The useful starting point is that the two most credible sources on this question disagree, and understanding why is more valuable than picking a side.

What the independent research found

The Pew Research Center analyzed the actual browsing behavior of 900 US adults across nearly 69,000 Google searches in March 2025. When an AI summary appeared at the top of the results, users clicked a traditional search result in 8% of visits. When no summary appeared, that figure was 15%. Links cited inside the AI summary itself were clicked in roughly 1% of visits, and users were more likely to end their browsing session entirely after seeing a page with a summary.

Two further findings matter more than the headline. About 18% of searches in the study produced an AI summary at all, and questions and longer, more conversational queries were considerably more likely to trigger one. Short, direct queries mostly did not.

What Google says

Google disputes the broader conclusion. In an August 2025 post, Google’s head of Search, Liz Reid, wrote that total organic click volume has remained relatively stable year over year, that average click quality has increased, and that Google is sending slightly more quality clicks to websites than a year earlier. Google defines a quality click as one where the user does not quickly return to the results. The same post argues that AI features are prompting people to search more often and with longer questions, which creates more opportunities to be clicked overall.

Treat that claim with the same care you would apply to any company describing its own product. The post does not publish the underlying data, and Google has an obvious interest in the conclusion. But it should not be dismissed either, because it is describing something the Pew data is consistent with.

How both can be true

Put the two together and a coherent picture emerges. AI features are absorbing a particular kind of click: the quick informational lookup, where someone wants a definition, a fact, or a short answer and leaves as soon as they have it. Those were always low value clicks for most businesses. They inflated traffic reports without producing much else.

Meanwhile, people who are actually evaluating a purchase, comparing providers, or ready to act still click through, because a summary cannot hire a contractor, sign an engagement letter, or book a consultation. Google’s own framing describes exactly this: an AI response gives the lay of the land, and people click when they want to go deeper.

For a service firm, that distinction maps almost perfectly onto your site. Your blog, especially the “what is” and “how does” articles written to attract search traffic, sits squarely in the zone AI summaries absorb. Your service pages, location pages, and contact paths sit mostly outside it. If your traffic has fallen and your inquiries have not, this is very likely why, and it is a far less alarming situation than the traffic chart suggests.

How to tell what is actually happening to you

Do not diagnose this from a single traffic graph. The answer is in data you already have.

Start in Search Console’s Performance report. Google’s documentation on AI features and your website confirms that clicks and impressions from AI Overviews and AI Mode are counted inside the standard Web search totals rather than reported separately. You cannot isolate AI traffic directly, but you can see its fingerprint.

Group your pages by intent. Put informational articles in one group and commercial pages (services, locations, pricing, contact) in another. Then compare the two over the same period.

The pattern that points to AI absorption looks like this: impressions on informational pages holding steady or rising while clicks and click-through rate fall. The page is still being shown; it is simply being summarized instead of visited. If your commercial pages show the same decline, AI summaries are a less likely explanation, and the cause is probably something else entirely. A drop that hits your money pages deserves a proper diagnosis before anyone blames AI for it.

Then check the only number that ultimately matters: are qualified inquiries down, or just sessions? If inquiries are stable, you have lost traffic you were never converting. That is worth knowing, and it is not a crisis.

What does not need to change

Google’s documentation is unusually direct on one point. There is no special markup, file, or structured data you need to add for AI Overviews or AI Mode, and no separate optimization is required. A page needs to be indexed and eligible to appear in Search with a snippet, and the existing fundamentals of being crawlable, well structured, and genuinely useful still apply.

That is worth knowing because a small industry has grown up selling special “AI markup” and similar shortcuts. Be skeptical of anyone promising that a technical trick will get you cited. The work that legitimately improves your odds of appearing in AI answers is real, but it is not a trick: clear structure, unambiguous information about who you are and what you do, content that answers the question precisely, and authority that other sites recognize. Most of that is ordinary good practice applied more deliberately.

What does need to change

Stop judging your blog by sessions. If informational articles were never producing inquiries, losing half their traffic costs you very little. Judge content by what it contributes: inquiries it starts, buyers it reaches who later convert, pages it sends people to next. If you are not measuring that yet, these are the numbers worth tracking.

Write what a summary cannot replace. An AI response can paraphrase a definition. It struggles to replace first-hand experience, a specific point of view, original data, or a detailed worked example from someone who has actually done the work. Generic explainers are the content most exposed to summarization. Content built on your own expertise, your own process, and your own judgment is the least exposed, and it is also the content most likely to be cited as a source rather than silently absorbed.

Invest in the pages buyers actually land on. If your informational traffic is shrinking, the share of your visitors arriving on service and location pages is growing. Those pages carry more weight than they used to, and most firms underinvest in them. Your service pages, not your homepage, are where buyers decide.

Build demand that does not depend on a results page. Branded searches, direct visits, referrals, and an email list are all traffic that an AI summary cannot intercept. A firm people search for by name is far less exposed than one that relies on ranking for generic questions.

Make sure you are eligible to be cited. Check that important pages are not blocked from snippets, that they are indexed, and that each one answers its central question clearly and early. This is not optimization for a special system. It is making sure the ordinary system can use your content.

