I’ve noticed that when I rely too heavily on micro-conversions, my PPC campaigns don’t quite perform as expected. This often leads to distorted CPA and ROAS figures. Here’s how I’m learning to refine my approach to micro-conversions and align my strategies with real revenue.
AI-powered ad bidding systems are remarkably advanced, yet I find myself grappling with conversion tracking that isn’t as evolved. While ad platforms nudge me to keep track of multiple actions, I’ve heard from experts that it’s actually more beneficial to zero in on final outcomes.
From my experience, neither approach is entirely foolproof. Both over-signaling and under-signaling can impact PPC campaigns negatively. Too many vague micro-conversions can introduce noise, steering the bidding process toward less valuable actions, hampering the actual results. Conversely, with too few signals, the system lacks sufficient data for learning.
This issue becomes particularly apparent in my work with Performance Max and similar setups. The optimization here leans heavily on whatever signals I provide, irrespective of their true business value.
I started reflecting on how micro-conversions can overshadow real conversions, leading me to explore why these bidding systems operate this way and how to create a conversion framework that better aligns signal volume with actual business impact.
The Myth of a ‘Data-Hungry’ PPC Algorithm
I had always believed that algorithms thrive on data, a notion reinforced by platform guides and numerous PPC articles. They often imply that more signals inherently equate to better learning.
Yet, I’ve realized that while bidding systems need a certain signal density, they don’t necessarily gain from indiscriminate micro-conversion logging. More data doesn’t equate to better data.
When I add low-intent or weakly related actions, performance can degrade. The system might start optimizing for actions not aligned with real revenue.
It’s clear to me that these machine-learning systems assess frequency, consistency, and predictability without discerning the strategic relevance of a signal.
My account often contains a blend of meaningful actions like purchases and others less significant, like pageviews. Without a value hierarchy, the algorithm treats all signals as viable targets, leaning toward easy, frequent actions that offer little business value.
As I adjust my approach, I’m finding the need to streamline my focus. By applying disciplined strategies and value-based bidding, I can align my signal structures more effectively with my business outcomes.
I recently delved into Google Search Console’s branded query filter, which has become a game-changer for SEO reporting. This feature now allows me to track brand awareness, diagnose performance drops, and truly measure the impact of my SEO efforts.
In November 2025, Google introduced a solution to a long-standing SEO challenge: the ability to distinguish branded from non-branded search performance directly within Google Search Console (GSC). The rollout is now complete for eligible properties, and I was ecstatic to try it out.
For so long, I’ve had to rely on regex filters, custom dashboards, or third-party tools, which weren’t always reliable. But GSC’s branded query filter simplifies the process, positioning it as a native feature in a platform widely used for organic reporting.
This change makes it easier for me to close a crucial gap in SEO reporting. Now, I can independently evaluate brand demand and discovery, leading to improved performance analysis supported by first-party data.
In essence, GSC’s new filter performs its function by sorting queries into two categories:
Branded queries that include recognized brand terms.
Non-branded queries covering all other discovery queries.
These features empower me to group queries by topic or intent, filter by branded and non-branded types, and create detailed reports without external processing.
Historically, separating branded from non-branded performance wasn’t new but maintaining consistency was challenging. I used to manually segment with regex, keyword tagging in rank-tracking tools, or through custom dashboards.
These methods worked but were fragile. Common issues included character limits on regex, language variants for international sites, and no shared standard for branded terms. With GSC’s update, I find these challenges largely eliminated.
Branded traffic is crucial, being both a signal of brand awareness and a major source of conversions. However, when mixed with non-branded data, it skews the interpretation of SEO performance.
By segmenting this data, I can now accurately identify brand demand versus discovery, allowing clearer insights. This helps me to better understand what’s genuinely boosting performance and address key questions like:
Are we enhancing brand demand or expanding non-branded reach?
Is our content strategy bolstering non-branded visibility?
Is the current strategy effective as anticipated?
Having used the filter, branded search trends have become one of the clearest indicators of brand health. Monitoring these trends reveals gaps and provides opportunities across various channels.
This functionality isn’t just a feature; it signifies a paradigm shift in SEO measurement. The consistency it brings to branded versus non-branded reporting is transforming how SEO work gets done, making reporting more consistent and actionable.
As I continue to evaluate and use these insights, I find that adopting this feature means less time spent reconciling data and more focus on interpreting results. This results in more confident and consistent communication, ultimately driving greater impact.
