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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.

Read more at Read More

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.

Read more at Read More

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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Prompts to programming: Vibe coding vs. traditional coding a website

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Good vibes can spark great ideas, and now they can even help you build a website. With vibe coding, you don’t need to start by writing lines of code. Instead, you tell AI what you want to create, refine the results through conversation, and watch your website take shape. While it won’t replace every aspect of traditional development, it’s opening the door for more people to bring their ideas online.

In this guide, we’ll explore what vibe coding is, how to build a website with it, and how it compares to traditional coding.

Table of contents

What’s the vibe around vibe coding?

The term vibe coding was coined in February 2025 by computer scientist Andrej Karpathy, co-founder of OpenAI and former AI leader at Tesla. He used it to describe a new way of building software: instead of writing code line by line, you simply explain what you want to create and let AI generate the technical implementation. In Karpathy’s words, you “say stuff, run stuff, and copy-paste stuff, and it mostly works.“

At its core, vibe coding is about shifting your role. Instead of acting as the developer who writes every line of code, you become the creative director. You describe your vision, review what the AI generates, ask for changes, and continue refining the result until it matches what you have in mind. Rather than focusing on syntax, frameworks, or debugging, your attention stays on the bigger picture: what you want to build and how it should work.

Overall, if I had to describe vibe coding in one sentence, “I’d say it feels less like programming and more like collaborating with an AI that speaks both your language and the language of code.“

What can you build with vibe coding?

Vibe coding isn’t limited to generating snippets of code. Today’s AI-powered tools can help you create everything from simple landing pages to complete websites, web applications, dashboards, and internal business tools!

what can you achieve with vibe coding

With vibe coding, you can:

  • Build websites and landing pages faster. AI can generate layouts, content, and page structures from a simple prompt, helping you move from idea to first draft in minutes.
  • Start with ideas instead of code. Rather than thinking about HTML, CSS, or JavaScript, you focus on your goals, audience, and the experience you want to create.
  • Generate a complete website foundation. AI can create pages, navigation menus, sections, and design elements that you can build on.
  • Focus on creativity rather than the technical setup. Spend more time shaping your messaging, branding, and design while AI handles much of the repetitive work and offloads technical tasks.
  • Customize and iterate quickly. AI-generated websites can be refined through multiple iterations, allowing you to adjust layouts, content, and other design elements until they match your needs.
  • Explore different ways to present the same content. AI can quickly generate different design approaches for the same information, making it easier to compare options and find the one that works best for your audience.

    For example, you could ask AI to create three versions of the same interface: one with low-contrast colors, one with vibrant colors, and one with a dark-mode option.

Understanding vibe coding for building a website

When the concepts of vibe coding are applied to website design, it means you can converse with an AI platform and bring your vision to life online without worrying about complex concepts like HTML, CSS, JavaScript, or other programming languages. Simply explain your brand in plain English and let AI handle much of the technical work.

For example, you could prompt an AI with something like:

“Create a modern website for a local coffee shop with warm colors, an online menu, customer testimonials, and a contact form.”

This conversational approach makes website creation much more accessible, especially for entrepreneurs, freelancers, creators, and small business owners who have ideas but little or no coding experience.

That said, vibe coding doesn’t eliminate the need for human input. AI can generate a strong starting point, but creating a successful website still depends on having a clear goal, understanding your audience, and reviewing the output before going online.

Key benefits of vibe coding a website

Vibe coding is changing the way people approach website creation. Instead of spending weeks learning programming languages or waiting for a first draft, you can start with an idea and let AI help you bring it to life.

For anyone with a website idea, that shift means more time to focus on what they want to achieve and less time spent worrying about the technical details. Even experienced developers are embracing vibe coding for rapid prototyping and repetitive development tasks because it helps them move from concept to execution much faster.

Here are some of the biggest reasons why vibe coding is gaining momentum:

Launch ideas in hours, not weeks

Every website starts as an idea, but turning that idea into something people can visit has traditionally taken time. Vibe coding shortens that journey considerably. By describing your website in natural language, AI can generate a functional first draft in minutes. Instead of staring at a blank screen, you’re reviewing something tangible and deciding how to improve it. That faster start means you can validate ideas, collect feedback, and get your business online sooner.

Also read: New: Yoast AI Content Planner turns a blank post into a structured draft

Experiment and prototypes faster

One of the biggest advantages of vibe coding is how quickly it lets you test and refine ideas. Instead of spending days or weeks building a prototype from scratch, you can describe your vision in plain language and let AI generate a working first draft in minutes.

Whether you’re exploring different layouts, validating a business idea, or creating a website for a client, you can quickly experiment, gather feedback, and improve the result through conversation, making it easier to iterate before investing significant time or resources.

Iterate through conversations

Perhaps the biggest shift isn’t that AI writes code, it’s that building a website becomes a conversation.

Instead of manually editing every element, you can ask AI to redesign a section, rewrite your homepage copy, add a testimonials section, or make the layout feel more modern. Each prompt builds on the previous one, allowing your website to evolve naturally over time.

Can you really build a website using prompts?

The answer is yes, but it’s not because AI has replaced websites with something entirely new. Instead, it has changed how we build them.

Think of prompts as the new interface for website creation. Instead of dragging elements onto a page or writing HTML and CSS, you describe your business, goals, and the kind of website you want. Modern AI website builders then translate those instructions into a functional first draft, which you can continue refining through conversation.

Also read: Perfect prompts: 10 tips for AI-driven SEO content creation

That doesn’t mean prompting replaces creativity or strategy. AI can generate the website, but it still relies on your direction. The clearer your vision and the more context you provide, the closer the final website will be to what you imagined.

How to vibe code a website in 5 steps

Building a website with vibe coding isn’t about finding the perfect prompt; it’s about following the right process. The more clearly you communicate your goals and refine the results, the closer your website will be to what you originally envisioned.

Here’s a simple five-step framework to help you get started.

1. Define the purpose and style of your website

Before you open an AI website builder, spend a few minutes thinking about what you want to build. The better you define your vision, the better AI can translate it into a website.

Start by answering a few simple questions:

  • Who is the website for?
  • What do you want visitors to do?
  • How should your brand feel?
  • What pages or features do you need?

For example, instead of writing: “Build me a business website.”

Try something more descriptive:

“Create a modern website for a boutique coffee roastery. Use warm earth tones, clean typography, and large product images. Include a homepage, About page, online shop, customer reviews, and a contact form. The overall style should feel welcoming, premium, and easy to navigate.”

It explains the website’s purpose, functionality, and personality, which is important for launching as a brand or business that people are searching for.

Also read: What is search intent and why is it important for SEO?

2. Choose an AI website builder

Once you have a clear idea of the website you want to create, the next step is choosing the right AI website builder.

AI tools can support website development in different ways. For example, coding agents such as Claude Code offer specialized capabilities for building websites, but they typically require you to work in a development environment or via a command-line interface.

Claude Code terminal works in a development environment

On the other hand, AI website builders offer a more accessible alternative: instead of working in a terminal or configuring a development environment, you can describe what you want through a conversational interface and start with a functional website that you can refine.

