We’ve updated this guide with information on LinkedIn’s new metrics for creators.
Using LinkedIn personally is very different from using it for marketing. Fully understanding the business social media site can boost your marketing efforts, but there is a lot to learn. To help you, we’ve put together this guide.
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LinkedIn is the largest social network for businesses and business people and an essential site for B2B marketing.
On LinkedIn, people can connect with colleagues, industry peers and potential employers. Companies can showcase products and services, generate leads, build brand awareness and establish “thought leadership” within their industry. This professional focus makes it an ideal place to find the people who may be interested in purchasing your product.
According to LinkedIn:
These numbers are self-reported, and it’s wise to keep that in mind. For example, LinkedIn says it has more than 199 million users from the U.S. That’s two-thirds of the nation’s total population and four-fifths of all Americans between the ages of 18 and 64. Many of these accounts are businesses, not individuals, so the numbers aren’t crazy. However, all social media sites have a certain percentage of ghost accounts.
They must be doing something right: LinkedIn’s 2022 ad revenue was $5.91 billion, It is projected to reach $10.35 billion by 2027.
Culture. Every social media site has its distinct culture and jargon. The site and its users put everything in the best possible light. As a result, many posts have all the liveliness and personality of a press release. This allows businesses and leaders with distinct personas to stand out. Think twice before directly dissing the competition, though. This is best done passive-aggressively: “It’s great to see [competitor] getting back up to speed. [Product] is their best work in years.”
Jargon. As with many products, LinkedIn’s descriptions can have confusing jargon. Consider these:
Everything on LinkedIn is marketing. Ads, posts, conversations, messages, etc. are all part of your campaign and need to be focused on achieving a goal. Here are the tools the site provides.
Your digital storefront on the platform. It’s where you showcase your brand, share company updates and connect with your audience.
LinkedIn offers a variety of ad formats to reach your ideal customers and drive specific actions.
All of these can be targeted according to factors such as:
Source: LinkedIn
LinkedIn Accelerate is a specialized campaign type designed to drive specific business outcomes, such as lead generation or website visits. It offers a streamlined setup process and pre-optimized targeting options, making it a more efficient choice for businesses with clear goals.
Accelerate also has gen AI tools to generate images, videos and other content.
New campaign objectives are being added — including brand awareness, engagement, website conversions and video views. That said, standard LinkedIn campaigns have more customization options and the ability to handle complex objectives.
Sales Navigator helps sales professionals identify and engage with potential customers. Its key features and functionalities include:
This lets you measure the effectiveness of your marketing efforts. It has metrics and reports to help understand your audience, track content performance, measure campaign effectiveness and benchmark performance against industry standards.
Key features and functionalities:
These are similar to Instagram Stories and allow users to share short, ephemeral content directly with their network. They can include photos, videos, text, and GIFs. The key features and benefits include:
These are specialized company pages that highlight specific products, services,or brand initiatives. They offer a focused platform for businesses to tailor their content and messaging to a particular audience, increasing engagement and interaction. Showcase Pages can also help build brand awareness and credibility for your featured offering, ultimately driving leads and sales.
As an experienced B2B marketer, you already know the basics of LinkedIn marketing. Content marketing and social selling work the same everywhere. So, let’s look at things you may not be familiar with.
This is the site’s version of event marketing. It lets you connect with your audience in real time. You can do this through:
LinkedIn Live is also useful for generating leads. You can get contact information by offering exclusive content to viewers who sign up for your email list. Contests or giveaways can also increase engagement and generate leads by offering prizes or discounts to people participating in the event or sharing it with others. And you can identify potential leads by seeing who has engaged with your content, asked questions or otherwise shown interest.
Employees can be a powerful force multiplier for marketing on LinkedIn. However, they need to know how to do it. Train them on best practices for LinkedIn usage and content sharing. Also, use a content calendar to ensure they share the right content at the right time. Of course, this means one more thing for them to do. So, recognize and reward employees who take the time to do this.
This has the additional benefit of encouraging employee ownership of the brand, which can contribute to a positive company culture. Furthermore, authentic comments from employees can build trust with potential customers and improve the company’s reputation.
Joining and participating in relevant LinkedIn groups lets you build relationships, network and share your expertise. Look for groups that align with your target audience and industry. Regular participation is the best way to raise your profile.
LinkedIn introduced a new feature for enhancing lead generation efforts. Users can now select “Lead Generation” as an objective when boosting posts on their LinkedIn feed. Previously, the quick boost option was limited to reach and engagement goals.
This simplified ad setup allows businesses to set a budget, target a specific audience, and attach a lead form template to their boosted post. The feature lets companies build their contact lists and gain valuable insights into their audience. And it can transform organic content into targeted ads that generate high-quality leads.
