marketing
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.
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.
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.
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.
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.
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.
Navigating the New Era of Mobile User Acquisition: Inside Zoomd’s 20-Year Journey
marketing 3 Feb 2026
Tell me about Zoomd’s business.
Zoomd is a mobile-first marketing solution company enabling global advertisers to generate new mobile app users cost-effectively as they grow their business profitably. By working with our proprietary UA platform, a mobile DSP, content creators, and Albert.ai technologies, Zoomd is able to deliver a full-funnel, holistic solution, running advertising campaigns, empowering our clients to generate new users across gaming, entertainment, commerce, and fintech verticals.
Zoomd has been working in User Acquisition for a while.
Zoomd (originally founded in 2007 as a search startup and later merged with Moblin in 2012), has nearly two decades of mobile user acquisition experience. We’ve managed user acquisition campaigns through all of the changes in the industry, from the pivot to social media and then video, to the implementation of privacy legislation and operating system changes, which restructured user acquisition best practices.
Through all of the industry changes, we’ve focused on uncovering opportunities for our clients and partners to cost-effectively manage their user acquisition campaigns across channels and regions. This experience has made Zoomd proactive and more nimble, enabling us to anticipate the changes being made by big tech, marketing, and end-users, resulting in profitable user acquisition campaigns for our clients.
Today, as a publicly traded company, Zoomd Technologies Ltd. (TSXV: ZOMD) (OTC: ZMDTF), offers comprehensive and privacy-friendly user acquisition across the leading platforms as well as programmatic and direct channels through the open mobile web.
How is User Acquisition different today?
User acquisition on mobile devices has changed a lot since we started running user acquisition campaigns in 2007. The initial campaigns were across a non-programmatic open mobile web or in-app ads in other apps. Back then, Facebook was a desktop website.
Apple’s App Store launched in July 2008 with 500 apps. Google Play, which unified the Android Market, Google Music, Google Movies, and Google Books into one app store, only launched in March 2012.
Today, user acquisition is more competitive and complicated, with campaigns running across social media networks, in-app, and programmatically over the open web via Demand and Supply-Side Platforms and exchanges.
Privacy, which wasn’t an issue in 2007, became important in 2018 when the General Data Protection Regulation (GDPR) took effect in Europe, followed in 2020 by the California Consumer Privacy Act (CCPA), the first of many US-based state-based privacy laws. In 2021, Apple limited Identifier for Advertisers (IDFA) when the company rolled out App Tracking Transparency (ATT) framework with the release of iOS 14.5. This required users to explicitly opt-in before they could be tracked for advertising purposes, significantly limiting the targeting data available for advertising. Google also rolled out enhanced privacy protocols, though they were less aggressive than Apple’s.
Having worked in user acquisition since there were just a few hundred apps, managing campaigns through all of the aforementioned changes in the industry has made Zoomd a stronger and more effective mobile marketing partner. We’ve literally seen and done it all, and are therefore ready to help companies manage the new changes and challenges that will happen in 2026 and beyond. For example, the EU’s Digital Markets Act (DMA) requires gatekeeper platforms like Apple’s App Store to allow European users to download apps from alternative app marketplaces and to use alternative payment systems outside of the app.
Today… is it all Google and Meta?
Though Google and Meta are important, there are a lot of other channels used in successful and profitable user acquisition campaigns. Depending on the target audience and geographies, Zoomd runs campaigns across many social platforms, including Snapchat, Reddit, Pinterest, Twitter, as well as on regional platforms, like Kwai and Bigo Live. For example, for one European campaign, Snapchat was the platform that delivered the most money-making users, which we anticipated based on our team’s experience.
Beyond the platforms, we’re big believers in the open mobile web. Through programmatic channels, we’re able to successfully convert lots of users cost-effectively across app categories and geographies through display and video ads, including user generated content videos.
What Key Performance Indicators (KPIs) are important for User Acquisition?
