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Google Ads AI Dashboards start appearing in advertiser accounts

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Google Ads’ new AI-powered Dashboards are rolling out to some accounts, letting advertisers create visual performance reports with simple text prompts.

Driving the news. AI Dashboards started appearing in some Google Ads accounts following Google’s announcement of the feature in August. You can use simple text prompts to turn account data into visual reports, Google said.

  • Instead of manually building a report to investigate a performance change, you can describe what you want to analyze and have Google generate the visualization.

Why we care. Building reports in Google Ads has traditionally required you to manually select metrics, dimensions, and visualizations. AI Dashboards shift much of that work to Gemini, letting you describe what you want to analyze while Google builds the report.

The details. The feature goes beyond building charts.

Each report also includes a real-time AI summary that explains the “why” behind the data, according to Google. That lets you use the dashboard to understand what changed in campaign performance and what may be driving those changes, rather than simply reviewing the underlying numbers.

The big picture. Google is increasingly putting AI between advertisers and their campaign data. It has also introduced AI-powered insights on the Google Ads homepage and Ask Advisor, its in-product AI agent, as it shifts toward workflows where advertisers can ask performance questions in natural language instead of manually navigating reports.

Bottom line. AI Dashboards reduce the work of building Google Ads reports, letting you spend more time interpreting insights rather than creating them.

First spotted. This update was first spotted by paid search expert Thomas Eccel, who shared it on LinkedIn.

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Microsoft Advertising removes Max CPC from new standalone bidding campaigns

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Microsoft Advertising will stop allowing advertisers to set Max CPC limits on new campaigns using several standalone automated bidding strategies starting Oct. 1, as the platform pushes advertisers toward conversion-based targets and other automated bidding controls.

Existing campaigns with Max CPC limits won’t immediately lose them, while portfolio bid strategies and several other bidding strategies will retain the option.

Why we care. This removes another manual control from Microsoft’s automated bidding strategies. Advertisers that use Max CPC as a safeguard against unexpectedly expensive clicks will have less direct control when creating certain new campaigns and will instead need to rely more heavily on budgets and conversion-based targets.

Whether that produces better results will depend on the quality of an advertiser’s conversion data, the targets they set and how effectively Microsoft’s bidding system responds to those signals.

What’s changing. Starting Oct. 1st, advertisers creating new campaigns using standalone Maximize Conversions, Maximize Conversion Value and Maximize Clicks bidding strategies will no longer be able to add a Max CPC.

Existing campaigns created before the deadline will retain their Max CPC settings. Microsoft Advertising Product Liaison Navah Hopkins also confirmed that Target Impression Share, eCPC and portfolio bidding strategies will continue to support Max CPC controls.

Why Microsoft is making the change. Microsoft says Max CPC limits can interfere with automated bidding by overriding an advertiser’s stated performance goals and potentially creating spend pacing irregularities.

The company says advertisers using conversion-based bidding with target CPA (tCPA) and target ROAS (tROAS) tend to have an easier time achieving their goals than advertisers relying on legacy controls such as Max CPC.

Instead of CPC caps, Microsoft wants advertisers to communicate their objectives through controls more closely tied to business outcomes, including budgets, tCPA, tROAS, conversion value rules and seasonality adjustments.

What happens to existing campaigns. There’s no immediate migration for campaigns already using Max CPC. Campaigns created before Oct. 1 will continue to retain the bidding control.

That creates an important distinction between existing and newly created campaigns: advertisers won’t necessarily lose Max CPC across their accounts on Oct. 1, but their ability to use it when building certain new campaigns will disappear.

Microsoft says further updates about the future of Max CPC will be provided later.

Get ahead of the change. Hopkins is encouraging advertisers to use optimization experiments to test removing Max CPC from existing campaigns before the deadline.

Doing so could give advertisers an indication of how campaigns perform when automated bidding has greater freedom, before the option disappears from newly created standalone campaigns.

That may be particularly relevant heading into the holiday season, when advertisers could otherwise encounter the new restriction while launching or restructuring campaigns.

Targets become the primary lever. Microsoft is encouraging advertisers to think of tCPA and tROAS as their primary volume and value levers, rather than trying to control automated bidding through individual CPC limits.

Hopkins also noted that Microsoft Advertising can allow campaigns to outperform their tCPA or tROAS targets regardless of whether they’re limited by budget. Where appropriate, Microsoft recommends using conversion value rules to provide the bidding algorithm with additional information about which conversions are more valuable to the business.

Bottom line. Microsoft Advertising is making automated bidding more target-driven by phasing Max CPC out of new standalone Maximize Conversions, Maximize Conversion Value and Maximize Clicks campaigns from Oct. 1 — while, for now, preserving the control for existing campaigns and portfolio strategies.

September 7th comms. Microsoft Advertising emailed advertisers with more information about this update.

Microsoft’s Max CPC restriction will also apply to Target CPA and Target ROAS campaigns, expanding the bid strategies beyond the Maximize Conversions, Maximize Conversion Value and Maximize Clicks strategies reported in August.

Microsoft also revealed a January 12th deadline for API users, tool providers and Google Import, after which Max CPC won’t be supported for new campaigns or existing campaigns not already using it. While existing campaigns using Max CPC by Oct. 1 can keep the setting, Microsoft now says removing it after that date is irreversible — advertisers won’t be able to add a Max CPC back later.

Dig deeper. Updates to Max CPC for new campaigns

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Google Ads adds new tools to drive and measure in-store sales

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Google is rolling out two new features designed to help multi-location retailers, restaurants and local service businesses reach nearby customers and connect their ad campaigns with in-store sales.

Driving the news. Google Ads Liaison Ginny Marvin announced Local Customer Optimization for Performance Max store goals campaigns and a new way to bring store sales data into Google Ads through Data Manager.

Both are aimed at reducing the work required to drive and measure offline sales ahead of the holiday rush.

Why we care. These updates make it easier to connect digital advertising with actual store visits and sales. Multi-location businesses can prioritize spend toward nearby, in-market customers across Maps, Waze and local Search, while simpler CRM and Google Sheets connections make it easier for advertisers to feed offline sales data back into Google Ads for measurement and optimization.

Zoom in. Local Customer Optimization is a new campaign-level toggle in Performance Max for store goals campaigns.

When enabled, Google will prioritize budget toward reaching consumers who are actively in-market nearby across Google Maps, Waze and local Search.

The feature is rolling out now.

Meanwhile. Google is also simplifying how businesses can share their offline sales data with Google Ads.

With Store Sales in Data Manager, advertisers will be able to connect their CRM or Google Sheets directly within Data Manager rather than relying on more technically demanding methods of sharing that data.

Advertisers can then use the information to measure in-store sales and optimize campaigns toward their highest-potential walk-in customers.

Google says the feature will begin rolling out in the coming weeks.

The big picture. The two updates are designed to work across opposite ends of the customer journey.

Local Customer Optimization uses real-time navigation and local intent signals to help businesses reach potential customers nearby, while Store Sales in Data Manager gives advertisers a simpler way to feed the resulting offline revenue data back into Google Ads.

For businesses with physical locations, that creates a more direct connection between finding nearby customers online and measuring what happens when they walk through the door.

What to watch: Local Customer Optimization is rolling out now, while Store Sales in Data Manager is expected in the coming weeks.

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Inside Google Maps: 72 ranking signals and the architecture behind local search

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Inside Google Maps- 72 ranking signals and the architecture behind local search

When a business appears in Google Maps, the listing you see is the end product of a much larger system.

Behind it sits a canonical geographic entity assembled from multiple data sources, connected to the Knowledge Graph and the web, scored by several ranking systems, filtered through geographic and semantic retrieval, personalized for the user, and finally passed to a rendering engine that decides what can actually appear on the map.

Our team recently obtained a binary exposing a non-public scope of Geostore, the system Google uses to represent geographic entities. We crossed it with Maps protocols, network traffic, the web index, mobile services, style tables, on-device components, and Google’s 2024 leak.

The recovered material includes:

  • 72 Geostore ranking signals.
  • 793 data source providers.
  • 446 local search intent types.
  • 50,998 Mapcore styles.
  • 12,936 label styles.
  • 10,936 searchable Geostore declarations.

The ranking signals will probably attract attention. But they’re only one layer.

The architecture around them tells a more important story about how Google understands places and what local SEO may become as Maps turns into a conversational product.

The first thing to understand: A listing isn’t the entity

A useful mental model starts with Geostore. Google represents geographic objects internally as Features. A Feature can be a business, building, road, city, station, area, transit element, or even a 3D object.

For an establishment, the object can contain identity, geometry, source information, websites, business-chain relationships, Knowledge Graph references, concepts, and ranking information.

The familiar Maps listing is assembled later. What a business owner edits in Google Business Profile isn’t necessarily what Google maintains internally as the entity.

Google builds a canonical representation of the place that can incorporate data from multiple sources, survive changes in geometry, and connect to other Google identifiers, including the Knowledge Graph machine ID (MID).

For local SEO, the entity is the more useful unit to consider. The listing is the interface. The entity sits underneath it.

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Google combines data from 793 providers

One of the most revealing parts of Geostore is its provenance system. 

A business doesn’t simply have one source. Its name might come from one provider, its phone number from another, its category from another, and its geometry from somewhere else entirely.

The corpus exposes 793 source providers, along with mechanisms for provenance, priority, trust, and conflation.

Conflation is the process used when several sources describe the same object and disagree.

Geostore contains generic mechanisms that can pick one value, merge several values, or combine them. It also models trust levels ranging from blocked or untrusted sources to trusted and super-trusted ones.

This gives a different interpretation to a common local SEO problem: changing a field in Google Business Profile doesn’t guarantee that Google’s canonical representation immediately becomes that new value.

The edit becomes another piece of evidence entering a system that may already have competing evidence.

For businesses struggling with persistent incorrect attributes, duplicate information, or changes that repeatedly revert, this architecture helps explain why the problem can be harder than editing a listing.

Dig deeper: The local SEO gatekeeper: How Google defines your entity

The archive makes the leak much more useful

The binary itself gives us structures, field numbers, and complete enumerations. Google’s March 2024 documentation often gives us something different: prose explaining what those structures mean.