Boosting LinkedIn posts can be a valuable tool for extending the reach of your top-performing content, while also serving as a data-gathering mechanism. This lightweight approach, however, lacks the granular targeting capabilities offered by LinkedIn’s Campaign Manager. For comprehensive campaign management, it’s essential to utilize the full suite of tools available within the platform.
LinkedIn’s new Companies feature in Campaign Manager is designed to provide B2B marketers with deeper insights into account-based marketing (ABM) performance. It is a centralized hub for viewing company-level engagement data across organic and paid LinkedIn campaigns.
It lets marketers:
Use cases include:
Companies data and functionality:
With Companies, B2B marketers should get valuable insights, refine their ABM strategies, and reach and engage audiences more successfully.
LinkedIn has rolled out several updates focusing on enhancing video presentation and discovery. They include bringing the full-screen, vertical video display format to desktop to make it more like the mobile app. The platform is also testing a larger video display in mobile and an option to display a member’s video uploads in a profile mini-view. These updates aim to make video posts more prominent and improve video discoverability.
Discovery is also a key focus, with LinkedIn now surfacing more videos in search results through a swipeable carousel. Additionally, LinkedIn is launching new video analytics, providing data on average watch time to help users understand video performance. To help users maximize video content, LinkedIn has also launched new nano-learning courses covering topics such as crafting compelling video hooks, editing, repurposing, and collaborating on video content.
These updates are significant because video content is on the rise on LinkedIn, with a reported 36% increase in watch time year-over-year. LinkedIn is actively trying to attract video creators by launching a TikTok-style vertical feed and prioritizing video content in its algorithm. By improving video presentation, discoverability, and providing analytics and learning resources, LinkedIn aims to capitalize on the growing popularity of video and encourage more users to incorporate video into their LinkedIn strategy.
LinkedIn updated its ad targeting and attribution features to help improve ad targeting and demonstrate return on investment. These updates include the Conversions API (CAPI) and Revenue Attribution Report (RAR) enhancements.
Conversions API:
Revenue Attribution Report:
LinkedIn is now a place for brands to leverage influencer marketing, according to The Sprout Social Index. The site offers a largely untapped opportunity for influencer marketing (only 12% of brands say LinkedIn is their preferred channel for influencers). Here’s why:
Example: Brandon Smithwrick partnered with Typeform, an online form-building software, for a LinkedIn post about how the platform enhances efficiency. The video got 380 engagements (reactions, comments and reposts), according to Sprout.
(The 2025 Sprout Social Index – registration required)
LinkedIn updated its Campaign Manager, focusing on three key areas: campaign planning and strategy, campaign creation and measurement and optimization.
The updates include a new Media Planner for forecasting campaign results, an Ads Duplication feature to streamline campaign creation, Dynamic UTMs for improved tracking, a Marketing Overview dashboard for small businesses, enhanced Measurement Insights for detailed performance analysis and an AI-driven Campaign Performance Digest for quick understanding of results.
Media Planner
Ads Duplication
Dynamic UTMs
Marketing Overview
New Measurement Insights
AI-driven Campaign Performance Digest
Polls, the least used format on LinkedIn, are getting a lot of push from the site’s algorithm, according to Metricool’s “2025 LinkedIn Study.” The report also suggests that marketers should make more use of carousels and video.
Here are the highlights:
Metricool’s 2025 LinkedIn Study is based on an analysis of 47,735 accounts and 577,180 posts between January 2023 to January 202. It is available here. (Registration required)
LinkedIn’s new Qualified Leads Optimization feature helps advertisers target high-intent accounts by aligning ad delivery with lead quality. Businesses can define lead quality through LinkedIn’s system or a CRM like HubSpot or Salesforce. Once leads are identified, their data is sent to Campaign Manager, which matches and optimizes ad delivery based on similar high-quality profiles. The tool also offers deeper attribution for pipeline and revenue impact.
LinkedIn recommends submitting at least five qualified leads every two weeks, within 30 days, for optimal results. The system takes two weeks to reach full performance. This feature applies to campaigns using the Lead Generation objective and works best for large-scale advertisers with an existing lead qualification process.
In early May 2025, Linked announced BrandLink, an expansion of what was formerly known as the Wire Program launched in 2024. BrandLink will offer video ad placements next to some of the LinkedIn platform’s most popular creator voices.
With the launch of BrandLink, LinkedIn continues to grow its investment in video. According to LinkedIn, 62% of B2B marketers believe video is most effective in reaching and influencing members of the buyer group.