The KPIs that advertisers monitor can vary widely based on the app’s maturity and function, but ultimately, they all share the same objective: driving growth and profitability. That’s why so many of our campaigns prioritize revenue-focused KPIs, such as first deposit amount or average order value. By centering user acquisition strategies around these profit-driven metrics, marketers can accurately measure campaign success and ensure they are acquiring valuable, high-quality users who contribute to the bottom line.
Vanity metrics, such as clicks and engagement rates, are also important for crunching data and gaining a better understanding of creative effectiveness, segmentation, etc. User acquisition creative must do more than generate clicks – it must drive prospective users into the funnel to download and install the app and take an action, like depositing money or placing an order.
What should a marketer new to User Acquisition understand before launching his or her first campaign?
A new marketer should first understand his or her company’s business model and the actions that deliver profitable users. That’s the marketing funnel and the north star that will lead marketing and user acquisition activity. Once the marketer understands the business, we’ll work together to set up the campaign based on the company’s business and our experience across similar geographies, target audiences, and product categories to ensure that the user acquisition campaign is on target and within the budget.
Closing thoughts on the future of User Acquisition?
As I said, there are changes happening in the app stores, like the opening up of alternative app stores and payment systems in Europe. App marketing is a dynamic market, which is why for user acquisition, it’s important to work with an agile partner having extensive experience with app marketing across verticals, geographies, and platforms. Artificial Intelligence (AI) is now in every aspect of our work and lives, and it’s also necessary for effective user acquisition. Advertisers need a partner that moves fast, with the trends, to ensure that their brand doesn’t fall behind. After nearly 20 years of actively working in user acquisition, Zoomd is a trusted partner that understands the market and can profitably manage user acquisition campaigns.
Beyond the Click: Why Transforming B2B Attribution Starts with AI
marketing 3 Feb 2026
To find out, I recently caught up with Chris Golec, a martech industry veteran who pioneered the ABM category and founded Demandbase. Chris is now the Founder and CEO of Channel99, an online platform that enhances attribution and marketing decision-making using AI.
To begin, why do you feel the current attribution standard (Click-Through Attribution, or CTA) is failing B2B marketers?
It all comes down to the increasing complexity of the B2B purchase process. Historically, to ensure proper attribution, we've expected a prospect to click an ad before purchasing. This "click-through" method is tidy and easy to track via UTM parameters.
If CTA is only a "tiny slice," what does the rest of the B2B sales funnel look like? What is the alternative?
The alternative is View-Through Attribution (VTA for short). This approach assigns credit when a buyer sees an ad, video, or piece of content and later converts, even if they never clicked the ad directly.
The results speak for themselves. Marketers who screen for VTA see a nearly 79% jump in conversion compared to CTA alone. It uncovers a wealth of insights, often revealing a hidden "first touch"—like organic social or industry review sites—that CTA completely misses. In today’s world, data-backed results are a matter of survival for marketers. VTA allows us to identify these audiences early and often.
If the data is so much stronger, why are nearly 25% of marketers still relying solely on click-through?
But this convenience comes at a cost. While CTA measures click-based campaigns well, it falls woefully short when asked to evaluate any other types of campaigns. Impressions, brand exposure, and social interactions often have just as much influence on deal closing as a digital ad does, yet CTA leaves these data points untouched.
That’s not even the worst part, though. Without view-through data, marketers naturally craft campaigns to attract high click volumes, but those often don’t speak to the intricacies of B2B sales, or connect with users in a position to buy. That’s why my #1 piece of advice to marketers is this: Craft your campaigns and measure success with sales at the center, nothing else. When it comes to attribution, that means leveraging VTA and CTA.
Let’s talk about implementation. For companies ready to modernize, what are the "traps" they need to avoid?
The biggest trap right now is privacy. Regulations like GDPR and CCPA have fundamentally reshaped what marketers can track. The third-party cookie is under threat, and consent requirements have already reduced attribution data volume by 30-40%.
In addition to privacy, are there any other issues that modernizing companies should look out for?