We published the two together. The resulting archive contains 10,936 Geostore declarations that can be searched by message name, package, field type, documentation text, tag number, status, and other properties.

In reverse-engineering work, an internal name can easily become a theory once it circulates through the SEO community. The archive lets you inspect the underlying evidence.

If a signal exists, you can find its declaration. If a field existed in the 2024 documentation, you can read the associated description. If a field has been stripped from the newer client scope, its protobuf tag still leaves a numbered hole.

The 2024 leak gave us many descriptions of Google’s systems. The newer binary gives us much more of their actual vocabulary.

Together, they provide a more useful picture than either source does on its own.

Oyster Rank contains 72 ranking signals

Geostore has its own ranking system. Internally, it’s called Oyster Rank. We recovered a complete visible enumeration of 72 signals, including:

  • Google reviews.
  • Web query volume.
  • Listing impressions.
  • Listing opens.
  • Direction requests.
  • Website clicks.
  • Chain membership.
  • Wikipedia signals.
  • Popularity.
  • Prominence.
  • Landmark information.
  • Road usage.

Out of 72 values, 25 are explicitly marked deprecated.

The important limitation is that we recovered the signal names, not their current weights.

The schema shows a pipeline in which raw observations are extracted, normalized, and mixed into the Feature’s rank. But the coefficients that would tell us how much each signal contributes are outside the scope we recovered.

So SIGNAL_GOOGLE_REVIEWS proves that reviews belong to the Oyster Rank vocabulary. It doesn’t prove that reviews currently carry a particular weight in a Maps search.

The 72 signals aren’t the Google Maps algorithm

This is probably the most important clarification for SEOs. Oyster Rank appears to characterize the importance of the entity inside Geostore. A user query still has to go through additional systems.

Maps must understand what the person means, identify a geographic context, generate candidates, evaluate semantic relevance, and serve a final result set.

A simplified pipeline looks more like:

Geostore entity -> query understanding -> semantic matching -> candidate generation -> geography and quality -> reranking -> results

There are additional complications. We also found a separate scorer running entirely offline on the device. It has eight signals across 13 tiers and is distinct from both Oyster Rank and server-side Places ranking.

There isn’t a single Maps ranking formula. Different scoring and retrieval systems operate at different stages.

Turning the 72 Oyster Rank signals into a checklist of 72 Google Maps ranking factors would miss most of the architecture.

Local search doesn’t have a fixed radius

We also tested the geographic layer directly. A common local SEO model imagines Google looking within a predefined radius around the user and ranking the businesses found inside it.

Our measurements show something more dynamic. Using the same origin in Paris, the geographic footprint changed considerably depending on the query.

A dense query, such as pharmacie produced a far smaller search area than a brand query, such as Carrefour.

The environment matters, too. The same pharmacy query expanded dramatically when run in a sparsely populated rural area.

Google appears to adapt the candidate space to both the query and what exists around the user.

We then removed geographic weighting from the same engine. Across 5,083 calls and 86,584 results, the median distance moved from 6.87 km with geography to more than 4,000 km without it.

More interestingly, the non-geographic order remained extremely stable.

This suggests geography is doing more than reordering the same list of candidates by distance. It changes what the retrieval system considers in the first place.

Distance is still fundamental in local SEO. But “I’m closer, I should rank higher” is an incomplete model.

Dig deeper: The proximity paradox: Beating local SEO’s distance bias

Maps and the web are connected through entities

The connection between Maps and classic web SEO may be one of the most consequential findings in the corpus.

Geostore Features can connect to the Knowledge Graph through a MID. On the web index side, documents can also carry MIDs.

Google has a layer called webref that associates documents with entities and stores information, including topicality, confidence, geographic metadata, and document-level scores.

The relationship also works at the document-ranking level. The recovered structures describe a relative ranking signal between different documents for the same entity, along with properties such as whether a page is an author page, publisher page, or reference page.

This creates a very different way of thinking about a store locator or location page. Its role may extend beyond ranking for queries such as “shoe shop Paris.” The document can become evidence about the underlying entity.

The SEO objective is then partly to make it easy for Google to establish:

  • Which entity the document describes.
  • How much of the document is actually about that entity.
  • How confident that association should be.
  • Whether the document is a useful reference for it.

Web SEO and local SEO are much less separate inside Google’s infrastructure than their interfaces suggest.

Google understands concepts, not just categories

The semantic layer goes considerably beyond the primary category visible on a listing. Google uses GConcepts, a shared conceptual vocabulary that can describe businesses, dishes, attributes, cuisines, service modes, and other concepts.

We followed a simple ramen query through several parts of the system. The search results themselves didn’t all belong to one category. Google connected the query with ramen restaurants, Japanese restaurants, Asian restaurants, and other related concepts.

Inside listings, the semantic representation goes deeper. Review topics, menu dishes, and other attributes can be represented as entities rather than plain strings.

For an AI system, this is extremely useful. Instead of rereading thousands of reviews every time someone asks whether a restaurant has long waits or good ramen, Google can work from structured themes, entities, and precomputed signals already attached to the place.

Semantic understanding becomes much more important when the interface starts answering complex questions.

Part of Google’s geographic intelligence lives on the phone

Not everything is calculated on Google’s servers. We found on-device structures associated with visits, place candidates, frequent places, trips, home and work, mobility patterns, and user location profiles.

One particularly interesting object is ChainAffinity, which suggests the system can model affinity toward a recurring retail chain.

There’s also the separate offline scorer mentioned earlier.

The exact evidence level differs between components. Some structures are explicitly named in the recovered schema, while parts of the persona layer can only be reconstructed from compiled structures.

But the broader architecture is clear: The phone itself participates in building geographic context.

That means personalization in Maps can combine server-side knowledge of the world with a local model of the user’s own geography.

Ranking still doesn’t guarantee map visibility

Search results are only one of the outputs of Maps. 

The visual map has another problem to solve: Thousands of potentially relevant entities cannot all receive labels simultaneously. That job belongs partly to Mapcore.

We recovered 50,998 Mapcore styles and 12,936 label styles. Label visibility can change with zoom and other rendering conditions.

A business can be eligible or highly ranked and still fail to appear as a visible name on the map. Search ranking and map visibility are separate optimization problems.

This distinction becomes especially important when people measure “Maps visibility” using screenshots or map grids. The visual surface includes a rendering decision after retrieval and ranking have already happened.

Then Google puts Gemini on top

The timing of the recovery is useful. Google is rapidly expanding Ask Maps and other AI-powered experiences, but much of the infrastructure required to answer complex questions was already present.

The system already has:

  • Canonical place entities.
  • Semantic concepts and attributes.
  • Reviews and extracted topics.
  • Knowledge Graph relationships.
  • Web evidence.
  • Geographic retrieval.
  • Behavioral signals.
  • Personal geographic context.
  • Listing composition.
  • Ranking systems.

Gemini adds a conversational interface over these layers. That changes what a local query can be.

“Best ramen near me” is relatively easy. “Where can six people eat near my hotel tonight, with one vegetarian, little waiting time, and good recent feedback about service?” requires a different kind of place representation.

Google needs to know what the restaurant is, what it serves, when it’s open, what people say about it, where it is, how it relates to the user’s route or context, and whether the available evidence is reliable enough to recommend it.

Maps has been building many of those ingredients for years. The AI layer gives Google a new way to use them.

Dig deeper: Google Ask Maps: How to optimize for visibility

What to optimize beyond the Business Profile

The most actionable conclusion from this research isn’t a new list of ranking factors.

Local SEO has traditionally concentrated heavily on optimizing the Google Business Profile: categories, reviews, photos, attributes, opening hours, and other listing fields.

Those remain important.

But Google’s architecture suggests a broader objective: improve the representation Google can build of the entity itself.

For a business or retail brand, I would increasingly ask:

  • What exactly is this place?
  • What does it offer?
  • Which brand or chain does it belong to?
  • Which concepts and attributes describe it?
  • Does its website describe the same entity clearly?
  • Which Web documents provide evidence about it?
  • Does Google see real demand for the brand?
  • What do reviews consistently say about specific aspects of the experience?
  • Which audiences and contexts could make this place relevant?
  • When should Google recommend it rather than another candidate?

The quality of the answer Google can produce depends on the completeness of that representation.

Semantic completeness may become the next local SEO battleground

Proximity, relevance, and prominence remain useful concepts.

AI adds another requirement. The system needs enough structured evidence to reason about a place.

A restaurant can have an optimized profile and hundreds of reviews, yet still be poorly represented for a specific question if Google can’t confidently connect the relevant attributes, concepts, web pages, and review themes to the entity.

A large retail brand has an additional problem.

Google models both chains and individual locations. We observed stores from the same brand in the same metropolitan area carrying different primary concepts, even though the chain model contains a canonical concept structure.

Consistency can’t be assumed simply because every location belongs to the same brand.

For multi-location SEO, the task extends across the brand entity, each local entity, the website, structured data, third-party sources, user-generated content, and the relationships between all of them.

That’s a much larger surface than a Business Profile.

Dig deeper: Multi-location SEO: How to structure geographic pages at scale

Get found by more local customers.

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The listing is only the surface

The 72 Oyster Rank signals are fascinating because they expose categories of information Google can use when estimating the importance of a place.

The deeper finding is the system around them:

  • Geostore builds a canonical geographic entity from competing sources.
  • The Knowledge Graph gives that entity semantic meaning.
  • Webref connects Web documents to it.
  • Search and Places interpret the query and retrieve candidates within a dynamic geographic space.
  • On-device systems contribute personal geographic context.
  • Mapcore controls what reaches the visual map.
  • Ask Maps and Gemini can finally reason over the resulting representation in natural language.

The Google Business Profile still matters. But Google’s architecture suggests looking beyond the listing itself and considering the representation Google has built of the business.

The key question is whether that representation is complete enough for Google to confidently recommend the business.

