marketing
How Collage is Reshaping Asset Management for Growing Brands
marketing 26 Feb 2026
You’re stepping into the CEO role at Collage as the company enters its next phase of growth. What’s your vision for Collage moving forward?
Collage was built on a simple belief: managing important brand content shouldn’t feel heavy, complicated, or out of reach for growing teams.
As we enter our next phase, our vision is to become the modern infrastructure layer supporting how brands organize, distribute, and ultimately get more value from their content. We’re focused on doing the core work of Digital Asset Management exceptionally well — search, organization, and sharing — while removing the enterprise complexity that has defined the category for a decade.
We’ve grown consistently by serving teams who felt priced out or overwhelmed by legacy systems. Moving forward, we’ll double down on that momentum: improving migration experiences, strengthening our distribution capabilities, and investing in performance and simplicity.
How do you define Digital Asset Management today, and where do legacy platforms fall short?
At its core, Digital Asset Management should be the system of record for brand content — the place where assets are structured, searchable, and controlled, ultimately with the purpose of optimizing the value created by that content.
Historically, though, DAM platforms were designed more like vaults — places to store and protect files. Distribution was treated as a secondary feature. And over time, that inward focus created layers of complexity and rising costs.
Today, content flows outward constantly — to agencies, retail partners, sales teams, distributors, media outlets, technology platforms. If distribution isn’t central to the DAM, teams default to workarounds: email attachments, shared drives, Slack threads, ad-hoc file transfers. That fragmentation is where control and efficiency break down.
A modern DAM needs to function as an engine, not just a vault. It should activate content — making it easy to find, permission, package, and distribute without adding operational overhead.
Where legacy platforms often fall short is assuming every team needs enterprise-level complexity. Many growing organizations don’t. They need speed, clarity, and workflows that match how content actually moves.
That’s the shift we believe the category needs to embrace. By shifting focus to what we believe matters most for these teams, we can fundamentally make the platform more affordable without sacrificing impact for the vast majority of brands out there.
Collage describes itself as “distribution-first.” What does that mean in practice?
Distribution-first means designing DAM around how content is actually used — not just how it’s stored. At Collage, we focus on two core audiences:
- The teams who create and manage assets
- The audiences who consume and amplify them
By stripping away unnecessary complexity and streamlining distribution, we make modern DAM fundamentally more cost-effective, scalable and user-friendly. Practically, that shows up in capabilities like branded distribution portals, dynamic share links, asset embeds, flexible permissions, and modern search. Instead of responding to constant requests, teams can proactively enable access — reducing friction, eliminating bottlenecks, and accelerating brand amplification.
What problem does Collage solve that other DAM platforms struggle with?
We solve the mismatch between modern marketing teams and legacy software expectations.
Many growing brands are managing thousands of assets, collaborating with agencies, dealers, distributors, or partners — but they don’t have enterprise budgets or supporting IT departments.
Legacy DAM platforms often assume both. Collage removes the friction around:
- High contract costs
- Complex platforms that are difficult to adopt
- Fragmented systems for managing content
- Inability to activate content even if centralized and organized
We provide the essential capabilities teams actually use — search, tagging, portals, custom metadata, structured organization — without forcing them into a heavyweight system.
That balance of power and simplicity is where we see the most traction.
Who is Collage built for, and how does it fit into the broader DAM market?
Collage operates squarely in the Digital Asset Management category, but our philosophy is different. While many legacy vendors prioritize enterprise breadth and highly specialized functionality, we emphasize:
● Simplicity over complexity
● Access and distribution
● Practical value over feature accumulation
Rather than building another feature-heavy system, we designed Collage from the ground up to focus on essential functionality with real impact. That focus allows us to deliver a powerful DAM experience without the cost or complexity that has deterred so many teams. That makes Collage especially well-suited for growing brands that have historically been underserved or priced out of DAM altogether — as well as modern teams that want speed, clarity, and control without enterprise overhead.
How do you think DAM needs to evolve to keep pace with modern marketing workflows?
Digital Asset Management needs to become more connected, more intelligent, and more flexible.
The future of DAM is less about managing files and more about orchestrating access — ensuring the right people can find and use the right assets at the right time.
First, platforms must integrate seamlessly with the tools marketers already rely on — design software, CMS platforms, automation tools, and perhaps most importantly, AI systems. DAM can’t be an isolated repository; it should facilitate a broader content ecosystem.
Second, search and metadata management must continue to evolve. As asset libraries grow, discovery becomes mission-critical. Teams need fast, precise ways to surface what matters. This is also essential for fully leveraging the emerging AI capabilities.
And finally, AI will reshape expectations entirely. Most of what we see as AI in the category will become commoditized. However, the platforms that evolve distribution into AI-ready infrastructure will be the ones that deliver lasting value.
The companies that embrace this shift — from vault to engine, from storage to orchestration — will define the next generation of DAM.
What does success look like for Collage over the next few years?
Success means making DAM feel approachable, effective, and indispensable for modern teams. If Collage can help brands reduce friction, save time, and get more value from their content - while reducing complexity - we're doing our job.
Ultimately, our goal is to redefine expectations for DAM: simpler, more affordable, and built for how content actually moves in today’s organizations now and in the future.
