If your entire Google Ads strategy consists of targeting brand and non-brand keywords, you’re limiting growth. If performance is declining, it’s not the platform — it’s the strategy.
People don’t discover you through non-brand search. They research on Reddit, ChatGPT, Facebook, LinkedIn, and YouTube. They watch demos, read testimonials, and learn about your brand long before they ever search for it.
If you have a complex sales process and a long customer journey, this shift is critical and requires a different approach. Here’s what you need to know to make this work in B2B.
AI-forward campaigns: A cost-effective growth gold mine
Google has been developing multi-channel, multi-asset campaigns for years — first with Performance Max, then with Demand Gen. These campaigns reach your audience across the web as they research and evaluate.
Your brand is front and center while your audience builds their shortlist. By the time they’re ready to pick vendors to take the next step, you’ve already built trust. Then they’ll find you by searching for your brand.
A Performance Max campaign with a variety of ad types, like image and video ads, can showcase demos or customer testimonials on YouTube. They can appear across the web via the Display Network. They can follow (retarget) your target audience as they research. That’s what drives the branded search that converts later.
These campaigns let you do all of this cost-effectively. In a Performance Max campaign, you can use keywords alongside your own customer data as signals. You’re not abandoning keywords. You’re using them smarter.
The search experience is changing. Your strategy should, too.
Google’s search results pages have evolved with AI Overviews and AI Mode. If this experience is changing so dramatically, isn’t it time to rethink your ad strategy as well?
I’m a fan of the 4S framework: search, scroll, stream, and shop.
I’d add “ask” to reflect how people now engage across AI tools. They ask ChatGPT or Gemini, search Google, scroll LinkedIn, stream YouTube videos, and shop across platforms. If your strategy only covers one or two of those behaviors, you’re missing how growth actually happens.
Focusing only on keyword targeting means you’re missing the bigger picture. Yes, brand keywords will convert better than non-brand keywords. But how do people even know to search for your brand in the first place? (The answer is that you’ve been showing up in their feed the whole time.)
This approach takes time, especially for B2B companies with long sales cycles.
It took us nearly a year to realize the value Performance Max was driving for a life science client. Most of their deals take months to close. Our account manager was about to pause the campaign at one point because the ad platform data didn’t look good.
But as we began piping in sales data, things started clicking. Once we got over the sales cycle hump and started seeing revenue data, Performance Max proved its value.
If you can sync data beyond MQLs — like Proposal Sent — that provides more data and signals to Google, and more peace of mind until you can add sales data.
Be patient, feed the system better data, and don’t give up too early. B2B sales cycles are complex.
You might have 100 people at an event that you promoted through a LinkedIn ad strategy. Some of those people caught an email promoting a webinar. Months later, they searched for you on Google and asked for a proposal. Still months later, they became a customer.
Even with the best-recorded data, you won’t see this happening right away in a long sales cycle.
If you don’t have a test-and-learn budget, reallocate 5% to 10% to introduce AI-forward campaign types. Test strategically. Don’t go all in, and don’t launch major tests during a busier time of year. Give yourself breathing room while the system learns.
This approach takes time. But it drives sustainable growth if you commit to the process. The advertisers who figure this out are building sustainable growth, while others are still stuck optimizing for a shrinking slice of demand.
A new version of the Google Ads API is out, bringing a handful of targeted updates across video, app campaigns, and audience planning tools.
Key changes in this release.
A new VideoEnhancement resource that surfaces whether a video ad is Google-generated or advertiser-provided — giving developers clearer visibility into auto-enhanced creative
A new AppTopCombinationView resource providing read-only insights into top-performing asset combinations in App campaigns
The ability to disable the hotel feed in Demand Gen campaigns via HotelSettingInfo.disable_hotel_setting
A new conversion metric for indirect first in-app installs across Campaign, Customer, and AdGroup resources
Several enhancements to ContentCreatorInsightsService and ReachPlanService
What to do. Upgrading to v23.2 requires updating both client libraries and client code — all updated libraries and code examples are already published.
Catch the walkthrough. Google is hosting a live release walkthrough on March 26 at 11am ET on Discord and YouTube Live, with a recorded version to follow for those who can’t attend.
Why we care. The VideoEnhancement gives developers the ability to programmatically identify whether a video ad is Google-generated or advertiser-provided, which has been a notable blind spot in Performance Max reporting. For agencies and teams building custom reporting tools, this is a meaningful step toward greater creative visibility.
The bottom line.A routine but useful release — the VideoEnhancement resource in particular is worth attention for any developer building tools around Performance Max creative reporting.
Refreshing creatives for every seasonal moment just got significantly faster — Google has quietly launched Asset Group Theming inside Performance Max, letting advertisers apply seasonal themes to existing asset groups without rebuilding from scratch.
