The most important details at a glance: Advanced Control 2026: Performance Max has become more transparent thanks to campaign-wide exclusions, detailed channel performance reports, and granular asset metrics, but it remains a system that needs tight guardrails.  Profitability Before Algorithm: Budgets and campaign splits should not be based on purely visual categories, but on hard business metrics such as margins, product lifecycles (evergreen vs. longtail), or customer value.  Signposts Instead of Targeting: Audience signals, search themes, and customer match serve as signposts for Google AI and must not be misunderstood as rigid, exact targeting. The focus must be on high-quality first-party data.  From ROAS to POAS: A high ROAS often covers up unprofitable sales segments. Advertisers should establish Profit on Ad Spend (POAS) as the primary steering metric via cart data import.  Hybrid Account Structures: Standard Search (for exact brand protection and precise intent) and Standard Shopping (for granular product control) retain their strategic justification alongside PMax.  By 2026, Performance Max campaigns are no longer the non-transparent black box that SEA managers complained about in the early days. Google has made massive technological upgrades and given advertisers tools that allow for fine-grained adjustments. These include campaign-wide negative keywords, optimized search term reports, transparent channel performance reports, deep asset metrics, segmentable reports for asset groups, as well as advanced demographic exclusions and device controls. Google's internal data shows that over one million advertisers now use PMax structures. Despite this technological maturity, a fundamental principle remains: a Performance Max campaign never optimizes itself in terms of your actual business model. The system operates purely opportunistically based on the data provided to it. If an unqualified, faulty contact form is counted as a successful conversion, the artificial intelligence scales exactly those low-quality lead sources. If expensive brand traffic artificially inflates the Return on Ad Spend (ROAS), the algorithm gratefully grabs it without generating real incremental revenue. For demanding SEA managers and marketing decision-makers, this means that optimization today no longer takes place primarily via manual bids, but through strategic data management, placing precise guardrails, and honest performance measurement.  Deeply Analyze Budget Distribution and Channel Performance   As soon as a Performance Max campaign shows a drop in performance, many market participants tend to immediately modify the target ROAS (tROAS) or target cost-per-conversion (tCPA). In practice, this lever is usually pulled too early and only treats symptoms instead of causes. The first analysis step must absolutely be looking at the budget distribution across the various networks.  The dedicated channel performance report reveals which budget shares are flowing into the Search, Shopping, YouTube, Display, Discover, Gmail, Maps channels or to search network partners. Although this report does not allow for direct, manual budget reallocation, it makes dangerous shifts transparent. If, for example, spending in the Display or YouTube network suddenly spikes and at the same time the final lead quality in the customer relationship management (CRM) system drops, the cause is not an incorrect bid level. Rather, the campaign is attracting low-quality clicks through visual placements because the underlying conversion signal is too weak or too easily manipulated.  As part of a deeper Performance Max optimization, search terms must be consistently analyzed and prioritized by total cost. Frequently, expensive search queries without any conversion action are much more revealing than historical winners. SEA managers should systematically identify and exclude unsuitable search terms. Typical negatives that should be placed in almost every professional B2B or e-commerce account include terms like: "jobs," "career," "salary," "support," "login," "free," "guide," "PDF," student research, irrelevant competitor names, or purely informational search phrases with no commercial intent.  Strategic Campaign Structure by Profitability   In many accounts, the structuring of Performance Max campaigns follows purely visual or catalog-based criteria. This is inefficient. A split into separate campaigns is only justified if this split enables targeted operational control – be it through differentiated budgets, specific target bids, differing conversion goals, margin structures, regional focus areas, or strict brand rule sets.  Segmentation Criterion  E-Commerce Approach  Lead Generation Approach     Profitability & Margin   Splits by high-margin (e.g., private labels) vs. low-margin (retail goods). Focus the budget on products with real return.  Differentiation by Customer Lifetime Value (CLV) or order volume (e.g., enterprise deals vs. SMB self-service).  Product & Service Dynamics   Separation of bestsellers (high-performers), seasonal goods, new arrivals, and so-called zombie SKUs (products without clicks).  Differentiation between high-margin core services and purely informational introductory offers (e.g., whitepaper downloads).  Database (Custom Labels / CRM)   Steering via the Google Merchant Center feed using defined custom labels for inventory and margin classes.  Steering via verified offline conversion data (MQL, SQL) instead of pure online form submissions.  The exact same economic principle applies to lead generation. Segmentation must be based on sales reality. Never structure your asset groups or campaigns primarily on audience signals. Since Google only interprets these signals as a non-binding recommendation, a purely audience-based campaign separation almost always leads to internal data overlap and inefficient budget allocation.  Align Search Themes, Audience Signals, and Customer Match Precisely  The introduction of search themes offers an excellent option for sharing contextual knowledge with Google AI. However, search themes should never be confused with classic keyword match types or seen as a complete replacement for structured search campaigns. Their strategic area of application is primarily where the system has too little historical data: during the market launch of completely new product lines, for highly complex B2B niche applications, for targeted promotion of competitor alternatives, or when the landing page offers too little semantic text content due to a minimalist design.  Even though Google allows up to 50 search themes per asset group, this limit should never be maxed out randomly if you want precise Performance Max optimization. Best practices suggest using a few, concise themes bundled strictly by search intent. Afterwards, the generated search term reports must be closely monitored to immediately prevent any misdirection of the algorithm.  The same applies to audience signals. They do not represent a hard, exclusive target, but rather act as an initial catalyst for machine learning processes. Advertisers should consistently rely on first-party data here. You will achieve the highest signal quality through:  Up-to-date customer match lists from your CRM (high-value buyers).  Granular website visitors (cart abandoners, returning users).  Specific app user data or qualified newsletter subscribers.  Isolate Brand Traffic and Secure Incremental Growth   It is one of the most common phenomena in SEA practice: a Performance Max campaign delivers outstanding ROAS metrics on paper, but real company growth stagnates. The reason lies in the uncontrolled skimming of existing demand. The system tends to target brand search queries (brand traffic), existing remarketing audiences, and loyal customers who would convert anyway in order to easily meet predefined efficiency targets.  Although Google prioritizes identical exact match keywords in regular search campaigns over a parallel PMax campaign, as soon as the search campaign hits a budget limit or is restricted by settings that are too tight, PMax takes over the brand auction. SEA managers must therefore check at regular intervals which search terms are being actively triggered within PMax and whether unwanted cannibalization effects are occurring with existing brand, generic, or competitor campaigns.  To drive genuine, incremental revenue, brand exclusions should be implemented directly in the campaign settings. For e-commerce, specialized search-only brand exclusions are also available. This feature suppresses pure text ads for brand terms within PMax, but still allows the algorithm to display visual brand shopping, which is highly profitable in most cases.  Optimize Data Quality in the Feed and Final URLs   Particularly in retail, Performance Max is often structurally much closer to a classic shopping campaign than an all-encompassing multi-channel campaign. Before making far-reaching bid adjustments, absolute data quality must be ensured in the Google Merchant Center. Optimizing product titles, product types, GTINs, high-resolution imagery, correct sale prices, precise stock status, and custom labels forms the bedrock.  Product titles should not simply be copied from internal ERP systems. They must include the attributes that customers are actively searching for. The optimal layout usually follows this logic: Brand + Product Type + Model Number + Material + Specification (e.g., size, color, compatibility).  An often overlooked pitfall lies in the uncontrolled activation of final URL expansion. This feature allows Google to replace the destination page with a supposedly more relevant URL on your website and automatically generate matching text assets. With a brilliantly structured, purely sales-oriented website architecture, this delivers excellent results. However, the setup becomes highly inefficient if informative blog posts, support documentation, career pages, or general advice articles unintentionally slip into the ad pool. Such URLs must be consistently blocked using explicit exclusion rules.  Link Bidding Strategies to Qualitative Conversion Signals   Choosing the right bidding strategy largely determines the success of a campaign. In e-commerce, the "maximize conversion value" strategy combined with a defined target ROAS is the gold standard – assuming revenue values are transmitted to the Google Ads account perfectly and without delay. A target ROAS that is selected too aggressively starves the algorithm of necessary liquidity and chokes campaign volume. A target value that is set too low generates massive revenue but is no longer economically viable at the margin level once all costs are considered. In the B2B segment and for lead generation, the exact definition of the conversion action is even more important than the bidding strategy itself. If you define the simple submission of a contact form as your primary conversion, you force PMax to maximize exactly these quantitative completions. The result is often a flood of spam leads or contacts with no real interest in buying. The solution lies in shifting optimization to qualified, deeper-funnel offline conversions via CRM import. Optimize for:  Marketing Qualified Leads (MQL) after successful initial vetting.  Sales Qualified Leads (SQL) after direct sales contact.  Generated pipeline opportunities or final "closed-won" deals.  A seemingly cheap Cost-per-Lead (CPL) that does not lead to measurable sales is not a marketing success; it feeds machine learning with useless training material.  Validate Incrementality Using PMax Experiments   Because Performance Max is excellent at funneling existing demand channels, evaluation must never occur in the silo of the campaign dashboard. SEA managers must isolate the real added value (incrementality). The integrated Performance Max experiments are ideal for this. Google provides these as scientific A/B tests with which strategic settings, creative directions, or completely new campaign setups can be compared in a statistically clean manner. Specific uplift tests also precisely measure the real additional benefit of PMax in direct comparison to already active search, video, and display campaigns. For a valid implementation in marketing practice, the following basic rules must be observed:  No testing during peak seasons: Never run experiments during extreme seasonal fluctuations (e.g., Black Friday or the holiday shopping season).  Single-variable principle: Never change the feed, budget, and bidding strategy simultaneously within a test run.  Allow sufficient runtime: Do not cancel experiments after just a few days; the algorithm needs an adequate learning and consolidation phase.  The ultimate success criterion is never the isolated ROAS of a single campaign, but whether the overall revenue, net profit, and qualified sales pipeline of the entire company increase significantly.  The Continued Relevance of Standard Search and Standard Shopping   Despite the omnipresence of PMax in 2026, switching your entire advertising account to this campaign type would be a fatal strategic error. Traditional campaign formats retain their fundamental place in a balanced overall strategy.  Classic standard search campaigns (Standard Search) are still indispensable for seamless brand defense, targeted and aggressive bidding on competitor keywords, highly regulated advertising claims, and specific B2B search queries with high exactness. Using exact match keywords ensures that the text ad written correlates perfectly with the user's search intent – a level of precision that PMax inherently cannot guarantee.  Similarly, Standard Shopping remains an incredibly powerful tool for tactical product control. When it comes to realizing targeted clearance sales, boosting so-called shelf warmers (zombie SKUs) with a specific budget, quickly reducing inventory, or running highly time-limited promotions for exclusive SKUs, Standard Shopping offers the required granular control at the product level. In the most successful ad accounts of 2026, a hybrid account model has been established: PMax serves as a scale-strong foundation for broad market coverage, Search secures high-quality intent, and Standard Shopping is used for surgically precise feed control.  The Paradigm Shift: From ROAS to POAS (Profit on Ad Spend)   The classic Return on Ad Spend is increasingly reaching its limits in modern e-commerce. It is a pure revenue metric. ROAS suggests success where financial losses may actually be occurring, as it completely ignores real gross profit. A product that generates $200 in revenue at a 20% margin must be evaluated completely differently from a business perspective than a product that generates $200 in revenue at a 60% margin. Purely revenue-based bidding treats both scenarios identically.  This is where the concept of Profit on Ad Spend (POAS) comes in. This metric relates the actual profit achieved to the advertising spend invested:  POAS = Gross Profit from Ad Investment / Ad Cost   To implement profit-based bidding in Performance Max, detailed shopping cart data and exact cost of goods sold (COGS) must be transmitted to Google Ads via the Google Merchant Center. Since PMax is naturally designed to realize the maximum conversion value within budget, the system runs the risk of heavily scaling low-margin bestsellers without this profit context, while neglecting highly profitable products due to a lack of initial search volume. A high ROAS does not protect against declining overall profitability.  Conclusion: Set Guardrails and Keep the AI Under Control   In 2026, Performance Max stands out as a highly sophisticated, excellently controllable marketing tool. The main task of SEA managers and marketing executives is no longer manually rebuilding every single ad auction. Your primary responsibility lies in defining crystal-clear guardrails. You must define where the algorithm is allowed to learn – and where it is rigorously blocked. Those who intelligently combine data quality, technological controls, and business logic like POAS will transform Performance Max from an unpredictable black box into a highly profitable growth engine.  FAQ on Performance Max Campaigns 2026   Should PMax completely replace Standard Search in 2026?   No. Performance Max is excellent for unlocking additional reach and incremental placements. However, it by no means replaces dedicated search campaigns where you need absolute control over keywords, exact ad copy, and the protection of your own brand.  Are audience signals in PMax equivalent to hard targeting?   No. Audience signals are purely guiding aids for Google AI to speed up the learning phase. They do not restrict ad delivery exclusively. To maximize signal quality, you should consistently feed in first-party data such as customer match lists, CRM segments, and deep website interactions.  When is it advisable to use PMax experiments?   Using them is highly recommended whenever you want to test the incrementality of your campaigns. Experiments show you in black and white whether PMax is generating genuine new revenue or merely claiming conversions that would have come in anyway through organic search or existing search campaigns.  Why is ROAS losing importance as a primary metric for PMax?   Because ROAS only measures the ratio of revenue to cost. Since PMax operates autonomously, it optimizes for revenue volume. If your product range has varying margin structures, this often leads to unprofitable products being pushed. POAS (Profit on Ad Spend) is the much more honest business metric here.  How often should Performance Max optimization take place? A weekly rhythm is recommended for controlling the channel mix, evaluating search terms, adding exclusions, and reviewing landing pages. Comprehensive audits of brand exclusions, analysis of SKU concentration, updating assets, and reconciling with CRM data should be carried out monthly. 