How CMOs are rethinking metrics moving beyond vanity KPIs toward lifecycle value and ROI, while balancing CFO pressure to automate with the need to preserve brand voice and integrity.
marketing 26 Feb 2026
How is NRF redefining the role of events by becoming a platform for ecosystem-building and community?
Events as a whole are an excellent way to create a larger sense of community. However, NRF specifically creates continuity across various retail partners, technology providers, and decision-makers, allowing ideas and relationships to develop past the show floor. This show in particular excels at purposeful networking and going beyond an annual meeting, operating as an ecosystem that builds connections and offers a platform to connect everyone involved.
For example, they host education sessions for practitioners to share their best practices. They also offer year-round engagement like virtual sessions, meetups, and gatherings on a global scale, underscoring the foundation’s core role in driving innovation to shape the industry.
NRF integrates data and AI to offer personalized attendee journeys and engagement experiences at every touch point, before, during and after the event. Doing this builds richer and more relevant connections, both to the content and other professionals.
In your view, what will differentiate high-impact events from “check-the-box” events over the next few years?
Traditionally, marketers have relied on surface-level metrics to determine the success of their events for years. However, in today’s complex landscape and with the increased use of digital tools, only counting vanity metrics, like number of attendees, falls short in showcasing the full value and business impact of an event.
With more AI and tech solutions at marketers’ disposal in the next few years, the impact of events will be measured by how effectively marketers can convert interactions into long-term value. AI tools can speed up this process by helping teams to look beyond the isolated actions taken at an event, such as registration data, engagement metrics, or survey feedback, so they can better understand how the attendees’ experiences impact tomorrow’s pipeline, revenue, and relationships.
The distinction between a “check-the-box” and high-impact event will ultimately come down to whether marketing teams can prove an event contributed this type of sustained momentum, or only captures activity in isolation.
How has the shift toward lifecycle value and long-term ROI changed the way marketing teams plan and evaluate campaigns?
The traditional linear funnel of sales no longer applies in the world of B2B. 44% of marketers believe they’re effective at running in-person events, but because buying decisions are becoming more complex with multiple stakeholders, teams must rethink how they can accelerate business goals and capture the best ROI from events.
This led to a more connected approach to planning and evaluating activations, where teams leverage AI to better analyze attendee behavior and engagement metrics to predict customer lifetime value, and estimate long-term revenue and ROI potential. Evaluation now centers on influence across the full journey, not just performance at a single point.
What challenges do CMOs face when trying to align metrics with executive expectations especially at the board and CFO level?
Attribution is one of the biggest challenges when it comes to marketing and aligning metrics with executive expectations. Opportunities aren’t driven by a single tactic, but rather influenced by a mix of campaigns, channels, content, timing, and follow-ups. Revenue is the result of multiple touchpoints working together, often over a long period of time. But that complexity doesn’t always translate easily in boardroom conversations. What boards want is clear ROI with data to back it up.
Time horizons and differences in priorities are another challenge. Boards and executives work on quarterly and monthly time horizons, while many marketing investments are set at longer times for impact. Focus areas are also intrinsically linked but different – boards care more about data like revenue growth, margins, and cash flow, and marketers focus more on share of voice, engagement, pipeline metrics, velocity, and customer lifetime value.
Overall, CMOs aren’t struggling to prove ROI from a lack of data, but rather how they connect that data to metrics discussed in the boardroom. In fact, the global martech market is expected to increase from $131B in 2023 to $215B by 2027. Despite the enormous industry growth, many companies still can’t tie their current marketing investments directly to business outcomes. Fragmented systems across marketing, sales, and events, only amplify these challenges, making it difficult to tell a cohesive story.
To demonstrate value and close this gap, more marketers will partner hand-in-hand with CFOs to establish shared KPIs and align on what real impact really means, while beginning to measure data beyond clicks and impressions, and managing martech like a strategic enterprise asset.
CFOs are pushing for automation and efficiency across marketing. Where do you see automation delivering value and where does it do more harm than good?
Pushing for automation where it drives operational clarity and scale can help improve overall operations and remove friction, reduce cost, and provide resource allocation. However, automation delivers the most value when it removes the burden of manual analysis and allows teams to act faster on metrics that actually influence business outcomes. For example, AI can synthesize attendee behavior across events, digital interactions, and post-event engagement to surface patterns that would otherwise take weeks or months to uncover. Examples of this include which buying groups are showing intent, which topics correlate with pipeline creation, and how long it typically takes for this momentum to turn into revenue. This helps marketing and sales teams prioritize follow-up, tailor messaging, and focus their time on the highest-value opportunities to boost rather than chasing every interaction equally.
On the other hand, it can cause issues when it replaces intentional experience or human connection. It’s important to keep authenticity in mind when it comes to automation. AI can be a powerful engine for fueling meaningful connections, but leaning on generic, AI-generated marketing content only adds to growing digital fatigue, and risks customer backlash. Automation should power precision and insight, not replace empathy and strategy.
What role does cross-functional alignment between marketing, finance, and technology play in redefining success metrics?
Cross-functional alignment is essential for moving from reactive reporting to proactive growth planning. Marketing, sales, finance, and technology all contribute unique perspectives and pieces of the data needed to understand true event impact. However, when these teams operate in silos, success looks different depending on the system or report being referenced, whereas alignment enables a shared view of performance that leadership can trust, build upon, and act on for tangible results.