How it works. Advertisers can clone a high-performing asset group and apply a theme — Google then generates themed image variations and suggests aligned headlines and descriptions, while leaving the original asset group completely untouched for safe testing.
Available themes cover.
Promotional: Sale, Studio/Editorial
Seasons: Winter, Spring, Summer, Fall
Cultural moments: Christmas, Black Friday/Cyber Monday, Halloween, Valentine’s Day, Easter, Mother’s Day, Father’s Day, Hanukkah, New Year, Lunar New Year, and Back to School
Where to find it. Look for the prompt inside Asset Groups ahead of major holidays, or via “Apply theme to existing asset group” when creating a new one.
Important caveat. This is a starting point, not a finished product. The tool uses existing images as a base and adds themed backgrounds — it does not replace videos, and typically only updates a handful of headlines to match the theme. Everything still needs to be reviewed and sense-checked before going live.
Why we care. Seasonal creative refresh has always been one of the more time-consuming parts of campaign management — requiring design resources, rebuilding asset groups, and risking performance drops on proven setups. This feature removes most of that friction, letting teams adapt their best performers to key moments in minutes rather than days.
The bottom line. Think of it as a creative assistant, not a replacement for a designer — but for advertisers managing multiple seasonal peaks across the year, the time savings alone make it worth exploring.
First spotted. This update was spotted by Google Ads specialist Bia Camargo who shared a screenshot on LinkedIn.
Aoostar has launched the MACO 470 mini PC in China. The device features the AMD Ryzen AI 9 HX470 processor and comes with 32GB of LPDDR5 RAM.
Aoostar MACO 470 Specifications
The mini PC has a compact design and uses a CNC-machined unibody aluminum chassis. The chassis has a seamless vent-free construction, which improves durability and gives the device a premium appearance. The mini PC weighs 0.66 kg and has dimensions of 13 x 15 x 5.5 cm. It also includes a fingerprint sensor with Windows Hello support, allowing secure and password-free logins.
The system is powered by the AMD Ryzen AI 9 HX470 processor, which is built on the Zen 5 architecture. This processor has 12 cores and 24 threads with boost speeds up to 5.2GHz. The device also features integrated Radeon 890M graphics, which use the RDNA 3.5 architecture with 16 compute units running at up to 3.1GHz.
The MACO 470 includes 32GB of LPDDR5 RAM operating at 8533MT/s. Aoostar has provided support for up to three M.2 PCIe 4.0 SSD slots to allow storage expansion.
The device uses a Glacier 4.0 cooling system to manage heat. This system includes a 5600mm² vapor chamber and a turbo fan with fluid dynamic bearings. The cooling setup ensures efficient heat dissipation while keeping noise levels low, even during heavy workloads.
The system also sports a well-rounded selection of ports across the chassis. The front includes two USB 3.2 Gen2 ports, one USB4 port (40Gbps), and a 3.5mm audio jack for quick access.
The rear comes with another USB4 port, two USB-A ports, HDMI 2.1, DisplayPort 2.1, and dual 2.5G Ethernet ports, ensuring stable high-speed networking and versatile display output options. It supports up to four displays simultaneously, including high-resolution output up to 8K. It also includes an OCulink port for external graphics docks.
Pricing and Availability
The MACO 470 barebones version costs 5,899 yuan ($826), the 1TB SSD variant is 7,299 yuan ($1,022), and the 2TB SSD version is 8,599 yuan (~$1,204). These are now available on JD.com.
For more daily updates, please visit our News Section.
Google is launching new Performance Max controls and reporting: audience exclusions, expanded reporting, and budget forecasting tools.
What’s new. Google announced a mix of “steering updates” and “actionable insights” for PMax:
First-party audience exclusions: You can exclude customer lists to shift spend toward net-new customer acquisition instead of repeat conversions.
Budget reporting: A new in-platform report projects end-of-month spend and shows how daily budget changes impact performance.
Full audience reporting: You get detailed breakdowns by demographics, including age and gender.
Network segmentation: You can segment placement reports by network, now under When and where ads showed.
Why we care. These updates help address concerns about PMax’s lack of control and transparency. Exclusions help you avoid wasting spend on existing customers, while improved reporting gives you clearer signals for optimization, budgeting, and brand safety decisions.
Google introduces Performance Max updates, including audience exclusions, budget projections, and expanded reporting to give advertisers more visibility and control over campaign performance.
While initially criticized as a black box, Performance Max has evolved into a fairly critical campaign type. With each passing quarter, Google has introduced more functionality and visibility.
Additional reporting is helpful, but what matters is what you can actually act on. While you can’t control everything in Performance Max, there are specific levers that can have a meaningful impact on performance. Here are the parts of PMax you can control and how to use them effectively.
