What’s New in Paid Media: June 2025 Industry Updates

Welcome to the latest edition of the Impression Paid Media Team blog, where we explore the newest updates and trends to keep you ahead of the game.

As summer begins to make its presence felt, the digital marketing scene is warming up too. June has delivered a new round of updates in paid media. Let’s dive into what’s changed and what it means for you.

Keep reading to find:

Google Ads PPC Updates June 2025: Deep Dive into AI-Driven Innovations Shaping Paid Media

Pay-per-click (PPC) advertising remains one of the most effective ways for businesses to connect with customers in the digital age. June 2025  marks a pivotal moment for Google Ads, with several major updates focused on integrating cutting-edge AI technology into ad placements, campaign automation, and reporting. These changes aren’t just incremental — they represent a fundamental shift in how advertisers will interact with the platform and their audiences.

If you’re invested in Google PPC campaigns, understanding these updates in detail will help you stay competitive and make informed decisions.

Google Ads Now Serve Ads Inside AI-Powered Search Overviews

Google has embedded its Gemini AI chatbot and generative AI technology into desktop search results through a new feature called “Search Overviews.” Announced in March 2025 and fully launched with new features in late May and early June, this feature generates comprehensive, conversational summaries for complex or multi-faceted search queries—think of it as an AI assistant providing detailed answers and context right on the search results page, referred to as AI Mode.

The game changer? Google Ads are now dynamically served inside these AI-generated search overviews, rather than being restricted to traditional ad slots like the top, bottom, or sidebars.

What’s Changed:

• Previous Model: Ads were shown alongside organic results in fixed slots, often with predictable visibility and placement.

• New Model: Ads now appear within the AI-generated answers. For example, when a user searches for a camera, instead of seeing just text links, they receive a rich AI overview summarizing key camera features, comparisons, and recommendations — and your Google Ads can appear directly inside this summary. Advertisers do not select this placement manually; Google’s ad-serving algorithms automatically determine when to place ads inside AI content based on your existing keyword, audience, and demographic targeting.

Advantages: This integration offers higher engagement during the research phase, capturing users precisely when they are seeking detailed insights, which increases the chance your ad is seen in a meaningful context. It also provides new inventory access, opening additional and real estate within Google Search and potentially increasing your impressions without extra bidding. Furthermore, appearing within AI summaries can enhance brand authority, positioning your brand as a trusted solution during user decision journeys.

Disadvantages: However, this new placement may lead to a lower Click-Through Rate (CTR), as users might consume the AI-generated answer fully within the overview without needing to click through to your landing page, which could reduce direct traffic. There’s also less control and transparency compared to traditional ad slots, as you have limited insight into exactly where and how your ads are integrated within AI content, making A/B testing and placement optimization trickier.

Why It Matters:

For advertisers, this update demands a shift in mindset: ads are no longer just standalone links but part of an AI-powered conversational search experience. It highlights the importance of creating compelling ad copy that stands out in more informative, content-rich environments. Furthermore, advertisers should adjust bidding strategies and monitor performance with an eye toward metrics beyond clicks, such as brand lift or assisted conversions.

Smarter Smart Bidding: Enhanced AI-Powered Automation with Greater Precision

What It Is:

Google’s Smart Bidding algorithms automate bid adjustments to maximize performance goals like conversions or conversion value. The June 2025 update supercharges Smart Bidding with deeper AI capabilities, better data integration, and more real-time decision-making.

What’s Changed:

• Expanded Data Signals: The AI now processes a wider array of signals — including first-party customer data, recent Browse behavior, device type, time of day, and contextual factors — to adjust bids at the auction level with laser precision.
• Improved Budget Pacing: Smarter pacing algorithms ensure your budget is optimally distributed across peak conversion windows, avoiding early depletion or under-spending.

Tighter Integration with Performance Max: AI automation is more tightly woven into Google’s Performance Max campaigns, which automatically optimize ad placements across Google Search, YouTube, Display, Discover, and more.