Using customer data integration (CDI) centralizes and aligns customer information across all touchpoints, such as sales, marketing, and service channels. For example, by integrating real-time behaviors with holistic customer journey records, businesses can create detailed, up-to-date customer profiles. Based on these customer profiles, companies can tailor communications and touchpoints to individual preferences, ensuring a more relevant and personalized experience.
CDI also facilitates an omnichannel approach that supports seamless, two-way communication and continuous feedback loops, streamlining workflows by breaking down silos between departments and enabling teams to work collaboratively.
How do data and storytelling coexist in a metrics-driven marketing organization?
Data provides the evidence of how buyers behave, how long it takes for impact to occur, and which touchpoints influence outcomes. Storytelling connects those insights into a narrative that explains what changed, why it matters, and what should happen next. Together, they allow organizations to move beyond dashboards and into strategic decision-making. Without storytelling, even sophisticated data fails to drive alignment or action.
What capabilities, technological or organizational will be most critical for CMOs to thrive in this evolving landscape?
AI is disrupting and transforming the way that we work, as more marketing organizations are already learning to vibe code to deliver more value quicker. The most successful teams will be the ones who embrace AI, but also inspire their teams to use it for better efficiency when it comes to automation, workflows, and automating tasks. This allows teams to focus on the strategic areas and accomplish more in advancing their company's objectives.
CMOs will also need the ability to see and understand the full, non-linear customer journey across digital, hybrid, and in-person experiences, rather than relying on isolated metrics from individual campaigns or channels. The CMOs who thrive will be those who can combine human judgment with predictive insight to turn interactions into sustained momentum, loyalty, and revenue over time.
As part of this, authentic thought leadership and messaging in the midst of AI and digital fatigue will be paramount. With more brands pivoting to AI to support their content engines, there is risk for undifferentiated, vanilla perspectives, meaning brands need to elevate their efforts to break through the noise.
How event technology is becoming a core go-to-market lever, enabling organizations to translate engagement signals into smarter pipeline and revenue strategies.
marketing 26 Feb 2026
What does “meaningful engagement” mean in today’s environment especially for audiences that are digitally saturated?
Meaningful engagement today is defined by intent and relevance, not volume. Audiences are inundated with content and notifications, meaning attention is no longer freely available, and has to be earned. Engagement becomes meaningful when the interaction is timely, context-aware, and clearly valuable to the individual on the other side of the device. Instead of asking the audience to passively consume more content, the focus should shift to creating moments where they can participate, signal interest, and move a relationship forward.
Increasingly, meaningful engagement has become a two-way exchange between the audience and the brand. This enables the audience to derive personalized value by actively participating in the conversation, experience, and community. It can be as simple as emoting on a livestream, or as complex as a tailored meeting with hand-picked experts, peers, and activities for that specific audience. Because most aspects of customers’ day-to-day lives have become digitally saturated, this type of valuable engagement also delivers purpose.
Event technology has evolved far beyond registration and badge scanning. What capabilities are now table stakes for delivering connected event experiences?
Today’s event technology must support the holistic attendee experience, going beyond a single event or program. To do this, a connected data foundation is essential to modern event experiences. Table stakes now include unified attendee profiles, real-time behavioral data capture, and bi-directional integration with marketing systems and CRM. These capabilities allow teams to understand more than simply who attended, but how and when they engaged, what they found valuable, and where that activity fits within the broader customer journey. Without this connective tissue, events remain disconnected moments rather than strategic touchpoints.
What role does real-time data play in adapting experiences while an event is still happening?
Real-time data allows teams to move beyond static execution models by providing immediate visibility into how attendees are engaging across sessions, networking, and event content. It can take an event from a fixed program to an adaptive environment. When teams have immediate visibility into session engagement, content interaction, and attendee movement, they can respond in the moment and adjust staffing, promote different content, or facilitate more meaningful connections. This shift away from static agendas and toward experiences that evolve based on actual behavior make the event more responsible and valuable for attendees.
Furthermore, events provide a wealth of first-party intent signals that can offer value beyond logistical management. The real-time discovery of an expansion opportunity, high-value meeting with sales, qualified lead for a partner, or connection with an influencer, are just the beginning of key customer milestones achieved through an event experience. These behaviors can drive the right next best action to accelerate the customer journey, whether it be another onsite experience, marketing campaign, sales engagement, or recommendation.
Many organizations now view event technology as a core part of their GTM stack. What’s driving this shift?
Events generate some of the strongest first-party engagement signals available to marketers. As traditional digital attribution becomes less reliable, organizations are prioritizing channels that provide clear indicators of intent. Event interactions, such as what session an attendee joins, who they meet with, what they participate in, offer high-confidence insights into buyer interest. When that data is connected directly into GTM systems, events move from beginning standalone moments to measurable accelerants to pipeline and revenue.
What challenges do organizations face when translating event engagement data into actionable pipeline insights?
Fragmentation is the biggest challenge. Event data is often captured across disconnected tools without a shared data model or consistent definitions of engagement. This makes it difficult to unify insights, act quickly, or deliver clear context to sales teams, which can slow down analysis and follow-up. When engagement data isn’t standardized or integrated, its value can decay rapidly after the event, limiting its impact on follow-up and pipeline acceleration.
How are sales using event intelligence to have more relevant, timely conversations with prospects?
