Your impressions might be up, but the traffic isn’t following. Competitors are showing up in AI search while your brand remains invisible.
How do you measure success when ChatGPT or Gemini doesn’t show traditional rankings? How do you define “winning” in a world where every query can produce a different answer?
Learn the SEO & GEO strategies enterprise brands are using to secure visibility in AI Overviews and large language models.
AI Mode is growing fast. Millions of users are turning to AI engines for answers, and brand visibility is now the single most important metric.
In this webinar, Tom Capper, Sr. Search Scientist at STAT Search Analytics, will guide you through how enterprise SEOs can adapt, measure, and thrive in this new environment.
Unless you have been living under a rock, you would have seen or experienced the evolution of marketing in recent years; often centered around the marketing leader and the chief marketing officer (CMO) role.
The CMO role has come under fire for performance, for the lack of big bang delivery, for not moving away from vanity metrics, and often being overly defensive at the leadership table.
Marketing Leadership Is Harder Than Ever
In coaching CMOs and equivalent titles, there are several recurring themes, one of which stands out in almost all coachees: Your job as a CMO is being a company executive first and then being a department leader.
You are in the C-Suite to represent the business needs, and business needs will trump your department and team needs, often going against how you are wired.
The business needs and the department needs shouldn’t be different. However, they are often at odds, especially when you, as the leader, haven’t placed the right guardrails; what often occurs is that you have followed poorly thought-through goals, key performance indicators (KPIs), and enabled disconnected objectives and key results (OKRs).
In other scenarios, the CMO role is being removed and not replaced, and the CMO title is removed. Repeatedly being replaced with VP, director, or “head of” titles, often resulting in the marketing leader not being in the C-Suite and regularly reporting one to two steps removed from the CEO.
Enter The Chief Growth Officer (CGO)
There are often reasons why there is a rebrand or title change within the C-Suite:
It is deliberate, changing the internal comms of the role. It demonstrates that, as a business, you are moving from marketing to growth or from old to new.
The removal of the previous CMO and legal requirements will dictate a change in title or a shift in job and description of the role.
If you work at a startup, it is often evolving the narrative with investors, which often helps frame previous struggles and drives the message that you are concentrating on growth.
There is also a showing of intent to the industry, often sending out press releases to show you are moving towards growth.
The Difference Between Marketing & Growth
The truth: The difference between marketing and growth setups is either negligible or a huge gulf.
Many confident marketing leaders would set up their teams in a very similar way; they would similarly set goals, but the department would work and operate in small ways.
Reducing and reframing the former way of defensive actions (Marketers have the hardest job and everyone thinks they can do marketing. Marketers have had to protect doing things that don’t scale and aren’t easily attributable).
Moving from not being connected to a truly cross-functional department.
Intentional reporting and proactively marketing more frequently and aggressively internally, which is the lost art in many marketing departments.
Like the best marketing organizations, the best growth departments are hyper-connected. They are intertwined cross-functionally, and they are pushing numbers constantly, reporting on the most important metrics and being able to tell the story of how it’s all connected. Reporting which KPI connects to which goal, how each goal connects up to the business objective, and how the brand brings performance.
Why The CGO Role Is Different
Skill Gaps
There are specific skill sets that differentiate successful CGOs from traditional CMOs – areas that often come up and stand apart marketing and growth. These include data fluency and the ability to crunch data themselves, adopting an experimentation-first mindset, being able to test, learn, and iterate as second nature, and everything CGOs do has revenue attribution baked in.
Customer Journey Ownership
Many CGOs are taking ownership of the entire customer lifecycle, and are happy to jump into product analysis and request missing product feature builds. There are many CMOs who struggle with the shift from leads and marketing qualified leads (MQLs) to customer lifetime values (CLVs).
Technology Integration
Often, CGOs have a greater understanding of tech stacks and the investment required in technical tools, and are more than comfortable working directly with product and engineering teams. Often the Achilles’ heel of CMOs.
Measurement Evolution
Growth leaders will often have sophisticated attribution models and real-time performance dashboards, focusing on performance across the board and being on top of numbers. Many CMOs can struggle with getting into the weeds of data and being able to talk confidently with the executive committee members.
External Stakeholder Management
CGOs will often have direct relationships with investors and board members, whereas “traditional CMOs” are regularly disconnected and have limited relationships with important management and investors.
Growth Department Challenges
In coaching CGOs, there are unique pressures that emerge in their sessions. The business requires its growth department to be accountable for every number and drive business performance through (almost all) marketing activities. No easy task.
The growth leader must evolve the former marketing approach into a fresh growth approach, which requires a new culture of performance, tactical refresh, a dedicated approach within teams in the department. That has to transform traditional disciplines following historical goals and tactics into the new growth approach. It’s no mean feat, especially in long-serving teams and traditional businesses.
The Long-Term Impact
Having built growth departments, holding both CMO and CGO titles, many long-term impacts are overlooked:
Stagnating Careers: Many team members can see their career stagnate if they are not brought onto the growth journey, and can feel because of their discipline, they are not considered a performance channel.
Specialist Struggles: In many marketing departments, there is a larger number of specialists and many specialists struggle with more integrated ways of working. It will be important for specialists to attempt to learn other skills and appreciate their generalist colleagues who will rely on them. Specialists are often those impacted most by the “marketing to growth” move.
Generalist Growth: Generalists are a crucial part of the move towards growth, often being relied upon to act as the glue and as the bridge. Generalists will need to understand the plan and connect with their specialist department colleagues, and help to shape and reshape.
Team Members Lost In The Transition: In any changeover, there will be team members who get lost. They will report to or through new managers, and will drift or will feel lost, and their performance will be hit. It is critical that all team members understand their plan and feel they are brought on the journey. Many middle managers are actually lost first. Ensure you keep checking in and have a plan co-created with the department lead.
Minding The Gap: The gap between teams can grow, and many teams can struggle to adapt to the change quickly enough. This also occurs when performance-based CGOs can overlook brand and retention teams.
Cultural Issues: Humans are averse to change. Now, opting out is the default, not opting in. It is on the team leads and the department head to bring everyone on the journey and make the hard decisions when members will not opt in.
The Path Forward: Lead Your Marketing Leadership Evolution
The shift from CMO to CGO isn’t just about changing titles or acting differently; it’s about fundamentally reimagining how marketing drives business growth.
For marketing leaders reading this, the question isn’t whether this evolution will happen, but how quickly you can adapt to lead the charge for departmental and business success.
Something I share in coaching is, if you’re a current CMO (or equivalent), you should step back and ask yourself the following questions:
Are you already operating as a “CGO”?
Are you deeply embedded in revenue conversations?
Are you able to connect and drive cross-functional alignment and drive change?
Do you positively obsess over business metrics that matter beyond your department?
If the answer is yes, you’re already on the right path. If not, it’s time to evolve before the decision is made above you or for you.
If this fills you with dread, then I can only be direct: You will have to learn to change your approach or get used to feeling the heat of business evolution.
For organizations considering this transition, remember that the best CGOs don’t just inherit marketing teams; they proactively transform them.
They build a culture where every team member understands their direct impact on business growth, where specialists learn to think and operate as generalists, and where the entire department becomes a revenue-generating engine rather than being considered a cost center.
Smart marketing leaders can also lead this transformation, but being able to prove they can evolve themselves and the people around them to this new way of working is critically important. A word to wise: Do not put yourself forward without knowing you are will be an essential leader in this new operating model and when it struggles you will be the leader they look to get the new system back on track.
The companies that get this transition right will see marketing finally claim its rightful seat (back) at the strategic table.
Those that don’t risk relegating their marketing function to tactical execution will see many of their competitors pull ahead with integrated growth strategies.
The choice now is yours: Evolve your marketing leadership to meet the demands of modern business, or watch as your competitors rewrite the rules of growth, while you’re struggling with metrics and influencing your business cross-functionally.