Other elements of BradnLink include:
Since launching the Wire Program in June 2024, LinkedIn advertisers report:
More information on BrandLin is available on the LinkedIn website.
Dig deeper: Marketing on Reddit: What you need to know
LinkedIn is adding more post analytics aimed at giving frequent posters and creators a clearer picture of how their content performs and what actions it drives. The new data shows things like how a post leads to profile visits or new followers. Premium members can also see how many people click their custom profile button, which is often used to send traffic to websites or newsletters.
The added metrics provide a better sense of how content performs in terms of visibility and outcomes. Data points like “followers gained from this post” help show growth over time, while “custom button interactions” give a clearer sense of how content leads to clicks—useful for anyone tracking conversions, especially in B2B. These also open the door for more targeted testing and content tweaks, giving users a better chance to see what works and what doesn’t. Marketers see this as a step toward stronger post-level attribution, which could make the platform more appealing for ad spend.
At the same time, LinkedIn quietly removed the basic hyperlink option from profiles on May 28. Now, the only way to add an external link is through a Premium-only custom button. That might nudge more people toward upgrading, though many top creators already have free access to Premium.
If you thought the hype around AI was receding, think again. Generative AI applications have become the key battleground between marketing services companies of all stripes, from creative to strategic consulting, to media planning and buying.
The American public believes generative AI will have a negative impact on U.S. society, according to recent polling by Pew and Forrester. But advertising clients clearly think the tech is a solution to their woes. Two-fifths of CMOs are already using AI for creative automation, and 37% report using agents to manage their media spending, according to Gartner’s latest CMO survey.
Which brings us to WPP Media, and the industry behemoth’s latest attempt to recover its once-leading market position. After last week’s rechristening, the media network (formerly known as GroupM) has launched “Open Intelligence”, an AI identity solution that WPP Media’s execs say can deliver more precise, privacy-conscious targeting for clients.
At its center is a “large marketing model” that, according to Evan Hanlon, CEO of WPP data business Choregraph, provides “an accurate representation of the world that helps us predict consumer behavior.”
In more straightforward terms, it’s an AI model filled with data drawn from consumer panels, retail media networks and CTV providers, rather than the Reddit posts behind large language models (LLMs) like ChatGPT.
From that “foundational” system, WPP Media plans to build bespoke AI models tuned to the needs of each client, combining its model with their first-party data. Those models can then be used to aid planning and targeting efforts, enabling targeting that relies on a mix of deterministic and probabilistic signals, and supposedly granting less waste, greater returns on digital marketing investments and faster turnarounds on media decisions. The ambition is to see “every brand having a predictive model built exclusively for them,” said InfoSum boss Lauren Wetzel.
The thing is, WPP Media is far from alone – it’s one of many marketing groups offering AI-enabled, -tinged, -derived or otherwise -powered tools. This week alone:
And of course, Meta’s Mark Zuckerberg unveiled plans to give marketers AI tools to create, plan and execute entire ad campaigns within its platform — an act that’s either indicative of the Facebook founder’s disregard for the ad industry’s expertise, or a grab for their lunch money, depending on who you ask.
That’s not to knock WPP Media’s AI effort. A product of parent company WPP’s ₤300 million ($403 million) annual investment in AI tech, Hanlon said the initiative involved a “Manhattan project” effort from staffers drawn out of AI unit Satalia, recent addition InfoSum, WPP Media and the holding company’s tech partners (which range from CTV firm FreeWheel to TikTok, Meta and Microsoft). Work on the initiative involved InfoSum prior to its acquisition by WPP in April, Wetzel said.
According to Hanlon, five of WPP Media’s clients have been using Open Intelligence on “relatively scaled” campaigns over the last year, as the company has developed prototypes of the tech. Hanlon claimed that Open Intelligence had been used to drive a 60% decrease in cost per acquisition for one “mobility” (industry code for a carmaker) brand; for an unnamed telecoms client, he said it had cut CPA by 15%. Hanlon declined to name the clients or provide financial specifics.
Open Intelligence is an illustration of WPP Media’s new data philosophy – something its execs hope can distinguish it with clients – in action.
It’s also an asset they hope can guard its market offer, as well as the media strategies of clients, against the changing tides of privacy regulation. Should identifiers for audience segments on CTV and the open web disappear tomorrow, Hanlon said WPP’s tool would still enable clients to segment and target consumer audiences in an effective way.
With Cannes Lions around the corner and no apparent end to CMO enthusiasm for AI solutions in sight, we should expect more companies to lean into the tech. Open Intelligence is just the latest of many holding company “bets”, as Wetzer phrased it, in play for the future of digital marketing.