If you’re building a website with AI, the Bluehost AI Website Builder is a great place to start. It asks a few questions about your business, goals, and preferred style before generating a personalized website that you can continue to customize. Because it’s built into Bluehost’s hosting platform, you can go from idea to a live website without juggling multiple tools or worrying about technical setup.

From there, your role shifts from building pages manually to guiding AI toward the website you envision.

3. Write your first vibe prompt

Your first prompt lays the foundation for everything that follows, so it’s worth spending a little extra time getting it right.

This is where the idea of context engineering becomes useful. Rather than trying to cram every instruction into one clever prompt, think about the information AI needs to understand your project: what you’re building, who it’s for, how it should look and feel, what pages it needs, and what you want visitors to do.

You can build this context around:

  • your business or project
  • your target audience
  • your preferred design style
  • the pages you need
  • important features or functionality
  • your brand’s tone of voice

The key idea is simple: don’t focus on writing a clever prompt; focus on giving AI the context it needs to make good decisions. And as you continue vibe coding, you can add or refine that context through subsequent conversations rather than trying to get everything right in your first message.

4. Refine your website through conversation

Don’t expect your first prompt to produce the perfect website, and that’s completely normal. Vibe coding is an iterative process. Once AI generates the first version, continue the conversation by making small, focused improvements.

For example, you could ask AI to:

  • make the hero section more visually engaging
  • rewrite your homepage copy in a friendlier tone
  • replace placeholder images with a cleaner layout
  • add customer testimonials
  • improve the mobile experience
  • make the call-to-action button more prominent

Making one change at a time usually produces better results than asking AI to redesign the entire website in a single prompt. Think of each prompt as another design review. With every iteration, your website moves closer to the experience you originally imagined.

5. Review, publish, and keep improving

Before publishing your website, take some time to review everything AI has generated.

Check that the content accurately represents your business, all links and forms work correctly, and the design looks good on both desktop and mobile devices. This is also the perfect time to replace placeholder text, add your own images, and make sure the website reflects your brand.

Once you’re happy with the result, connect your custom domain and publish your website.

One of the biggest advantages of vibe coding is that the process doesn’t stop after launch. Need to add a new service, update your homepage, or create a landing page for a campaign? Simply return to the AI website builder, describe what you want, and continue refining your website through conversation instead of starting from scratch.

How to avoid the vibe coding doom loop

vibe coding doom loop representation

One of the biggest advantages of vibe coding is how quickly you can turn an idea into a working website. But as you continue refining your prompts, there’s a chance you’ll run into what’s commonly known as the vibe coding doom loop.

This happens when AI gets stuck trying to solve the same issue repeatedly without actually fixing it. It may suggest several “solutions,” but each one either introduces a new problem or brings you back to where you started. Instead of moving your website forward, you end up going in circles.

The doom loop usually happens when AI loses track of the bigger picture. This can occur if your original prompts are too vague, you keep changing your website’s direction midway through the process, or you request too many unrelated changes at once. As the conversation becomes more complex, the AI may struggle to connect all the pieces together, leading to inconsistent results.

How to fix this situation

The good news is that the vibe coding doom loop is usually avoidable. A few simple habits can help you keep AI on track and make the website-building process much smoother.

Get clarity on what you want

As mentioned earlier, context engineering is about giving AI the information it needs to make better decisions, rather than relying on increasingly elaborate prompts. Before asking AI to make changes, consider whether you’ve provided enough context about what you’re trying to achieve, who the website is for, and what you want the change to accomplish.

For instance, if you’re refining your homepage, explain the outcome you’re aiming for and provide relevant context about your audience, messaging, layout, or user experience. This gives AI a better basis for making the change and reduces the chances of it solving one problem while creating another.

Manage AI mistakes instead of chasing them

It’s important to remember that AI isn’t perfect. Treat each suggestion as a draft rather than a final answer. Review every change before moving on, make one improvement at a time, and don’t hesitate to undo or rephrase a prompt if the output isn’t what you expected. A little oversight goes a long way in preventing small issues from turning into bigger ones.

Vibe coding vs. traditional coding: What’s the difference?

By now, you’ve probably noticed that vibe coding doesn’t completely reinvent website development; it simply changes the way you interact with it. Instead of writing every line of code yourself, you’re guiding AI with prompts and refining the results through conversation.

That doesn’t mean traditional coding has become obsolete. Developers still rely on it to build highly customized websites, complex web applications, and features that require complete control over every aspect of the codebase. Vibe coding, on the other hand, focuses on speed, accessibility, and helping people bring ideas to life without getting bogged down in technical details.

Ultimately, both approaches can produce excellent websites. The biggest difference isn’t the final outcome; it’s the path you take to get there.

Website aspect Traditional coding Vibe coding
Primary input Programming languages like HTML, CSS, JavaScript, and PHP Natural language prompts
Required skills Coding and web development knowledge Clear communication and prompt writing
Development process Manual coding and debugging AI-assisted generation and refinement
Time to first draft Usually longer Usually much faster
Customization Unlimited control High, depending on the AI tool
Best suited for Complex, highly customized websites and applications Business websites, portfolios, landing pages, MVPs, and rapid prototyping
Barrier to entry Higher Lower, making it accessible to non-coders

Vibe coding a website: FAQs

Who should vibe code a website?

Vibe coding is a great fit for entrepreneurs, freelancers, creators, marketers, and small business owners who want to launch a professional website without having to learn to code. It’s also useful for designers and developers who want to prototype ideas quickly before investing time in a full-scale build. If you have a clear vision but limited technical experience, vibe coding can help you bring that vision online much faster.

Which tools are best for website vibe coding?

If you’re building a website, an AI website builder is the easiest place to start because it combines AI-generated design, hosting, and customization in one workflow. For example, the Bluehost AI Website Builder helps turn your prompts into a personalized website that you can continue to refine through conversation. If you’re creating an online store, Bluehost AI Store provides AI-powered ecommerce capabilities, while the Bluehost AI All Access Pack brings together multiple AI tools for building, managing, and growing your website.

Once your website is live, you can complement it with Yoast SEO AI+. It helps you prepare for the future of search by monitoring how your brand appears in AI-powered search experiences, tracking AI visibility over time, and providing insights into your brand’s presence alongside competitors.

How long does it take to create a website with an AI website builder?

The answer depends on your website’s complexity, but one of the biggest advantages of vibe coding is how quickly you can create a functional first draft. Many AI website builders can generate a website within minutes, while the remaining time is usually spent refining the design, content, and functionality to match your goals.

If you’d like a detailed breakdown of what influences the timeline, read our guide on how long it takes to build a website with AI.

Your prompts build the site. SEO helps people find it

Creating a website is an exciting milestone, but publishing it is only the beginning. Whether you’ve built your website through traditional development or vibe coding, it still needs to be discovered by the people you’re trying to reach. That’s where SEO comes in.

Today, your website isn’t just being read by search engines. AI assistants and other intelligent systems are also interpreting your content to answer questions, recommend businesses, and surface relevant information. That means your website needs to communicate clearly with both people and machines.