A note on where this is heading

Everything above describes the situation as of the research available in 2025 and 2026, and this is the least stable topic in search. AI features are appearing on more queries over time, AI Mode is expanding, and click behavior will keep shifting. Revisit your own data quarterly rather than relying on any article, including this one, as a fixed picture.

What will not change is the underlying logic. AI search is very good at answering questions and very poor at replacing the people who do the work. If your marketing depends on being the answer to a question, it is exposed. If it depends on being the firm buyers choose once they understand the question, it is in a far stronger position than the traffic chart suggests.

The post AI Search Is Taking Your Clicks. It Is Not Necessarily Taking Your Customers. appeared first on Griffon Webstudios.

That is the short version of ChatGPT Ads, and the mechanical differences matter less than the strategic question underneath them: is this a channel you should be spending money on right now, or a channel you should understand and revisit? The answer depends less on the platform than on what you sell, and most of the coverage skips that part entirely.

What actually exists today

OpenAI began testing ads inside ChatGPT in early 2026 for logged in adult users on the Free and Go tiers, then opened a self serve Ads Manager and expanded internationally over the following months. By late August 2026, OpenAI reported the ads business had reached a billion dollar annualized revenue run rate in under 200 days and was operating across more than 40 countries, with self serve buying opening across India, Europe, the Middle East, and North Africa.

That growth rate is the useful signal here. Whatever you conclude about whether to advertise, this is not a curiosity that will quietly disappear.

Two structural facts shape everything else. Ads appear only to people on the free and lower cost tiers, since the paid tiers remain ad free. And ads sit beneath the response, clearly labeled and visually separated, rather than woven into the answer. OpenAI describes this as answer independence: advertisers cannot shape, rank, or influence what ChatGPT actually says, and they do not receive access to people’s conversations.

How the targeting works, and why it breaks your habits

Advertisers coming from Google or Meta will recognize the structure and misuse it immediately.

The hierarchy is familiar: campaign, ad group, ad. The campaign sets your objective, budget, and dates. The ad group holds your bid and your targeting. The ad is the creative, meaning a title, copy, an image, a landing page, and your favicon.

The objectives are also familiar. OpenAI’s help documentation describes two buying options: a Reach objective optimizing for impressions and paying per thousand, and a Clicks objective optimizing for clicks and paying per valid click. You set a maximum bid at the ad group level.

Then the familiarity stops. There are no keywords. The targeting input is called a context hint, and OpenAI describes it as a plain language description of the conversations, topics, or keywords where your product may be relevant. Critically, the same documentation notes these are not exact match keywords and do not guarantee delivery in any specific conversation.

This is where most advertisers get it wrong on the first attempt. The instinct is to paste in a keyword list. The system is semantic, not keyword based, so a list of fragments gives it very little to work with. What performs better is a description of a situation: the kind of person, the problem they are working through, and the decision they are trying to make. Write it the way you would describe your ideal customer to a new employee, not the way you would build a keyword group.

Beyond context hints, your levers are geography, your own first party data, and the wording of your ad and landing page. Landing page relevance appears to factor into whether you win placement, which will feel familiar to anyone who has worked with quality scoring on other platforms.

What it costs, and how confident to be about that

Reported benchmarks cluster around a few dollars per click, with guidance in the range of three to five dollars, and CPM figures circulating in the vicinity of sixty dollars. Treat those as directional rather than reliable. The platform is young, competition varies enormously by category, and benchmark numbers published during a land grab phase tend not to survive contact with a crowded auction. Your own first thirty days of data will be worth more than any published average.

What is more useful than a benchmark is the reasoning behind the price. Clicks in ChatGPT cost more than social clicks because the person is usually in the middle of working something out rather than passively scrolling. Whether that intent premium is worth paying is exactly the question the next section answers.

Who this is actually for right now

Here is the part most coverage will not tell you, because “new platform, act now” is a more exciting article.

If you are a local service business, this is probably not where your next dollar belongs. Someone searching for an emergency plumber is not deliberating in a chat window, they are searching and calling. Your highest return spending remains search, where intent is explicit and immediate, and your Business Profile and local placements, where local buyers actually decide. Test ChatGPT Ads with money you can afford to learn from, not with budget moved out of a channel that is producing.

If you sell something people research before buying, the case is stronger. Considered purchases, comparisons, and category education are exactly the conversations happening inside these tools. Software, professional services, and higher ticket products fit the format better than impulse purchases do.

If you sell to consumers on the younger end, note the audience skew. Ads serve to free and lower tier users, and ChatGPT’s user base skews younger than the general population. That is an advantage for some businesses and a mismatch for others.

And if your paid media is currently leaking, fix that first. Adding a new platform to an account that is already wasting budget on placements nobody chose multiplies the problem instead of solving it.

How to test without wasting money

If you do test, run it as a test rather than a launch.

Start with the Clicks objective. You get faster feedback on what people actually respond to, and you can measure downstream conversion from the first week. Impression based buying needs more volume and more patience before it tells you anything.

Set a budget you would be comfortable writing off entirely, because that is the honest framing for any channel with no track record in your category.

Write context hints as scenarios, not keyword lists. Describe the person, their situation, and the decision. Specificity beats breadth here: hints that are too generic tend to lose relevance scoring to better targeted advertisers.