I’m excited to share with you the newest feature in Profound: Custom Dashboards! This innovative tool lets me create personalized, fully configurable, and shareable views of my data, all tailored to fit my unique needs.
Having the ability to build these dashboards transforms how I interact with my data. With just a few clicks, I can design views that help me better understand and analyze crucial insights. Whether for personal use or sharing with a team, these dashboards are an invaluable addition to my data toolkit.
The convenience and flexibility of Custom Dashboards have genuinely enhanced my workflow. Now, I can focus on making data-driven decisions with confidence, knowing that my data is presented precisely the way I need it. Join me in exploring this exciting feature, and let’s make the most of our data together.
Have you ever wondered if your Google Ads attribution window is truly representing how your customers purchase? That’s a question I faced when working with one of my clients, a direct-to-consumer (DTC) retailer in a fiercely competitive industry.
At first, we used the default 30-day click attribution window in Google Ads. But as I discovered, my client’s customers typically converted within 2.2 days. This discrepancy meant that many conversions were mistakenly credited long after the initial interaction.
I realized that to capture the genuine impact of our advertising efforts, particularly the impulse-buying behavior, we needed a shorter attribution window. So, in January, we transitioned the account from a 30-day to a 7-day click window. Here’s what we found.
Our main focus was on Meta Ads, the primary recipient of the marketing budget. With both Meta and Google Ads reporting high sales due to the initial 30-day window, it was challenging to assess where advertising dollars were best spent.
Before making any changes, I delved into the conversion path data, which revealed that customers converted on average in just 2.2 days. A sizable portion of these conversions occurred within a single day.
Rather than abruptly altering our primary conversion action, we decided to carefully test by setting up a new 7-day conversion as a secondary action. This cautious approach helped us monitor any disruptions.
The process went as follows:
Step 1: We duplicated the primary purchase conversion, setting a 7-day click window as a secondary conversion action.
Step 2: We monitored performance over two weeks.
Step 3: We transitioned to primary optimization on January 12, 2026.
Let’s see what happened after we made this change. By comparing data 30 days post-switch to a previous period, we observed changes and improvements.
Results:
Spend decreased by 6.3%.
Conversions rose by 42.9%.
Conversion value increased by 52.1%.
ROAS jumped by 62.3%.
The signs were promising, but I still wanted to check the actual business impact. Examining Shopify sales data, I found a 20% increase in total sales and a 30% increase in net profit.
Our Marketing Mix Modeling (MMM) data revealed:
Google’s incremental ROAS improved by 10% to 1.82.
Meta’s incremental ROAS fell by 25% to 0.59.
Clearly, the 7-day window gave us better clarity on channel contribution. But I must admit, we were also refining campaigns, which contributed to these outcomes. Still, performance remained stable, and transparency increased.
With Google’s window shortened, we successfully limited overlap with Meta, which had previously been capturing credits for conversions likely influenced by other channels. It’s now easier to gauge the incremental impact of our efforts.
The quicker attribution provided faster insights into campaign performance, tightening feedback loops for optimization. Here’s how we benefited:
Reduced delayed attribution.
Enhanced feedback loops for optimization.
Improved performance diagnostics.
This shift also affected Smart Bidding by providing fresher signals for bid strategies, enabling the system to respond quicker to changes like bid adjustments and budget shifts.
I found that a cleaner attribution structure built stronger confidence for campaign optimizations, helping my client make smarter investments.
Ultimately, while not a miracle solution, this adjusted approach significantly complemented other campaign enhancements, improving overall strategy.
Do consider potential trade-offs if you plan to shorten your attribution window like this. Be prepared for an initial dip in reported conversions and a recalibrating phase for smart bidding. Most importantly, ensure this approach aligns with your sales cycle.
In summary, the core objective wasn’t merely updating platform metrics. It was about improving insights and facilitating well-informed decisions. The right solution depends on the congruence between your attribution settings and actual buying behaviors.
When I hear the terms “incremental” and “incrementality” in affiliate marketing, I sometimes wonder if they truly reflect their intended meaning. Often, they don’t indicate an actual increase in sales, new customers, or revenue. Many affiliate marketers seem to focus only on the affiliate channel, overlooking the broader company impact.
I’ve learned to question whether sales would occur without an affiliate program to assess true incrementality. This helps me determine if a partner genuinely brings new customers and revenue or just diverts those already heading towards checkout.