Event intelligence gives sales teams context before and after contact. As opposed to starting conversations cold, reps can see what a prospect engaged with in the past, topics of interest, and where interest was concentrated. This allows outreach to be relevant and rooted in the attendee’s experience, not just within a generic sales narrative.
As part of this, event insights can also greatly inform post-engagement follow ups. When data is delivered quickly and shows which attendees had the highest levels of engagement or interest, follow up conversations are not only timely, but better aligned with buyer intent.
What organizational shifts are required to treat events as a revenue driver rather than a brand-only channel?
It starts with intentional planning and shared ownership. Events have to be designed with GTM outcomes in mind from the beginning, supported by shared KPIs across events, marketing, and sales. Organizations need to move away from managing disconnected tools and toward orchestrating outcomes by relying on technology to handle complexity while teams focus on strategy, alignment, and execution.
Revenue teams should also be an active participant in pre-, during, and post-event planning, to drive audience acquisition, craft personalized experiences, and have better oversight in engagement. These elements should be readily available and a part of co-planning efforts in order to support nomination goals, activities like timely follow ups, and management oversight.
What innovations in event technology are you most excited about from a GTM and revenue perspective?
The most meaningful innovation is the shift from task automation to decision support. Emerging, agent-based approaches can help teams identify buying signals, guide attendees through relevant experiences, and recommend the next best action in real time. Rather than replacing human judgement, these systems augment it, helping teams recognize key moments as they happen, and act with greater precision across the event lifecycle to ultimately connect the full spectrum of events to the customer journey.
Perfecting Personalized Promotions, The secret to achieving advanced personalization, and what’s holding retailers back
marketing 25 Feb 2026
Jeff Baskin, Chief Revenue Officer
Promotions are a key performance area for all retailers, and effectively implementing truly personalized offers at scale has been a goal for enterprise retailers for decades. Eagle Eye’s CRO Jeff Baskin shares his thoughts on how technology is making this goal more attainable, the legacy approaches that are holding retailers back, and what impacts they can expect from genuine one-to-one promotional engagement.
1. What are the biggest inefficiencies you see in traditional promotional models today and why are so many retailers still relying on broad, mass-discount approaches?
The biggest flaw with traditional mass discounting is that it often incentivizes customers who would have purchased anyway, while failing to influence behavior where it matters. This inefficiency is largely driven by legacy systems and the inertia of “what’s always worked.” Most retail infrastructure was built to support blanket offers or, at best, broadly segmented campaigns, not individualized promotions at scale. For years, that was effective, but as competition intensifies and technology improves, more retailers are embracing approaches that enable one-to-one engagement. After all, dynamically creating an offer for the exact brand of organic snacks the individual customer is most likely to respond to at the exact discount level most likely to prompt them to action is inherently more effective – and efficient – than placing a generic discount on similar items in the weekly circular.
2. Why has true one-to-one promotional personalization been so difficult to achieve?
Manual processes, unstructured data, and legacy platforms are the main roadblocks to true one-to-one personalization and are what keep retailers relying on broad-based approaches. There are also two issues of scale: first, the volume of data (customer data, SKUs, multiple sales channel data) retailers must manage has increased exponentially; and second, the ability to deploy personalized offers at enterprise level across millions of transactions remains out of reach. Few incumbent promotional systems support the real-time decisioning or on-the-fly offer creation necessary to deliver unique promotions to individual customers across a multi-store network, let alone a portfolio of banners.
3. What has changed (technologically or operationally) that is now making individualized promotions possible at enterprise scale?
From a tech perspective, AI and machine learning models can now analyze behavior and generate custom offers in milliseconds. Cloud infrastructure handles the computational demands of real-time adjudication across millions of shoppers or loyalty members. Operationally, retailers now have the customer data and digital touchpoints necessary to identify individual shoppers and deliver offers at checkout, online and in-app. These two components are equally important; even the most advanced AI will deliver irrelevant promotions without data-based insights into what individual customers care about, and all the customer data in the world is useless without the technology make it actionable.
4. Retailers often struggle to know whether an offer is actually influencing behavior or simply rewarding shoppers who would have purchased anyway. How can retailers start measuring true incremental impact?
Attribution at the individual shopper level is essential. You need systems that track each customer's baseline purchasing patterns, then measure how behavior changes when specific offers are delivered. Closed-loop reporting that connects offer allocation, redemption, and actual sales lift reveals which promotions are working. Of course, this requires technology that follows the complete customer journey from offer to purchase, across platforms and channels, and incorporating both marketing-exclusive systems (like retail media networks) and traditionally analog interaction points (like physical stores).
5. Boston Consulting Group has estimated that shifting even a portion of mass promotion spend into personalized offers can dramatically improve ROI. What does that tell us about how much promotional budget is currently being misallocated?
BCG estimates enterprise retailers can generate over $100 million in topline impact from scaling personalized offer execution. That suggests that retailers’ current promotional spending is underperforming, delivering little incremental value for the budget. It tells us that when retailers offer undifferentiated incentives to customers with existing purchase intent, or offer deeper discounts than necessary to change behavior, they’re essentially paying for sales they already had.
7. As shoppers’ expectations for immediate value increase, how are promotions emerging as a new competitive battleground for retailers?