Advantages: These enhancements lead to maximized ROI, with smarter bids resulting in higher conversion volume and value within your existing budget. Advertisers will benefit from less manual management, as automation reduces tedious bid adjustments and guesswork. Additionally, better audience reach is achieved through real-time bid changes, allowing your ads to show to the most relevant users at the optimal moment.

Disadvantages: Despite the advantages, this increased automation could reduce transparency, as the complexity of AI decisions can obscure why certain bids were made or why performance changed. Advertisers also face limited manual override capabilities, which may be challenging for campaigns needing specific strategic targeting. Furthermore, the system’s effectiveness is highly dependent on data quality; poor quality or incomplete data inputs can negatively impact AI bidding decisions.

Why It Matters:

For advertisers, embracing smarter automation is essential to scale campaigns efficiently and compete in increasingly competitive auction environments. However, it requires trust in AI systems, constant monitoring, and readiness to intervene when necessary. This evolution calls for marketers to refine data collection practices and focus on higher-level strategy and creative optimization.

Smart Bidding Gets a Major Upgrade with Exploration Mode

What It Is:

In June 2025, Google Ads introduced Smart Bidding Exploration Mode — the most significant enhancement to its bidding system in over a decade. This new feature enables advertisers to tap into untapped, high-potential search queries without altering audience targeting or campaign segmentation.

What’s Changed:

• Exploration Layer Added to Smart Bidding: The algorithm now includes an exploratory bidding layer, which dynamically adjusts bids on previously ignored or low-volume keywords that show signs of high intent or conversion potential.
• Segmentation Integrity Maintained: Unlike keyword expansion tactics that dilute targeting, Exploration Mode retains your original audience and geographic settings, keeping campaigns tightly aligned with core segments.
• Works Seamlessly with Existing Strategies: This feature integrates directly with Target ROAS and Maximize Conversions, leveraging historical performance data and predictive signals to discover new queries without compromising efficiency.

Advantages: This mode promises increased conversion volume by bidding into untapped queries, unlocking new growth without expanding budgets or reworking audience lists. Advertisers also benefit from hands-off optimization, as Smart Bidding Exploration handles discovery automatically—eliminating the need for extensive keyword research or manual testing. Finally, campaigns gain better query diversity, exposure to a broader set of converting queries, helping scale performance while maintaining strategic alignment.

Disadvantages: However, there is a potential for volatility; as the system tests unfamiliar query patterns, initial fluctuations in cost-per-conversion or ROAS may occur. Advertisers also face reduced control since keyword choices are more opaque, offering less visibility into exactly which new queries are being tested and why. Lastly, a learning curve is required for success, which relies on understanding the data signals and allowing sufficient time for the model to learn effectively.

Why It Matters:

Smart Bidding Exploration Mode marks a major leap in Google’s automation strategy. It’s designed for growth-hungry advertisers who want to scale without sacrificing audience precision. With the rising complexity of search behavior, this feature helps you stay competitive by letting AI do what it does best: find the conversions you didn’t know you were missing.

YouTube Ads Introduce Store Pickup Tag

What It Is:

In June 2025, YouTube rolled out the Store Pickup tag, a new feature for video ads that highlights when a product is available for local in-store pickup. This tag appears directly on YouTube video creatives, enabling brands to connect online engagement with offline shopping intent — effectively supporting click-to-brick journeys.

What’s Changed:

• Local Availability Tag in Video Ads: Advertisers can now add a “Store Pickup Available” label to their YouTube ads, signaling to viewers that the product they’re seeing can be picked up nearby.
• Real-Time Location Integration: The feature dynamically uses store-level inventory and viewer location to display accurate pickup info, helping bridge the gap between digital interest and physical store traffic.
• Works with Local Inventory Ads & Performance Max: Seamlessly integrates with Google Merchant Center and Performance Max for Store Goals, feeding real-time stock data into the video ad experience.

Advantages: This feature significantly boosts omnichannel conversions, encouraging users to act on purchase intent by visiting local stores and reducing friction in the buying journey. It also improves upper-funnel ROI by tying YouTube’s top-of-funnel reach to measurable offline sales, giving marketers a stronger case for brand video investment. Furthermore, it enhances local relevance as viewers are shown product availability based on their actual location, making ads more personalized and timely.

