional, and aligned with a clear use case. For marketers, this means being strategic about which signals you capture and how you activate them across channels.

So, what qualifies as “right data”? It goes beyond demographic or behavioral. The right data is contextual, consented, and actionable. For example, understanding that a B2B buyer has returned to your pricing page three times in a week is more valuable than knowing they downloaded an unrelated eBook months ago. With the right data, powered by predictive AI, clean architecture, and intelligent orchestration, you can deliver messaging that aligns with real-time intent—not just historical guesses.

Brands that embrace this shift are already seeing results. Case in point: a client leveraging Resonate’s website personalization solution saw the rate of first-touch personalization on their website rise from a mere 8% to 53%. This resulted in not only a much better experience for their visitors, but a 20% increase in conversions to the client’s premium product. The difference wasn’t more data; it was a smarter data strategy and solution.

To get there, marketing and tech leaders must work in lockstep. Start with a collaborative audit: What’s being collected, what’s being used, and what’s delivering value? Then invest in tools that help prioritize, unify, and activate high-value data across your stack.

As AI continues to evolve, personalization will become more predictive, conversational, and real-time. But those gains will depend not on the size of your database, but on the clarity of your strategy. In the battle between breadth and precision, precision wins.

The future of personalization doesn’t belong to the brands with the most data. It belongs to those who know what matters and use it well.

The post Is the Future of Personalization About More Data, or Smarter Data? appeared first on MartechView.

Customers have become used to being hyperconnected, “always on,” in the digital world. They receive marketing messages on every platform, at every interaction, from pop-up ads to social media sponsored posts to reviews from their favorite influencers.

There’s a lot of competition for their attention, making it more challenging for brands to develop genuine relationships – no matter how much data is available. Personalization without authenticity falls flat, but understanding the behavioral data, what makes the customer tick, can be the key to building the relationship you want.

What Is Behavioral Data?

Behavioral data is the information that reflects how customers interact with a brand, product, or service. This can include website visits and clicks, purchase history, cart abandonment, email open and click-through rates, time spent on pages, and app usage patterns. You can also glean valuable insights from social media interactions like comments and shares or customer service communications.

Unlike demographic or psychographic data, which merely tells you who a customer is or what they value, behavioral data tells you what they do. These insights are more dynamic and can provide an authentic, real-time picture of customer intent and preference, which you can then use to deliver more personalized, timely, and relevant experiences.

How Does Behavioral Data Affect Authenticity?

Customers are increasingly skeptical of traditional marketing tactics. A report from Edelman Trust Barometer found that 71% of consumers say that if they perceive a brand as putting profit over people, they lose trust in the brand.

It’s clear—customers want authenticity. They prefer communications that feel personal, relevant, and rooted in a genuine understanding of their needs. Behavioral data can help you respond to actual customer behavior instead of your own assumptions for more accurate ways to highlight their preferences, anticipate their needs, and engage with them on a human level.

Strategies to Use Behavioral Data to Build Authentic Relationships

Contextual Personalization

Personalization isn’t just about including a first name in an email greeting. Behavioral data is a valuable tool for tailoring content recommendations and messaging based on the customer’s interactions and journey.

For example, a customer may visit a restaurant’s website and check out the brunch menu, but doesn’t book a reservation. This is a restaurant marketing opportunity to use behavioral data to trigger a geo-targeted ad offering a limited-time weekend brunch special or send a personalized SMS reminding them to finish booking. It’s timely, relevant, and based on real behavior, not guesswork.

Timing Is Everything

Behavioral signals can inform not only what you say but also when you say it. For example, triggering a discount offer after a cart abandonment or delivering onboarding content after a customer signs up for a subscription are timely, behavior-driven communications demonstrating attentiveness and respect for the customer’s journey. They reduce friction, support the customer’s goals, and make them feel understood.

Enabling Predictive Engagement

Tools like AI can forecast what customers are likely to do next based on their past behaviors. For example, data demonstrates that customers who engage with a brand’s blog for two months convert into paid subscribers after reading a case study. This information can be used to proactively suggest case studies to engage these users further and speed up the conversion.