“We are not going to be the last,” Hanlon said. “But we think that our early decision to take this path, to make these investments, leaves us really well situated right now to set the pace and to maintain a lead moving forward.”
The designer and animator Tristan Huschke began his creative journey not with design, but with painting. He studied for a degree in fine art working solely with oil on canvas; “during that time, I not only learned a lot about technique but also about the creative process,” Tristan says. “How to develop a personal style, engage with my work, and find a visual language that goes beyond mere decoration.” After graduating, Tristan realised he needed to take his work outside of the sometimes stuffy environments of museums and galleries; the typically elitist nature of art contended and his desire to be seen by more people. “Design provides exactly that opportunity,” Tristan says, “it has a function, is accessible, and reaches people where they are.”
In comparison to the famously slow practice of oil painting, Tristan’s work is fast-paced, digital and energetic – these very sensibilities drew him to the music scene where his practice is particularly at home. Whilst some practicalities of his work differ from painting, the ‘canvas’ structure has continued. Tristan is naturally drawn to the poster as his primary medium, using its limitations as a way to explore the capacity of his creative practice whilst offering it to a much broader audience. “My interest in posters arose from the desire to make art more accessible,” Tristan says. “I wanted my work to be present in public spaces, to reach a wider audience, and digital tools provided me with exactly that opportunity.”
Tristan’s systematic approach to form and pattern is the foundation of his practice. “What I often find fascinating is the transition between harmony and disharmony,” he says, usually creating a more subdued poster before then causing chaos. “Miles Davis once said, ‘it’s not the note you play that’s the wrong note – it’s the note you play afterwards that makes it right or wrong,’” Tristan adds. As such, Tristan is meticulous in the composition and character of his posters, creating contrast with broader, bolder forms alongside delicate disruptions of type and illustration. “It creates tension that tells a story and brings the two elements into direct dialogue,” Tristan ends.
As paid media marketers, we often default to the “big” platforms: Google, Meta, and increasingly, TikTok.
However, there’s a quiet powerhouse in the app marketing world that too many advertisers overlook: Apple Search Ads (ASA).
If you work with apps or even if your business uses an app as a secondary conversion point, ASA is one of the most intent-driven ad platforms you can leverage.
Unlike other platforms where discovery can feel like throwing spaghetti at the wall, ASA puts you directly in front of users already searching for what you offer.
That’s not just high intent. That’s purchase-ready behavior.
So, why aren’t more marketers fully embracing Apple Search Ads? Usually, it’s because they either assume it’s only for app developers or they’re intimidated by yet another ad platform to learn.
With a bit of strategic setup and a clear understanding of how ASA differs from other platforms, you can unlock a high-performing new channel.
This guide will walk you through everything you need to know.
Apple Search Ads is Apple’s proprietary platform that lets advertisers promote apps directly inside the Apple App Store.
It operates similarly to paid search platforms: Advertisers bid on keywords and pay when users tap their ads.
Instead of driving traffic to websites or landing pages, ASA drives users directly to your App Store product page. From there, users can immediately download or purchase the app.
So, why should that matter to marketers?
If you’re investing in user acquisition or app engagement, Apple Search Ads deserves to be part of the conversation.
If you think that ASA placements are strictly within the App Store search results, think again.
Currently, your ads can appear in four key placements.
This is the most coveted placement. Ads appear at the very top when a user searches for a keyword. This is where intent is at its peak.
Image credit: ads.apple.com, May 2025
Ads appear before a user types in a search term. This is a great placement for brand awareness and introducing your app to broader audiences.
Image credit: ads.apple.com, May 2025
These ads show up on the App Store’s homepage, which is the first thing users see when they open the App Store. It’s ideal for major launches or branding campaigns.
Image credit: ads.apple.com, May 2025
Ads appear when users scroll through other app product pages. These placements capture users who are in browsing mode, often comparing similar apps.
Image credit: ads.apple.com, May 2025
Each placement serves a different purpose, from brand awareness to high-intent acquisition.
At first glance, Apple’s two solutions, “Basic” and “Advanced,” might seem like they serve similar purposes. They don’t.
This solution is designed for small app developers or businesses without dedicated marketing teams.
It’s entirely automated: You enter a monthly budget (up to $10,000), and Apple does the rest. It handles targeting, bidding, and ad delivery.
You get very limited reporting and zero visibility into which keywords or placements are driving installs. There’s no ability to control cost-per-tap, and optimization is virtually non-existent.
This solution, on the other hand, is a fully-featured platform that gives you control over every element of the campaign: keywords, audience targeting, bidding, scheduling, and performance measurement. It’s what any performance marketer should be using.