Disadvantages: A key disadvantage is the requirement for accurate inventory feeds; success depends on real-time local inventory data, as outdated feeds can create poor customer experiences. The feature is also limited to eligible campaign types, only working with select campaign structures and formats (e.g., video action campaigns with store goals). Finally, it may not be ideal for all brands, offering limited value for e-commerce-only or DTC brands without a retail footprint.

Why It Matters:

As consumer behavior continues shifting toward hybrid shopping (research online, buy in-store), YouTube’s Store Pickup tag empowers brands to capitalize on offline buying intent directly from video ads. It’s a powerful tool for retailers looking to enhance omnichannel campaigns and turn awareness into foot traffic — especially during product launches, local promos, or peak retail seasons.

June 2025 Paid Social Media Updates: What’s New and How It Transforms Your Campaigns

In June 2025, major platforms like Meta (Facebook & Instagram), TikTok, and LinkedIn rolled out exciting new features and improvements, many powered by advanced AI, designed to make campaigns smarter, more engaging, and easier to manage.

Meta Launches AI-Driven Dynamic Creative Optimization (DCO) for Stories and Reels Ads

What It Is:

Meta’s new Dynamic Creative Optimization leverages generative AI to automatically create, test, and serve personalized video and image ads specifically for Stories and Reels placements. Instead of manually assembling multiple ad variations, advertisers upload core assets (text, images, video clips), and Meta’s AI generates dozens of tailored creative combinations optimized for individual viewer preferences in real time.

What’s Changed:

• Before: Advertisers had to manually create multiple creative variants for Stories and Reels, a time-consuming process with limited scalability.
• Now: The AI handles creative assembly and personalization, instantly adapting formats, visuals, and messaging based on user engagement signals.

Advantages: This update offers increased engagement because personalized content resonates better with viewers, driving higher watch times, swipe-ups, and conversions. It also provides significant efficiency, saving creative development time and budget, especially for brands targeting diverse audiences. Finally, the AI enables continuous learning, optimizing creative combinations dynamically based on real-time performance data.

Disadvantages: Some brands may express concerns about brand control, worrying about losing creative consistency with AI-generated assets. There’s also a possibility of quality variance, where automated creatives may occasionally produce combinations that don’t align perfectly with brand voice or messaging. Additionally, the dependence on data means AI effectiveness hinges on robust audience data and engagement signals.

Why It Matters:

Video Stories and Reels continue to dominate user attention on Meta platforms. This update empowers advertisers to scale high-impact, personalized ads without ballooning production costs. For companies, this means better user engagement and ROI, plus the ability to reach audiences with messages that truly connect.

TikTok Expands AI-Powered Audience Segmentation and Predictive Targeting

What It Is:

TikTok has enhanced its AI-driven audience targeting by introducing predictive segmentation tools that automatically identify high-value user groups based on behavior, content consumption patterns, and past purchase signals. Advertisers can now create campaigns that dynamically adjust audience targets to focus on users most likely to convert.

What’s Changed:

• Before: Audience segmentation relied heavily on manual setup and fixed interest categories.
• Now: TikTok’s AI continuously refines segments and predicts user intent, updating campaign targeting on the fly for better precision.

Advantages: This expansion leads to higher conversion rates as ads reach users with higher intent and purchase probability. It also results in time savings, with less manual audience research and guesswork. Ultimately, there’s improved ROI as the budget is allocated to segments showing stronger engagement and conversions.

Disadvantages: A potential drawback is less transparency, as advertisers receive less insight into how segments are formed or adjusted. There are also data privacy concerns because increasing reliance on AI raises questions about data use and consent compliance. Finally, some advertisers may develop a potential over-reliance on AI without layering strategic inputs.

Why It Matters:

TikTok’s rise as a paid social powerhouse demands advertisers harness its AI to reach the right users at the right time. By automating audience refinement, campaigns become more efficient and effective, unlocking higher sales and brand growth opportunities in this fast-moving environment.

