Store Goals with Performance Max (Replaces ‘Local Ads’)

The overarching purpose of Performance Max with Store Goals is to link digital ad exposure to offline sales by predicting when and where shoppers will make in-store purchases after seeing the ads. Approval for a new local ad campaign must be formulated into a business case and presented to Google’s ad approval team. Failure to do so leads to a complete suspension of your Google Ads account. Local campaigns for Store Goals prepare auto-generated ads that help shoppers discover physically nearby stores, but they lack AI Smart Bidding; it has its own logical group and structural advantages.

With predictive AI at the wheel, Marketing fulfills its role as the steward of first-party data to orchestrate activity and leverage the marketing stack’s automation capabilities. Performance Max for Store Goals requires all marketing features must be enabled and that the Business Profile must be linked. Pixel tracking should confirm proper installation. Store visit conversions must be set up before launching the new campaign. Smart bidding enables predictive footfall modeling to identify latent stores in the area that would likely make an in-store purchase. These will help determine when and where people visit stores.

The Transition from Local Campaigns to Performance Max for Store Goals

Effective omnichannel experiences require marketers to treat online and offline sales as a single journey, with campaigns designed to drive the most valuable footfall and achieving Store Goals with Performance Max offers the best way to do just this. Local Ads have retired, and Store Goals are part of Performance Max, Google’s next-generation campaign type that harnesses machine learning and a broader set of business signals to match creatives with real-time demand. Therefore, a link between a Business Profile and a Performance Max campaign is required to unlock merchant-specific store phone numbers and address-specific ads.

Store Goals allow brands to use omnichannel marketing to drive more visits, calls, and directions while connecting those interactions to in-store sales. Positioning a campaign with the Store Goals objective lets Google Ads optimize customers’ online-to-offline journeys, identifying online behavior across multiple channels and helping them complete their task in the real world. The new objective uses AI Smart Bidding to maximize Store Visits and optimizes creatives for each user. Moreover, Store Goals enable Unified Online-to-Offline Measurement to track in-store results accurately and assess how all channels contribute to store performance.

Why Google Retired Local Ads

Performance Max for Store Goals has emerged from the retirement of Local Campaigns, and Google Store Goals have effectively replaced Local Ads. For Google, the move represents a consolidation of capabilities rather than a disappearance of the format. Performance Max as its name implies automates and amplifies marketing performance across channels by routing the right assets to the right audience in the right place at the right time. Store Goals leverage all of that Performance Max power and apply it specifically to in-person store visits and sales.

The shift reflects the growing importance of omnichannel shopping to marketers, consumers, and Google itself. As omnichannel commerce becomes the new normal, marketers increasingly need to connect their campaigns to store visits and sales to close the loop. At the same time, Google’s systems are now advanced enough to analyze user signals across its entire network including Search, YouTube, Display, and Maps and identify the combinations that maximize store visits and sales. In other words, optimization for physical destinations now requires integrated, cross-channel measurement and AI Smart Bidding capabilities that were not available at the time Local Campaigns were built.

The Rise of Omnichannel Retail and AI-Driven Store Marketing

The shift to omnichannel retail is a defining trend of the 2020s. To capture that transition, Google has made a bet on AI particularly, using AI smart bidding to maximize store visits and measure offline impact across its ad platforms. Performance Max for Store Goals is the first result of that investment and after of years of data gathering and learning offers a viable pathway for retailers and local businesses to use Google ads to drive foot traffic into stores.

Two key elements of Google’s Store Goals objective are automation and measurement. Behind the scenes, hundreds of signals drive optimization and attribution, producing intelligent bidding that estimates in-store visits and visits when stock is available. Those estimates provide a bridge between online ad activity and offline revenue, creating real-world countable events store visits and local actions that can be imported into Google Ads and linked to other online actions.

What Are Store Goals with Performance Max?

Store Goals for Performance Max is the replacement for Local Campaigns and Local Ads, which are being deprecated. This objective helps marketers reach new customers online while driving store visits, calls, and direction requests in a single campaign. It automatically optimizes creative assets across the Google ecosystem to respond to local intent and increase visits to Business Profile locations.

Store Goals for Performance Max is rooted in the omnichannel retail approach and AI-driven store marketing. The goal of store marketing is to drive real-world activity traffic to stores, calls to stores, and requests for directions with advertising. To measure this activity, marketers can select the Store Visits, Store Visit Conversations on YouTube, and Store Goal conversions insights, which use a combination of online and offline data sources.

Definition and Purpose

Store Goals enable marketers to unify performance measurement for in-store sales with exposure on YouTube, Search, Display, and Discover by harnessing online signals and AI. Achieving store outcomes   offline visits, interactions, and calls   is fundamental to omnichannel success and omnichannel relevance. Optimizing store visitation from online demand is therefore a natural objective for AI Smart Bidding, and optimizations typically travel beyond the maps interface and thus require cross-channel attribution.

Store Goals represent a specific type of Performance Max campaign where the underlying business objectives center on in-store actions, such as directions clicks, calls, and visits. When supported by local signals, such as inventory feeds and Business Profile data, these actions inform automated attribution modeling, enabling media return on investment to be calculated in store visits and transactional value; these tracked signals can also supply real-time data for Store Visit Smart Bidding.

How P-Max for Store Goals Works

Core performance-level signals drive inventory-based asset optimization (including images and video). Both audience signals (first-party segments) and user intent help inform how the campaign and local signals work, while supporting assets increase overall relevance.

Maximizing store visits relies primarily on asset selection and how Performance Max uses intelligent machine-learning modeling to fine-tune placement. Signals feed both models and auction-time optimization across Google’s entire network of destinations. More information on Local Inventory targeting can be found in the “Data Sources and Signals” section; supported objectives (and their impact on Store Goal campaign and in-store optimization) are discussed in “Supported Business Objectives.”