The future belongs to leaders who can bridge the gap between marketing’s art and growth’s science. The title will change and revert, but the question is: Will you be one of the modern marketing leaders, or could you be left behind?
For multi-location brands, local search has always been competitive. But 2025 has introduced a new player: AI.
From AI Overviews to Maps Packs, how consumers discover your stores is evolving, and some brands are already pulling ahead.
Robert Cooney, VP of Client Strategy at DAC, and Kyle Harris, Director of Local Optimization, have spent months analyzing enterprise local search trends. Their findings reveal clear gaps between brands that merely appear and those that consistently win visibility across hundreds of locations.
Multi-generational search habits are shifting. Brands that align content to real consumer behavior capture more attention.
The next wave of “agentic search” is coming, and early preparation is the key to staying relevant.
This webinar is your chance to see these insights in action. Walk away with actionable steps toprotect your visibility, optimize local presence, and turn AI-driven search into a growth engine for your stores.
📌 Register now to see how enterprise brands are staying ahead of AI in local search. Can’t make it live? Sign up and we’ll send the recording straight to your inbox.
We’re more than halfway through 2025, and SEO has already changed names many times to take into account the new mission of optimizing for the rise of large language models (LLMs): We’ve seen GEO (Generative Engine Optimization) floating around, AEO (Answer Engine Optimization), and even LEO (LLM Engine Optimization) has made an apparition in industry conversations and job titles.
However, while we are all busy finding new nomenclatures to factor in the machine part of the discovery journey, there is someone else in the equation that we risk forgetting about: the end beneficiary of our efforts, the user.
Why Do You Need Behavioral Data In Search?
Behavioral data is vital to understand what leads a user to a search journey, where they carry it out, and what potential points of friction might be blocking a conversion action, so that we can better cater to their needs.
And if we learned anything from the documents leaked from the Google trial, it is that users’ signals might actually be one of the many factors that influence rankings, something that was never fully confirmed by the company’s spokespeople, but that’s also been uncovered by Mark Wiliams Cook in his analysis of Google exploits and patents.
With search becoming more and more personalized, and data about users becoming less transparent now that simple search queries are expanding into full funnel conversations on LLMs, it’s important to remember that – while individual needs and experiences might be harder to isolate and cater for – general patterns of behavior tend to stick across the same population, and we can use some rules of thumb to get the basics right.
Humans often operate on a few basic principles aimed at preserving energy and resources, even in search:
Minimizing effort: following the path of least resistance.
Minimizing harm: avoiding threats.
Maximizing gain: seeking opportunities that present the highest benefit or rewards.
So while Google and other search channels might change the way we think about our daily job, the secret weapon we can use to future-proof our brands’ organic presence is to isolate some data about behavior, as it is, generally, much more predictable than algorithm changes.
What Behavioral Data Do You Need To Improve Search Journeys?
I would narrow it down to data that cover three main areas: discovery channel indicators, built-in mental shortcuts, and underlying users’ needs.
1. Discovery Channel Indicators
The days of starting a search on Google are long gone.
According to the Messy Middle research by Google, the exponential increase in information and new channels available has determined a shift from linear search behaviors to a loop of exploration and evaluation guiding our purchase decisions.
And since users now have an overwhelming amount of channels, they can consult in order to research a product or a brand. It’s also harder to cut through the noise, so by knowing more about them, we can make sure our strategy is laser-focused across content and format alike.
Discovery channel indicators give us information about:
How users are finding us beyond traditional search channels.
The demographic that we reach on some particular channels.
What drives their search, and what they are mostly engaging with.
The content and format that are best suited to capture and retain their attention in each one.
For example, we know that TikTok tends to be consulted for inspiration and to validate experiences through user-generated content (UGC), and that Gen Z and Millennials on social apps are increasingly skeptical of traditional ads (with skipping rates of 99%, according to a report by Bulbshare). What they favor instead is authentic voices, so they will seek out first-hand experiences on online communities like Reddit.
Knowing the different channels that users reach us through can inform organic and paid search strategy, while also giving us some data on audience demographics, helping us capture users that would otherwise be elusive.
So, make sure your channel data is mapped to reflect these new discovery channels at hand, especially if you are relying on custom analytics. Not only will this ensure that you are rightfully attributed what you are owed for organic, but it will also be an indication of untapped potential you can lean into, as searches become less and less trackable.
This data should be easily available to you via the referral and source fields in your analytics platform of choice, and you can also integrate a “How did you hear about us” survey for users who complete a transaction.
And don’t forget about language models: With the recent rise in queries that start a search and complete an action directly on LLMs, it’s even harder to track all search journeys. This replaces our mission to be relevant for one specific query at a time, to be visible for every intent we can cover.
This is even more important when we realize that everything contributes to the transactional power of a query, irrespective of how the search intent is traditionally labelled, since someone might decide to evaluate our offers and then drop out due to the lack of sufficient information about the brand.
2. Built-In Mental Shortcuts
The human brain is an incredible organ that allows us to perform several tasks efficiently every day, but its cognitive resources are not infinite.
This means that when we are carrying out a search, probably one of many of the day, while we are also engaged in other tasks, we can’t allocate all of our energy into finding the most perfect result among the infinite possibilities available. That’s why our attentional and decisional processes are often modulated by built-in mental shortcuts like cognitive biases and heuristics.
These terms are sometimes used interchangeably to refer to imperfect, yet efficient decisions, but there is a difference between the two.
Cognitive Biases
Cognitive biases are systematic, mostly unconscious errors in thinking that affect the way we perceive the world around us and form judgments. They can distort the objective reality of an experience, and the way we are persuaded into an action.
One common example of this is the serial position effect, which is made up of two biases: When we see an array of items in a list, we tend to remember best the ones we see first (primacy bias) and last (recency bias). And since cognitive load is a real threat to attention, especially now that we live in the age of 24/7 stimuli, primacy and recency biases are the reason why it’s recommended to lead with the core message, product, or item if there are a lot of options or content on the page.
Primacy and recency not only affect recall in a list, but also determine the elements that we use as a reference to compare all of the alternative options against. This is another effect called anchoring bias, and it is leveraged in UX design to assign a baseline value to the first item we see, so that anything we compare against it can either be perceived as a better or worse deal, depending on the goal of the merchant.
Among many others, some of the most common biases are:
Distance and size effects: As numbers increase in magnitude, it becomes harder for humans to make accurate judgments, reason why some tactics recommend using bigger digits in savings rather than fractions of the same value.
Negativity bias: We tend to remember and assign more emotional value to negative experiences rather than positive ones, which is why removing friction at any stage is so important to prevent abandonment.
Confirmation bias: We tend to seek out and prefer information that confirms our existing beliefs, and this is not only how LLMs operate to provide answers to a query, but it can be a window into the information gaps we might need to cover.
Heuristics
Heuristics, on the other hand, are rules of thumb that we employ as shortcuts at any stage of decision-making, and help us reach a good outcome without going through the hassle of analyzing every potential ramification of a choice.
A known heuristic is the familiarity heuristic, which is when we choose a brand or a product that we already know, because it cuts down on every other intermediate evaluation we would otherwise have to make with an unknown alternative.
Loss aversion is another common heuristic, showing that on average we are more likely to choose the least risky option among two with similar returns, even if this means we might miss out on a discount or a short-term benefit. An example of loss aversion is when we choose to protect our travels for an added fee, or prefer products that we can return.
There are more than 150 biases and heuristics, so this is not an exhaustive list – but in general, getting familiar with which ones are most common among our users helps us smooth out the journey for them.
Isolating Biases And Heuristics In Search
Below, you can see how some queries can already reveal subtle biases that might be driving the search task.
Bias/Heuristic
Sample Queries
Confirmation Bias
• Is [brand/products] the best for this [use case]? • Is this [brand/product/service] better than [alternative brand/product service]? • Why is [this service] more efficient than [alternative service]?