Match your landing page to the hint. If the context describes someone comparing options, sending them to a generic homepage wastes the click. This is ordinary conversion discipline, and it applies with more force on a platform where relevance appears to influence delivery.

Give it enough time and volume to produce a signal, then judge it on cost per acquired customer rather than cost per click. A cheap click on a new platform is still a cost, not an outcome.

Expect the product to keep changing. Formats, targeting options, and availability have all shifted repeatedly since launch, and anything you read about this platform, including this article, has a short shelf life.

The part that has not changed

A new ad platform does not change the arithmetic underneath it. You are still buying attention, and attention still has to survive contact with your website and your follow up before it becomes revenue. A ChatGPT click that lands on a slow page or generates a lead nobody calls back is the same waste it would be from any other source, except now it is arriving from a channel with less proven performance.

The businesses that will do well here are the ones that already have their conversion path in order and are adding a channel to a working machine. The businesses that will conclude ChatGPT Ads does not work will mostly be the ones whose funnel was already leaking before they arrived.

Should you try it?

This is a real platform with real scale, growing faster than almost any advertising product in recent memory, and it is worth understanding now rather than in a year. It is also early, its costs are unsettled, and its best fit is a specific kind of business rather than every business.

Understand it now. Test it with money you can lose. Do not move budget out of a channel that is currently producing customers in order to chase a channel that has not yet proven it can.

The post ChatGPT Ads: What They Are and Whether You Should Buy Them Yet appeared first on Griffon Webstudios.

So how do you make sure your business shows up on AI Search Engines? Let’s break it down.

What Makes AI Search Engines Different?

Unlike Google’s classic algorithmic ranking system that prioritizes backlinks and keyword density, AI search engines like Perplexity, Google AI, and even ChatGPT’s browsing capabilities are designed to understand context, intent, and authority at a much deeper level. They pull from multiple sources, synthesize information, and deliver it back to the user in a natural way.

That means the old tricks like keyword stuffing, thin content, or chasing backlinks won’t cut it anymore. Instead, success depends on how well your website communicates expertise, clarity, and structured knowledge.

Step 1: Build Content That AI Can Understand

AI thrives on clarity. To improve your chances of being featured in an AI search engine result:

• • Write in plain, direct language that answers questions fully.
  • Anticipate user queries-what’s the exact question your customer would type or speak? Then answer it directly in your content.
  • Use headers and sub-headers to create clean structure. AI crawlers love clear hierarchy.

Think of your website like a teacher’s guide: the clearer you explain, the more likely AI is to quote you.

Step 2: Strengthen Your E-E-A-T

Google introduced E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) years ago, and now AI search engines are adopting similar principles. They want to showcase information that’s reliable and backed by authority.

Ways to build this:

• • Add author bios showing real-world expertise.
  • Cite reputable sources and link out where necessary.
  • Publish case studies, testimonials, and data-driven insights to prove your credibility.

AI systems weigh your reputation heavily before pulling your site into an answer.

Step 3: Use Structured Data

Schema markup isn’t just for Google. AI search engines use it too. Structured data gives AI context on what your product is, what your article covers, who wrote it, and more.

For example, adding FAQ schema to a service page makes it more likely that AI will surface those Q&As in a conversational result. Similarly, product schema ensures your listings can be recognized as official and accurate.

Step 4: Cover Topics in Depth, Not Just Keywords

If AI is trying to summarize the web, shallow content won’t make the cut. Instead of writing a 300-word blog post stuffed with keywords, go deeper. Address related questions, explore “what if” scenarios, and include actionable takeaways.

Example: If your keyword is “domestic battery defense attorney”, don’t just define what the term means. Go further and explain the types of charges, possible defenses, the legal process, what clients should expect in court, and common misconceptions. A page that thoroughly answers these connected questions is far more likely to be chosen by an AI search engine than one with just a brief definition and contact info.

AI favors sources that give complete, well-rounded answers.

Step 5: Optimize Beyond Google

Yes, Google is experimenting with AI Overviews, but don’t stop there. Platforms like Perplexity, ChatGPT with browsing, and niche AI search engines are gaining users quickly. Make sure your brand appears wherever your customers might be searching.

That could mean:

• • Publishing on multiple platforms (Medium, LinkedIn, Quora).
  • Ensuring your website is crawlable and fast.
  • Staying active with authoritative guest posts and thought leadership.

The more touch points you create, the more likely AI will recognize your site as a trusted source.

Getting Picked Up by an AI Search Engine

This isn’t about gaming the system, it’s about making your website the kind of resource AI wants to recommend. If your content is clear, authoritative, and well-structured, you stand a strong chance of being surfaced in conversational results that millions of users now rely on.

Search is evolving, but visibility is still the name of the game. The difference is that now you need to think about how both humans and machines read your content. Those who adapt fastest will own the next era of online discovery.

The post Get Your Website Picked Up by AI Search Engines appeared first on Griffon Webstudios.

Online sellers who ignore changing market conditions will start to disappear from digital platforms but forward-thinking companies will gain substantial benefits because product discovery now depends on conversations instead of simple clicks.

This article identifies all changes taking place in the industry along with specific business implications of ChatGPT’s shopping functions while providing step-by-step guidelines to make your store ready for the AI shopping revolution.