Control what you can: Search terms and placements
One of the most exciting updates in the last year to Performance Max has been the ability to add these campaign-level negative keywords.
In the past, you could contact Google to add these in. It was somewhat cumbersome and involved filling out an Excel doc, forwarding it to Google, and giving them permission to implement.
With the inclusion of the search terms report, we’re now able to select a keyword and quickly add it to the campaign-level negative keyword list, just as we can with a search or shopping campaign.
Another way to optimize PMax is to review and monitor the placements report. Most recently, Google has moved the Performance Max placements report out of the reporting section of the Google Ads account and into the Where ads have shown section at the campaign level. While this makes analysis easier by removing additional steps, we still only have impression-level reporting on placements.
We can use this information to decide whether to add these placements as negative placements at the account level. This is found in Tools > Content suitability > Advanced settings > Excluded placements.
While this isn’t ideal, there’s still useful insight we can glean from this report, such as ads appearing in kids’ programming or driving a high number of impressions from mobile apps.
Also located in the When and where ads showed section is the ad schedule. Even if you hadn’t selected an ad schedule when creating the campaign, Google automatically dayparts performance hourly.
Google typically recommends an open ad schedule, but if you have a limited budget, restricting your ad schedule during off-peak or non-converting hours is an excellent way to increase efficiency.
You can do this by creating a campaign-level ad schedule within Campaigns > Audiences, keywords, and content > Ad schedule. Make sure your Performance Max campaign is selected in the top left dropdown menu.
Demographic exclusions are a relatively new feature at the campaign settings level for Performance Max. Unfortunately, reports for these campaigns are hard to obtain, limiting informed decisions on demographic exclusions.
This functionality is helpful if you’re aware of specific demographics that aren’t actively in the market for specific products or services. To make adjustments, go to Campaign-level settings > Other settings > Demographic exclusions. From here, you can turn on age or gender exclusions:
While PMax initially didn’t even provide device-level reporting, a new feature lets you opt out of serving on certain devices.
If you opt into all device targeting when launching a PMax campaign, you should periodically review device performance and adjust accordingly. This is best done by segmenting at the campaign or asset group level by device. Device-level data is extremely helpful for determining which device is better suited to reach your goal.
Likewise, if you almost always opt out of certain devices when launching a campaign, this data makes it easier to either launch with all device targeting enabled and monitor performance, or add a device you hadn’t initially added to see how it impacts performance. Device-level targeting is also available at the campaign level, under Other settings.
Improve inputs: Creative and AI assets
Ad assets play a large role in the display, YouTube, and Discover network performance of a PMax campaign. For many, there’s still a gap in producing high volumes of quality image and video creative.
While still evolving, AI assets are getting closer to filling these gaps — enabling us to more effectively target these additional networks. As newer iterations of LLMs emerge, this will become a primary way to generate video content and professional-looking images.
Google already offers generative AI image assets from shopping feed products that look relatively impressive. But we’re still a ways out from seeing high-quality AI-generated videos without the well-known glitches we typically see in this type of content.
Understand the limits of control in Performance Max
The channel controls report gave more insight into where ads were serving. I have an unpopular opinion on this report. While helpful, there’s little we can do within the campaign to improve performance. Because of this, the report is frustrating.
We’ll likely see channel controls available within Performance Max in the near future — similar to what we already have in Demand Gen campaigns. For now, adjust creative and bids to sway volume within certain networks. To opt out of certain networks completely and focus on shopping, then a feed-only Performance Max campaign will do just that.
Performance Max is evolving from a black box to a critical asset in a marketer’s toolkit. The steady stream of new functionality, from campaign-level negative keywords to detailed placement and ad schedule reports, shows Google’s commitment to providing greater control.
Use these levers — strategic exclusions, device adjustments, and budget-aware scheduling — to move beyond set-it-and-forget-it and run Performance Max campaigns with precision and efficiency.
LinkedIn Ads consistently delivers some of the highest-quality B2B leads in paid media. But it also has a reputation for being very expensive — for both cost-per-click (CPC) and cost-per-lead (CPL) metrics.
Because of that reputation, I wanted to test a theory: that I could get low CPCs and low-cost qualified leads from LinkedIn Ads by creating a highly valuable, audience-specific piece of content.
As an agency, we usually run LinkedIn Ads campaigns for our clients. We don’t really run many paid ads for ourselves. However, to have the most control over this test, I decided that Saltbox Solutions would be the guinea pig. (Disclosure: I’m the director of strategy at Saltbox Solutions, a B2B-focused PPC and SEO agency.)
The results were impressive.
We spent less than $1,000 and generated a significant volume of leads at a sub-$10 CPL. For advertisers on a shoestring budget, LinkedIn Ads may not be out of reach as previously thought. It just requires a solid strategy.