If you’re running a WordPress website, Yoast SEO Premium helps lay that foundation. It automatically generates structured data that helps search engines and AI systems better understand your pages, your content, and your expertise. Features such as schema aggregation create a unified view of your website’s structured data, while the Bot Blocker gives you more control over which AI systems can use your content for training.

A smarter analysis in Yoast SEO Premium

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SEO doesn’t stop once your website is published, either. Creating optimized titles and meta descriptions for every page can be time-consuming, especially as your website grows. Yoast SEO Premium helps speed up that process with AI-powered suggestions that can draft SEO titles and meta descriptions for individual pages or in bulk. Every suggestion remains fully editable, giving you the final say before anything goes live.

The post Prompts to programming: Vibe coding vs. traditional coding a website appeared first on Yoast.

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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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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.

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Anthropic AI watermarking: What it means for content and SEO

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AI compass

On Aug. 11, Anthropic announced it would begin adding machine-readable watermarks to Claude’s outputs. The reaction was immediate and predictable. LinkedIn and X filled with the usual takes:

  • “All AI writing is now fully traceable!”
  • “This is the death knell for AI content farms!”
  • “SEO is dead. Again.”

Sensing the uproar, Anthropic quickly followed up with a blog post, FAQs, and a technical demo showing that the watermark had no practical effect on output quality.

A few days later, Dario Amodei posted on X about AI’s broader crisis of trust, arguing that the public’s skepticism runs deeper than any one company’s messaging.

The technical explanations were clear. The demo was impressive. Yet public reaction remained largely negative.

In this article, I want to separate the hype from the reality and explore why what appeared to be a straightforward regulatory compliance announcement may instead become a flash point dividing the Eloi who embrace AI from the Morlocks who oppose it.

A quick history of watermarking

Craftspeople have marked their work for centuries.

In 1266, the English Parliament required bakers to use distinctive marks on their bread. By 1282, papermakers in Fabriano, Italy, were creating translucent watermarks with wire molds embedded in the paper.

The principle was simple: this is someone’s work, and the maker should be identifiable.

In the digital era, stock image libraries adopted the same idea. You’ve seen Shutterstock’s repeating patterns and Getty Images’ overlays stamped across preview images. The goal was the same: identify the original creator and discourage unauthorized use.

The EU rule Anthropic is answering

Anthropic’s decision is a direct response to Article 50(2) of the EU AI Act (Regulation 2024/1689). The provision requires providers of systems that generate synthetic text, images, audio, or video to mark those outputs in a machine-readable format so they can be detected as artificially generated or manipulated. The technical measures must be effective, interoperable, robust, and reliable, “as far as this is technically feasible.”

That final phrase carries significant weight. It’s not a precise legal standard.

To give companies a practical compliance path, the EU published a Voluntary Code of Practice on Transparency of AI-Generated Content. Most major providers (Anthropic, OpenAI, Google, Meta, Microsoft, Mistral, Cohere) signed it. xAI did not.

What ‘text watermarking’ actually means here

The term itself is causing confusion, so it’s worth being precise.

Traditional text watermarking typically relied on orthographic steganography: inserting hidden characters, zero-width spaces, or other invisible markers into finished text. These methods alter the form of the text. Once you know what to look for, they’re relatively easy to detect and remove.

Anthropic is using a different approach: statistical, or generative, watermarking.

When a language model generates text, it doesn’t always choose the single most likely next word. Instead, it samples from a range of plausible candidates. That controlled randomness helps keep the writing from becoming flat and repetitive. Statistical watermarking replaces some of that randomness with choices guided by a secret key. To the user, the output still appears natural. To the provider, the sequence of choices creates a detectable statistical signature.

Anthropic has said the method doesn’t insert hidden characters, identify individual users, or have any practical effect on output quality. A developer also released a demonstration tool based on the SynthID-Text approach. The engineering is sound.

Yet public reaction remained largely negative, even after Anthropic’s explanations.

That’s because the company answered the technical objections while largely missing the concerns that matter most to the people who use these tools every day — or who still need convincing to use them.

The real problems

1. It treats AI use itself as the problem

Imagine buying a set of kitchen knives and having the government assign someone to monitor you around the clock to make sure you don’t stab anyone. Don’t worry, they say. As long as you only use the knives to cut vegetables, you’ll be fine.

That’s the logic behind this approach.

Historically, watermarking existed to protect creators. Here, it’s meant to protect the potential victims of people who use AI.

Yes, scammers will use AI for fraud. Yes, people will be misled by synthetic content.

Those risks are real. But this policy rests on the assumption that the default use of AI is suspect, so the tool itself must bear a permanent mark.

Anyone who’s worked in SEO has seen this pattern before: white text on white backgrounds in the 1990s, paid links in the 2000s, private blog networks in the 2010s. The tactics worked for a while, then the market and the platforms adapted.

We didn’t need a special regulatory regime treating every form of content creation as potentially fraudulent. Existing fraud and consumer protection laws, along with Google’s incentive to protect the quality of its search results, were enough.

AI is a tool. It can be used well or poorly. Building the system on the assumption that users can’t be trusted isn’t a good way to earn their trust.

2. A positive detection becomes a Scarlet Letter

This is the practical issue that matters most to people doing the work.

Statistical watermarking can’t distinguish between high-value and low-value uses. If Claude performs light editing, rewriting, translation, or tone adjustment, the output can still carry a watermark. The watermark indicates the text was processed by Claude, not that Claude was the original author.

That distinction will be lost on most people. In practice, a detected watermark is likely to become a negative signal — a sign that the work is somehow less legitimate. Ironically, the people producing the lowest-value content will have the strongest incentive to strip or evade the watermark. Its absence will prove almost nothing.

The technique also isn’t especially durable. Just when we thought we were past the endless “we cracked Google’s algorithm” cycle, we’re about to start the same cat-and-mouse game again. Once reliable detectors exist, people will test how much paraphrasing, human editing, or multi-model processing it takes to weaken the signal.

3. It treats writing like a math problem to be optimized

I studied both computer science and English. When I read Anthropic’s explanations, the computer scientist in me was intrigued. The description of the sampling process was clear, and the demonstration tool was genuinely instructive.

The English major in me cringed.

Read these three sentences and see if you can spot the difference:

  1. Four score and seven years ago our fathers brought forth on this continent, a new nation, conceived in Liberty, and dedicated to the proposition that all men are created equal.
  2. Eighty-seven years ago, our forefathers established upon this continent a new nation, born in liberty and devoted to the principle that all men are created equal.
  3. Fourscore and seven years past, those who came before us brought into being on this continent a new nation, conceived in freedom and committed to the truth that all men are created equal.

From a narrow technical perspective, all three are grammatical, coherent, and “high quality.” From the perspective of someone who values good writing, only one is doing the work of literature. The other two are competent paraphrases.

An engineer or computer scientist might not even notice the difference. Readers will.

AI writing already has recognizable patterns: a heavy reliance on em dashes, the familiar “It’s not X, it’s Y” construction, overuse of words like “delve,” “leverage,” and “underscore” where simpler language would do, neatly balanced but empty phrasing, and a lack of specific, independently verifiable details that could only come from real experience.