Supported Business Objectives (Visits, Calls, Directions, Local Actions)

Google encourages marketers to focus on four key objectives driving Store Visits, Directions Clicks, Local Actions, and Call Conversions when using Performance Max for Store Goals. These actions provide valuable local online signals and feed into the broader measurement framework that captures offline success, even if attribution occurs after the local intent recedes.

1. Store Visits are modeled conversions based on detected visits to any of the associated locations (linked via Business Profile). Granular attribution (device, geography, proximity) supports offline revenue modeling and reporting.
2. Directions Clicks indicate mobile and desktop directions requests through Maps and Search. Improved physical visibility of stores and their offerings drives engagement.
3. Local Actions encompass three micro-conversion events: “Get a Quote,” “Buy,” and “Get Offer.” Activation nudges a considered audience closer to action and strengthens prompting for Store Visits.
4. Call Conversions measure actual calls initiated from an ad. Nurturing these connections/categories improves omnichannel experience, ultimately driving in-store sales.

Where Your Store Ads Appear Across Google’s Network

Store ads for campaigns with a Store Goals objective are served in places where Google believes they will have the most positive local impact and are therefore of most interest to users. In addition to Google Search, Google typically shows these assets on Google Maps and Google Search on Maps, Display and YouTube, Discover, and Gmail. For details about how different ad placements support various local marketing goals and how Performance Max delivers ads for Store Goals, see The Rise of Omnichannel Retail and AI-Driven Store Marketing.

Search Ads on Google

Store Visit marketing is firmly rooted in Google Search. Store Goals campaigns launch contextual text ads triggered by user interest in particular products and proximity to the store. Google continuously optimizes the ad copy for nearby users through a constantly updating inventory of messages. Local Ads partners benefit from an additional layer of optimization by Google   Store Visit conversions   a bidding signal that feeds into Smart Bidding models for Store Visit goals.

Display Network and YouTube

Video, image, and shopping ads on YouTube and the Display Network are key for user discovery. When users are browsing YouTube and the rest of the Display Network, they are often searching for fun or relevant content rather than looking to make a purchase; as a result, the intent is more about exploration than high purchase intent. Store Goal ads in this context help build brand identity and presence. Users exposed to website visitors or display ads have been shown to have better performance in subsequent campaigns; that is, exposure to identity-building campaigns generates future incremental sales and supports broader sales goals.

Discover and Gmail Placements

Store ads can also appear on Discover and are included in Gmail promotion boxes. Unlike Ads on YouTube and on the Display Network, these placements enable consideration-based ads for Store Goals, catching interested users while they are browsing yet not committing to a specific action.

Google Search and Maps

Store ads housed by campaigns optimized for store goals appear in    the places most often consulted just before setting out for a store nearby. Supported placements include local search results, the Local Pack area in regular search results, and Google Maps. In these environments Purchase Behavior audience signals play a prominent part in advertising, and key proximity signals are also involved. Signals from Display Network placements outside the local area do not factor into the direct performance of store visits, but the asset group structure enables them to help maximize impressions, attribute possible sales across the entire advertising ecosystem, and drive local actions   distillations of interest from online audiences that require additional nurturing.

Purchasing behavior around Store Goals from Store Visits creates obvious reasons to optimize the campaign creatives for local impact. Integrated across assets, dynamic location extensions automatically highlight the advertiser’s physical presence and provide store-specific links for directions and calls. Furthermore, local inventory feeds enhance reach and appeal by showing not just what the advertiser wants people to know, but what they can indeed go and buy.

YouTube and Display Network

Ads for store goals are placed across Google’s extensive network, utilizing multiple formats and destinations to support traffic-driving objectives. The likely benefit of each placement is summarized below, with references to additional information where appropriate.

Engagement on YouTube and the Google Display Network comes from audiences with high intent and interest, and is therefore expected to deliver visits and calls to your stores. Campaigns using these placements are optimized to drive traffic, with high feeds for performance on visits and call conversions. For advertising focused on store closings, optimized campaigns are driven by proximity to the store.

Discover and Gmail Placements

Performance Max stores its automation and asset optimizations across all Google properties notably, Discover and Gmail. Both placements reach high-intent users, and their scaled reach makes them ideal for effectively running local inventory ads for omnichannel retailers.

Discover Placements

Google Discover is a feed of articles, videos, and posts curated for users based on their interests. It offers several ad formats, including native ads, Discovery ads, image carousels, and Collection ads. These placements reach users when they are not actively searching but still open to engaging with relevant content, making it a suitable audience for ads focused on local inventory.

Gmail Placements

Gmail Ads leverage three formats: Discovery ads (displaying as branded content), animated ads, and native Gmail ads. Discovery ads work best when integrated into a broader Discovery Ads and feed strategy; branded emails capture fresh, engaged audiences. Gmail placements are particularly effective for promoting offers and sales that can drive in-store visits.

Benefits of Using Performance Max for Store Goals

Performance Max for Store Goals provides unified measurement of store traffic across Google channels, with seasonal smart bidding that leverages local availability during high-demand periods. Attribution combines YouTube engagement data with signals from Discovery placements and Display impressions to assess impact on offline sales. Local Inventory Ads signal in-store product availability; Dynamic Location Extensions provide automated store linkages and calls-to-action on YouTube videos.

Using Performance Max for Store Goals to drive footfall offers four main advantages: By letting Google’s AI derive attribution and bidding signals, marketers can improve return on advertising spend. Aiming to maximize store visits enables seasonal optimization of creatives and budget allocation against local demand. During peak periods, exposure to ads that mirror customer intent improves the probability of store visits.

Unified Online-to-Offline Measurement

USC’s “Using Your Data Responsibly” initiative describes how using data responsibly leads to superior experiences for users and advertisers. Google uses advanced modeling techniques to intelligently aggregate performance at scale while protecting users’ privacy. Optimization signals built into Performance Max campaign types leverage first-party data combined with modeling based on behavioral insights and contextual information. Store visits and Store visit conversions are among the supported data-driven conversions.