If you care about scalability, performance optimization, or insight into where your spend is going, the decision is easy.
Advanced is the only real option. Basic may work for small developers, but if you’re reading this guide, it’s probably not for you.
If you’re coming from a Google Ads or Meta Ads background, ASA will feel both familiar and refreshingly simple, but it wouldn’t be a proper ad platform without its own quirks.
Here’s a quick walkthrough of what to expect when navigating the platform:
There is one key difference between this platform and the Google Ads platform, and that comes in the form of ad creatives.
You won’t create ads in the traditional sense like other platforms. Apple Search Ads automatically pulls your app’s name, icon, screenshots, and description from your App Store listing.
While this limits creative flexibility, it ensures that ads align perfectly within the app’s branding.
For more custom creatives, there is the option to create custom product pages within Apple App Store Connect, but we’ll cover that later in this guide.
Keyword targeting is at the heart of Apple Search Ads, and while it borrows concepts from Google Ads, there are some critical differences.
ASA offers two main match types: exact and broad.
Exact match is exactly what it sounds like. Your ad will only appear when the user’s search matches your keyword or a very close variation.
Broad match is more flexible, allowing your ad to appear for related terms, synonyms, and phrases. Broad match is helpful for keyword discovery, but can sometimes cast too wide a net if not monitored closely.
You can also opt into Search Match, which lets Apple automatically match your app to relevant search terms.
It uses metadata from your app listing (like your title, keywords, and category) to decide where your ad should show.
While it can be helpful in discovery campaigns, you’ll want to keep a close eye on what it’s actually matching to, as it often surfaces low-quality or irrelevant terms.
Now, here’s the kicker: Apple does allow negative keywords, but managing them is far more frustrating than it should be.
Unlike Google Ads, you can’t easily apply negatives across multiple campaigns in bulk or through a shared library.
There’s also no built-in keyword suggestion tool to help you filter or negate irrelevant terms based on live data. If you want to block poor-performing keywords, you have to manually upload them one by one into the ad group or campaign.
There is another option to copy/paste them into the interface, but I’ve found that you have to build them out in Excel by match type, then use a Notepad (or something similar) to format it the way Apple can ingest it.
You can’t paste a linear list like most platforms can. You’ll need to format negative keywords something like this:
[exact negative keyword A],[exact negative keyword B],[exact negative keyword C]
This makes proactive negative keyword management a bit of a time suck.
Keyword management is doable, but it’s not frictionless. You’ll need a spreadsheet handy and some patience, especially if you’re working across multiple campaigns.
Read More: AI-Enhanced Keyword Selection In PPC
The structure of your Apple Search Ads campaigns is one of the biggest levers you can pull for performance and efficiency.
It helps you control budgets, isolate performance by keyword type, and make smarter bid decisions.
In my experience, the most successful campaign structure includes four campaign types/categories:
Your brand campaign captures people already searching for your app by name.
It usually delivers the cheapest installs and highest conversion rates, making it a reliable foundation.
This campaign targets searches for other apps in your space.
For example, you’re marketing a personal budgeting app. If someone searches for “Mint” or “YNAB” (which stands for You Need A Budget), you can show up as an alternative.
These campaigns are competitive, so expect higher CPTs.
This campaign focuses on generic terms like “budget app” or “meal tracker.”
These users are high intent but still evaluating their options. It’s a great area for differentiation.
Lastly, your discovery campaign should use broad match and search match to find new terms.
Keep bids lower here and treat it as a research engine.
Once you build out this structure, you’ll be able to track which intent tiers are performing, allocate budget accordingly, and avoid muddy data from mixed-match types.
It’s the first step toward scale and clarity.
Lastly, once you’ve mastered the basics of Search campaigns in Apple, I’d recommend branching out to the broader campaign types (Search Tab, Product Page, Today Tab).
While Apple Search Ads is primarily keyword-driven, there are a few targeting levers you can pull to refine who sees your ads.
They’re not as deep as what you’d get on Meta or TikTok, but they’re still useful.
You can refine your audience by:
While these refinements are helpful, they don’t work like standard audience building in Google Ads or Meta Ads. You won’t be building layered lookalike audiences or behavior-based segments.
ASA targeting leans more on keyword intent, with these settings helping you narrow the lens.
Used thoughtfully, these refinements help stretch your budget further and ensure you’re reaching the right slice of users without completely overhauling your campaign structure.
Apple Search Ads operates on a cost-per-tap bidding model. You set the maximum amount you’re willing to pay for a tap (essentially a click), and Apple runs an auction to determine whether your ad gets shown.
What makes ASA different is that the auction isn’t just about who bids the most.