Customers now expect offers that reflect their actual shopping behavior; generic discounts feel irrelevant. In this way, promotions have become a de facto indicator of whether retailers truly understand their customers. Those who do can deliver timely, meaningful incentives, build stronger engagement and capture more share of wallet. Those who don't risk spending promotional dollars with little measurable return. In a marketplace with more choice than ever, relevance is a clear competitive advantage.
8. When promotions are personalized at the individual level, how does that change the way shoppers engage with offers and deliver value at the right moment?
Personalization ensures that customers receive offers that feel relevant and appropriate rather than random or excessive. When offers align with consumers’ actual preferences, purchase patterns and contextual cues, engagement naturally increases. Delivered through digital channels or at checkout in the moment of decision, personalized incentives create a higher-value experience that encourages repeat behavior and strengthens ongoing loyalty. They also drive results for retailers; Eagle Eye’s AI-powered Personalized Challenges, which creates personalized, incremental goals for each shopper based on their purchase history, has generated 7:1 ROI for high-profile retailers that have implemented the solution
Cancer Awareness Month Can Highlight Real Community Support
marketing 19 Feb 2026
Each February, the country turns pink. Landmarks glow, national campaigns launch, and stories of survival and resilience fill television screens and social feeds. The scale of support is both inspiring and necessary, reminding millions that they are not alone in the fight against cancer. Yet beyond the national spotlight, something quieter is happening.
In hospital waiting rooms, volunteers sit beside patients before chemotherapy begins. In community centers, local nonprofits coordinate rides so no one misses treatment. In church basements and neighborhood gathering spaces, families come together for support groups because healing is not only physical, but emotional. In kitchens across America, neighbors prepare meals for someone too exhausted to cook.
These moments rarely make headlines, but they form the backbone of the fight.
Cancer is deeply personal. It touches families street by street and house by house, and the organizations responding most immediately are often local and deeply rooted in the communities they serve. They know the names behind the diagnoses. They understand the practical barriers patients face. And they continue showing up long after awareness campaigns fade from view.
Cancer Awareness Month offers a powerful national platform. The opportunity before us is to extend that platform to the people doing this work closest to home.
Imagine if the storytelling strength that powers major national campaigns also illuminated the hospital down the road, the screening event at the high school gym, or the local survivor who turned personal hardship into community action. When people see their own community reflected back to them, something shifts. Engagement becomes personal. Support becomes immediate. Action feels tangible.
Today, we have the technology to elevate local organizations with the same creative quality and reach once reserved for large national causes. Through modern media channels, community based nonprofits can share their stories at scale, connecting households to resources and reminding viewers that help is not abstract. It is nearby.
National momentum and local action do not compete with one another. They reinforce each other. Broad awareness drives conversation, while local visibility drives participation. Together, they create a stronger and more responsive support system for patients and families.
Awareness is most powerful when it becomes tangible, when it connects a household to a place they recognize, a service they can access, or a story they understand. It is measured not only in dollars raised or campaigns launched, but in rides provided, meals delivered, appointments kept, and hands held during uncertain moments.
The fight against cancer lives in communities, carried forward by neighbors, volunteers, caregivers, and local leaders who work tirelessly, often without recognition. This Cancer Awareness Month, as we honor the national movement, let us also make space to elevate the people doing the work closest to home. Their impact is real, immediate, and deeply human. They deserve to be seen.
The Content Bottleneck Has Shifted from Production to Operations
marketing 17 Feb 2026
Q1: This is now the fourth year of Canto’s State of Digital Content report. What stood out to you most in this year’s findings compared to previous years?
Just how tangible the cost of fragmentation in brands’ content and creative operations has become. We’ve been seeing teams acknowledging the problem, saying “yeah, our digital assets are scattered, our workflows are messy.” But this year the data puts real business consequences behind that. The survey found 44% of folks reporting employee burnout tied directly to poor asset management. Wasted budget, duplicated work, missed revenue, and delayed launches are no longer hypothetical risks of fragmentation, they are measurable business outcomes.
The other thing that struck me is product content and information as a major theme. Previous reports focused heavily on creative assets, but this year we saw just how much brands are struggling to manage product information alongside their digital assets. 88% of teams can’t keep product content consistent across channels, and more than half are still managing product data completely separately from the assets used to actually market and sell those products. That disconnect is creating real friction, especially as e-commerce demands keep growing.
Q2: The report found that 82% of content teams saw volume increase in the past year, with three in four attributing at least some of that growth to AI. How are teams actually keeping up with that pace, and where are they falling short?
AI is driving the content volume up, but it’s also the thing helping teams manage the surge. 75% of content professionals told us AI has increased their output, and 30% said that increase was significant. At the same time, about half of teams are already using AI to accelerate creation, tagging, and organization. So the same technology pushing volume higher is also becoming the relief valve.
A critical part of the data, though, shows how teams are falling short on the operational side. The volume is growing but the underlying systems and workflows haven’t yet caught up. Only 43% of teams describe their digital content workflows as standardized and automated. The rest are still dealing with manual processes, inconsistencies, and fragmented tools that slow everything down. You can produce content faster with AI, but if your team can’t find the right asset, doesn’t know which version is current, or has to manually push updates across channels, you’re just creating more chaos. The content bottleneck has shifted from production to operations.