An Offline Conversion Uploads feature enables inclusion of offline conversions in Smart Bidding. Google recommends implementing import models using Attribution and Data Studio for a first-party overview of performance, while its Customers Match function provides additional Smart Bidding optimization signals by letting advertisers upload their online patrons directly into Google Ads.

AI Smart Bidding for Store Visits and Sales

Automated bid strategies for store visits and sales are designed to maximize in-store outcomes at the campaign level. AI models consume a wide variety of data signals to predict store visit counts and allocate budgets across retail locations and channels. Store Goals campaigns can benefit from unified online-to-offline measurement, so that search ad traffic driving sales in physical locations is accurately credited important for omnichannel performance analysis. These automated systems handle a highly complex optimization task across millions of advertisers, locations, and variables to improve performance even while you sleep.

Bids for Store Goals campaigns can be set to Maximize Store Visits (new recommendation for in-store outcomes) or Target ROAS (when an offline revenue model is configured). A Target ROAS strategy will make more efficient use of a Store Goals campaign for driving in-store sales when set alongside the matching conversion type.

Cross-Channel Attribution and Budget Efficiency

Predicting in-store outcomes from customer interactions across multiple channels remains a challenge for many marketers. As a result, advertising campaigns that focus exclusively on driving online actions, such as add-to-cart clicks or completed purchases on an e-commerce website, miss out on valuable in-store sales opportunities by excluding store visitors from the convergence path. Attribution errors occur in the opposite direction, too: purely offline signals do not capture the numerous users who interact with a business online before ultimately making an offline purchase.

By moving towards Store Goals, Performance Max consolidates both online and offline attribution paths into a single source of truth. As customer journeys become more integrated, supporting both local and online sales leads to a more efficient budget. Store Goals signals open the door for deeper collaboration across P-Max campaigns, with department managers actively managing store visits, local inventory dips, and product-specific price incentives.

Despite This synergetic approach, seasonal demands may still differ considerably across online and offline channels. If larger bucket campaigns target online sales, consider combining Store Goals with Performance Max for Online Sales. In these cases, store visit conversions can still provide valuable insights into your audience.

Real-Time Optimization for Local Inventory and Demand

Offline metrics have an intrinsic lag that doesn’t really fit Lord Fletcher’s adage that “the first to take advantage wins.” Even when the ad is measured over a long period, changes can happen in weeks or months (for example, a recession, a new competitor, or an e-commerce shift), leading to miscalculated ROAS. But other datasets can provide faster, real-time signals outside of those longer offline metrics. They shift the optimization decision to the side of search engagement, minus the boot-up time of having stores sell again.

Optimizing for store visit metric or similar local actions (direction clicks, local actions, calls) feeds into Store Visit Conversions (modeled). These finally link back into classical store visit modeling, but by utilizing the large amount of online data inside Google. By focusing on the search engagement metric, the Model F approach means that it should, in principle, be useful across all segments, offering strategic priorities based on different strategies.

How to Set Up a Store Goals Campaign (Step-by-Step)

Setting up a Store Goals campaign with Google Ads follows a simple sequence of six steps. Advertisers should:

1. Link their Business Profile to Google Ads.
2. Select Store Goals as the campaign objective.
3. Add creative assets (text, images, video, and product feeds).
4. Choose a bidding strategy (Target Return on Ad Spend or Maximize Store Visits).
5. Review location targeting and budget settings.
6. Launch the campaign and monitor performance using store visit conversions.

The following sections provide detailed instructions for each step.

Step 1: Link Google My Business / Business Profile

Conditions for proper linking are a natural consequence of store visit modeling and optimization with Performance Max Store Goals. Google requires accurate matching between local data across your Store Goals, Google Business Profile (GBP), and Ads accounts to support the location signals that drive bid optimization and campaign attribution. This enforcement improves campaign performance by helping Google assign store visits more confidently to user interactions. Discrepancies in business names, addresses, phone numbers, and other key data will hinder performance and could result in display network exclusions.

Follow these guidelines to ensure a smooth linkage process: the Google Ads location address must match the GBP business address exactly, including location attributes (e.g., “suite”); business name must only differ if geolocation context permits simplified display (e.g., “Supermarket” vs. “Supermarket 1848 6th Ave”); city name must be stated consistently (e.g., “Las Vegas” vs. “Las-Vegas”); location phone number must differ by country code if present; and the site URL in GBP should not be a third-party landing page. If there’s a quality mismatch and you detect that audiences are not being defined confidently, check GBP for your business presence.

Step 2: Choose Store Goals as the Campaign Objective

Setting Performance Max for Store Visits as the campaign objective directs the AI toward store outcome-based signals instead of site or app actions.

Store Goals campaigns are optimized to enable more store visits. When this objective is selected, Performance Max for Store Goals focuses on actions such as directions requests, call clicks, and local actions that are most likely to lead to a store visit. In contrast, when online-only goals associated with digital purchases, form completions, or engagement on site or app are activated, AI Smart Bidding for Performance Max campaigns prioritizes such online-centric signals, which may not generate real, offline store traffic and result in store visits that materialize only later or are not attributable back to the campaign.

Selecting Store Goals permits potential attribution of in-store revenue to the Performance Max campaign and sets it up to be measured with Store Visit conversions, as described in “Understanding Local Signals and Data Sources.” Store Visit conversions capture modeled store visits that are influenced by the campaign but not necessarily confirmed by point-of-sale data (like conversion modeling for digital purchases), as well as actual store visits where point-of-sale data match the store location. Such integration provides in-store attribution and insight into whether the campaign is driving incremental visits, in-store engagement, and, ultimately, sales.

Step 3: Add Assets   Text, Images, Video, and Product Feeds

When adding assets to a Performance Max for Store Goals campaign, consider prioritizing local relevance, maintaining branding consistency, and ensuring product feeds are updated with sufficient freshness. The asset requirements differ from those for regional and national Store Visits campaigns (the assets are the same as for other Store Goals campaigns, but the explanatory notes differ). Easily replaced holiday or seasonal templates may also be useful; however, despite limited creative resources, do not overlook the customization opportunities that these campaigns offer. Localization is one of the major opportunities to leverage local Signals effectively.

