Posts

Are AI Overviews Stealing Your Clicks? How Paid Search Teams Are Adapting to the Answer Engine Era

/0 Comments/in /by

Key Takeaways

  1. AI Overviews can reduce paid search click-through rates by more than 50 percent for affected queries, making impression share a critical visibility metric.
  2. Informational queries are most vulnerable. AI answers resolve research intent directly in the SERP, reducing the number of users who scroll to ads.
  3. Transactional and brand queries hold up better. Teams reallocating budget toward high-intent searches see more consistent engagement.
  4. Measurement frameworks need to expand. Click-through rate alone no longer tells the full story when impressions rise but clicks fall.
  5. Search is no longer a single channel. Brands that extend paid strategy to YouTube, Pmax, Demand Gen, Reddit, TikTok, and AI platforms capture demand earlier and across more touchpoints.

Your impression numbers look healthy. Your click-through rate tells a different story.

For many paid search teams, this is the new reality. AI Overviews now appear at the top of Google search results for millions of queries, answering user questions before they ever reach the ads. Impressions hold steady or climb. Clicks get harder to come by.

Research from Seer Interactive found that when AI Overviews appeared in search results, paid click-through rate dropped to 9.87 percent compared to 21.27 percent on the same queries without an overview. That translates to a 53.6 percent reduction in traffic.

Let’s look into why certain query types are more exposed than others and what paid search teams are doing right now to adapt their strategy, targeting, and measurement.

AI Overviews Are Reshaping the Search Results Page

When Google introduced AI Overviews, it fundamentally changed the architecture of the SERP. The AI-generated summary now occupies the most visible real estate at the top of many search results, answering the user’s question before they interact with anything else on the page.

For paid search, the implications are significant. Ads that once appeared near the top of the page now often appear below the AI summary. Users scroll past a detailed AI-generated answer before they encounter a paid result.

Google SERP showing an AI Overview summary occupying the top of the page with paid search ads appearing below the overview section

This is not just a visual shift. Seer Interactive’s research found that the presence of an AI Overview correlates with a 12 percentage point decrease in paid click-through rate. Across a full dataset, that translated to a 53.6 percent reduction in traffic compared to searches where no AI Overview was shown.

The core issue: paid search visibility is no longer the same as paid search attention. An impression in a SERP dominated by an AI Overview does not carry the same weight as an impression on a traditional results page.

If teams assume all impressions carry equal value, their performance data will remain difficult to interpret. Impressions go up. Clicks stay flat. Revenue and ad costs become harder to predict.

Understanding this requires analyzing which query types most frequently trigger AI Overviews and the resulting implications for budget allocation.

Why Informational Queries Are Becoming Less Valuable for Paid Search

Not all queries are equally at risk. AI Overviews appear far more often on informational queries than on high-intent queries, and that distinction matters for budget allocation. This is closely tied to the broader trend of zero-click searches, where users get what they need from the SERP itself and never click through to a website.

Now the AI summary answers the question on the spot. The research phase that once sent users scrolling through several pages of results has been compressed into a single AI-generated box. Users read the answer, get what they need, and move on without clicking.

Transactional queries tell a different story. Searches with clear purchase intent, such as pricing inquiries, product comparisons, and demo requests, are less likely to trigger an AI Overview. When they do, ads still perform reasonably well. According to the same Seer research, brand queries with AI Overviews present still generated a 16.36 percent click-through rate, well above the average for informational query types.

The practical implication: budget allocated to queries that consistently trigger AI Overviews is at higher risk of generating impressions without clicks. Identifying which queries in your account fall into that category is a practical first step toward protecting performance.

10 Paid Search Pivots Teams Are Making Right Now

Paid search teams are not waiting for Google to solve this. The following pivots reflect what practitioners are already doing to protect performance and adapt to a more competitive SERP.

Shift Budget Toward Transactional Queries

Informational searches increasingly resolve in the SERP. Queries like “what is a CRM” or “how does ROAS work” are prime territory for AI Overviews, which means fewer users scroll to ads.

Transactional searches behave differently. “Best CRM for small business,” “Salesforce pricing,” and “schedule a demo” queries still generate strong ad engagement. Auditing your campaigns for intent and moving spend away from informational keywords toward conversion-ready queries is one of the most direct ways to protect revenue.

Structure Campaigns Around Intent, Not Just Keywords

Traditional keyword groupings by topic are giving way to segmentation by intent stage. Organizing campaigns into informational, commercial, and transactional buckets allows teams to allocate budget with more precision and adjust quickly as AI Overview coverage expands.

When informational campaigns are isolated from high-intent traffic, reducing or pausing them becomes a cleaner decision. You can act without disrupting the campaigns that are still driving results.

Defend and Expand Brand Search

Brand queries are among the most resilient in an AI-driven search environment. Users searching for your company by name carry strong purchase intent, and brand ads still convert at high rates even when AI Overviews appear.

Without an active brand campaign, competitors can bid on your brand terms and capture that traffic directly. Protecting brand terms is a baseline priority that pays off consistently.

Make Ads More Visually Competitive

Ads appearing below an AI summary need to work harder to earn attention. Every available asset matters. Sitelinks add navigation options. Callouts reinforce value propositions. Structured snippets give product category detail. Pricing extensions answer a buyer’s primary question before they click.

A well-extended ad standing out below an AI Overview will consistently outperform a barebones text ad in the same position.

Write Ad Copy That Moves the Decision Forward

The user who sees your ad has likely already read an AI-generated summary of the topic. Ad copy should not repeat what the AI already covered. It should move the decision forward.

“Get a free audit” does more work than “Learn more about SEO.” Specificity converts when users are already past the information-gathering stage. Copy focused on differentiation, pricing clarity, or a clear next action earns the click that a generic brand message will not.

Expand Competitor Conquesting

AI Overviews frequently name specific products and brands when summarizing a category. After reading a summary that lists top CRM tools, a user often searches immediately for a specific brand’s alternatives or pricing. That is a conquesting opportunity.

Bidding on “[Competitor] alternative” and “[Competitor] vs [Your Brand]” queries reaches users at the moment they are actively comparing options. These searches happen right after the AI Overview has done the initial filtering for them.

Invest More in Remarketing and Audience Targeting

AI Overviews compress the research phase, but they rarely close the decision entirely. Many users read the summary, step away, and return to search again before converting. Remarketing lets you reconnect with those users in that return window.

First-party data becomes more valuable here. Building audience segments from site visitors, email lists, and CRM data gives teams the targeting precision that broad keyword bidding alone cannot provide.

Use Broader Match to Capture Conversational Queries

AI-influenced searches tend to be longer and more natural in phrasing. Users accustomed to conversational AI tools bring that style to their search queries. Exact match lists built for shorter, traditional keyword patterns will miss a growing share of that traffic. Revisiting your paid search bidding strategies with this in mind is worth the time.

Performance Max campaigns and broader match types help capture the longer, less predictable queries that are becoming more common. The trade-off is less control, which makes ongoing performance monitoring more important.

Rethink How You Measure Search Performance

Click-through rate dropping while impressions hold is not necessarily a failure. In an AI Overview environment, it is often an expected outcome. The mistake is treating CTR as the primary health indicator when the SERP environment has fundamentally changed.

