Every year, Search Engine Land is delighted to celebrate the best of search marketing by rewarding the agencies, in-house teams, and individuals worldwide for delivering exceptional results.
Today, I’m excited to announce all 18 winners of the 11th annual Search Engine Land Awards.
The 2025 Search Engine Land Awards winners
Best Use Of AI Technology In Search Marketing
15x ROAS with AI: How CAMP Digital Redefined Paid Search for Home Services
ATRA & Jason Stone Injury Lawyers – Leveraging CRM Data to Scale Case Volume
Best Commerce Search Marketing Initiative – PPC
Adwise & Azerty – 126% uplift in profit from paid advertising & 1 percent point net margin business uplift by advanced cross-channel bucketing
Best Local Search Marketing Initiative – PPC
How We Crushed Belron’s Lead Target by 238% With an AI-Powered Local Strategy (Adviso)
Best B2B Search Marketing Initiative – PPC
Blackbird PPC and Customer.io: Advanced Data Integration to Drive 239% Revenue Increase with 12% Greater Lead Efficiency, with MMM Future-Proofing 2025 Growth
Best Integration Of Search Into Omnichannel Marketing
How NBC used search to drive +2,573 accounts in a Full-Funnel Media Push (Adviso)
Best Overall SEO Initiative – Small Business
Digital Hitmen & Elite Tune: The Toyota Shift That Delivered 678% SEO ROI
Best Overall SEO Initiative – Enterprise
825 Million Clicks, Zero Content Edits: How Amsive Engineered MSN’s Technical SEO Turnaround
Best Commerce Search Marketing Initiative – SEO
Scaling Non-Branded SEO for Assouline to Drive +26% Organic Revenue Uplift (Block & Tam)
Best Local Search Marketing Initiative – SEO
Building an Unbeatable Foundation for Success: Using Hyperlocal SEO to Build Exceptional ROI (Digital Hitmen)
Best B2B Search Marketing Initiative – SEO
Page One, Pipeline Won: The B2B SEO Playbook That Turned 320 Visitors into $10.75M in Pipeline (LeadCoverage)
Agency Of The Year – PPC
Driving Growth Where Search Happens: Stella Rising’s Paid Search Transformation
Agency Of The Year – SEO
How Amsive Rescued MSN’s Global Visibility Through Enterprise Technical SEO at Scale
In-House Team Of The Year – SEO
How the American Cancer Society’s Lean SEO Team Drove Enterprise-Wide Consolidation and AI Search Visibility Gains for Cancer.org
Search Marketer Of The Year
Mike King, founder and CEO of iPullRank
Small Agency Of The Year – PPC
ATRA & Jason Stone Injury Lawyers – Leveraging CRM Data to Scale Case Volume
Small Agency Of The Year – SEO
From Zero to Top of the Leaderboard: Bloom Digital Drives Big Growth With Small SEO Budgets
“I’m going to SMX Next!”
Select winners of the 2025 Search Engine Land Awards will be invited to speak live at SMX Next during our two ask-me-anything-style sessions. Bring your burning SEO and PPC questions to ask this award-winning panel of search marketers!
Congrats again to all the winners. And huge thank yous to everyone who entered the 2025 Search Engine Land Awards, the finalists, and our fantastic panel of judges for this year’s awards.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/10/best-use-of-AI-in-search-marketing-search-engine-land-awards-2025-BqBNkX.webp?fit=1920%2C600&ssl=16001920http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-27 14:00:002025-10-27 14:00:00Search Engine Land Awards 2025: And the winners are…
Many PPC advertisers obsess over click-through rates, using them as a quick measure of ad performance.
But CTR alone doesn’t tell the whole story – what matters most is what happens after the click. That’s where many campaigns go wrong.
The problem with chasing high CTRs
Most advertisers think the ad with the highest CTR is often the best. It should have a high Quality Score and attract lots of clicks.