Apple weighs relevance, meaning that apps with higher conversion rates and better alignment to the search query can win placements with lower bids.
That means throwing money at ASA doesn’t guarantee success. Smart bidding is about segmenting intent and adjusting bids based on performance.
Here’s how to frame your approach to bidding:
You’ll also want to make frequent bid adjustments. Unlike Google Ads, ASA doesn’t offer much in the way of automated bidding or budget pacing.
This means manual optimization matters a lot more, and performance can shift quickly based on ranking changes or user behavior.
The takeaway? Stay active. Set up a regular cadence to adjust bids and keep your spend aligned with what’s driving installs.
If you’ve worked with Apple Search Ads in the past, you might remember Creative Sets. That’s the old name of this feature.
Today, you create ad variations using Apple’s Custom Product Pages.
These are alternate versions of your App Store product page with different screenshots, app previews, and promotional text. When paired with specific ad groups or keywords in ASA, they allow you to show different visuals depending on the search intent.
Creating custom product pages requires a few things:
For example, if you’re running a meditation app, you might build one page emphasizing sleep content and another emphasizing stress relief.
Then, when a user searches [meditation for sleep], your ASA campaign can direct them to the custom page showing your sleep-focused content.
These variations not only improve relevance, but they can meaningfully lift conversion rates when executed properly.
Since ASA doesn’t allow you to change much else about your ad creative, this is one of the few levers you can pull to align creative with user intent.
Even seasoned marketers trip over Apple Search Ads’ simplicity. It’s not a complicated platform, but it is easy to get wrong if you treat it like something it’s not.
One of the most common missteps is relying too heavily on search match. It sounds like a time-saver, but it often matches your app to irrelevant or low-converting keywords.
If you do use it, pair it with a discovery campaign and monitor the search terms closely.
Another pitfall is ignoring custom product pages. Most advertisers just run with the default App Store listing, missing an easy opportunity to align visuals with user intent.
It’s a mistake that can silently eat away at your conversion rate.
Then, there’s bid stagnation. ASA doesn’t come with automated bid rules, which means if you’re not manually adjusting CPTs, your performance will erode over time.
Finally, many marketers forget to actively review negative keyword opportunities. If you’re not trimming irrelevant traffic, you’re probably paying for taps that will never convert.
The good news? Most of these mistakes are fixable once you know what to look for and take the time to make deliberate optimizations.
If you market an app, or even plan to in the future, Apple Search Ads is absolutely worth testing.
It puts your brand in front of users with the highest purchase intent available in the app ecosystem.
While it lacks some of the advanced audience targeting of other ad platforms, it compensates with simplicity, clear keyword intent, and an ecosystem designed for conversions, not just clicks.
Like any paid media channel, success comes from thoughtful campaign structure, active management, and the willingness to iterate.
If you’ve been putting Apple Search Ads on the back burner, now’s the time to give it the attention it deserves.
More Resources:
Featured Image: GamePixel/Shutterstock
IAB Tech Lab has used its annual summit to announce two major initiatives aimed at modernizing digital advertising infrastructure and content governance, as well as addressing some of the fundamental challenges that generative AI and LLMs pose to content monetization.
The two-part announcement details two key initiatives with the LLM Content Ingest API Initiative, addressing publishers’ concerns prompted by AI agents and large language models, as well as AI-driven search summaries that reduce publisher traffic. Meanwhile, its Containerization Project is geared toward the development and maintenance of programmatic infrastructure (see more below).
IAB Tech Lab is inviting publishers, brands, LLM platforms, and AI agent developers to provide feedback on the proposals, with a workshop for the LLM Content Ingest API scheme planned for next month. Elsewhere, the Tech Lab Containerization Project Working Group is responsible for leading the separate effort, with representatives there also requesting feedback on the initiative.
The two-pronged announcement marks the fruition of the standards body’s earlier pledge to release up to 31 new or updated specifications this year, with the efforts targeting sub-sectors of the industry, including CTV, conversion tracking, and curation.
However, it is the rising tide of generative AI and LLMs that has proven the most fundamentally concerning shift in recent years, with the number of related job losses in 2025 alone a striking concern. IAB Tech Lab CEO Anthony Katsur discussed this and the latest initiatives with Digiday ahead of this week’s IAB Tech Lab Summit, detailing his belief that every publisher should ink licensing deals with LLMs, how brands, too, need to protect themselves amid the “contextual soup.”
When quizzed on mass layoffs across the sector, Katsur also gave recommendations on how individuals can future-proof their careers in this new paradigm of the internet economy.
The conversation below has been lightly edited for brevity and clarity.