High-intent traffic is frequently mistaken for incremental value. But just because someone is ready to make a purchase doesn’t mean this touchpoint wouldn’t exist without affiliates. For instance, a coupon site might target consumers already at checkout, simply searching for brand discounts on Google.
Closing an affiliate program today might mean touchpoints still occur without extra costs like commissions and fees. Sure, this traffic involves high intent—it’s consumers in the checkout line. Nonetheless, I might be losing money if the touchpoint provides low or no value.
Note: Not all coupon or deal sites are detrimental. Some might genuinely add value, so I always ensure to test if sales remain consistent without the program before deciding.
The more customers heading to my checkout, the more top-ranking affiliates on Google earn. They depend on intercepting my traffic, which is why they’re sometimes labeled as parasitic. This is where incrementality becomes crucial.
Do touchpoints that consistently occur without your program constitute incremental sales? It’s vital for me to define incremental sales and value clearly.
Incremental sales are those driven by partners, which wouldn’t occur without them. Incremental value arises when affiliates enhance customer value through means your company couldn’t achieve, like increasing cart size or building trust for more conversions.
As a brand, I can offer discounts without an affiliate program. Even without the program, I could submit deals to sites that rank for my brand + coupons, achieving similar sales without incurring network fees, commissions, or salary costs.
If partner-exclusive deals drive sales through unique platforms, it demonstrates incremental value. That’s something unattainable without them, making the affiliate an asset.
Here are some content types and programs adding real incremental value.
Product and brand comparisons
Product and brand comparisons represent two key areas where affiliates can drive value. The affiliate decides which brand or retailer secures the sale, influencing customer choices. For smaller brands, appearing in comparisons with major players can establish credibility and drive incremental revenue.
Affiliates who present unbiased comparisons and reviews cultivated trust, adding value and potentially broadening my customer base.
Tip: Utilizing non-affiliates for brand comparisons can be a more cost-effective strategy.
For instance, I might pay a one-time fee for an independent comparison versus ongoing affiliate commissions, potentially saving money long term.
Moreover, for a smaller brand, being included in comparative reviews can be a significant opportunity to weave into larger brand traffic and attract their customer base.
Types of partners that can offer this value include:
Review and comparison websites.
Listicle sites (SEO and PPC).
YouTubers.
Communities and forums with user-generated content and shopping guides.
When it comes to creators, both those who review and those who don’t, they possess unique content styles that can enhance incrementality.
Some creators add significant value simply through brand mentions and their trusted recommendations—whether they produce detailed reviews or provide other engaging content.
Ultimately, I’ve found that detailed data analysis and testing help me navigate what incrementality means for my business. This involves discerning between true incremental partners and those who merely capitalize on existing customer journeys.
I’ve learned that as AI-driven searches and fragmented media reshape brand discovery, the outdated “set it and forget it” mindset in marketing measurement is no longer effective.
Understanding impact isn’t just about watching dashboard data. Strategically, measurement is a dynamic feedback loop, guiding ad platform adjustments, which then yields better results and insights for my business.
Allow me to share how I construct a measurement flywheel that propels my growth efficiently.
The 4-step measurement cycle
Imagine, like me, you’re managing a Bay Area SaaS company, PowerLoop, specializing in AI-powered analytics. Heavy investments in Google Search, LinkedIn, and AI publication sponsorships are underway.
However, Google Ads boasts impressive ROAS, yet our CRM signals a critical gap: leads and opportunities aren’t directly traceable to specific campaigns, making it tricky to demonstrate marketing’s true board-level impact.
1. Platform ROAS
With Platform ROAS, I dive into platform data—be it Google Ads or Meta—powered by pixel and conversion APIs. Though beneficial for real-time optimization, platforms generally accentuate their impact.
At PowerLoop, Google Ads reports a $50 CPA, aligning well with targets, yet LinkedIn’s engagement doesn’t fully equate to conversions, raising concerns about unattributed leads.
The next phase, Back-end ROAS, leverages CRM intelligence—Salesforce, Shopify, etc.—linking ad investment to tangible database outcomes, crucial for filtering out ‘noise’ like refunds and fake leads.
In practical terms, PowerLoop reveals that many Google-signups were either incomplete or out-of-target market, prompting adjustments in targeting and campaign focus on LinkedIn.
iROAS tackles the “So what?”—unveiling the sales truly impacted by ads through mix modeling and incrementality tests, like geo-lift or holdout tests.