Google rankings are no longer the only goal: If you want to appear in AI-generated answers, you need to rethink E-E-A-T.    In our GEO study , we analyzed over 100,000 search queries. The result: The rules of the game for visibility have fundamentally changed. Google AI Overviews, ChatGPT Search, Perplexity, and other LLM-based systems decide independently which sources to trust; and the parameters they use to decide do not always match those we know from classic SEO. Appearing in Google SERPs does not automatically mean you will be cited by AI — and in the worst case, you become invisible. But what criteria should content follow to be structured for LLM optimization? And what does the SEO-GEO discrepancy mean for long-standing concepts like E-E-A-T?   E-E-A-T refers to a principle that Google has been describing in its Quality Rater Guidelines for years – Experience, Expertise, Authoritativeness, Trustworthiness. Spoiler alert: Even in the era of ChatGPT and similar tools, this concept is still highly relevant. In this article, we'll explain why.  The essentials at a glance: E-E-A-T remains relevant – but the criteria are shifting. The domain is no longer the central trust signal; instead, it's the person behind it. AI systems increasingly evaluate the author, the depth of the content, and the overall digital footprint rather than isolated ranking factors.  "Experience" is the strongest signal in the AI era. Authentic experience reports, proprietary data, and concrete case studies are hard for language models to imitate – and are therefore preferred when citing sources. Generic, redundant content, on the other hand, is ignored.  Citability requires AI-readable content. Clear author profiles, structured data (Schema markup), backed-up claims, and paragraphs broken down into small "chunks" determine whether a source appears in Google AI Overviews, ChatGPT, or Perplexity.  What has specifically changed for businesses  Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) are changing how people search and find information. Click rates are taking a back seat, while snippets and AI citations are taking their place. These three changes make E-E-A-T more relevant than ever:   1. From page to author  Historically, the domain was the key trust signal. Today, the person behind the content takes center stage. Language models try to understand who wrote the content and whether that person is considered an expert in their field. Anonymous content or generic corporate copy without clear authorship is losing traction.  2. From quantity to depth  If your strategy so far was to produce as much content as possible for as many keywords as possible, you are hitting new limits: AI systems prefer content that truly dives deep into a topic – with real data, concrete case studies, and a clear point of view structured in short, citable paragraphs ("chunks"). Shallow, redundant content gets ignored.  3. From website to digital footprint  In the AI era, E-E-A-T is no longer limited to your own website. AI models know the entire web. If you are cited in trade publications, speak at conferences, participate in podcasts, or are recognized as a voice on a topic on social networks, you are strengthening your E-E-A-T signals even without direct SEO measures.  How important is E-E-A-T for LLMs?  The original acronym EAT (Expertise, Authoritativeness, Trustworthiness) was expanded by Google in 2022 to include an extra "E" for Experience. Since then, the model has represented four building blocks of quality that together determine whether content is deemed trustworthy:  E   EXPERIENCE   Does the author have first-hand, real-life experience with the topic? Real case studies and personal insights are strong signals of quality.  E   EXPERTISE   Does the author or organization possess proven expertise? Depth of knowledge, correct terminology, and verified/backed-up claims demonstrate competence.  A   AUTHORITATIVENESS   Is the source cited by other recognized authorities? External links, mentions in industry media, and listings in structured databases build authority.  T   TRUSTWORTHINESS   Is the source transparent and accurate? Clear details about origin, authors, sources, and potential conflicts of interest form the basis of trust.  The first "E" for Experience is especially critical in the AI era: Language models are trained to spot generic knowledge. On the other hand, real-life experience reports, specific numbers from your own projects, and hands-on practices are hard to fake, which makes them prime targets for AI citations.  How AI systems evaluate E-E-A-T signals  Traditional search engines evaluate E-E-A-T primarily through links, structured data, and page quality. AI systems go a step further: they read and analyze content semantically. This has far-reaching consequences. Instead of focusing only on classic ranking factors like keywords, AI systems implicitly ask: Which source would a human expert recommend? They look closely at factors like context, entity, and relationship. Therefore, if you want to be cited, you need both the right content elements and a format that is easily readable for AI models. Among other things, LLMs look for:  Author profile and biography: Is the author named? Are qualifications, background, or publications clearly visible? AI models connect author names with information available about them across the web.  Sources and citations: Content that references other reliable sources is perceived as thorough and accurate. Unsupported claims, however, flag an instant risk.  Consistency across channels: consistently sharing similar core messages on your website, LinkedIn articles, industry media, and in podcasts builds a cohesive knowledge identity that is much easier for AI systems to grasp.  Structured data / Schema Markup: AI-readable article data, location details, brand info, listicles, and FAQ elements help language models form correct associations between content, authors, and topics. The less the AI has to guess, the more credible it rates the content.  Mentions in external sources: When well-regarded industry media, Wikipedia articles, or other authoritative pages mention a source, the likelihood of being deemed an authority by AI systems increases significantly.  What can you do? Five E-E-A-T actions for successful LLM optimization  E-E-A-T is not a quick checklist of tactics, but a strategic positioning effort. Building it early creates a solid competitive edge. Concretely, this means:  Introduce and maintain author profiles: Every piece of content should be attributed to a real person. Biographies that link to LinkedIn, highlight qualifications, and showcase main topics greatly increase credibility.  Publish your own studies, data, and case studies: Exclusive insights are one of the strongest E-E-A-T signals possible. Proprietary surveys, anonymized customer data, or internal analyses carry immense value.  Implement structured data: Using schema markup for articles, people, and organizations helps AI systems make connections accurately.  Proactively manage PR and digital mentions: Guest posts in industry media, interviews, and Wikipedia entries: external mentions build long-term brand authority.  Consolidate content instead of spreading it thin: A few deep, well-structured pieces of content on specific areas of expertise are far more effective than many shallow articles on broad topics.  Conclusion: E-E-A-T is here to stay – just in a slightly different way  AI search doesn't change what makes content great. It only changes how that content is found. E-E-A-T remains a core ingredient in GEO as well — but it’s no longer the only one: setting up content to be AI-friendly and readable is a vital addition. Combining E-E-A-T with this principle creates a highly resilient foundation for being cited.  FAQs about E-E-A-T in AI Search  Is E-E-A-T still relevant in the age of ChatGPT and similar tools? Yes. AI search doesn't change what makes good content, only how it gets found. E-E-A-T remains a fundamental element for visibility.  Which of the four E-E-A-T signals is the most important for LLMs? The first "E " for Experience. Language models are built to recognize generic information. Hands-on experience, specific numbers from actual projects, and personal insights stand out and are preferred by AI systems.  How do I make my content citable for AI systems? Setting up author profiles is a great first step. Publishing proprietary studies increases citable value, while technical optimization assists with AI readability. Similarly, carefully curating content and building PR outside of your own domain can have a powerful impact.   How visible are you in AI search?   We analyze how LLMs rate your E-E-A-T content and show you concrete steps to actively improve your visibility in Google AI Overviews, ChatGPT, and Perplexity.  → Request your free GEO Quick-Check now!