Teams shifting their measurement frameworks are tracking impression share, top-of-page visibility rate, branded search volume growth, and assisted conversions alongside traditional metrics. Together, those signals give a fuller picture of what search is actually contributing to business outcomes.

Measurement of search performance.

Source: The Media Captain

Diversify Beyond Search Ads

Zero-click trends reduce the available inventory of high-quality search clicks. As explored in the zero-click future of search, search still matters, but it cannot carry the same weight alone that it once did.

Demand Gen campaigns, YouTube, Display, and paid social all help reach users earlier in the funnel before they arrive at Google ready to buy. Search then becomes the capture mechanism for demand built elsewhere. The full paid media mix has to work together more tightly than before.

Paid Search Measurement Is Changing

The instinct to look at click-through rate when paid performance dips is understandable. It is one of the most visible metrics in any search account. In an AI Overview environment, though, it is an incomplete signal.

Rising impression counts with declining click-through rate is not always a campaign failure. It often reflects a change in SERP composition. Search Engine Land’s analysis of paid search teams confirms that AI Overviews are lowering CTR and raising CPCs simultaneously, compressing the buyer journey and requiring a measurement evolution rather than just a performance fix.

The Adthena interface.

Source: Adthena

Impression share tracks how often ads appear for eligible queries. A high impression share with low CTR confirms visibility is strong but engagement is soft. That is a different problem than an impression share problem, and it calls for a different solution.

Branded search volume is a proxy for overall demand. If awareness campaigns and upper-funnel efforts are working, brand search volume should rise over time. It is one of the cleaner ways to confirm whether broader marketing spend is translating into search intent.

Assisted conversions show how search contributes to outcomes that close on a different channel or in a later session. Search often does awareness and consideration work that surfaces in the last-click data of another touchpoint entirely.

Top-of-page rate tracks the share of impressions appearing in the highest-visibility positions above organic results. In an AI Overview environment, that position matters more than it ever has. Semrush’s AI Overviews study found that AI Overview prevalence varies significantly by industry, which means teams with niche-specific data will have an advantage in calibrating how aggressively to adjust their measurement benchmarks.

A SEMrush graphic about industries impacted by AI overviews.

Source: Semrush

The Bigger Shift: Search Is Becoming an Ecosystem

Google is still the dominant search platform. But as AI SEO continues to reshape how content gets discovered, search as a behavior now happens across a much wider set of surfaces.

Users looking for product reviews turn to Reddit. Short-form how-to content lives on YouTube and TikTok. AI tools like Perplexity and ChatGPT answer research queries directly. Younger audiences often bypass Google for discovery entirely, using social platforms as their primary search interface.

A graphic showing many different marketing channels.

Source: Yewx

For paid teams, search advertising strategy has expanded to match. Visibility on Google still matters. So does presence on the platforms where users form opinions and compare options before they ever open a search bar.

Paid search budgets are increasingly being redistributed to reflect this. Teams that once concentrated the majority of digital spend in Google search are now testing YouTube, PMax, Demand Gen, Reddit Ads, and TikTok in parallel. The goal is not to abandon search but to meet demand at every point it forms.

FAQs

What Is the Impact of Generative AI on Paid Search and PPC?

Generative AI has compressed the buyer research journey and pushed ads lower on the page. Seer Interactive’s research found paid click-through rate drops by more than 53 percent on queries where an AI Overview appears. The effect is most pronounced on informational and question-based searches. Transactional queries with clear purchase intent remain more resilient.

How Will AI Mode Redefine Paid Search Advertising?

Google’s AI Mode delivers deeper, more conversational answers than standard AI Overviews, which may further compress informational search traffic. For paid teams, this reinforces the shift toward transactional keywords, stronger ad creative, and multi-channel investment. Teams monitoring how AI-powered search is evolving will be better positioned to adapt their bidding and targeting structures before the impact hits performance.

What Solutions Help Improve AI-Driven Search Visibility in Paid Search?

Focus on transactional keyword targeting, expand ad extensions to maximize SERP real estate, and invest in brand defense campaigns. Pairing paid strategy with SEO content that earns AI Overview citations also improves overall search presence. Impression share reporting and top-of-page rate data in Google Ads are the most direct indicators of where visibility is slipping.

What Tools Help Analyze Paid Search Ads in AI-First Search Environments?

Google Ads provides impression share, top-of-page rate, and CTR data needed to diagnose AI Overview impact. Platforms like Adthena track how AI search changes are affecting competitive ad positioning in real time. Tools like Semrush and Ahrefs are also useful for AI Overview keyword tracking, helping your team understand what keywords are triggering AIOs.

Conclusion

The teams that adapt their targeting, measurement, and channel strategy will find that paid search still delivers. The approach that worked in 2022 or 2024 just needs a serious audit.

AI Overviews have compressed the research phase, shifted where attention falls on the SERP, and exposed the limitations of click-through rate as a standalone KPI. Marketers who recognize those shifts early and adjust accordingly will stay competitive as Google’s search experience continues to evolve.

Search is not disappearing, but the way people use it is. The paid media strategies built for that evolution will outperform those still built for a world where clicking through to a website was the default outcome of every query.

For a deeper look at how paid and organic strategies work together in this environment, explore the complete guide to Google Ads and the SEO strategy guide to see how these channels can reinforce each other. Our Google Ads Grader will also help make sure the ads you do make are best positioned to succeed.

Read more at Read More

How High-Growth Companies Actually Measure Marketing

/0 Comments/in /by

Key Takeaways

  1. No single measurement method can answer all the questions modern marketing leaders face. A layered stack combining multiple tools is necessary.
  2. The challenge of marketing attribution is structural: it assigns credit to touchpoints but cannot prove causality. It works best for tactical optimization, not strategic decisions.
  3. Marketing mix modeling identifies marginal returns and channel saturation, helping guide long-term budget allocation.
  4. Incrementality testing is the most reliable way to determine whether marketing activity actually created outcomes, rather than captured demand that already existed.
  5. Organizing measurement teams into pioneers, settlers, and planners ensures each type of work gets the right standards and decision-making speed.

Most marketing leaders know the challenge of marketing attribution well: you have dashboards full of data, but the numbers don’t reliably answer which investments are actually driving growth. The instinct is to search for a better tool, a smarter model, or a more accurate attribution system. But the organizations getting measurement right have moved past that instinct.

They have stopped looking for a single source of truth. The challenge of marketing attribution is part of a broader problem: modern marketing environments are too complex for one method to cover everything. Discovery happens across too many platforms, buyer journeys are too fragmented, and privacy changes have eroded too much signal for any single tool to give a complete picture.

What works instead is a layered approach. Different measurement methods answer different questions, and high-growth organizations combine them deliberately. Marketing mix modeling guides strategic budget allocation. Incrementality testing validates whether a specific activity caused a result. Platform data handles day-to-day campaign optimization. Each plays a defined role. None of them works as a standalone strategy.

This is the second piece in a three-part series on modern marketing measurement. The first part examined why traditional metrics like traffic, rankings, and ROAS are becoming less reliable. This piece covers how to build a measurement system that actually supports growth decisions.

Why No Single Measurement Method Works Anymore

The digital marketing attribution tools most teams rely on were built for a different environment. They worked well when user journeys were relatively linear, cookies tracked reliably across sessions, and most discovery happened through channels that were easy to log. That environment is gone.