Absolutely. Once you solve the privacy piece, you still have to contend with company culture. The insights that VTA can provide are so substantial compared to CTA that a lot of old assumptions get called into question as soon as the new data comes in. Having a company-wide growth mindset and being willing to abandon time-tested strategies for a new, data-backed approach isn’t always easy, but it’s essential for success.
For our readers who are ready to make the switch, how do they begin? Is there a roadmap?
Absolutely. For any organization that’s ready to go all-in on VTA, I would recommend these five steps:
- Build a new foundation: Adopt privacy-forward account identification. Leave third-party cookies behind and focus on identifying accounts, not just anonymous users.
- Unify your datasets: B2B buyers don't live in a single channel. You need to integrate tracking across every touchpoint—display, ABM, organic social, email, etc.—to connect exposures to revenue.
- Screen for data validity: New data streams are useless if they are full of noise. Incorporate rigorous ad verification to ensure you are measuring actual target audience impressions, not bot traffic.
- Recalibrate performance benchmarks: Be prepared to throw out the old click-based playbook. Reallocate budget toward tactics that are truly driving influence from your target accounts and addressable market, the rest is noise..
- Loop in the sales team: Don't keep these insights in the marketing silo. Share them promptly so both teams can understand which accounts are generating deals.
Any final thoughts?
By shifting to AI-powered, view-through attribution, a far more robust and complete strategy picture comes into focus for marketers. It’s past time to move beyond the click.
Beyond Static Floors: What Publishers Need to Know About Dynamic Flooring in Programmatic Advertising
marketing 23 Jan 2026
1) When publishers talk about their “programmatic floor strategy,” what does that typically look like in practice today? How are most teams approaching this?
In practice, most publishers’ “floor strategy” is still largely manual and backward-looking.
Typically, teams set static CPM floors in GAM (via UPRs) or in their wrapper/Prebid configuration, segmented by broad buckets like geo, device, or placement. These floors are usually based on historical averages, past performance, or rough heuristics rather than real-time demand signals.
They revisit and tweak them periodically — weekly, monthly, or around seasonal moments — but the core approach rarely changes: fixed prices applied across highly variable auctions.
So most “floor strategies” today are really just static rule sets designed for a slower market. They are not built for the speed, variability, or complexity of modern programmatic auctions.
2) Modern programmatic auctions happen in milliseconds with DSPs constantly repricing based on real-time signals. How should that reality influence the way publishers think about setting price floors?
It should completely reshape how publishers think about pricing.
If buyers are making decisions in real time, then floors cannot be static. They need to reflect what is actually happening in that specific auction — not what happened last week or last month.
Instead of asking, “What should my average floor be?” publishers should be asking, “What is this impression worth right now, given who is in the auction and how they are behaving?”
That means treating floors as dynamic, responsive signals — not fixed thresholds — that react to live demand, bidder behavior, and competition in real time.
3) Many publishers measure price floor success by looking at CPM changes, often comparing this week to last week. Is this the most effective way to measure this?
No — it is one of the weakest ways to evaluate floors.
CPM alone is an incomplete metric. It only tells you the price of impressions that are sold. It says nothing about suppressed demand, lost bidders, or auctions that never cleared.
On top of that, before-and-after comparisons (this week vs last week) are fundamentally flawed because the market is constantly changing. You end up measuring market volatility more than pricing impact.
This is why floor changes often look “good” or “bad” depending on timing — not because of the floor itself, but because of shifting demand conditions.
4) What other metrics should publishers actually be tracking to understand whether their floor strategy is working?
The single most important metric is holistic RPM — revenue per thousand ad opportunities (requests) across all programmatic channels (Prebid, Amazon, AdX, Open Bidding).
This metric captures:
● Price impact (CPM)
● Volume impact (fill)
● Buyer participation and routing effects
Crucially, this must be measured per ad unit first, then aggregated to site level. Site-wide averages hide too much.
Beyond holistic RPM, publishers should also track:
● Bid density (bids per auction) — a proxy for competition
● Win rates by bidder — to see which partners are reacting to floors
● Timeouts / drop-offs — signs of demand suppression
● Clearing price distributions — where auctions are actually settling
Together, these give a much clearer picture of whether floors are helping or hurting.