The Rise of Conversational Shopping

Traditional eCommerce starts with a keyword. AI-led commerce begins with a conversation. Instead of typing “best headphones under $200”, today’s consumer might ask:“I need a noise-cancelling headphones that work well for flights and long work calls, ideally under $200.”

The difference is subtle but powerful. This new model understands intent, context, and personalization. And ChatGPT, with its shopping and search integration, is turning into a digital shopping assistant that responds to these prompts with curated, real-time results.

ChatGPT’s Shopping Features: What’s New

The shopping functionality and browsing options within ChatGPT enable users to:

• • Access fresh product information which draws data from the internet
  • Suggests shopping options through a combination of user goals along with the context of their conversation
  • Suggest alternative headphone styles by asking “Do you prefer over-ear or in-ear headphones?”

The platform generates links to product pages from reliable websites which display product images along with specifications and customer reviews and pricing information. These aren’t gimmicks. They’re functional pathways that guide users through entire buyer journeys without ever visiting a traditional search engine or marketplace homepage.

The Broader AI Shopping Shift

ChatGPT isn’t alone. The AI shopping era is being shaped by:

Google’s Search Generative Experience (SGE): AI-generated summaries that include products and reviews

Amazon’s AI integrations: Personalized suggestions based on behavior and previous prompts

Visual search tools: Like Google Lens and Pinterest Lens enabling product discovery from images

AI-powered chatbots and product advisors: Custom-built for Shopify and WooCommerce stores

The new battleground? Visibility within these AI systems. The principles for this market diverge completely from conventional SEO or advertising techniques.

How Online Sellers Should Respond: Your AI-Era Strategy

1. Optimize for AI Discovery, Not Just SEO

AI assistants base their operations on clear structured data with descriptive information instead of relying only on keywords.

What to do:

Product titles should include detailed features while descriptions should be both lengthy and feature-oriented:

E.g., “Men’s Waterproof Hiking Jacket – Breathable Shell – 3 Zipper Pockets – Under $100”

Schema markup should be added to every product page with price information as well as availability and review and image details.

Use natural language and FAQs on your pages that AI can pull from directly

2. Build AI-Friendly Content Ecosystems

The blog content, help center materials and guide pages serve two functions: they enhance search engine optimization and provide data sources for ChatGPT and other AI models.

What to do:

The articles should use questions which mirror how we speak (For example, “What’s the best carry-on backpack for digital nomads?”)

Use conversational tone and include clear, actionable answers. The creation of comparison posts (Product A vs. B), “best for” guides, and “how to choose” articles should be a priority.

The content needs to include interlinks which lead to relevant product pages.

3. Get Your Products in External Feeds Used by AI

AI platforms retrieve product information through shopping aggregators instead of accessing data from individual websites.

What to do:

Your website must appear on both Google Shopping and Bing Shopping as well as suitable marketplaces, if you’re selling online. Product feeds must be submitted through the Google Merchant Center platform. Join affiliate networks (ShareASale, Rakuten, etc.) if ChatGPT or other AI tools access their feeds

4. Invest in Smarter Product Imagery and Alt Text

AI tools which use visual search or show images in chat require both quality images and alt text for their operation.

What to do:

• • Upload multiple high-resolution images per product
  • Add multiple images that demonstrate product size and show its usage and material features
  • Write detailed alt text descriptions for product images that include description of the image (Example: “Leather crossbody bag – tan color – zip closure – worn over shoulder”)

5. Prepare for AI-Powered Conversations on Your Own Store

AI tools beyond ChatGPT enable customers to interact with your store through AI assistant solutions.

What to do:

AI chatbots integrated into your store can:

• • Recommend products
  • Answer questions in real time
  • Provide guided shopping experiences

You can establish AI chat functionality through Heyday, Tidio AI and Shopify Magic tools which integrate AI chat into your store. You should track customer inquiries to enhance your content creation and product descriptions.

6. Maintain accurate inventory and pricing data across all sales channels.

AI assistant functions through data access that stays current in real time. Data inconsistencies between platforms result in customer distrust and lost sales opportunities.

What to do:

• • The inventory should be synchronized between your website and Google Shopping and all feed channels.
  • Use tools like Shopify Flow, Zapier and custom scripting tools help automate the update process.
  • The quick removal of out-of-stock products from feeds should be enabled for both flagged and unflagged products.

7. Detailed reviews with context

Ai tools like ChatGPT uses publicly accessible reviews to determine the strengths and weaknesses of a product

What to do:

Ask customers to mention: What they used the product for, how it compares to alternatives, Specific features they liked or disliked. Your product pages should display reviews prominently for customers to view.

8. Monitor What AI Says About You

You can ask ChatGPT:

“For example: What are the best organic skincare brands for dry skin?”  You must intervene when your brand appears in incorrect information or fails to get mentioned in the results.

What to do:

• • You should ask ChatGPT and Bard and other AI tools to generate information about your specific industry on a regular basis.
  • Perform an audit of the content that AI tools present by analyzing blogs and reviews along with Reddit posts and forums.
  • Submit your content to trusted sources for potential reference by AI tools.

9. Experiment with AI-Generated Content (Strategically)

The ability of AI systems to produce large amounts of supporting content helps businesses maintain their agility.