• Text Assets: Text assets should be crafted with local audience intent in mind. Try to develop several variations of the short description to test different creative directions, and ensure that at least some of the text alternatives reflect localized promotions.
• Image Assets: Image assets should ideally showcase the local store as an environment where visitors can expect a branded experience. Whenever possible, use images that capture visitors experiencing the store.
• Video Assets: Video assets offer another opportunity to showcase the store experience and cultivate local brand affinity. As with image assets, the key is to present the store as a branded experience and to allow visitors to envision themselves in the location.
• Product Feeds: Local Inventory Ads (LIAs) are supported. When local inventory feeds are used, the feeds must be updated regularly and the availability of products must be maintained. Otherwise, Local Inventory Ads will not be optimized. The freshness and availability of products in the feed also affect Google’s ability to display the Local Inventory Ads.

Step 4: Select Bidding Strategy (Target ROAS or Maximize Store Visits)

Two bidding options enable distinct stores to achieve the same goal: attract customers. The first strategy uses traffic signals and conversion value to forecast performance, seeking a target ROAS. It benefits stores with distinct products that want to drive sales across channels. The second strategy aims to maximize store visits for either pure visits or re-engagement via lower-funnel touchpoints. It’s especially relevant for limited-time promotions, events, or launches.

Selecting Maximize Store Visits as the bidding strategy mandates that Store Visit conversions are enabled; this allows for campaign launch, setup, and audience expansion without traffic signals. Success depends on Store Visits as the primary conversion event but actually increasing real-world visits is an implicit intent behind Store Goals. Like other bidding options, Google’s algorithms monitor the evolving local landscape detecting cross-device conversions, observing proximity signals, and assessing probability of visits then make real-time adjustments to maximize volume.

Step 5: Review Location Targeting and Budget Settings

Highlight options for radius-based geo-targeting, cover your selected budget type and any local bid modifiers, then confirm that your campaign is set to pace for seasonal demand with the potential to alter that strategy in the following step.

Store Goals campaigns target a radius around your business, with native support for store visit tracking and Dynamic Location Extensions. As a default, the budget setting drives delivery at an even pace, but when demand is expected to fluctuate over the course of the campaign, it can be adjusted to take seasonality into account.

Radius and Geofencing Logic: The targeted radius can be configured when the campaign is created (on the Create new campaign form). When selecting a distance, remember that bid adjustments for proximity are also enabled as a standard feature pacing more budget to the searchers who are nearest to one of your stores.

You have the option to set Up to 30 Days Before Season to enable advanced seasonal budget pacing; in that case, the campaign will receive more budget in the days and weeks leading up to its seasonal peak, while Budget Type is set to Standard, thus pacing funds gradually.

Step 6: Launch and Monitor with Store Visits Conversions

To assess the effectiveness of your campaign in driving store visits, launch it with Store Visits conversions as the primary conversion type. Following the launch, monitor its performance every 2-3 weeks more frequently if the Budget limit is likely to be reached before new evaluations can be made. Store Goals campaigns produce placements in Google Maps, Search, YouTube, Display Network, Discover, and Gmail; each source has distinct visitor transaction patterns; and individual changes in performance can vary widely by source. Hence, regular evaluation ensures that drops in performance on any individual signal source can be detected quickly and appropriate corrective actions taken.

For campaigns optimized for seasonal demand, scaling down the Budget sufficient to sustain performance just before the busy period starts helps maximize profitable purchases. Seasonality adjustments also build in for lower local demand in less-trafficked periods or ahead of stock clearance: reducing bids in advance when local inventory levels move to low positions can ensure coverage within the available traffic without sacrificing overall ROI.

Understanding Local Signals and Data Sources

Local signals tell Google your business’s proximity to potential customers and they guide campaign outcomes and performance optimization. Fueling Performance Max campaigns configured to drive Store Goals, these signals allow for sophisticated AI bidding and attribution, connecting advertising exposure to bricks-and-mortar sales success.

Three data sources primarily influence local signals and internal attribution in a Store Goals Performance Max campaign: the linked Google Business Profile / Business Profile, local inventory feed, and geo-targeting settings.

A well-structured Google Business Profile is pivotal. Store-specific information from hours and holiday closings to menu offerings and product availability must be accurate. The Business Profile also informs radius-based location targeting, ensuring that the logical distance between business location and audience exposure is accurately incorporated into proximity signals.

The locations of users exposed to Performance Max ads determine whether Campaign Manager models that exposure as a relevant interaction with the advertising business. When evaluating store visit conversions for these ads, Google models the visits only when users’ smartphones are detected in proximity to the store.

With proper setup and reliable signals, Store Goals campaigns positioned to drive store visits provide a powerful connection between ads, marketing exposure, and local business success: they offer multiple ways to connect with stores, allow Google to add direction requests as dedicated local actions, and, because store visits are modeled based on driving directions, support measurement of ROAS and performance against offline revenue goals.

Store Visit Conversions (Modeled and Observed)

are defined by the nature of the data that confirms offline visits. By design, some visits are modeled based on a range of factors, including demographic characteristics, device usage, and the location’s proximity. Such modeling helps optimize the targeted audience, creative assets, Smart Bidding, and attribution but requires additional caution. In particular, the modeled conversions should not be used to inform or set a target ROAS, as they do not indicate the same level of certainty as observed visits. If a large percentage of your store visits are modeled, they are less reliable and should be validated against other available data streams before impacting decision-making.