5) What happens at the auction level when a publisher sets a floor that doesn’t align with what DSPs are willing to bid in that moment?
Two things typically happen, depending on the direction of misalignment:
If the floor is too high, DSPs don’t negotiate — they exit. Bidders reduce participation, route budgets elsewhere, or stop bidding altogether. Fill drops, and auctions become less competitive.
If the floor is too low, auctions clear too easily. Bidders don’t need to bid aggressively, competition thins out, and you end up leaving meaningful value on the table — selling inventory below what buyers were actually willing to pay and losing yield in the process.
In both cases, revenue is lost — it just appears differently: either as lower fill or lower effective prices.
6) When publishers analyze floors using site-wide or monthly aggregated data, what critical dynamics are hidden from view?
A lot.
Aggregated data hides:
● How different ad units respond to floors
● How specific bidders behave in specific geos or devices
● Time-of-day demand patterns
● Differences between mobile vs desktop, app vs web, or browser types
You might see “healthy” site-wide CPMs, but underneath that some placements could be massively underpriced while others are choking demand.
Most auction-level behaviors — like bid density shifts or bidder pullback — get completely washed out in monthly rollups.
7) How do price floors influence which demand partners and campaigns even enter an auction? Is it just about setting a minimum price?
It’s more than just having a minimum price.
In modern auctions, price is a signal — but a static price is a weak one. Without a reliable, real-time signal, DSPs have to guess the likely clearing price, which makes pacing and confident bidding harder, even when the audience match is strong.
Dynamic floors strengthen that signal. By adjusting in real time, they give buyers a clearer view of where the market is clearing right now, which makes them more willing to participate and bid at the true value of the impression.
Dynamic floors can also help unlock demand. When a higher, real-time clearing price is visible, it can influence which campaigns DSPs prioritize for that impression.
Finally, dynamic floors improve how inventory moves through SSP and DSP throttling systems. Requests that are priced in line with real demand are more likely to clear — and therefore more likely to pass throttling and access available budgets.
In short, dynamic floors act as a real-time market signal that shapes participation, routing, and budget access — not just a guardrail on price.
8) If a Head of Programmatic wanted to properly test whether a floor change improved performance, what would that test need to look like?
They would need a true real-time A/B test, not a before-and-after comparison.
That means:
● Splitting traffic into a floored cohort and a control cohort running simultaneously
● Measuring holistic RPM per ad unit across both groups
● Ensuring both cohorts see the same demand conditions
● Tracking bidder behavior (bid density, win rates, drop-offs) alongside revenue
Only with this setup can you isolate the true impact of pricing from normal market volatility.
9) What patterns have you seen when publishers shift from static to more dynamic floor strategies? What typically changes in their auction outcomes?
The most consistent pattern is that auctions become healthier.
Typically we see:
● Higher holistic RPM (often 10–16%)
● Slightly higher CPMs (around 7-11%)
● Stable or improved fill
In other words, pricing aligns better with real demand. Publishers capture more value without suppressing liquidity.
Perhaps most importantly, they stop seeing the wild performance swings that plague static floor setups.
10) Where do you see price floor strategies evolving as the programmatic ecosystem becomes increasingly algorithmic on both buy and sell sides?
On the web, pricing is becoming more holistic, unified, and visible inside the auction.
Two recent shifts are accelerating this:
● Amazon now participates in Prebid as a bidder, meaning Prebid floor decisions can directly influence Amazon demand instead of pricing it in a separate silo.
● Google now allows separate pricing for AdX demand in GAM, giving publishers far more control over how AdX is treated relative to other demand.
Prebid floors themselves aren’t new — they’ve existed since early header bidding. What’s changing is that more major demand sources now sit inside a common pricing perimeter.
As a result, I expect floors to become more holistic and consistent across channels, delivering clearer RPM impact and giving publishers greater control over how their inventory is positioned and monetized.