Everything at a glance: In 2026, AI agents will handle research, comparison, and in some cases even parts of the checkout process on behalf of users According to the internetwarriors GEO study (May 2026): Over 80% of ChatGPT citations do not come from the Google Top 50 FAQ pages, how-to guides, and comparison tables are the most cited formats in AI systems Schema.org markup is becoming a mandatory infrastructure requirement, not just an optional add-on AI Overviews reduce the click-through rate of classic search results by up to 67.8% and require a new paid media logic What Agentic Commerce means for businesses and their visibility Agentic Commerce describes the shift from a click-driven e-commerce model to a system where AI agents research products, evaluate options, consider constraints, and prepare specific purchase suggestions. In this model, the online shop is no longer just a sales space, but also a data source, a basis for decision-making, and a transaction infrastructure. From a technical standpoint, this development is accelerated by new protocols and standardized interfaces. In 2026, the Model Context Protocol (MCP), the Agentic Commerce Protocol (ACP), and the Agent Payments Protocol in particular will become more visible, as they are designed to make context, commerce data, and payment approvals more accessible to AI systems. The separation between discovery and checkout is key here. Shopify describes Agentic Storefronts in a way that products become discoverable in AI channels via the Shopify Catalog, while the final purchase can take place either in the shop or directly in the respective interface, depending on the channel. It is precisely this decoupling that changes the logic of digital commerce: visibility, recommendation, and checkout no longer need to happen on the same interface. GEO instead of just SEO: What the internetwarriors study shows The third GEO study by internetwarriors shows that classic SEO visibility and AI visibility only overlap to a limited extent. For the study, 240 prompts from 12 industries in Germany were analyzed; a total of 5,317 URLs were included in the analysis, of which 4,794 were unique URLs. The numbers mark a turning point. Of the URLs linked in Google AI Mode, only 15.6 percent are found in the Top 10 of organic Google searches. For ChatGPT, this figure is even lower at just 9.2 percent. At the same time, over 70 percent of AI Mode links and over 80 percent of ChatGPT citations lie outside the Google Top 50. These results do not mean that SEO is becoming irrelevant. Rather, they show that GEO follows its own selection mechanisms. Ranking well organically still offers benefits in terms of authority and domain trust, but it does not guarantee being cited by generative systems. Why strong domains alone are no longer enough A particularly revealing result of the study concerns the role of strong domains. 51.3 percent of the citations in Google AI Mode and 33.0 percent of the citations in ChatGPT come from domains represented in the Top 10 of organic search – though often with different subpages than in classic Google search. This is a crucial difference. Classic SEO often rewards the single best URL for a topic. In contrast, generative systems search a trusted domain for the specific page that answers a query most precisely. It is not the strongest homepage that wins, but the most relevant subpage. As a result, the focus is shifting from keyword placements to topic coverage, entity clarity, and depth of answers. Businesses must not only be visible, but also interpretable as a reliable source for machines. Which content AI systems prefer The internetwarriors study clearly shows which page types are preferred in AI answers. FAQ, help, and how-to pages account for 22.8 percent in Google AI Mode and 26.3 percent in ChatGPT. Blog posts follow at 19.4 percent and 17.5 percent respectively, and comparison tables at 10.5 percent and 12.1 percent respectively. This breakdown makes sense. FAQ and how-to pages provide compact, clearly structured answers. Blog posts offer the necessary context. Comparison tables are particularly valuable for AI systems because they make products, services, or options directly comparable based on specific features. Classic product detail pages, on the other hand, play a smaller role than many retailers might expect. In Google AI Mode, only 3.5 percent of citations lead to product detail pages, and 4.7 percent in ChatGPT. This suggests that AI systems often prefer aggregating or explanatory pages over isolated product views. Page Type   Google AI Mode   ChatGPT   FAQ / Help / How-to  22.8 %  26.3 %  Blog posts  19.4 %  17.5 %  Comparison tables  10.5 %  12.1 %  Product detail pages  3.5 %  4.7 %  How search intent changes the choice of sources Search intent also changes content preferences. For informational prompts, FAQ/how-to content and blog posts dominate. In Google AI Mode, FAQ/how-to pages sit at 30.46 percent and blog posts at 26.39 percent; for ChatGPT, they are at 31.63 percent and 23.53 percent respectively. With transactional prompts, the pattern shifts significantly. Comparison tables, service pages, and homepages gain weight, while product detail pages grow but still do not become dominant. This suggests that AI systems often structure purchasing decisions through consolidated comparison pages first, before individual products play a larger role. This is an important insight for merchants: optimizing only product detail pages is not enough. Generative search and shopping environments require an additional layer of content consisting of FAQs, comparisons, advisory content, and clear service pages. Why structured data is becoming a mandatory infrastructure requirement With the rise of Agentic Commerce, structured data is turning into a vital infrastructure issue. It helps AI systems reliably interpret prices, availability, product attributes, delivery terms, return policies, and organizational details. This also changes the role of technical SEO. Product, Offer, FAQ Page, Organization, Local Business, and, depending on the business model, Merchant Return Policy data are becoming more important because they make information machine-readable, comparable, and actionable. The more consistently and clearly this data is maintained, the better systems can evaluate a brand or offer. In essence, it is about transforming a website from just a readable page into a decision-ready source. Agentic commerce rewards good data structures, not just good design. Shopify and Shopware: How platforms are reacting The infrastructure of major platforms already shows where the market is heading. With Agentic Storefronts and the Shopify Catalog, Shopify relies on a model where discovery takes place in AI channels and checkout is handled either in the shop or directly within the interface of the respective system, depending on the channel. As a result, attribution is becoming highly relevant again. Shopify tracks orders from Agentic Storefronts using channel or referrer attribution. Visibility in AI systems is therefore not just a matter of reach, but can increasingly be measured as a commerce channel. Shopware is moving in a similar direction in May 2026. The new sales channel type for Agentic Commerce, OpenAI product feeds, JSONL exports, and AI referral tracking show that product feeds, data formats, and performance measurement are becoming standard tools for the next phase of commerce. Area   Shopify   Shopware   Discovery  Shopify Catalog for AI channels  Agentic Commerce Sales Channel and OpenAI Product Feed  Checkout  Depending on the channel in the shop or via Direct Checkout  API- and feed-based connection  Tracking  Channel and referrer attribution  AI Referral Tracking  Data Format  Catalog and product data mapping  JSONL export and feed structures  How AI Overviews shift paid media logic The rise of generative search interfaces is also changing the logic of paid visibility. When an AI summary already does the research work, users are less likely to click on classic ads or standard organic results than before. The key statistic: a click-through rate of 19.70 percent without AI Overview drops to 6.34 percent with AI Overview – a relative decline of around 67.8 percent. This figure is more important as a strategic signal than as an exact universal number. It shows how much generative interfaces can disrupt previous click behavior. At the same time, a new opportunity arises: when brands are cited within the AI Overview, the click-through rate of their paid ads placed below increases by up to 91 percent. This makes it clear why GEO and Paid Media are no longer separate disciplines. For Paid Media, this does not mean moving away from the existing model, but rather realigning it. Being present in the answer logic of generative systems, in product feeds, and in subsequent decision paths not only improves organic visibility, but also enhances the impact of paid campaigns. Why B2B is particularly affected In the B2B sector, Agentic Commerce is potentially even more profound than in B2C. Procurement processes there are based on specifications, approvals, boundary conditions, compliance requirements, and recurring supply relationships. This is precisely why structured information, comparability, and reliable data are so relevant for AI-supported selection processes. A B2B agent needs to compare not just products, but also delivery availability, certifications, contract options, minimum order quantities, or service levels. Companies that present this info only in PDFs, unstructured tables, or vague marketing speak make it harder for machines to evaluate them. Providers with clearly structured, robust data will gain a massive advantage. This is why B2B showcases that Agentic Commerce is not just a UX topic. It is an infrastructure, data, and trust project. Simply editing website text without systematically organizing product and service data will often leave a company invisible to the new procurement logic. What internetwarriors calls the "AI-AI Bias" As an analytical working concept at internetwarriors, we refer to a specific pattern as the AI-AI Bias: the tendency of AI systems to systematically prefer providers with highly clear, structured, and fact-rich information because this data is easier to process, compare, and reuse with less uncertainty. This mental model corrects a common misconception: the most emotional brand message does not automatically win; instead, it is often the source requiring the least interpretation. Especially in B2B markets, where products are complex and differences need explanation, this bias can decide which providers make the shortlist in the first place. The 95:5 rule in the Agentic Web The 95:5 rule – originally from B2B marketing research by the LinkedIn B2B Institute and the work of Les Binet and Peter Field – simply states that the vast majority of potential buyers are not actively in target purchase mode at any given time. Brands must therefore build long-term memory structures instead of just reacting to immediate demand. In the context of Agentic Commerce, this logic can be expanded. A brand must be present not only in human minds, but increasingly in the data spaces, knowledge graphs, and trained preference patterns of systems. If you only start organizing your structure, content, and entities at the moment of a specific purchase request, you are often too late. That is why brand building in the agentic web should not be seen as the opposite of performance marketing. Rather, it is a prerequisite for a brand to appear as a trustworthy source, a preferred domain, or a logical recommendation. Governance, trust, and transaction security Delegating purchase decisions to machines significantly increases the demands on governance, authentication, and transaction security. According to recent industry surveys, 78 percent of financial institutions expect an increase in fraud cases driven by AI shopping agents. This is pushing the development of

The essentials in brief   Today, structured data plays a key role in deciding whether AI systems like ChatGPT, Perplexity, and Google AI Overviews recognize and cite your brand as a source.  The real competitive edge doesn't come from FAQPage and Product , but from the rarely used types – first and foremost DefinedTerm and sameAs (Wikidata/Wikipedia).  Schema is an amplifier, not a magic switch: The markup must match the visible content.  For years, using structured data was a topic exclusive to Google.  Under the umbrella term "markup for rich snippets," Google continues to have its own rules for handling structured data on a website. With the rise of ChatGPT, Perplexity, Google AI Overviews, and Google AI Mode  (and others), this has evolved into something else: the infrastructure through which AI systems recognize, categorize, and cite your brand as a source.    The exciting news for the current handling of structured data: The biggest leverage no longer lies in the classic implementations for FAQPage and Product (which everyone has adopted by now), but in the schema.org types that almost no one uses. That is precisely where a head start is being created.  From Rich Snippets to Entity Infrastructure  Anyone who does SEO knows structured data as a means to an end: integrate markup to get star ratings and FAQ accordions into Google search. This job still exists and remains important. But the actual shift is happening one level deeper.  AI search engines synthesize answers from multiple sources instead of displaying ten blue links. For a brand to appear in this answer at all, the system must understand: What is this? Which entity? Which facts belong to it? Is the source trustworthy? A cleanly implemented schema markup answers exactly these questions.   The turning point came in March 2025. Within a few days, both major players commented on the role of structured data for their AI systems: Fabrice Canel (Principal Product Manager at Microsoft Bing) confirmed on stage at SMX Munich that schema markup helps Microsoft's LLMs understand web content (Source LinkedIn ). Shortly after, Google emphasized at Search Central Live in New York (March 20, 2025) that structured data is valuable for their AI systems. (Source Search Engine Roundtable ). With that, the years-long debate over whether AI systems "even use" schema was officially answered, at least for search-driven systems (Bing Copilot, Google AI Overviews, and AI Mode).  The well-known schema.org types. The mandatory program  Before diving into the exciting types, let's briefly look at the foundation. These belong on every serious page. One could even go so far as to say that these mandatory types are no longer a competitive advantage, as they have become industry standard.  Organization / LocalBusiness: anchors the brand as an entity  Article: with author, publisher, and date as credibility signals  FAQPage: question-answer pairs that LLMs love to use directly as answers  Product / Offer: for e-commerce areas  HowTo and BreadcrumbList: process content and page hierarchy  The underestimated types. This is where you get the head start  DefinedTerm and DefinedTermSet   This is by far the most underrated markup. If you take away only one type from this article, let it be this one. Hardly any site uses it, but it is incredibly valuable for AI systems. The effort is usually minimal because the glossary content is already on the page anyway.  DefinedTerm turns your glossary into a structured key-value resource: term, synonyms, definition, URL. Instead of parsing flowing text, the AI system gets a clean "this term means exactly this." For any brand with specialized vocabulary (e.g., in B2B, SaaS, niche products), this is a direct lever for definition queries.  