Today, a buyer might encounter a brand through an AI-generated answer, research it on YouTube, discuss it in a private message thread, and convert through a branded search three weeks later. The attribution system credits the last touchpoint. The channels that actually shaped the decision get little or nothing.

This is the core structural problem. Marketing attribution models are designed to assign credit, not establish cause. Even sophisticated multi-touch attribution marketing approaches still operate within the same fundamental constraint: they can show which touchpoints preceded a conversion, but they cannot prove that removing any of them would have changed the outcome.

What high-growth organizations have recognized is that different measurement tools answer different questions. Attribution modeling answers: which touchpoints were present before a conversion? Marketing mix modeling answers: where are marginal returns strongest across channels over time? Incrementality testing answers: did this specific activity actually change outcomes? 

A graphic talking about how strong measurement incorporates more than one method.

Each question matters. Each requires a different approach. According to NP Digital research, 90 percent of high-growth marketers prioritize incrementality testing, 61 percent use attribution modeling, and 42 percent use marketing mix modeling. The most effective teams use all three, weighted by the decision at hand.

Marketing Mix Modeling as Strategic Guidance

Marketing mix modeling, or MMM, takes a different approach to measurement than attribution. Rather than tracking individual user journeys, it uses aggregated historical data to model the relationship between marketing spend and business outcomes across channels over time. The result is a view of marginal returns that attribution systems cannot provide.

A graphic talking about when timing matters more than touchpoints.

MMM is most useful for identifying where each additional dollar of spend in a channel produces diminishing returns. A channel running at a strong blended ROAS may look efficient in a dashboard while the last 30 percent of its budget is generating negligible incremental revenue. MMM surfaces that inefficiency. It also helps identify cross-channel effects, such as how video or brand investment upstream affects conversion rates in paid search downstream.

For strategic budget allocation, this makes MMM the most reliable tool available. It does not require user-level tracking, which means privacy changes and cookie deprecation do not erode its accuracy the way they do for attribution. Quarterly MMM runs can consistently improve long-term budget decisions even when day-to-day attribution signals are noisy.

MMM does have real limits. It struggles to quantify upper-funnel brand building accurately, because the lag between a brand impression and a downstream conversion is too long and too indirect for historical correlations to capture cleanly. Organizations using MMM for strategic guidance while supplementing it with brand tracking and perception studies get the most complete picture.

<h2> Incrementality Testing as the Causal Engine </h2>

If MMM provides strategic direction, incrementality testing provides causal proof. The question it answers is specific: would this outcome have happened if this marketing activity had not occurred? That is a fundamentally different question from what attribution models ask, and the answer is far more useful for deciding where to invest.

The most common incrementality approaches include geo experiments, holdout tests, and campaign pauses. In a geo experiment, matched geographic markets are identified and spend is withheld in one group while maintained in another. The difference in outcomes between the two groups isolates the causal lift from the marketing activity. Holdout tests apply the same logic at the audience level. Campaign pauses, while cruder, can also reveal whether results drop when spend stops. 

For teams running Amazon attribution or other marketplace-based measurement, incrementality testing is especially valuable because platform-reported conversions often reflect demand that already existed rather than demand the campaign created.

NP Digital research tracking incremental versus attributed conversions across channels found meaningful gaps in almost every case. Organic social showed 13 percent incremental lift against 3 percent attributed lift. Paid social showed 17 percent incremental lift against 24 percent attributed, suggesting attribution was over-crediting that channel. These gaps directly affect where budget should go, and they are invisible without incrementality testing.

A graphic talking about incremental lift by channel.

Incrementality testing requires planning and clean data, but it does not require a large budget. Even a single well-designed geo holdout on a major channel provides more reliable insight into causal impact than months of attribution reporting.

Platform Data Still Matters, But Only for Optimization

Platform dashboards from Google, Meta, and other ad platforms remain useful, but their role is narrower than most teams treat it. The attribution blind spots built into platform reporting are structural, not accidental. Platforms are designed to optimize campaign performance within their own ecosystems. They are not designed to tell you whether that performance changed your business.

For day-to-day decisions, platform data is the right tool. Pacing spend against budget, adjusting bids based on performance signals, identifying creative fatigue, and diagnosing delivery issues all rely on platform metrics. These are operational decisions, and platform data handles them well.

Where platform data becomes unreliable is in strategic decisions. Algorithms optimize toward users most likely to convert, which means they systematically favor demand capture over demand creation. A high ROAS figure in a platform dashboard may reflect an efficient algorithm, not effective marketing. 

According to NP Digital research, poor attribution costs small businesses an average of 19.4 percent of ad spend, mid-market companies 11.5 percent, and enterprise brands 7.7 percent. That wasted spend is largely invisible in platform reporting because the platforms have no incentive to surface it.

A graphic talking about ad spend wasted due to ppor attribution.

The practical guidance is to use platform metrics for what they are: tactical steering, not strategic truth.

The Pioneer–Settler–Planner Measurement Model

Building a layered measurement system is not just a technical challenge. It is an organizational one. There are three distinct roles that every effective measurement organization needs: pioneers, settlers, and planners.

  • Pioneers work at the edges of what is currently measurable. They run incrementality experiments, build initial marketing mix models, test geo holdouts, and pressure-test assumptions that may no longer hold. Their work is uncertain by design. Pioneers do not deliver certainty; they deliver direction. Holding them to the same standards of statistical confidence as operational reporting will stop this work before it produces value.
  • Settlers take what emerges from experimentation and turn it into repeatable processes. They refine models, tighten assumptions, and connect insights back to planning decisions. This is where early MMM runs mature into playbooks, and where incrementality test results become frameworks teams can apply consistently. Settlers build trust by translating directional insight into systems that can actually be run.
  • Planners keep daily operations running. They rely on platform data, attribution signals, and conversion mechanics to manage spend in real time. This layer is necessary; without it, execution falls apart. But planners should not be asked to explain long-term growth or diagnose structural shifts in performance. Their focus is optimizing efficiency within channel constraints.

The failure mode most organizations fall into is applying planner-level standards of certainty to pioneer-level work. Requiring 95 percent statistical confidence from experiments that need time to develop guarantees that nothing new gets built. A model with 60 percent directional confidence, paired with fast iteration, consistently outperforms a perfect answer that arrives a quarter too late.

How High-Growth Companies Allocate Measurement Resources

NP Digital research tracking measurement practices across Canadian brands found a clear divide between average organizations and high-growth ones. Average teams allocate roughly 65 percent of their measurement influence to platform dashboards and 25 percent to attribution tools, leaving little room for more strategic methods.

High-growth brands with over $750,000 in annual media investment look meaningfully different. Platform dashboard reliance drops to around 45 percent. Attribution tool usage decreases to 15 percent. MMM grows from 5 percent to 20 percent. Incrementality testing reaches 10 percent, and early generative search optimization work accounts for another 10 percent.

These organizations are not abandoning attribution or platform data. They are reweighting them. The logic is straightforward: in markets that keep changing, you build measurement capability where change is happening, not where familiarity feels safe. The goal across all of these methods is directional confidence, meaning enough signal to make better budget decisions faster, not perfect certainty that arrives after the opportunity has closed.

Three-tier pyramid diagram from NP Digital showing the outcomes-first measurement stack, with business outcomes at the top, demand signals in the middle, and visibility and influence metrics forming the base.