Familiarity Heuristic
• Is [brand] based in [country]? • [Brand]’s HQs • Where do I find [product] in [country]?
Loss Aversion
• Is [brand] legit? • [brand] returns • Free [service]
Social Proof
• Most popular [product/brand] • Best [product/brand]
You can use Regex to isolate some of these patterns and modifiers directly in Google Search Console, or you can explore other query tools like AlsoAsked.
If you’re working with large datasets, I recommend using a custom LLM or creating your own model for classifications and clustering based on these rules, so it becomes easier to spot a trend in the queries and figure out priorities.
These observations will also give you a window into the next big area.
3. Underlying Users’ Needs
While biases and heuristics can manifest a temporary need in a specific task, one of the most beneficial aspects that behavioral data can give us is the need that drives the starting query and guides all of the subsequent actions.
Underlying needs don’t only become apparent from clusters of queries, but from the channels used in the discovery and evaluation loop, too.
For example, if we see high prominence of loss aversion based on our queries, paired with low conversion rates and high traffic on UGC videos for our product or brand, we can infer that:
Users need reassurance on their investment.
There is not enough information to cover this need on our website alone.
Trust is a big decision-mover, and one of the most underrated needs that brands often fail to fulfill as they take their legitimacy for granted.
However, sometimes we need to take a step back and put ourselves in the users’ shoes in order to see everything with fresh eyes from their perspective.
By mapping biases and heuristics to specific users’ needs, we can plan for cross-functional initiatives that span beyond pure SEO and are beneficial for the entire journey from search to conversion and retention.
How Do You Obtain Behavioral Data For Actionable Insights?
In SEO, we are used to dealing with a lot of quantitative data to figure out what’s happening on our channel. However, there is much more we can uncover via qualitative measures that can help us identify the reason something might be happening.
Quantitative data is anything that can be expressed in numbers: This can be time on page, sessions, abandonment rate, average order value, and so on.
Tools that can help us extract quantitative behavioral data are:
Google Search Console & Google Merchant Center: Great for high-level data like click-through rates (CTRs), which can flag mismatches between the user intent and the page or campaign served, as well as cannibalization instances and incorrect or missing localization.
Google Analytics, or any custom analytics platform your brand relies on: These give us information on engagement metrics, and can pinpoint issues in the natural flow of the journey, as well as point of abandonment. My suggestion is to set up custom events tailored to your specific goals, in addition to the default engagement metrics, like sign-up form clicks or add to cart.
Heatmaps and eye-tracking data: Both of these can give us valuable insights into visual hierarchy and attention patterns on the website. Heatmapping tools like Microsoft Clarity can show us clicks, mouse scrolls, and position data, uncovering not only areas that might not be getting enough attention, but also elements that don’t actually work. Eye-tracking data (fixation duration and count, saccades, and scan-paths) integrate that information by showing what elements are capturing visual attention, as well as which ones are often not being seen at all.
Qualitative data, on the other hand, cannot be expressed in numbers as it usually relies on observations. Examples include interviews, heuristic assessments, and live session recordings. This type of research is generally more open to interpretation than its quantitative counterpart, but it’s vital to make sure we have the full picture of the user journey.
Qualitative data for search can be extracted from:
Surveys and CX logs: These can uncover common frustrations and points of friction for returning users and customers, which can guide better messaging and new page opportunities.
Scrapes of Reddit, Trustpilot, and online communities conversations: These give us a similar output as surveys, but expand the analysis of blockers to conversion to users that we haven’t acquired yet.
Live user testing: The least scalable but sometimes most rewarding option, as it can cut down all the inference on quantitative data, especially when they are combined (for example, live sessions can be combined with eye-tracking and narrated by the user at a later stage via Retrospective Think-Aloud or RTA).
Behavioral Data In The AI Era
In the past year, our industry has been really good at two things: sensationalizing AI as the enemy that will replace us, and highlighting its big failures on the other end. And while it’s undeniable that there are still massive limitations, having access to AI presents unprecedented benefits as well:
We can use AI to easily tie up big behavioral datasets and uncover actionables that make the difference.
Even when we don’t have much data, we can train our own synthetic dataset based on a sample of ours or a public one, to spot existing patterns and promptly respond to users’ needs.
We can generate predictions that can be used proactively for new initiatives to keep us ahead of the curve.
How Do You Leverage Behavioral Data To Improve Search Journeys?
Start by creating a series of dynamic dashboards with the measures you can obtain for each one of the three areas we talked about (discovery channel indicators, built-in mental shortcuts, and underlying users’ needs). These will allow you to promptly spot behavioral trends and collect actions that can make the journey smoother for the user at every step, since search now spans beyond the clicks on site.
Once you get new insights for each area, prioritize your actions based on expected business impact and effort to implement.
And bear in mind that behavioral insights are often transferable to more than one section of the website or the business, which can maximize returns across several channels.
Lastly, set up regular conversations with your product and UX teams. Even if your job title keeps you in search, business success is often channel-agnostic. This means that we shouldn’t only treat the symptom (e.g., low traffic to a page), but curate the entire journey, and that’s why we don’t want to work in silos on our little search island.
Your users will thank you. The algorithm will likely follow.
Are your AI writing tools helping or hurting your SEO performance?
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This post was sponsored by Cloudways. The opinions expressed in this article are the sponsor’s own.
Wondering why your rankings may be declining?
Just discovered your WooCommerce site has slow load times?
A slow WooCommerce site doesn’t just cost you conversions. It affects search visibility, backend performance, and customer trust.
Whether you’re a developer running your own stack or an agency managing dozens of client stores, understanding how WooCommerce performance scales under load is now considered table stakes.
Today, many WordPress sites are far more dynamic, meaning many things are happening at the same time:
Every action a user takes, from logging in, updating a cart, or initiating checkout, relies on live data from the server. These requests cannot be cached.
Tools like Varnish or CDNs can help with public pages such as the homepage or product listings. But once someone logs in to their account or interacts with their session, caching no longer helps. Each request must be processed in real time.
This article breaks down why that happens and what kind of server setup is helping stores stay fast, stable, and ready to grow.
Why Do WooCommerce Stores Slow Down?
WooCommerce often performs well on the surface. But as traffic grows and users start interacting with the site, speed issues begin to show. These are the most common reasons why stores slow down under pressure:
1. PHP: It Struggles With High User Activity
WooCommerce depends on PHP to process dynamic actions such as cart updates, coupon logic, and checkout steps. Traditional stacks using Apache for PHP handling are slower and less efficient.
Order creation, cart activity, and user actions generate a high number of database writes. During busy times like flash sales, new merchandise arrivals, or course launches, the database struggles to keep up.
Platforms that support optimized query execution and better indexing handle these spikes more smoothly.
3. Caching Issues: Object Caching Is Missing Or Poorly Configured
Without proper object caching, WooCommerce queries the database repeatedly for the same information. That includes product data, imagery, cart contents, and user sessions.
Solutions that include built-in Redis support help move this data to memory, reducing server load and improving site speed.
4. Concurrency Limits Affect Performance During Spikes
Most hosting stacks today, including Apache-based ones, perform well for a wide range of WordPress and WooCommerce sites. They handle typical traffic reliably and have powered many successful stores.
As traffic increases and more users log in and interact with the site at the same time, the load on the server begins to grow. Architecture starts to play a bigger role at that point.
Stacks built on NGINX with event-driven processing can manage higher concurrency more efficiently, especially during unanticipated traffic spikes.
Rather than replacing what already works, this approach extends the performance ceiling for stores that are becoming more dynamic and need consistent responsiveness under heavier load.