Keep in mind that there’s no manipulation or pressure here. Instead, this approach offers value that is aligned with the customer’s current behavior and their likely needs. It gives your brand credibility while gently guiding the customer through the buying journey.

Driving Two-Way Conversations

Behavioral data doesn’t just help brands speak to customers. It helps them listen, too. For example, if customers frequently search for a feature that doesn’t exist or leave feedback in chat interactions, this may indicate they have needs that aren’t met.

You have an opportunity to acknowledge and respond to these insights. You could add a requested feature, improve support workflows, or reach out for more details to better serve the customer. This shows that you’re actively listening and care, which is the foundation of an authentic and valuable business relationship.

Some brands leverage behavioral data to adapt tone and channel preference. Some customers won’t check your marketing emails, but they’re happy to listen when you use SMS or social media messages. That signals that they need you to meet them where they are.

Segmenting with Purpose, Not Stereotypes

Traditional marketing segmentation often groups customers into static categories like “High-Income Professional” or “Gen X Mom.” Leaving people in broad categories can miss the important nuances of each customer, and your messaging could fall flat.

Behavioral data offers more fluid, accurate segmentation. You can group customers by shared behaviors, such as “customers who use social media purchase buttons” or “customers who download content often.” That’s inspiration to design experiences around those actions and deliver more meaningful interactions to these segments.

Improving the Experience Instead of Selling

A key aspect of an authentic relationship is to provide value without always asking for something in return. Behavioral data can help identify friction points in the customer journey, or places where the customer may get stuck, confused, overwhelmed, or frustrated.

If it’s bad enough, the customer may drop off completely. For example, if you see a lot of customers abandoning ships before the final step of a form, it may indicate that there’s a design flaw or that the pages are lagging. You can proactively fix the issue without discussing it with the customer – or waiting for a complaint – and show that you are attentive to the customer’s needs.

Ethical Use of Behavioral Data

Authenticity also relies on transparency and respect. Customers should know how their data is being used and can opt out of data collection or targeting. Personalization can quickly become ” creepy, ” but committing to ethical data practices and privacy can help you avoid these scenarios and maintain trust.

Let Customer Behavior Guide the Experience

Behavioral data helps you cut through the noise with meaningful, personalized experiences. You can respond to real customer actions, not your own assumptions, to build trust, loyalty, and authentic, long-term relationships. 

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Marketers have heard the advantages of designing customized materials. They can make target audiences feel more understood, enticing them to become repeat customers. Alternatively, some shoppers are uneasy when they see hyperpersonalized campaigns. They believe companies are using their personal information in manipulative ways. How can experts find a balance and avoid ethical quandaries associated with modern marketing?

Ethical Considerations in Custom Marketing

Automating personalization can help organizations reach customers who are more likely to convert. However, there is a fine line between using personalized data for meaningful marketing versus overstepping boundaries. 

With data breaches and consumer privacy scandals hitting headlines, people are becoming increasingly aware of how marketers use their information. These are some of the most prominent concerns professionals should be worried about before they engage with curated campaigns:

• Privacy violations: Using information without consent or expressing how the data will be used
• Discrimination: Personalizing in a way that promotes unfair, demographic-based pricing or filter bubbles
• Manipulation: Exploiting customers with sensitive information for corporate gain, making them feel vulnerable
• Data security: Exposing and misusing data that could lead to cybersecurity concerns

Another novel concern is artificial intelligence (AI). Generative AI, which 45% of marketers claim to be engaging with already, is prone to synthesizing outcomes incorrectly. Experts must fact-check AI determinations to prevent miscommunications or inaccurate portrayals of target audiences. 

Advice for Ethical Personalization

Get the most out of available data without crossing lines with these trade tips.

Obtain Explicit Consent

Marketers should always have easy-to-understand consent forms for customers. These should detail what information they will collect and how long they will store and use it. Describing the applications is crucial for making consumers commit without question. Robust opt-in features and confirmation emails can require multiple verifications and approvals of understanding.

Prioritize Data Security

Marketers must work alongside cybersecurity analysts to protect consumers. Hackers are finding new methods to pull data daily, requiring teams to outsource expert aid. Professionals know the best ways to protect specific programs and systems, ensuring they cooperate with compliance and security protocols. They recommend using immutable storage, updating encryption practices, and incorporating data minimization.