Using Offline Conversions to Show Cross-Channel Success The other conversion action permitted during the Store Goals campaigns or in a P-Max account with Store Goals is “store visit conversion (offline)” an upload of sales conversions with a store location value linked to the Visitor. This is most common where other online conversion include a remarketing pixel tracking with actual offline revenue triggered within that window. Any Store Goals ad can be thought of as having two audiences: 1) the connected user, and 2) those that becomes visitors after direct or indirect exposure; both need to be nurtured.

Local Inventory Feeds and Product Availability

To activate local inventory asset optimization, retailers must submit an up-to-date local inventory feed in the Google Merchant Center. This structured feed supplies crucial product availability data for local markets and exposes it to all channels across Google Ads, Google Search, Shopping, and Display. A well-maintained feed enhances Performance Max campaigns by making Google aware of the latest stock levels, facilitating online-to-offline sales connections, and supporting local product inventory ads.

Combining feeds with visual assets enriches storytelling, generating relevant and compelling ads that speak to audience intent. Regular feed maintenance is essential to prevent out-of-stock product promotion and support accurate time-bound local offers.

Geo-Targeting and Proximity Signals

A radius-based targeting approach is utilized for Store Goals campaigns, wherein ads are delivered on a per-user basis to Google users located within a specified distance of a business location. The selection of which business location to show, when several locations are targeted, is determined by proximity signals, with priority given to the nearest store. The radius is frequently augmented by opening-hours targeting, as well as by Dynamic Location Extensions. Whether All Stores within Campaign or Locations by Data Feed are selected as the location targeting option affects the granular operation of these two methods see the Help Center and the section on Using Dynamic Location Extensions for further details.

Dynamic Location Extensions dynamically generate a location-specific link and store-specific callout information in Display, Video and Discovery ads that link users to the nearest store. Traffic from these links is inevitably local, increasing the likelihood, for instance, that users click through to a product that’s now in stock in their own neighborhood store.

Optimizing Creative Assets for Local Impact

Creative relevance greatly influences store-centric outcomes. Assets should evoke store-specific context and audience intent, supported by multiple dimensions of research to guide localized creative direction. Group-specific asset analysis and audience intel on stage-of-market maps can enhance appeal. Dynamic Location Extensions further enliven creativity with automatic store-specific callouts and routing links. For sustained engagement, local offers and promotions should be timely. Within campaign delivery, engaging video and image asset work can bring store experiences to life.

Optimization of creative assets for Performance Max campaigns with Store Goals hinges on enhancing local impact, whether incremental or within cross-channel attribution. Such ads connect with relevant in-market audiences when local timing aligns, and these consumers interact with stores directly rather than through tagged visits. The choice of bidding strategy can also drive on- and off-site outcomes simultaneously. Local market conditions affect in-store fluctuations and hence offer scope for dynamic local offers and seasonal adjustments linked to local inventory. Store-Specific Context and Stage of Market A keen understanding of campaign context within local markets and citywide competition is essential for tailoring assets to maximize the impact of the shop-in-ad traffic. Information sources include viewpoint reports that map at-risk areas, audience empathy studies, and creative feedback audits.

Performance Max formats supporting a Product Feed enrich creative storytelling about the audience effect. Video assets can replicate the discovery theater in non-linear ways across the Google Display Network. An ambiance-evoking image library depicting the storefront and surroundings appeals to considered lifestage shoppers developing shortlist predispositions for in-store action. Local Inventory Feeds for stores plus Influencers provide scope for brand endorsements to widen Demand in-market. Together, these dimensions address local stores and their relevance during consideration.

Using Dynamic Location Extensions

Dynamic Location Extensions enable automatic store-specific callout texts and links, allowing potential customers to access essential information about your nearest location directly from your ads. Using this resource also boosts the quality and relevance of your creatives by making them more useful for users looking for local information. Results outline that advertisers who take advantage of Dynamic Location Extensions achieve improved engagement metrics for their ads.

When using Performance Max campaigns for Store Goals, Dynamic Location Extensions are automatically activated to display store-specific information in the assets. Store address and phone number will be added on those campaigns, with the clicks directing users to the store’s Business Profile. Advertisers can also take the chance of linking promotions, local inventory, and store apps into these ads.

Incorporating Local Offers and Promotions

Time-limited local offers and clearly visible local inventory provide two major signals that a consumer’s purchase intent is an in-store visit, not a click, tap, or call. Strategically using Local Ads (now P-Max for Store Goals) to highlight such offers, when relevant, can greatly enhance the likelihood of driving actual in-store sales. Adding a limited-time offer, such as “Free first drink with purchase,” to ads running in a store’s area has proven to significantly increase foot traffic. In fact, advertisers that incorporate local offers into their Store Goal ads see 50–100% more store visit conversions than those using non-local offers. Why does it work? The offer, combined with the local signal, turns the ad into a direct response ad offering something the consumer can’t get online. As with any advertising or marketing, consumers have a choice of where to shop, when to shop, and what to buy. The consumer is being presented with an internal dialogue that leads them to choose a certain store, a certain time, and a certain product.

The Product Listing ads are made even stronger if the advertiser also has inventory feeds connected to the Google Merchant Center (GMC). These ads show the exact product available in that specific store at that specific time, giving the consumer the highest chance of converting when they are in an intent-focused stage. With all these signals, Google can detect that this specific consumer is likely going to visit a physical store, and they provide the advertiser with a store visit conversion to help with further analysis of true marketing effectiveness.

Video and Image Assets That Showcase Your Store Experience

When developing creative assets for campaigns using Store Goals, marketers should prioritize text, images, and video that emphasize their specific retail location. Because Performance Max campaigns with Store Goals as the primary objective present ads to customers looking for nearby stores, make use of these assets to tell a story about either the experience of visiting the store or the products that can be found in stock. Featuring images of the storefront can help customers quickly understand whether the store fits their needs. Reassuring those who see the ad and its visual elements of the experience awaiting them at the store can play a large role in their decision to visit.

Consider the following concepts and ideas for local storytelling of physical retail locations:

– Local presence  

Ensure your assets are true to the local experience. Key elements of your store experience, whether customers’ favorite part of being hosted in the store or local legends about the area and the store’s role in the community, can create positive anticipation for store visits.