Q3: One of the more striking data points is that teams with full connectivity between their digital assets and product information are more than four times as likely to report significant ROI improvements. Why is that gap so wide?
That 4x multiplier surprised even us, but when you think about what connectivity actually enables, it makes sense. When your product information and digital assets live in the same environment, you eliminate an enormous amount of duplicated effort. Teams aren’t hunting across systems to match the right image with the right product description, nor manually updating the same information in five different places every time something changes.
The data backs this up across the board. Teams with fully connected systems told us that tasks like locating assets, maintaining brand consistency, and collaborating across teams were “extremely easy” at rates two to three times higher than everyone else. 67% of fully connected teams said locating assets was extremely easy, compared to 21% of those without full integration. That speed and confidence compound across every campaign, every channel update, every product launch. Just as importantly, it all shows up directly in revenue. 65% of teams that can make real-time content updates reported significant revenue increases, versus just 16% of those with slower timelines.
Q4: Only 35% of respondents said they feel very confident that employees are using the most current, approved version of brand assets. What’s driving that confidence gap, and what does it cost organizations?
That number reflects the reality of how most brands manage their content today. When assets are spread across cloud drives, local desktops, email threads, and multiple platforms, it becomes almost impossible to guarantee that everyone is working from the same source of truth. 62% of teams are using cloud file storage, 44% have content on local servers, and 41% still rely on individual hard drives. That’s a lot of places where an outdated logo or last quarter’s product spec can be sitting around, ready to get used by mistake.
The cost shows up in a few ways. 30% of respondents reported publishing off-brand or inconsistent content as a direct consequence of poor asset management. You also see it in the 30% who flagged legal or compliance risk. When you’re operating in regulated industries or across global markets, using the wrong version of an asset can have real legal and financial consequences.
Beyond those specific risks, there’s the broader drag on team productivity. People spend time second-guessing whether they have the right file, chasing down approvals, or recreating something that already exists somewhere in the organization.
Q5: The data shows that 51% of teams still rely on spreadsheets to manage product information, and 56% manage product content separately from digital assets. What needs to change operationally before those numbers start to shift?
I think a lot of brands have grown into this situation organically. Spreadsheets are familiar, they’re flexible, and when you only have a handful of products or channels, they work fine. But when you’re managing hundreds or thousands of SKUs across e-commerce platforms, retail partners, marketplaces, and your own website, spreadsheets stop scaling. You end up with version control nightmares, no clear ownership, and inconsistencies that erode customer trust.
The shift really starts with recognizing that product content and creative assets are two sides of the same coin. A product image, its description, its specifications, its pricing…those all need to move together when something changes. When 78% of teams are using two or more separate solutions just to manage product content, every update becomes a multi-system coordination exercise. Teams told us the improvements that would deliver the most benefit include making product data easier to access across teams, eliminating duplicate or outdated information, and managing product data alongside creative assets. Those are all fundamentally about bringing things together rather than continuing to manage them in silos.
Q6: AI adoption for content is nearly universal at 96%, but only 30% of teams describe their use of AI as widespread. What’s holding back deeper adoption?
The adoption curve is real, and I think it’s actually healthy that most teams are taking a measured approach. 47% are using AI in limited ways, and 16% are planning to adopt. That’s a lot of momentum. But moving from experimentation to deep integration requires trust, infrastructure, and governance, and those things take time.
On the trust side, the news is actually encouraging. 81% of content professionals expressed confidence in AI’s accuracy for tagging and organizing assets, and that confidence gets even stronger with hands-on experience. Among teams using AI most extensively, 77% reported high confidence. The hesitation seems to be more about the operational layer. When we asked about top worries over the next two years, integrating new technologies like AI tied for the number one concern alongside security and access control, both at 30%. Teams want to adopt AI more broadly, but they want to do it in a way that doesn’t compromise brand consistency or introduce new security risks. Brands seeing the biggest returns are embedding AI into a centralized, governed environment rather than layering it on top of fragmented systems.
Q7: Teams with advanced, standardized workflows were dramatically more likely to see significant ROI gains - 48% versus 0% among teams with ad hoc processes. What does workflow maturity actually look like in practice for content and creative teams?
That 48% to 0% gap is one of the most compelling findings in the report, and it really underscores that operational maturity isn’t merely a nice-to-have.
In practice, workflow maturity starts with processes for creating, reviewing, approving, and distributing content that are documented and consistent across teams rather than reinvented every time. On top of that, the repetitive work (like tagging, metadata generation, format conversions, routing assets for approval) is automated instead of eating up people’s time. Additionally, the tools are connected so that updates flow through the system rather than requiring someone to manually copy information from one platform to another.
The teams doing this well are also much more likely to have invested in AI, analytics, and template-driven approaches. When we looked at what high-ROI organizations are doing differently, they’re significantly more likely to be introducing automation to reduce manual work, expanding template and modular content approaches, and measuring content performance to refine their processes.
Q8: The report highlights security, AI integration, and brand consistency as the top concerns about managing content at scale over the next two years. How should content leaders be prioritizing those?
I’d say these concerns are actually more interconnected than they might appear at first. Security and access control, AI integration, and brand consistency all improve when you centralize how content is managed and governed. If your assets are scattered across disconnected systems with inconsistent permissions, you have a security problem, a brand consistency problem, and a much harder time rolling out AI in a controlled way.