An example of usage in JSON-LD   {    "@context": " https://schema.org ",    "@type": "DefinedTermSet",    "name": "GEO Glossary",    "url": " https://www.internetwarriors.de/glossar ",    "hasDefinedTerm": [      {        "@type": "DefinedTerm",        "name": "Generative Engine Optimization",        "alternateName": "GEO",        "description": "The optimization of content for visibility in AI search engines such as ChatGPT, Perplexity, and Google AI Overviews.",        "url": " https://www.internetwarriors.de/glossar/geo ",        "inDefinedTermSet": " https://www.internetwarriors.de/glossar "      }    ]  }  The structure has two levels: a container and its entries:  The outer level = the glossary itself ( DefinedTermSet )   @context: tells every parser "the vocabulary here is schema.org". It almost always sits right at the top.  @type: "DefinedTermSet": the declaration "This is a collection of technical terms," i.e., a glossary.  name / url : Name and address of this exact glossary collection: your glossary overview page goes here.  The inner level = the individual entries ( hasDefinedTerm )   hasDefinedTerm: the square brackets […] make this a list. All the individual terms live in here: in the example above only one, but you can chain as many as you like (separated by commas).  Each entry in this list is a DefinedTerm with:  @type:"DefinedTerm" :   "This is a single defined term."  name :   the term itself: "Generative Engine Optimization".  alternateName :    synonyms or abbreviations, in this case "GEO". This is extremely practical because it covers different search queries.  description :    the actual definition of the term. AI often pulls its information directly from this content.  url :   the specific detailed/subpage (or an anchor) for this exact term.  inDefinedTermSet :   the backlink to the parent glossary (the same URL as the set above). This clearly assigns the entry to the glossary, closing the loop between the two levels.  sameAs – the inconspicuous property with the biggest impact   Technically speaking, sameAs is not a schema.org type in its own right, but a property—and of all things, it is almost globally neglected. Most implementations just link to LinkedIn, for example, and call it a day. The real added value lies elsewhere: Wikidata and Wikipedia.   Wikidata is the canonical knowledge registry behind Google, ChatGPT, Claude, and Perplexity. If you anchor your entity there, you tap directly into the source where these systems get their knowledge of the world. This is the most verifiable step possible, not least because it connects directly with the Knowledge Graph instead of vague LLM assumptions.  An example of usage in JSON-LD   {    "@context": " https://schema.org ",    "@type": "Organization",    "name": "internetwarriors GmbH",    "url": " https://www.internetwarriors.de ",    "sameAs": [      " https://www.wikidata.org/wiki/Q ...",      " https://de.wikipedia.org/wiki/ ...",      " https://www.linkedin.com/company/internetwarriors ",      " https://www.crunchbase.com/organization/ ..."    ]  }  Dataset - If you have your own data, show it as data   Do you have your own studies, benchmarks, market figures, or evaluations? Then signal with Dataset that this is original data, not recycled facts. AI systems prefer primary sources because they lower the risk of hallucination. This is exactly how you stand out from the crowd of secondary content sites.  Info and implementation examples at: https://schema.org/Dataset   ItemList and ClaimReview – structure for unique statements   With ItemList , you make rankings, comparisons, and enumerations machine-readable, for instance for "best X for Y" articles that users search for before making a purchasing decision. Instead of having to extract a list from continuous text, the search engine gets the exact order served on a silver platter.  ClaimReview identifies individual, verified statements, originally intended for fact-checking. Google has scaled back its functionality by now, so don't expect miracles. But if you want to clearly indicate what a statement is based on, this is still a solid choice.  Info and implementation examples at: https://schema.org/ItemList   and at https://schema.org/ClaimReview   Achieving the greatest impact: combine types instead of using them individually  The biggest mistake is relying on a single "magical" type. Analyses consistently point in one direction: it's the combination that works. In practice, a stacked approach of Article + FAQPage + BreadcrumbList + DefinedTerm + HowTo clearly beats pages with only one schema type. But even here, we must remain realistic: more isn't always better.   Let's be honest: schema is an amplifier, not a magic switch  A word of perspective, as the market is currently overflowing with golden promises. Much of what circulates as "340% more AI citations" statistics is not independently verified and often comes from sources that sell exactly this service. Google itself clarifies that schema alone does not guarantee inclusion in AI Overviews.  And there is an important technical caveat: tests show that LLMs sometimes simply read JSON-LD as additional text on the page rather than necessarily as a parsed structure.   In plain English, this means a good part of the effect does not come from the schema label , but from the fact that structured data forces you to organize your facts cleanly, unambiguously, and in a machine-readable way. The label helps search-based systems like Bing or Google, while clean content helps everyone.  This is not a weakness of the strategy—on the contrary. It just means markup without clean, matching page content is useless. The two must fit together.  Unsure if your structured data is ready for AI search, or if your brand even appears in ChatGPT, Perplexity, and Google AI Overviews? This is exactly where we come in. The internetwarriors will audit your existing schema markup, anchor your brand as an entity (think Wikidata), and show you the levers that will make the biggest difference for you. Schedule your free initial consultation now.   FAQ  Which schema type brings the most benefit for GEO? The most underestimated and at the same time most verifiable lever is sameAs with links to Wikidata and Wikipedia, closely followed by DefinedTerm for specialized vocabulary. The greatest overall effect comes from combining several types.  Is JSON-LD enough, or do I need Microdata? JSON-LD is the format preferred by Google and all major platforms. Microdata and RDFa work, but are not recommended.  Does schema guarantee visibility in AI answers? No. Schema is an amplifier, not a switch. It makes your brand and facts unique and clear. Inclusion also depends on content quality, authority, and the alignment of your markup with the actual page content.  How do I check if my markup is correct? By using Google's Rich Results Test and the Schema.org Validator. Both will show you errors and warnings. Invalid markup has no utility. This test should therefore be part of your routine before every launch.  You can find the links to the tools here   

At a glance: The key takeaways   End of an era: Google is phasing out standalone Display campaigns as a separate campaign type. The layout shift and full migration to Demand Gen will be wrapping up by 2027.  GDN is here to stay: The Google Display Network (GDN) isn't going away. Instead, it will serve purely as an inventory placement within Demand Gen, and you can still target it exclusively if you prefer.  A holistic approach: Demand Gen brings GDN, YouTube (In-Stream & Shorts), Discover, Gmail, and Google Maps together under one unified technological roof.  Performance boost: According to Google's data, advertisers using GDN through Demand Gen see an average ROI increase of 9.5%.  Action required: Google is launching an upgrade tool starting June 2026. However, advertisers should proactively manage the transition rather than waiting for the automatic migration.  If you've been relying on classic Display campaigns in Google Ads for years, it's time to shift gears: Google has officially announced the end of standalone Display campaigns. The migration will be fully completed by 2027. All Display activities are moving permanently into the Demand Gen campaign type, which was introduced in 2023. There is much more to this than just cosmetic renaming. It marks the final step in a strategic realignment: moving away from the rigid, silo-based management of individual channels and heading toward AI-powered, cross-platform steering of visual assets.  The Timeline: What happens when?   The transition is happening in phases to give advertisers plenty of time to test and adapt:  Starting June 2026: Google will gradually roll out an integrated migration tool in accounts. Eligible advertisers will be able to easily move existing Display campaigns directly into Demand Gen structures.  Moving forward: The option to create completely new, standalone Display campaigns will be deactivated. Future updates and new features will be developed exclusively for Demand Gen.  By 2027: The automatic migration pipeline will be completed, and any remaining Display campaigns will be migrated automatically by Google's systems.  Source: Google - https://blog.google/products/ads-commerce/google-display-ads-demand-gen/   Google's reasoning behind this step matches the reality of modern e-commerce: customer journeys are no longer linear. Potential customers bounce between YouTube Shorts, Discover feeds, Gmail, and traditional blogs in a matter of minutes. Demand Gen was built precisely to connect these touchpoints seamlessly.  What is Demand Gen, and what happens to GDN?   Briefly put: Demand Gen is designed to actively generate demand (mid and upper funnel) in contrast to just capturing existing search volume. Ads are served across Google's highest-reaching and most visually prominent surfaces: YouTube, Discover, Gmail, Google Maps, and the Google Display Network.  Good news for pure Display strategies: if you prefer to advertise exclusively on the Google Display Network (GDN) for budget or branding reasons, you can still keep that control. Advanced channel controls within Demand Gen let you limit delivery purely to the GDN if needed. That means the migration doesn't force you into producing video or using YouTube; it simply opens those doors as powerful options.  Key changes for advertisers   This consolidation brings some structural shifts to daily campaign management:  Algorithms over micromanagement   Classic Display campaigns often allowed for very granular, manual targeting at the placement or ad group level. Demand Gen shifts that focus: AI takes over most of the real-time optimization. Because of this, the advertiser's leverage shifts heavily from technical settings to strategic audience targeting and creative supply.  Brand safety and exclusions   A critical point in any automated transition is brand safety. Google guarantees that existing content exclusions and brand safety settings will be preserved when migrating with the official tool. Even so, it's highly recommended to manually verify all exclusions in your new setup after the upgrade.  Reporting and data logic   The isolated reporting level for pure Display data is going away. While you can still filter channel-specific data within Demand Gen reports, the attribution and analysis logic follows Google's holistic multi-channel approach.    Source: Google - https://blog.google/products/ads-commerce/google-display-ads-demand-gen/   Is the switch worth it? A look at the numbers   The first performance data released by Google shows highly promising trends: advertisers enjoy an average of 9.5% more ROI when using GDN inside Demand Gen. In a global case study with food delivery service GoFood, the combined setup led to a 24% lower CPA alongside a 19% increase in conversions.  Source: Google - https://blog.google/products/ads-commerce/google-display-ads-demand-gen/   Even though studies from platforms themselves always reflect ideal conditions, real-world practice confirms: Demand Gen rewards first-party data and high-quality visual assets. Advertisers with clean customer lists (Customer Match) and tailored lookalike audiences will see noticeable performance benefits from AI-powered delivery.  Strategic Roadmap: What you should do now   Waiting for the forced, automatic migration means missing out on valuable optimization time and losing control over your historical data. We recommend taking the following steps:  Audit your current setup: Analyze your current Display campaigns. Which ones are serving retargeting, and which ones are purely for brand awareness? This clustering will shape your future Demand Gen setup.  Strengthen your audience infrastructure: Since Demand Gen relies heavily on Google's audience intelligence, make sure your custom segments, Customer Match lists, and lookalike structures are flawlessly implemented.  Ramp up asset production: While static banners will do for a start, Demand Gen truly shines when combined with video (such as Shorts). Use this time to build short, visually engaging video assets.  Run parallel tests: Set up your own Demand Gen campaigns alongside your core Display campaigns early on to help the algorithms learn and to draw direct performance comparisons.  The Verdict   The end of standalone Display campaigns marks the end of manual banner management in Google Ads. However, the Google Display Network isn't dying; it is simply moving into a modern, AI-driven ecosystem that is much better equipped for today's fragmented user paths. Planning your transition strategically and updating your creatives now will give you a noticeable competitive advantage early on.  Need help with the migration or want to future-proof your Google Ads setup? Get in touch with our Paid Ads team for a data-driven migration strategy without losing search or placement reach.   FAQ – Frequently Asked Questions about the Display Migration   When exactly will Display campaigns be discontinued?   The entire process is set to wrap up by 2027. Google will provide a migration tool in the interface starting June 2026, and the creation of new standalone Display campaigns will be disabled step-by-step moving forward.  Should I wait for Google's automatic tool?   While the tool simplifies the technical transfer of budgets and smart signals, it is still highly recommended to manage the transition manually or with professional support. This ensures your target groups and creatives are perfectly aligned with Demand Gen's requirements from day one.  Can I still advertise exclusively on the GDN within Demand Gen?   Yes, you can. By using advanced channel controls, you can specifically restrict ad delivery to the Google Display Network, so you aren't forced to serve ads on YouTube or Gmail inventory.  What happens to my previous exclusions and target audiences?   When using the official upgrade tool, your existing settings and historical signals are carried over to the new campaign structure. However, double-checking your brand safety guidelines manually right after the switch is highly recommended.  Is Demand Gen worth it for small daily budgets?   Yes, though AI-assisted campaigns like Demand Gen do need a certain amount of data to successfully complete their learning phase. With very small budgets, you should give the learning phase a bit more time and avoid analyzing performance too early.  Where can I find official details about the change?   Google regularly publishes updates, best practices, and detailed migration guides on the official Google Ads Help Center as well as the Google Products Blog. 