However, in most cases, lower CTR ads usually outperform higher CTR ads in terms of total conversions and revenue.
If all I cared about was CTR, then I could write an ad:
“Free money.”
“Claim your free money today.”
“No strings attached.”
That ad would get an impressive CTR for many keywords, and I’d go out of business pretty quickly, giving away free money.
When creating ads, we must consider:
Type of searchers we want to attract.
Ensure the users are qualified.
Set expectations for the landing page.
I can take my free money ad and refine it:
“Claim your free money.”
“Explore college scholarships.”
“Download your free guide.”
I’ve now:
Told searchers they can get free money for college through scholarships if they download a guide.
Narrowed down my audience to people who are willing to apply for scholarships and willing to download a guide, presumably in exchange for some information.
If you focus solely on CTR and don’t consider attracting the right audience, your advertising will suffer.
While this sentiment applies to both B2C and B2B companies, B2B companies must be exceptionally aware of how their ads appear to consumers versus business searchers.
B2B companies must pre-qualify searchers
If you are advertising for a B2B company, you’ll often notice that CTR and conversion rates have an inverse relationship. As CTR increases, conversion rates decrease.
The most common reason for this phenomenon is that consumers and businesses can search for many B2B keywords.
B2B companies must try to show that their products are for businesses, not consumers.
For instance, “safety gates”is a common search term.
The majority of people looking to buy a safety gate are consumers who want to keep pets or babies out of rooms or away from stairs.
However, safety gates and railings are important for businesses with factories, plants, or industrial sites.
These two ads are both for companies that sell safety gates. The first ad’s headlines for Uline could be for a consumer or a business.
It’s not until you look at the description that you realize this is for mezzanines and catwalks, which is something consumers don’t have in their homes.
As many searchers do not read descriptions, this ad will attract both B2B and B2C searchers.
The second ad mentions Industrial in the headline and follows that up with a mention of OSHA compliance in the description and the sitelinks.
While both ads promote similar products, the second one will achieve a better conversion rate because it speaks to a single audience.
We have a client who specializes in factory parts, and when we graph their conversion rates by Quality Score, we can see that as their Quality Score increases, their conversion rates decrease.
They will review their keywords and ads whenever they have a 5+ Quality Score on any B2B or B2C terms.
This same logic does not apply to B2B search terms.
Those terms often contain more jargon or qualifying statements when looking for B2B services and products.
B2B advertisers don’t have to use characters to weed out B2C consumers and can focus their ads only on B2B searchers.
How to balance CTR and conversion rates
As you are testing various ads to find your best pre-qualifying statements, it can be tricky to examine the metrics. Which one of these would be your best ad?
15% CTR, 3% conversion rate.
10% CT, 7% conversion rate.
5% CTR, 11% conversion rate.
When examining mixed metrics, CTR and conversion rates, we can use additional metrics to define our best ads. My favorite two are:
Conversion per impression (CPI): This is a simple formula dividing your conversion by the number of impressions (conversions/impressions).
Revenue per impression (RPI): If you have variable checkout amounts, you can instead use your revenue metrics to decide your best ads by dividing your revenue by your impressions (revenue/impressions).
You can also multiply the results by 1,000 to make the numbers easier to digest instead of working with many decimal points. So, we might write:
CPI = (conversions/impressions) x 1,000
By using impression metrics, you can find the opportunity for a given set of impressions.
CTR
Conversion rate
Impressions
Clicks
Conversions
CPI
15%
3%
5,000
750
22.5
4.5
10%
7%
4,000
400
28
7
5%
11%
4,500
225
24.75
5.5
By doing some simple math, we can see that option 2, with a 10% CTR and a 7% conversion rate, gives us the most total conversions.
How do you dissuade users who don’t fit your audience from clicking on your ads?
How do you attract your qualified audience?
Are your ads setting proper landing page expectations?
By considering each of these questions as you create ads, you can find ads that speak to the type of users you want to attract to your site.