What to do:

Use AI tools to generate product descriptions and meta tags and FAQs but review the content to maintain accurate information and correct tone. You need to prioritize quick market launches of your campaigns and guides during seasonal trends. Don’t forget to use A/B testing to determine which content performs best when AI summarizes it.

10. Stay Agile and Monitor Industry Changes

The AI shopping ecosystem undergoes rapid development at this time. The methods which succeed today may experience alterations tomorrow.

What to do:

Subscribe to updates from OpenAI, Google AI, Shopify, and Amazon and regularly monitor competitor listings through AI tools for visibility benchmarking purposes. Also, consider consulting with AI-savvy eCommerce strategists like Griffon Webstudios.

Adapt Now—Not Later

We are moving into a time where search bar is a conversational tool while category filters are intelligent assistants and the homepage is wherever the products are first recommended. Every e-commerce business needs to adapt to the AI shopping era because it has become the norm for users to interact with the brand

Your business will become more discoverable and trustworthy and profitable as soon as you bring your store and content and data into alignment with AI systems.

The post Future-Proofing Your Store for the AI Shopping Era appeared first on Griffon Webstudios.

Google’s AI Overview feature transforms how search results interact with users. The AI technology presents quick summaries that gather information from multiple sources instead of showing the traditional blue link results.

Understanding how to rank on AI Overview provides businesses and content creators with the ability to gain the top position in search results.

The process of achieving AI Overview rankings demands a strategic and updated SEO methodology which advances past traditional content writing and link-building practices.

A step-by-step guide together with concrete examples and detailed strategies will lead you toward achieving AI Overview success for your website.

What is Google’s AI Overview?

AI Overview functions as Google’s platform to provide complex question solutions using artificial intelligence summary technology.

Instead of directing users to single pages it retrieves information from trusted multiple sources to generate an immediate answer based on collective knowledge.

The AI Overview ranking requires your content to fulfill the following conditions:

• • Be clear, factual, and highly useful.
  • The content needs to be both technologically enhanced for AI comprehension and simple to understand.
  • Demonstrate expertise, authority, and trustworthiness (E-E-A-T).

You need to have one of the highest-quality information sources that both human readers and artificial intelligence systems will accept.

Google AI Overviews appear in approximately 30-47% of searches.

Key Strategies to Rank on AI Overview

Here’s the core strategy blueprint:

1. 1. Align Topics with User Intent
   2. Create AI-Friendly, High-Utility Content
   3. Implement Proper Schema Markup
   4. Focus on E-E-A-T Signals
   5. Optimize Technical SEO
   6. Keep Content Fresh and Updated
   7. Boost Engagement and User Interaction

The following section provides an in-depth analysis of each strategy.

1. Align Topics with User Intent

The path to success begins with a thorough understanding of what users really need.

1. Analyze micro-intents: Think beyond broad keywords. Consider a different focus such as “sustainable fashion trends for 2025” instead of the general term “fashion trends”

2. Use Google’s “People Also Ask” and “Related Searches”: They reveal the angles AI Overview prioritizes.

3. Create topic clusters: Build multiple supporting articles around your main topic.

Example:

Let’s say you own a logistics company and you need to create additional content on your website that supports your goal of ranking on AI Overview for the search term “best shipping solutions for ecommerce businesses.” Your website should include content that addresses the following topics:

• • Standard and expedited shipping selection methods
  • Best practices for international shipping
  • Strategies for small businesses to minimize their shipping expenses

Your website establishes authority in logistics through these topics which enables Google AI to identify your expertise and choose your content for AI Overview.

2. Create AI-Friendly, High-Utility Content

The content needs to follow a structured format which AI and users can understand quickly when targeting AI Overview.

• • 1. Directly answer common questions early in your content.
  • 2. Content should feature short paragraphs limited to two or three sentences each.
  • 3. Use bulleted lists, tables, and comparison charts wherever possible.
  • 4. Summarize key points in a TL; DR section.
  • 5. The addition of multimedia content through simple videos and infographics enhances information retention rates.

Example:

If you run an interior design company and publish an article about “best living room design trends 2025,” you could start with a clear summary:

 

Quick Summary: The top living room design trend of 2025 is minimalistic luxury, featuring neutral color palettes, layered textures, and sustainable materials.

 

Google’s AI can easily extract this information because the summary is concise, directly answers the query, and uses clear language.

Photo by Growtika on Unsplash

3. Implement Proper Schema Markup

Schema enables you to express your communication to Google’s AI system through its native communication method.

• • FAQ Schema: Ideal if your page answers multiple questions.
  • How-To Schema: Essential for step-by-step tutorials.
  • Article Schema: Ensure blog posts are marked up properly.
  • Review Schema: For product or service recommendations.

Your AI Overview ranking opportunities improve with proper markup because it makes page structure more understandable for the AI system.

The free Google’s Rich Results Test tool enables you to check your schema implementation for correct validation.

Example:

Suppose you own an ecommerce store selling fishing sunglasses. You create a detailed blog post titled “How to Choose the Best Fishing Sunglasses for Different Water Types.”

 

You mark each step in the process of choosing the right lens color and the correct frame shape for saltwater conditions by using How-To Schema.