Meta Expands Advantage+ Campaigns & Launches Opportunity Score

What It Is:

In June 2025, Meta rolled out a major update to Advantage+ campaigns, introducing new goal-specific campaign options and unveiling the Opportunity Score — an AI-powered diagnostic tool that scores campaigns from 0 to 100 based on their alignment with Meta’s best practices.

What’s Changed:

• Expanded Campaign Objectives: Advantage+ now supports multiple conversion goals, including Video Views, Link Clicks, and Website Conversions. This lets advertisers tailor automation to their unique funnel stage and KPIs.
• Opportunity Score Introduced: A new AI-powered metric that evaluates how well a campaign adheres to optimal structure, creative formats, placements, and budget strategies. Scores range from 0–100, with higher scores correlating with lower cost-per-result and higher efficiency.
• In-Platform Recommendations: When scores are low, Meta provides actionable guidance to improve performance, including creative tweaks, bidding suggestions, and targeting adjustments.

Advantages: These updates lead to improved cost efficiency; early tests show campaigns with high Opportunity Scores can reduce cost-per-result by ~5% or more. They also facilitate smarter campaign optimization, as the AI doesn’t just automate—it teaches advertisers how to improve outcomes with data-backed recommendations. Finally, custom automation is now possible, allowing marketers to choose goals that better match their funnel strategy, rather than relying on broad “sales” targeting.

Disadvantages: A key concern is limited human control as automation deepens, which may frustrate advanced media buyers. While promising, performance consistency may vary for newer goals like video views, which are still training under the Advantage+ system. Additionally, a high Opportunity Score doesn’t always guarantee it’s the right strategic move for your business model or audience.

Why It Matters:

This update shows Meta’s push to make Advantage+ not just automated, but intelligent and strategic. With Opportunity Score, marketers now get real-time coaching on how to improve their ads — creating a smarter, more responsive way to scale without needing a full in-house media team. It’s a major step in Meta’s effort to democratize high-performance media buying.

LinkedIn Introduces New Video Ad Tools for B2B Marketers

What It Is:

In June 2025, LinkedIn launched a suite of premium video ad formats tailored for B2B marketers seeking to drive brand awareness, influence decision-makers, and improve upper-funnel performance. These tools expand LinkedIn’s video-first capabilities across desktop and mobile, with new reservation-based ad placements designed for strategic, high-impact moments.

What’s Changed:

• First Impression Ads (New Format): A full-screen vertical video ad that appears as the very first ad a user sees each day. Ideal for major launches, key announcements, or time-sensitive events, its exclusivity drives higher recall and brand visibility.
• Reserved Ads for Sponsored Content: Brands can now secure top-of-feed placement for a variety of Sponsored Content formats: Thought Leader Ads, Single Image Ads, and Document Ads. This guarantees visibility in prime real estate, boosting engagement for critical content.
• CTV Expansion (in parallel updates): LinkedIn also signaled broader Connected TV (CTV) experimentation, expanding beyond the LinkedIn feed to engage B2B audiences across multiple screens.

Advantages: These new tools offer premium brand positioning, as guaranteed placement gives brands control over when and where their message appears—especially valuable during product launches or tentpole campaigns. They also introduce vertical video for professional audiences, with LinkedIn now supporting mobile-native, full-screen video, a format proven to drive attention and storytelling effectiveness. Lastly, the ability to reserve space for Document and Thought Leader Ads boosts thought leadership by supporting deeper brand narratives in high-traffic zones.

Disadvantages: However, these reserved placements come with a higher cost barrier, potentially limiting access for smaller B2B advertisers. There’s also limited inventory due to the exclusive and limited daily slots, especially around major industry events. Finally, brand suitability needs monitoring as full-screen video and top-of-feed placements demand high-quality creative to avoid wasted impressions or negative perception.

Why It Matters:

With decision-makers increasingly consuming content across devices and during off-hours, LinkedIn’s new video ad tools give B2B marketers powerful visibility tools to stand out in a crowded landscape. These formats blur the line between consumer and business media — elevating the creative bar and enabling B2B brands to act more like B2C in how they capture attention and build brand equity.