Seven Steps to Evolve Your Measurement System

Rebuilding a measurement system does not require replacing everything at once. The organizations that do this well evolve gradually, adding capability in the right order rather than attempting a full overhaul.

  1. Map your current measurement inputs. List every tool and data source your team uses and identify where each one sits: operational platform data, attribution modeling, MMM, or incrementality. Most teams discover they are heavily concentrated in the first two.
  2. Identify the decision gaps. Be explicit about which strategic questions your current stack cannot answer. The challenge of marketing attribution is most visible here: where are you making budget decisions based on blended ROAS without visibility into marginal returns? Where are you crediting channels that may just be capturing existing demand?
  3. Introduce basic modeling. Even a simple quarterly MMM run provides more strategic direction than attribution alone. Start with your highest-spend channels and the business outcomes most directly tied to revenue.
  4. Run your first incrementality test. Pick one major channel and design a geo holdout or holdout audience test. The goal is not perfection; it is building the organizational capability and comfort with this type of measurement.
  5. Adapt governance expectations. Attribution reports will not disappear from leadership reviews overnight. Running a parallel track that shows incrementality and MMM findings alongside attribution data builds confidence in the new approach without requiring a full transition.
  6. Build processes gradually. Settlers turn pioneer experiments into repeatable workflows. Each incrementality test should produce a documented methodology that makes the next one faster and cheaper.
  7. Increase decision cadence. One of the advantages of directional confidence over perfect certainty is speed. Weekly budget adjustments based on incrementality signals and MMM outputs outperform quarterly reallocations based on attribution reports.
Four-panel action plan from NP Digital showing the first week of a 30-day measurement reset, covering reporting audits, profit-aware KPIs, definition standardization, and data hygiene improvements.

FAQs

What Is Marketing Attribution?

Marketing attribution is the process of assigning credit to the marketing touchpoints that contributed to a conversion. Common marketing attribution models include last-click, first-click, linear, and data-driven attribution. Each assigns credit differently across the customer journey. Attribution is most useful for optimizing campaign performance within channels, but it cannot establish whether marketing caused a business outcome.

How Do You Measure Marketing Attribution?

Attribution is measured by connecting conversion data to the touchpoints that preceded it, using tracking pixels, UTM parameters, and CRM data to map the path. Marketing attribution software platforms automate this process and offer different attribution models to choose from. The key limitation to understand is that all attribution approaches assign credit based on correlation, not causality.

Which Is the Best Software for Tracking Marketing Attribution?

The best marketing attribution software depends on your business model and measurement goals. Google Analytics 4 and platform-native dashboards handle basic attribution well. Tools like Northbeam, Triple Whale, and Rockerbox are built for direct-response and e-commerce contexts. For strategic decisions, attribution software works best when paired with MMM and incrementality testing rather than used in isolation.

Conclusion

The challenge of marketing attribution is not a problem that better software alone solves. It is a structural limitation of what attribution can do. Credit assignment and causal proof are different things, and conflating them leads to budget decisions that favor demand capture over demand creation.

High-growth organizations have addressed this by building layered measurement systems where each tool plays a defined role: platform data for operational steering, attribution for tactical signals, MMM for strategic allocation, and incrementality testing for causal validation. The next piece in this series examines how marketing leaders use these signals together to decide where the next dollar of investment should go.

If you want to go deeper on where attribution breaks down before moving to that piece, this breakdown of marketing attribution blind spots covers the specific failure modes in detail. For a broader view of how to connect measurement to revenue decisions, this guide to digital marketing attribution is a useful reference.

Read more at Read More

One in five ChatGPT clicks go to Google: Study

/0 Comments/in , /by
Traffic funnel few winners

Over 30% of outbound clicks go to just 10 domains, with Google alone taking more than 20%, according to a new Semrush study published today.

ChatGPT also relies less on the live web, triggering search on 34.5% of queries, down from 46% in late 2024.

The big picture. ChatGPT’s growth has plateaued, and its role in how users navigate the web is evolving unevenly.

  • Referral traffic from ChatGPT grew 206%, comparing January 2025 to January 2026.

The details. Most ChatGPT referral traffic still goes to a small set of sites, even as more sites receive some traffic.

  • Google accounts for 21.6% of all ChatGPT referral traffic.
  • The next nine domains bring the top 10 to just over 30% of referrals.
  • Most other sites get a long tail of minimal traffic.
  • The number of domains receiving referrals expanded, peaking at around 260,000 in 2025 before settling near 170,000.

Why we care. Visibility in ChatGPT doesn’t translate evenly into traffic, and you’ll likely see marginal referral impact. The decline in search-triggered queries also limits your chances to earn citations and traffic.

When ChatGPT searches. It defaults to pre-trained knowledge and uses web search in specific cases, including:

  • User requests for sources.
  • Questions about recent events.
  • Situations where the model lacks confidence.

Behavior shift. Most ChatGPT prompts still don’t resemble traditional search queries.

  • Between 65% and 85% of prompts don’t match standard keywords, reflecting more complex, conversational inputs.
  • Meanwhile, engagement is deepening. Queries per session jumped 50% in late 2025.

About the data. Semrush analyzed more than 1 billion lines of U.S. clickstream data from October 2024 to February 2026 across a 200 million-user panel, tracking prompts, referral destinations, and search usage.

The study. ChatGPT traffic analysis: Insights from 17 months of clickstream data

Read more at Read More

New Google Maps features: Local Guides redesign, AI captions, photo sharing

/0 Comments/in , /by
Google Maps AI updates

Google is rolling out new Google Maps features that make it easier to contribute photos, reviews, and local insights, while adding Gemini-powered caption suggestions.

Local Guides redesign. Contributor profiles are getting more visibility. Total points now appear more prominently, Local Guide levels are easier to spot, and badge designs have been refreshed.

  • Top contributors will also stand out more in reviews with new gold profile indicators.

AI caption drafts. Google is also introducing AI-generated caption drafts. Gemini analyzes selected images and suggests text you can edit or discard.

  • Caption suggestions are available in English on iOS in the U.S., with Android and broader global expansion planned.

Media sharing. Google Maps now shows recent photos and videos directly in the Contribute tab, making uploads faster.

  • If you enable media access, Google Maps will suggest images from your camera roll that are ready to post with a tap.
  • This feature is now live globally on iOS and Android.

Why we care. Google is making it easier to create and scale fresh local content, which can directly affect rankings and visibility. At the same time, stronger contributor signals may influence which reviews users trust and which businesses win clicks.

Read more at Read More

Web Design and Development San Diego

5 priorities for lead gen in AI-driven advertising

/0 Comments/in , /by
5 priorities for lead gen in AI-driven advertising

Many of today’s PPC tools were designed to be easily accessible to ecommerce. That doesn’t mean lead gen can’t take advantage of them, but it does mean more intentional application is required.

Lead gen with AI still requires a creative approach, and many conventional ecommerce tools still apply — but not always in the same way.

Here are the priorities that matter most for succeeding with lead gen using AI.

Disclosure: I’m a Microsoft employee. While this guidance is platform-agnostic, I’ll reference examples that lean into Microsoft Advertising tooling. The principles apply broadly across platforms.

1. Fix your conversion data first

This is the single most important thing you can do as AI becomes more embedded in media buying.