Der Onlinehandel hat in den vergangenen Jahren eine dynamische und meist positive Entwicklung erlebt. Während der Pandemie erreichten viele Unternehmen ungewohnte Wachstumsschübe. Budgets wurden ausgeweitet, Prozesse beschleunigt und Strukturen aufgebaut, die dem damaligen Marktumfeld entsprachen. Heute im Jahr 2026 hat sich die Lage jedoch gewandelt. Das Umsatzwachstum ist rückläufig, gleichzeitig bestehen Fixkosten aus Wachstumsphasen fort. Besonders stark wirken sich dabei zwei Bereiche aus: Software und externe Dienstleistungen bzw. Agentur-Partnerschaften. In beiden Feldern wurden in den Boomjahren Entscheidungen getroffen, die aus damaliger Sicht sinnvoll erschienen, heute jedoch zu einer hohen und oft unnötig komplexen Kostenbasis führen. Software wurde lizenziert, erweitert und ergänzt. Agenturen wurden beauftragt, um Wachstum und Projekte voranzutreiben. 2026 zeigt sich, dass viele dieser Ausgaben neu bewertet werden müssen, nicht aus Sparzwang, sondern um Budgets wieder konsequent an Wirkung auszurichten. Genau hier liegt das größte Potenzial, Effizienz zu steigern und Investitionen gezielt dorthin zu lenken, wo sie spürbaren Business-Impact erzeugen. Dieser Beitrag untersucht, wie Unternehmen im E-Commerce durch die Optimierung ihrer Softwarelandschaft und durch klare Agenturstrukturen ihre Profitabilität nachhaltig verbessern können. Der Fokus liegt darauf, wie Transparenz entsteht, welche typischen Fehler auftreten und welche strategischen Maßnahmen die Budgeteffizienz dauerhaft steigern. Der Status Quo: Hohe Fixkosten, geringe Transparenz Viele Onlinehändler sehen sich heute mit einer Kostenstruktur konfrontiert, die in Wachstumsphasen entstanden ist, aber nicht mehr zum aktuellen Umsatzniveau passt. Was ursprünglich als Investition gedacht war, hat sich zu einem dauerhaften Fixkostenblock entwickelt. Besonders im Bereich Software wurden in den vergangenen Jahren zahlreiche Lösungen gekauft, lizenziert und implementiert. Der Grund lag häufig im Bedarf nach Geschwindigkeit und Flexibilität. Im Agenturumfeld ist eine ähnliche Entwicklung sichtbar. Strategische Partner wurden beauftragt, um Aufgaben auszulagern, Know-how zu ergänzen oder Projekte schneller umzusetzen. Die dadurch entstandenen Budgets waren im Kontext steigender Umsätze vertretbar. Heute treffen die gleichen Kosten oft auf eine völlig andere Marktrealität. Zwei Faktoren eint beide Bereiche: Es fehlt vielen Unternehmen an systematischer Transparenz. Es existiert kaum eine etablierte Routine für Kostenkontrolle und Vertragsmanagement. Ohne Übersicht wird optimiert, ohne zu wissen, welche Programme, Leistungen oder Verträge überhaupt aktiv, notwendig oder redundant sind. Dies führt dazu, dass Kosten über Jahre wachsen, ohne dass eine bewusste Entscheidung dahinter steht. Software als unterschätzter Kostentreiber Software ist zu einem der größten Fixkosten-Posten im E-Commerce geworden. Das liegt nicht an den grundsätzlichen Anforderungen des Onlinehandels, sondern an der Art, wie Software eingeführt, genutzt und verlängert wird. Studien zeigen, dass knapp die Hälfte aller Softwarelizenzen in Unternehmen ungenutzt bleibt. Die Kosten dafür sind enorm, denn Software-Anbieter setzen auf automatische Verlängerungen, Stufenmodelle und nutzerbasierte Preise. In der Praxis bedeutet das, dass für Funktionen gezahlt wird, die entweder nicht verwendet oder nur von wenigen Mitarbeitenden genutzt werden. Typische Ursachen für hohe Softwarekosten Ungeplante Tool-Expansion: Teams kaufen Tools für spezifische Aufgaben, ohne vorhandene Lösungen zu prüfen. So entstehen Überschneidungen, Dopplungen und isolierte Systeme. Überlizenzierung: Viele Unternehmen zahlen für mehr Nutzer als benötigt. Onboarding erfolgt schnell, Offboarding selten. Unklare Verantwortlichkeiten: Es gibt häufig keinen definierten Software-Verantwortlichen. Dadurch wird nicht geprüft, ob ein Tool seinen Zweck erfüllt oder ob der Preis noch angemessen ist. Automatische Verlängerungen: Viele SaaS-Verträge verlängern sich jährlich oder monatlich automatisch, oft zu höheren Preisen als im Vorjahr. Fehlende Konsolidierung: In Wachstumsphasen wurden Tools ergänzt statt ersetzt. Das führt zu Funktionsüberschneidungen, die kaum jemand wahrnimmt. Warum Softwarekosten so schwer zu reduzieren sind Software gilt vielen Unternehmen als „notwendig“. Selbst wenn der Nutzen gering ist, scheuen Teams eine Kündigung, weil sie vermeintlich wichtige Prozesse beeinträchtigt sehen. In Wahrheit sind viele Tools austauschbar oder lassen sich durch bestehende Systeme ersetzen. Zusätzlich spielt Bequemlichkeit eine Rolle. Eine Lizenz zu kündigen bedeutet, Prozesse zu prüfen, Alternativen zu evaluieren und Verantwortlichkeiten zu klären. Ohne klaren Prozess wird es daher oft aufgeschoben. Agentur-Partnerschaften strategisch optimieren Neben Software sind Agenturen der zweite zentrale Kostenblock, der 2026 stärker unter strategischer Betrachtung steht. Agenturleistungen decken ein breites Spektrum ab: Strategieentwicklung, Marketing, Content, Tracking, UX, SEO und viele weitere Bereiche. Der Boom der letzten Jahre führte dazu, dass Unternehmen mehrere Agenturen parallel beauftragten, häufig ohne zentrale Steuerung. Retainer wurden ausgebaut, Zusatzprojekte umgesetzt und Leistungsmodelle über Jahre fortgeführt, oft ohne regelmäßigen Abgleich zwischen Zielbild, Prioritäten und tatsächlichem Business-Impact. Zentrale Herausforderungen im Umgang mit Agenturen Fehlende Leistungs- und Erfolgskontrolle: Viele Unternehmen erhalten monatliche Berichte, ohne klare KPIs, Zieldefinitionen oder Erfolgsmessung. Leistungen werden umgesetzt, aber nicht konsequent bewertet. Unklare Aufgabenteilung: Nicht selten übernehmen mehrere Partner Aufgaben, die sich überschneiden. Das führt zu Doppelarbeit und unnötiger Komplexität. Pauschale Retainer ohne konkrete Leistung: Ein fixer Betrag wird gezahlt, unabhängig davon, ob Leistung und Umfang klar nachvollziehbar sind. Fehlende Struktur in der Steuerung: Ohne klare Prozesse, Ansprechpartner und Prioritäten entsteht operative Reibung, und damit indirekter Aufwand auf beiden Seiten. Hohe Wechselbarrieren: Unternehmen scheuen einen Partnerwechsel, weil sie Wissenstransfer, Reibungsverluste oder Verzögerungen fürchten. Dadurch bleiben ineffiziente Strukturen bestehen. Warum Agenturverträge neu ausgerichtet werden sollten Die Marktsituation hat sich gedreht. Budgets werden in vielen Unternehmen gezielter geplant und stärker an messbaren Ergebnissen ausgerichtet. Dadurch entsteht die Chance, Agenturmodelle neu zu gestalten: klarer in der Leistung, transparenter in der Steuerung und stärker an Wirkung orientiert. Unternehmen, die ihre Agentur-Partnerschaften strukturiert überprüfen, schaffen häufig klarere Leistungsdefinitionen, bessere Planbarkeit und eine effizientere Budgetverteilung, bei gleichbleibend hoher Qualität und besserer Ergebnisorientierung. Hebel zur Optimierung von Softwarekosten Eine systematische Optimierung der Softwarelandschaft beginnt mit einer vollständigen Bestandsaufnahme. Ziel ist eine klare Übersicht über alle bestehenden Lizenzen, Kosten, Funktionen und Nutzungsgrade. Schritte zur Budget-Effizienzsteigerung Software-Inventar erstellen: Alle Tools, Lizenzen, Preise, Vertragslaufzeiten und Nutzer erfassen. Ein aktuelles Inventar ist die Grundlage jeder Entscheidung. Nutzung prüfen: Welche Tools werden aktiv genutzt, welche nur selten, welche gar nicht. Tools mit geringer Nutzung gehören auf den Prüfstand. Funktionsüberschneidungen erkennen: Viele Tools bieten ähnliche Funktionen. Eine Konsolidierung senkt Kosten und reduziert Komplexität. Lizenzmodelle prüfen: Enterprise- oder Premiumtarife werden oft bezahlt, obwohl Basisversionen ausreichen. Verträge aktiv verhandeln: Viele Softwareanbieter bieten Rabatte auf Nachfrage an, besonders bei längeren Laufzeiten oder höherem Lizenzumfang. Alternative Anbieter evaluieren: Open-Source-Lösungen, modulare Systeme oder Anbieter mit flexibler Preisstruktur bieten Kostenvorteile. Hebel zur Optimierung von Agenturstrukturen Agenturen sollten genauso strukturiert betrachtet werden wie Software. Ein professionelles Partner- und Vertragsmanagement kann die Budgeteffizienz erheblich steigern, ohne die Qualität zu senken. Schritte zur Optimierung Leistungs- und Zielabgleich durchführen: Was wird tatsächlich geliefert, wie zahlt es auf die Unternehmensziele ein und wie lässt sich Wirkung messbar machen? Retainer strukturieren: Fixe Budgets sollten klare Leistungsblöcke enthalten, die nachvollziehbar, messbar und steuerbar sind. Vergütungsmodelle modernisieren: Statt starrer Tagessätze rücken 2026 zunehmend wertorientierte Modelle in den Fokus. Entscheidend ist nicht die bezahlte Anwesenheit, sondern der messbare Beitrag zur Zielerreichung. So entsteht eine faire, transparente Budgetlogik, mit klarer Verknüpfung zwischen Aufwand, Ergebnis und Wirkung. Doppelstrukturen reduzieren: Wenn zwei Partner ähnliche Aufgaben erfüllen, entstehen parallele Kosten. Eine klare Aufgabenteilung verbessert Effizienz und Kommunikation. Leistungsbasierte Modelle prüfen: Erfolgsabhängige Vergütung schafft Fokus auf Ergebnisse und erhöht die Verbindlichkeit in der Zusammenarbeit. Verträge flexibel halten: Sinnvolle Laufzeiten und klare Kündigungsfristen sorgen für Agilität und verhindern langfristige Abhängigkeiten. Warum Transparenz der Schlüssel zu jeder Optimierung ist Transparenz ist die Voraussetzung für jede Form der Kostensteuerung. Unternehmen, die alle Verträge, Tools und Kostenstellen zentral dokumentieren, treffen bessere Entscheidungen. Transparenz führt automatisch zu höherer Effizienz, da Verantwortlichkeiten klar zugeordnet und Entscheidungen begründet werden müssen. Ein professionelles Vertrags- und Kostenmanagement umfasst: automatische Erinnerungen bei Kündigungsfristen regelmäßige Kosten-Reviews Verantwortliche pro Vertrag klare Entscheidungskriterien für Verlängerung oder Kündigung Ohne diese Struktur lassen sich selbst große Hebel nicht systematisch nutzen. Eine klare, regelmäßige Analyse zeigt schnell, wo Doppelstrukturen vorliegen, wo Abos in teuren Enterprise-Plänen laufen, obwohl die Nutzung deutlich darunter liegt, und wo Verträge seit Jahren unverändert durchlaufen. Unternehmen, die hier konsequent aufräumen, verbessern nicht nur ihre Kostenbasis, sondern schaffen auch ein stabileres technisches Setup. Denn weniger Tools bedeuten weniger Komplexität, weniger Schnittstellen und weniger Risiko in kritischen Prozessen. Mit zunehmender Transparenz verschiebt sich auch die Art der Entscheidungen. Es geht nicht mehr darum, Tools aus Gewohnheit weiterzuführen oder Agenturverträge aus Bequemlichkeit zu verlängern. Es geht darum, jede Investition an Wirkung zu messen: Welche Tools schaffen echten Wert und tragen zu Umsatz, Effizienz oder Sicherheit bei? Welche Partnerschaften sind strategisch notwendig und welche binden Budget, ohne die Organisation voranzubringen? Was erfolgreiche Unternehmen 2026 anders machen Erfolgreiche Händler setzen nicht auf kurzfristige Kürzungen, sondern auf strukturelle Optimierung. Statt einzelne Tools oder Partnerschaften isoliert zu beenden, entsteht ein langfristiges System, das Budgets