Adding a statistical bias on top of those tendencies introduces another artificial constraint on the output. The stronger the required signal, the more constrained — and less human — the writing is likely to feel.

4. It applies a regional rule globally

Anthropic didn’t write the EU regulation; it’s simply responding to it. Still, the decision to apply the watermark worldwide at launch, rather than limiting it to the jurisdictions where the law applies, was deliberate and speaks volumes.

The company’s stated reason was the “lack of a durable way to scope the feature by region.” That may be technically inconvenient, but it’s hardly impossible.

Companies routinely adapt product behavior to local legal requirements. Choosing not to do so here — especially for a user base that extends well beyond the EU — suggests a surprising disconnect from its users, many of whom are sophisticated enough to switch to open-weight or non-watermarked models when they want maximum flexibility.

The deeper problem

On the surface, the past week looks like a tech company solving a technical problem to meet a regulatory requirement. To Anthropic’s credit, it moved first and was transparent about the change.

Where it went wrong was the audience it seemed to be addressing. Its explanations were clear to people who already understand how language models work. They did little to address the broader crisis of trust.

A few days after the announcement, Dario Amodei posted on X that the public’s negative view of AI is fundamentally a crisis of trust.

  • “I do agree that the public has a negative view of AI (and that this is a big problem), but I don’t think it is primarily caused by me or any other AI leader warning about AI’s risks.  I think it is fundamentally a crisis of trust.”

He has the diagnosis right. What’s less convincing is the cure.

He went on to argue, correctly, that glitzy marketing won’t fix the problem, and neither will simply claiming AI will cure cancer. The real solution, he suggested, is actually curing cancer.

That framing misses the point. It’s a blind spot shared by many AI executives.

AI won’t cure cancer. Humans will.

AI can surface connections, identify patterns, and accelerate parts of the work. But it’s still a tool. Behind every meaningful result is human judgment and human responsibility.

The same gap appears at a more ordinary level.

Outside of work, AI has improved my life. I’ve already shared how it helped me improve my health. I’ve also used it to plan vacations, adapt recipes, repair my car, and research my family history.

None of those uses will change the world. But they changed mine. Not because I picked the right model, but because I knew how to use it.

I’ve found the same is true for many long-time SEOs. Good SEOs know how to ask questions. We know how to challenge what a computer gives us, refine our prompts, and decide when to accept an answer and when to push back.

Most people haven’t had that experience. Their exposure to AI is largely limited to viral videos and a steady stream of horror stories: mass layoffs, data centers straining local resources, and executives accumulating fortunes that would make the old robber barons blush. With all due respect to Amodei, actually curing cancer won’t change any of that.

Talking as though the technology itself will deliver the breakthrough turns people into spectators instead of participants. Worse, some hear that message and conclude the companies quietly share Agent Smith’s view in “The Matrix”: humans are the problem, and AI is the solution.

What will close the gap is the same force that drove mainstream internet adoption in the 1990s: people discovering tangible benefits in their own lives. That happened because the early internet was built in a spirit of openness rather than control.

The internet scaled because its architects favored open protocols and worked in a culture that was skeptical of concentrated power, whether in government or corporations. Vint Cerf, Bob Kahn, Tim Berners-Lee, Jon Postel, Linus Torvalds, Richard Stallman, Paul Mockapetris, and many others still aren’t household names. Most never became multimillionaires or sought public recognition, yet their contributions to daily life are immeasurable. The political class’s greatest contribution was restraint.

Today, the major AI labs are responding to pressure by adding constraints and tightening control. Too often, the visible motivation seems to be who can produce the biggest exit. That’s a very different spirit from the one that built the early internet.

What actually matters

There’s a useful parallel here for SEOs. You’ve always been able to distinguish between using a technique to create real value and using it to game the system.

This article is a good example. I wrote it the old-fashioned way, drafting it myself and using AI only for research.

Once I had a draft, I used AI to organize, prune, and refine it. I didn’t blindly accept every suggestion. I pushed back and, in some cases, overrode it.

A good example is the H.G. Wells “The Time Machine” analogy above. AI kept urging me to expand that paragraph and explain the reference. I said no. I think enough of this audience will get it immediately. The rest of you can spend five seconds Googling it (or, better yet, check the book out from your local library).

The difference between quality work and slop isn’t whether it passes a detection tool. It’s whether people engage with it, share it, and convert. Everything else is secondary.

It’s also telling which tool I chose. I’ve been using Claude all month for real work. For this piece, I switched to Grok precisely because it doesn’t fingerprint its output.

Part of that decision was rational. Part was emotional. Companies ignore that mix at their own risk.

Read more at Read More

Calls and clicks keep falling as Google Maps becomes the destination

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In February, we wrote that local rankings were holding steady while calls and website clicks quietly disappeared. By April, our data pointed to something much more dramatic, enough that we declared “local SEO” was dying on stage at BrightonSEO.

We were wrong about the scale.

Q1 data was revised retrospectively. After testing the corrected numbers against a full second quarter, we found a clearer, more specific story than either version suggested.

Why Q1 looked like a crisis

Sterling Sky and Jepto’s analysis of 179 Google Business Profiles found that AI-powered local packs often show just two businesses instead of three, frequently without a click-to-call button, and surface only 32% as many unique businesses as the traditional Map Pack. Most rank trackers couldn’t see any of it.

Joy Hawkins, Claudia Tomina, and Matt McGee were independently reporting the same pattern: rankings held steady while performance declined.

Our initial reading of the Q1 data, presented at BrightonSEO in April, looked even more severe. Actions and impressions appeared to have fallen by roughly half across our U.S. portfolio. The drop was dramatic enough that we advised some agency customers to invest in paid search.

It didn’t hold up.

The corrected Q1 number, and what Q2 adds

Rechecking Q1 revealed a much less dramatic picture. Year over year:

  • U.S. website clicks and calls each fell 15.8%, while direction requests rose 31.3%.
  • Desktop Search impressions increased 12.3%.
  • Mobile Search impressions fell 20.6%.
  • Desktop Maps impressions declined 17.9%.

We don’t put much weight on the mobile Maps figure alone. A small number of large advertisers can significantly shift that metric.

Q1 2026 vs. Q1 2025, corrected year-over-year Google Business Profile performance. (Source: GMBapi data)

Q2 data tests that corrected the baseline.

U.S. calls and website clicks are still falling, down 11.9% and 12.5%, and direction requests are still growing, up 21.1%, all three decelerating against Q1’s sharper moves.

The standout change is desktop Maps: down 17.9% in Q1, up 3.2% in Q2, a genuine reversal, specific to the US. Mobile Maps impressions are up 30.4%, desktop search is up 13.9%, and mobile search is down 20.1%, enough on its own to erase the other gains.

Q2 2026 vs. Q2 2025, year-over-year Google Business Profile performance across the US, EU and UK. (Source: GMBapi data)

Our read is that U.S. customers are increasingly completing the entire journey within Google Maps rather than starting on a search results page. The reversal in desktop Maps impressions suggests that the shift is no longer limited to mobile.