Programmatic Advertising Updates June 2025: Unlocking Smarter Automation & Privacy-First Innovations

In June 2025, there are major steps forward in AI-powered bidding, privacy-compliant targeting, and holistic attribution — all aimed at making campaigns more efficient, relevant, and measurable than ever before.

AI-Enhanced Predictive Bidding Algorithms Become the New Standard

What It Is:

Demand-Side Platforms (DSPs) now embed highly advanced AI bidding models that predict the value of every single ad impression in real time. These algorithms go beyond simple cost-per-click or conversion goals — they analyze multiple variables such as user device, Browse behavior, time of day, location, ad placement context, historical conversion patterns, and external factors like market trends to forecast the likelihood that a specific impression will deliver a conversion or meaningful engagement.

What Has Changed:

Prior to June 2025, bidding automation was largely rule-based or used basic machine learning models with limited granularity. Advertisers set target CPA (cost per acquisition) or ROAS (return on ad spend) goals, but the system adjusted bids at a broad audience or campaign level. The newest AI models bid on each impression individually, dynamically increasing or decreasing bids to maximize the chance of success while optimizing overall spend.

Practical Example:

Suppose you’re running a programmatic campaign for a travel brand promoting last-minute holiday deals. The AI might detect that a user Browse on a mobile device near the airport at 6 pm on a Friday has a higher likelihood to book than someone Browse on a desktop mid-week. It bids more aggressively on the former impression, potentially winning the auction and converting that user — while lowering bids on less valuable impressions, saving budget.

Advantages: This advancement enables hyper-targeted spend, directing money to the impressions with the highest predicted value, which leads to improved ROI with smarter bids resulting in better conversion rates at lower costs. The AI also offers exceptional adaptability, reacting instantly to changing user behavior, device usage, and competitive dynamics, and provides significant time savings as marketers no longer micromanage bids and pacing.

Disadvantages: However, a major concern is algorithm transparency; these AI models operate as black boxes, and advertisers may struggle to understand bid rationale, which complicates troubleshooting or strategic adjustments. The effectiveness is also heavily reliant on data quality, where incomplete or noisy data can degrade prediction accuracy, causing inefficient bidding. Lastly, new campaigns require a learning period or “warm-up” phase where the model gathers sufficient data to perform well.

Why It Matters:

Predictive bidding represents a quantum leap in programmatic efficiency. It means advertisers can compete smarter in real-time auctions without wasting budget on low-value impressions. The result is campaigns that are both cost-effective and scalable, enabling marketers to focus on broader strategy and creative innovation rather than bid minutiae.

Cross-Channel Attribution Models Deliver Unified Performance Insights

What It Is:

Cross-channel attribution is a method of analyzing how different digital marketing channels work together to drive conversions. In June 2025, programmatic platforms integrated data from display, video, connected TV (CTV), mobile apps, and paid social into unified AI-driven attribution models.

This allows advertisers to see how their programmatic ads contribute to conversions throughout the entire customer journey — not just the last click or interaction — enabling more accurate measurement and optimization.

What Has Changed:

Historically, programmatic campaigns were measured largely in isolation or with rudimentary attribution models (last-click or last-touch). These approaches often undervalue impressions and interactions that assist conversion earlier in the funnel.

The new models use AI to allocate conversion credit fairly across multiple touchpoints and channels, giving marketers a clearer picture of programmatic’s true impact.

Practical Example:

A consumer electronics brand running campaigns across CTV, mobile video, and display ads can now see that users typically first engage via a CTV ad at home, then click a mobile video ad during lunch break, before finally converting through a display ad on desktop. The attribution model credits each channel appropriately, informing budget allocation decisions.

Advantages: This integration leads to improved budget allocation, allowing brands to invest more in channels and tactics that genuinely move the needle. It provides a holistic view of the full customer journey, not just isolated touchpoints, and enables real-time optimization as campaigns can be adjusted dynamically based on multi-channel performance data. Ultimately, it allows for stronger business cases, demonstrating programmatic’s value to stakeholders with data-backed ROI.