The full study includes the reconstructed architecture, experiments, and technical evidence. The companion archive exposes the 10,936 recovered Geostore declarations alongside the 2024 documentation so the underlying schemas, enums, fields, and signals can be inspected directly.

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Real Estate Marketing: A Complete Guide for 2026

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If you’re running marketing for a real estate brand, you already know the deal. Zillow, Realtor.com, and Redfin sit at the top of nearly every high-volume query, and AI Overviews are quietly compressing what’s left of the informational clicks below. That’s the real estate marketing challenge in 2026. Everyone else has already adopted the basics, so pulling ahead now takes a creative, targeted strategy.

This guide walks through a five-step framework for building a real estate marketing plan. From there, you’ll get a breakdown of how AI search and portal competition are reshaping the search engine results pages (SERPs), plus a client case study showing what real growth looks like when the strategy lands.

You’ll finish this guide with a clear picture of what to do next for your real estate marketing strategy in 2026.

Key Takeaways

  • Zillow, Realtor.com, and Redfin dominate head-term SERPs, so marketing wins come from targeting long-tail and hyperlocal queries the portals ignore. 
  • Google’s AI Overviews are causing people to click less. Structure content for AI citation and reinforce E-E-A-T signals to stay visible. 
  • A five-step marketing plan (ICPs, brand identity, website, omnichannel, offline) is the foundation. Skip a step and everything downstream weakens. 
  • Owned channels like email and direct mail are how “the little guy” can fight against the real estate giants by routing buyers around third-party portals. 
  • NP Digital used the tactics in this guide to stack significant wins for our client, Homeway Real Estate.

What Is Real Estate Marketing?

Real estate marketing is the practice of promoting properties available for sale or rent. It takes the Four Ps of marketing (Product, Price, Place, and Promotion) and adapts them to the real estate market. 

The marketing plan is important because of the hyper-competitive nature of the real estate industry. A proper strategy could give you a powerful competitive edge. 

Here are the three main benefits of marketing for real estate professionals: 

  • Builds trust with the customer base: A consistent presence assures potential buyers that they’re dealing with the right person to help them find their dream property. 
  • Makes the brand more memorable: A recognizable brand increases the chances that buyers will seek the brand out when they enter the market. 
  • Enables more efficient selling: A long-term marketing strategy provides established channels to quickly promote new properties when they become available, cutting down on promotion and admin costs. 

National Association of Realtors (NAR) data tells us that every buyer uses the internet at some point during their home search, and 46 percent take their first step online (versus 20 percent who start by contacting an agent). The marketing strategy used could be the difference between a brokerage that compounds visibility for years and one that keeps starting from zero on every new listing.

How to Build a Real Estate Marketing Plan

You can develop a solid real estate marketing management plan in five simple steps. The order matters because each step sets up the next, so don’t skip anything. It usually means going back to fix something later.

We’ll start with the most important step of any marketing plan: defining who you’re trying to reach. From there, we’ll discuss your brand’s presence and finish with the optimal digital strategy, as well as offline channels you can use to supplement it. 

Here’s how the framework breaks down.

1. Define Your Ideal Customer Profiles (ICPs)

What type of people would make ideal sellers or renters?

Even if you understand your ideal customer, it’s important to map them out clearly on paper. These definitions will form the basis of the real estate marketing plan you implement, so ICPs could always use more fine-tuning. The more detailed, the better.

Most agents typically target several distinct ICPs that cover a whole portfolio of properties. 

Here are three example ICPs: 

  • Three to four-person families with a joint annual income of $150,000 looking to purchase suburban detached or semi-detached houses.
  • Young professionals with high disposable income searching for inner-city apartments to rent in the $2000 to $3000/month price range with an active local nightlife nearby.
  • Retirees with a budget of $300,000 to $500,000 in the market for semi-rural properties with good car access to local amenities like hospitals, supermarkets, and shops.

Research the following attributes when putting together your customer profiles:

  • Budget for rent or purchase
  • Desired location and property type
  • Specific property features (garage, bathrooms, garden)
  • Timeline (weeks or months)
  • Required nearby amenities (schools, transport, roads, etc.)

Treat these definitions as segmentation blocks you can plan against. Each ICP needs its own creative and channel mix. It also makes sense to give each segment its own definition of a win.

Take the three examples above. The suburban family segment probably converts best on Facebook and YouTube with school-district and neighborhood-tour content, and you might measure campaign success on booked showings per dollar. The young-professional rental segment leans on Instagram Reels and TikTok walkthroughs of the units and the neighborhood, with success measured by rental applications submitted. Retirees might respond better to a Google search or a referral-driven email, and success there could be measured by the number of in-person consultations booked.

2. Build a Consistent Brand Identity

Branding is a long-term play. The visual below, based on NP Digital research, shows that it takes five years or more to build a recognizable brand. Consistency and a long-term outlook are key. 

A visual showing it took five years for 7.2 percent of 502 companies surveyed to build a recognizable brand. It took 10 years for that number to grow to 18.6 percent. 

However, once you’ve passed that five-year mark, you’ll have an incredibly valuable asset, one that will help sell properties more quickly, and often at a higher price point, than other listings. 

Here are the three main components of effective brand building:

  • Regular content output: Repeatedly appearing in front of your audience means they’re more likely to think of, or recommend, you when they’re ready to buy. 
  • Tone of voice:  Is your messaging polished and professional to resonate with luxury buyers, or is it a more conversational, casual tone to appeal to families and first-time buyers?
  • Recognizable brand assets: Assets include your logo, website color scheme, email template, etc.

3. Build a High-Converting Real Estate Website

When potential buyers or renters visit your sites, they begin converting from marketing-qualified leads (MQLs) to sales-qualified leads (SQLs).

An SQL is a lead that has shown a high degree of interest in a specific property or listing and is ready for a salesperson to walk them through the buying process.

With this in mind, it’s critical that your real estate website marketing converts, like the Keller Williams Luxury example below. 

Keller Williams Luxury is an example of a high-converting real estate website, optimizing the right elements.

Source: https://luxury.kw.com/search/sale/Austin-TX-USA/929288?viewport=30.74016159213386%2C-96.85265755%2C29.850865913177792%2C-98.61047005&q=Austin%2C+TX%2C+USA

Here’s a quick checklist for high-converting real estate website design:

  • Sleek, minimal design
  • Easy-to-use search filters
  • High-quality images 
  • Video walkthroughs of properties
  • Comprehensive property descriptions
  • Transparent pricing 
  • A map for visualizing locations
  • Clear “Book a Viewing” calls to action (CTAs)
  • Visible address and phone number
  • Customer testimonials if appropriate

While all these site elements are essential to optimizing your real estate site for search, there’s a technical side to consider as well:

  • Core Web Vitals: Google’s Core Web Vitals measure things like page loading, responsiveness, and visual stability. Elements like Hero images and property carousels can cause issues, so it’s best to constantly monitor website pages with tools like PageSpeed Insights.
  • Mobile-first indexing: Google crawls and ranks sites off the mobile version, so keep a user’s smartphone browsing experience in mind as you tweak pages and other content.
  • RealEstateListing schema markup: Structured data helps crawlers better interpret property listings for rich results and AI Overview citations, potentially increasing click-through rates (CTRs). 
  • Portal strategy: Zillow and Realtor.com will always outrank you on head terms like “homes for sale in [city],” so use them for reach and your own site for email capture and retargeting.

4. Build an Omnichannel Digital Strategy

You’ve defined an ICP, refined your tone and brand identity, and designed a website primed for conversions. 

Now it’s time to start promoting listings. 

My team and I found that the average customer requires 11.1 marketing touchpoints before making a purchase. This means that an omnichannel strategy incorporating multiple marketing techniques is critical to success. 

A bar graph showing that customers require an average of 11.1 touchpoints before purchasing in 2025. That number is up from 8.5 in 2021. 

Source

Social media should be a sizable part of your omnichannel real estate marketing strategy. Per NAR’s Technology Survey, 39 percent of agents cite social media as their top lead-generating technology.

Short-form video platforms like TikTok are a growing channel in 2026. However, still only 16 percent of agents use them for marketing, presenting an untapped opportunity for marketers and agents looking to build visibility for their real estate sites.

Here are the avenues you should consider for your marketing mix:

  • YouTube: Publish property walkthroughs and more general video content, such as “The Top 10 Places to Live in Chicago,” that might appeal to your target market.
  • Pay-per-click (PPC): Run PPC ads for property listings targeting hyperlocal keywords, such as “best real estate agent in [TOWN]” or “luxury homes specialist [CITY]” that have lower SEO difficulty than the ones the big portals are targeting (see image below).
  • SEO: Target long-tail neighborhood queries and optimize for ranking in local search rather than fighting for head terms. Also, claim and optimize your Google Business Profile (GBP) to show up in local business and map-based searches.
  • Organic social: Post short-form video walkthroughs and neighborhood content weekly, not just when a new listing drops. NAR data shows 58 percent of buyers found social video posts useful during their home search. 
  • Paid social: Boost your top-performing organic posts to lookalike audiences of your ICPs instead of starting cold.
  • Email: Send weekly or biweekly listing roundups segmented by the ICPs you defined in Step one. Segmented campaigns generate 30 percent more opens and 50 percent more click-throughs than broadcasts.
The Ubersuggest keyword research report for “best real estate agent in Austin, TX” shows an SEO difficulty of four, demonstrating how hyperlocal search terms can provide more opportunities for real estate agents against industry giants like Zillow and Realtor.com.

5. Identify Viable Offline Channels

Online marketing may currently be the most prominent real estate marketing strategy, but there are still significant (often underleveraged) offline opportunities. 

For example, research shows that direct mail has a 161 percent return on investment (ROI) when sent to house lists, outperforming both email and search. 

Consider all of the following marketing channels as part of a multi-pronged plan:  

  • Print media
  • In-person events
  • Direct mail
  • TV and radio
  • Cold calling

Offline channels round out the framework. Now, let’s look at the two 2026-specific challenges reshaping how to approach the SERPs: AI Overviews and portal competition.