5. Your WordPress Admin Slows Down During Sales Seasons
During busy periods like seasonal sales campaigns or new stock availability, stores can often slow down for the team managing the site, too. The WordPress dashboard takes longer to load, which means publishing products, managing orders, or editing pages also becomes slower.
This slowdown happens because both shoppers and staff are using the site’s resources at the same time, and the server has to handle all those requests at once.
How To Architect A Scalable WordPress Setup For Dynamic Workloads?
WooCommerce stores today are built for more than stable traffic. Customers are logging in, updating their carts, taking actions to manage their subscription profile, and as a result, are interacting with your backend in real time.
The traditional WordPress setup, which is primarily designed for static content, cannot handle that kind of demand.
Here’s how a typical setup compares to one built for performance and scale:
Component
Basic Setup
Scalable Setup
Web Server
Apache
NGINX
PHP Handler
mod_php or CGI
PHP-FPM
Object Caching
None or database transients
Redis with Object Cache Pro
Scheduled Tasks
WP-Cron
System cron job
Caching
CDN or full-page caching only
Layered caching, including object cache
.htaccess Handling
Built-in with Apache
Manual rewrite rules in NGINX config
Concurrency Handling
Limited
Event-based, memory-efficient server
How To Manually Setup A Performance-Ready & Scalable WooCommerce Stack
If you’re setting up your own server or tuning an existing one, are the most important components to get right:
1) Use NGINX For Static File Performance
NGINX is often used as a high-performance web server for handling static files and managing concurrent requests efficiently. It is well suited for stores expecting high traffic or looking to fine-tune their infrastructure for speed.
Unlike Apache, NGINX does not use .htaccess files. Rewrite rules, such as permalinks, redirects, and trailing slashes, need to be added manually to the server block. For WordPress, these rules are well-documented and only need to be set once during setup.
This approach gives more control at the server level and can be helpful for teams building out their own environment or optimizing for scale.
2) Enable PHP-FPM For Faster Request Handling
PHP-FPM separates PHP processing from the web server. It gives you more control over memory and CPU usage. Tune values like pm.max_children and pm.max_requests based on your server size to prevent overload during high activity.
3) Install Redis With Object Cache Pro
Redis allows WooCommerce to store frequently used data in memory. This includes cart contents, user sessions, and product metadata.
Pair this with Object Cache Pro to compress cache objects, reduce database load, and improve site responsiveness under load.
4) Replace WP-Cron With A System-Level Cron Job
By default, WordPress checks for scheduled tasks whenever someone visits your site. That includes sending emails, clearing inventory, and syncing data. If you have steady traffic, it works. If not, things get delayed.
You can avoid that by turning off WP-Cron. Just add define(‘DISABLE_WP_CRON’, true); to your wp-config.php file. Then, set up a real cron job at the server level to run wp-cron.php every minute. This keeps those tasks running on time without depending on visitors.
5) Add Rewrite Rules Manually For NGINX
NGINX doesn’t use .htaccess. That means you’ll need to define URL rules directly in the server block.
This includes things like permalinks, redirects, and static file handling. It’s a one-time setup, and most of the rules you need are already available from trusted WordPress documentation. Once you add them, everything works just like it would on Apache.
A Few Tradeoffs To Keep In Mind
This kind of setup brings a real speed boost. But there are some technical changes to keep in mind.
NGINX won’t read .htaccess. All rewrites and redirects need to be added manually.
WordPress Multisite may need extra tweaks, especially if you’re using subdirectory mode.
Security settings like IP bans or rate limits should be handled at the server level, not through plugins.
Most developers won’t find these issues difficult to work with. But if you’re using a modern platform, much of it is already taken care of.
You don’t need overly complex infrastructure to make WooCommerce fast; just a stack that aligns with how modern, dynamic stores operate today.
Next, we’ll look at how that kind of stack performs under traffic, with benchmarks that show what actually changes when the server is built for dynamic sites.
What Happens When You Switch To An Optimized Stack?
Not all performance challenges come from code or plugins. As stores grow and user interactions increase, the type of workload becomes more important, especially when handling live sessions from logged-in users.
To better understand how different environments respond to this kind of activity, Koddr.io ran an independent benchmark comparing two common production setups:
A hybrid stack using Apache and NGINX.
A stack built on NGINX with PHP-FPM, Redis, and object caching.
Both setups were fully optimized and included tuned components like PHP-FPM and Redis. The purpose of the benchmark was to observe how each performs under specific, real-world conditions.
The tests focused on uncached activity from WooCommerce and LearnDash, where logged-in users trigger dynamic server responses.
In these scenarios, the optimized stack showed higher throughput and consistency during peak loads. This highlights the value of having infrastructure tailored for dynamic, high-concurrency traffic, depending on the use case.
WooCommerce Runs Faster Under Load
One test simulated 80 users checking out at the same time. The difference was clear:
Scenario
Hybrid Stack
Optimized Stack
Gain
WooCommerce Checkout
3,035 actions
4,809 actions
+58%
Screenshot from Koddr.io, August 2025
LMS Platforms Benefit Even More
For LearnDash course browsing—a write-heavy and uncached task, the optimized stack completed 85% more requests:
Scenario
Hybrid Stack
Optimized Stack
Gain
LearnDash Course List View
13,459 actions
25,031 actions
+85%
This shows how optimized stacks handle personalized or dynamic content more efficiently. These types of requests can’t be cached, so the server’s raw efficiency becomes critical.
Screenshot from Koddr.io, August 2025
Backend Speed Improves, Too
The optimized stack wasn’t just faster for customers. It also made the WordPress admin area more responsive:
WordPress login times improved by up to 31%.
Publish actions ran 20% faster, even with high traffic.
This means your team can concurrently manage products, update pages, and respond to sales in real time, without delays or timeouts.
It Handles More Without Relying On Caching
When Koddr turned off Varnish, the hybrid stack experienced a 71% drop in performance. This shows how effectively it handles cached traffic. The optimized stack dropped just 7%, which highlights its ability to maintain speed even during uncached, logged-in sessions.
Both setups have their strengths, but for stores with real-time user activity, reducing reliance on caching can make a measurable difference.
Stack Type
With Caching
Without Caching
Drop
Hybrid Stack
654,000 actions
184,000 actions
-7%
Optimized Stack
619,000 actions
572,000 actions
-7%
Screenshot from Koddr.io, August 2025
Why This Matters?
Static pages are easy to optimize. But WooCommerce stores deal with real-time traffic. Cart updates, login sessions, and checkouts all require live processing. Caching cannot help once a user has signed in.
The Koddr.io results show how an optimized server stack:
Helps scale without complex performance workarounds.
These are the kinds of changes that power newer stacks purpose-built for dynamic workloads like Cloudways Lightning, built for real WooCommerce workloads.
Core Web Vitals Aren’t Just About The Frontend
You can optimize every image. Minify every line of code. Switch to a faster theme. But your Core Web Vitals score will still suffer if the server can’t respond quickly.
That’s what happens when logged-in users interact with WooCommerce or LMS sites.
When a customer hits “Add to Cart,” caching is out of the picture. The server has to process the request live. That’s where TTFB (Time to First Byte) becomes a real problem.
Slow server response means Google waits longer to start rendering the page. And that delay directly affects your Largest Contentful Paint and Interaction to Next Paint metrics.
Frontend tuning gets you part of the way. But if the backend is slow, your scores won’t improve. Especially for logged-in experiences.
Real optimization starts at the server.
How Agencies Are Skipping The Manual Work
Every developer has a checklist for WooCommerce performance. Use NGINX. Set up Redis. Replace WP-Cron. Add a WAF. Test under load. Keep tuning.
But not every team has the bandwidth to maintain all of it.
That’s why more agencies are using pre-optimized stacks that include these upgrades by default. Cloudways Lightning, a managed stack based on NGINX + PHP-FPM, designed for dynamic workloads is a good example of that.