Here’s what I did, why it worked, and how you can apply the same framework to your own campaigns — regardless of your advertising budget.
The campaign setup
The goal of this campaign was to get our target audience to download our 2026 B2B Demand Gen Playbook — a hefty, 23-page guide created specifically for B2B marketing decision-makers. The timing was key because many marketing leaders were already planning for 2026 in Q4 2025.
For this LinkedIn Ads campaign, I used a document ad format + a lead generation objective. The document ad lets the audience flip through and preview the content before downloading, with four pages available to preview before requiring a download to access more.
I also used a lead gen form for contact capture, since it’s fairly frictionless — the form lives within the LinkedIn platform and autofills most of the contact information from a user’s profile. There was just one campaign for this test, with three ad copy variations for the document ad.
In terms of budget and bid strategy, the campaign used a $600 lifetime budget and a $15 manual bid.
This is what allowed for such low CPLs. Before writing a single word, I did deep audience research to figure out what they really cared about and what would be useful to them.
I knew exactly who I wanted to talk to (and who would be a good fit for the agency): B2B marketing decision-makers at larger companies with a dedicated marketing team. They worked mostly in a demand generation capacity and needed help prioritizing the channels that would make sense for their 2026 goals.
From there, the research focused on understanding what they would actually need in that planning process. It involved:
Mining client meeting notes and calls for recurring questions, common pain points, and frequent requests that kept coming up during planning season.
Using SparkToro to plug in my ideal customer profile (ICP) details and explore the questions, topics, and channels the audience was already engaging with.
Scanning LinkedIn, where I’m active and where a majority of my network is in B2B marketing, for real-time insight into what people were worried about.
Reviewing Reddit threads and B2B marketing communities I’m part of, which were super helpful for getting at the questions marketing leaders had.
The main question throughout this process was, “If I were in my audience’s shoes, what resource would actually be helpful right now?”
One big advantage I had: My audience is me. I’m a B2B marketer talking to other B2B marketers. Being plugged into the same communities and conversations made it much easier to put a personal spin on the content and write like a human.
Once I had a clear picture of what my audience needed, the focus shifted to going deep. The goal was to create a genuinely useful resource, not a thinly veiled sales pitch disguised as a playbook.
That took time to get right. But that depth is likely what drove the 76% lead form completion rate. When people could preview the document in their feed and see that it was substantive, they trusted it was worth downloading.
A few other notes on creating the playbook:
Timeliness: It was created to address a very timely and important marketing activity – annual planning. Because of that, 2026 became the focal point of the cover, and the content was framed around the moment the audience was already in.
Contextual CTAs: Calls to action to get a free audit were sprinkled into sections that dealt with PPC and SEO/GEO, which are the services we actually provide. The CTAs felt earned rather than forced because they were relevant to the surrounding content.
Cover design: A lot of effort went into how the guide looked. Knowing it would be promoted as an ad, the goal was to make it pop in the LinkedIn feed and grab the audience’s attention.
The targeting strategy
For audience targeting, I used a few different layers:
I also excluded a few attributes deliberately after viewing the audience insights:
The resulting audience was about 54,000 people. It could’ve been smaller and still delivered great results.
Job title targeting would also be worth testing. The leads were qualified as-is, but it would be interesting to see what the results would look like with more specific role targeting.
Three ad variations were used to test different copy angles. All three used the same document ad format and lead gen form. The only variable was the copy.
Here are the variations.
Version 1:
Version 2:
Version 3:
A few principles guided the ad copy process:
Each variation led with a strong hook. The first sentence had to grab attention and make people want to keep reading.
The copy ran longer than you typically see in ads to give a clearer sense of the guide’s tone and value before the click.
Common fears and questions the audience already had were addressed, such as translating high-level strategy into execution and staying visible in AI search results.
The tone leaned into a “we’ve got you” approach rather than being overhyped or promotional. B2B buyers are skeptical and respond to guidance and valuable information, not pressure.
The copy also had some personality, with a slightly cheeky edge while staying professional. For example, it called out common situations, such as having a beautiful strategy deck but never executing the plan.
Campaign and ad results
Recapping the campaign’s overall performance from Jan. 5 to Jan. 31:
One interesting note is that while the CPC bid was set at $15, the average CPC actually came in way under that at $5.41.
The average CTR was also above LinkedIn’s typical benchmark of 0.50%, and the lead form completion rate was over 75%.
LinkedIn lead gen campaigns have delivered strong results across many client engagements. But even by those standards, this performance was pretty good.
And for the specific ads, V2 was the winner by far:
The LinkedIn Ads algorithm zeroed in on that one and gave it pretty much all the airtime. It makes sense — that had the most eye-catching hook, “Steal our best demand gen ideas.”