Between evolving attribution models, privacy changes, different platform connections, and shifts in how consumers engage with brands, it’s reasonable to ask whether your data is still telling an accurate story.

Start by auditing your CRM or lead management system. Make sure the data you pass back to advertising platforms is clean, consistent, and intentional.

In most cases, data issues stem from human choices rather than technical failures. Still, there are a few technical checks that matter:

  • Confirm conversions are firing consistently.
  • Regularly review conversion goal diagnostics.
  • Validate that lead status updates and downstream signals are actually flowing back.

If AI systems are learning from your data, you want to be confident that the feedback loop reflects reality.

Dig deeper: How to make automation work for lead gen PPC

Your customers search everywhere. Make sure your brand shows up.

The SEO toolkit you know, plus the AI visibility data you need.

Start Free Trial
Get started with

Semrush One Logo

2. Make landing pages easy to ingest and easy to understand

Lead gen campaigns often have multiple conversion paths, which can be helpful for users. But from an AI perspective, ambiguity is a risk.

Your landing pages should make it clear:

  • What action you want the user to take.
  • What happens after action is taken.
  • Which conversions matter most.

Redundant or unclear conversion paths can confuse both users and systems. If AI crawlers detect that anticipated outcomes are inconsistent, they may begin to question the accuracy of what your site claims to do. That can limit eligibility for certain placements.

Language clarity matters just as much. Avoid jargon, eccentric terminology, or internally focused phrasing when describing your services. Clear, plain language makes it easier for AI systems to understand who you are, what you offer, and how to match creative to the right audience.

A practical test: Put your website content into a Performance Max campaign builder and review how the system attempts to position your business. If you agree with the messaging, imagery, and framing, your site is likely easy to understand. If not, that feedback is valuable.

You can also paste your site content into AI assistants and ask them to describe your business and services. If the response aligns with reality, you’re in a good place. If it doesn’t, that’s a signal to refine your content.

Behavioral analytics tools, like Clarity, can help you understand exactly how humans are engaging with your site and how often AI tools are crawling your site.

Dig deeper: AI tools for PPC, AI search, and social campaigns: What’s worth using now

3. Budget across the entire funnel

Lead gen has always struggled with long conversion cycles. That challenge doesn’t go away, and in some ways, it becomes more pronounced.

AI-driven systems increasingly weigh sentiment, visibility, and contextual signals, not just last-click performance. If all of your budget and reporting focuses on immediate traffic, you may miss meaningful impact higher in the funnel.

That means:

  • Budgeting intentionally across awareness, consideration, and conversion.
  • Applying the right metrics at each stage.
  • Looking beyond traffic as the primary success indicator.

In many lead gen models, citations, qualified leads, and eventual revenue tell a more accurate story than clicks alone.

Dig deeper: Lead gen PPC: How to optimize for conversions and drive results

Get the newsletter search marketers rely on.

See terms.

4. Clean up your feeds and map data

You may not think you have a “feed” in your lead gen setup, but that absence can put you at a disadvantage.

Feeds help AI systems understand your business structure, services, and site architecture. Even if you don’t have hundreds of pages, a simple, well-maintained feed in an Excel document can provide valuable context when uploaded to ad platforms.

Clean up your feeds and map data
Example of a feed for lead gen

Feed hygiene matters. Use clear, specific columns. Follow platform standards for text, images, and categorization. Make sure all relevant categories are represented.

On the local side, claim and maintain all map profiles. Ensure information is accurate and consistent. If you use call tracking in map placements, review your labeling carefully. AI systems may pull data from map listings or your website, and mismatches can create attribution confusion, particularly for phone leads.

Account for potential AI-driven inflation in reporting, whether you’re looking at map pack data, direct reporting, or site-level performance. Any changes you make should also be reflected correctly in your conversion goals.

5. Pressure-test your creative for clarity

Creative assets may be mixed, matched, or shortened using AI. In some cases, you may only get one headline to explain who you are and why someone should contact you.

If your value proposition requires three headlines, or a headline plus a description, to make sense, that’s a risk.

Review your existing creative and identify assets that stand on their own. You should have at least some options where a single headline clearly communicates:

  • What you do
  • Who you help
  • Why it matters

If that clarity isn’t there, AI-driven placements can quickly become confusing.

Dig deeper: Why creative, not bidding, is limiting PPC performance

The fundamentals that still move the needle

Lead gen today doesn’t need to be complicated.

Most of the actions that matter today are things strong advertisers already do: clean data, clear messaging, intentional budgeting, and disciplined execution. What changes is how attribution may shift, and how much weight systems place on different signals.

The fundamentals still win. The difference is that AI makes weaknesses more visible and strengths more scalable.

If you focus on clarity, accuracy, and alignment across your funnel, you give both people and systems the best possible chance to understand your business — and that’s where sustainable performance comes from.

Read more at Read More

See how your brand appears in AI-generated answers, for free

/0 Comments/in , /by

Eligible Yoast customers can now run a free Yoast AI Brand Insights scan and get a personalized report showing how ChatGPT, Perplexity, and Gemini see your brand. Your brand is part of the AI conversation whether you’re monitoring it or not. Yoast AI Brand Insights, part of the Yoast SEO AI+ plan gives you visibility into what AI tools say about you, how often you appear, and whether the picture they paint matches reality. To help you see that for yourself, we’re offering eligible customers a free, one-time scan. 

What you’ll see

  • Your AI Visibility Index: a clear score showing how present your brand is across ChatGPT, Perplexity, and Gemini 
  • Sentiment analysis: whether AI describes you positively, neutrally, or in a way that needs attention 
  • Competitor benchmarking: how often your competitors appear alongside you, so you know where you stand 
  • Citation tracking: which sources AI is drawing on when it talks about your brand 

How it works

Add your brand details, set your location, and generate your queries. Your personalized report is ready in minutes.

Current customers can locate Yoast AI Brand Insights inside their MyYoast account

Who is eligible

Existing customers on one of the following plans can log in and try a brand scan for free today.

  • Yoast SEO Premium 
  • Yoast WooCommerce SEO 
  • Yoast SEO Google Docs add-on 

Look out for your invitation inside the product the next time you log in. 

Claim your free scan for the Yoast AI Brand Insights tool from your MyYoast. 

The post See how your brand appears in AI-generated answers, for free appeared first on Yoast.

Read more at Read More

The SEO Update by Yoast – April 2026

/0 Comments/in , /by

The SEO Update by Yoast – April 2026



Will take place on



4 PM CET / 11 AM EDT

Don’t want to miss it?

Register now! (Opens in a new browser tab)

Hosts

Alex Moss

Principal SEO

Carolyn Shelby

Principal SEO

Don’t miss the next SEO Update by Yoast

Search is changing fast – make sure you’re not falling behind.

Sign up for the next SEO Update by Yoast and get expert-led clarity on what’s happening in SEO right now and what it means for your strategy.

Join Carolyn Shelby and Alex Moss as they unpack the most important SEO news, algorithm shifts, and industry developments – so you can focus on what actually moves the needle.

Who should sign up?

This update is ideal if you:

  • Want expert insight into recent SEO changes and trends
  • Need help refining or validating your SEO strategy
  • Have SEO questions you’d like answered live

Event details

  • Level: Intermediate
  • Duration: 1 hour
  • Live Q&A with our SEO experts
  • Free registration
  • Recording available after the session

Don’t want to miss it?