 

Adding an FAQ Schema that answers questions such as “Are polarized lenses necessary for fishing?” or “Which sunglasses are best for deep-sea fishing?”, will make your page even more AI-friendly and increase your chances to appear inside AI Overviews.

4. Focus on E-E-A-T: Experience, Expertise, Authority, Trust

AI Overview selection relies heavily on the ranking signals that consist of Experience, Expertise, Authority and Trustworthiness (E-E-A-T).

• • Author bios: Always list credentials for your writers.
  • Share actual examples along with case studies and first-hand stories to demonstrate your points.
  • Cite trusted sources: Link to studies, government reports, or recognized industry leaders.

Example:

When you run a wellness clinic or med spa and write a blog post about “Benefits of Microneedling for Skin Rejuvenation,” you should strengthen E-E-A-T by:

• • Adding an author bio that includes your licensed aesthetician or medical professional.
  • Sharing before-and-after treatment photos (with permission).
  • Linking to clinical studies that explain the science behind microneedling.

When you demonstrate real experience, credible expertise, and cite trustworthy sources, you increase your chances of ranking on AI Overview because Google’s AI favors content that is verifiable and professionally backed.

5. Optimize Technical SEO

Your website cannot appear on the AI Overview ranking unless it offers fast loading times and functions correctly and search engine bots can easily navigate through it.

• • Improve page load speed: Target <2 seconds.
  • Ensure mobile responsiveness: The AI Overview platform delivers content to mobile users before others.
  • Pass Core Web Vitals: The most important factors are Largest Contentful Paint (LCP), First Input Delay (FID), and Cumulative Layout Shift (CLS).
  • Customize your sitemap.xml and robots.txt files to guarantee that new or modified pages get properly indexed.

Example:

Your company operates a website that provides oil and gas services through bulk fuel delivery and fuel management.

 

The AI system of Google will likely ignore websites containing large technical PDFs and slow-loading images and poor mobile compatibility when generating overviews for search terms such as “marine fuel delivery company.”

 

Your website will have a higher probability of being selected by AI Overviews when it delivers fast page loading times and mobile-friendly design and optimized technical reports for speed and crawlability.

6. Keep Content Fresh and Updated

• • The time of content publication affects how a webpage appears in AI Overview search results.
  • Update statistics and facts and recommendations should be refreshed every six months.
  • When the industry advances researchers should incorporate fresh FAQs and new sections into their work.
  • A meta title and description should get re-optimized with new keywords if search trends experience changes.

Example:

Your criminal defense law firm operates as a business and you have published an article which explains Indianapolis battery laws.

 

Google’s AI will probably ignore your content if state regulations change or new landmark cases set fresh precedents and your article still reflects outdated information from two years ago.

 

Your page will signal relevance and authority and boost your chances to rank on AI Overview when users search for legal guidance if you regularly update your article with the latest battery law definitions, penalties, and real case examples.

 

7. Boost Engagement

Google uses user engagement to evaluate the value of content.

• • Strong opening hooks: The first 100 words should grab attention.
  • Internal linking: Guide users naturally to related articles.
  • Answer snippet-friendly: The probability of getting included in both the snippet and overview increases when you respond to direct questions within a 40–60 words limit.

70% of marketers say improving user experience directly improves rankings.

Example:

Your run a screen enclosure company which provides custom shades and patio enclosures to customers. The blog post “Why Install a Screen Enclosure for Your Atlanta Home?” serves as the main topic.

Your opening statement should use a powerful emotional hook which begins:

Do you want to end your struggle with bugs and debris and unpredictable weather conditions? A screen enclosure enables you to create a year-round sanctuary in your outdoor area which simultaneously increases your home value.

In the blog post, you internally link to additional resources including:

• “5 Benefits of a Screen Enclosure”
• “How to Maintain Your Patio Screen for Long-Term Durability”

You may also respond to a direct question about screen enclosures early on with this snippet-friendly answer like

What does a screen enclosure primarily offer as its main advantage?

A screen enclosure serves three main functions by protecting outdoor areas from insects and debris and UV rays while providing safety features and reduced maintenance needs to create better outdoor living experiences.

Your content structure which includes a powerful opening and strategic internal linking and snippet-ready answers will enhance user interaction signals and boost your chances of ranking on AI Overview.

Real-Time Example: Ranking for “Best Neighborhoods to Buy a Home in Miami 2025”

The following content strategy outlines how to create a competitive real estate search ranking for “best neighborhoods to buy a home in Miami 2025”:

• The first element should be a TL; DR summary which appears at the beginning of the page:

The top Miami neighborhoods for home purchases in 2025 include Coral Gables for luxury residents and Brickell for young professionals and Coconut Grove for families.

• Provide answers to questions that can be used as snippet excerpts.

What Miami neighborhood offers the most suitable environment for families?

“The family-friendly neighborhood of Coconut Grove provides excellent schools and lush parks together with walkable streets.” Include table that compare median home prices with downtown proximity and crime rates between different neighborhoods in the content you write.

The article should link internally to related content such as:

• • Top 10 Things to Know Before Buying a Home in Miami
  • Miami Real Estate Market Trends for 2025
  • The How-To and FAQ Schema formats enhance AI understanding of the content.