Disadvantages: The implementation can involve integration complexity, requiring advanced data infrastructure and cooperation across platforms. There’s also a data privacy impact, as limits on user-level tracking can reduce attribution precision. Finally, interpreting models can be challenging, as different attribution approaches (linear, algorithmic, time decay) may produce conflicting insights, complicating decisions.

Why It Matters:

Attribution is the foundation of marketing optimization and budget justification. Cross-channel attribution ensures advertisers don’t undervalue programmatic ads’ role in multi-touch conversion paths. It fosters smarter spend, better campaign performance, and stronger alignment between marketing and business objectives.

MiQ Sigma – Unifying Programmatic & Cross‑Channel Insights

What It Is:

Launched on June 9, 2025, MiQ Sigma is a next-gen, AI-powered cross-platform programmatic solution that centralizes data and decision-making across all major digital ad channels. It merges over 300+ data streams — including display, video, CTV, mobile apps, and paid social — into a single dashboard for holistic planning and activation.

What’s Changed:

• One Unified Platform: Instead of siloed insights across DSPs and formats, MiQ Sigma offers a “single point of entry” into all key programmatic channels.
• AI-Led Activation & Forecasting: Integrated AI models optimize bidding, audience building, and media mix decisions in real time using predictive performance modeling.
• Cross-Channel Forecasting + Attribution: Marketers can plan and evaluate full-funnel performance across every major digital medium, from top-of-funnel awareness to conversion.

Advantages: MiQ Sigma eliminates data silos, finally bringing display, video, mobile, and social performance into one cohesive measurement and activation engine. It enables smarter budget allocation as AI evaluates which channel offers the best return for your goals and shifts spend accordingly. Campaign decisions become faster with real-time dashboards and unified forecasting reducing time-to-launch and decision-making complexity.

Disadvantages: Full benefits require platform adoption, meaning brands must consolidate data sources and workflows into MiQ Sigma, which can be a potentially large migration. The system’s effectiveness is dependent on AI trust, as automated optimizations may reduce transparency into why spend is shifting between channels. Lastly, the integration effort may require technical coordination with CRM, analytics, and ad ops teams.

Why It Matters:

MiQ Sigma is the first real attempt at true cross-platform programmatic orchestration, blending deep AI with seamless channel connectivity. In a fragmented digital media world, it empowers advertisers to maximize ROI holistically — not just channel by channel.

InnovidXP – Pixel-Free Purchase Attribution

What It Is:

Launched on June 11, 2025, InnovidXP’s new update introduces pixel-free, full-funnel attribution, allowing brands to measure the true impact of CTV and converged TV ads — all without relying on third-party cookies or tracking pixels.

What’s Changed:

• No-Pixel Attribution: Innovid now maps ad exposure to actual sales using privacy-compliant data partnerships and household-level identifiers — no JavaScript tags, cookies, or invasive tracking needed.
• CTV-Centric Measurement: Finally delivers accurate ROAS and sales lift reporting for streaming and linear TV buys, long a gap in attribution tools.
• Incrementality Insights: Advertisers gain visibility into what portion of conversions were directly driven by ad exposure versus organic behavior.

Advantages: InnovidXP is privacy-safe by design, solving attribution in a cookieless world without needing invasive tracking. It proves TV ad effectiveness, giving media buyers real proof of impact from their CTV and streaming campaigns. Being enterprise-ready, it’s tailored for large advertisers with complex media mixes and long sales cycles.

Disadvantages: Success requires clean data feeds, as attribution relies heavily on well-matched purchase and exposure data sets. It may be limited to certain publishers/partners, potentially not yet supporting 100% of CTV platforms depending on integrations. While strong on aggregate insights, it may offer less granular data compared to user-level detail available in legacy pixel-based systems.

Why It Matters:

As the industry exits the third-party cookie era, InnovidXP offers a future-proof attribution model, especially for brands heavily invested in TV, CTV, and omnichannel media. It’s a vital tool to connect upper-funnel media with real sales outcomes — without sacrificing consumer privacy.