dauerhaft kontrollierbar macht. Die wichtigsten Merkmale sind: klare Softwarearchitektur definierte Prozesse für Tool-Evaluierungen transparente Agentursteuerung regelmäßige Vertragsgespräche quartalsweise Kostenanalysen vollständige Dokumentation aller Ausgaben Diese Unternehmen steigern nicht nur ihre Budgeteffizienz, sondern erhöhen auch die operative Schlagkraft. Optimierung ist daher nicht per se negativ, sie sorgt für Fokus, Stabilität und bessere Ergebnisse. Fazit Der E-Commerce steht 2026 vor einer klaren Herausforderung: Viele Kostenstrukturen stammen aus Wachstumsphasen, passen aber nicht mehr zum aktuellen Marktumfeld. Softwarelandschaften und Agenturmodelle haben sich zu großen, oft unkontrollierten Fixkostenblöcken entwickelt. Genau in diesen Bereichen liegt das größte Potenzial, Profitabilität und Effizienz nachhaltig zu verbessern. Die Optimierung beginnt nicht mit pauschalen Kürzungen, sondern mit Transparenz und klaren Entscheidungsgrundlagen. Wer weiß, welche Tools genutzt werden, welche Partner welche Leistungen erbringen und welche Verträge wann enden, gewinnt Kontrolle. Wer zusätzlich konsolidiert, verhandelt und klare Prozesse etabliert, erzielt oft fünf- bis sechsstellige Effizienzgewinne pro Jahr, ohne operative Leistungsfähigkeit oder Qualität zu verlieren. Kostenprobleme entstehen selten über Nacht. Sie entstehen in kleinen Schritten: durch fehlende Kontrolle und durch Strukturen, die nicht aktiv gepflegt werden. Die Lösung besteht darin, die eigenen Systeme bewusst zu gestalten. Software und Agentur-Partnerschaften sind dabei die zentralen Stellschrauben. Unternehmen, die diese Bereiche 2026 konsequent angehen, schaffen sich einen klaren Vorteil. Sie erhöhen ihre Profitabilität, gewinnen Flexibilität und können Investitionen wieder dorthin lenken, wo sie Wirkung erzeugen. Genau das entscheidet in einem Markt, in dem Wachstum schwieriger geworden ist. Für alle Onlinehändler, die ihre Kostenstruktur nicht manuell verwalten möchten, haben wir unseren Service für Vertragsmanagement und -optimierung entwickelt. Wir schaffen Transparenz, setzen klare Prozesse auf und unterstützen bei Verhandlungen, damit Budgets planbar bleiben und gezielt dort wirken, wo sie Profitabilität und Wachstum stärken. Text über den Autor: Alexander Steireif ist Gründer und Geschäftsführer der Strategie- und Technologieberatung Alexander Steireif GmbH. Seit über 20 Jahren unterstützt er mittelständische Unternehmen dabei, ihren Vertrieb zu digitalisieren, leistungsfähige E Commerce Lösungen aufzubauen und klare Strategien für nachhaltiges digitales Wachstum zu entwickeln.

A strong ad is only half the battle: only the right landing page determines whether a click actually turns into a conversion. If you invest in Google Ads, Meta, or LinkedIn, you should pay at least as much attention to the landing page as you do to the ad creative. In this article, we’ll show what makes a successful paid landing page, which components are essential, and which tips and tricks you can use to get the most out of your campaigns.  The key points at a glance  A paid landing page (also called a conversion page or PPC landing page) is a page created specifically for paid advertising campaigns with a clear conversion goal.  Unlike a classic website, it avoids distracting navigation and focuses on a single action, such as a purchase, a signup, or lead generation.  Successful campaign pages convince with a clear headline, a strong USP, trust-building elements, and a prominent call to action.  Mobile optimization, short loading times, and consistent message match between the ad and the landing page determine success or failure.  A/B testing and clean tracking are essential for continuously improving performance.  What is a paid landing page?   A paid landing page, often also referred to as a campaign page, conversion page, or PPC landing page, is a website that is designed specifically for a paid advertising campaign. Unlike a classic homepage, it pursues one single goal: to turn visitors who arrive via a Google Ads, Meta, LinkedIn, or other paid ad into customers or leads.  The term "paid" refers to the traffic source. Unlike organically reached users who come to the page via search engines, social media posts, or recommendations, visitors arrive at the landing page exclusively through paid ads. Every click costs money, which is exactly why the page must be designed so that this click reliably leads to an action. The difference from a classic website   While a company website covers many topics and serves different target groups, a landing page is minimalist and purpose-driven. There is no main navigation, no distracting links, and no unnecessary content. Everything on the page works toward one single call to action, whether that is a purchase, filling out a form, or a download.  The two formats also differ significantly when it comes to measuring success. While a company website is measured by metrics such as sessions, time on site, or page views, a landing page is practically judged by just one metric: the conversion rate. Every element on the page, from the image to the headline to the button text, is consistently aligned with that goal.  Why do you need a dedicated landing page for paid campaigns?   When you run ads, you pay for every click, regardless of whether it leads to a conversion. If you simply send visitors to the homepage, a lot of potential is often lost: the ad message is not picked up, users get lost in the navigation, and leave the page.  A dedicated lead landing page ensures that the promise made in the ad is delivered immediately. Specific campaign pages usually achieve significantly higher conversion rates than general websites. In addition, advertising platforms such as Google Ads reward relevance with better quality scores, which in turn lowers click prices and makes the ad budget more efficient.  The most important building blocks of a successful landing page  A good conversion page follows a clear structure.   These elements should never be missing:  Clear headline and convincing USP:   The headline is the first thing visitors see, and within seconds they decide whether to stay or click away. It must clearly communicate which problem is being solved or which benefit awaits. Directly below it, a subheadline specifies the unique selling point.  Convincing visuals:    Images and videos convey messages faster than text. Authentic photos have more impact than generic stock images, and product videos or explainer clips can noticeably increase the conversion rate.  A prominent call to action:    The CTA button is the centerpiece of every campaign page. It should stand out visually, be clearly worded ("Try it free now", "Book a consultation") and ideally appear multiple times on the page without being pushy.  Build in trust elements:   Trust is the decisive factor, especially when the brand is new to visitors. Customer testimonials, reviews, seals of approval, well-known reference logos, or awards work wonders. Transparent information about privacy and delivery terms also lowers barriers.  Mobile optimization and short loading times:   More than half of all paid clicks now come from mobile devices. A landing page must work just as well on a smartphone as it does on desktop. Loading times over three seconds lead to massive drop-offs — every additional second can reduce the conversion rate by double-digit percentages.  Tips & tricks for more conversions:   With a few targeted adjustments, a good landing page can become a truly strong one.  Message match: the ad and landing page must align:   If an ad promises a free demo, that demo must be shown prominently on the landing page as well. The so-called message match — meaning the content and visual alignment between the ad and the destination page — is one of the biggest levers for higher conversion rates.  A/B testing as a must:   Even small changes can have a big impact: a different headline, a new button color, another image. A/B tests help you find out which version actually performs better instead of relying on gut feeling.  Set up clean tracking:   Without valid data, nothing can be optimized. Conversion tracking, heatmaps, and session recordings show what works on the page and where visitors drop off. Tools like Google Tag Manager, GA4, or Hotjar provide valuable insights for this purpose.  Keep forms as short as possible:   Every additional field costs conversions. Only ask for what is truly needed. On a lead landing page, name, email address, and one or two specific details for later qualification are often enough.  Avoid common mistakes on campaign pages:   Many companies underestimate how quickly a landing page can fail. Classic pitfalls include too much text, unclear CTAs, missing mobile optimization, the wrong target audience, or landing pages that are simply copies of the homepage. Missing trust elements or insufficient GDPR notices also have a negative impact.  It is also problematic to launch paid campaigns without preparing a matching destination page. If you want to appear professional and not burn through your ad budget, you should create a dedicated page for each campaign, or at least for each main target group.  Conclusion: paid landing pages are not a nice-to-have   A well-thought-out landing page is the decisive lever between click and conversion. It saves ad budget, boosts the performance of your campaigns, and creates a professional brand experience. Anyone investing in paid channels should therefore pay at least as much attention to the destination page as to the ad itself, because even the best campaign is useless if the landing page does not convince.  At the same time, a landing page is never truly "finished." User behavior, platform algorithms, and the competitive environment are constantly changing, which is why successful companies treat their campaign pages as an ongoing optimization process. Anyone who thinks strategically from the start and aligns headline, visuals, CTA, trust elements, and tracking properly can turn expensive traffic into profitable customer relationships — and turn an average paid campaign into a truly successful one.  FAQ   What is the difference between a landing page and a campaign page?   The terms are often used synonymously. A campaign page is a specific type of landing page created for a particular marketing campaign, such as a product launch or a time-limited promotion.  Do I need a separate landing page for every ad?   Ideally, yes — at least for each target group or offer. The more closely the page matches the ad content, the higher the conversion rate and the better the quality score on platforms like Google Ads.  How long should a PPC landing page be?   That depends on the offer. Simple lead generation works with short pages, while products that require more explanation or higher-priced offers need more content, arguments, and trust elements.  How do I measure the success of a conversion page?   By clearly defined KPIs such as conversion rate, cost per conversion, bounce rate, and time on page. Tools like GA4, Google Ads, and heatmap software provide the data needed for a solid evaluation.   