In practice, PowerLoop’s geo-lift experiment reveals Google Ads’ limited incremental impact compared to the potent brand awareness uplift from AI sponsorships.
Finally, Marginal ROAS guides my decision on where to allocate the next dollar, as channels reach efficiency peaks following the law of diminishing returns.
Analyzing PowerLoop’s spend, I observe that while Google’s spend plateaus, AI sponsorships yield untapped growth and potential, urging a budget reallocation.
Why the cycle never ends
In truth, marketing measurement is a continual evolution, always grappling with the ever-fluctuating landscape, be it Google strategies today or ChatGPT impacts tomorrow.
I’ve embraced this at PowerLoop, adapting to new channels with an openness knowing past success doesn’t guarantee future outcomes, especially when relying solely on platform data risks wastage.
The objective isn’t finding a fixed ideal number, but maintaining agility, using iROAS and mROAS signals to drive innovation and efficiency across campaigns and channels.
Have you ever faced the daunting question from leadership: “Why isn’t our marketing achieving more?” As a marketer, I find this question challenging but vital.
To answer this, let’s dive into a scenario with a fictional location analytics company we call Acme Area Analytics.
At Acme, our reports suggested that everything was functioning correctly. Campaigns were running, leads were coming in, and performance metrics seemed stable. Yet, our sales weren’t gaining momentum, and pinpointing the reason was difficult.
Insights were scattered across different platforms like site analytics, brand monitoring, SEO tools, CRM systems, and paid media dashboards. Each told part of the story, but none revealed the entire narrative.
This disconnection illustrates how well-meaning “data-driven decisions” can mislead. Let’s explore how we at Acme, and you, can resolve this issue.
When Data Leads You Astray
With global, multi-channel campaigns like Acme’s, the toughest times come when everything seems right, yet sales stall without any clear indication of what to change next.
Subtle signs of trouble surfaced—non-brand CPCs increased, and a competitor named Spotter Intelligence became more visible in branded searches.
As part of Acme’s marketing team, we revisited our reports with the burning question: Which tactic is faltering?
Delving into the data, we concluded: our remarketing for the API seemed weaker, conversion rates slightly dropped, and efficiency slipped. It appeared logical to reduce spending to match demand due to audience fatigue.
Yet, limiting spend without the right questioning could lead us astray. Was demand truly decreasing, or were we falling short in generating new upstream interest?
The reality became evident once we evaluated data across systems. Our industry still possessed growth potential, but our product wasn’t engaging new, interested audiences despite the potential interest indicated by site analytics and Search Console data.
We shifted our approach towards engaging awareness, emphasizing trust-building and relevance through additional campaign layers. Initial results didn’t surface immediately, but our confidence persisted due to monitoring early signs of progress.
This holistic approach taught us the importance of strategic patience and broader insight from integrated data to identify true momentum beyond dashboard narratives.
In my journey, discovering significant marketing insights hinges on understanding how various data points connect.
Removing data silos is less about proving causality and more about acting on emerging opportunities and realizing which metrics quietly indicate building demand.
The victorious teams excel in sensing and capitalizing on emerging momentum, shaping their strategy before visible metrics catch up and validate their approach.
If I hear “always be testing” one more time, I might just scream. It was excellent advice back in 2016, but in 2026, it’s more like watching your budget go up in flames.
Back then, with flexible budgets and forgiving platforms, chaotic testing methods were all the rage. Launching multiple audience tests at once or swapping several creative variables was the norm. Why not, right?
But times have changed. We’re dealing with tighter budgets, longer learning phases, and fragmented signals. Now, a poorly structured test can distort results for weeks, compounding your performance issues rapidly.
Modern experimentation has become both costly and risky. Instead of sticking with outdated practices, why not leverage agentic AI? I’m not talking about using AI as a quick fix to churn out more ad variants—that’s just burning budgets faster.
Instead, it’s time to employ agentic AI to craft smarter experimentation systems.
The Real Cost of Unstructured Testing
In the “always be testing” era, launching random tests was as common as Oprah giving away cars or Taylor Swift packing stadiums. We’d throw ideas around at the start of the week, hoping for a pleasant surprise by Friday.
These days, the costs are astronomical. Algorithms thrive on stability. Research shows that ad sets stuck in learning phases have CPAs 20-40% higher than stable ones.