Some publishers are starting to do content licensing deals with the LLMs, and every publisher should do a content licensing deal with their LLMs, full stop.
Every publisher should know that every LLM is crawling your content, so do a licensing deal, stop the bleed, and get paid for your content. Any LLM that is effectively ransacking publisher content without paying for it; that’s intellectual property theft, in my opinion.
The challenge, though, is we don’t believe that the crawling approach has is a long term, feasible approach. By introducing a standardized set of APIs [LLM Content Ingest API], we can get the industry to lock arms and shut down the crawling, lock them at the IP level. Then we can create an open-source, standardized API that gives structure to this content, and that structure does a number of things.
For example, you can now create a gateway that you know allows access to the LLMs that reflect the business terms of a contract that you sign with them. The problem is there are publishers with different tiers of content: your archival, always-on content, then there’s your day-to-day, and the same [monetization or paywall model] should exist for the LLMs.
There’s a logging component to the API, so now you can audit the crawls and make sure you’re invoicing correctly and getting paid appropriately for your content. And then fourth, and I would argue, maybe the most important, is the tokenization of the content so it demonstrates a source of truth. The issue with the LLM, while promising, is that they’re still nascent in their development, and they are prone to [making factual] mistakes.
Containerization is arguably the biggest development in programmatic since Open RTB. In today’s current server-to-server architecture, Open RTB is a [meta-protocol] and is an HTTP request that makes a wide area network call, so even if a DSP and SSP are in the same data center, it isn’t necessarily smart enough to know to stay in the same data center.
The beauty of containerization is you can leverage the GRCP protocol and protocol buffers to make a containerized version of the RTB protocol. So, what we’re doing is we’re taking that 300-to-500 milliseconds and potentially shrinking that down to 50-to-100 milliseconds… and what you can do with that [saved] time is a lot.
The connection between DSP and SSP will open and close much faster, or you can keep the connection open, just keep streaming new requests through it, that works great for scaling live events [opening programmatic up to new content types such as live sports].
Agentic AI, or purpose-driven AI that is not so task-driven, is the one that really comes into play in terms of media buying and optimization, combating fraud. They’ll be able to spot patterns in the supply chain or performance patterns or creative optimization.
I think my advice to anyone in our ecosystem is to learn and become an expert in working with those tools. It’s early days, but I think those that embrace this can get an edge from a learning curve perspective.
A new billboard has (quite literally) dropped for the upcoming Final Destination: Bloodlines film. Bringing the horror of the 2000s franchise to life, the terrifyingly clever campaign pushes the boundaries of what billboard ads can be, thinking outside the box to create a truly heart-palpitation-inducing display.
It seems that billboard advertising has been stepping up lately, with interactive displays and sonic activations redefining the world of OOH advertising. Expertly capitalising on the film’s real-world horror, this ingenious campaign is a prime example of how a simple concept can have a huge impact when it comes to creating eye-catching advertising.
Final Destination is back, with a scary billboard To promote the first film in 14 years, a shattered billboard appeared in a Paris mall, as if death just missed its mark… 👀 (Creative Agency: Jellyfish Paris) #FinalDestination pic.twitter.com/3KydUcUDMGMay 14, 2025
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Starting Monday, June 9 WKYC in Cleveland, Ohio will launch a live stream from 7 to 9 a.m. on its plus channel.
The show is a continuation of its GO! Morning show and features 3News’ Danita Harris, Dave Chudowsky, Matt Wintz, Brissa Bradfield, David Greenberg, Candice Hare and Monica Robins.
The stream is free and available on Roku, Apple TV, Fire TV and mobile devices.
The station even has a quick tutorial to show how to get it. Click here to watch.
When it comes to social media feedback, it’s easier to focus on the negative, but it isn’t the whole story . When some of the more toxic comments started coming in, Benjamin was pleasantly surprised to find other designers coming out to bat for the new rebrand, unbidden by him or Templo. “Isn’t it great to see the creative industries starting to look at what other people are saying and actually self-policing?” he says. Perhaps we could all be a bit bolder when it comes to calling out this kind of toxicity when we see it.
Anoushka, meanwhile, preaches patience. While we might want to judge a rebrand on day one, they really only reveal their value over time (something the graphic design legend Paula Scher has spoken about often). Anoushka cites the Airbnb rebrand as an example. “That was met with a huge amount of backlash,” she recalls, “but now it’s five or ten years later and it’s just the logo, it’s the brand; we accept it, we don’t even think twice about it, we use it and move on.” Only time will tell if the same will be true of GF Smith’s new branding.