These ads are rarely your best CTRs. These ads balance the appeal of high CTRs with pre-qualifying statements that ensure the clicks you receive have the potential to turn into your next customer.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/10/OSHA-compliance-Google-Ads-hT8Y2X.webp?fit=871%2C574&ssl=1574871http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-27 13:00:002025-10-27 13:00:00Why a lower CTR can be better for your PPC campaigns
The web’s purpose is shifting. Once a link graph – a network of pages for users and crawlers to navigate – it’s rapidly becoming a queryable knowledge graph.
For technical SEOs, that means the goal has evolved from optimizing for clicks to optimizing for visibility and even direct machine interaction.
Enter NLWeb – Microsoft’s open-source bridge to the agentic web
At the forefront of this evolution is NLWeb (Natural Language Web), an open-source project developed by Microsoft.
NLWeb simplifies the creation of natural language interfaces for any website, allowing publishers to transform existing sites into AI-powered applications where users and intelligent agents can query content conversationally – much like interacting with an AI assistant.
Developers suggest NLWeb could play a role similar to HTML in the emerging agentic web.
Its open-source, standards-based design makes it technology-agnostic, ensuring compatibility across vendors and large language models (LLMs).
This positions NLWeb as a foundational framework for long-term digital visibility.
Schema.org is your knowledge API: Why data quality is the NLWeb foundation
NLWeb proves that structured data isn’t just an SEO best practice for rich results – it’s the foundation of AI readiness.
Its architecture is designed to convert a site’s existing structured data into a semantic, actionable interface for AI systems.
In the age of NLWeb, a website is no longer just a destination. It’s a source of information that AI agents can query programmatically.
The NLWeb data pipeline
The technical requirements confirm that a high-quality schema.org implementation is the primary key to entry.
Data ingestion and format
The NLWeb toolkit begins by crawling the site and extracting the schema markup.
The schema.org JSON-LD format is the preferred and most effective input for the system.
This means the protocol consumes every detail, relationship, and property defined in your schema, from product types to organization entities.
For any data not in JSON-LD, such as RSS feeds, NLWeb is engineered to convert it into schema.org types for effective use.
Semantic storage
Once collected, this structured data is stored in a vector database. This element is critical because it moves the interaction beyond traditional keyword matching.
Vector databases represent text as mathematical vectors, allowing the AI to search based on semantic similarity and meaning.
For example, the system can understand that a query using the term “structured data” is conceptually the same as content marked up with “schema markup.”
This capacity for conceptual understanding is absolutely essential for enabling authentic conversational functionality.
Every NLWeb instance operates as an MCP server, an emerging standard for packaging and consistently exchanging data between various AI systems and agents.
MCP is currently the most promising path forward for ensuring interoperability in the highly fragmented AI ecosystem.
The ultimate test of schema quality
Since NLWeb relies entirely on crawling and extracting schema markup, the precision, completeness, and interconnectedness of your site’s content knowledge graph determine success.
The key challenge for SEO teams is addressing technical debt.
Custom, in-house solutions to manage AI ingestion are often high-cost, slow to adopt, and create systems that are difficult to scale or incompatible with future standards like MCP.
NLWeb addresses the protocol’s complexity, but it cannot fix faulty data.
If your structured data is poorly maintained, inaccurate, or missing critical entity relationships, the resulting vector database will store flawed semantic information.
This leads inevitably to suboptimal outputs, potentially resulting in inaccurate conversational responses or “hallucinations” by the AI interface.
Robust, entity-first schema optimization is no longer just a way to win a rich result; it is the fundamental barrier to entry for the agentic web.
By leveraging the structured data you already have, NLWeb allows you to unlock new value without starting from scratch, thereby future-proofing your digital strategy.
NLWeb vs. llms.txt: Protocol for action vs. static guidance
The need for AI crawlers to process web content efficiently has led to multiple proposed standards.