The campaign was intentionally stopped at 60 leads. We’re a small, boutique agency, and the goal was to be thoughtful about nurturing the leads generated rather than flooding the funnel with volume that couldn’t be followed up on well.
Of the 60 leads, roughly 56 were qualified — a remarkable outcome for a prospecting campaign.
Our approach to working these leads has been organic LinkedIn engagement rather than a hard sell. No cold pitch sequences. Just showing up in their world as a familiar, credible presence.
As the person who wrote the playbook, I’m also personally reaching out to downloaders to ask for feedback on what they found useful and what they were hoping to see that wasn’t there. That insight will directly shape the next version of the guide and any future content assets created.
The campaign is still in the nurture phase. The primary goal of this test was to validate the model, not generate an immediate pipeline. On that measure, it exceeded expectations.
What made this work and what could be done differently
Looking back at the campaign as a whole, a few things stand out as the real drivers of performance:
Audience research came first. The target audience was clearly defined before anything was created. The content, the targeting, and the copy all flowed from that. As a result, it was very specific.
The content was timely. Releasing a 2026 planning guide early in the year, when everyone was back from the holidays, really worked in this campaign’s favor.
Depth built trust before the form appeared. The preview paired with substantive ad copy had a positive impact on lead form completion rate.
The copy sounded like a person, not a brand.
What could be done differently next time:
Despite the high conversion rates, adding a bit more friction to the form completion process may help. The fact that it was so easy to fill out the form means that the audience may not remember actually downloading it.
Following up with the leads faster after downloading would be a priority. The same approach of asking for feedback would still apply, rather than a sales pitch.
Running it longer and getting more leads would provide a larger data set to learn from.
Testing more ad copy variations against the winner.
How to do this yourself
Whether you’re running lead gen for a client or testing it on your own business, here are some tips to make it work:
Do your audience research before you create the asset: Reddit, SparkToro, community forums, and your own client conversations are all underutilized sources of real audience pain points, and you get pointers on the language they use.
Build something genuinely useful: If it’s a thinly veiled promotion, you’re wasting your audience’s time.
Match your content topic to a timely moment your audience is already in: What season, event, or planning cycle are they navigating right now?
Give your ad copy some personality: Test a hook that stands out, or at least is something that sounds like it was written by a real person.
Start small intentionally: Validate CPL and lead quality before scaling. A $500 test can tell you a lot.
Let the winner run: Early creative testing gives you the signal you need to spend efficiently at scale.
Align your content and your targeting precisely: If you wrote the guide for marketing decision-makers, make sure the campaign isn’t picking up sales roles.
We plan to relaunch this campaign once we’ve gathered enough feedback from the first wave of downloaders. The playbook itself is a living document. It will be updated as the industry shifts, particularly with the wave of ads in AI Overviews and responses.
This was one content asset and one campaign. More are in the works, and this test gave a lot of confidence in the approach.
The platform isn’t the problem. The strategy and offering might be what is driving up the cost.
If you’re willing to put the work into research, producing a quality asset, and getting the messaging right, LinkedIn Ads can be one of the most efficient B2B lead generation channels available.
SEAVIV has launched the AideaMini R10, a compact and affordable mini PC powered by the AMD Ryzen 5 3500U processor. The barebones version, which does not include storage, costs 879 yuan ($127).
SEAVIV AideaMini R10 Specifications
The AideaMini R10 features a quad-core, eight-thread AMD Ryzen 5 3500U processor. It runs at a base clock of 2.1GHz and can boost up to 3.7GHz. The integrated Radeon Vega 8 graphics operates at up to 1200MHz, supporting 4K video playback, casual gaming, and basic design tasks.
The system supports up to 32GB of memory via two DDR4-2400 SO-DIMM slots. It includes an M.2 2280 SSD slot compatible with SATA and PCIe 3.0 ×4 drives, as well as a 2.5-inch SATA drive bay, allowing up to 2TB of combined storage for speed and capacity.
The AideaMini R10 comes with SEAVIVClaw software for direct OpenClaw deployment. It provides features like one-click updates, AI model configuration, system diagnostics, and chat integration. SEAVIV also includes a one-click recovery feature that allows users to restore the system by pressing F9 during startup.
The mini PC measures 127.5 x 112.4 x 39.9mm and weighs 420g. It supports VESA mounting, making it easy to attach behind a monitor or place on a desk. It uses a dual heat pipe and single fan cooling system to maintain low noise levels and improve energy efficiency.
For connectivity, the mini PC includes two HDMI 2.1 ports, one USB Type-C port with DisplayPort support, multiple USB 3.2 ports, a microSD card slot, and a Gigabit Ethernet port. The system also supports Wi-Fi 6 and Bluetooth 5.2 for wireless connectivity.