Your page becomes more likely to appear in Google’s AI Overview results when you organize content with clear answers and comparison tools and smart internal linking.

Mastering the Future of SEO

The ability to rank on AI Overview represents more than a simple SEO technique since it signifies the mastery of future information consumption patterns.

To achieve success, you need to focus on three essential aspects.

• • Focus on helpful, well-structured content.
  • Build real authority and trust.
  • Stay technically perfect and constantly updated.

Our team at Griffon Webstudios develops AI-optimized strategies and smart content systems which help your brand achieve AI Overview rankings. We deliver the essential tools and established methods to place your website in the leading position of this emerging era.

The post A complete strategy guide to rank on Google AI Overview appeared first on Griffon Webstudios.

Unfortunately, the cost of developing a high-quality mobile app can be anywhere from $20,000 to $30,000. If you have a big company like Uber, you would probably pay over a million dollars to develop your mobile app. That should give you some idea of the complexity and sophistication that goes into developing high-end apps like the Uber.

Of course, nobody expects your mobile app to compete at the same level as Uber. However, you can still develop a high-quality mobile app for a reasonable price. All you need to do is learn the best strategies for cutting costs on mobile app development. Then, you may find an affordable path to developing your dream mobile app.

Below are six smart strategies to reduce your mobile app development costs.

1) No-Code App Development Platforms

The internet is crawling with dozens of no-code mobile app development platforms. Most of them have drag-and-drop user interfaces that make it easy to modulate your apps with the functions and designs you want them to have. You won’t need to know or write coding because the entire app development process is graphical and visual.

In 2025, the mobile app market is a massive, global phenomenon, with over 257 billion apps downloaded worldwide, generating over $935 billion in revenue

Some examples of no-code app development platforms include:

• Airtable
• Webflow
• Thunkable Live
• Kintone
• Zoho Creator
• Softr

Each app development platform tends to focus on specific types of app creation, such as web apps, business apps, or game apps. The pricing is typically a low monthly subscription or one-time fixed fee that most small businesses and individuals can afford.

2) Build Cross-Platform Apps Rather Than Native Ones

You can build your mobile apps with either a cross-platform or native approach. The difference pertains to the number of operating systems they can run on.

For example, a native approach to mobile app development means you are only building an app for one particular mobile operating system, such as Android or iOS. Coding in a different programming language is required to create an app for each operating system. That will cost you more time and money.

On the other hand, the cross-platform approach to mobile app development allows you to use one programming language to create an app compatible with multiple operating systems. Although cross-platform mobile apps have less flexibility and sophistication, they can certainly save you time and money by allowing you to build one app for all operating systems.

Statistically,  developing a single cross-platform app is up to 20% less costly than building two or more native apps.

3) Cost Comparisons

You don’t need to settle for the first qualified mobile app developer you come across. Take the time to pick out at least three or four qualified developers for your consideration. Then, conduct price comparisons of each service to determine which accommodates your budget best.

The best choice doesn’t necessarily mean the cheapest one. Try to find a developer offering a balance of quality and affordability in their service. That is usually the rule of thumb for hiring the ideal developer. Look through their work portfolios to see if their development experience aligns with your needs.

4) Open-Source Frameworks and Tools

The most expensive way to develop an app is if you do it from scratch. However, you don’t need to do that if you use an open-source framework as the basis for your app. Open-source frameworks are customizable, pre-written mobile app-building tools containing reusable coding and libraries.

Since they are “open source” tools, you don’t need to pay for a license or anything like that. Open-source tools are 100% free for you to use or modify into other apps as you see fit. That doesn’t mean you merely rebrand the open-source framework with your brand. You must use the framework as a template to build from to save time and money from developing a framework from scratch.

5) Find a Partner

There is no need to take on the financial burden of your app development project alone if you are on a tight budget. If you cannot bear the project’s cost, consider finding a business partner to assist you. They can put up a percentage of the investment needed to build your app. Then, in return, they will probably expect to be repaid in full or from a share of the revenue generated by the app.

For example, you could seek a venture capitalist to invest in your mobile app idea. Just ensure you have a business plan prepared to show the venture capitalist. It might persuade them to invest in your app development project if it is good enough.

Hire an Affordable Mobile App Developer

Griffon Web Studios specializes in mobile app development for businesses and individuals worldwide. Call our team at (212) 739-0374 or email contact@griffonwebstudios.com to schedule a consultation and seek assistance completing your app development project.

The post Smart Strategies to Cut Costs on Mobile App Development appeared first on Griffon Webstudios.

Using artificial intelligence to boost engagement and expand customer personalization has many benefits for small businesses all the way to enterprise organizations. Here are a few of the many ways you could use AI to automate your marketing goals.

Enhancing Customer Interactions with Chatbots

You’ll find chatbots everywhere online. These are AI-powered tools that operate as customer service professionals for online interactions. Maybe you’ve dealt with an AI chatbot while booking a hotel or getting a refund request taken care of for a sweater you ordered that doesn’t quite fit right.

Claude.ai is an example of a chatbot builder. GitLab, Midjourney, and Menlo Ventures are all utilizing this technology to greet website visitors, collect relevant data, and provide a “human” face to their brands.