A comparison between NLWeb and the proposed llms.txt file illustrates a clear divergence between dynamic interaction and passive guidance.
The llms.txt file is a proposed static standard designed to improve the efficiency of AI crawlers by:
Providing a curated, prioritized list of a website’s most important content – typically formatted in markdown.
Attempting to solve the legitimate technical problems of complex, JavaScript-loaded websites and the inherent limitations of an LLM’s context window.
In sharp contrast, NLWeb is a dynamic protocol that establishes a conversational API endpoint.
Its purpose is not just to point to content, but to actively receive natural language queries, process the site’s knowledge graph, and return structured JSON responses using schema.org.
NLWeb fundamentally changes the relationship from “AI reads the site” to “AI queries the site.”
Attribute
NLWeb
llms.txt
Primary goal
Enables dynamic, conversational interaction and structured data output
Improves crawler efficiency and guides static content ingestion
Operational model
API/Protocol (active endpoint)
Static Text File (passive guidance)
Data format used
Schema.org JSON-LD
Markdown
Adoption status
Open project; connectors available for major LLMs, including Gemini, OpenAI, and Anthropic
Proposed standard; not adopted by Google, OpenAI, or other major LLMs
Strategic advantage
Unlocks existing schema investment for transactional AI uses, future-proofing content
Reduces computational cost for LLM training/crawling
The market’s preference for dynamic utility is clear. Despite addressing a real technical challenge for crawlers, llms.txt has failed to gain traction so far.
NLWeb’s functional superiority stems from its ability to enable richer, transactional AI interactions.
It allows AI agents to dynamically reason about and execute complex data queries using structured schema output.
The strategic imperative: Mandating a high-quality schema audit
While NLWeb is still an emerging open standard, its value is clear.
It maximizes the utility and discoverability of specialized content that often sits deep in archives or databases.
This value is realized through operational efficiency and stronger brand authority, rather than immediate traffic metrics.
Several organizations are already exploring how NLWeb could let users ask complex questions and receive intelligent answers that synthesize information from multiple resources – something traditional search struggles to deliver.
The ROI comes from reducing user friction and reinforcing the brand as an authoritative, queryable knowledge source.
For website owners and digital marketing professionals, the path forward is undeniable: mandate an entity-first schema audit.
Because NLWeb depends on schema markup, technical SEO teams must prioritize auditing existing JSON-LD for integrity, completeness, and interconnectedness.
Publishers should ensure their schema accurately reflects the relationships among all entities, products, services, locations, and personnel to provide the context necessary for precise semantic querying.
The transition to the agentic web is already underway, and NLWeb offers the most viable open-source path to long-term visibility and utility.
It’s a strategic necessity to ensure your organization can communicate effectively as AI agents and LLMs begin integrating conversational protocols for third-party content interaction.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/10/ChatGPT-Image-The-agentic-web-is-here-Why-NLWeb-makes-schema-your-greatest-SEO-asset-VCuOt6.png?fit=1920%2C1080&ssl=110801920http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-27 12:00:002025-10-27 12:00:00The agentic web is here: Why NLWeb makes schema your greatest SEO asset
Nearly 90% of businesses are worried about losing organic visibility as AI transforms how people find information, according to a new survey by Ann Smarty.
Why we care. The shift from search results to AI-generated answers seems to be happening faster than many expected, threatening the foundation of how companies are found online and drive sales. AI is changing the customer journey and forcing an SEO evolution.
By the numbers. Most prefer to keep the “SEO” label – with “SEO for AI” (49%) and “GEO” (41%) emerging as leading terms for this new discipline.
87.8% of businesses said they’re worried about their online findability in the AI era.
85.7% are already investing or plan to invest in AI/LLM optimization.
61.2% plan to increase their SEO budgets due to AI.
Brand over clicks. Three in four businesses (75.5%) said their top priority is brand visibility in AI-generated answers – even when there’s no link back to their site.
Just 14.3% prioritize being cited as a source (which could drive traffic).