The key points at a glance   Google has fundamentally changed: Instead of blue links, AI-generated answers dominate the search results page — with direct effects on Google Ads.  AI Overviews have been active in Germany since spring 2025. Ads can already appear above, below, and in some cases within the AI responses.  Ads directly in Google AI Mode are currently being tested in the US and will soon also come to Germany.  Only certain campaign types qualify for these new placements — above all Broad Match, AI Max for Search, Performance Max and Shopping Ads .  Anyone who still works exclusively with Exact Match or a rigid campaign structure today will lose visibility in the future exactly at the moments that matter.  AI Max for Search is currently the fastest-growing AI feature in Google Ads and a key lever for the new placements.  Anyone who optimizes their campaign structure, data quality and assets now will secure a decisive head start.  Search has fundamentally changed   Anyone searching on Google today increasingly gets not a list of links, but a direct answer. The search results page advertisers have grown used to over the years looks fundamentally different in 2026 than it did just two years ago.  Two technologies are driving this change:  AI Overviews are AI-generated summaries that have also been active in Germany since spring 2025. They appear at the top of the page for more complex or informational search queries and often answer the question so completely that many users do not scroll any further. This changes where and how ads are perceived and which ones are served at all.  Google AI Mode has taken things a step further. Available in Germany since October 2025, it is a standalone, conversational search interface. Users no longer type in individual search terms, but have real dialogues, similar to an AI assistant. The intent behind them is often much more layered, the context more complex.  For Google Ads advertisers, this means: Reaching the right audience no longer depends only on precise keywords, but on understanding intent, context and conversation flow. The AI decides and it decides based on data and signals, not manually maintained keyword lists.  Where do ads actually appear — and which campaigns qualify?   This is the most practical question advertisers ask: Where exactly do my ads appear, and what do I need to do for that?  In AI Overviews   Ads can appear in three places around an AI Overview: above, below, or directly within the AI answer. Placement above and below is already available in all markets where AI Overviews are active, including Germany. Integration directly into the answer text is currently limited to English-language markets.  Important to understand: There is no separate opt-in for these placements. If you use the right campaign types and have relevant ads, you are automatically considered. Just as little can this placement be specifically excluded.  Google evaluates both the actual search query and the content of the AI-generated answer to decide whether an ad fits. This is a key difference from classic keyword logic: relevance is now measured in the context of the entire answer, not just the individual search term.  In Google AI Mode   Tests are currently running here in the US. Ads appear there directly embedded in the conversational responses — not as separate blocks, but as an integrated part of the AI answer. This is an even tighter context than with AI Overviews. The global rollout, including for Germany, has been announced, but no specific date has been set yet.  Which campaign types are actually qualified?   This is the point where many advertisers get stuck. Not every campaign is automatically served in AI Overviews or AI Mode. Google has clearly defined which campaign types qualify:  Search Ads with Broad Match keywords   AI Max for Search Performance Max (PMax)   Shopping Ads   Campaigns that work exclusively with Exact Match or Phrase Match are not qualified for these placements. This is a structural turning point: anyone who still relies on hyper-granular keyword structures today will, over time, lose impression share exactly at the moments when users are most ready to buy.  AI Max for Search: What is behind it and why is it so relevant right now?   AI Max in Google Ads is not a new campaign type, but a feature package that can be integrated into existing search campaigns. Activated with one click in the campaign settings, it fundamentally changes the campaign logic.  Specifically, AI Max combines two approaches: first, the familiar Broad Match technology, which also matches search queries when the exact wording differs from the entered keywords. Second, so-called keywordless serving — similar to Dynamic Search Ads in the past, but much smarter. The AI independently recognizes which search queries an ad would be thematically relevant for, even without a stored keyword.  To this are added three other core features:  Automated text adaptation: Google generates new headlines and descriptions based on existing ad titles, descriptions, and landing page content — and selects in real time the combination that best fits the respective search query. Since February 2026, text guidelines have been available worldwide for all advertisers: there you can define which wording the AI may use and which it may not.  URL expansion: Users are automatically sent to the page on your website that best matches the search query — not necessarily the URL stored in the campaign. Certain pages can be excluded from the system.  Brand controls: Advertisers can define for which brands ads should appear and for which they should not. This is especially relevant for accounts that actively manage competitor or brand campaigns.  When does AI Max pay off — and when does it not (yet)?   AI Max shows its strengths above all in accounts that already have enough conversion data and target broad audiences. In e-commerce and with B2C products with high search volume, results are typically strongest.  In niche markets, with very explanation-heavy B2B products, or accounts with only a few daily conversions, the rollout should be more cautious. An A/B test with a 50/50 split between the existing campaign and the AI Max version is the most sensible first step here.  What applies in any case: the foundation has to be right. Clean conversion tracking, a data-driven attribution model, and clear conversion goals in the account are mandatory. Anyone activating AI Max without this foundation leaves the AI in charge without a map or compass.  Performance Max: Google’s preferred channel for AI Overviews   Performance Max is not new, but its role has shifted. Google increasingly sees PMax as the main format for serving in AI-driven surfaces. This is because PMax was built from the ground up for data-driven, cross-channel serving: it provides the AI with text, images, videos and audience signals, and leaves the optimal combination to it.  For advertisers, this means: Anyone who has already set up PMax properly and regularly maintains asset groups is well positioned for AI Overviews and the AI Mode. Anyone not yet using it should start now at the latest — with clear goals, enough assets and regular monitoring of search terms.  A good sign: PMax has become significantly more transparent in recent months. Negative keywords can now be added directly, and the channel reporting shows which channel (Search, YouTube, Display, Gmail, Discover) contributes what to performance — without additional scripts or workarounds.  What this means for campaign structure   Many accounts have grown historically: strict match type separation, single keyword ad groups, dozens of ad groups for minimal differences. That used to make sense to maintain control. Today, this structure works against the AI.  If you split data across too many campaigns, you give the algorithm too little material to learn from. Instead of quickly recognizing patterns and optimizing, it stalls.  The current approach that has proven effective in practice looks like this: topic-based campaigns with a manageable number of keywords, a combination of Exact and Broad Match, Smart Bidding as standard. Not maximally granular, but maximally data-dense.  That does not mean giving up control completely. Negative keywords, audience signals, text guidelines and regular review of search queries remain active levers.  The foundation: data quality decides   Here is a mistake that runs through almost all accounts: people discuss campaign types and features before the data foundation is right. But the rule is: Garbage in, garbage out. If you feed the AI bad data, you are only automating budget burn.  Server Side Tracking (SST) is the foundation. Classic browser tracking increasingly loses data due to ad blockers, cookie restrictions and iOS updates. Server Side Tracking bypasses these hurdles and, in practice, delivers at least 12% more usable data points — signals that Smart Bidding and AI Max urgently need for optimization.  In addition, advertisers should actively use the following data sources:  First-party data / customer lists : Existing and new customers can be evaluated differently in a targeted way via Customer Match lists. In the area of new customer acquisition, Smart Bidding can be prompted to weight new customers more heavily — with concrete effects on bid logic.  CRM data (offline conversions) : Especially in B2B, it makes no sense to treat every lead equally. Anyone feeding back CRM data (e.g., from HubSpot or Salesforce) via offline conversions gives Google Ads the signal to distinguish between "poor" and "valuable" — and that is exactly the prerequisite for sustainably profitable growth.  Conclusion: Act now before the market does   Google Ads in 2026 is a data-driven system, not a manual tool. The question is no longer whether to use AI Max, AI Overviews and modern tracking structures — but when. Anyone who actively shapes the transformation now secures visibility at the moments that really matter.  As an experienced Google Ads agency, we guide you through exactly this process: from tracking infrastructure to campaign structure to AI Max and Performance Max. Get in touch now →   FAQ   Will my Google Ads be served automatically in AI Overviews? Not automatically. Ads appear in AI Overviews when the ad matches both the search query and the content of the AI answer. Another requirement is that you use Broad Match, AI Max or Performance Max.  What does advertising in Google AI Mode cost more than classic Search Ads? There is no separate pricing model for AI Mode ads. Google's auction system stays the same — placement is determined by relevance, quality score and bid.  Can I exclude my ads from AI Overviews? No. Google currently does not offer a way to specifically disable these placements.  Do I get separate reporting for AI Overview ads? Not yet in full. At present, ads in AI Overviews are counted as "Top Ads" and appear accordingly in standard reports. Dedicated segment reporting has been announced for the future, but is not yet available.  When will ads in Google AI Mode also come to Germany? There is no official date yet. Ads in AI Mode are currently being tested in the US (as of March 2026). The international rollout has been announced.  Does AI Max also make sense for smaller accounts? That depends on the individual case. In principle, AI Max needs a solid data foundation — meaning enough conversions, clean tracking and clear goals. For accounts with only a few daily conversions, we first recommend a controlled A/B test before the entire campaign is switched over.  Do I need to create new campaigns to appear in AI Overviews? No. Existing campaigns qualify automatically, provided the right campaign types and match types are used.  What is the difference between AI Overviews and AI Mode? AI Overviews are AI summaries within the normal Google search. AI Mode is a separate, conversational search interface for complex, multi-step queries — comparable to an AI chatbot directly in search. 

The world of online marketing is spinning faster today than ever before. While we've been fighting for clicks and conversions at internetwarriors since 2001, we're currently experiencing the most radical upheaval in our history. The trigger: Agentic Commerce . We are transitioning from mere information search to task-oriented execution. Today, a user no longer just asks for products; they instruct a AI shopping agent to autonomously handle the entire purchase process. In this article, I'll show you why the failure of OpenAI's "Instant Checkout" is not the end of the hype, but the starting point for a new technical infrastructure that you need to know as a retailer now. The OpenAI Pivot: From Shopping Cart to Discovery Platform In March 2026, OpenAI ended its "Instant Checkout," prompting one of the most discussed debates in e-commerce. Failure or strategy? We reveal what is really behind the pivot and what it means for retailers. What was Instant Checkout? In September 2025, OpenAI launched the Agentic Commerce Protocol (ACP) with Stripe, bringing "Instant Checkout" to ChatGPT. The vision: users find a product in the chat and buy it directly without leaving the platform. Etsy, Walmart, and Shopify were the first partners – Shopify president Harley Finkelstein called it a "new frontier" for online retail. Why did direct checkout fail? In early March 2026, OpenAI pulled the plug. What critics dismiss as the failure of Agentic Commerce is, upon closer inspection, a strategic pivot from which we can learn a lot. OpenAI underestimated the immense complexity of global commerce. Three critical factors made direct purchase completion in the chatbot impossible: The three technical killers:   1. Lack of real-time synchronization: The inventory data of millions of retailers could not be reconciled at the required speed – outdated prices and stock immediately shattered user trust.   2. Compliance hurdles: Systems were missing for automated calculation of regional taxes (in the US alone, thousands of local tax jurisdictions) and for compliance with local laws like the Price Indication Regulation (PAngV) in Europe.   3. Fraud prevention: Agent-based transactions require completely new security architectures to prevent automated abuse. Another factor that is rarely mentioned in reporting: the withdrawal comes immediately after Amazon's $50 billion investment in OpenAI. Amazon controls 40 percent of US e-commerce and is building its own AI shopping tool with Rufus . Whether coincidence or strategic calculus – the timing is remarkable. 