Two ranking systems and reviews as the connective tissue

Traditional Maps rankings still rely on proximity, relevance, engagement, and prominence. The signals behind AI Mode and Gemini differ: web context, entity matching, brand authority, and review sentiment are layered on top of the Google Business Profile.

One system determines whether you appear on the map. The other determines whether Google’s AI trusts what it knows about your business enough to answer a customer’s question directly, without a click.

Reviews are becoming the raw material for that second system. Google now prompts reviewers with structured tags such as atmosphere, price, and cleanliness, rather than relying solely on free text, and also encourages customers to review businesses they’ve visited.

It’s building cleaner data so its AI can answer questions without sending users to a website first — a plausible explanation for direction requests rising while calls and website clicks decline.

The EU and UK aren’t a slower version of the US

The picture outside the U.S. is different. In the EU, desktop Maps impressions fell faster in Q2, dropping from -16.5% in Q1 to -34.7%, even as direction requests continued to grow, slowing from +21.7% to +13.1%.

The U.K.’s smaller dataset showed the opposite reversal. Mobile Maps impressions swung from +22.4% in Q1 to -70.8% in Q2, while direction requests and website clicks continued to rise.

Neither market appears to be simply trailing the U.S. They’re following different paths.

Rank tracking alone won’t tell you this

SOCi’s 2026 Local Visibility Index found AI platforms recommend far fewer locations than Google’s 3-pack: 1.2% on ChatGPT, 7.4% on Perplexity, and 35.9% on Google. A key reason is profile accuracy, which averages 68% on ChatGPT and Perplexity versus 100% on Gemini, which pulls directly from Google Maps data. Search Engine Land covered the report in detail.

Reviews matter here, too. Locations recommended by ChatGPT average 4.3 stars, while those recommended by Perplexity average 4.2, suggesting review quality is becoming a gate for AI recommendations, not just a ranking signal.

What this means for your reporting

If your reporting still leads with call volume, you’re measuring only part of the customer journey. Direction requests deserve equal weight. Businesses with the most complete, accurate Google Business Profile data capture more of the remaining clicks, calls, and directions.

For multi-location brands, agencies, and SMBs focused on the bottom of the funnel, local SEO has grown up. It’s starting to look a lot more like SEO.

More detail on GMBapi’s Q2 Local SEO trend data is available here.

Read more at Read More

Best PPC Companies of 2026

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Key Takeaways

  • The best pay-per-click (PPC) agencies specialize in specific verticals. NP Digital leads in data-driven omnichannel campaigns, while many agencies focus on B2B, SaaS, or ecommerce.  
  • PPC agencies typically charge 10 to 20 percent of monthly ad spend, a flat retainer of $1,500 to $25,000 per month, or a hybrid of both. Setup fees run from $2,500 to $10,000.  
  • Google’s AI-powered tools like Smart Bidding and Performance Max are now baseline competencies for competitive PPC. Ask any agency you’re evaluating how they configure target return on ad spend (tROAS) and monitor AI-driven campaigns.  
  • Define your goals and success metrics before the discovery call. If you show up without clear key performance indicators, you’ll waste their time and money. You could even have less leverage at the negotiating table.  Check agencies against third-party review platforms like Clutch and G2. Then pressure-test their case studies against your own industry. 

Pay-per-click (PPC) advertising delivers an average return on investment (ROI) of 200 percent.  

Marketers are taking notice. About 93 percent of them view PPC advertising as effective or highly effective, second only to content marketing. 

A lot of that success depends on the PPC agency running your campaigns. Agencies aren’t interchangeable, though. The wrong partner can burn through your budget and compromise your results before you even launch. 

This article breaks down the best PPC agencies of 2026. Each one is highlighted for their expertise and specific strengths, helping you find the perfect partner who aligns with your goals and drives real results. 

Define Your Goals Before Hiring a PPC Agency

PPC campaigns can target drastically different outcomes, so you need to set clear goals and expectations before you start looking for an agency. Without them, you’ll have a hard time making an educated decision and could waste your time and money. 

Negotiating services with your agency of choice may also be a struggle without a clear target in mind. Scoping and pricing conversations generally fall to the agency if you don’t bring clear goals to the table, putting you in a weaker position at the signing table.  

So, before you get started, sit down with your team and decide what success looks like for your PPC campaigns.  

Your goals may include: 

Clear campaign goals matter more when you’re building a paid media strategy around AI-driven campaign types like Performance Max. Campaigns like these rely on inputs from your business, including target return on ad spend (tROAS), target cost per action (CPA), or conversion value. Only you know what those numbers should be. 

If you can’t articulate what a successful conversion looks like to your business, the AI will optimize toward the wrong outcome faster than a human team ever could.  

Best PPC Companies of 2026

Each of the top PPC agencies below earned its spot by delivering measurable results across a specific business type, budget range, or channel focus. Some specialize in enterprise omnichannel work, while others lean into niches such as B2B lead generation or paid social. The right fit depends on the goals you defined above, so keep them in mind as you browse. 

NP Digital — Best Data-Driven Omnichannel PPC Agency 

he homepage for npdigital.com showcases how Neil Patel Digital can help customers find you everywhere from Google to ChatGPT. The page also displays logos of high-profile clients like Intuit, Mitsubishi, Marriott, SoFi, Hewlett-Packard, and more.

 Running a successful PPC campaign involves more than placing ads on one platform.  

At  NP Digital, we create  omnichannel campaigns that connect with your audience wherever they are, whether they’re searching on Google, scrolling social media, or shopping online. This approach strengthens  brand awareness and drives engagement, turning leads into customers while maximizing ROI. 

Our track record speaks volumes.  

One client, ZAGG, needed to boost paid search revenue while reducing costs. Its existing campaigns had inefficient conversion rates from warranty offerings, requiring a strategic pivot to improve profitability. 

As part of our process, we: 

  • Restructured campaigns and consolidated ad groups for clearer tracking and better attribution. 
  • Implemented tROAS bidding and Performance Max campaigns for growth while managing underperforming categories with standard shopping campaigns. 
  • Enhanced product categorization to focus on smaller product groups, increasing visibility and profitability. 
  • Tested ad copy in smaller environments, scaling proven combinations to maximize impact. 
  • Expanded broad match adoption, capturing untapped long-tail queries and driving efficiency at scale. 

Results: 

  • Winner of the 2024 Online Media, Marketing, and Advertising (OMMA) Best SEM Campaign award 
  • 28 percent increase in revenue from paid search 
  • 38 percent boost in media spend efficiency, reducing cost-per-conversion by 18 percent 
  • 17 percent lift in return on ad spend (ROAS) for power products 
  • 13 percent decrease in cost-per-click (CPC) 
  • 23 percent boost in ROAS and 26 percent increase in sales per click (SPC) for the Keyboards and More product category 
  • 6 percent lift in campaign efficiency and 30 percent boost in ROAS month-over-month from broad match testing

Here’s what ZAGG Digital Marketing Director Brayden Martin praised the team’s hands-on approach and the ROI results NP Digital delivered on ZAGG’s PPC campaigns. 