In related news, Asus recently launched the ExpertCenter PN55 with Ryzen AI 400 series CPUs and up to 96GB RAM, while Mechrevo launched the iMini E300 with a Ryzen 7 7445HS processor, USB 4, and OpenClaw AI support.
For more daily updates, please visit our News Section.
Samsung Browser, Samsung’s mobile internet browser, is now available to download for Windows 11 and Windows 10 PCs with new AI and smart device features.
The launch comes after four months of beta testing; however, you may have noticed that the app is no longer named Samsung Internet. That change is already reflected on mobile devices, and it’s also appearing on the PC app.
Cross-device features
The basic feature package includes bookmarks, home page setup, history, settings, and added Samsung smart apps on the right-hand toolbar.
Besides the basics, you can continue browsing a page from mobile to PC with a cross-device experience. For example, you have been reading about a new car on your Galaxy S26 Ultra, and with one tap, you can open it on your PC to read the same info on a large display.
You can import all of your Samsung Pass passwords saved in your mobile device through the Samsung account and save new ones on your PC.
AI Integration with Perplexity
The company has officially confirmed that Samsung Browser on Windows PCs integrates Perplexity AI agents for natural language processing, finds content on pages, and processes information that would take time when done manually.
AI features
Understand Context: You can ask the browser to create a schedule for a visit to a certain place with a travel plan based on the current open page. The AI will browse the page and come up with a plan on the screen.
AI actions – Samsung Browser can also take actions on your behalf, for example, you can ask the browser what type of video you want to watch or understand the content within the video.
Browsing history – History is one important feature that has long been part of browsers, but its exploration might get tricky, especially when you are searching for that one web page that you explored somewhere a week ago or maybe longer. Samsung Browser could help you to get that page, for things like a laptop they were looking at last week.
Multi-Tab AI – This new browser can also analyze all of the opened tabs and summarize their content at once.
Where to get?
Samsung Browser is available for all PCs with Windows 11 and 10 (1809 and above) starting today. However, the AI features are limited to the U.S. and South Korea, with an expansion planned for more markets. You can download this browser from Samsung Browser’s official website.
You launch a new TikTok ad. Early metrics look great — low CPCs, high engagement, and a ROAS that makes you look like a pro. Then, a few days later, performance slips.
Ad frequency creeps up, the hook rate drops, and you’re suddenly back at the drawing board.
Some call it creative fatigue. On TikTok, it’s closer to creative exhaustion.
A TikTok ad’s “half-life” is shorter than any other platform. If you’re still treating it like a Meta ad campaign, you’ll lose.
To win, treat creative like a supply chain, not a campaign asset.
Why TikTok creative decays so quickly
On intent-based platforms like Google, Amazon, or Pinterest, people search for things. On social platforms, people look for family, friends, and other people. On TikTok, above all, people go for entertainment (though they still discover things and people).
TikTok’s algorithm favors variety, and you consume content at lightning speed. The moment something feels repetitive or stale, you swipe.
Your creative decays faster because the platform runs on high-velocity novelty. You’re competing with thousands of creators and brands.
If your process relies on long feedback loops — from storyboarding to shooting to editing — you’ll fall behind. By the time your ad goes live, the trend has shifted, the audio is dated, the hooks are stale, and your audience has moved on.
Use ongoing content capture to avoid bottlenecks and keep up with TikTok’s shrinking content half-life.
Modular creative: Record five hooks, three body segments, and four CTAs. Get 60 ad permutations from one hour of filming. Block time on your calendar to shoot.
Creator-in-residence: Don’t rely on one-off shoots. Hire creators in-house or on retainer to capture footage and document the brand daily. Make content creation more efficient and effective.
The 80/20 fidelity rule: Keep 80% of your content lo-fi and native, as if it were shot on a phone. Use the other 20% for higher-production, polished hero assets. Blend into the feed, maximize performance, and elevate your brand where it matters.
Every high-performing TikTok ad can be broken down into three distinct modules.
The hook (0:00-0:03)
The most volatile part. It stops the scroll and fatigues fastest.
Film 5–7 variations for each concept. Use pattern interrupts—start mid-action, zoom in, throw a box. Try a negative constraint: “Stop doing [common mistake] if you want [result].”
Use green screen reactions with trending news or customer reviews as the backdrop, with your commentary over it. Strong statements and questions keep it open-ended.
The body (0:04-0:15)
This is where you retain attention, deliver value, and show the “why” or “how.” It’s more educational or narrative and lasts longer than the hook.
Test “us vs. them” in a split-screen showing your product solving a common problem.
Test first-person use in real settings—at home, in the kitchen, outside, at the gym, or at work.
The CTA (last 3-5 seconds)
This is where you close. Test psychological triggers to see what moves the needle:
Use scarcity: “Our last drop sold out in 48 hours—don’t miss this one.”
Test low-friction angles: “Take the 2-minute quiz to find your best fit.”