AI Search and Platform Competition

Two structural shifts define real estate search in 2026. First, Google’s AI Overviews now appear on roughly 18 percent of all searches, according to a Pew Research study. The same data shows that users click a traditional result 8 percent of the time when an AI Overview is present, versus 15 percent when it isn’t. For informational real estate content (“what does escrow mean,” “how does closing work”), this compresses organic clicks even for pages that rank, but targeting queries with question words like these gives you the best shot at AI visibility, as the graph below illustrates.

A bar graph citing Pew Research Center data that shows the distribution of queries surfacing AI summaries.

Source: https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/

To stay visible, structure content so that AI systems can cite it, with a clear header structure pointing to direct one-sentence answers. FAQ sections that mirror how buyers phrase questions improve alignment with search intent and user language.

Reinforce experience, expertise, authoritativeness, and trustworthiness (E-E-A-T) signals by making agent credentials and client results visible on-site, backed by content that demonstrates genuine local expertise. Focus your keyword strategy on finding the keywords buyers search locally, where AI Overviews appear less frequently.

A more thoughtful keyword research strategy also addresses the second challenge: competing with the likes of Zillow and Realtor.com.

Portals dominate popular terms, such as “homes for sale in Denver.” The AI-era strategy is less about outranking them than becoming a source that AI systems cite.

Earning local backlinks can strengthen the signals search and AI-powered discovery systems use to assess a business’s relevance and authority. Just be sure to pursue linking opportunities from reputable sources, like regional publishers and community sites, similar to traditional SEO best practices.

Positioning agents as the authority through backlink building and a targeted local keyword approach should help grow their email lists, which they can use to retarget buyers directly without a portal or an AI Overview in between.

Real Estate Marketing in Action: An NP Digital Case Study

What do these real estate marketing tips look like in practice? Here’s how my team at NP Digital implemented some of the strategies you’re reading about now for Homeway Real Estate, a regional brokerage that competed against national portals in its home market and achieved impactful wins.

Situation: Homeway Real Estate, a growing Pennsylvania brokerage serving the Lehigh Valley, Stroudsburg, and Lancaster markets, needed a website that would drive sales and build brand awareness across its expanding footprint.

Strategy: NP Digital focused on SEO fundamentals: strengthening the technical foundation, targeting competitive keywords for high-priority pages, and refreshing existing content while producing new pieces aimed at Homeway’s target buyers.

Outcomes (12 months):

  • 200 percent increase in organic impressions
  • 350 percent increase in organic traffic
  • 17,794 ranking keywords earned

An even more meaningful result was helping Homeway succeed in finding homes for 500 families. Homeway CEO Alex Lopez was happy with the results and appreciated the NP Digital team for crafting and executing a personalized, winning strategy:

Homeway Real Estate CEO, Alex Lopez, thanks NP Digital for creating a plan and guiding his team toward reaching the goals for his business.

Real Estate Marketing Tools

Having the right tools makes real estate marketing management easier. Here’s a simple starter tech stack kit that hits all the essentials you’ll need:

Tool Primary Use
Hootsuite Schedule posts across multiple social platforms from one dashboard
Canva Design listings, ads, and social graphics without a designer
Follow Up Boss (fub by Zillow) Track leads and automate follow-ups from a real-estate-specific customer relationship management (CRM) tool
Ubersuggest Keyword research, content ideas, and rank tracking
Mailchimp Segmented email campaigns and lead capture forms
Google Ads and Meta Ads Paid search and paid social ad platforms
ChatGPT Draft listing descriptions, content briefs, and outreach copy

FAQs

What is a real estate marketing plan?

A real estate marketing plan is a documented strategy for how a brokerage will attract buyers and sellers. Its job is to align every marketing decision, from brand voice to channel spend, around a clearly defined customer so nothing gets built or bought without a reason. The value of documenting it is that scattered channel activity begins to compound into brand equity over time.

How do you create a real estate marketing plan?

A real estate marketing plan starts with the audience. Once the target customer profiles are documented specifically enough for a marketer to plan against, everything downstream becomes an extension of that definition rather than a separate strategic exercise. Skip the audience work, and every other decision is a guess.

How do you do real estate marketing?

Real estate marketing is the ongoing work of putting a brokerage in front of buyers and sellers at the moment they’re making decisions. The winning approach is a consistent presence across the channels that actually reach the target audience, not picking a single perfect tactic.

How can you improve real estate marketing?

The biggest gains in real estate marketing come from narrowing focus rather than adding more activity. Marketers and agents who win in 2026 double down on hyperlocal content and neighborhood expertise. Structure that content so AI systems can cite it in their answers, and invest in owned channels, such as email and direct mail, that route buyers around third-party portals.

What should you look for in a real estate marketing agency?

The best real estate marketing agencies show a portfolio of real-world results rather than repackaged ecommerce case studies. Beyond the results, look for transparent reporting and a strategy grounded in your local market. Agencies that treat brokerages as long-term brand assets outperform those focused only on running ads.

Conclusion

Sound real estate marketing fundamentals are the best way to succeed, despite how competitive the industry is in 2026. 

Assess your current channel mix against the five-step framework above. Identify the gaps where you’re currently not showing up and begin reaching those audiences with hyper-local or long-tail keyword content.

Don’t try to stand toe-to-toe against the giants like Zillow and Realtor.com for popular search terms. They may seem “sexy,” but marketing specificity is the way to win. Plus, targeting rarer, long-tail queries with question words is a better strategy for AI visibility.

This guide gives you the foundational tips and tools you need to get started. If you’re looking for more specific guidance, reach out to my team at NP Digital and let’s see how we can put these marketing principles to work for you.

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Meta Tags for SEO: What They Are, Why They Matter, and How to Use Them

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Key Takeaways

  • Meta tags are HTML snippets in the <head> section of your page that tell search engines and AI tools what your content is about. 
  • Human readers don’t see meta tags directly, but the tags shape how your pages show up in search and social previews. 
  • Not all meta tags for SEO move the needle. The title tag, canonical, and robots meta tag directly affect SEO. Meta descriptions, viewport, and Open Graph tags contribute indirectly through click-through rate (CTR), mobile usability, and social sharing. 
  • Google rewrites roughly 76 percent of title tags it analyzes, so a well-optimized title still matters even when Google might overwrite it. 
  • The same meta tag hygiene that helps you rank in Google can also improve visibility in AI-powered features like Google AI Overviews, since AI parsers rely on structured signals to interpret and cite your pages. 
  • Meta tags decay over time. Run a full audit at least quarterly, or after any major site migration or content management system (CMS) update.

What does the term “meta tags” cover, and which ones matter for SEO?

Some of the advice out there is outdated, and plenty of tags that sound important don’t really affect your rankings.

This guide covers the meta tags for SEO that influence how search engines and AI tools read your pages. I’ll walk you through what each tag does and how to implement them correctly. I’ll also show you which tags you can safely ignore and how to audit your meta tag setup to strengthen your visibility across traditional and AI search.

What Are Meta Tags?

Meta tags are a type of HTML tag that provides search engines with information about a website page.

Two tags do most of the work: your title tag and meta description.

Your title and meta description tags create the cover for your web page. They’re the first impression most visitors get about your site. That’s where the importance of meta tags in SEO lies. They’re the elements that control how your pages appear in search.

An annotated screenshot of the Google result for Neil Patel’s “What is Digital Marketing? Your Ultimate Guide” blog, underlining the title tag and meta description.

Meta tags shape so much of your site’s visibility, yet they operate entirely behind the scenes. They live in the <head> HTML section of your site’s source code. They’re invisible to visitors but instrumental in explaining the content and structure of your pages to search engines, browsers, and social media platforms. 

Humans and machines view the internet in different ways. What machines see when they visit your site looks something like this:

A screenshot of the source code for neilpatel.com showing the <head> section and all the meta tags contained within it for this particular page. 

A search engine like Google takes all that machine-readable information and organizes it for people. To do that, it relies on tags that provide context. So meta tags aren’t just about improving rankings. They also help your site deliver a smoother user experience.

The right meta tags can strengthen your email marketing and marketing automation efforts, too, by helping you track performance on emailed links.

On the social front, bloggers, YouTubers, and social media influencers will have an easier time sharing your content if you use the right meta tags. Clean tags make sure your content displays cleanly and let you track which social media platforms your content clicks are coming from.

Think of meta tags for SEO as the packaging for your product. If your packaging is off, even a great product struggles to sell.

Which Meta Tags Are Important for SEO?

Not every meta tag is worth your time. Some directly affect how Google ranks your pages, some help indirectly by shaping user behavior, and some do nothing for SEO in 2026. 

Here’s how the HTML meta tags for SEO break down by impact.

High SEO impact:

  • Title tag: A confirmed ranking factor and the primary clickable element in the search engine results page (SERP), which can drive click-through rate (CTR).
  • Canonical tag: Tells Google which version of a page to rank when duplicate or near-duplicate URLs exist.
  • Robots meta tag: Controls what searchers see in the SERPs by telling Google whether to index a page or follow its links.

Moderate SEO impact:

  • Meta description: Not a ranking factor but drives CTR from the SERP.
  • Viewport tag: Advises your browser on how to render pages on your mobile device. This signifies mobile-friendliness to Google, which is good for its mobile-first indexing model.
  • Open Graph tags: Shape how your pages appear on social platforms and how AI tools describe your content.

Meta Tags That Don’t Affect SEO

Here’s a reference table of the HTML meta tag attributes and the values each one accepts.

A list of the different HTML5 meta tags.

Four tags you’re likely to encounter that have less effect on rankings:

  • Author tags: An author meta tag identifies who wrote a piece of content. Google once used author information to filter results, but it’s no longer a ranking factor. However, you might use author tags if you run a multi-author blog.
  • Keyword tags: The keywords meta tag was originally used to list terms a page wanted to rank for. Google confirmed in 2009 that it ignores this tag due to widespread abuse, and that position has not changed.
  • Charset tag: The charset meta tag declares the page’s character encoding (usually UTF-8), so browsers render text correctly. It’s a rendering instruction, not a search signal, and a modern content management system (CMS) platform will likely add it automatically.
  • Http-equiv tags: Http-equiv tags emulate HTTP response headers directly within the HTML, controlling things like page refresh and content type. These are server-level concerns, and modern implementations handle them at the server rather than in the markup.