Register now! (Opens in a new browser tab)

First upcoming events

Introduction to Yoast SEO webinar
16 April 2026

Hosts

Michael Quaranta

Support Team Lead

Mabel Adekola

Support engineer

Introduction to Yoast SEO webinar

Discover how to effectively use the Yoast SEO plugin. Get hands-on tips…

Register now (opens in a new tab)

Learn more

WordCamp Asia 2026
April 09 – 11, 2026

Who will be there:

Samah

Wouter

Carl

Réka

Sergey

Jose

WordCamp Asia 2026

Team Yoast is Attending, Sponsoring, Yoast Booth at WordCamp Asia 2026! Click…



Learn more

The SEO Update by Yoast – April 2026
28 April 2026

Hosts

Alex Moss

Principal SEO

Carolyn Shelby

Principal SEO

The SEO Update by Yoast – April 2026

Expert analysis of the latest SEO & AI news developments with Carolyn…

Register now (opens in a new tab)

Learn more

The post The SEO Update by Yoast – April 2026 appeared first on Yoast.

Read more at Read More

AI search engines cite Reddit, YouTube, and LinkedIn most: Study

/0 Comments/in , /by
AI citations

Reddit ranks as the most-cited domain in AI-generated answers, followed by YouTube and LinkedIn, based on a new analysis of 30 million sources by Peec AI, an AI search analytics tool.

The findings. Reddit was the most-cited source across ChatGPT, Google AI Mode, Gemini, Perplexity, and AI Overviews. YouTube, LinkedIn, Wikipedia, and Forbes also ranked in the top five. Review platforms like Yelp and G2 appeared often in recommendation queries.

The research showed which domains models rely on:

  • ChatGPT favored Wikipedia, Reddit, and editorial sites like Forbes.
  • Google leaned toward platforms like Facebook and Yelp.
  • Perplexity emphasized Reddit, LinkedIn, and G2 for B2B queries.

Why we care. To win in AI search, you need authority beyond your site. Brands that appear consistently across trusted third-party platforms are more likely to be cited.

Why these sources? AI systems prioritize perceived authority plus authentic user input:

  • Reddit leads because it captures real user discussions.
  • YouTube dominates video citations via transcripts and descriptions.
  • Wikipedia serves as both a live source and a training dataset.

About the data. The analysis covered 30 million sources across ChatGPT, Google AI Mode, Gemini, Perplexity, and AI Overviews, measuring domains directly cited in answers to isolate what shapes responses.

The study. Top domains cited by AI search: Analysis based on 30M sources

Dig deeper. More citation research:

Read more at Read More

15 Key Marketing Automation Statistics

/0 Comments/in /by

The marketing tech stack is always evolving. Marketing automation software enables improved efficiency with various features, from customer segmentation to campaign management.

What’s the marketing automation industry market size? What are the adoption rates of marketing automation, and what benefits does it bring to businesses? Continue reading as we’ll cover answers to these questions with these recent marketing automation statistics.

Here’s what you’ll find on this page:

  • Marketing Automation Industry Revenue
  • Leading Players in the Marketing Automation Software Industry
  • Marketing Automation Budget Changes
  • Top Channels Where Marketing Automation is Used
  • Top Benefits of Marketing Automation
  • Marketing Automation and Customer Data Platform Integration

Marketing Automation Industry Statistics

In 2026, marketing automation is steadily growing, with spending reaching billions every year.

This section presents key statistics to show market automation revenues, top players in the industry, and expected budget changes on automation among marketers.

  • Between 2026 and 2032, the worldwide marketing automation industry revenue is forecasted to grow by 62.4% from $8.44 billion to $21.7 billion – Statista

Year Marketing automation market revenue (worldwide)
2021 $4.79 billion
2022 $5.19 billion
2023 $5.86 billion
2024 $6.62 billion
2025 $7.47 billion
2026 $8.44 billion
2027 $9.53 billion
2028 $10.76 billion
2029 $12.14 billion
2030 $13.71 billion
2031 $17.2 billion
2032 $21.7 billion
  • Revenue of marketing automation solution vendors is forecast to reach $6.6 billion in 2026 (up from $2.9 billion in 2020) – Frost & Sullivan
  • Around 68% of surveyed marketers expect an increase in their budget for marketing automation for the upcoming year – Ascend2
Marketing Automation Budget Share of marketers
Increasing significantly 14%
Increasing moderately 54%
Staying the same 21%
Decreasing moderately 9%
Decreasing significantly 2%
  • HubSpot dominates the marketing automation software market, holding a market share of 29.58%. Other commonly used marketing automation tools include RD Station (9.25%), Welcome (7.38%), All In Marketing Cloud (6.87%), and Cheetah Digital (6.86%) – Datanyze

  • As of March 2026,  at least 454 companies provide marketing automation software solutions – G2

Marketing Automation Usage and Performance Statistics

Marketing automation software is widely used as a part of an effective marketing tech stack.

This section highlights key insights into current adoption and planned usage of marketing automation and its impact on helping achieve business objectives.

  • Email (58%), social media management (49%), and content management (33%) are the most reported areas for currently using marketing automation – Ascend2

Area Marketing Automation Usage
Email marketing 58%
Social media management 49%
Content management 33%
Paid ads 32%
SMS marketing 30%
Campaign tracking 28%
Landing pages 27%
Live chat 24%
SEO efforts 22%
Workflows/visualization 20%
Account-based marketing 20%
Sales funnel communications 19%
Push notifications 18%
Dynamic web forms 18%
Lead scoring 17%
  • 29% of surveyed marketers are planning to implement marketing automation for social media management and paid ads. Another 28% claim they will be adding marketing automation to email marketing programs – Ascend2

Planned Marketing Automation Usage

Area Planned Marketing Automation Usage
Social media management 29%
Paid ads 29%
Email marketing 28%
Landing pages 21%
SMS marketing 21%
Content management 20%
Campaign tracking 18%
Live chat 18%
Push notifications 17%
Account-based marketing 16%
Workflows/visualization 16%
SEO efforts 15%
Dynamic web forms 14%
Sales funnel communications 13%
Lead scoring 9%
  • Optimizing overall strategy and improving data quality are top goals for improving marketing automation, according to 37% and 34% of B2B and B2C marketers surveyed, respectively – Ascend2

Marketing Automation Primary Goals

Primary Goal for Improving Marketing Automation Share of Marketers
Optimize overall strategy 43%
Improve data quality 37%
Identify ideal customers/prospects 34%
Optimize messaging/campaigns 31%
Increase personalization 30%
Decrease costs/drive efficient growth 21%
Decrease automation across the customer journey 19%
Integrate technologies/data 15%
Increase employee adoption/usage 13%
  • Around 41% of marketers report that their customer journeys are “mostly automated” or “fully automated” – Ascend2
Extent of Marketing Automation across the Customer Journey Share of Marketers
Fully automated 9%
Mostly automated 32%
Partially automated 59%
  • 30% of surveyed marketers strongly agree with the statement that their “marketing automation platform makes it easy to build effective customer journeys” – Ascend2
“Marketing Automation Platform Makes It Easy to Build Effective Customer Journeys” Share of Marketers
Strongly agree 30%
Somewhat agree 59%
Somewhat disagree 10%
Strongly disagree 1%
  • About 1 in 4 (26%) of marketers say their multi-channel marketing strategy is fully or mostly automated. Another 22% claim it’s not automated at all – Ascend2
Extent of Multi-Channel Marketing Strategy Automation Share of Marketers
Fully 5%
Mostly 21%
Partially 29%
Very little 23%
Not at all 22%
  • Pricing is considered a key factor by 53% of marketers when deciding on a marketing automation tool. Ease of use (54%) and customer service (27%) are regarded as the other top factors driving automation tool purchase – Ascend2