Offer incentives beyond “Shop Now” or “Link in bio”: “Use code (X) for (% off) your first order.”
When a winning ad fatigues, don’t kill it. Keep the body and CTA, swap in a new hook. TikTok weights the first seconds for audience matching — use that to reset fatigue and extend performance.
When to pause or reallocate
A common mistake is cutting an ad too soon and missing its potential—or letting it run too long and wasting budget.
Your intuition matters, but TikTok’s algorithm sees more. An ad may fatigue with one audience and find a second life with another, so don’t give up too quickly. Here’s when to pause and when to move it elsewhere:
Kill signal: If your thumb-stop rate (3-second views/impressions) drops below your benchmark for three straight days, your hook isn’t working—pause it. If your hook is very fast, use 2-second views/impressions.
Iterate signal: If engagement is high but conversions are low, your creative may work, but your offer, CTA, or landing page is adding friction.
Algorithm reallocation: Before you delete any asset, test broad targeting — especially with Smart+ campaigns. Let the algorithm find a new audience that hasn’t seen your ad and compare performance to manual targeting.
With fast iteration cycles, your TikTok budget can’t be static. Dedicate 20% to 30% of your monthly budget to testing new creative concepts. This budget isn’t for hitting your target ROAS — it’s for buying data and insight.
Once you find a winner, move it into scaling campaigns. This prevents performance from dropping when a single creative hits its half-life.
Brands winning on TikTok aren’t the ones with the biggest budgets or name recognition. They create and test the most.
Capture everything—packaging, shipping, unboxings, product use, customer testimonials—as raw material in your creative supply chain. Shorten the distance between a brand event and launch.
The shrinking ad half-life won’t slow you down. It will become your advantage.
For the past several years, marketing strategy has reorganized itself around a simple premise. Third-party data is fading. Privacy expectations are rising. The solution, we are told, is first-party data.
Collect more of it. Centralize it. Build the customer view around it.
In many ways, the shift was necessary. Direct relationships with customers are more durable than rented audiences. Consent and transparency matter. Organizations that invested early in their own data ecosystems are better positioned today than those that relied entirely on external signals.
But the industry’s confidence in first-party data has grown so strong that it now obscures a more complicated reality.
Owning customer data does not automatically translate into understanding customers.
Most marketing leaders have sensed this tension already. Despite increasingly sophisticated technology stacks, many organizations still struggle with familiar questions. Which records represent active individuals? Which identities are stale or misattributed? How much of the customer view reflects current behavior versus historical assumptions?
These are not philosophical concerns. They surface in everyday operational decisions. Campaigns that reach fewer real customers than expected. Personalization efforts that plateau. Measurement models that appear precise but produce inconsistent outcomes.
The problem is not the absence of data. If anything, the opposite is true.
The problem is the assumption that the data sitting inside our systems still reflects reality.
When first-party data becomes historical data
One of the quiet characteristics of customer data is how quickly it shifts from present tense to past tense.
Most organizations gather identity information at moments of interaction. Account creation, purchases, subscriptions, service requests. These events create durable records that enter CRM systems, marketing platforms and data warehouses.
From that point forward, the records largely persist as they were captured.
What changes is the world around them.
Consumers rotate devices. Email addresses evolve from primary to secondary. People move, change jobs, create new accounts, abandon others. Behavioral patterns shift with new platforms, new habits, and new privacy controls.
The record still exists, but the certainty surrounding the identity begins to loosen.
Marketing teams encounter this reality in subtle ways. Lists that appear healthy but deliver diminishing engagement. Customer profiles that fragment across systems. Identity graphs that require constant reconciliation as signals drift out of alignment.
None of this means first-party data is wrong. It simply means it ages.
The moment of collection is precise. The months and years that follow are less so.
The distance between records and reality
The idea of a unified customer profile has become foundational to modern marketing infrastructure. Customer data platforms, identity graphs and advanced analytics environments all attempt to bring scattered signals together into a coherent picture.
When the signals align, the results can be powerful.
But the effectiveness of these systems depends heavily on the integrity of the identifiers entering them. Email addresses, login credentials, device associations and other identity anchors serve as the connective tissue between records.
When those anchors drift or degrade, the unified profile begins to lose clarity.
This is not a failure of the technology itself. Most identity platforms perform exactly as designed. They connect the signals available to them.
The challenge is that many of those signals were captured months or years earlier, during moments when the system had limited visibility into the broader identity context surrounding the individual.
As the digital environment evolves, the original record becomes one reference point among many.
Marketing leaders recognize this gap when their systems produce technically accurate profiles that still fail to explain current customer behavior. The database reflects what was known. The customer reflects what is happening now.
Closing that gap requires something more dynamic than stored attributes alone.