The Most Important Meta Tags for SEO

Here’s a closer look at each of the seven meta tags that shape how search engines, social platforms, and AI tools understand your pages.

1. Title Tag

It’s not technically a meta tag, but it appears in the header and is treated as one.

Aim for title tags for SEO that are roughly 50 to 60 characters. Google frequently rewrites page titles in search results. One study finds that it does so for about 76 percent of the pages analyzed. While you can’t prevent rewrites entirely, writing clear, descriptive titles gives Google a stronger starting point.

Also note that just because 60 is the generally recommended limit doesn’t mean you should use all those characters. Google really assesses your title tag by pixels. The limit is 580 to 600 pixels. The character limit is just a good benchmark within that range.

Your title should also match the intent behind the search. That’s where long-tail keywords make a difference, especially with AI answer engine optimization (AEO).

Here are the top meta titles in the SERPs for “How to make content marketing effective.”

The top organic results in Google SERPs for “how to make content marketing effective.”

By adding the year, we get an entirely new set of results for “how to make content marketing effective 2026.” 

Someone searching without a date may want evergreen advice, while someone including the year is probably looking for the latest strategies. 

The top organic results in Google SERPs for “how to make content marketing effective 2026.”

You’ll also see the AI Overview results change for each query if you conduct this experiment yourself. 

AI Overview results for the search term “how to make content marketing effective.”

AI Overview results for the search term “how to make content marketing effective 2026.”

Including modifiers like a year, location, or audience in your title can help reinforce the search intent you’re targeting. Whatever title you choose, place your primary keyword near the beginning whenever it reads naturally. That makes it easier for both users and search engines to understand what your page is about.

Here’s what a title tag looks like in HTML:

<head>

<title>The Best Title Example I Could Come Up With</title>

</head>

In WordPress, your page title often becomes the title tag by default. 

WordPress’s new content pane enables you to give every page its own title, the easiest way to give your page a title tag.

If you want more control, install an SEO plugin like Yoast SEO. It lets you customize your title tag, meta description, and URL slug without touching your site’s code. 

That’s all there is to it!

2. Meta Description

A meta description tag provides an overview of the page’s content.

They’re limited to around 160 characters and aren’t directly tied to Google’s search algorithms at all.

Here’s what the HTML looks like:

<head>

<meta name= ”description” content=”This is an example of the text that will show up in search results. Read on to learn more about description tags.”>

</head>

Although they aren’t directly tied to your rankings, it’s worth getting them right. Writing a well-crafted description can boost your CTR and social engagement. 

Google frequently rewrites meta descriptions when it believes a snippet of on-page content better meets a user’s search intent, a finding observed in multiple industry studies and reflected in Google’s own documentation.

You can’t prevent rewrites entirely, but writing a clear, accurate meta description gives Google a strong candidate to use. Follow the best practices I’ve laid out in my meta description guide to improve your chances. 

3. Canonical Tag

The rel=”canonical” tag tells search engines which version of a page you consider the preferred URL when duplicate or near-duplicate content exists across multiple addresses. Google Search Console can show you which of your pages currently have this tag:

A screenshot of Google Search Console’s Page Indexing report showing "Duplicate without user-selected canonical" or "Alternate page with proper canonical tag.

Source

Duplicate content occurs more often than site owners might realize. It shows up in instances such as:

  • Pagination
  • URL parameters (like tracking codes or filters)
  • HTTP vs. HTTPS versions
  • www vs. non-www variations
  • Syndicated content on other sites

Without a canonical tag, Google decides which URL to index and consolidate ranking signals around, and it doesn’t always pick the one you want.

Broken canonical tags won’t do you any favors, either. Avoid these common mistakes:

  • Canonicalizing to a page that returns a non-200 status code (like a 404 or redirect) sends mixed signals and can prevent Google from recognizing your preferred page.
  • Placing multiple conflicting canonical tags can confuse Google.

Canonical tags are a fundamental of technical SEO and are worth auditing on any site with more than a few dozen pages. Using self-referencing canonicals (a page pointing to itself) is fine and recommended per Google’s guidance.

Here’s how the HTML should look:

<head>

<link rel=”canonical” href=”https://example.com/your-page/” />

</head>

4. Robots Meta Tag

I need to get something out of the way up front: The robots meta tag is completely different from a robots.txt file. I don’t want you getting the two confused.

The robots meta tag controls indexing, while a robots.txt file controls crawling. A page can be crawled by Google and still noindexed, and a page blocked in robots.txt can still show up in Google’s index if Google finds links pointing to it from elsewhere.

By default, all the pages and links you create on your website are indexed as ‘follow’ by search bots and web crawlers.

Whenever you want to change that, you’ll need a robots meta tag.

It’s useful for syndicated and duplicate content that your customers or readers could use, but you don’t want credit in search indexes. You can also use it for links you don’t necessarily want to endorse.

Here’s the noindex HTML code:

<html><head>

<meta name=”robots” content=”noindex” />

(…)

</head>

Here’s the HTML for a robot nofollow:

<meta name=”robots” content=”nofollow”>

You can also combine directives in a single tag:

<meta name=”robots” content=”noindex, nofollow”>

Other common directives include noarchive (don’t show a cached copy), nosnippet (don’t show a text snippet in the SERP), and noimageindex (don’t index images on the page). 

Common use cases for noindex are:

  • Staging and development environments
  • Thank you pages
  • Admin pages
  • Thin pages
  • Downloadable content

Common use cases for nofollow are:

  • User-generated content like blog comments
  • Paid or sponsored links
  • Any outbound link you don’t want to endorse

Recently, Google changed how we use the nofollow tag. Each nofollow use case mentioned above needs to be paired with its appropriate related attribute. 

Blog comments and other user-generated content should use rel=”ugc.” Paid or sponsored links use the rel=”sponsored” attribute. Everything else stays as rel=”nofollow.”

5. Viewport Meta Tag

You probably don’t spend much time thinking about viewports, but they’re especially important in today’s mobile-first world of search. They should be on every one of your pages, and most CMS platforms add them automatically. You just need to verify by inspecting the page source. 

When you browse on a mobile device, webpages are shown in a pop-up window called a viewport that extends past the device’s border.

Here’s an illustration of what I’m talking about.

A comparison illustration showing how a website would look on a mobile device in default mode on the left versus displaying in a viewport on the right. 

Developers can set the viewport size to increase mobile usability.

WordPress users can check this tag to learn this information for their templates.

You probably didn’t know that unless you’re already a web developer.

Since Google increasingly focuses on mobile-friendly websites, this meta tag could be the difference between success and failure on mobile.

Here’s a look at the HTML: 

<head>

<meta name=”viewport” content=”width=device-width, initial-scale=1″>

</head>

6. Open Graph Tags

Open Graph (OG) tags define how a page appears when shared on social platforms. The protocol was developed by Facebook in 2010 and is now used across most major social and messaging platforms. 

The four essential tags are og:title, og:description, og:image, and og:url. There are optional tags that go even deeper, but these are four must-haves.

For og:image, Facebook recommends 1200×630 pixels, a default size that translates well to other major platforms, such as LinkedIn and Discord.

OG tags also matter because they influence how appealing the preview is, and that can have an impact on engagement and click-through from social shares.

X (formerly Twitter) uses its own Card tags (twitter:card, twitter:title, twitter:description, twitter:image), but most implementations fall back to OG tags when Twitter-specific tags aren’t present. However, it’s helpful to use X’s card tags because they provide platform-specific data that OG tags don’t. 

Most SEO plugins, including Yoast and Rank Math, auto-generate OG tags from your title and meta description. Review and override where needed.

These code snippets show what Open Graph (OG) tags might look like in your page’s source code.

Source: https://ogcheck.com/blog/twitter-cards-vs-open-graph

7. Structured Data Markup (Schema)

Structured data uses JSON-LD to provide explicit information about page content, like the ingredients and cooking time of a recipe. Added as a script block in the <head>, it’s separate from your visible HTML.

A screenshot of Google’s Rich Results Test tool with a JSON-LD example being validated. Shows readers exactly how implementation looks in practice.

Source

Structured data also helps search engines better understand your content, as clear structured data can make the information on your pages easier for machines to interpret.

The most commonly used schema markup types are:

  • Article: Provides search engines with critical data like an article’s headline, author, publication date, modification date, and content sections. BlogPosting is a subtype of Article structured data specifically for blogs. 
  • Product: Enables rich results in Google that display important information like pricing and reviews. Product schema also helps machines understand details like pricing and availability, which may improve how products are represented across search experiences. 
  • HowTo steps: Helps crawlers understand step-by-step instructional content where the order of the steps matters. HowTo schema helps search engines understand instructional content with ordered steps.
  • Organization: Highlights company-specific information like name and logo, and is typically placed on an organization’s homepage or about us page. 
  • BreadcrumbList: Explains your site structure to AI and search engine crawlers. Google can use this to display breadcrumb trails in the SERPs rather than solely URLs. This schema also helps show search engines the topical relevance of a particular page, based on its position within your site. 

Note that Google fully deprecated FAQ rich results in May 2026, so FAQPage schema no longer generates a visible SERP feature, though the markup can still help systems interpret Q&A content.

Implementation is easiest through plugins like Yoast, Rank Math, or a dedicated schema plugin. 

How to Audit Your Meta Tags

Auditing meta tags is essential to any on-page SEO cheat sheet. The tags you set up years ago often no longer match the pages they describe now, and a single CMS update or plugin swap can overwrite what you configured without you even realizing it. 

A quarterly audit keeps things aligned.

Start with a site crawler. Tools like Screaming Frog, Ahrefs Site Audit, and Semrush identify meta tag issues at scale. A crawl will also identify pages missing canonical tags or carrying robots directives that conflict with what you actually want indexed. 

From there, move to Google Search Console for the problems that only come up in production. 

The Page indexing report flags pages Google has excluded from the index due to noindex directives. 