Create a Culture of Transparency

Every team member must prioritize authentic marketing equally. Otherwise, there could be inconsistencies at any campaign phase, including strategic planning and deployment. Establishing a transparency-focused culture requires regular training on how the organization will use and collect information. 

The effort will also refine existing privacy and data-usage policies, ensuring deletion happens as needed and access is limited. This is crucial for omnichannel campaigns because strategies can vary from platform to platform. This complexity may be why confidence in them has decreased. Only 24% of professionals believe omnichannel experiences were successful compared to 35% from the prior year.

Respect User Control

Customers should have complete control over their data. Even if they previously gave consent, they should be able to withdraw it at any point. Knowing they can revoke access to personal information whenever they want can make them feel better about sharing it with marketers in the first place. Incorporate simple-to-access opt-out options. People should be able to customize them, including complete deletion or toggling some sharing settings.

Focus on Value and Relevance

Customers will dismiss ads and abandon brand loyalty if they see irrelevant campaigns. Those who consent to giving data want to see it in action, so businesses need to prove they understand their audience. 

Every design, slogan and product should be justified as a valuable life improvement. So, focus on personalizing offers and ads based on what customers need and participate in regularly, instead of trying to manipulate them into deals or services that feel disingenuous. 

Avoid Discriminatory Practices

Sometimes, personalized marketing data can skew pricing and design choices. For example, a program may show a predominance of customers of specific genders or races. Overrelying on these insights could make marketing materials exclusionary or not representative of the target audience. Even if unintentional, these missteps can lead to alienating some consumers. Auditing data stores to reduce gaps and biased algorithms prevents these issues. 

Regularly Review and Update Practices

Buyer sentiments change often, and so should personalized marketing. Demonstrating adaptability in ads and products is essential for keeping people engaged. This requires attentiveness to updating regulatory policies and a desire to constantly overhaul the company’s perception of its buyer personas. Schedule consistent data and marketing audits to ensure everything is current and aligned with best practices.

Personalization Without Privacy Concerns

The average shopper is becoming more attuned to marketing tactics. They spend hours exploring social media and notice the increase in ads during their favorite shows, so marketers must become more intentional with data use. It should feel personalized to keep people engaged without making them feel betrayed. These techniques will help organizations set a valuable precedent for a future with even more information driving business decisions.

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Businesses operating in today’s saturated media landscape face a daunting challenge—how to capture and maintain consumer attention amidst a tsunami of content. For decades, the media industry has measured success by the sheer volume of consumer attention, aptly reflected in metrics like hours watched or clicks amassed. However, insights from McKinsey’s recent report, “The Attention Equation: Winning the Right Battles for Consumer Attention,” highlight a critical paradigm shift toward measuring the quality of attention rather than just its quantity. This new outlook is poised to revolutionize media monetization and redefine strategies for advertisers and content creators alike.

From Quantity to Quality  

Traditionally, the media industry viewed consumer attention in terms of scale—chasing larger audiences and greater consumption hours. The assumption was that increased exposure would inevitably lead to monetization success. However, this approach neglects the core truth that not all attention is equally valuable. One consumer glued to a piece of content with intent and focus generates more meaningful engagement—and, by extension, more revenue value—than multiple distracted viewers casually skimming through it.  

The McKinsey report introduces the “attention equation” to address this blind spot, encapsulating both the quantity and quality of attention. The research is grounded in a comprehensive survey of 7,000 global participants, including 3,000 consumers in the United States. It argues that the ultimate determinant of attention’s value lies in two primary factors—the commercial quotient (CQ) and the attention quotient (AQ).

The Formula for Attention Value  

The “attention equation” brings clarity to what drives meaningful engagement. It integrates two complementary components:

Commercial Quotient (CQ)

This measures traditional variables, including market dynamics, consumer demographics, advertising effectiveness, and platform sophistication. According to the report, CQ explains about two-thirds of the variability in how attention translates into monetization. For example, legacy media formats like magazines and live events remain highly profitable due to factors such as scarcity and cultural relevance.

