Last updated: February 10, 2026
Ecommerce is the single largest vertical spending on Google Ads, and it's also the most demanding. You're not managing a handful of keywords and a few ad groups. You're managing hundreds or thousands of products, each with its own margins, stock levels, competitive dynamics, and seasonal demand patterns. Your Shopping feed needs constant attention. Your Performance Max campaigns pull budget across channels in ways you can't fully see. And every day you're not optimising at the product level, you're leaking money to competitors who are.
The ecommerce advertising landscape heading into 2026 is more powerful and more complex than it's ever been. Google's Power Pack framework combines Performance Max, Demand Gen, and AI Max into a full-funnel system. Performance Max now offers channel-level reporting, campaign-level negative keywords (up to 10,000), expanded search themes, and asset-level performance data. Standard Shopping campaigns remain essential for granular product control. And Google's AI Overviews are creating entirely new shopping surfaces where users can compare products and buy without ever reaching a traditional search results page.
The average ecommerce ROAS across Google Ads dropped to 2.87:1 in 2025, with median CPA climbing 12.35% to $23.74. CTR improved by 7.49%, but conversion rates fell 9.28%. Shoppers are clicking more but converting less, and it's costing more to acquire each customer. In this environment, every element of your Google Ads strategy needs to work harder.
This is the complete playbook for making that happen.
The days of running a single Shopping campaign and calling it done are long gone. A profitable ecommerce Google Ads account in 2026 requires a multi-campaign architecture where each campaign type serves a specific strategic purpose.
The most effective ecommerce accounts run a hybrid approach using three to five campaign types working together.
Standard Shopping for brand defence and high-control products. Standard Shopping campaigns give you transparent bidding, visible search terms, product-level performance data, and manual bid control. Use them for your highest-margin products, bestsellers that need precise bid management, and branded product searches where you need to defend against competitors. About 80% of all ecommerce Google Ads spend goes through Shopping placements, making feed quality and Shopping structure the foundation of your entire strategy.
Performance Max for prospecting and cross-channel reach. PMax campaigns extend your product ads across Search, Shopping, Display, YouTube, Gmail, Discover, and Maps from a single campaign. They excel at finding new audiences, retargeting past visitors, and scaling reach beyond what Shopping alone can achieve. Feed-only PMax configurations (no uploaded creative assets) typically see 30% to 45% lower cost per sale compared to full-asset campaigns because they eliminate budget waste on awareness placements that don't drive immediate conversions. For product-based ecommerce, starting with feed-only PMax and adding creative assets only after establishing a performance baseline is the recommended approach.
Search campaigns with AI Max for high-intent queries. Standard Search campaigns capture shoppers who are searching with specific purchase intent: "buy red running shoes size 10," "best wireless headphones under $200." With AI Max enabled, your Search campaigns expand to match broader intent signals and keywordless targeting, similar to how Dynamic Search Ads worked but with significantly more sophistication. Keep brand and non-brand Search campaigns separate to prevent branded traffic from inflating your prospecting metrics.
Demand Gen for remarketing and visual discovery. Demand Gen campaigns serve visually rich ads across YouTube, Discover, and Gmail. For ecommerce, they're particularly effective for remarketing to cart abandoners and past site visitors, where they routinely deliver 600% to 1000% ROAS. The addition of Google Display Network inventory and shoppable Connected TV placements in 2025 has made Demand Gen a meaningful conversion driver, not just an awareness channel.
Standard Shopping catch-all at low priority. This is an advanced tactic that experienced advertisers use to capture profitable clicks that PMax passes on. Set up a Standard Shopping campaign with low priority and very low bids ($0.10 to $0.20). It acts as a safety net that catches auctions where PMax doesn't bid aggressively enough, picking up additional conversions at extremely low CPCs. It won't generate massive volume, but the clicks it does capture tend to be pure profit.
The key decision in campaign structure is how to segment your products across campaigns. The right segmentation depends on your catalog size, margin variation, and management capacity.
For catalogs under 100 SKUs, a simple structure works: one PMax campaign, one Standard Shopping campaign for your top sellers, and one Search campaign for non-brand terms. Total: 3 campaigns.
For catalogs of 100 to 1,000 SKUs, segment by product category or margin tier. High-margin products get their own PMax campaign with an aggressive ROAS target. Low-margin products get a separate campaign with a more conservative target. Add Standard Shopping for bestsellers and a Search campaign. Total: 4 to 6 campaigns.