Over in the Performance report, pages with high impressions but unusually low click-through rates deserve a closer look. Review the title tag and meta description to see whether they match search intent, and check how Google is displaying the page in search results. 

Structured data problems surface separately in the Rich Results reports.

Prioritize fixes by traffic, since errors on high-impression pages carry more weight than errors on pages nobody visits. Also, run a fresh crawl after any major site migration or CMS update.

How to Add Meta Tags

Here’s how to use meta tags for SEO on the three most common platforms.

WordPress With an SEO Plugin

The two most popular options are Yoast and Rank Math. Both are free at the entry level and expose the same set of controls, so the choice usually comes down to preference.

Both plugins add a meta box below the post editor with the tags you’ll edit most often. The SEO tab covers your title tag and meta description with a live snippet preview. 

Deeper controls live under Advanced, where you can override the robots meta tag and canonical URL, and under Social, where you can set custom Open Graph and Twitter Card values.

For site-wide defaults, Yoast uses the Search Appearance menu, and Rank Math uses Titles and Meta. Both let you set templates that apply across every post type on your site.

Current Yoast meta box on a post edit screen

Other CMS Platforms

Most modern CMS platforms include a built-in interface for the basic meta tags. 

Shopify’s Edit website SEO section handles title tags and meta descriptions on every page. 

Squarespace uses the SEO/AI Visibility panel. 

Wix has the most complete built-in interface of the three, with an SEO Panel that handles every major meta tag on every page. 

Advanced tags like canonicals and robots vary in support and may require a theme edit or third-party app on Shopify and Squarespace.

Custom HTML

If you have direct HTML access, add meta tags inside the <head> element of each page, or set them once through a <head> template if your framework supports it. Verify what actually renders by viewing the page source or running the URL through Screaming Frog or Ahrefs Site Audit.

Meta Tags and AI Search

Your meta tags shape how features like Google AI Overviews interpret your page. A page with a clear title tag and well-implemented structured data is easier for AI parsers to cite than one that relies on unstructured body text alone.

Open Graph tags matter here, too. AI crawlers like GPTBot read og:title and og:description when generating link previews and citations, so a strong OG image and description also shape how AI systems describe your page.

The important thing to grasp here is that good SEO hasn’t changed. AI SEO still relies on many of the same on-page and technical SEO elements we’ve all been talking about for years. 

FAQs

How many types of meta tags are there?

There isn’t an official count, since HTML’s <meta> element accepts many different attribute combinations. For SEO purposes, only a handful materially affect performance, and they’re covered in the sections above.

Do meta tags help SEO?

Yes, though the impact varies by tag. The title tag directly affects rankings. Canonical and robots tags shape how Google indexes your pages. Meta descriptions don’t influence rankings but do drive click-through rate from the SERP.

Which meta tags are important for SEO?

The title tag matters most, since it’s a confirmed ranking factor. The viewport tag would come next, since Google uses mobile-first indexing. Right behind it are the canonical tag (controls duplicate content handling) and the robots meta tag (controls indexing). Beyond those, meta descriptions and Open Graph tags contribute indirectly through CTR and social sharing.

How do you create meta tags for SEO?

It depends on your platform. WordPress users typically add them through an SEO plugin like Yoast or Rank Math. Most other major CMS platforms have built-in interfaces for the basics. For custom HTML, add meta tags directly to the <head> section of each page.

Conclusion

The meta tags for SEO covered in this guide form the foundational layer of on-page communication between your site and the systems that decide who sees it. 

Get them right, and you give search engines and AI tools a clearer read on your pages. Get them wrong, and even strong content can struggle to earn visibility.

Meta tags are a must for any site to have a reliable presence in traditional search and AI-powered features. Pair clean fundamentals with an SEO audit workflow that catches drift early, and you’ll compound value across every channel where discovery happens.

If you’re looking for a professional opinion on your on-page setup, the team here at NP Digital can help you with an on-page audit. We’ll make sure your meta tags are doing the most to boost your visibility and help you implement strategies that help you climb up the SERPs. 

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The August 2026 SEO Update by Yoast recap

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Each month, we host the SEO Update by Yoast, covering the latest in search and AI. In this edition, Carolyn Shelby and Alex Moss discussed Google’s new tools for publishers, the impact of AI on search visibility, and how to adapt to the evolving digital landscape.

Watch the full recap on YouTube to dive deeper into these topics, hear examples, and get answers to audience questions.

Key stories from August 2026

Google Trends adds comparison over time data

Google Trends now allows users to compare search interest over time, providing deeper insights into keyword trends and seasonality.

Why it matters:

– Helps identify long-term trends and seasonal fluctuations in search behavior.

– Useful for content planning, especially for topics like news, e-commerce, and seasonal events.

Actionable takeaway:

– Use Google Trends to refine your content strategy by focusing on rising or consistently popular topics.

– Compare related keywords to understand user intent shifts over time.

Explore Google Trends.

Cloudflare introduces new controls for AI content access

Cloudflare announced new default settings for AI crawlers, blocking “Training” and “Agent” crawlers by default for new domains starting September 15, 2026. However, Google has stated it will not honor these blocks for its search crawlers.

Why it matters:

– Publishers now have more control over how AI systems access their content.

– Google’s refusal to honor these blocks means AI-driven search visibility may still be impacted.

Actionable takeaway:

– Review Cloudflare’s new AI traffic settings to understand how they affect your site.

– If you rely on AI-driven traffic, ensure your content remains accessible to Google’s crawlers.

Read Cloudflare’s announcement.

Google updates product structured data guidance

Google has updated its product structured data guidelines, emphasizing the importance of rich snippets for e-commerce sites. The update includes new properties like “category” to improve product visibility in search results.

Why it matters:

– Structured data helps search engines better understand and display product information.

– E-commerce sites can benefit from enhanced visibility in rich results and AI-driven search.

Actionable takeaway:

– Audit your product schema to ensure compliance with Google’s updated guidelines.

– Use Yoast SEO to implement structured data effectively.

View Google’s Product Structured Data Guidelines.

Google Search Console expands reporting for social and video platforms

Google Search Console now includes platform properties, allowing creators to track how their content on Instagram, TikTok, X, and YouTube performs in Google Search and Discover.

Why it matters:

– Provides a holistic view of how social and video content contributes to search visibility.

– Helps creators and businesses optimize their content for broader reach.

Actionable takeaway:

– Set up platform properties in Google Search Console to monitor social and video performance.

– Compare data across platforms to identify gaps and opportunities.

Learn more about platform properties.

Google’s Preferred Sources button

Google has expanded its Preferred Sources feature, allowing users to prioritize specific publishers in their search results. Publishers can now embed a button on their sites to encourage readers to add them as a preferred source.

Why it matters:

– Publishers can increase visibility in AI Overviews and Top Stories by becoming a preferred source.

– Users benefit from personalized search results tailored to their trusted sources.

Actionable takeaway:

– Add the Preferred Sources button to your website to encourage readers to prioritize your content.

– Monitor performance in Google Search Console to see how preferred source status impacts visibility.

Guide to Preferred Sources in Google Search.

AI Overviews can now generate images

Google’s AI Overviews now have the capability to generate images directly in search results, enhancing the visual appeal of AI-driven answers.

Why it matters:

– Publishers must ensure their content is descriptive and accurate to avoid misinterpretation by AI image generators.

– Visual content in AI Overviews can drive engagement but may also raise copyright concerns.

Actionable takeaway:

– Provide detailed descriptions in your content to guide AI image generation.

– Monitor AI Overviews for inaccuracies and report issues to Google.

Read About AI image generation in search.

Google claims AI search sends billions of clicks to websites

Google stated that its AI search features are sending billions of clicks to websites weekly, though the quality and intent of these clicks remain debated.

Why it matters:

– AI-driven search is becoming a significant traffic source for publishers.

– Not all clicks may be valuable, as some could be generated by AI systems rather than human users.

Actionable takeaway:

– Monitor AI-driven traffic in Google Search Console and Microsoft Clarity.

– Focus on optimizing for high-intent queries to maximize the value of AI-generated clicks.

Read about Google’s AI search clicks claim.

Google loses key claims in lawsuit against SerpApi

A U.S. federal court dismissed key claims in Google’s lawsuit against SerpApi, a move that could preserve access to third-party SEO tools that rely on SerpApi for data.

Why it matters:

– The outcome ensures that SEO professionals can continue using tools that depend on SerpAi.

– Highlights the ongoing tension between Google and third-party data providers.

Actionable takeaway:

– Stay informed about legal developments that could impact SEO tooling.

Read about the SerpApi lawsuit outcome.

Microsoft Clarity adds branded and non-branded AI query reporting

Microsoft Clarity now offers AI query reporting, allowing website owners to distinguish between branded and non-branded queries that lead to AI citations.

Why it matters:

– Provides insights into how AI systems cite your content.

– Helps identify opportunities to improve visibility for high-value queries.

Actionable takeaway:

– Set up Microsoft Clarity to track AI query performance.

– Use insights to refine content strategy and target high-intent queries.

Explore Microsoft Clarity’s AI query reporting

Anthropic adds watermarks to Claude content

Anthropic announced that Claude models will now include watermarks in AI-generated content to comply with EU regulations and improve transparency.

Why it matters:

– Watermarking helps distinguish AI-generated content from human-created content.

– Businesses must ensure compliance with emerging AI regulations.

Actionable takeaway:

– Familiarize yourself with AI watermarking and its implications for content creation.

– Use AI tools responsibly and transparently to maintain trust with audiences.

Learn about Claude’s watermarking

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The next SEO Update by Yoast is on September 25, 2026, at 4:00 PM CET (10:00 AM EST). Sign up to join live!

The post The August 2026 SEO Update by Yoast recap appeared first on Yoast.

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Google AdSense to change how it counts impressions: Begin-to-Render

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Google notified AdSense publishers that it is switching how it counts impressions for display ads.  Google will switch to a Begin-to-Render methodology that will count an impression only once the ad has loaded and rendered on the user’s device.

What’s changing. Google wrote:

  • “Currently, AdSense counts an impression when an ad starts to download to a user’s device.”
  • “Beginning on February 17, 2027, we will move to the Begin-to-Render methodology. This means an impression will only be counted once the ad has successfully loaded and has started to render on the user’s device.”