The value of activity signals
In recent years, some organizations have begun looking beyond the traditional boundaries of customer records and focusing more closely on signals that indicate whether an identity is still active within the broader digital ecosystem.
Activity signals provide a different kind of intelligence.
Instead of asking what information was collected about a customer in the past, they ask whether the identity attached to that information continues to exhibit real-world behavior today.
Is the email address still being used?
Does the identity appear in recent digital interactions?
Are the signals surrounding it consistent with genuine consumer activity?
These questions are becoming increasingly important for teams responsible for both growth and risk management.
For marketing, activity signals help clarify which audiences remain reachable and which identities have quietly gone dormant. For fraud teams, they help differentiate legitimate consumers from synthetic identities that appear valid on the surface but lack authentic behavioral patterns.
Both disciplines are ultimately trying to answer the same question.
Does this identity correspond to a real person who is active in the digital world right now?
Stored data alone rarely answers that question with confidence.
A more durable identity anchor
Among the many identifiers circulating through the digital ecosystem, one has proven particularly resilient over time.
Email.
For decades it served as both a communication channel and a persistent identity anchor. It appears in authentication systems, commerce transactions, subscriptions, customer service interactions and countless other digital touchpoints.
That ubiquity produces a secondary effect. Email addresses generate a continuous stream of activity signals that reflect how identities move through the online world.
When those signals are analyzed across large networks, they reveal patterns that extend far beyond a single company’s customer database.
They can indicate whether an identity is actively engaged in digital life or has fallen silent. They can highlight inconsistencies that suggest risk. They can surface connections that help reconcile fragmented customer views.
In other words, they transform a simple identifier into a dynamic indicator of identity health.
Organizations that understand this dynamic tend to treat email differently. It becomes less of a campaign endpoint and more of a reference point for understanding identity across channels.
Rethinking what it means to know the customer
Over the past decade, marketing technology has made extraordinary progress in storing and organizing customer data. Few organizations today lack the infrastructure to capture and analyze enormous volumes of information.
The next frontier is not accumulation. It is validation.
Knowing a customer increasingly depends on the ability to verify that the identities inside a database still correspond to real individuals with ongoing digital activity.
This shift changes how teams think about data quality.
Instead of focusing solely on completeness, forward-looking organizations pay closer attention to vitality. Which identities remain active. Which have quietly faded. Which exhibit patterns that suggest fraud or synthetic creation.
These distinctions influence everything from campaign reach to attribution accuracy to risk exposure.
When identity signals are strong, the rest of the marketing ecosystem performs more reliably. Personalization becomes more relevant. Measurement reflects real outcomes. Customer experiences align more closely with actual behavior.
When identity signals weaken, even the most advanced tools begin operating on uncertain ground.
Moving beyond the illusion
The industry’s embrace of first-party data was an important correction after years of dependence on opaque third-party sources.
But ownership alone does not guarantee clarity.
Customer records capture moments in time. The people behind them continue to evolve.
For organizations that want to truly understand their customers, the challenge is no longer simply collecting data. It is maintaining an accurate connection between stored identities and real-world activity.
That requires looking beyond the database itself and paying closer attention to the signals that reveal whether an identity remains alive in the digital ecosystem.
Companies that make that shift discover something important.
The most valuable customer data is not the information they collect once.
It is the intelligence that helps them keep that data connected to real people over time.
Reddit is rolling out new Dynamic Product Ad features, including a shoppable Collection Ads format and Shopify integration, the company announced today.
What’s new.
Collection Ads: A new Dynamic Product Ad format that pairs a lifestyle hero image with shoppable product tiles in one carousel, bridging discovery and purchase. Early adopters following best practices are seeing an 8% ROAS lift.
Community and Deal overlays: Reddit-native labels like “Redditors’ Top Pick” and automatic discount callouts surface social proof and pricing signals without extra work from you.
Shopify integration: Now in alpha, this simplifies catalog and pixel setup for new DPA advertisers, automatically matching products to the right users and context.
The numbers. Reddit DPA delivered an average 91% higher ROAS year over year in Q4 2025. Liquid I.V. reports DPA already accounts for 33% of its total platform revenue and outperforms its other conversion campaigns by 40%.
Why now. Reddit has seen a 40% year-over-year increase in shopping conversations. Also, 84% of shoppers say they feel more confident in purchases after researching products on Reddit.
Why we care. The new tools, especially the Shopify integration, lower the barrier to getting started with Dynamic Product Ads. Reddit might still be viewed by some as an undervalued paid media channel, but there’s an opportunity to get in before competition and costs rise.
Bottom line. Reddit is increasingly a serious performance channel for ecommerce, and these tools make it easier to get started. If you’re not yet running DPA on Reddit, the combination of undervalued inventory and improving ad formats makes this a good time to test.
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