Stop spinning, start gaining traction in 2026
marketing 22 Jan 2026
Marketers are at a strategic turning point. AI is on track to generate nearly 90% of online content. Gartner predicts that 30% of a brand's perception will soon come from generative AI, not human-created work. In this new reality, marketers are asking: How can we build real connection with our customers?
The answer, increasingly, is storytelling. The Wall Street Journal recently reported that companies are “desperately seeking storytellers” as AI floods every channel with look-alike content. Why? Because stories are central to the human experience. Stories help us learn. They make us care.
The real tension in 2026 won’t be whether marketers can produce things, but whether what they produce will matter. As sameness becomes the enemy, the teams that gain traction will be the ones who tell a damn good story.
Disciplined storytelling will cut through the noise.
Research shows that 75% of marketers say AI is now more important to their strategy than it was last year, but more content doesn’t always equal a deeper connection. Producing more video, more copy, more social, more bylines—none of that will matter unless it’s anchored in a coherent story.
The real differentiator in 2026 will be discernment. Producing more will be easy. Producing what actually moves an ideal customer to action will not.
The best brands will resist “random acts of marketing” (a term coined by MKT1 founder Emily Kramer) and create less, better: one core story, reinforced everywhere with precision and intent. When everyone is shouting, consistency and clarity quietly resonate.
Marketers who think in systems, not just trends, will win.
For years, marketing rewarded speed above all else. Now, speed without structure is collapsing under its own weight. The real edge in 2026 will come from how well teams connect the dots between moments, platforms, and outcomes.
The strongest marketers will stop chasing every trend and start architecting connected journeys that mean something to customers, where product, paid marketing, organic social, communications, search, and customer care reinforce each other under a singular, differentiated narrative. This is much harder to get right.
Some narrative bets will fail, but the right ones—the ones grounded in real customer insight and a deep understanding of what customers are up against—will shine. As AI makes execution easier, the connective tissue behind it becomes the true differentiator.
Consumers will reward brands with a unique voice.
The strongest brands in 2026 will stay grounded in who they are, while staying deeply attuned to who their customers are becoming. Authenticity and empathy won’t compete with one another, but rather compound each other.
What’s changing is how voice gets managed. As brand health, message testing, and continuous feedback loops become standard, voice is shifting from a creative flourish to a measurable asset. The SurveyMonkey Trends 2026 report shows a 167% increase in brand attribute measurement templates and a 75% rise in brand tracking, clear signs that brands are now engineering—not guessing about—their impact.
Trust will become the most measurable metric.
AI casts a long shadow over trust, truth, and responsibility. According to HubSpot and SurveyMonkey, 70% of consumers notice AI in marketing communications, but only 47% trust brands to use it responsibly. In 2026, communicators won’t treat trust as an abstract brand value. They’ll track it in real time and link it directly to performance.
With continuous feedback and brand health data becoming standard, marketers will measure how trust builds, erodes, and recovers—then connect those shifts to business strategy. Data from marketing’s brand health tracker should make its way into every executive business review, influencing everything from product decisions to taglines, your refund policy to what your support chatbot says.
Turn motion into momentum in 2026.
Volume and timeliness are out—AI has leveled that field. The real difference-makers will be the humans who bring judgment, restraint, creativity, and connection back into the work.
Going forward, marketing success will belong to the brands that rise above AI-generated sameness with clearer thinking, sharper stories, and a deeper understanding of the people they’re trying to reach. Brand strength will be defined by how deliberately leaders guide creation, and how often they ask the question: why? That responsibility can’t be delegated to technology or tools. It belongs to humans.
What Makes a Great CMO in an AI-Accelerated, Martech-First Market
marketing 20 Jan 2026
Great CMOs are being judged less on marketing output and more on revenue outcomes. That change is not philosophical. It is operational. When pipeline targets rise and buying cycles get messier, the CMO becomes the person who turns strategy into a working system across people, process, data, and technology.