For catalogs over 1,000 SKUs, segment by performance tier (top sellers vs long tail), margin tier, product category, and seasonal relevance. Each segment gets its own PMax campaign with tailored ROAS targets and budgets. Total: 6 to 12 campaigns.
The critical rule: only segment if each campaign will receive at least 30 conversions per month. Campaigns without sufficient conversion data can't optimise effectively. Over-segmenting a small account creates multiple data-starved campaigns that all underperform.
Your product feed is the single most important factor in Shopping and PMax performance. Google's own research estimates that 80% of Shopping success depends on feed quality. Your ads, your bids, your budgets, none of it matters if your feed doesn't accurately and compellingly represent your products.
Your product title is the most heavily weighted attribute for search matching. Google uses it to determine which queries trigger your Shopping ads. A vague title like "Blue Shirt" loses to "Men's Slim Fit Oxford Button-Down Shirt Navy Blue Size Medium" on virtually every relevant search.
Structure your titles with the most important information first: brand, product type, key attributes (colour, size, material), and model number if applicable. Different product categories benefit from different title structures. Apparel works best with Brand + Gender + Product Type + Colour + Size. Electronics benefits from Brand + Model + Key Spec + Product Type. Home goods performs well with Brand + Product Type + Material + Dimensions.
Front-load your titles with the terms shoppers actually search for. If your analytics show that people find your products by searching "wireless noise cancelling headphones," make sure those words appear at the front of your title, not buried after the brand name and model number.
Fill in every available attribute in your Merchant Center feed. Colour, size, material, pattern, age group, gender, condition, GTIN, MPN. The more attributes Google has, the better it matches your products to relevant searches. Missing attributes don't just reduce visibility. They actively disadvantage your products in auctions against competitors with complete data.
Product descriptions should be keyword-rich but natural. Include the search terms that shoppers use to find products like yours, along with specific technical details, measurements, and use cases. Descriptions don't appear in Shopping ads directly, but Google uses them for matching and relevance signals.
Your product image is the first thing shoppers see in a Shopping ad. It needs to clearly convey what you're selling, stand out from competitor listings, and set accurate expectations. Use high-resolution images on clean white backgrounds for your primary image. Show the product clearly without visual clutter. For differentiation, supplemental images showing the product in use, from multiple angles, or with size context can improve engagement.
Products with customer reviews convert 2 to 3 times better than those without. If your feed supports review integration, enabling it should be a top priority.
Custom labels in your Merchant Center feed are what enable sophisticated campaign segmentation. You get five custom label fields (custom_label_0 through custom_label_4) that you can populate with any values meaningful to your business.
Use them to tag products by margin tier (high, medium, low), performance tier (bestseller, steady, long tail), seasonality (evergreen, seasonal, clearance), price range, or any other business-specific attribute. These labels then become the basis for your campaign and asset group segmentation in Google Ads, allowing you to set different ROAS targets and budgets for different product groups based on their actual economics.
Here's where theory meets reality, and where most ecommerce advertisers hit a wall.
Managing Google Ads for a 50-product store is a fundamentally different challenge from managing it for a 500-product store. And managing 5,000 SKUs is a different universe entirely. The complexity doesn't scale linearly. It scales exponentially, because every additional product creates new interactions with keywords, audiences, competitors, and budgets.
At this scale, managing bids at the product level becomes physically impossible for a human. With 500 products across multiple campaigns, you have potentially thousands of product-level bid decisions to make. Each product has its own conversion rate, margin, competitive dynamics, and seasonal demand pattern. The optimal bid for Product A at 9am on Tuesday is different from the optimal bid for Product B at 3pm on Saturday. No human can process this volume of decisions at the speed required.
Search term management becomes overwhelming. A 500-product Shopping campaign generates hundreds of search queries daily. Reviewing them, identifying irrelevant terms, and adding negative keywords is a multi-hour weekly task that most advertisers eventually stop doing consistently. The moment you stop, waste accumulates. Within 30 to 60 days of neglected search term management, 15% to 30% of your budget is going to irrelevant clicks.
Budget allocation across product categories requires constant attention. Your winter coats should get more budget in October and less in April. Your swimwear follows the opposite pattern. Your everyday basics need steady year-round investment. Manually adjusting budgets across hundreds of products for seasonal shifts is a scheduling nightmare.
At this scale, everything above gets worse, plus new problems emerge.
Inventory synchronisation becomes critical. When products go out of stock, their ads need to pause immediately. When new products arrive, they need to be added to the right campaigns with appropriate bids. With 5,000 SKUs and constantly shifting inventory, manual synchronisation creates a lag that wastes money on out-of-stock products and delays exposure for new arrivals.