A small group said they need both.
Top concerns. “Not being able to get my business found online” ranked as the biggest fear, followed by the total loss of organic search and loss of traffic attribution.
About the survey. Smarty surveyed 300+ in-house marketers and business owners, mostly from medium and enterprise companies, with nearly half representing ecommerce brands.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-24 17:10:572025-10-24 17:10:5790% of businesses fear losing SEO visibility as AI reshapes search
Yelp just unveiled its 2025 Fall Product Release, a sweeping AI-driven update that turns the local discovery platform into a more conversational, visual, and intelligent experience.
Driving the news: Yelp’s rollout includes over 35 new AI-powered features, headlined by:
Yelp Assistant, an upgraded chatbot that instantly answers customer questions about restaurants, shops, or attractions—citing reviews and photos.
Menu Vision, which lets users scan menus to see photos, reviews, and dish details in real time.
Yelp Host and Yelp Receptionist, AI-powered call solutions that handle reservations, collect leads, and answer questions with natural, customizable voices.
Natural language and voice search, allowing users to search conversationally (“best vegan sushi near me”) for smarter, more relevant results.
Popular Offerings, which highlights a business’s most-mentioned services, products, or experiences.
Why we care. Yelp’s new AI tools make it easier to capture and convert high-intent customers at the moment of discovery. With features like Yelp Assistant, AI-powered call handling, and natural language search, businesses can respond instantly, stay visible in smarter search results, and never miss a lead. The update turns Yelp from a review site into an always-on customer engagement platform—giving advertisers more efficient ways to connect, communicate, and close.
What’s next. Yelp plans to make its AI assistant the primary interface for discovery and transactions in 2026, merging instant answers, booking, and customer messaging into one seamless experience.
The bottom line. Yelp’s latest AI release gives brands smarter tools to engage customers in real time—turning everyday search and service interactions into instant connections.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/10/Screenshot-2025-10-21-at-19.18.04-kjKpB5.png?fit=776%2C1386&ssl=11386776http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-21 18:25:342025-10-21 18:25:34Yelp’s new tools help brands connect faster and engage customers in real time
OpenAI announced the launch of its first web browser, which they named ChatGPT Atlas. Atlas is currently available on Mac only right now and has all the features you would expect from an AI browser. But the most surprising part is that its built-in search features seem to be powered by Google and not Microsoft Bing, its early partner and one of its largest investors.
How to download Atlas. If you are on a Mac, you can download ChatGPT Atlas at chatgpt.com/atlas. From there, the web browser will download to your computer, you double click on the installer and then drag the application to your application folder.
What Atlas does. It is a web browser, first and foremost. You can go directly to web pages and browse them, but as you do that, there is ChatGPT available on the sidebar, like other AI powered web browsers. You can ask ChatGPT questions, you can have it re-write your content in Gmail and other tabs, offers personalization and memory, plus it will help you complete tasks, code and even shop using agentic features.
Search in Atlas. The interesting thing is that when you search in ChatGPT Atlas, it gives you a ChatGPT like response but also adds search vertical tabs to the top, like you have in other search engines. Like web, images, videos, news and more. Then when you go to those tabs, there is a link at the top of each set of search results to Google.
Here are screenshots:
More details. ChatGPT Atlas is launching worldwide on macOS today to Free, Plus, Pro, and Go users. Atlas is also available in beta for Business, and if enabled by their plan administrator, for Enterprise and Edu users. Experiences for Windows, iOS, and Android are coming soon.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/10/chatgpt-atlas-search-web-1-58f6Gh.png?fit=1862%2C1302&ssl=113021862http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-21 17:58:422025-10-21 17:58:42OpenAI launches a web browser – ChatGPT Atlas
Google Merchant Center is rolling out a new Issue Details Page (IDP) to help advertisers more easily diagnose and resolve account or product-level problems.