This is also where many teams struggle. Misalignment between Sales and Marketing is expensive. It shows up as wasted time, abandoned deals, duplicate tooling, and budget that can’t be defended. In a martech-first world, alignment breaks for a very specific reason: teams have more tools, more data, and more dashboards, but no shared operating model that turns those inputs into repeatable decisions. AI doesn’t introduce a new problem so much as it speeds up existing ones, making weak alignment and unclear ownership harder to hide.
For many mid-market companies, the gap isn’t ambition. It’s leadership bandwidth. That’s why fractional marketing leadership is showing up more often: not as a “part-time marketer,” but as senior CMO-level ownership focused on building the revenue system and the team habits that make performance repeatable.
The CMO’s Core Job: Revenue Alignment, Not Activity
A strong CMO makes marketing accountable to pipeline, revenue, retention, and expansion. They do it by creating shared definitions and shared accountability with Sales, RevOps, and Customer Success. That means agreeing on what a qualified opportunity is, what pipeline coverage is required, and what “good” looks like at each stage.
The practical implication is that marketing does not “hand off” and disappear. It stays connected to the buyer journey through enablement, nurture, sales readiness, and post-sale growth signals.
They earn their keep by aligning the revenue team around the scorecard, the definitions, and the decisions, not by shipping more assets.
Strategy That Holds Up Under Execution Pressure
A strategy is only useful if it survives contact with the calendar. Great CMOs build a full-funnel plan that connects ICP and segmentation, positioning and messaging, demand creation, nurture and conversion, and expansion and retention.
They also treat brand and demand as one system. Brand makes demand generation cheaper by improving response rates and win rates. Demand gen keeps brand honest by forcing clarity, proof, and focus.
Most importantly, strong CMOs are comfortable saying no. Fewer initiatives, executed well, create more throughput than an overloaded roadmap that nobody can measure. It brings outside-in prioritization and decision discipline when internal teams are stuck in reactive execution.
Turning Martech into a Growth System
A great CMO treats the martech stack like infrastructure. It must be purpose-built, integrated, and adopted. Buying tools is easy. Building a growth system is harder, and it shows up in three choices.
First, they prioritize data flow over feature checklists. The core is clean movement of data across CRM, marketing automation, web analytics, and BI. If lifecycle stages and attribution fields do not travel reliably, reporting becomes politics.
Second, they design for capacity. A complex stack with low adoption is worse than a simpler stack that the team actually uses. This is where governance matters: documented lifecycle stages, required fields, routing rules, and naming conventions for campaigns so performance can be compared over time.
Third, they run an alignment cadence that makes the stack useful. Regular sales and marketing syncs and closed-loop reporting become standard operating practice for teams that want real feedback on lead quality and pipeline movement.
Measurement Executives Trust
Great CMOs build measurement that the CEO, CFO, and CRO can use to make decisions. They define a KPI model that connects daily work to revenue outcomes through leading indicators, pipeline metrics, and efficiency metrics.
They also acknowledge the limits. Attribution is directional, not absolute. It works best when combined with pipeline review, cohort performance, and channel experimentation. The goal is consistency, not perfection.
Measurement is one of the fastest ways to make alignment real because it forces shared definitions and exposes tradeoffs, especially in organizations where marketing leadership is being shared across a fractional CMO, internal operators, and agency partners.
When Fractional Marketing Leadership Is the Right Move
Fractional marketing can be the right fit when you need senior revenue accountability but can’t justify a full-time CMO, have channel executors but no one owning the full-funnel plan, or are drowning in tools and reporting and need governance plus a clear measurement model.
What matters most is not the title or the engagement model. It is whether the person in the seat can see the whole revenue system, recognize where it is breaking down, and make clear decisions about what moves the business forward now versus what can wait. In an AI-accelerated, martech-heavy environment, that judgment is the difference between motion and momentum.
How AI Is Rewriting Trust and Buying Behavior in 2026"
marketing 12 Jan 2026
AI Digital recently released their annual Media Trends report. Can you briefly summarize what you’ve learned and why it matters for marketers right now?