– Storefront ambiance   

Video and image assets that establish the atmosphere of the physical store environment can make your offer more compelling than others online. Picture sequences or one-off clips that showcase store employees ready to help customers, that present the checkout experience, that prepare visitors for in-store product discovery, or that introduce playful product and service aspects can be effective.

Establishing a strong presence with these materials will make the campaign and its performance boost the customer experience when brands are physically present at local locations.

Advanced Strategies for Store Goals Campaigns in 2025

After establishing key setup elements for Store Goals campaigns in Performance Max, forward-looking tactics can help optimize these campaigns for in-store revenue generation. By integrating high-quality first-party data, leveraging seasonality and local inventory signals, and combining Store Goals with Performance Max for online sales, advertisers can maximize their chances of success.

The strength of any Performance Max campaign is its ability to harness valuable data signals from across the Google ecosystem. When brands can supplement that data with their own high-quality first-party information, Performance Max can go from a capable performer to a top-tier driver of results. Integrating Customer Match lists or first-party CRM data into the customer journey is not only a best practice, but also an important strategy to consider for Store Goals campaigns that offer attribution across channels. Enriching the model with first-party data through customer match ROAS creates a stronger performer, as the model operates at a deeper level and can also assist in building kitchen cabinets.

Performance Max for Store Goals can run concurrently alongside a sales-driving Performance Max campaign focused on online sales. Advanced machine-learning technology from Google can serve ads to the right audience for the right objective, regardless of which campaign they belong to. Having this dual-campaign structure in town makes a lot of sense and allows a retailer to unify-store attribution across online and offline sales. For example, a search query for “running shoes” may show a Display ad for an online sale, while a “running shoes near me” style query may show a Performance Max ad with a Store Goals objective driving a store visit. The advantage of using Store Goals is that attribution happens across both campaigns if the sale is completed within the preallocated 30 days (or converted using the Customer Match list).

Monitoring and managing seasonal demand is always a priority, and Store Goals campaigns are no exception. Brands should keep a close eye on stock levels and raise and lower bidding accordingly during key periods. Local inventory adjustments should be made to inform shoppers of upcoming demand for trendy items and promotions that are available for a short time.

Integrating First-Party Data and Customer Match

To leverage first-party data, advertisers can upload their Customer Relationship Management (CRM) lists into Customer Match. These lists are compared against signed-in users on Google, while respecting privacy controls. Higher-quality matches correlate with better performance since the audience is more similar to previous customers. However, advertisers must hold on to this audience and have their first-party data in a usable format before incorporating this tactic.

To help close the loop between online and offline customer journeys, advertisers can also upload offline conversion data from purchases and in-store visits. These conversions can then be associated with earlier online engagements, enabling improved cross-channel attribution and smarter machine-learning algorithms. Tagged stores take prioritization a step further by showing ads based on actual traveling routes.

Combining Store Goals with Performance Max for Online Sales

Store Goals and Performance Max for Online Sales are complementary capabilities. To maximize the local impact of their online business, retailers should balance in-store and online priorities within a single Performance Max campaign, informed by cross-channel attribution.

Performance Max allows optimization for revenue across all channels, making it possible to promote both in-store sales and online orders within the same campaign. Ads using Store Goals for in-store visits can therefore also be used to drive online sales, helping brands assess the full impact of their online investments in the physical world and conversely the online contribution to offline sales. With store visit conversions enabled, the underlying models support dedicated attribution of online sales to a nearby store.

A Store Goals campaign with Target Return on Ad Spend bidding and a local audience is particularly well suited for driving online transactions. The estimation of sales uplift from local online ads then includes conversion modeling for both sales claimed online and visits to the store location. For maximum synergy with in-store outcomes, however, brands should ensure that customer experience is preserved when driving online sales through their local stores for example, by maintaining good inventory availability and transferring parcel delivery as a preferred fulfillment option.

Leveraging Seasonality and Local Inventory Adjustments

Strategic advertising campaigns recognize that individuals’ needs fluctuate over time, and so should their bids and ad assets. If the seasonal shifts in demand are significant, set up your Performance Max with Store Goals campaign to dynamically adjust these elements. Consider directing your Performance Max for Store Goals campaign to bid higher when your audience is in-market for your offer, rather than during quieter periods. This is particularly effective for holidays or special events – Halloween, Black Friday, Easter, Back-to-School Season, etc. Seasonal adjustments that allow you to take full advantage of high-demand periods, for example Peak Christmas Season to Be-jolly Period, Advertise More in The Run-up to The Date, etc.

Inventory levels can change frequently for retail stores. While bids must consider demand, inventory also influences the decision to buy or not. Based on your predicted stock levels and local demand for those products, Performance Max for Store Goals campaigns can be tuned to advertise more actively when the products are available, and less when the stock is low. Consequently, all bids will be adjusted down for any period when no products are available or no stock-level feed is connected.

Offline Conversion Uploads and CRM Integration

Integrating offline conversion data helps Performance Max campaigns more accurately measure, optimize, and attribute value. For in-store sales, CRM data enables marketers to establish broader connections with customers, leveraging rich details about browsing, purchasing, and lifecycle management. Anonymized customer data can also be safely used for audience building via Customer Match.

To transfer CRM data to Google, upload a list of first-party contacts with hashed email addresses or similar identifiers. This information is matched against online and offline signals stored in Google systems, allowing for more personalized experiences across the Google ecosystem.

Marketers should prioritize sufficient coverage when uploading customer lists; sources such as loyalty programs typically yield the highest match rates. Attention to privacy best practices is also critical; using Customer Match requires that first-party data was acquired in a lawful manner, and advertisers should familiarize themselves with their obligations. When de-identified data is made available, it may be used to refine audiences and inform future creative decisions.