How it works:
Located under the “Needs attention” tab, the page provides a consolidated overview of current issues.
It surfaces recommended actions, business impact metrics, and sample affected products — giving merchants a clearer sense of what to fix first.
Why we care. Until now, identifying and fixing issues in Merchant Center often required navigating multiple sections and reports. The new Issue Details Page (IDP) in Google Merchant Center gives advertisers a single place to view and fix account or product issues.
It highlights the problem’s impact, recommends actions, and shows affected products, helping advertisers resolve issues faster and keep listings active. In short, it saves time, improves visibility, and helps prevent lost sales.
The big picture. The update is part of Google’s broader push to improve Merchant Center usability ahead of the holiday shopping season, when product accuracy and uptime are critical for advertisers.
The bottom line.Google’s new IDP could save advertisers time and guesswork by putting all issue diagnostics and solutions in one place.
First seen. The newly released help doc was spotted by PPC News Feed founder, Hana Kobzová
Google Ads appears to be testing an automatic assignment of New Customer Value within New Customer Acquisition (NCA) campaigns — and it’s doing so without advertisers’ explicit consent.
The change, first spotted by performance marketer Bilal Yasin, has led to unexpected reporting shifts and frustration among advertisers.
“Without any heads-up, and without it being in the change history, a new customer value has suddenly been applied to a customer,” Yasin wrote on LinkedIn. “It was set to 200 DKK… One thing is that Google has assigned a value, but another is that I can’t remove it again!”
Why we care. Advertisers rely on New Customer Value settings to determine how campaigns optimize toward acquiring new users. When Google sets those values automatically, it can distort revenue reporting and campaign efficiency metrics.
Yasin noted several issues:
Google doesn’t know the true lifetime value of a new customer.
Many conversions are still classified as “unknown,” further clouding data.
What they’re saying. Google Ads Liaison Ginny Marvin confirmed the behavior is part of an experiment.
“This guidance is part of an experiment aimed at helping advertisers use settings that will improve results—specifically, to increase new customer ratios,” Marvin wrote.
She added that when the New Customer Value is too low—or not set—it can hinder campaign optimization.
What’s next. Google says it plans to roll out new customer reporting for all purchase conversion campaigns “in the next couple of quarters.”
The bottom line. While Google frames the test as a way to improve campaign performance, advertisers are raising alarms over transparency — especially when automatic value assignments alter reported revenue without clear notice or control.
It’s true that YouTube Ads perform very well for ecommerce advertising aimed at consumers. But YouTube can also help drive B2B leads.
You might be scratching your head and saying, “But I’ve tried YouTube for B2B. It doesn’t convert.” And you would be right.
YouTube Ads for B2B rarely convert directly into leads. Complex products with long sales cycles are not going to sell themselves in one video.
But YouTube campaigns definitely have a positive influence on B2B lead generation – we’ve seen it across nearly all of our B2B clients.
Here are two case studies, featuring very different advertisers, that show how YouTube Ads can be used to increase B2B conversions.
Case study 1: Enterprise B2B SaaS advertiser
One of our enterprise B2B SaaS clients offers multiple business solutions.
Paid search is a strong lead source for most of them, but two struggled to convert – traffic was steady, yet the cost per lead was high.
When we dug in, we found that users weren’t aware of these solutions or how they addressed specific business needs. The landing page content wasn’t persuasive enough.
We tested YouTube video campaigns that clearly explained each solution’s value. The impact was undeniable.
Comparing search performance from the quarter before video to the quarter during, we saw key metrics – CTR, CPC, cost per lead, and conversion rate – all improve.
Here, CTR improved significantly with the video live, which indicates that users had a better understanding of the solution after seeing the video.
This led to a lower CPC, which, combined with a slightly improved conversion rate, lowered cost per lead by 30%.
With the second solution, the results were even more dramatic.
For this solution, front-end metrics actually got worse: CTR declined, and CPC increased.