Brayden Martin, Digital Marketing Director at ZAGG, praises Neil Patel Digital for their hands-on service and impressive ROI results handling ZAGG’s PPC advertising. 

Our client  ConnectWise needed to shift from marketing qualified lead (MQL) volume to a strategy focused on sales qualified opportunity (SQO) and pipeline growth. The challenge was a lack of visibility into campaign-level ROI. 

As part of our process, we: 

  • Mapped Power BI data to paid search campaigns, aligning investment with mid-funnel key performance indicators (KPIs) and pipeline contributions. 
  • Identified and reallocated $180,000 per month in savings from low-quality campaigns to high-performing campaigns. 
  • Focused budgets on campaigns driving the highest SQO conversion rates, improving cost efficiency. 

Results: 

  • 166 percent increase in SQO conversion rates across regions 
  • 100 percent improvement in ROAS, with a 68 percent pipeline growth 
  • 25 percent reduction in cost-per-SQO, driving higher-quality leads at a lower cost 

ConnectWise’s results came from paid search, but our reach extends further. We hold premier or select partner certifications from Google, Meta, Microsoft Advertising, and Amazon Ads, so you get certified expertise and direct platform support regardless of where your campaigns run.  

NP Digital holds a 4.5 rating on Clutch, with reviewers frequently citing the team’s professionalism and project management. 

Directive Consulting — Best for B2B and SaaS Businesses  

Directive Consulting’s homepage invites prospective clients to “Rethink the Potential of Your B2B Agency.” 

Directive Consulting is the top choice for B2B, SaaS, and enterprise businesses aiming to convert ad spend into predictable revenue. It focuses on connecting with high-intent buyers through precise targeting based on deep total addressable market (TAM) analysis and detailed ideal customer profile (ICP) modeling that factors in the fragmented buyers’ journey AI answer platforms are creating. 

Directive’s expertise spans paid search, social, account-based marketing (ABM), and programmatic advertising. The agency prioritizes metrics like  customer lifetime value (CLV), customer acquisition costs (CAC), and net sales margin to optimize campaigns for revenue growth. 

On Clutch, Directive carries a 4.8 rating across more than 50 reviews, with B2B clients consistently highlighting the team’s proactive approach and strategic depth. 

For businesses looking to scale PPC campaigns efficiently and grow their sales pipeline, Directive Consulting provides a results-driven approach tailored to your industry and goals. 

Stryde — Best for Ecommerce Businesses

Stryde’s homepage touts their expertise in scaling online visibility and marketing performance for ecommerce brands.  

Ecommerce brands need PPC campaigns that bring in and convert traffic to build long-term revenue.  Stryde focuses on helping ecommerce businesses stand out and grow through well-executed paid search and social strategies. 

Stryde’s team specializes in crafting PPC strategies on Google and organic SEO or generative experience optimization (GEO) strategies geared toward ecommerce brands. It creates campaigns that guide potential customers from initial awareness to the final purchase, ensuring each step of the process is intentional and impactful. 

Stryde carries a 4.5 rating on Clutch, with ecommerce clients praising the team’s flexibility and responsiveness. 

For ecommerce businesses looking to grow their audience and revenue, Stryde offers a proven strategy that’s designed to drive results while building customer loyalty. 

Disruptive Advertising — Best for Data-Driven Google and Meta Campaigns

Disruptive Advertising’s homepage lists their marketing results and logos of their bigger accounts, such as Guitar Center and PennyMac.

Disruptive Advertising is one of the most-reviewed PPC agencies on Clutch, with more than 360 client reviews and a 4.8-star rating spanning a decade and a half of work across Google Ads, paid social, and search engine marketing (SEM).  

It’s hard for any agency to stay in the business that long without providing real results for their clients, which is why Disruptive’s Premier Verified status on Clutch comes as no surprise.  

Its model emphasizes personalized strategy and transparent ROI reporting, backed by a performance guarantee and no long-term contract requirement. Disruptive is best suited for mid-market and growth-stage businesses that want an agency with high review volume and documented results across multiple industries. 

KlientBoost — Best for Landing Page Design 

KlientBoost’s homepage bills the company as The Outcome Marketing Agency That Hits Bigger & Bigger Goals. 

KlientBoost takes PPC campaigns further by focusing on what happens after the click. Its 400-plus verified reviews and 4.9-star rating on Clutch are proof that its expertise in PPC management and landing page design turns traffic into measurable results. 

The agency’s in-house team of developers and designers creates landing pages that drive conversions. It focuses on A/B testing and advanced analytics to continually improve performance, ensuring campaigns deliver maximum value. 

For businesses that want PPC campaigns paired with high-performing landing pages, KlientBoost delivers solutions that turn clicks into meaningful revenue growth. 

AdVenture Media— Best for CRO

AdVenture Media’s homepage positions it as a digital marketing strategist for service-based businesses.  

Campaigns start with an account audit and competitor research to establish where quick wins exist. From there, the team builds out targeted ads on Google, Meta, and LinkedIn, backed by continuous A/B testing on landing pages and creative. Every campaign gets monitored closely, with reporting centered on ROAS rather than vanity metrics. 

AdVenture Media pairs experienced strategists with its proprietary AI platform, SHERPA, to sharpen conversion rate optimization (CRO) across every campaign. The platform pulls in data from Google, Meta, Shopify, and CRM tools, then flags the patterns and opportunities a team would otherwise spend days finding. This combination lets AdVenture Media catch high-intent leads early and turn traffic into real, trackable outcomes for both ecommerce brands and lead generation businesses. 

AdVenture Media has built a reputation on this data-driven approach to CRO, earning recognition from Google and a spot on the Clutch 1000 list of global B2B leaders. With offices across New York, Philadelphia, and Fort Lauderdale, the agency works with businesses ranging from fast-growing ecommerce brands to established lead gen operations looking to get more out of their ad spend.  

Ignite Visibility — Best for Paid Social

Ignite Visibility’s homepage says they’re the Trusted Digital Marketing Agency for More Traffic, Leads, & Revenue. They also offer a free audit to potential clients. 

Ignite Visibility specializes in paid social advertising, using platforms like Meta, TikTok, and LinkedIn to drive growth. Its Premier Verified status and five-star rating, based on more than 170 reviews on Clutch, prove it understands what gets its clients results. 

The agency’s approach goes beyond running ads. Ignite Visibility builds funnel-based strategies that engage target audiences on the right platforms. The team runs branded challenges on TikTok and connects B2B brands with decision-makers on LinkedIn. A/B testing and ongoing adjustments help their campaigns consistently perform. 

With over a decade of experience and awards for its work, Ignite Visibility has become a trusted partner for businesses looking to grow through paid social advertising. 

SmartSites — Best for Small and Mid-Sized Businesses

SmartSites advertises itself as an Award-Winning Digital Marketing Agency and showcases its premier partnerships with Google, Microsoft, Meta, and Amazon. The page also displays SmartSites’ 9-year run in the Inc. 5000 and A+ rating with the Better Business Bureau (BBB). 