The practical starting point is getting your foundation right. That means establishing a centralized, structured environment where assets are governed, versioned, and accessible to the right people with the right permissions. Once that’s in place, you can layer in AI capabilities, things like smart tagging, visual search, and content recommendations, with confidence that the AI is working within guardrails rather than amplifying existing chaos. And brand consistency becomes much more manageable when there’s one source of truth rather than dozens of repositories where outdated files can linger.
I think content leaders should also be paying attention to the product content dimension. Managing storage costs at 26% was one of the top concerns, and that’s only going to grow as content volume keeps climbing. The teams managing costs most effectively can reduce duplication, improve reuse, and avoid recreating assets that already exist somewhere in the organization.
Q9: What’s one thing you’d want a marketing or content operations leader to take away from this year’s report and act on immediately?
Audit your fragmentation! Take an honest look at how many systems, folders, drives, and platforms your team is using to manage content and product information today. The data is really clear that fragmentation is the single biggest drag on performance. Teams using two or more systems to manage digital assets are significantly more likely to experience delays, missed revenue, burnout, and wasted budget compared to those working from a unified approach.
You don’t have to solve everything at once, but understanding the full scope of the problem is the first step. Once you can see where assets and product information are scattered, you can start making intentional decisions about what to centralize, what to connect, and where to apply AI and automation to eliminate the most painful bottlenecks.
"First-Party Data Isn’t Enough Anymore”.
marketing 17 Feb 2026
By: Scott Kozub, VP, Product at Experian Marketing Services
For years, first-party data has been positioned as the answer to nearly every challenge in digital advertising. Lose cookies? Build first-party relationships. Privacy gets more complicated? Lean into owned data. Measurement becomes murky? Go direct to the source.
That logic still holds, but only up to a point.
What many marketers are discovering in practice is that first-party data alone creates depth without scale. It offers rich insight into customers a brand already knows, but far less visibility into the audiences it still needs to reach. In a fragmented, privacy-conscious ecosystem, relying exclusively on first-party signals often results in limited reach, frequency challenges, and diminishing returns on prospecting
The next phase of targeting will be defined by how well marketers combine first-party, third-party, contextual, and geographic signals to drive growth, improve efficiency, and strengthen customer relationships.
Why first-party and third-party data are better together
The biggest challenge facing modern targeting is not the loss of identifiers. It is the growing fragmentation of signals across devices, channels, and environments. In that reality, identity does not disappear. It becomes more important as the connective layer that brings different data sources together for planning, activation, and measurement
First-party data remains essential. It provides accuracy, consent, and a reliable foundation for personalization and measurement. But on its own, it reflects only a partial view of the market. Most first-party data sets skew toward existing customers, logged-in users, or known devices, leaving significant gaps in reach and understanding.
This is why third-party data is so valuable. Not as a standalone solution, but as a complementary layer that expands perspective beyond what first-party data can capture alone. Responsibly sourced third-party data adds demographic, behavioral, interest, and purchase context that helps marketers understand who they should be reaching next, especially in an environment shaped by privacy constraints and signal fragmentation.
First-party data on its own is limiting. Third-party data on its own is incomplete. The real power comes from connecting the two through identity, allowing marketers to plan, activate, and measure across fragmented environments with greater accuracy and confidence.
Contextual and geographic signals as privacy-safe extensions
Contextual and geographic targeting are not new tactics. They are proven approaches that have evolved alongside changes in technology, privacy expectations, and data availability.
Today, data-informed contextual targeting goes far beyond keywords or simple page adjacency. When contextual signals are combined with audience insights, they help marketers understand where high-indexing audiences naturally spend time, regardless of channel or environment. Certain content consistently attracts users with shared behaviors, demographics, or purchase intent. Identifying those patterns allows advertisers to reach relevant audiences in ways that are both effective and privacy-safe.
Geographic data functions in a similar way. People with similar lifestyles, needs, and behaviors often cluster in similar locations. When geographic signals are informed by behavioral and demographic data, rather than used as blunt radius targeting, they become a meaningful proxy for intent. This is especially important for categories like retail, CPG, and automotive, where location continues to influence decision-making.
These signals are not replacements for first- or third-party data. They are additional layers that strengthen a modern data strategy while supporting privacy-forward activation.
AI as decision intelligence in a fragmented ecosystem
Artificial intelligence plays an increasingly active role in making fragmented signals and multi-source data strategies manageable.
AI is not replacing targeting strategy. It is enabling it. By interpreting fragmented signals at scale, machine learning models help marketers connect identity, first-party data, third-party insights, contextual signals, and geographic information into actionable intelligence. Models trained on both structured and unstructured data can identify patterns across content, timing, device behavior, and location, then optimize delivery in real time.
This shift allows campaigns to move beyond static audience definitions and toward dynamic decisioning. As performance signals change, activation strategies can adapt accordingly, without relying on persistent identifiers or exposing sensitive personal data.
What this means for marketers in 2026
Marketers who want to create and activate campaigns more efficiently in 2026 will need integrated approaches that reflect how fragmented the ecosystem has become. Success will not come from betting on a single data type, but from building flexible systems that connect signals through identity and intelligence.
First-party data alone is no longer sufficient. Marketers who combine it with third-party, contextual, and geographic signals will be better positioned to plan, reach, and measure advertising in an environment defined by fragmentation, evolving privacy standards, and constant change.