Search competition in this space was stiffer during the “after” period, which pushed CPCs up.
However, the campaigns still saw a 25% decrease in cost per lead, and conversion rates more than doubled.
In this instance, the video campaigns really helped explain how the solution can benefit users, which directly translated into better conversion rates from search.
For the first five months of 2025, this advertiser ran a small YouTube video campaign intended to drive consideration.
We had hoped the video would directly drive a few leads, and ran it on a Maximize Conversions bid strategy, but it never generated a single lead.
At the same time, CPLs across the entire account were rising, so in early June, we decided to pause YouTube and use the budget on campaigns that were directly driving leads.
That turned out to be a mistake.
CPLs on brand search campaigns rose by 47% when we stopped video.
This is a business without much seasonality, and brand is usually less impacted by seasonality anyway, so at first, we were puzzled. Then we decided to relaunch video.
Voila! Brand search CPLs returned to their previous levels.
We suspected the video campaigns were contributing to the success of the brand campaigns, so we decided to try adding a Demand Gen campaign to the mix.
Brand CPLs decreased by 47%.
Not only were we able to return brand search CPLs to their original levels, but we were also able to cut them nearly in half when combined with YouTube and Demand Gen campaigns.
During the whole nine-month period, YouTube and Demand Gen campaigns only generated two conversions directly. However, the positive impact on brand search performance was indisputable.
It’s important to stress here that we made other optimizations during the test periods for both clients, so the improvements in search are probably not 100% attributable to the addition of the video campaigns.
However, in the case of the enterprise client, the improvements for the solutions that ran video outpaced performance across the rest of the account.
And the fact that two very different advertisers saw correlated improvements in search performance lends further credence to the theory that video played an important role.
Even though these two cases involved very different clients, here are the key practices that made both video campaigns successful:
Use custom segments made up of high-performing search keywords. Don’t use broad targeting or in-market audiences unless you have a very large awareness budget.
If you have first-party audiences and want to run Demand Gen, use them for a lookalike audience. Otherwise, custom segments of strong search keywords work best.
Make your geo-targeting spot-on. Don’t waste spend on irrelevant regions. For the local B2B client, we carefully selected areas of the city that best met their needs. For the enterprise client, even though they wanted to reach a global audience, we took care with which countries we targeted.
Use short videos – no more than 15-30 seconds – and include your brand name and logo in the first few seconds.
Choose a Target CPV bid strategy. We were able to get CPV below $0.01, which got our message in front of as many users in the target audience as possible.
The more videos, the better. If you have 3, 4, 5, or more videos, use them. Even slight variations help minimize video fatigue and grab attention.
You don’t need huge budgets for this to work – in both cases, we spent less than 5% of the client’s total budget on video.
With the right targeting, you can keep costs very reasonable – and the campaigns pay for themselves in lower CPLs in search.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/10/Enterprise-B2B-SaaS-advertiser-Solution-1-yzX8m1.jpg?fit=375%2C131&ssl=1131375http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-21 13:00:002025-10-21 13:00:00How to use YouTube Ads to drive B2B conversions
Marketing mix modeling (MMM) is having a moment in marketing measurement.
As privacy regulations limit user-level tracking, marketers are turning to it for reliable, cross-channel measurement. (We love it at my agency – MMM analyses often lead to smarter budget allocation with significant downstream impact.)
But as adoption grows, so do execution errors and misconceptions about what MMM can and can’t do.
Despite its strategic potential, it’s often misused, misinterpreted, or oversold – leading to costly mistakes and credibility loss from unrealistic expectations.
MMM isn’t a black box. To produce meaningful insights, it demands context, strategy, iteration, and strong data.
Context is especially critical. Without it, MMM becomes what I call a mathematical echo chamber – no external inputs and little connection to reality.
This article breaks down how to approach MMM correctly, avoid common pitfalls, and turn your analysis into real business value.
Execution errors
Too often, teams fixate on the modeling technique and overlook the broader system – data quality, assumptions, and stakeholder context.