Google will only count the impression when the ad fully loads.

The impact. Google said that when this change goes live, “you may see a change in total impressions.” Google said “this is because impressions that started to download but never actually rendered will no longer be counted (e.g., if a user left the page before the ad began to render).”

More details. Google posted a help document with more details that says “Currently, Ad Manager and AdSense already use or are compliant with begin-to-render for native, app, and video inventory impression counting. The shift to count display ads via BTR unifies our impression counting methodologies.”

Why we care. If you run AdSense ads on your site, expect impressions and maybe earnings to be lower after February 17, 2027. Advertisers may notice more engagement on their ads for less money, I assume.

This is a big change for AdSense display ads and we have several months to prepare for it.

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10 technical SEO audit mistakes that lead to bad recommendations

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10 technical SEO audit mistakes that lead to bad recommendations

Most technical SEO audits produce plenty of findings. Then the document sits in a shared drive for six months, and nothing gets deployed.

Sometimes that’s the client’s fault. More often, it’s the audit’s. The findings were never validated, ranked by a tool’s notion of severity, or written in a way no developer could act on.

Here are 10 mistakes that keep showing up — and what to do instead.

1. Crawling without JavaScript execution enabled

Screaming Frog will show you both versions of the page in a single crawl, as long as JavaScript rendering is on and you’ve enabled storing both the original and rendered HTML.

The comparison shows you any body copy, internal links, canonical elements, or meta robots directives that exist in the rendered DOM but not in the initial HTML response.

You can also see the diff side by side in the View Source tab.

screaming frog original vs rendered

Google renders most pages without issue, but content that only appears after JavaScript runs is still less reliable. A blocked resource, a script error, or a timeout can leave it out of the index entirely.

Most AI crawlers don’t execute JavaScript at all, so a page can rank in Google and still be invisible to the systems generating AI answers.

If you find a gap, confirm it with the URL Inspection tool in Search Console. That gives you Google’s own view of the rendered page, which is harder for a developer to argue with than a screenshot from a third-party crawler.

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2. Ignoring the Page indexing report in Search Console

You’ll find it under Indexing > Pages. It’s the only report where Google tells you directly whether a URL is indexed, crawled but not indexed, discovered but not indexed, a soft 404, or something else.

The Page indexing report looks like this:

Not every URL in the “Not indexed” bucket is a problem, which is where people go wrong with this report. Alternate page with proper canonical tag, excluded by noindex tag, and page with redirect are all normal outcomes of a site that’s set up correctly.

The exclusions worth investigating are the ones you didn’t expect. Pages you want ranking that sit in Crawled – currently not indexed, or a Discovered – currently not indexed count that keeps climbing.

3. Sampling URLs at random instead of by template

Pull URLs by page type rather than at random, so you’re covering product pages, category pages, blog posts, filtered views, paginated series, and whatever else the site generates. Most technical issues worth reporting are template issues.

Get the canonical rule wrong on a product template, and you’ve broken it on all 40,000 product pages at once. If your sample includes three blog posts and a contact page, you’ll miss that and report something trivial instead.

Sampling by template also makes the fix cheaper to scope. A developer can estimate “change the canonical logic on the PDP template” in about a minute. Nobody can estimate a list of 40,000 URLs.

Dig deeper: Technical SEO testing: How to build a stronger experiment

4. Auditing from a single data source

Every tool is blind to something. A crawling tool only finds what’s linked or what you feed it, so orphaned pages stay invisible unless you supply them.

Search Console tells you Google’s verdict but not the reason behind it. Analytics only records visits where the tracking code runs, so crawler activity mostly doesn’t show up.

Server logs are the only source that shows every request Googlebot or AI crawlers make to your server and what they get back. Rate limiting, intermittent 5xx errors, and crawl activity concentrated on URLs you don’t care about only turn up here.

If you don’t have server logs, use the Crawl Stats report in Search Console. It’s sampled data, but you can still use the crawl request breakdown to see examples of URLs Google requested.

Crawl requests breakdown

You don’t need all of them for every finding. But anything you’re about to hand to a development team should be confirmed in at least two places, and when two sources disagree, that disagreement is usually the more interesting finding.

5. Treating tool classifications as facts

Crawlers report missing titles and H1s on pages where the content renders fine, and they log 429 and 503 status codes that the site only returned because the crawl was running too fast.

Before a finding goes in the report, open the page and check it yourself. To confirm a status code like the example above, run a curl command.

Curl status code

It takes a couple of minutes per finding, and it prevents a developer from spending half a day chasing a problem that was never there. Developers who’ve been sent after one phantom issue tend to read the rest of your document with suspicion.

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See terms.

6. Documenting symptoms instead of causes

“The site has 12,000 duplicate URLs” is an observation, not a finding. The finding is whatever produces them, which might be faceted navigation without parameter handling, session IDs appended to URLs, or a CMS that generates a second copy of every page under a different path.

A developer can delete the 12,000 URLs in an afternoon. They come back the next time someone adds a filter because nothing about the underlying behavior changed. Tracing a duplicate back to its source takes longer than exporting the list, and it’s the part of the job a tool can’t do for you.

7. Prioritizing by tool severity instead of business impact

A crawler assigns severity based on the type of issue. It has no idea which templates generate revenue, which categories the business is pushing next quarter, or which pages the sales team sends prospects to. So you get audits where tons of low-value warnings sit at the top of the list, and a rendering failure on the highest-margin product template sits on page four.

In the example below, it looks like there are some high-priority issues with Page Titles: Outside <head>. What Screaming Frog doesn’t know is that all those URLs come from a template the team is deleting in the upcoming redesign.

Fixing this means asking the client questions the tool can’t answer. What are the priority products or services? Which pages convert? What’s launching this year? Then rank your validated findings against those answers instead of against a severity column.

Dig deeper: The biggest technical SEO time-wasters to avoid

8. Recommending changes without understanding site architecture

Redirects, canonical changes, URL removals, and noindex directives all have second-order effects. A noindex on a filtered category eventually cuts off the internal links to the products underneath it. A batch of old URLs redirected to the homepage will often end up classified as soft 404s.

Before you recommend any of these, map what links to the pages in question and what they link to in turn. Check whether they appear in navigation, sitemaps, or breadcrumbs. What you want to know is whether the page is the only route to something else, and whether the pages it links to have another way in.

9. Writing recommendations developers can’t act on

“Improve site speed” isn’t a recommendation. Neither is “fix canonicalization” nor “strengthen internal linking.”

A usable recommendation includes the affected URLs or templates, the root cause, the expected outcome, and enough detail for someone to estimate the work. If a developer has to come back and ask what you actually want, the ticket goes to the bottom of the backlog and stays there.

Compare “improve site speed” to something a developer can pick up, like “The LCP element on the PDP template is a hero image loading through a lazy-load script, so it needs loading=”lazy” removed and fetchpriority=”high” added, with LCP under 2.5 seconds.”

10. Prescribing the implementation instead of the outcome

Write the outcome and the constraints. The canonical on paginated pages needs to be self-referencing. Primary product content must be included in the initial HTML response. Then let the developer decide how.

You can absolutely suggest an approach if you have one, and on smaller sites you might be right. But you don’t know the framework’s limitations, what else depends on that component, or what the team already has planned for that part of the codebase.

Acceptance criteria give a developer something to build against and something to check their work against when they’re done. A prescription just invites a debate about whether your approach is the right one.

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What a good audit looks like

A crawler produces a list of problems in 10 minutes. Clients are paying for everything that happens after that, when someone checks which of those problems are real, determines which ones matter to the business, and assigns a cost to each fix.

Read more at Read More

Google vs. Microsoft AI Max: What’s the same and what’s different

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AI Max Google vs. Microsoft

With the global launch of Microsoft AI Max for Search campaigns, it’s worth spending some time unpacking where the features align and differ from Google’s version.

Much of the core functionality is the same:

  • Search term matching expanding your reach beyond static keyword lists. AI Max uses your keywords, ads, and landing pages, along with intent and contextual signals, to uncover relevant searches you may not qualify for with keywords alone. This is especially helpful for complex conversational queries.
  • Text customization allowing ads to adapt to high-value placements and prospects. AI Max uses your existing assets and website content to generate and test additional messaging variations. It then selects the most appropriate combinations at auction time, helping you to deliver more relevant ad creative, including in AI-native experiences.
  • Final URL expansion that routes people to the page on your site that best matches their intent. Instead of always sending traffic to a static landing page, AI Max can route people to the page that best matches what they’re looking for.  This ensures a more consistent user experience across query, creative, and website.

However, there are some key nuanced differences. We’ll dive into:

  •  Where Google and Microsoft AI Max are the same.
  •  Key points of differentiation between Google and Microsoft AI Max.
  •  How to leverage AI Max successfully in new and existing account structure.
Microsoft AI Max

(Note: I am a Microsoft Advertising employee and wrote this post as platform-agnostically as possible. Features discussed in this post are based on publicly available help documentation as of September 2026.)

What’s the same between Google and Microsoft AI Max?

Aside from the core functionality, the following AI Max mechanics remain the same across both Google and Microsoft.

AI Max is a setting, not a campaign type.

Unlike Performance Max (PMax), Demand Gen, Audience ads, and other unique campaign types, AI Max represents optional settings within Search campaigns. These settings are designed to work better together, and advertisers tend to see the most benefit when they opt into all three.

Here’s how advertisers benefit when they leverage all three core AI Max features across Google and Microsoft:

  • Search term matching allows for net new queries and that can bring the advertiser into auctions their static ad might not accurately reflect. By allowing the text to adapt in the moment, you’ll ensure your ads are relevant for those new queries.
  • To deliver the most relevant landing page for unique queries, Final URL expansion can be really helpful. Final URL expansion is linked with Text asset generation as platforms need to be able to adapt creative to make a promise the dynamic landing page can deliver on.
  • If you’re going to allow for text generation, you’ll get the most bang for your optimization buck by also allowing for intelligent matching of URLs and relevant queries.