Every significant change in creative, audience, or budget risks resetting this learning. Run overlapping tests that each cause resets? You’re essentially imposing a volatility tax on all your media spend.
Then there’s the issue of waste. Most A/B tests yield no significant lift. If you’re not discerning about what tests to run, you’re wasting resources to confirm that most ideas are inconsequential. Without proper guardrails, “always be testing” spirals into “always be destabilizing.”
From Random Tests to a Real Experimentation Engine
We’re shifting focus now. It’s no longer about “AI, write me 10 new headlines.” It’s about “AI, craft the most efficient next experiment within our budget, considering our risk tolerance and current learning status.”
This transition from just generating creatives to configuring a comprehensive experimentation framework is where the real advantage lies.
Here’s a seven-step guide to evolve testing from a mere habit to a strategic powerhouse.
Step 1: Set Hard Guardrails (Humans Draw the Lines)
Before integrating AI into your testing strategy, establish constraints. Without these, AI has no context. With them, it becomes a disciplined strategic ally.
Define and document five key constraints.
Budget allocation: Dedicate a fixed percentage, like 10%, exclusively for testing.
Maximum volatility: “Ensure no test increases CPA by more than 15% over five days.”
Learning phase sensitivity: Tailor reset criteria for each platform.
Leading indicators: Use early signals (CTR, engagement drops) to terminate underperforming tests before they impact significantly.
Brand risk: Define untested areas (like avoiding discount-heavy strategies in upscale markets).
Maintain these in a single document (e.g., experimentation-guardrails.md) to guide AI in ensuring test viability. Your AI agent must refer to this before suggesting any tests.
Step 2: Let AI Audit Your Experiment History
Most teams have amassed data over time but don’t utilize it effectively. Feed your last six months of test results into an AI system to analyze changes, duration, performance shifts, statistical relevance, and platform resets.
Have it spot patterns like:
Over-tested variables: Testing CTA buttons multiple times with negligible results? That’s not a useful variable.
False failures: Tests often fail due to lack of statistical significance. AI can verify statistical power and highlight inconclusive outcomes.
Volatility patterns: Your highest CPA weeks might not be market shifts or poor ads but the result of multiple simultaneous tests.
This is the essence of AI as your analytical partner.
Step 3: Write Real Hypotheses
Instead of jumping straight from concept to launch, let AI enforce hypothesis discipline.
Weak: “Let’s test a new headline.”
Strong: “Emphasizing ‘faster time-to-value’ over ‘ease of use’ could boost demo requests by 10-15% among mid-market companies, as analysis shows speed is crucial for them.”
Documenting hypotheses builds institutional knowledge. Later, when someone suggests retesting “speed messaging,” you’ll know past results and reasoning.
Step 4: Risk-Score Every Proposed Test
Budget and algorithm stability are limited. Your AI agent should evaluate proposed tests on five criteria, assigning a risk score.
Budget impact (e.g., less than 5% vs over 15%).
Algorithm disruption level (minor update vs new campaign).
Audience overlap.
Brand sensitivity.
Learning value.
High risk with low learning potential? Drop it. Low risk with high potential? Proceed.
Example: Testing a new positioning statement is risky in a paid campaign. Your AI might suggest verifying it with organic LinkedIn posts first. Low risk. High insight.
Step 5: Pre-test With Synthetic Audiences
This under-utilized AI application can simulate how varied personas might respond to messaging, saving real-world testing costs.
Research by Stanford and Google DeepMind has shown digital agents match human survey responses with 85% accuracy and mimic social behavior with 98% accuracy.
While not a replacement for actual data, synthetic audiences serve as a cost-effective early test.
Define demographic archetypes such as the Skeptical CMO, Growth-focused VP, and margin-driven CFO, and test their responses to messaging.
For example, you may find that phrases like “All-in-One” are seen negatively, prompting a shift to terms like ‘Integrated’.
Step 6: Sequence Tests, Don’t Stack Them
Tweaking audience, creative, and landing pages simultaneously teaches you nothing. Your AI should monitor campaigns to avoid conflicts and recommend proper test sequencing.
A sensible approach is to:
Weeks 1-2: Audience testing.
Weeks 3-4: Creative tests with the proven audience.
When unavoidable, establish clear control groups to maintain data integrity.