Performance Max campaign structure needs dynamic segmentation. At this scale, static product groups become stale within weeks as performance data shifts. Products that were bestsellers last month might be underperformers this month due to competitor activity, seasonal shifts, or pricing changes. The product groupings in your PMax campaigns need continuous reorganisation based on current data, not quarterly manual reviews.
Price competitiveness monitoring becomes essential but unmanageable. With 5,000 products, you're competing against dozens or hundreds of other sellers on each SKU. Your price position changes constantly as competitors adjust their pricing. A product that was price-competitive yesterday might be 20% overpriced today, and your ads will still run, consuming budget while converting poorly because shoppers can see a cheaper option right next to yours.
The SKU scaling problem is not a human problem with a human solution. A 3-person PPC team managing 5,000 SKUs across Search, Shopping, and PMax cannot physically keep up with the volume of decisions required to optimise at the product level in real time. They can manage at the campaign level, maybe at the product group level. But product-level optimisation across thousands of SKUs, adjusted hourly for auction dynamics, inventory changes, and competitive shifts, that requires a system that never sleeps and never gets overwhelmed by scale.
groas was built specifically for this challenge. Its autonomous AI manages bid decisions across every product in your catalog, adjusts budgets based on real-time performance signals, continuously monitors search terms to eliminate waste, and adapts to seasonal demand patterns without manual intervention. Whether you have 100 products or 50,000, the system applies the same depth of product-level attention to every SKU. The three-person team that was drowning in spreadsheets and search term reports can redirect their energy toward creative strategy, product development, and business growth, the areas where humans genuinely add value, while groas handles the data-intensive operational work that machines do better.
How you distribute your budget across campaigns matters as much as how much you spend in total. The wrong allocation sends money to your least profitable products while starving your highest performers.
Many ecommerce advertisers split their budget evenly across campaigns or product categories. If you have four PMax campaigns, each gets 25% of the budget. This is convenient for planning and terrible for performance, because your campaigns don't contribute equally to profit. One campaign might generate a 5:1 ROAS while another limps along at 1.5:1. Giving them equal budgets guarantees you're over-investing in the underperformer and under-investing in the winner.
The better approach allocates budget in proportion to proven ROAS, weighted by volume opportunity. A campaign generating 5:1 ROAS with room to scale should receive budget increases until its marginal ROAS approaches your target threshold. A campaign at 1.5:1 ROAS should have its budget reduced and reallocated to better performers.
In practice, this means reviewing campaign performance at least weekly and shifting budget toward what's working. At higher spend levels ($10,000 per month and above), daily reallocation becomes important because auction dynamics shift quickly enough to make weekly adjustments too slow.
This is another area where autonomous management provides an outsized advantage. groas reallocates budget dynamically throughout the day based on real-time conversion data, cost signals, and performance trends. If your accessories category is converting at twice the rate of your electronics category on a Wednesday morning, budget shifts automatically. No weekly review meeting. No manual adjustment. Just continuous, data-driven allocation that maximises profit at every hour.
One of the most common ecommerce budget mistakes is capping your best campaigns. If a campaign is generating a 6:1 ROAS and hitting its daily budget cap by 2pm, you're leaving money on the table every afternoon. The campaign is telling you it can find more profitable traffic, but you've built a ceiling that prevents it.
For your top-performing campaigns, set daily budgets at least 50% higher than their average daily spend. This gives the algorithm room to capitalise on high-opportunity periods without artificial constraints. Your ROAS target acts as the real spending governor, not the budget.
Ecommerce advertising has a rhythmic quality that most other verticals don't experience. Demand spikes and troughs predictably throughout the year, and the advertisers who plan for these cycles dramatically outperform those who react to them.
Google's bidding algorithms learn from recent conversion data. If you suddenly double your budget on Black Friday without building up to it, the algorithm has no data at that spend level. It doesn't know which auctions are profitable at the higher bid levels required during peak competition.
Start increasing budgets 3 to 4 weeks before major sales periods. Increase by 15% to 20% per week, giving the algorithm time to learn at each new spend level. By the time Black Friday arrives, your campaigns are already operating at near-peak budget with a well-calibrated algorithm.
During high-demand periods, you should typically lower your ROAS targets rather than raise them. This sounds counterintuitive, but the logic is sound: peak periods have higher conversion rates and higher average order values. A slightly lower ROAS per conversion is offset by dramatically higher volume, which often produces more total profit than maintaining a strict ROAS target that limits your reach during the most lucrative days of the year.