It’s not just about speed. It’s also about backend stability during high traffic. Admin logins stay fast. Product updates don’t hang. Orders keep flowing.
Joe Lackner, founder of Celsius LLC, shared what changed for them:
“Moving our WordPress workloads to the new Cloudways stack has been a game-changer. The console admin experience is snappier, page load times have improved by +20%, and once again Cloudways has proven to be way ahead of the game in terms of reliability and cost-to-performance value at this price point.”
This is what agencies are looking for. A way to scale without getting dragged into infrastructure management every time traffic picks up.
Final Takeaway
WooCommerce performance is no longer just about homepage load speed.
Your site handles real-time activity from both customers and your team. Once a user logs in or reaches checkout, caching no longer applies. Each action hits the server directly.
If the infrastructure isn’t optimized, site speed drops, sales suffer, and backend work slows down.
The foundations matter. A stack that’s built for high concurrency and uncached traffic keeps things fast across the board. That includes cart updates, admin changes, and product publishing.
For teams who don’t want to manage server tuning manually, options like Cloudways Lightning deliver a faster, simpler path to performance at scale.
Use promo code “SUMMER305” and get 30% off for 5 months + 15 free migrations. Signup Now!
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Recent research highlights intriguing paradoxes in consumer attitudes toward AI-driven marketing. Consumers encounter AI-powered marketing interactions frequently, often without realizing it.
According to a 2022 Pew Research Center survey, 27% of Americans reported interacting with AI at least several times a day, while another 28% said they interact with AI about once a day or several times a week (Pew Research Center, 2023).
As AI adoption continues to expand across industries, marketing applications – from personalized recommendations to chatbots – are increasingly shaping consumer experiences.
According to McKinsey & Company (2023), AI-powered personalization can deliver five to eight times the ROI on marketing spend and significantly boost customer engagement.
In this rapidly evolving landscape, trust in AI has become a crucial factor for successful adoption and long-term engagement.
The World Economic Forum underscores that “trust is the foundation for AI’s widespread acceptance,” and emphasizes the necessity for companies to adopt self-governance frameworks that prioritize transparency, accountability, and fairness (World Economic Forum, 2025).
The Psychology Of AI Trust
Consumer trust in AI marketing systems operates fundamentally differently from traditional marketing trust mechanisms.
Where traditional marketing trust builds through brand familiarity and consistent experiences, AI trust involves additional psychological dimensions related to automation, decision-making autonomy, and perceived control.
Understanding these differences is crucial for organizations seeking to build and maintain consumer trust in their AI marketing initiatives.
Cognitive Dimensions
Neurological studies offer intriguing insights into how our brains react to AI. Research from Stanford University reveals that we process information differently when interacting with AI-powered systems.
For example, when evaluating AI-generated product recommendations, our brains activate distinct neural pathways compared to those triggered by recommendations from a human salesperson.
This crucial difference highlights the need for marketers to understand how consumers cognitively process AI-driven interactions.
There are three key cognitive factors that have emerged as critical influences on AI trust, including perceived control, understanding of mechanisms, and value recognition.
Emotional Dimensions
Consumer trust in AI marketing is deeply influenced by emotional factors, which often override logical evaluations. These emotional responses shape trust in several key ways:
Anxiety and privacy concerns: Despite AI’s convenience, 67% of consumers express anxiety about how their data is used, reflecting persistent privacy concerns (Pew Research Center, 2023). This tension creates a paradoxical relationship where consumers benefit from AI-driven marketing while simultaneously fearing its potential misuse.
Trust through repeated interactions: Emotional trust in AI systems develops iteratively through repeated, successful interactions, particularly when systems demonstrate high accuracy, consistent performance, and empathetic behavior. Experimental studies show that emotional and behavioral trust accumulate over time, with early experiences strongly shaping later perceptions. In repeated legal decision-making tasks, users exhibited growing trust toward high-performing AI, with initial interactions significantly influencing long-term reliance (Kahr et al., 2023). Emotional trust can follow nonlinear pathways – dipping after failures but recovering through empathetic interventions or improved system performance (Tsumura and Yamada, 2023).
Honesty and transparency in AI content: Consumers increasingly value transparency regarding AI-generated content. Companies that openly disclose when AI has been used – for instance, in creating product descriptions – can empower customers by helping them feel more informed and in control of their choices. Such openness often strengthens customer trust and fosters positive perceptions of brands actively embracing transparency in their marketing practices.
Cultural Variations In AI Trust
The global nature of modern marketing requires a nuanced understanding of cultural differences in AI trust. These variations arise from deeply ingrained societal values, historical relationships with technology, and norms around privacy, automation, and decision-making.
For marketers leveraging AI in customer engagement, recognizing these cultural distinctions is crucial for developing trustworthy AI-driven campaigns, personalized experiences, and region-specific data strategies.
Diverging Cultural Trust In AI
Research reveals significant disparities in AI trust across global markets. A KPMG (2023) global survey found that 72% of Chinese consumers express trust in AI-driven services, while in the U.S., trust levels plummet to just 32%.
This stark difference reflects broader societal attitudes toward government-led AI innovation, data privacy concerns, and varying historical experiences with technology.
Another study found that AI-related job displacement fears vary greatly by region. In countries like the U.S., India, and Saudi Arabia, consumers express significant concerns about AI replacing human roles in professional sectors such as medicine, finance, and law.
In contrast, consumers in Japan, China, and Turkey exhibit lower levels of concern, signaling a higher acceptance of AI in professional settings (Quantum Zeitgeist, 2025).
The Quantum Zeitgeist study shows that regions like Japan, China, and Turkey exhibit lower levels of concern about AI replacing human jobs compared to regions like the U.S., India, and Saudi Arabia, where such fears are more pronounced.
This insight is invaluable for marketers crafting AI-driven customer service, financial tools, and healthcare applications, as perceptions of AI reliability and utility vary significantly by region.
As trust in AI diverges globally, understanding the role of cultural privacy norms becomes essential for marketers aiming to build trust through AI-driven services.
Cultural Privacy Targeting In AI Marketing
As AI-driven marketing becomes more integrated globally, the concept of cultural privacy targeting – the practice of aligning data collection, privacy messaging, and AI transparency with cultural values – has gained increasing importance. Consumer attitudes toward AI adoption and data privacy are highly regional, requiring marketers to adapt their strategies accordingly.
In more collectivist societies like Japan, AI applications that prioritize societal or community well-being are generally more accepted than those centered on individual convenience.
This is evident in Japan’s Society 5.0 initiative – a national vision introduced in 2016 that seeks to build a “super-smart” society by integrating AI, IoT, robotics, and big data to solve social challenges such as an aging population and strains on healthcare systems.
Businesses are central to this transformation, with government and industry collaboration encouraging companies to adopt digital technologies not just for efficiency, but to contribute to public welfare.
Across sectors – from manufacturing and healthcare to urban planning – firms are reimagining business models to align with societal needs, creating innovations that are both economically viable and socially beneficial.
In this context, AI is viewed more favorably when positioned as a tool to enhance collective well-being and address structural challenges. For instance, AI-powered health monitoring technologies in Japan have seen increased adoption when positioned as tools that contribute to broader public health outcomes.
Conversely, Germany, as an individualistic society with strong privacy norms and high uncertainty avoidance, places significant emphasis on consumer control over personal data. The EU’s GDPR and Germany’s support for the proposed Artificial Intelligence Act reinforce expectations for robust transparency, fairness, and user autonomy in AI systems.
According to the OECD (2024), campaigns in Germany that clearly communicate data usage, safeguard individual rights, and provide opt-in consent mechanisms experience higher levels of public trust and adoption.
These contrasting cultural orientations illustrate the strategic need for contextualized AI marketing – ensuring that data transparency and privacy are not treated as one-size-fits-all, but rather as culture-aware dimensions that shape trust and acceptance.