Step 7: Build A Living Knowledge Base
Treating tests as one-off experiments overlooks their value. Have AI summarize each test by assessing:
Success reasons.
The audience impacted.
Lift durability.
Variable interaction.
Over time, this database can provide unmatched advantages. Anyone can access the same audience targeting, but few have a database of 100+ customer insights.
The Bigger Shift: From Activity to Architecture
“Always be testing” may have worked in a growth-centric era, but in 2026, success comes from “always be compounding intelligence.”
Instead of maximizing tests, build a competitive edge through structured, risk-aware experiments that maintain algorithm stability and tie directly to revenue.
When asked why you’re not testing more, show your testing architecture and confidently say, “We’re building an intelligence engine, not just running experiments.”
As someone passionate about video advertising, I’ve noticed how easily videos can now be distributed across platforms like YouTube, paid social media, and connected TV. It’s an immense opportunity for exposure.
However, I often find myself questioning the real effectiveness of these videos. Campaigns sometimes show impressive metrics, but lack in tangible business impact due to strategic missteps.
The issue isn’t so much about targeting or budget; it’s about focusing more on outputs—views, impressions—rather than crucial outcomes like attention and persuasion. That’s where most video strategies falter.
Misunderstanding Attention: A Common Pitfall in Video Ads
Many video ads operate under the assumption that they’re just like TV commercials, but that’s a misunderstanding of how attention works today.
In past meetings, we’ve defined success by views and impressions, not realizing these metrics don’t always translate to engagement or conversion.
True success lies in transforming impressions into meaningful actions, and that requires a drastic shift in strategy.
I’ve learned that the opening seconds of a video ad are critical. Initially, I assumed upfront branding mattered most, but ads that opened with engagement hooks performed better.
View-through rates don’t equate to persuasion. Real impact happens before the viewer can skip the ad.
An effective hook makes all the difference, whether it’s striking visuals or compelling questions. That initial grab of attention sets the stage for success.
Scrappy Ads Often Outperform Polished Productions
It’s surprising how often simple videos outperform higher quality productions. Authenticity resonates more with audiences than polished, overtly professional content.
Audiences and algorithms favor content that feels genuine over what looks like an ad. It’s about fitting in with the platform’s native content style.
Through experience, I’ve realized that the optimal length for an ad depends on the message itself. Sometimes a longer duration with a well-crafted story outperforms shorter clips.
A well-paced narrative keeps viewers engaged, making them more receptive to the brand’s message, regardless of duration.
Understanding Metrics: Decoding Signals, Not Outcomes
The abundance of data can be misleading, with metrics often misinterpreted as outcomes. I’ve seen campaigns with high completion rates fail to drive any business impact.
The true measure of success is how video metrics correlate with real-world actions and conversions.
Aligning Briefs with Creative Outcomes
A common issue is poorly defined briefs leading to lackluster creative. Clear objectives and a deep understanding of the target audience guide more effective video strategies.
Knowing precisely who you’re speaking to and what action you desire them to take results in more intentional and impactful creative.
Creative and Distribution: An Inseparable Duo
Strategically planning how and where ads are distributed is just as crucial as content creation. I’ve witnessed great ideas fall flat due to mismatched platform contexts.
Designing ads tailored for specific platforms ensures they resonate and are effective in their intended environment.
Insight-Driven Testing: Beyond Mere Variance Generation
Effective testing focuses on key elements that engage audiences. Hypothesis-driven testing yields insights far more valuable than superficial variant testing.
Ultimately, I’m looking for tools that prove reliable in predicting real-world outcomes, enhancing creative confidence well before any campaign goes live.
Despite evolving platforms and algorithms, I’m convinced that the core elements of attention, curiosity, and trust remain constantly human.
The most successful video ads I’ve been part of focused on relevance, respecting viewers’ time, and delivering valuable content. That’s what truly captivates audiences.
Success in video advertising comes from understanding people—not just appealing to platform metrics.
As someone who’s deeply involved in the world of e-commerce, I know how crucial it is to understand whether your Shopify store’s pages are being referenced by LLMs (Language Learning Models). Up until now, that insight has remained elusive for those of us using Shopify. But everything is about to change.
Partnering with Nostra, Profound is bringing comprehensive Agent Analytics capabilities to Shopify brands for the very first time. This groundbreaking opportunity means that we can finally gain an overview of how our web presence is echoed in the digital realm, opening the doors to advanced marketing and strategy opportunities.