If your normal Target ROAS is 400%, consider dropping to 300% to 350% during peak weeks. Monitor total profit daily during these periods rather than focusing on ROAS, because total profit is what matters when you're capturing outsized demand.
After peak periods, competition drops rapidly but so does consumer demand. Gradually reduce budgets over 2 to 3 weeks rather than cutting abruptly. An abrupt budget cut can trigger the algorithm's learning phase as it readjusts to the new spend level, costing you performance during the transition.
Also use the post-peak period to clear seasonal inventory. Create dedicated clearance campaigns with more aggressive ROAS targets (meaning lower, to drive volume) for products that need to move before they become dead stock.
Beyond the obvious Q4 holiday peak, most ecommerce categories have their own seasonal rhythms. Fitness equipment peaks in January. Outdoor gear ramps in spring. Back-to-school drives demand in August. Wedding-related products spike in engagement season (November to February) and wedding season (May to October).
Build a seasonal calendar for your specific product categories and plan budget shifts 3 to 4 weeks ahead of each demand cycle. The advertisers who proactively reallocate budget to seasonal opportunities capture demand at lower CPCs than those who react after competition has already increased.
Shopping campaigns don't use traditional keywords for targeting. Google matches your product feed to search queries based on your product titles, descriptions, and attributes. This means you don't choose which queries trigger your ads, and Google's matching is often broader than you'd like.
Without negative keywords, your Shopping ads show for a wide range of queries that include irrelevant searches. Someone searching "how to clean running shoes" might see your running shoe Shopping ads. Someone searching "free shipping coupon codes" might trigger your products. Someone looking for a DIY tutorial might see your product ads because the product name appears in their query.
Each irrelevant click costs money and produces zero revenue. Across a typical ecommerce account, Shopping campaigns without active negative keyword management waste 15% to 25% of ad spend on queries that never had any chance of converting.
Start with universal negatives that apply across your entire account: "free," "DIY," "how to," "tutorial," "used," "repair," "cheap" (if you sell premium products), "review" (if you want to exclude research-phase queries), and any competitor brand names if you don't want to show for competitor searches.
Then review your search term reports weekly. In Standard Shopping campaigns, search terms are fully visible. In Performance Max campaigns, search term reporting was significantly expanded in 2025, and you can now add up to 10,000 campaign-level negative keywords. Sort your search terms by cost and filter for terms with zero conversions. These high-cost, zero-conversion terms are your biggest waste sources.
Build category-specific negative lists. Your electronics campaign should exclude searches related to product categories you don't sell. Your clothing campaign should exclude sizes you don't carry. Your premium product campaign should exclude budget-oriented terms.
Negative keyword management isn't a one-time task. New irrelevant queries appear constantly as search behaviour evolves and Google's matching algorithms expand. A negative keyword list that was comprehensive last month has gaps this month because new query patterns have emerged.
This is exactly the kind of continuous, data-intensive work where groas excels. The platform monitors search queries across all campaigns in real time, automatically identifying and excluding irrelevant terms before they can accumulate significant wasted spend. For ecommerce accounts with hundreds of products generating thousands of search queries daily, this continuous automated management saves meaningful budget that would otherwise evaporate between manual reviews.
Benchmarks are useful as directional guides, not absolute targets. Your specific margins, AOV, and competitive landscape determine what "good" looks like for your business. That said, here's where ecommerce performance sits in 2025 and 2026 across key metrics.
Overall ecommerce ROAS averaged 2.87:1 across all channels and 3.68:1 specifically on Google Ads in 2025 according to Triple Whale's analysis of 18,000 brands. Search campaigns delivered the highest returns at 5.17:1, followed by Performance Max at 2.57:1 and Display at 0.12:1.
Ecommerce CPA averaged $23.74, up 12.35% year over year. Sub-vertical variation is substantial: fashion and apparel tends toward $15 to $25, electronics runs $20 to $40, home goods sits at $25 to $45, and health and beauty ranges from $15 to $35.
Click-through rate for ecommerce improved to around 1.5% to 2.5% on Shopping, with Search campaigns in the 5% to 8% range depending on query specificity.
Conversion rate for ecommerce on Google Ads averaged around 3% to 4.5% for Shopping and 2.5% to 4% for Search, though top performers regularly exceed 6% on well-optimised product pages.
Average order value held roughly steady in 2025 with only 0.57% growth year over year, meaning advertisers can't rely on rising AOV to offset increasing costs. Efficiency improvements have to come from better targeting, better feed optimisation, and smarter budget allocation.