There are plenty of possible mistakes, but the ones I see most often are:
Using inconsistent, incomplete, or unvalidated spend and performance data.
Assuming immediate or linear responses to media spend, which oversimplifies reality.
Interpreting statistical relationships as proof of impact without experimentation.
Using MMM for daily campaign decisions despite its strategic design and lagging granularity.
Building models that are over-optimized in-sample but fail in the real world.
If you make any of these, your MMM efforts will be muddled and ineffective, and you will not get much buy-in for the initiative going forward.
Faulty expectations vs. reality
When run properly, MMM can offer highly valuable insights, but only within its appropriate use case.
With good modeling and inputs, you can:
Reallocate budgets based on marginal ROI and saturation.
Forecast sales impact from various budget scenarios.
Set spending caps to avoid diminishing returns.
Show long-term contributions of brand versus performance channels.
Track media effectiveness over time and support cross-functional alignment.
What you cannot expect MMM to do:
Optimize daily media buying decisions.
Attribute at the user or creative level.
Replace lift tests or experimentation (which are a necessary complement to MMM).
In other words, treat MMM as a strategic GPS that needs other inputs to work well, not a tactical turn-by-turn navigation tool.
Misreadings of output
You can give three marketers the same MMM output, and they might have three very different interpretations of what it means and what to do next.
We’ve got a handy chart of the ways people misread the data (and how to fix those mistakes):
The misinterpretation I’d like to spend a bit of time on here is the correlation/causation dynamic.
Marketers need to understand that MMM is essentially a fancy correlation analysis that needs to be supplemented by incrementality testing, such as geo lift testing, to establish causation.
MMM does involve coding, but it’s a lot more than that.
It’s a cross-functional discipline involving data science, marketing, finance, and strategy.
To get it right, you need:
1. Clean, longitudinal data
One note before I dive into the data elements you need to run MMM: data density is critical.
For businesses without a huge pool of revenue-generating events (think of big SaaS platforms or car dealerships advertising online), use strategic proxy metrics that happen earlier in the purchase journey and provide strong predictors of revenue generation.
With that in mind, here’s the data needed (or recommended) for your model:
Weekly data across 2–3 years.
Media spend by channel and campaign. (Region is recommended.)
Control variables (all recommended): Promos, pricing, and competitors.
Note: seasonality is baked into the model for Meta’s Robyn, one of my favorite MMM options.
2. Advanced modeling techniques
Adstock/lag functions to reflect delayed impact.
Saturation models (e.g., Hill curves) for diminishing returns.
Regularization or Bayesian priors to stabilize estimates.
3. Validation and iteration
Running an MMM analysis once and taking the results at face value is never going to get you the best possible insights.
If you’re serious about adopting MMM, prepare to include the following in your process:
I highly recommend running analyses more than once and using different methods/platforms to identify commonalities and differences.
In the visual comparing Robyn and Meridian’s output from a recent client analysis, both models attributed similar influence across most channels – a good sign that helps validate the model.
But there’s a wrinkle: for channel 0, Meridian showed much higher organic influence and a slight bump in paid.
That suggests we need additional testing before moving to action items.
4. Stakeholder engagement
Even with top-tier MMM analyses, how you communicate the findings – and what they enable – is critical to getting buy-in from clients or management.
Before you start, align with stakeholders on KPIs, ROI definitions, and model assumptions to prevent surprises or misunderstandings later.
When you share results, include uncertainty ranges and clear action items that flow directly from your data.
If you can’t answer the inevitable “So what?” question, you’re not ready to present your findings.
Better MMM becomes a competitive edge
Overall, the shift away from user-based tracking is healthy for the marketing industry.
Initiatives like incrementality testing and MMM are finally getting their due as core parts of campaign analysis.
As major platforms level the optimization playing field with automation, running these analyses more effectively than your competitors is one way to drive differentiated growth.