The report shows that the media has crossed a structural threshold. AI now determines visibility, credibility determines recommendation, and premium environments determine performance. The research makes clear that many marketers are still optimizing for reach and efficiency in systems that no longer govern outcomes — for example, brands are still buying on CPM when AI systems don't prioritize based on volume anymore. Right now, the gap between brands adapting to this shift and those clinging to legacy playbooks is widening fast, and it shows up directly in performance.
One of the report’s big takeaways is that AI is changing how discovery works. What does that mean for marketers who still rely heavily on clicks and last-click metrics?
Discovery is no longer a journey, it is a decision moment. AI-driven answers are collapsing multi-touch funnels into single conversations, reducing organic click-through rates by 34–64%. The report is blunt: if a brand is not present inside the AI response, it is not part of the consideration set. This forces marketers to move beyond clicks and last-touch metrics toward measuring visibility, credibility, and influence earlier in the decision cycle. The priority should be ensuring brand information is structured and authoritative enough to appear in AI-generated answers.
Why does trust matter more now, and what should brands focus on to build it?
Trust now operates at two levels, human and machine. Consumers remain skeptical of AI-generated content, and AI systems themselves prioritize reliable, consistent signals when deciding what to surface. Our research has shown that transparency, premium inventory, and verifiable data inputs are no longer brand hygiene, they are performance levers. Brands that treat trust as infrastructure consistently outperform those that treat it as messaging.
How does first-party data factor into building trust and driving performance today?
First-party data is the strongest signal brands actually control. The report shows that campaigns anchored in first-party identity deliver lower CPAs and higher conversion rates, while third-party dependency continues to erode. AI Digital frames unified CRM, CDP, and clean-room infrastructure not as a tech upgrade, but as the foundation for privacy-safe personalization and sustainable performance.
Many marketers are investing in AI tools, but still struggling with performance. Where do you see teams getting stuck or overlooking foundational issues like data and transparency?
AI amplifies everything — good strategy and bad. Teams see breakthrough results when they ensure clean data, transparent supply chains, and clear success metrics before adding AI to the stack. Without these fundamentals, automation simply scales inefficiency faster. We consistently see performance improve only after teams address basics like fraud exposure, measurement integrity, and signal quality. The AI isn't the problem; it's a mirror that reflects the strength or weakness of what's already there.
Why are channels like CTV, retail media, and audio becoming more important, and how are brands underusing them today?
These channels combine attention, trust, and closed-loop measurement, but remain underutilized. CTV now reaches nearly all U.S. households and supports real attribution. Retail media is approaching $69B with full-funnel capability built on first-party data. Audio and podcasts deliver high engagement with relatively low saturation. The report shows brands still undervalue these channels because they don't fit traditional reach-and-frequency or last-click attribution models, even though they increasingly shape how decisions are made. Marketers need to rebuild their measurement frameworks around these environments, not force them into outdated metrics.
Redefining Marketing Seniority in the Age of AI: Why Adaptation Matters More Than Experience
marketing 7 Jan 2026
What traps do traditional marketers fall into when they rely on past knowledge instead of exploring new AI capabilities?
Incorrect assumptions and stagnation. Past knowledge is great, but without layering it with AI, marketers put themselves in a position to be chasing current trends and user behaviors constantly.
As people adopt AI across all aspects of their lives, they begin to expect more, whether it’s quicker answers, higher quality, or more relevant information. Relying on past knowledge without the addition of AI can lead to marketing in ways that don’t resonate with current consumers and cause marketers to react too slowly to underperforming tactics.
Today, a marketer with just a few years of experience can run fully automated, personalized campaigns using AI. How does this change the definition of “seniority” in marketing?
Seniority is traditionally defined by length of time in a career, and for good reason: knowledge is gained with experience. The longer someone worked in the field, the more experience and working knowledge they had, making them more valuable. In most cases, this was the only way to gain that experience.
AI is quickly leveling that playing field in many areas. For instance, let’s say a team is running a paid ad campaign. The veteran marketer would traditionally be leading the team, using his experience to guide the design of the ad. They’d choose the copy, the creative, the target audience, etc. If the campaign was underperforming, they’d have to make assumptions for the reasons why and make changes accordingly. Today, AI tools can assist with all of these tasks by relying on large sets of data, helping to optimize campaigns before they go live, and then adjusting, if needed, once they do.