Marketing Automation Solution Purchase Factors

Factors Driving Marketing Automation Solution Purchase Share of Marketers
Price 58%
Ease of use 54%
Customer service 27%
Customization options 24%
Integration capabilities 22%
Breadth of features 21%
Depth of features 19%
Data visualization/analytics 13%
Streamlined onboarding/training 11%
Data consolidation capabilities 10%
  • Only 18% of B2B marketers state they use marketing automation that’s integrated with a customer data platform (CDP). Another 42% say they use B2B marketing automation but don’t have CDP in their current tech stack. Other 40% have both B2B marketing automation and CDP, but they aren’t integrated – Adobe

Marketing Automation Benefits Statistics

Marketing teams can greatly improve their effectiveness through the use of automation software, which offers a number of benefits, from improving customer experience to enabling better use of marketing budgets.

  • Improving customer experience (43%), enabling better use of working hours (38%), and better decision making (35%) are the most commonly reported advantages of using marketing automation among surveyed marketers – Ascend2

Marketing Automation Benefits

Advantage of Marketing Automation Share of Marketers
Improves customer experience 43%
Enables better use of staff time 38%
Better data and decision-making 35%
Improves lead generation and nurturing 34%
Enables better use of the budget 33%
Increases personalization options 24%
Increased ability to measure important metrics/KPIs 23%
Aligning marketing efforts to adjacent departments 21%
  • Around 2 in 3 (66%) surveyed marketing professionals state that their current marketing automation is “somewhat successful” in helping to achieve marketing objectives. Another 25% of respondents say it’s “very successful”. Only 9% of marketers report no success from their marketing automation efforts – Ascend2

Conclusion

We hope you enjoyed this list of marketing automation statistics.

We frequently update this list of statistics. So feel free to check this stats page later for new insights.

The post 15 Key Marketing Automation Statistics appeared first on Backlinko.

Read more at Read More

How To Boost a Post on Linkedin

/0 Comments/in /by

Key Takeaways

  • Boost posts that are already winning organically, not the ones you hope will catch on.
  • Paid spend won’t fix weak content. Only boost LinkedIn posts that have social proof. 
  • Your campaign objective tells LinkedIn who to show your post to. Choose your goal strategically so LinkedIn doesn’t optimize for the wrong audience.
  • Start with one or two targeting filters. Too broad wastes budget on junk impressions, and too narrow spikes costs and limits delivery.
  • Impressions and clicks are vanity metrics. Rate comparisons between boosted and organic rates tell you what’s actually working.

If you’re not getting views on your LinkedIn posts, you’re losing business.

How do I know that?

LinkedIn is where buyers vet your credibility and compare options before they ever book a call. The platform has become a powerful lead-gen engine.

That’s why LinkedIn can be your highest-leverage channel in B2B, where 89 percent of marketers use it for driving leads. 

The challenge, though, is that solid content can still flop.

That’s where boosting comes in. Paid reach behind the right posts breaks you out of the “great content, tiny distribution” trap. Your message suddenly starts reaching the people who truly matter.

Before you hit the Boost button, though, it helps to know which posts are worth putting money behind.

What Does It Mean to Boost a Post on LinkedIn?

Boosting a post on LinkedIn means taking something you published organically and turning it into a paid promotion so more of the right people see it.

Think of it as putting fuel on a fire that’s already burning.

There’s no need to start from scratch in LinkedIn Campaign Manager. All you have to do is pick an existing post from your company page, choose a goal (like more engagement or website visits), define a basic audience, and set a budget. 

LinkedIn does the rest, extending your post’s reach beyond your followers. Here’s what that looks like from your Page posts dashboard:

NP Digital LinkedIn Page Posts dashboard with Boost button

Source: NPD LinkedIn

Here’s how boosting stacks up against your other options:

  • Organic posts rely on the algorithm and your existing network. If it hits, great. If it doesn’t, it disappears fast.
  • Building a campaign gives you more control over targeting through advanced marketing metrics, but it requires more setup and management.

Boosting sits in the middle. It’s designed for speed and simplicity, not for hyper-specific targeting or complex funnels. 

For a deeper look at LinkedIn’s full toolkit, my LinkedIn marketing guide is a good place to start. 

The Challenge of Getting Views on LinkedIn

LinkedIn is the world’s largest professional network with more than 1 billion members. 

That sounds like a marketer’s dream, until you try to earn consistent views. The numbers reflect the challenge:

  • Organic reach is getting squeezed. Richard van der Blom’s 2025 Algorithm Insights Report, which analyzed more than 1.8 million posts, says it has dropped nearly 50 percent. 
  • Most people scroll past without engaging. Socialinsider’s benchmark data shows the engagement rate per impression at about 5.2 percent, meaning about 95 out of 100 people who see a post don’t interact with it. 
  • Timing alone won’t save a post. LinkedIn’s continued push toward relevance over recency means even well-timed content can get buried if the algorithm deems it less relevant to a given user. 

That’s exactly why boosting works. It stops the guessing game on distribution and puts paid visibility behind posts that already deserve a wider audience.

When Does It Make Sense to Boost a LinkedIn Post?

Boosting only makes sense when the post does. Put paid spend behind weak content, and you’re wasting marketing dollars.

You should boost a post when:

  • It’s already showing strong early signals. Comments and saves in the first few hours, for example, tell you the content is resonating.
  • The post is tied to a hard deadline. Events, product launches, webinars, and hiring pushes all have a window where visibility directly drives action.
  • You have one clear conversion goal, such as a download or follow.
  • You need reach beyond your existing network, and organic distribution won’t get you there fast enough.

Hold off on boosting when:

  • The post isn’t gaining momentum on its own.
  • The call to action (CTA) is vague. “Thoughts?” is not a measurable conversion goal, for example.
  • You haven’t defined what success looks like before you spend.

It pays to be selective because LinkedIn’s audience is genuinely valuable: LinkedIn data says 4 out of 5 members drive business decisions. 

However, just because decision-makers use the platform doesn’t mean they’ll see your post. LinkedIn’s algorithm weighs credibility heavily in distribution, and verified members see up to 50 percent more engagement on their posts as a result.

Boosting works in a similar way. It amplifies what’s already credible, not what’s struggling to find its footing. Boost your winners, not your wishes.

How to Boost a Post on LinkedIn (Step by Step)

Boosting is straightforward, but the results depend on the decisions you make before you hit publish. Here’s how to do it right.

Choose the Right Post to Boost

Start with posts already showing signs of life. 

Look for strong early engagement (especially comments and saves) or a clear spike in impressions versus your usual baseline. If a post isn’t earning attention organically, paid reach won’t magically fix it. 

That’s why you should boost only what’s already working.