Attention Quotient (AQ)

Adding a new layer to understanding, AQ quantifies the quality of engagement by considering two critical dimensions:

• Level of Focus: How actively involved the consumer is with the content. For example, activities like reading a book or attending a live concert demand high focus compared to multitasking behavior (e.g., scrolling social media while watching TV).  
• Job to Be Done: Why the consumer is engaging with the content. Motivations range from light entertainment and relaxation to seeking education, social connection, or background ambiance.

The interplay between the CQ and AQ provides a fuller picture of attention’s value. For instance, while a live concert offers limited scalability, it elicits deep focus and enjoyment, making it one of the most profitable mediums, with an average monetization value of $17 to $33 per hour.

Consumer Attention Patterns and Media Monetization  

The McKinsey findings reveal striking disparities in how various media formats monetize attention. Live experiences, video gaming, and sports emerge as the highest-value drivers, generating significant revenue per hour of consumption. Meanwhile, digital formats such as podcasts and streaming services often lag in monetization efficiency despite capturing vast consumer attention. For example, digital music monetizes at just $0.12 per hour, while legacy mediums like books and magazines monetize significantly better despite dwindling market share.

This divergence underscores the importance of understanding why consumers pay attention. Younger audiences, for example, demonstrate focused attention on interactive mediums like video games while older generations engage more deeply with traditional formats like newspapers. Interestingly, focus correlates directly with spend—consumers in the top quartile of focus spend twice as much on media content as those in the bottom quartile. High-focus engagement thus holds immense untapped potential for media companies.

Segmenting Attention into High-Value Groups  

To leverage this nuanced understanding of attention, McKinsey proposes segmenting consumers based on their attention value and spending potential. Three standout groups offer particular promise for media stakeholders:

Content Lovers (13% of consumers)

These passionate superfans are eager to consume cross-platform franchises, from movies to merchandise. They spend 2.4 times more on media than the average consumer, representing a lucrative, high-conversion audience.  

Interactivity Enthusiasts (16%)

Competitive and community-oriented, this group is drawn to interactive spaces like gaming and sports betting. However, they often experience decision fatigue and cost barriers, highlighting opportunities for tailored offerings.

Community Trendsetters (10%)

Sociable and connected, these consumers heavily influence online culture and spend disproportionately on collective experiences like live concerts or themed attractions. They are also highly receptive to aligned advertising efforts.

Importantly, the research finds that segments with the highest attention value also dominate media spend, offering a compelling case for targeting these groups with precision.

Strategic Recommendations for Advertisers and Creators  

The “attention equation” offers actionable insights for two key stakeholder groups—advertisers and media creators and distributors:

For Advertisers:

• Match Content with Attention Value: Ads should align with consumer engagement levels and the job to be done. For example, a humorous ad might find resonance in light-entertainment contexts, while emotionally driven narratives could better suit deep-focus viewer segments.
• Identify Underpriced Attention: Platforms like mobile gaming and video streaming offer undervalued opportunities despite their strong focus and monetization potential. Advertisers should rethink budget allocation toward these attention sweet spots.  

For Creators and Distributors:

• Prioritize Quality Attention: Content strategies should focus on formats that elicit high-focus engagement. For instance, producers could emphasize original intellectual property and communal experiences that command undivided viewer attention.  
• Diversify Content Portfolios: Balancing content that serves different “jobs to be done” enables creators to tap into multiple lucrative consumer segments. Offering both educational and entertainment-driven programming broadens audience appeal.
• Leverage Data and Technology: Platforms with superior recommendation algorithms see higher focus scores. Investment in these tools can optimize engagement and, subsequently, subscriber value.

The Road Ahead  

McKinsey’s “attention equation” shifts the conversation in the media business toward a more insightful, value-driven understanding of consumer behavior. By emphasizing the quality of attention—evaluated through focus and intent—companies can create better strategies to engage high-value audiences, maximize monetization potential, and thrive in the attention economy.

Organizations adopting this research-backed approach will be well-positioned to capture the fragmented, immensely valuable, consumer attention landscape. For media stakeholders, the opportunity lies not merely in winning more attention but in securing the right kind of attention—focused, intentional, and high-impact.

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