To measure the in-store impact of performance marketing on CRM audiences, combine Customer Match with offline conversion uploads. By establishing a link between online interactions and offline purchases, marketers can evaluate full-funnel performance for different customer segments and channels. When uploading these conversion records, be sure to apply the Google Ads Management Account ID as the conversion label, as this field specifies the appropriate action tracking setting to use.

In addition to enriching optimization, data uploads facilitate robust attribution modeling: during the modeling window, any online click or impression made by an offline conversion customer is considered an influenced touchpoint. Modeling can thus harness the full range of first-party behavioral signals. To make data-driven predictions about future offline sales, upload conversion data from previous periods. The higher the modeled coverage, the better the model can predict results for similar customers.

Common Mistakes to Avoid

Four pitfalls can hinder success with Store Goals campaigns two stemming from a failure to track in-store visits and two affecting the quality and relevance of assets. Addressing these issues builds on previous coverage of how signals are optimized and why asset optimization is so critical.

Not Enabling Store Visit Tracking

For Store Goals campaigns to drive store visits, store visit conversions must be enabled and correctly configured. Without accurate store visit tracking, modeled store visit conversions heavily determine the campaign outcome, leading to inaccuracies in performance measurement and forecasting. If you haven’t already done so, enable store visit tracking by placing the Google tag on your website; this process is detailed in “Store Visit Conversions.” Installation choices include a dedicated conversion action, within a standard conversion tracking setup, or with a Google Ads remarketing tag.

If using a Google Tag Manager installation, ensure that Store Visits can be selected when configuring conversions. After launch, track traffic on the Store Visits conversion action. Once enough data accumulates, assess whether modeled or observed Store Visits dominate performance measurement. If observed Store Visits remain limited but performance is poor, consider whether location targeting is too broad.

Overlapping Location Targeting or Outdated Profiles

Careful setup and ongoing management of the campaign are crucial to achieving success with Store Goals using Performance Max. As with any machine-learning system, the quality of outcomes is only as good as the signals processed by the model. Neglecting to put all pieces in place means that optimization will either focus on the wrong audiences or, worse still, be based on inaccurate information.

Store Visit conversions feed into different machine-learning learnings, and careful eye needs to be placed on ensuring they are set up if stores and offline events are important to your business. To ensure performance goals are being met, use Store Goals and check what goals have been chosen and are actually driving success. You can choose from a list covering store visits, directions clicks, local actions, and calls.

Using Generic Creatives Without Local Context

Localization is a key differentiator within the store goals framework. Creative assets lacking relevant detail run the risk of becoming bland and unengaging. Tailoring images and videos to the local context and intended audience ensures that the message resonates. Specific suggestions for location-focused creatives can be found in a prior article detailing video and image storytelling elements that connect with the retail audience.

The store goals landscape is evolving quickly as recent developments underscore a broader realignment of omnichannel retail strategies and AI capabilities. As the demand for a truly omnichannel strategy intensifies, the need for a dedicated Google product that measures in-store productivity and success relative to advertising spend behind digital activations becomes even more critical. Performance Max for Store Goals bridges the gap between spending for online engagement and attributed online sales while also delivering relevant and optimized store-centric advertising across the entire Google ecosystem.

Key Metrics to Track in Store Goal Campaigns

Store Goals with Performance Max connect in-store outcomes with online signals. Metrics that track relevant outcomes for these budgets include Store Visits, Directions Clicks, Local Actions, Call Conversions, ROAS, Conversion Value, and Impression Share by Geography and Device.

Store Visits and Directions Clicks

Both  are modeled conversions. MIT-ML’s estimates indicate how many users visited the store after they engaged with the ad, but these visits can’t be confirmed with GPS or WiFi data from the users’ devices. Store Visits conversions appear as a Store Visit conversion action in Google Ads, associated with an attribution model. Accurate attribution, like for all modeled conversions, hinges on having enough observed data from other real-world signals around store foot traffic if there’s not enough from Store Visit conversions, attention will shift to the next best signal in the attribution model. The attribution for Directions Clicks conversions is still last click.

Store Visit conversions are particularly important because they mark the start of interaction with a store, and they feed into offline conversion models. For example, a query for shoes might lead to a store ad that gets a Store Visit conversion. The combination of Search + P-Max + directions click + Store Visit gets assigned to the offline revenue from that interaction.

Local Actions and Call Conversions

Local actions and call conversions allow the tracking of various interactions that indicate local intent but do not measure store visits directly. These local actions encompass driving direction clicks, clicks to call, and interactions with call extensions. While not optimization objectives in themselves, they contribute data to gauge the effectiveness of the Store Goals campaign for driving physical visits.

Local actions, similar to Store Visit Conversions, are attributed based on touchpoints with the predicted customer journey, providing assistance in reporting and optimizing. Call conversions represent calls from ads with call extensions or call-only ads that take place from your business profile within the 30-minute attribution window.

Defining a clear pathway to an offline purchase is an objective of Google’s 2025 strategic roadmap, and Credit Union NZ appears to be advancing in that direction by getting ahead to optimize their campaigns for more local and direct conversion paths.

ROAS and Conversion Value for Offline Sales

Conversion tracking with store-visit measurement supports attribution of in-store revenue to advertising expenditure, providing Google Ads users with an offline ROAS comparable to that for online sales. When a user interacts with the advertisement, either with a click or an impression, this is marked. If they visit the advertiser’s physical store during the attribution window and later make a purchase, the conversion is recorded by store-visit monitoring. By monitoring the sales associated with each ad, Mariachixton is able to assign a conversion value to an online click using the corresponding product.

Store visits also feed into offline conversion models. These can take multiple shapes, but the main idea is to use the known sales data of the physical store to infer how much these store visits cost. This indication can then be used in the “store visits” section in Google Ads to calculate an ROAS. However, given that keywords and other audience parameters cannot be seen for visits, the actual modeling required Google to develop a model. Hence, the indicated value is an estimate on the conversion side as compared with online conversions.