SmartSites maintains a strong presence on Clutch, with more than 350 verified client reviews and Premier Verified status. Much of that stems from small businesses and local advertisers who emphatically back their services with a 4.9-star rating. 

Founded in 2011 by brothers Alex and Michael Melen, the agency runs PPC as a core service alongside web design and other marketing channels. Client reviews cite responsiveness, transparent billing, and measurable gains in lead generation as consistent upsides to working with SmartSites. 

How Much Do PPC Agencies Charge in 2026?

By now, you’ve probably spotted a few agencies that match what you’re looking for. The next question is what they’ll cost. The answer can be a little tough to find without some digging.  

Many agencies quote on discovery calls rather than post rates online. Pricing transparency seems hard to come by in the industry, so here’s a general breakdown of what to expect. 

Generally, three pricing models dominate the market in 2026: 

  • Percentage of ad spend. Agencies charge 10 to 20 percent of monthly ad spend, typically landing at 15 percent. This model dominates accounts spending $15,000 or more per month. 
  • Flat monthly retainer. Retainers typically range from $1,500 to $10,000 per month for small- and mid-market accounts, with enterprise retainers reaching $25,000 or more. 
  • Performance-based or hybrid. Pure performance pricing is rare in 2026 due to attribution challenges stemming from iOS privacy changes and cookie deprecation. Hybrid structures that combine a base retainer with a percentage layer above a spend threshold are more common. 

The ballpark monthly ranges by tier are: 

  • Small businesses: $1,500 to $3,000 
  • Mid-market: $3,000 to $7,500 
  • Enterprise: $7,500 to $25,000 or more, or a percentage of large ad spend 

Clutch’s PPC Pricing Guide shows the average monthly PPC project cost across their verified client reviews at about $7,165, with most PPC projects falling between $10,000 and $49,999 in total. You’ll also need to account for setup fees, which Solid Marketing estimates at anywhere from $2,500 to $10,000, depending on the agency. 

Pricing shouldn’t be the deciding factor, though. An agency that misfires on strategy could easily burn through more in wasted ad spend than you’d save on a lower fee. 

What Makes a Great PPC Agency?

The path to finding the best PPC marketing agency isn’t a straight line. Your research will take some twists and turns. Some excel at specific advertising types, and others specialize in creating excellent customer experiences across every platform.  

One isn’t necessarily better than the other, but it ultimately depends on what you’re looking for. Use these characteristics as a baseline for creating a list of viable options: 

  • Extensive industry knowledge: A top-tier PPC agency understands the specifics of your industry. It knows how to target the right audiences with the right keywords based on real-world experience and proven results. Check case studies to see if the agencies you’re considering have delivered outcomes for businesses like yours. NP Digital does a great job of this, as seen in the examples above. 
  • Advanced analytics and reporting: Clear reporting is key to understanding the success of your campaigns. The best agencies provide detailed performance data, showing where your money goes and what it brings back. They use this information to fine-tune campaigns and cut waste. 
  • Intent-driven keyword strategy: Keyword strategy can make or break a PPC campaign. Effective campaigns target transactional keywords, the terms people search for when they’re ready to take action. Great agencies avoid keywords that generate traffic without meaningful results and use intent-based research to guide their strategy. 
  • First-party data and partnerships: Agencies with direct access to platforms like Google and Meta, along with first-party data, deliver stronger campaigns. These connections allow them to stay ahead of trends and tap into audience behaviors others might miss. 
  • Mobile optimization: With mobile devices accounting for 58.3 percent of paid clicks, agencies must prioritize mobile-friendly campaigns. This means creating ads and landing pages that look great and perform well on smaller screens.
  • Multi-channel PPC services: Running ads across multiple platforms helps you reach a wider audience. The ideal scenario is to have the agency managing your Google Ads campaigns also manage social media, programmatic advertising, and more, so your campaigns stay consistent across channels. NP Digital offers a wide range of services and makes it super easy for anyone to find what they need. 
  • AI bidding and automation expertise: Smart Bidding, Performance Max, and tROAS configuration are now baseline competencies for competitive PPC. Ask how the agency structures Performance Max campaigns, sets bidding targets, and monitors AI-driven optimization to catch drift before it burns through budget. NP Digital’s ZAGG campaign above shows what tROAS bidding and Performance Max produce when applied with proper human oversight. 
  • Well-versed in all things digital marketing: The best PPC agencies work to create effective campaigns and PPC strategies to support your overall business goals. The strategies they use should fit nicely into your broader digital marketing strategy. Great agencies understand the big picture and can often help improve other parts of your digital marketing system because they’re great marketers themselves.  

How AI Is Changing PPC in 2026

Campaign management has shifted from keyword-by-keyword control to setting inputs for machine learning systems. Three shifts define how AI is reshaping paid search in 2026: 

  • Consolidated campaign management. Performance Max rolls multiple Google platforms into one campaign optimized by AI. You provide goals and creative assets, and the algorithm decides where each ad serves and to whom.
  • Automated bidding. Google’s bidding framework now runs on AI. Strategies like Target CPA, Target ROAS, Maximize conversions, and Maximize conversion value optimize bids in every auction based on your conversion signals. 
  • Machine-assembled creative. Responsive Search Ads and Performance Max asset groups both feed headlines, descriptions, images, and video into machine-learning systems that assemble and test combinations in real time. 

As you interview agencies, ask specifically how they structure Performance Max campaigns and monitor AI-driven optimization. It’s critical to set up Google Ads Manager with clean conversion tracking and make sure there’s human oversight. That’s what will keep your ad spend from going to waste.  

NP Digital’s ZAGG results earlier show what AI advertising can do when the inputs are set correctly. 

How to Work With a PPC Agency

Working with a PPC agency can reshape how you approach paid advertising. Here’s what to expect at every step of the process: 

  1. Discovery and Onboarding: Your agency will start by learning everything about your business. You’ll discuss your budget, goals, target audience, and what sets you apart. Sharing details about your audience, like their habits and preferences, helps your agency develop a strategy tailored to your needs. 
  1. Planning and Testing: Next, your agency creates a roadmap and identifies key performance indicators (KPIs). Pilot campaigns often follow, testing audience segments, ad creatives, and copy. These tests provide valuable data to guide full-scale campaigns and improve targeting. 
  1. Execution and Monitoring: Once the campaign launches, the agency tracks performance and makes adjustments. Expect regular updates on key metrics like impressions, click-through rates (CTR), conversion rates, and CPA. This feedback keeps you informed and helps refine the strategy. 
  1. Measuring Results and Next Steps: At the campaign’s end, the agency reviews the data with you. They’ll highlight successes, explain what could be better, and suggest ideas for future campaigns. This phase is about learning from the results to achieve even better outcomes next time. 

To avoid confusion, designate a single point of contact for communications from your agency. This ensures there are no miscommunications or wasted time from several people managing that relationship. 

  The best PPC agencies are experts in their space, and their advice usually reflects patterns they’ve seen work across dozens of accounts. This could be advice on improving your home page for conversions or redesigning landing pages to increase sales.  Perhaps it’s a suggestion to improve your ad copy or headline.  