Hofstede’s (2011) cultural dimensions theory offers further insights into AI trust variations:
High individualism + high uncertainty avoidance (e.g., Germany, U.S.) → Consumers demand transparency, data protection, and human oversight in AI marketing.
Collectivist cultures with lower uncertainty avoidance (e.g., Japan, China, South Korea) → AI is seen as a tool that enhances societal progress, and data-sharing concerns are often lower when the societal benefits are clear (Gupta et al., 2021).
For marketers deploying AI in different regions, these insights help determine which features to emphasize:
Control and explainability in Western markets (focused on privacy and autonomy).
Seamless automation and societal progress in East Asian markets (focused on communal benefits and technological enhancement).
Understanding the cultural dimensions of AI trust is key for marketers crafting successful AI-powered campaigns.
By aligning AI personalization efforts with local cultural expectations and privacy norms, marketers can improve consumer trust and adoption in both individualistic and collectivist societies.
This culturally informed approach helps brands tailor privacy messaging and AI transparency to the unique preferences of consumers in various regions, building stronger relationships and enhancing overall engagement.
Avoiding Overgeneralization In AI Trust Strategies
While cultural differences are clear, overgeneralizing consumer attitudes can lead to marketing missteps.
A 2024 ISACA report warns against rigid AI segmentation, emphasizing that trust attitudes evolve with:
Media influence (e.g., growing fears of AI misinformation).
Regulatory changes (e.g., the EU AI Act’s impact on European consumer confidence).
Generational shifts (younger, digitally native consumers are often more AI-trusting, regardless of cultural background).
For AI marketing, this highlights the need for flexible, real-time AI trust monitoring rather than static cultural assumptions.
Marketers should adapt AI trust-building strategies based on region-specific consumer expectations:
North America and Europe: AI explainability, data transparency, and ethical AI labels increase trust.
East Asia: AI-driven personalization and seamless automation work best when framed as benefiting society.
Islamic-majority nations and ethical consumer segments: AI must be clearly aligned with fairness and ethical governance.
Global emerging markets: AI trust is rapidly increasing, making these markets prime opportunities for AI-driven financial inclusion and digital transformation.
The data, drawn from the 2023 KPMG International survey, underscores how cultural values such as collectivism, uncertainty avoidance, and openness to innovation, shape public attitudes toward AI.
For example, trust levels in Germany and Japan remain low, reflecting high uncertainty avoidance and strong privacy expectations, while countries like India and Brazil exhibit notably higher trust, driven by optimism around AI’s role in societal and economic progress.
Measuring Trust In AI Marketing Systems
As AI becomes central to how brands engage customers – from personalization engines to chatbots – measuring consumer trust in these systems is no longer optional. It’s essential.
And yet, many marketing teams still rely on outdated metrics like Net Promoter Score (NPS) or basic satisfaction surveys to evaluate the impact of AI. These tools are helpful for broad feedback but miss the nuance and dynamics of trust in AI-powered experiences.
Recent research, including work from MIT Media Lab (n.d.) and leading behavioral scientists, makes one thing clear: Trust in AI is multi-dimensional, and it’s shaped by how people feel, think, and behave in real-time when interacting with automated systems.
Traditional metrics like NPS and CSAT (Customer Satisfaction Score) tell you if a customer is satisfied – but not why they trust (or don’t trust) your AI systems.
They don’t account for how transparent your algorithm is, how well it explains itself, or how emotionally resonant the interaction feels. In AI-driven environments, you need a smarter way to understand trust.
A Modern Framework For Trust: What CMOs Should Know
MIT Media Lab’s work on trust in human-AI interaction offers a powerful lens for marketers. It breaks trust into three key dimensions:
Behavioral Trust
This is about what customers do, not what they say. When customers engage frequently, opt in to data sharing, or return to your AI tools repeatedly, that’s a sign of behavioral trust. How to track it:
Repeat engagement with AI-driven tools (e.g., product recommenders, chatbots).
Opt-in rates for personalization features.
Drop-off points in AI-led journeys.
Emotional Trust
Trust is not just rational, it’s emotional. The tone of a voice assistant, the empathy in a chatbot’s reply, or how “human” a recommendation feels all play into emotional trust. How to track it:
Sentiment analysis from chat transcripts and reviews.
Customer frustration or delight signals from support tickets.
Tone and emotional language in user feedback.
Cognitive Trust
This is where understanding meets confidence. When your AI explains itself clearly – or when customers understand what it can and can’t do –they’re more likely to trust the output. How to track it:
Feedback on explainability (“I understood why I got this recommendation”).
Click-through or acceptance rates of AI-generated content or decisions.
Post-interaction surveys that assess clarity.
Today’s marketers are moving toward real-time trust dashboards – tools that monitor how users interact with AI systems across channels. These dashboards track behavior, sentiment, and comprehension all at once.
According to MIT Media Lab researchers, combining these signals provides a richer picture of trust than any single survey can. It also gives teams the agility to address trust breakdowns as they happen – like confusion over AI-generated content or friction in AI-powered customer journeys.
Customers don’t expect AI to be perfect. But they do expect it to be honest and understandable. That’s why brands should:
Label AI-generated content clearly.
Explain how decisions like pricing, recommendations, or targeting are made.
Give customers control over data and personalization.
Building trust is less about tech perfection and more about perceived fairness, clarity, and respect.
Measuring that trust means going deeper than satisfaction. Use behavioral, emotional, and cognitive signals to track trust in real-time – and design AI systems that earn it.
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More Resources:
References
Hofstede, G (2011) Dimensionalizing Cultures: The Hofstede Model in Context, Online Readings in Psychology and Culture, 2 (1), scholarworks.gvsu.edu/cgi/viewcontent. cgi?article=1014&context=orpc (archived at https://perma.cc/B7EP-94CQ)
ISACA (2024) AI Ethics: Navigating Different Cultural Contexts, December 6, www.isaca. org/resources/news-and-trends/isaca-now-blog/2024/ai-ethics-navigating-different-cultural-contexts (archived at https://perma.cc/3XLA-MRDE)
Kahr, P K, Meijer, S A, Willemsen, M C, and Snijders, C C P (2023) It Seems Smart, But It Acts Stupid: Development of Trust in AI Advice in a Repeated Legal Decision-Making Task, Proceedings of the 28th International Conference on Intelligent User Interfaces. doi.org/10.1145/3581641.3584058 (archived at https://perma.cc/SZF8-TSK2)
KPMG International and The University of Queensland (2023) Trust in Artificial Intelligence: A Global Study, assets.kpmg.com/content/dam/kpmg/au/pdf/2023/ trust-in-ai-global-insights-2023.pdf (archived at https://perma.cc/MPZ2-UWJY)
McKinsey & Company (2023) The State of AI in 2023: Generative AI’s Breakout Year, www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023- generative-ais-breakout-year (archived at https://perma.cc/V29V-QU6R)
MIT Media Lab (n.d.) Research Projects, accessed April 8, 2025
OECD (2024) OECD Artificial Intelligence Review of Germany, www.oecd.org/en/ publications/2024/06/oecd-artificial-intelligence-review-of-germany_c1c35ccf.html (archived at https://perma.cc/5DBS-LVLV)
Pew Research Center (2023) Public Awareness of Artificial Intelligence in Everyday Activities, February, www.pewresearch.org/wp-content/uploads/sites/20/2023/02/ PS_2023.02.15_AI-awareness_REPORT.pdf (archived at https://perma.cc/V3SE-L2BM)
Quantum Zeitgeist (2025) How Cultural Differences Shape Fear of AI in the Workplace, Quantum News, February 22, quantumzeitgeist.com/how-cultural-differences-shape-fear-of-ai-in-the-workplace-a-global-study-across-20-countries/ (archived at https://perma.cc/3EFL-LTKM)
Tsumura, T and Yamada, S (2023) Making an Agent’s Trust Stable in a Series of Success and Failure Tasks Through Empathy, arXiv. arxiv.org/abs/2306.09447 (archived at https://perma.cc/L7HN-B3ZC)
World Economic Forum (2025) How AI Can Move from Hype to Global Solutions, www. weforum.org/stories/2025/01/ai-transformation-industries-responsible-innovation/ (archived at https://perma.cc/5ALX-MDXB)
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I’ve spent 30 years navigating the turbulent waters of what was once called “internet marketing” and is now called “digital marketing.”