Impression Share by Geography and Device

Segmentation by geography and device type provides insight into relative performance distribution and helps identify issues or opportunities in particular markets or regions. Impression share by geography shows how often ads are shown to users in different locations. Impression share by device indicates how ads perform on computers, mobile devices, and tablets.

If *Store Visits* and *Directions Clicks* metrics are being used, evaluate the breakdown of these events by geography. Significant differences may indicate areas where specific attention is required to improve performance. For instance, if bidding and targeting are correct but Store Visits are considerably lower on mobile than other devices, check whether creative assets are designed for a mobile audience and suitable for restricted screens.

Future of Local Advertising with Performance Max (2025–2030)

The Next Five Years of Local Advertising with Performance Max for Store Goals

Footfall Modeling, AR Ads, and Maps Integration (2025–2030)

Several trends and enhancements may shape the future of local advertising over the next five years. Predictive footfall modeling could use Google’s AI capabilities to deliver insights on future footfall by location at various levels of granularity and accuracy, based on historical patterns and other signals such as weather, seasonality, and external events such as nearby construction. Businesses could use these predictions to inform their planning, allocating resources to specific locations and times, and identifying price points, experiences, or inventory likely to engage or pull in customers. In an online context, predictive footfall signals could also inform budget allocations and bidding for omnichannel objectives across different marketing channels.

Another exciting trend focuses on the development of immersive formats on YouTube and the Display Network, providing more eye-catching ad creatives for advertisers. Over time, Google may also integrate a business’s ads into the Google Maps experience, incorporating inventory available and relevant to the user’s search. Full integration with the Merchant Center Next initiative will allow businesses and platforms across the merchant ecosystem to inform offer and product availability on ads running on Google Maps and elsewhere across Google Search and YouTube.

Predictive Footfall Modeling with AI

Predictive footfall modeling with AI by leveraging traffic data and an understanding of the influences on footfall behavior is the future of local marketing on the Performance Max platform. In-store footfall predictions and planning are commonly used for decision making, such as optimizing online ad spend to balance demand and supply, making staffing decisions, and managing stock levels. In 2025, Google is expected to make this capability available to advertisers by developing a predictive model for footfall that uses a combination of former Traffic data and an understanding of the influences on footfall behavior, such as seasonality events, and online ad signals. Advertisers will then be able to use this footfall forecast during the optimization and planning processes for campaigns that are expected to generate Store Visits.

Businesses will also be able to understand the potential impact of their advertising spend on footfall. The AI algorithm will be able to automatically assess whether advertising activity is driving footfall above or below the predicted level. Key signals that Google expects to draw on to assess these trends include foot traffic estimates from the Traffic data, changes to prediction patterns in the Traffic data that occur as a result of seasonality events, and incremental effect models that measure the impact of Performance Max ad spend on footfall. This predictive footfall capability is initially planned for the Store Visits Google Ads attribute and the Store Visits conversion action in Google Ads.

AR/VR Store Previews and Immersive Ads

With the rise of AR and VR technologies, the future of local marketing is even more exciting. From immersive, explorable experiences to straightforward ads that allow a look into your storefront or an AR piece of furniture for your house, there are countless ways in which you can create ads that let customers engage with the shopping experience without leaving home. Start small and experiment with a few campaigns to see what customers respond to best.

Integrating fit-to-space AR product previews into your ads. Showcasing your store’s ambiance and product showcase with an exploration campaign. Allowing customers to remotely step into your store with a fully explorable 3D asset. Creating an AR-enabled ad that lets viewers see how a piece of furniture fits into their house. Marketing to a 6–12 month long pipeline with videos of your tented special events. Using a collection ad to catch attention with products from your store while still closing customers immediately online.

Test the formats. Find the right way to engage customers where customers are engaged. It’s one aspect of how Google is making the online advertising experience more immersive to attract local customers.

Full Integration with Google Maps and Merchant Center Next

Performance Max with Store Goals represents the future of local advertising, offering advantages in measurement, bidding, attribution, and real-time optimization of local inventory. Integration with Google Maps and Merchant Center Next will deepen these capabilities   making store visits and calls both a campaign objective and a measurement outcome.

In the long term, the destination for a store visit will be as relevant for attribution and bidding signals as the ad itself. By 2030, Google expects to model foot traffic to stores based not only on exposure, but also on contextual signals such as closing time, reviews, available promotions, local product availability, day of week, and season   establishing a predictive footprint signal for customers. AR/VR Store Previews will provide shoppers with immersive ways to explore the store experience and browse inventory. Ads will control Store Preview content, thanks to full integration with Google Maps and continuity with Merchant Center Next.

Why Performance Max for Store Goals Is the Future of Local Marketing

Setting store visits or phone calls as key objectives within an evolving omnichannel strategy that embraces AI optimization for Prediction , Management and Unified Measurement represents a sophisticated approach to tracking and supporting real in-store goals.

The omission of Local Ads at Google is anything but a casual decision. In a time of rampant AI adoption and digital performance-driven marketing playing an increasingly dominant role, Google Schwartz has quietly unified its Store Goals for Performance Max campaigns and Store Visit Conversions. Performance Max has not only become the new campaign type for driving search and YouTube website sales and lead generation, but it has also taken over Local Campaign for footfall-driving store visits. Omnichannel retail deals with the integration and seamless functioning of different channels of Retail Marketing And Communications – Online and Offline, the use of AI in Marketing, Marketing performance modelling and Unified Measurement of all the channels involved .

Store Goals using Performance Max is the evolution of Google’s Local Campaigns introduced earlier for A4A Oberlo. Locally relevant ads within Google Search, YouTube, Display Network, Discovery, Gmail , and Google Maps are served, helping store customers discover products nearby, make purchase decisions quickly and efficiently, and provide business details and driving directions. AI and Boum Optimisation now ensure that store visit conversions are driven and revenue accomplished for an omnichannel strategy using Prediction , Management and Unified Measurement .