None of us want to work in an industry where every new project is met with universal, uncritical praise – that kind of back-slapping, saccharine positivity would also be bad for the design industry. But we need to find that line between constructive critique and vicious vitriol. Otherwise we might well be doing more harm than good. “I love that people aren’t afraid to express opinions, to have a debate and say, ‘I like this, I don’t like that.’ That’s why I love the creative industry so much,” says Benjamin. “But let’s take the toxicity out of it.”
A patent recently filed by Google outlines how an AI assistant may use at least five real-world contextual signals, including identifying related intents, to influence answers and generate natural dialog. It’s an example of how AI-assisted search modifies responses to engage users with contextually relevant questions and dialog, expanding beyond keyword-based systems.
The patent describes a system that generates relevant dialog and answers using signals such as environmental context, dialog intent, user data, and conversation history. These factors go beyond using the semantic data in the user’s query and show how AI-assisted search is moving toward more natural, human-like interactions.
In general, the purpose of filing a patent is to obtain legal protection and exclusivity for an invention and the act of filing doesn’t indicate that Google is actually using it.
The patent uses examples of spoken dialog but it also states the invention is not limited to audio input:
“Notably, during a given dialog session, a user can interact with the automated assistant using various input modalities, including, but not limited to, spoken input, typed input, and/or touch input.”
The name of the patent is, Using Large Language Model(s) In Generating Automated Assistant response(s). The patent applies to a wide range of AI assistants that receive inputs via the context of typed, touch, and speech.
There are five factors that influence the LLM modified responses:
The first four factors influence the answers that the automated assistant provides and the fifth one determines whether to turn off the LLM-assisted part and revert to standard AI answers.
There are three contextual factors: time, location and environmental that provide contexts that are not existent in keywords and influence how the AI assistant responds. While these contextual factors, as described in the patent, aren’t strictly related to AI Overviews or AI Mode, they do show how AI-assisted interactions with data can change.
The patent uses the example of a person who tells their assistant they’re going surfing. A standard AI response would be a boilerplate comment to have fun or to enjoy the day. The LLM-assisted response described in the patent would generate a response based on the geographic location and time to generate a comment about the weather like the potential for rain. These are called modified assistant outputs.
The patent describes it like this:
“…the assistant outputs included in the set of modified assistant outputs include assistant outputs that do drive the dialog session in manner that further engages the user of the client device in the dialog session by asking contextually relevant questions (e.g., “how long have you been surfing?”), that provide contextually relevant information (e.g., “but if you’re going to Example Beach again, be prepared for some light showers”), and/or that otherwise resonate with the user of the client device within the context of the dialog session.”
The patent describes multiple user-specific contexts that the LLM may use to generate a modified output:
Here’s a snippet that talks about various user profile related contextual signals:
“Moreover, the context of the dialog session can be determined based on one or more contextual signals that include, for example, ambient noise detected in an environment of the client device, user profile data, software application data, ….dialog history of the dialog session between the user and the automated assistant, and/or other contextual signals.”
An interesting part of the patent describes how a user’s food preference can be used to determine a related intent to a query.
“For example, …one or more of the LLMs can determine an intent associated with the given assistant query… Further, the one or more of the LLMs can identify, based on the intent associated with the given assistant query, at least one related intent that is related to the intent associated with the given assistant query… Moreover, the one or more of the LLMs can generate the additional assistant query based on the at least one related intent. “
The patent illustrates this with the example of a user saying that they’re hungry. The LLM will then identify related contexts such as what type of cuisine the user enjoys and the itent of eating at a restaurant.
The patent explains:
“In this example, the additional assistant query can correspond to, for example, “what types of cuisine has the user indicated he/she prefers?” (e.g., reflecting a related cuisine type intent associated with the intent of the user indicating he/she would like to eat), “what restaurants nearby are open?” (e.g., reflecting a related restaurant lookup intent associated with the intent of the user indicating he/she would like to eat)… In these implementations, additional assistant output can be determined based on processing the additional assistant query.”
The system and device context part of the patent is interesting because it enables the AI to detect if the context of the device is that it’s low on batteries, and if so, it will turn off the LLM-modified responses. There are other factors such as whether the user is walking away from the device, computational costs, etc.
This patent is important because millions of people are increasingly engaging with AI assistants, thus it’s relevant to publishers, ecommerce stores, local businesses and SEOs.
It outlines how Google’s AI-assisted systems can generate personalized, context-aware responses by using real-world signals. This enables assistants to go beyond keyword-based answers and respond with relevant information or follow-up questions, such as suggesting restaurants a user might like or commenting on weather conditions before a planned activity.
Read the patent here:
Using Large Language Model(s) In Generating Automated Assistant response(s).
Featured Image by Shutterstock/Visual Unit