Based on my experience, the past year has been nothing short of a perfect storm for chief marketing officers (CMOs).
As the Director of Corporate Communications for Ziff-Davis, I helped to launch Yahoo! Europe in 1996. We faced several key challenges as the joint venture began offering customized versions of Yahoo!’s leading “Internet guide” in France, Germany, and the United Kingdom.
We had to overcome language, cultural, operational, and competitive hurdles to succeed in a rapidly evolving digital landscape with “annual growth rates in excess of 80%.”
Four years later, I was the VP of Marketing of WebCT when the dot-com bubble burst on March 10, 2000.
A month earlier, the board of directors had asked me why we had not joined the other 14 dot-com companies that spent $2.2 million to run a 30-second spot during Super Bowl XXXIV.
A month later, the board told me to cut my marketing budget in half. (So, our strategic goal flipped overnight from lighting our money on fire to slowing our burn rate.)
Yet, even with that backdrop, the confluence of challenges CMOs have faced in the last twelve months is unprecedented.
Let’s analyze why this current period has been particularly grueling and evaluate some critical data, market trends, strategic insights, fresh examples, and tactical advice for navigating these unusually rough seas.
A Perfect Storm Of Challenges
We are witnessing a surprising mix of factors:
Changing Consumer Behavior
The COVID-19 pandemic permanently reshaped consumer behaviors and preferences.
CMOs have had to rapidly adapt to increased demand for digital engagement, personalized experiences, and a heightened focus on sustainability.
Understanding and responding to these evolving expectations is paramount for maintaining brand loyalty.
Increased Competition
The digital marketing environment is more turbulent than ever, with brands fiercely competing for consumer attention across numerous channels.
CMOs are tasked with differentiating their brands in a saturated market, which necessitates innovative strategies and truly creative campaigns to stand out.
Rapid Technological Advancements
The pace of technological change continues to accelerate, with new tools and platforms emerging at a dizzying rate.
CMOs are not only expected to stay on top of these developments but also to seamlessly integrate advanced technologies like artificial intelligence (AI), machine learning (ML), and data analytics into their strategies, all while ensuring their teams are proficient in using them.
Economic Uncertainty
Global economic fluctuations, marked by inflation and supply chain disruptions, have forced CMOs to operate with tighter budgets and contend with shifting consumer spending habits.
This volatility makes forecasting marketing return on investment (ROI) and allocating resources effectively incredibly difficult.
Establishing clear metrics and accountability for marketing performance is essential, yet it remains challenging in such a rapidly changing environment.
Navigating A Perfect Storm
This powerful combination of negative circumstances leads to a significantly worse outcome than if those circumstances had occurred separately. This explains why the role of the CMO has never been more complex, nor more critical.
But, how does a CMO successfully navigate a perfect storm?
In this maelstrom, Google is often seen as both a catalyst for these challenges and a beacon for solutions. So, CMOs may turn to “Think with Google,” which was recently updated to provide the equivalent of a nautical chart of “marketing in the AI era.”
The redesigned Think with Google has organized its content into five critical categories: Consumer Insights, Search & Video, AI Excellence, Future of Marketing, and Measurement.
These can provide a strategic framework for CMOs to not only weather the current turbulence but to emerge stronger, more agile, and more effective.
1. Consumer Insights: Marketing To The Predictably Unpredictable Customer
In an age of endless choice and constant connectivity, the consumer journey is anything but linear.
Understanding the “predictably unpredictable” customer is paramount. This means moving beyond demographic segmentation to truly grasp intent, context, and micro-moments.
Critical Data: New research indicates video plays a vital role in the shopping journey, especially on YouTube, where consumers seek in-depth information and trusted creator recommendations.
YouTube influences various shopping behaviors, from “rookie” to “quest for the best,” and can shorten the purchasing journey.
Shoppers turn to YouTube for product reviews and information more than other social platforms, leading to increased purchase confidence.
Market Trends: Social media drives brand awareness, but trusted recommendations boost conversions. According to a recent Traackr survey, YouTube is a top platform for product reviews.
Shoppers are increasingly relying on content from creators and honest product reviews to make their buying choices, which has, on average, cut six days off their purchasing journey, according to a Google/Material survey.
Strategic Insight: The modern consumer expects hyper-personalization without sacrificing privacy.
CMOs must build deep empathy for their audience, anticipating needs before they are explicitly stated and delivering value at every touchpoint. This requires a shift from broad-stroke campaigns to highly individualized experiences.
Fresh Example: Sephora expanded its holiday social media campaigns by collaborating with seven creators on a Shorts-only Demand Gen campaign that featured timely gift guides.
This strategy significantly increased traffic to Sephora.com, leading to an 82% rise in “Sephora holiday” searches and top-tier brand awareness.
Tactical Advice:
Invest in First-Party Data Strategies: As third-party cookies deprecate, building robust first-party data collection mechanisms becomes non-negotiable. This includes loyalty programs, direct customer interactions, and consent-driven data capture.
Map the Non-Linear Journey: Utilize analytics to understand the actual paths customers take, identifying key decision points and moments of influence, rather than relying on outdated funnel models.
Embrace Empathy-Driven Content: Create content that directly addresses customer pain points, aspirations, and questions, rather than simply pushing products.
Conduct Market and Audience Research: Both are crucial for understanding a business’s potential and success, but they differ in scope and focus. Market research explores the overall market landscape, while audience research delves into the specific characteristics and behaviors of a target group.
2. Search & Video: Meeting Customers Where They’re Searching, Streaming, Scrolling, And Shopping
Search and video are no longer distinct channels but intertwined ecosystems where consumers search, stream, scroll, and shop.
So, you must “influence audiences in all the places they go to consume content about your topic,” as Rand Fishkin says.
Critical Data: New research from Boston Consulting Group (BCG) indicates that four key consumer behaviors (streaming, scrolling, searching, and shopping) have fundamentally changed how consumers find and interact with brands.
For CMOs, it is crucial to understand each of these “4S behaviors” and adjust their marketing strategies accordingly to effectively reach, connect with, and ultimately sell to their target audiences.
Market Trends: The increasing prevalence of the “4S behaviors” creates an opportunity and a threat for CMOs.
While these behaviors make the consumer’s path to purchase more unpredictable and difficult to track, they also open new doors for brands to connect with, influence, and convert potential customers.
Strategic Insight:Visibility and discoverability are paramount. CMOs must ensure their brands are present and compelling across all forms of search and video consumption, anticipating evolving user behaviors, including voice and visual queries.
Fresh Example:Rare Beauty, founded by Selena Gomez, used AI-powered advertising to connect with Gen Z and drive business growth.
It leveraged Google AI with YouTube and Search strategies to deliver relevant messages, leading to a 7X return on ad spend as well as increased traffic and conversions through their own site and Sephora.com.
Tactical Advice:
Optimize for Generative AI in Search: Understand how AI-powered summaries and answers will impact organic visibility. Focus on providing comprehensive, authoritative content that AI models can readily synthesize.
Adopt “Search Everywhere Optimization”: Optimize content not just for text-based queries but also for voice search (conversational language, long-tail keywords) and visual search (high-quality images, structured data).
Master YouTube SEO and Strategy: As I outlined before, YouTube is a powerhouse. Focus on strong titles, descriptions, tags, and compelling thumbnails. Prioritize audience retention and engagement signals.