How's this "Returns Shouldn’t Be Tolerated — They Should Be a Strategic Differentiator"
marketing 13 Feb 2026
Your research shows returns are now a routine part of shopping, not a seasonal issue. What does the data reveal about how frequently consumers are returning items, and why should CX leaders care?
It’s true, what we uncovered with our survey is that returns are no longer a seasonal anomaly, but a meaningful brand interaction, a routine part of commerce, and a stepping stone to building lasting relationships. When our survey was conducted in early January, 55% of respondents had already made or planned to make a post-holiday return, and 21% of shoppers said they return an item as frequently as once a month. This means returns are a recurring touchpoint that happens across the customer lifecycle, not just in peak holiday periods. Given the volume of returns, even small inefficiencies become points of real friction, and that’s tied directly to loyalty and CSAT. CX leaders in retail and ecommerce should recognize returns as a high-value touchpoint and focus on making the process an opportunity for brand affinity and trust, not frustration.
More than half of shoppers say a bad returns experience could impact future purchases. Why do returns have such an outsized effect on loyalty compared to other post-purchase moments?
Returns matter because they’re consequential and emotional. While purchase experiences are driven by anticipation and reward, a return is triggered by disappointment. How a brand handles that disappointment fundamentally shapes trust. 57% of consumers say a bad return experience would influence whether they buy from that brand again, regardless of previous loyalty. It’s a high-stakes moment. If brands can’t resolve a problem quickly, transparently, and with a bit of empathy, they risk turning a one-time issue into long-term disengagement.
More than 60% of consumers say they’d use an AI-powered agent to handle returns. What are shoppers actually hoping AI will fix at that moment?
Speed, clarity, and resolution are the top three things consumers expect from returns. While only a small percentage currently prefer chatbots (12%), 60% of respondents in our survey said they would use an AI-powered agent if it could instantly answer questions and process their return. This is customers signaling a desire for accurate, real-time assistance that gets the job done, with as little friction as possible. Only 36% of survey respondents say they are "very satisfied" with the returns process today, leaving significant room for improvement. AI, when done well, can eliminate many of the pain points consumers feel, including long wait times, confusing policies, and shipping hassles.
For retail leaders evaluating AI investments in 2026, why should returns be prioritized alongside acquisition and personalization efforts?
Trends in retail tech investment continue to focus on personalization and AI integrations to help the buyer build confidence. But what happens after the first purchase often determines whether the brand will get a second purchase, a third purchase, and so on. Returns are one of the few moments in the journey where customers are actively questioning their relationship with a brand, and that moment in time is where differentiation matters the most. AI investments in customer service are maturing quickly, proving that they can handle sensitive, complex situations with clarity and human-like empathy, all of which are critical to a successful returns process. But AI is not a “set and forget it” proposition. CX leaders must invest in training and empowering their teams to ensure their AI can grow, learn, and evolve alongside the needs of their customers. If a brand provides a strong purchase experience, but then loses the customer during a frustrating return experience, all those early investments in acquisition are at risk.
Trust remains a major concern with AI. According to your research, what conditions make consumers comfortable using AI for returns?
Earning consumer trust will be an ongoing challenge for brands as they continue to integrate AI into their practices. Our recent survey took a deeper look into why consumers lack trust in AI currently. It found that consumers worry AI will be less efficient than a human, will have difficulty understanding their issue, or will provide inaccurate information. All of these concerns can be addressed by ensuring that the AI agent is given accurate customer data and policy information from the brand, and is overseen by well-trained ACX managers and teams.
At Ada, we know this can be done well because our customers are seeing significant results from their AI investments today. One of our customers, IPSY, operates one of the largest beauty subscription networks in the world, serving more than 20 million community members across its brands. At that scale, customer experience isn’t just about support. It’s about relationship management, where every improvement compounds.
In just four months, IPSY, GenAI agent, Glam Bot, which is built and managed through Ada’s ACX Platform, unlocked:
→ a 41% lift in CSAT,
→ a 943% ROI on their generative AI investment,
→ 64% increase in autonomous resolution, and
→ It remains one of the largest AI deployments inside the company to date.
The key to ensuring consumers are comfortable with AI isn’t removing humans, but creating a seamless integration with humans, including transparent escalation paths.
Returns should no longer be an interaction that consumers tolerate, but a strategic differentiator for brands using AI to turn problems into opportunities.
Looking ahead, how do you expect AI to reshape post-purchase CX over the next 12–24 months, particularly around returns?
In the next 12-24 months, AI will become increasingly agentic. This means it will do more than answer simple queries – it will automate increasingly complex tasks end-to-end with context, accuracy, and even empathy. This would include checking inventory at nearby stores for pickup, processing payments, and making repurchases of the same products easy. We will see AI become more deeply capable in policy, status updates, logic, and personal preferences, which can make returns virtually frictionless by default. Brands will also increasingly measure the success of their ACX investments not simply in resolution rates, but in revenue generation, both from cross-sell/upsell opportunities and in reduced customer churn. But this requires a thoughtful approach to AI management and adoption, as well as a team that’s empowered to grow and evolve their own agents. Brands that win will understand AI success isn’t just a technology deployment, it’s a management discipline. You cannot delegate your transformation to a vendor.
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