Select Your Campaign Objective

Open the post from your company page and hit Boost. Then choose the objective that matches what you’re trying to do:

  • Brand awareness, if you’re launching something new or want to grow your share of voice in a category
  • Post engagement, if you want to grow followers or keep your brand top of mind
  • Video views, if your post is a video and watch time is the priority
  • Website visits, if you want to drive traffic to a landing page or lead capture form

Here’s what that looks like within LinkedIn.

LinkedIn boost post campaign goal selection screen

Define Your Audience

Keep targeting focused enough to be relevant, but not so narrow that it limits delivery. Start with one or two core filters: job title or function, seniority, industry, company size, or location. 

If your audience is too broad, you’ll buy cheap impressions that don’t convert. If it’s too tight, your costs will spike and your delivery won’t be consistent. Keep in mind that relevance beats reach every time. 

Here’s what setting your audience parameters looks like in-platform:

Filters you can use to target your LinkedIn audience
Filters you can use to target your LinkedIn audience 2

Set Your Budget and Duration

Set a lifetime budget and choose your start and end date. If your post is tied to a deadline-driven event like a webinar, set your end date accordingly.

Start with a modest test budget, and give the campaign enough time to generate meaningful data. A few hours won’t tell you much.

LinkedIn boost post budget and schedule settings

Watch your frequency as your boosting campaign runs. If the same audience sees your post too many times, engagement may drop and your spend will likely be less efficient. 

Review and Launch

Before you hit Boost, run through this quick checklist. Make sure that:

  • Your copy and visuals look exactly as intended.
  • Your messaging matches your campaign goal.
  • There are no grammar or spelling errors.
  • All links are working.
  • You confirm your audience targeting and budget.

Once everything checks out, it’s time to boost.

Best Practices for Boosting LinkedIn Posts

Boosting isn’t magic. It just gives a good post more distribution, but it can’t rescue a weak one. Here’s how to make sure your post is worth putting money behind.

Lead with Native-First Content

If your goal is to increase views and engagement, it’s best to keep people on the platform. Native formats like video or documents are built for feed consumption. A Metricool study shows video post growth up 53 percent, while clicks on linked content are up 28 percent. 

The format should follow your goal. Native content keeps readers in the feed and builds engagement. Links work when you want to drive traffic to a specific destination. Documents are strong for capturing attention before directing readers off the platform.

Test what works, and track the results.

Write Like a Person

Keep your copy tight and human. LinkedIn posts allow for up to 3,000 characters, but that doesn’t mean you should use them all. 

Readers might be quickly scrolling through LinkedIn over lunch or during a coffee run. They’ll read what’s worth reading and skip over everything else. So be direct and to the point. Use plain language, and focus your post on one specific point or outcome.

Win the First Line

On mobile, LinkedIn previews cut off at about 200 characters. On desktop, it’s around 300. Sponsored posts can show an even shorter preview. Everything after that lives behind a “see more” click that many people won’t tap. 

Your first line is your hook, and its job is to grab the reader’s attention.

A few approaches that work:

  • Lead with a surprising stat or a bold claim.
  • Ask a question the reader wants answered.
  • Open with a contrarian take on something familiar.
  • Set up a story with an unexpected outcome.

Nicolas Cole’s opening line in the post below is a good example: “Over the last 10 years, I’ve made $10,000,000+ as a writer.” It’s a single stat that stops the scroll. The second line (“The secret?”) creates just enough tension to earn the click. 

Two sentences, and you’ve got your hook. 

Nicolas Cole LinkedIn post example with strong hook

Source: https://sproutsocial.com/insights/linkedin-best-practices/

The hook is just the beginning, though. Once you have a reader’s attention, provide so much value that they keep coming back. For example, you might offer your latest lead magnet.

A strong lead magnet gives readers a reason to act beyond the post itself. The graphic from Pathmonk below covers the most effective options for B2B audiences. It includes: 

  • E-books
  • White papers
  • Webinars
  • Free trials 
  • Demos
  • Case studies 
  • Success stories
  • Quizzes 
Best types of B2B lead magnets infographic

Source: https://pathmonk.com/best-b2b-lead-magnets-8-tactics/

Odds are your team already has at least one of these in some form.

Use One Clear CTA

Each post should have one job and clearly direct the reader on what to do next, like subscribing or downloading. 

The more CTAs you stack, the more you dilute the click. LinkedIn sponsored content formats are built around a single CTA path for good reason.

To get the best results, match your CTA language to your post’s intent. If you want them to download your checklist, say, “Get the checklist.” Saying something like “Learn more” gives the reader no clear direction and no reason to move.

Watch Early Results and Pause Fast

Give a boosted post 24 to 48 hours before drawing conclusions. That’s enough time to collect a meaningful signal but not so much time that you waste spend on something that’s not working. Test ad variations with LinkedIn’s A/B testing workflows and review their performance. 

How do you diagnose where your post has gone wrong? The best place to start is your click-through rate (CTR). If you have a low CTR, then there’s an issue with your creative (post copy or visuals). If you have a high CTR but a low conversion rate, the landing page or form you’re using could be the issue. 

How to Measure the Success of a Boosted Post

A boosted post’s results can be misleading if you measure the wrong things. Start with the metrics that match your objective:

  • Engagement: Track your engagement rate by totaling the post’s social signals and dividing by the number of total impressions. Comments matter more than likes because they signal real interest, not drive-by approval.
  • Website visits: Watch CTR. See how many people are landing on your website from your boosted post. Compare those numbers against a similar organic post to see whether the boost is moving traffic or just generating impressions.
  • Brand awareness: Look at your follower growth and repeat engagement from the same audience over time. These are signal metrics that tell you whether the right people are paying attention.

From there, look at whether rates moved, not just totals. If impressions climbed but CTR and engagement rate stayed flat, the post reached more people without changing their behavior. 

More visibility without action is not a success metric. A boost works when it drives the specific outcome you set your objective around. That’s the only measure that matters.

FAQs

How do I boost a post on LinkedIn?

Go to your LinkedIn company page in admin view, open the post, and click Boost. Then choose your objective, audience, budget, and duration. Keep it simple by focusing on one goal, one or two audience filters, and one CTA. 

How much is it to boost a post on LinkedIn?

You can often begin with as little as $10, making it one of the more accessible ways to advertise on LinkedIn. It’s typically best to start small for a few days, and then scale only if results justify it. For a deeper look at LinkedIn advertising costs overall, check out my LinkedIn ads pricing guide. 

Can you boost carousel posts on LinkedIn?

Not if it’s a multi-image carousel. Boosting doesn’t support posts with more than one image. If you want a “carousel feel,” use a document or PDF post and promote it through Campaign Manager instead. 

Conclusion

LinkedIn marketing doesn’t need to be a mystery. The platform is one of the most powerful tools your business has for reaching real decision-makers, and the right approach can make it a game-changer.

Start by publishing content your audience actually wants to read. Then use boosting to put paid reach behind what’s already earning attention organically. That way, the right people see your post on your timeline, not whenever the algorithm gets around to it.

Consistent social media measurement is what separates marketers who scale from those who guess. Track your rates and compare them against your organic baseline. When something isn’t working, cut it fast.

Use data to make smart boosting decisions, and you’ll earn more qualified attention that leads to real business results.

Read more at Read More