Marketers who master the AI tools necessary to run optimized marketing programs become the ones trusted to lead efforts moving forward. Experience has its place, but it no longer serves as a defining predictor of success.
You’ve said that marketers who don’t adapt will be left behind. What signs are you seeing today that confirm this shift is already happening?
Businesses are becoming either AI-first or integrating AI into their products and day-to-day processes. Those who don’t adapt to AI won’t understand the business and how to help it grow. To see this in the world, look at the description of any current job opening. Nearly everyone, whether it’s marketing, engineering, or sales, specifically mentions proficiency with AI tools as a requirement.
You’ve emphasized that AI won’t replace people, but people who use AI will replace those who don’t. How do you explain this distinction to marketing leaders who are hesitant?
Let’s look at it from a brand perspective. Let’s say a company wants to run a video campaign that helps with brand positioning in the market. There are nuances to a company’s brand, competitors, and current events that only humans know. AI won’t understand each of these and create a perfect campaign from A to Z. However, AI can help provide ideas, creative suggestions, targeting segment recommendations, and the tools to create the final product.
In this scenario, AI doesn’t replace humans; it helps them execute faster and smarter.
In your opinion, what are the areas of marketing where AI can boost performance without needing a full transformation?
This can happen at nearly every level of an organization. I already mentioned paid ads and the ability to produce content more quickly, and there are plenty more. Let’s look at email marketing.
Brands rely on automated emails because they generate 37% of all email sales from just 2% of messages sent. Traditionally, building automated email flows took time and best practice knowledge. That’s no longer the case. Email platforms offer pre-built workflows and email templates to streamline creation, and infuse AI throughout the process to create more efficient messaging based on individual brands.
What once took marketers days, if not weeks, to create (if they had the knowledge), can now be done in minutes by junior team members. This is the type of transformation that’s possible with AI.
You recommend that marketers make time each day to learn and experiment with AI. What does that look like in practice, 15 minutes, a daily workflow test, or something else?
There are many different AI tools and platforms, learning them all is next to impossible. Gradually increasing knowledge is the key to long-term success. I recommend people spend at least 15 minutes each day learning something new with AI. Of course, if you can spend more, you should.
Spending 15 minutes each day can easily be done by anyone. For example, let’s say you already use ChatGPT for something simple, like brainstorming ideas. Spend 15 minutes experimenting with the prompt and learning which ones produce more or less favorable results. Then, push the boundaries to see if you can get a “pie in the sky" result, even though it may not seem or be possible. Failing can help users understand the limits of a tool, which further informs them of alternate means of achieving a goal, whether it’s through promoting or combining the output with other tools.
If you have more time to invest each day, make a list of things you wish could be streamlined in your day-to-day or “genie in the bottle” projects you wish could be done, and start testing different tools to help you accomplish these goals. With AI, even failure is winning.
Looking ahead, what will differentiate marketers who thrive in an AI-driven future from those who fall behind?
The ability to stack information, combined with critical thinking. As I mentioned, learning how different platforms can achieve different goals is essential. This will be the first line of separation. However, if we assume many modern-day marketers will learn these new tools, there will be a need for a second set of differentiation. Here is where critical thinking, and maybe even experience, come into the equation.
If we have 20 marketers who understand the fundamentals and maybe even advanced use cases of different platforms, no one is ahead or behind. Using critical thinking to determine how different programs can benefit their own department while connecting the results to different areas, like sales, marketing, and customer success teams, is how marketers can prove value to an organization.
I mention experience here because while seniority may be redefined, experience is still experience. Those with experience tend to understand how different teams operate and see the bigger picture. Combining experience with critical thinking, marketers can determine when AI-generated outputs don’t perfectly align with organizational goals — something only humans can determine.
When marketers become reliant on AI and assume every output is “correct,” the result is blandness, unoriginality, and stagnation. This is what separates the pack.
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