Always remember they’re experts, and you hired them for a reason. Take the time to listen and keep an open mind throughout the process. Working with a PPC agency should feel like a partnership, with clear communication and results-driven strategies leading the way. 

How to Choose the Right PPC Agency for Your Business

There are hundreds (if not more) of PPC agencies to choose from. Choosing the right one is often the hardest part of getting started.  

But the best PPC agencies for you specialize in the types of campaigns you’re interested in. They should also have in-depth knowledge of your specific industry and be proficient in the AI paid advertising focus areas I mentioned earlier.  

An agency that’s been around for a while and uses savvier tactics, such as analyzing your competitors’ paid ads, also helps. Ultimately, every brand’s needs are unique, but some combination of these characteristics should provide your best results.  

It may also help to make a list of your expectations and requirements before starting your search.  

From there, list the companies you’re considering. Be sure to include: 

  • Their specialty areas 
  • What makes them stand out to you 
  • Why they seem like a good fit 
  • Pricing if it’s available online
  • Any negatives about their business 

Then, when you hire a PPC manager, you can use your requirements and expectations to cross off agencies that don’t match what you need. Client reviews on the top PPC agencies of 2026 from platforms like Clutch or G2 can help you narrow the field even further. 

Once you’ve narrowed down your short list, schedule calls with those agencies. This is your chance to interview them just as much as it’s their chance to interview you. Ask all your questions and take notes throughout the meeting so you have all the information in front of you when making your final decision.  

FAQs

What should I expect from a PPC management agency?

A PPC management agency handles paid campaigns all the way from strategy to post-campaign reporting. The process should start with a discovery call, with ongoing communication about ad performance and creative testing throughout. 

How much do PPC agencies charge?

PPC agencies typically charge 10 to 20 percent of monthly ad spend, a flat retainer of $1,500 to $25,000 per month, or a hybrid of both. Pricing will vary by agency and depend on your business size or the complexity of your campaign. 

How much is PPC with a marketing agency?

Monthly PPC costs range from $1,500 for small businesses to $25,000 or more for enterprise accounts, plus setup fees of $2,500 to $10,000. You’ll typically have to speak to the agency you’re interviewing about their pricing model for your business size and campaign goals.

How do I avoid wasting money hiring a PPC agency?

Define your goals and KPIs before signing, and require a monthly reporting cadence tied to business outcomes rather than vanity metrics. It’s also smart to check the agency’s case studies against your industry and avoid long-term contracts without performance clauses. 

What should a transparent PPC agency monthly report include?

A transparent monthly report should include spend by campaign and channel, conversions and conversion value, cost per acquisition or return on ad spend, click-through and quality score benchmarks, and specific optimization actions taken during the reporting period. 

Conclusion

Hiring a top PPC company is a smart choice if you’re looking to save time, strategize with experts in your industry, and get short-term results (when compared to something like SEO). 

However, choosing a PPC agency you can trust is harder than it sounds. If you’re looking for someone to manage your PPC campaigns, use the tips and recommendations in this guide. 

Of course, you can always reach out to my team at NP Digital for a conversation. We have a proven track record of building impactful paid media strategies for our clients and can’t wait to show you what our omnichannel approach to PPC can do for you. 

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Google expands direct booking for Local Services Ads

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Google is significantly expanding booking capabilities for Local Services Ads (LSAs), increasing support from around 20 Reserve with Google booking partners to more than 500.

The expansion means substantially more LSA advertisers can let potential customers book directly from their ads on Google Search and Maps, according to Google Ads Liaison Ginny Marvin.

What’s new. Starting now, advertisers whose Google Business Profile already has an active partner booking link will automatically have that booking capability enabled for their Local Services Ads.

There’s no additional manual linking required. Eligible customers can move directly from seeing the LSA to making a booking through the advertiser’s existing booking partner.

A big expansion. Google says Local Services Ads now support more than 500 Reserve with Google booking partners, up from just 20.

That dramatically increases the number of businesses that could potentially use direct booking through their ads without adopting a different scheduling provider.

Bookings become paid leads. Direct bookings generated through LSAs aren’t simply an additional interaction with the ad. Google says they will flow into LSA lead reporting as paid leads.

Advertisers can continue viewing and managing those booking leads from within their Local Services Ads profile.

Advertisers still have controls. Businesses that don’t want to use a particular booking provider can adjust their preferences.

Booking settings and individual partner links can be managed under Profile & Budget > Settings within the LSA dashboard, including the ability to disable a specific partner link.

The Google Ads migration. These settings won’t disappear as Google moves LSA accounts into Google Ads over the coming months.

Marvin said advertisers’ booking preferences will carry over as part of that migration, helping preserve existing configurations through the transition.

Why we care. Direct booking reduces the number of steps between seeing an LSA and becoming a lead. Expanding support from 20 to more than 500 booking partners could make that experience available to significantly more advertisers. But because those bookings are treated as paid leads, businesses should also monitor booking quality and costs rather than viewing the feature simply as a free conversion improvement.

Bottom line. Google is turning direct booking into a much more widely available Local Services Ads feature, automatically connecting existing eligible Business Profile booking links and letting customers go from an ad on Search or Maps directly to making an appointment.

Dig deeper. Ginny Marvin announcement

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Google Local Services Ads will charge for some missed calls starting Oct. 1

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Google is updating its Local Services Ads lead charge policy, changing how and when advertisers are charged for calls generated by their ads.

Starting Oct. 1st, certain missed calls and subsequent calls that meet Google’s valid lead criteria will become chargeable.

What’s changing. Missed calls during business hours will now be charged as valid leads when a user stays on the line for more than 20 seconds, with some exceptions.

That means advertisers could be charged for a call even if nobody at the business actually answers it.

Subsequent calls. If an initial call doesn’t qualify as a charged lead, Google says subsequent follow-up calls between the business and the user will be charged if they meet its valid lead criteria.

Call routing exceptions. Google is introducing an exception for businesses whose call-receiving setup requires customers to press a key to reach the appropriate department.

In these cases, the 20-second timer won’t begin until the customer presses the key. Businesses won’t be charged if the customer doesn’t press a key and therefore isn’t routed.

Spam protections. Google says it is introducing new safeguards intended to limit robot calls and address spam call abuse as part of the change.

The announcement doesn’t provide further details about how those protections will work.

Why Google is making the change. Google says Local Services Ads customers often have immediate needs and expect to connect quickly with a trusted local professional.

The company says the policy change is intended to help the platform meet those customer expectations while rewarding businesses that provide “excellent responsiveness.”

Why we care. The change puts more importance on advertisers’ ability to answer calls during business hours. Businesses could now pay for missed calls that last longer than 20 seconds, making call responsiveness and routing setup more directly connected to LSA costs.

Bottom line. Starting Oct. 1, Google will broaden what can qualify as a charged Local Services Ads call lead, including missed calls lasting more than 20 seconds during business hours and qualifying subsequent calls.

First spotted. This updated was spotted by a digital marketer on X.

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