🟢 Update: March 25, 2026 OpenAI has simultaneously launched a completely new shopping experience with the checkout withdrawal: visual product browsing, side-by-side price comparisons, and image upload for product searches. Seven major US retailers – including Target, Sephora, Nordstrom, and Best Buy – are already live via ACP. Walmart operates a dedicated In-ChatGPT app with loyalty integration and native Walmart payment. This is not a withdrawal – this is a pivot. The new Warrior reality: OpenAI is primarily focusing on Product Discovery through ACP. The checkout returns to the retailer – but the decision of which retailer gets the order is increasingly made by the agent. Agentic Shopping works – just not yet in the West Anyone who believes that the failure of Instant Checkout proves Agentic Shopping is just hype is making a categorical mistake. Alibaba's Qwen-App is already completing food orders, travel bookings, and product purchases entirely in a single conversation – and at scale. The decisive difference: Alibaba owns the AI model, the marketplace, the payment infrastructure, and the logistics all from one source. OpenAI attempted to replicate the same without owning this stack. It was structurally doomed to fail. Google UCP: The new operating system of commerce While OpenAI is correcting, Google is creating facts with the Universal Commerce Protocol (UCP) . Unlike closed systems, UCP is an open standard that allows AI agents to communicate directly with merchants' backends – from discovery through checkout to post-purchase management. For you as a retailer, this means: Your Google Merchant Center (GMC) becomes the critical interface for AI in e-commerce . Google has introduced new attributes to make your products machine-readable: ·         product_faq – questions and answers directly extractable from the feed for AI agents ·         product_use_cases – specific scenarios in which your product offers the best solution ·         native_commerce – a switch signaling whether your product is ready for autonomous checkout The advantage for Germany: Google Merchant Center and Google AI Mode are already active in DACH. Retailers who optimize their feed now secure a real time advantage. SEO alone is no longer enough: Welcome to the era of GEO Our analysis of German e-commerce shops shows a clear picture: A top ranking in traditional search does not guarantee visibility in AI responses. Over 60 percent of URLs linked in AI overviews do not rank in the top 50 of traditional Google search. The rules have changed. This is where Generative Engine Optimization (GEO) comes into play – the discipline of optimizing content not for human clicks but for extraction by AI systems. Feature Classic SEO Generative Engine Optimization (GEO) Target Group Human users AI agents & Large Language Models Primary KPI Click-through rate (CTR) & rankings Mention rate & citation authority Content Logic Keywords & readability Semantic depth & fact density Technical Basis Crawlability & loading speed Structured data & API connectivity Success Measurement Google Search Console (rankings) Brand mentions in LLM responses Warriors Insight: In Germany, AI overviews already appear in 33 percent of all search queries. If you don't opt for GEO now, you will become invisible to the "agent customer" before they even arrive at a website. Strategic Warriors Knowledge: Brand power and the 95:5 rule In the Agentic Web, it's not just the keyword that counts anymore, but the authority of your brand as an "entity" – how a Large Language Model knows, categorizes, and recommends your brand. The 95:5 rule in B2B Only 5 percent of your target group is currently ready to buy (In-Market). The remaining 95 percent need to be reached through thought leadership and trust building in the long term. AI agents prefer brands that are anchored as expert entities in the knowledge graphs of Large Language Models. Those who only optimize for transactional keywords lose the majority of their potential customers before they are ready to buy. Preferred Sources: The Democratization of the Algorithm Google now allows users to actively mark their preferred sources. These "Preferred Sources" receive a permanent visibility boost – regardless of algorithm updates. This fundamentally changes the game: Trust is the new currency. You must persuade users to actively choose your brand as trustworthy – not just ranking well. Checklist: Make your shop agent-ready now For German retailers, the groundwork begins today, even though fully autonomous Agentic Shopping in DACH is still 12–24 months away. Product data excellence in Merchant Center: Maintain GTINs, precise attributes, and new UCP fields (product_faq, product_use_cases). A flawed feed is the largest KI visibility obstacle you can control yourself. Technical infrastructure for AI agents: Implement an llms.txt file (the robots.txt for AI crawlers) and consistently use JSON-LD – specifically the Product, FAQPage, and Article schemas. These are the signals that AI agents prioritize. API-First strategy: Ensure that inventories and prices can be retrieved in milliseconds via interfaces. Outdated data was the main reason for OpenAI's checkout failure – and the same mistake will be costly for retailers once agents actively book. Semantic enrichment with the Query Fan-Out Principle: Answer the questions an AI asks when comparing products on behalf of a customer: For which use cases is the product optimal? What alternatives are there? What are common purchase barriers? This depth distinguishes cited from ignored content. GEO strategy and build brand authority: Ensure that your shop is perceived as an expert entity in relevant categories – in ChatGPT, Perplexity, and Google AI Mode. More on this in our GEO audit → Secure DACH compliance early: PAngV and GDPR apply to AI-mediated purchases as well. Price reductions must disclose the lowest price of the last 30 days as a reference – and this must be machine-readable. Clarify this early with your legal advisor. Conclusion: Become a leader of the new era Agentic Commerce is no longer a science fiction scenario – it's the technological reality of today, still in development, but unstoppable. What OpenAI buried with Instant Checkout is a specific business model: the chatbot as a transaction facilitator between retailer and customer. What lives on – and is accelerating – is the underlying logic: AI shopping agents take over discovery, filter options, prepare purchase decisions. This already happens, daily, for millions of users. The question for retailers is no longer whether , but if they are visible when the agent decides . The companies that are ahead in two years are not the ones with the biggest budget. They are the ones with the best data, the strongest GEO presence, and the clearest understanding of how Artificial Intelligence in e-commerce is used as a lever rather than a threat. Frequently Asked Questions about Agentic Commerce What is the difference between Agentic Commerce and traditional e-commerce? Traditional e-commerce follows the Search & Click principle: The user actively searches, compares manually, and buys themselves. Agentic Commerce follows the Ask & Done principle: An AI shopping agent takes over product search, price comparison, availability check, and – if authorized – the purchase completion fully autonomously. What is Agentic Shopping? Agentic Shopping is the practical manifestation of Agentic Commerce: The user formulates a concrete goal – such as "Order printer cartridge XYZ at the best price by tomorrow" – and an AI shopping agent carries out all steps independently: search, comparison, purchase. Why did OpenAI discontinue Instant Checkout? OpenAI faced three technical hurdles: lack of real-time inventory synchronization across millions of retailers, no infrastructure for tax collection, and no fraud prevention for agent-based transactions. OpenAI is now pivoting to Product Discovery – the checkout remains with the retailer. What is the difference between SEO and GEO? SEO (Search Engine Optimization) optimizes content for the Google search algorithm and for human users – the goal is the click. GEO (Generative Engine Optimization) optimizes for AI systems and Large Language Models that extract content and output as a direct answer – without the user clicking on a website. Both disciplines complement each other and build on each other. Is my shop legally safe for AI purchases in Germany? In the DACH region, you must pay particular attention to GDPR and PAngV (Price Indication Regulation). Price reductions must always disclose the lowest price of the last 30 days as a reference – also machine-readable for AI agents. Clarify this early with your legal advisor before you register for Agentic Commerce protocols. When is Agentic Commerce coming to Germany? ACP and the new ChatGPT shopping hub are currently US-first. However, Google Merchant Center and Google AI Mode are already active in DACH – AI overviews already appear in 33 percent of all German search queries. Experts predict that AI agents could reach a market share of 20-30 percent in European e-commerce in two to three years. The preparation starts now. Is your shop ready for AI shopping agents? We analyze your GEO visibility, your product feed, and show you where you are currently invisible to AI agents – and how you can change that. Request GEO analysis now → Sources & further links: CNBC, March 2026: “OpenAI revamps shopping experience in ChatGPT after struggling with Instant Checkout” – cnbc.com Forrester Research: ConsumerVoices Market Research Survey, March 2026 Gartner: Bob Hetu, Analyst, gegenüber CNBC, March 2026 The Information, March 2026: First report on the Instant Checkout withdrawal OpenAI Blog, March 2026: Official statement on Instant Checkout and new shopping experience Google: Universal Commerce Protocol – Announcement January 2026

One of the main differences between SEA and SEO is time. While SEO measures need time to show growth and performance improvements, paid campaigns require quick actions as any delay costs money. Even if your campaigns appear to be set up correctly at first glance, you can’t rely on hope and a good gut feeling if they aren’t delivering profitable results.  In the following article, I will demonstrate three signs that help you recognize unprofitable campaigns at first glance and what could be behind them. Additionally, I will show you specifically how you should optimize your Google Ads campaigns in these cases.  However, before we get started, there are three points that can provide a quick explanation for poor performance. If your campaigns still perform poorly despite these factors, you should choose a different approach to improve the figures and reduce Google Ads CPCs .  Your tracking isn't working  It’s a commonly underestimated problem: Unexpected changes on your website, such as the creation of new landing pages or migration to other data platforms, can disrupt your tracking. This can result in your campaigns showing 0 conversions. Ideally, the Google Ads managers are informed in advance about such planned changes, but in reality, that’s not always the case. An example: Once, a client of mine removed a CPA button that we had measured as a soft conversion goal. My campaigns began to struggle significantly, and I had to quickly find a solution to reduce Google Ads costs. In the end, we couldn’t see any conversions because there was literally no conversion action on the website that could trigger conversions in Google Ads.  Tip: Regularly check if your tracking is functioning correctly. Without working tracking, you cannot optimize your Google Ads. It’s still possible for conversions to be generated, but they won't appear in Google Ads, only in the backend. Once the tracking problems are resolved, your campaign might perform well again.  Your campaign is still in the learning phase  Paid campaigns need patience, even though we all want to see good results as quickly as possible. That would prove our expertise and help us further optimize and scale the Google Ads campaigns. However, new campaigns cannot always work wonders, as the algorithm needs time to learn and improve performance. The official learning phase usually lasts up to four weeks. Depending on the business model, this process can also be shorter because the quicker the campaign generates conversions, the faster the algorithm learns. However, this development is not always guaranteed. For instance, the average customer journey in the B2B sector generally takes more time. Additionally, it often includes several touchpoints before achieving the desired result.  Tip: Be patient during the learning phase.  Your main goal is not clear  Unrealistic expectations usually lead to disappointments - not only in life but also in Google Ads. If marketing goals are vague, clear results will not follow either. If the goals are clear, but you don’t know which campaign types are suitable for them, the figures will also disappoint.  For example, if you work with display or video ads, you should not automatically expect to receive many high-quality leads. Not because your setup is wrong, but because these campaign types pursue different goals. They are meant to increase the awareness of your product and cover the early phase of the customer journey. Moreover, the ad formats are tailored to this goal - think of skippable ads on YouTube. They are there to promote your brand and convey a message. However, it is not realistic to expect good leads from them, as they are likely to be skipped, with the customer taking no further action. If your shopping campaigns don’t deliver results for weeks, this is at least alarming.  Tip: Define clear objectives for each phase of the funnel and choose the appropriate campaign types. Only then can you effectively optimize your Google Ads campaigns.  There is a Budget-Killer in the House  But let's go back to the three clear signs that a budget-killer is present in your account:  Campaigns with traffic but no conversions  Rising CPAs  Decreasing ROAS  If your goal is conversions and you see none or increasingly fewer, there’s a problem. Especially if your tracking is functioning and the learning phase is complete. If the campaign still does not deliver the desired conversions, this impacts not only your KPIs but also the performance of your automated bidding strategies. For instance, if you optimize for tCPA or tROAS, declining conversions will lead to a higher CPA, a lower ROAS, and overall restrictions on bidding strategies.  Here is a list of factors that could explain the decline in conversions you are observing. These include:  Landing page – Any change that worsens the user experience can negatively influence the conversion rate as well as the bounce rate.  Competition - Especially in e-commerce, competition through lower prices can affect the number of conversions as well as the conversion rate.  Seasonality - If your business experiences significant declines during certain periods, you should adjust your marketing strategy accordingly.  Irrelevant Traffic - Ensure that your ads don’t appear for irrelevant search queries to reduce Google Ads costs for poor traffic. This often helps to lower Google Ads CPC.  Faulty Targeting – A reasonable campaign setup is vital in Google Ads. However, despite optimal campaign setups, certain target groups or keywords may perform less well than expected. For this reason, you should quickly optimize the targeting of your Google Ads campaigns if the desired results are not there.  Google Ads campaigns are not static. What works well today can perform poorly tomorrow. As a marketing manager, you should thoroughly understand the business model and goals, select the appropriate campaign types, set KPIs, and set realistic expectations. The rest lies in flexible and smart Google Ads optimization. Additionally, your task extends beyond Google Ads as overall performance is influenced by many other factors described above. For example, dramatic political or economic developments can have the same negative impact as a poorly optimized campaign. Your Google Ads expertise should go hand in hand with thorough market analysis so that you can see the bigger picture and take the right actions.  If you need assistance with this or if you want to scale your existing campaigns, our SEA team is happy to advise you. Contact us now!