Embrace Shoppable Video: Integrate ecommerce directly into video content, allowing seamless transitions from viewing to purchasing.
3. AI Excellence: Transform Your Marketing With AI And Boost ROI
Artificial intelligence is no longer a futuristic concept; it is a present-day imperative for marketing transformation.
From automating routine tasks to powering hyper-personalization and predictive analytics, AI is reshaping every facet of the marketing function.
Critical Data: A recent report on AI in the Workplace by McKinsey Digital found:
“Almost all companies invest in AI, but just 1 percent believe they are at maturity. Our research finds the biggest barrier to scaling is not employees – who are ready – but leaders, who are not steering fast enough.”
Market Trends: The democratization of generative AI tools is making sophisticated AI capabilities accessible to more marketers. The focus is shifting from simply using AI to mastering AI for strategic advantage.
As I suggested previously, AI should be integrated into a continuous improvement loop, where insights from AI inform strategy, leading to better execution and further data collection.
Strategic Insight: CMOs must view AI not as a replacement for human creativity but as an indispensable co-pilot.
The strategic adoption of AI can unlock unprecedented efficiencies, enhance decision-making, and significantly boost return on investment.
Fresh Example: Jill Cress, H&R Block’s CMO, has increased AI-powered marketing tool usage by 24% by focusing on empathy and education.
Her strategy aligns AI with brand values like expertise and empathy, leading to innovations like AI Tax Assist and localized marketing efforts. This human-centered approach provides a model for AI leadership.
Tactical Advice:
Automate Mundane Tasks: Use AI for tasks like ad copy generation, email subject line optimization, social media scheduling, and basic content creation to free up human marketers for strategic work.
Personalization at Scale: Deploy AI-powered tools for dynamic content delivery, personalized product recommendations, and adaptive website experiences based on real-time user behavior.
Predictive Analytics for Campaign Optimization: Leverage AI to forecast campaign performance, identify optimal audience segments, and predict customer churn, allowing for proactive adjustments.
4. Future Of Marketing: Lead The Charge With The Latest Innovations And Ideas
This section of the overhauled Think with Google resource for marketers, advertisers, and creatives provides the least helpful content to CMOs in an unexpected mix of events.
But in a crisis, advice for how to grow your career in marketing to become a CMO is the first thing that current CMOs will toss overboard to lighten the ship.
In a crisis, time can seem to speed up. So, the perception of the “Future of Marketing” alters from 4.3 years (which is the average tenure of CMOs, according to Spencer Stuart) to 4.3 months, which is when CMOs who don’t successfully navigate economic uncertainty are likely to exit their roles.
Unfortunately for them, the most recent article from Think with Google that addresses economic uncertainty was published in 2022.
This article analyzed how economic uncertainty impacts consumer behavior and spending intentions. It also discussed how businesses need to build trust with customers in an uncertain market.
Two days later, OpenAI released ChatGPT on Nov. 30, 2022.
In November 2023, when Think with Google in Europe, Middle East & Africa published their predictions for 2024, the focus shifted to “growth” – even though economic uncertainty was predicted to continue.
Since then, the topic of economic uncertainty has only popped up in a Think with Google UK article in 2025. But it appears that Think with Google is avoiding this topic in the U.S.
But, the best source is proprietary market research, which enables a CMO to understand changing customer needs, identify new opportunities, and make informed decisions, helping them adapt and thrive in a challenging market.
In the U.S., eMarketer offers a comprehensive suite of resources, including advertising and marketing research as well as a toolkit on “Navigating Uncertainty in 2025.”
In the U.K., the IPA Bellwether Report has found marketing budgets often decrease during economic downturns, like the 2008 financial crash and the 2020 COVID-19 lockdown, showing that the willingness of British businesses to invest in their brands is closely tied to the economic climate.
Strategic Insight:Agility and a willingness to experiment are the hallmarks of future-ready marketing leaders. This involves fostering a culture of continuous learning and embracing technologies that redefine customer engagement.
“We constantly live in uncertain times. Periods of tranquility are actually an aberration, if not an illusion.”
He adds:
“Rougher waters don’t sink all boats.”
Although his examples are from the Great Recession of 2008 and the COVID-10 pandemic of 2020, they offer “four strategic approaches for the uncertainty-conscious marketer.”
Build Agile Marketing Teams: Structure teams to be cross-functional and adaptable, capable of rapid iteration and quick pivots in response to market shifts.
Assemble All Hands on Deck: According to Spencer Stuart, 16% of Fortune 500 marketing leaders have marketing plus another function in their title (such as chief marketing and communications officer). If this function does not report to the CMO or SVP of marketing yet, then include Communications in all-hands meetings to ensure everyone is working towards a shared purpose.
Invest in Continuous Learning: Encourage teams to stay abreast of the latest technological advancements and marketing methodologies.
5. Measurement: Build Business Advantage With Your Data
In an increasingly data-rich environment, the ability to effectively measure marketing performance and translate data into actionable insights is the ultimate competitive advantage.
Without robust measurement, CMOs are just using dead reckoning.
Critical Data: Earlier this year, I asked, Where are the missing data holes? Back then, 67.9% of users of the Google Merchandise Store over the previous 28 days had arrived from the direct channel, according to the GA4 demo account.
Today, 77.6% of users are arriving “direct,” which means GA4 cannot determine the specific referral source of more than three out of four visitors.
Screenshot by author from GA4, July 2025
Market Trends: This month, I asked, why CMOs need to rethink attribution. I also said they should conduct brand lift studies and audience research to successfully navigate the reduced visibility that is a significant consequence of a perfect storm.
Strategic Insight: CMOs should read Avinash Kaushik’s article in The Marketing < > Analytics Intersect Newsletter. He advises shifting from activity-based marketing metrics to profit-driven outcomes like “Profit On Investment” (POI).
This innovative approach protects CMOs and secures budgets by demonstrating true business value. Kaushik also recommends cutting underperforming campaigns and retraining teams to achieve positive POI, stressing the importance of profitability even with AI Search.
Fresh Example: Lululemon used an AI-powered playbook to boost its performance marketing. This involved restructuring shopping campaigns, building a new customer acquisition engine, and strengthening measurement foundations.
The strategy led to reduced customer acquisition costs, increased new customer revenue, and an 8% boost in return on ad spend (ROAS).
Tactical Advice:
Implement Robust Attribution Models: Move beyond last-click attribution to multi-touch attribution models that give credit to all touchpoints in the customer journey, providing a more accurate picture of ROI.
Data Governance and Quality: Establish clear processes for data collection, cleaning, and storage to ensure accuracy and compliance with privacy regulations.
Integrate Data Silos: Break down departmental silos to create a unified view of customer interactions across marketing, sales, and service. This often involves Customer Data Platforms (CDPs) or robust data warehousing solutions.
Focus on Business Outcomes, Not Just Marketing Metrics: Connect marketing efforts directly to revenue, customer lifetime value, and market share, demonstrating clear business impact to the C-suite.
Conclusion: Thriving In The New Marketing Era
The digital marketing environment is indeed a perfect storm, but it is also brimming with unprecedented opportunities for those CMOs willing to adapt, innovate, and lead.
The redesigned Think with Google offers a framework to circumnavigate these challenges, even if the “Future of Marketing” team needs to recalibrate their time horizon, revise their editorial calendar, and refresh their helpful content on the topic of economic uncertainty.
By deeply understanding the predictably unpredictable customer, mastering the dynamic search and video ecosystem, embracing AI as a strategic partner, proactively exploring the future of marketing, and building a robust, data-driven measurement infrastructure, CMOs can transform their marketing organizations.
The future belongs to the agile, the data-informed, and the customer-obsessed.
By focusing on these strategic categories, CMOs can not only weather the storm but steer their brands towards unprecedented growth and sustained competitive advantage.