The most important benchmark isn't any single metric in isolation. It's your blended profitability: total revenue from Google Ads minus total ad costs minus total product costs minus management fees. If that number is positive and growing month over month, your strategy is working. If it's declining or negative, everything else is vanity.
The recommended structure combines Standard Shopping for high-margin and branded product searches, Performance Max for prospecting and cross-channel reach (start with feed-only configuration), Search campaigns with AI Max for high-intent non-brand queries, and Demand Gen for remarketing to cart abandoners and past visitors. Advanced accounts add a low-priority Standard Shopping catch-all to capture profitable clicks that PMax passes on. Segment products across campaigns by margin tier and performance tier, ensuring each campaign receives at least 30 conversions per month.
Use both in complementary roles. Standard Shopping provides transparent bidding, visible search terms, and product-level control for your most important products. Performance Max excels at reaching new audiences across all Google properties, scaling reach, and automating management at scale. PMax now offers full channel-level reporting, search term visibility, and up to 10,000 campaign-level negative keywords, addressing many of the transparency concerns that previously made advertisers hesitant.
In Standard Shopping campaigns, add negative keywords at the campaign or ad group level through the Keywords section. In Performance Max campaigns, you can now add up to 10,000 campaign-level negative keywords directly in the interface. Start with universal negatives like "free," "DIY," "how to," "repair," and "used." Then review search term reports weekly and exclude high-cost, zero-conversion queries. Build category-specific negative lists to prevent your products from showing for irrelevant product categories.
Average ecommerce ROAS was 2.87:1 overall and 3.68:1 on Google Ads specifically in 2025. However, "good" depends entirely on your margins. A 50% margin business breaks even at 2:1 ROAS. A 25% margin business needs 4:1 just to break even. Search campaigns typically deliver the highest ROAS (5.17:1 average), followed by Performance Max (2.57:1). Shopping campaigns for competitive products may run lower but drive high volume. Always evaluate ROAS against your specific break-even point.
Focus on product titles first since they're the most important attribute for search matching. Structure titles with brand, product type, key attributes (colour, size, material), and model number. Fill in every available Merchant Center attribute including GTIN, colour, size, material, and condition. Use high-resolution product images on white backgrounds. Implement custom labels to tag products by margin, performance tier, and seasonality for strategic campaign segmentation.
At scale, manual management typically leads to mismatched bidding strategies, stale product groups, and insufficient conversion data per campaign. Over-segmenting a large catalog creates many campaigns that each lack the 30 or more monthly conversions needed for Smart Bidding to optimise effectively. Consolidating products into fewer campaigns with stronger data density, or using autonomous AI management like groas that handles product-level optimisation across the entire catalog simultaneously, resolves this issue.
Start ramping budgets 3 to 4 weeks before major sales periods by increasing 15% to 20% per week. Lower ROAS targets during peak periods (a reduced target compensated by higher volume often generates more total profit). Wind down gradually over 2 to 3 weeks after peaks to avoid algorithm learning phase disruptions. Build a seasonal calendar for your specific product categories and proactively shift budget ahead of demand cycles rather than reacting after competition has already increased.
groas manages bid decisions across every product in your catalog individually, adjusts budgets in real time based on performance signals, continuously monitors and excludes irrelevant search terms, and adapts to seasonal patterns without manual intervention. For ecommerce specifically, this means product-level optimisation across thousands of SKUs simultaneously, something a human team physically cannot maintain. Its deep integration with Google's AI ecosystem, including Performance Max, AI Max, and Smart Bidding, ensures each product receives the optimal bid at every auction throughout the day.
Google's built-in Smart Bidding strategies (Target CPA, Target ROAS, Maximize Conversion Value) provide a baseline level of AI-driven optimisation. However, these tools optimise within individual campaigns and don't account for cross-campaign budget allocation, product-level margin differences, or total business profitability. Autonomous AI platforms like groas layer additional intelligence on top of Google's systems, optimising across all campaigns simultaneously while factoring in real business economics. This combined approach typically delivers 30% to 50% better results than relying on Google's native tools alone.
For ecommerce accounts spending over $10,000 per month with 500 or more products, the management workload exceeds what most agencies can deliver cost-effectively. Agency fees of $2,000 to $5,000 per month buy you a team that reviews your account periodically but can't optimise at the product level in real time. Autonomous AI platforms like groas provide continuous product-level optimisation across your entire catalog at a fraction of agency costs. The math is straightforward: if your management solution can't make bid decisions for every product at every hour of the day, it's leaving money on the table. For ecommerce at scale, autonomous AI isn't a nice-to-have. It's the only way to operate at the speed and granularity the platform demands.