That said, if advertisers want to begin with a more conservative test, they absolutely can.

For example, an ecommerce brand selling products with similar margins might feel more comfortable testing URL expansion because it allows them to get fuller coverage of their products without building out unique ad groups, but don’t want to leverage search term matching. While Brand Controls exist and can help ensure certain brands aren’t included (more on that later), it’s fair for the advertiser to start with just Final URL Expansion and Text customization.

Whether an advertiser tests just one, two, or all three parts of AI Max, these tests can be done safely with experiments. While both platforms support controlled testing, Google’s AI Max experiments are designed to run within the existing campaign by diverting traffic, whereas Microsoft Advertising’s Search Experiments compare a standard campaign against a cloned test version with AI Max enabled.

AI Max experiments

Here is guidance on how to experiment with AI Max:

  • Start with your strongest campaign. Choose a campaign with stable performance and enough volume to generate results. Experiments work best when there’s enough traffic to detect meaningful differences.
  • Create a 50/50 split. Split traffic evenly between the control and experiment. Keeping traffic balanced makes it easier to determine whether performance differences are due to the change you’re testing.
  • Let the experiment learn. One common mistake is ending a test too quickly. New bidding strategies and AI-powered features need time to learn. If you’re testing AI Max specifically, go directly into an A/B test and allow sufficient learning time before drawing conclusions.
  • Measure business outcomes, not just clicks. Focus on:
    • Conversion rate
    • Cost per acquisition (CPA)
    • Return on ad spend (ROAS)
    • Changes in revenue or conversion value

Conversion-based bidding is a core part of search term matching   

AI Max’s search term matching relies heavily on conversion data in order to be successful. This is why both Google and Microsoft AI Max require conversion-based bidding when enabling this part of AI Max.

Conversion-based bidding works when there’s accurate conversion data flowing into the platform. It is ideal to have at least 15-30 conversions in a 30-day period before working with conversion-based bidding.

If your brand likely won’t hit the threshold, it makes sense to wait on trying AI Max until you can or to use micro-conversions with strategic conversion values. This means setting conversion values aligned to each stage of the journey and TROAS that reflects the focus on important steps.

For example, if a brand wanted to secure applications for their financial product, they might include conversion goals reflecting different completion milestones (beginning, mid-way, completed, and accepted). These conversion goals would get a profit-related conversion value associated:

  • Beginning application: $10
  • Mid-way: $20
  • Completed: $50
  • Accepted: actual conversion value using offline conversion uploads.

Brand controls exist on both, though there are some differences in what’s available.

Google and Microsoft both understood advertisers need messaging and branding controls to ensure ad creative stays within style guides. That’s why both AI Max variants allow advertisers to set brand inclusions, exclusions, term exclusions, and message constraints.

To make it easier to understand the mechanics for each, here’s the breakdown:

Google:

  • Brand inclusions: 10 brand lists per campaign, with up to 5,000 brands per list
  • Brand exclusions: 10 brand lists per campaign, with up to 5,000 brands per list
  • Term exclusions: 25 per campaign
  • Message constraints: 40 per campaign

Microsoft:

  • Brand inclusions: 20 brand lists per campaign, with up to 100 brands per list
  • Brand exclusions: 20 brand lists per campaign, with up to 100 brands per list
  • Term exclusions: 25 per campaign
  • Message constraints: 40 per campaign

Microsoft offers disclaimers that do not take up ad real estate, and work with AI Max. Google is piloting disclaimers as of this writing that would take up description line #2.

AI Max disclaimer

What’s unique between Google and Microsoft AI Max?

While most of the core AI Max functionality remains the same, there are a few key differences to account for when moving between ad platforms.   

Consolidation of settings vs being able to pick and choose all three.

AI Max features work better together, so it makes sense to opt into all three. However, if you want to test individual settings before committing to the full suite, Microsoft makes this easier by offering all three features as opt-in toggles.

Google opts all advertisers who turn on AI Max at the campaign level into search term matching, which can be turned off at the ad group level.

Ad group settings for AI Max

While Microsoft maintains all existing ad group targeting settings (including location targeting, scheduling, and time zone selection), there are no AI Max-specific ad group settings.

Google also supports Locations of interest, URL inclusions, and Brand inclusions at the ad group level.

This means Google’s AI Max management asks advertisers to make more decisions at the ad group level, while Microsoft focuses AI Max settings at the campaign level.

Matching mechanics and search term transparency are different.

Both Google and Microsoft recommend leaning away from syntax-oriented keywords as they can get in the way of being eligible to serve for complex and longer queries, especially in AI experiences. 

However, Google and Microsoft take different approaches to matching and search term reporting. Microsoft offers full search term reporting for any query resulting in a click for AI Max, PMax, and traditional Search and Shopping campaigns. These can be found in the reporting templates, Search term and Search term landing page.

Google hides some search terms for privacy reasons, which also means there isn’t full transparency on whether the query is relevant. To mitigate this, Google allows for more close variant mechanics in negative keywords.

When it comes to matching, due to different ecosystems, there are inherently different signals about how Google and Microsoft will match user queries to advertiser campaigns. Here’s a breakdown of the main signals by each ad platform:

Google:

  • YouTube data
  • Previous search behavior
  • Conversion data
  •  Landing page
  • Other keywords in the ad group
  • Audiences: In-market, demographics, and other 1P audiences like Customer Match). 

Microsoft:

  • LinkedIn data
  • Previous search behavior
  • Conversion data
  • Landing page
  • Other keywords in the ad group
  • Audiences: Impression-based remarketing, In-market, demographics, and other 1P audiences like Customer Match

How to leverage AI Max in existing and new account structures?

AI Max brings the best of AI functionality to search campaigns, and it’s understandable that advertisers will want to test some or all of the AI Max feature suite. However, there are some key considerations for accounts bringing AI Max into their structures.

Here are the top five considerations:

  1.  Do you trust your conversions measurement  and do you meet conversion thresholds?
  2. Will your landing pages be a help or a hindrance in conveying what your brand offers to AI systems as they address human questions?
  3. Are your existing ad assets on brand or do they have serious deviation?
  4. Is PMax already part of your account structure?
  5. Have you budgeted for the targets you’re setting?

Let’s dig deeper into each one.

Do you trust your conversion measurement and do you meet conversion thresholds?

AI Max and conversion-based bidding are joined at the hip because conversions are a critical signal AI Max uses to help advertisers connect with the right customers at the best ROI. If your campaigns don’t have accurate conversions feeding into the system, or don’t have enough conversion data (15-30 conversions in a 30-day period is the minimum), it will be very hard for AI Max to make intelligent matching choices.

If you have an existing account with at least 90 days of accurate conversion data, AI Max is a no-brainer.

Newer accounts should work on building conversion data so they can leverage AI Max and conversion-based bidding once the account ramps up.

Will your landing pages be a help or a hindrance in conveying who you are to the ad platform?

Landing pages are a critical signal that ad platforms use to understand your utility to a potential user. If your landing page is accessible in phrasing and visuals, this translates to easier content consumption for AI and humans alike.

A really great example of this is including alt-text on images and videos on the landing page, which ensures there can be no doubt about the subject matter. On the flip side, if you don’t make it clear what you’re offering and why your customers enjoy working with you, it can be hard to translate those messages to AI creative and matching.

A common mistake brands will make is disallowing all bots from crawling their landing pages, which deprives AI from critical insights into who you are and how you can help your customers.

A good way to understand whether your landing pages are AI Max compatible is to plug them into the PMax campaign creation flow. If you strongly disagree with the assets Google or Microsoft generated for you, that can be a sign to adjust landing page copy/mechanics before turning on AI Max.  

Are your existing ad assets on brand or do they have serious deviation?

Both Google and Microsoft AI Max rely on existing text assets to inform potential new ad creative. Beyond adding in Brand Controls (including term exclusions and message constraints), it’s really important that your ad assets reflect the guidance you share.

For example, if you give a style guide note that all headlines should be sentence-cased, but your existing headlines are title-cased, that can cause confusion in the system. It’s important to audit your ad creative and landing pages for phrasing choices that might not align or were included unintentionally.  

Is PMax already part of your account structure?

AI Max takes the best parts of PMax AI and layers them as optional boosts to Search campaigns. That means there are inherently fewer net-new opportunities for AI Max to unlock in accounts already running mature PMax campaigns.

However, AI Max can still add value when PMax is focused primarily on Shopping, is budget-constrained, or isn’t fully capturing your search opportunity. Rather than evaluating AI Max and PMax in isolation, focus on whether the combination is driving incremental account-level growth in conversions, revenue, or efficiency

PMax, by its nature, is cross-channel, and has a strong affinity for ecommerce. It can be useful to have AI Max as a search specific tool to go after parts of your business you don’t want exposed to non-search inventory.

In short, running AI Max and PMax in the same account isn’t inherently good or bad. If you want the full AI performance lift, Performance Max will have an easier time delivering that because it’s not restricted to search only surfaces. AI Max represents the useful AI gains of PMax in choice oriented and search-specific experience.

Have you budgeted for the targets your setting?

AI Max requires leaning into conversion-based bidding (“Smart” on Google and “Auto” on Microsoft). One of the biggest reasons any campaign can fail is it’s asked to go after too many targets for the budget.

For example, if you’re targeting customers for your plumbing business, it won’t be useful to ask the same campaign to go after minor repairs and a burst pipe. This is because the services have different costs, levels of urgency, and service capacities.

As a general rule, all services/products within a campaign should be within 20-30% of each other. If there’s too much variance, make sure you’ve set up accurate conversion values/TROAS goals, URL exclusions, and budgeted enough to cover the spread.

Final takeaways

Ultimately, Google and Microsoft’s AI Max are fairly similar. The differences have more to do with platform-specific mechanics.

Both agree that you’ll see the best results when you opt into all three core AI Max features.

Google puts more AI Max functionality at the ad group level, while Microsoft makes it a campaign-level choice.  

Both platforms actively take on advertiser feedback, so if there is a preference for one style of management vs another, it’s worth sharing through support.

Read more at Read More