Predictive Analytics for Smarter Decisions

AI in marketing relies on data input. Knowing what to do with that data is crucial to predicting what could happen next. AI scans through massive data stores through predictive analytics. The AI looks at historical data to detect patterns and then forecast future trends, from a website owner running a blog to McDonald’s predicting the next big Happy Meal toy.

A casino is another example where using predictive analytics for financial forecasting is relevant to how many dealers to have at the craps table on any given day of the year. Even QuickBooks offers automated invoice generation based on predictive analytics.

72% of organizations have now adopted some form of AI, up from ~50% in previous years.

Creating Content with AI-Powered Tools

Content creation is all the rage lately. Popular AIs like Jasper, ChatGPT, and Dall-E use advanced algorithms and processes to generate text, images, and video. These pieces of content can be generated using prompts written in common parlance. Such innovations erase the barriers to adaptation by many marketers, allowing swifter onboarding of social media content calendars, long-form blog articles, and even sales letters for fundraising campaigns.

Optimizing Campaigns Across Channels

One of the more intriguing aspects of AI in marketing is behavioral analysis. Say you have a website, Facebook profile, TikTok, and email campaign. Using a universal platform like Hootsuite with AI allows you to make judgments based on the gathered data from user behavior.

For example, if you have much higher open rates on a specific email campaign compared to your latest Facebook content, you can automatically focus on more resources to gain greater lead generation or customer satisfaction traction.

Personalizing Customer Experiences

Personalization is always going to be the bread and butter of AI in marketing. People want to feel like they are unique to a brand. They want a mutual relationship, and a little appreciation goes a long way.

You can automate these personalizations through advanced email campaigns run in MailChimp or ActiveCampaign. Segmentation, email sequencing, and tailored messages all lead to higher engagement and read-through rates.

Why Businesses Should Embrace AI in Marketing

The point is that AI in marketing comes in many forms and features. Each one has a way to improve operational efficiency and optimize campaign performance, but you still need a human at the rudder to steer the ship down the right path occasionally.

Whether you wish to integrate a brand new chatbot for automating your boat rental business or have a new email sequence you wish to test for your SaaS enterprise, always work with experts who have experience with AI tools.

At Griffon Webstudios, we specialize in building AI-driven solutions tailored to enhance your marketing and operational processes. Based in NYC, we stay at the forefront of emerging technologies to ensure our clients remain competitive. Whether it’s AI-powered automation, predictive analytics, or personalized customer engagement, we can help integrate AI seamlessly into your strategy. Give us a call today to explore how AI can transform your business.

The post The Growing Use of AI Tools and Automation in Marketing appeared first on Griffon Webstudios.

Let’s break down how the world of sports is being transformed, on and off the field.

1. Performance Analysis and Player Tracking

You might be wondering if there’s some way of fine-tuning those strategies or if there is some way a coach could predict what the future holds for a player still to come. The answer is data and lots of it. The use of computer vision AI underpins player tracking systems that capture live data on movement, speed, angles, and even the stamina of players on the field. This data allows coaches to make evidence-based decisions around team tactics or ways in which an individual player may be improved.

For example, Hawk-Eye in tennis, a computer vision system that tracks the path of the ball to an accuracy of within a few millimeters, provides valuable information about players’ performance. Similar systems are now used in football: FIFA’s VAR (Video Assistant Referee) system looks at multiple camera feeds to help officials make decisions about offsides, goals, etc. These systems correct human error and create data that coaches and analysts can use to figure out how to play the game better.

Real-Time Data: More Than Just Numbers

AI player-tracking is not just a set of numbers; it is a living portrait of what an individual player is doing on the pitch. An excellent example of this is the heatmap, a visual representation of where a player was located most during a match, available for every player on the pitch. This information can be used by coaches to better position players in order to take advantage of an opponent’s weaknesses or because they have been playing too far from their own goal.

Meanwhile, computer vision tools can measure sprint speed, shot accuracy, and stamina, among other attributes, so that coaches can provide tailored feedback to players on the areas that could be improved. It’s not simply about who can run the fastest, but who can do so in the most effective manner, at the right time, in the right direction, etc.

Enhanced Strategies for Teams

Nor does it just help the individual player: AI systems that can track formations and spot spaces in defenders’ lines are being utilized in team sports such as soccer. This gives teams such as Manchester City a leg-up in refining training routines and tactics to encompass a greater range of moves, making teams more versatile and more responsive to different styles of play. A coach can review video data with the input of AI insights to devise a plan based on the cold, hard facts instead of guesswork.

Computer vision is both the ideal assistant coach and a tool that sees things the human eye cannot. Some of the best minds in sports use these tools to gain a competitive advantage. Performance analysis is one of the most powerful applications for computer vision in sports.

2. Training and Injury Prevention

Besides game-day analytics, computer vision is also changing the way athletes train and stay healthy by monitoring how they move their bodies during training sessions and flagging swings in body mechanics that could lead to injury before they become serious problems. This is a major advancement in sports science, in which performance-boosting techniques are now quickly matched by the development of training aids that prevent injury.

Personalized Training Plans

A creative application of computer vision in sports is the ability to create individual training plans. By learning about how an athlete moves, stands, and the positions of her joints, AI can tailor exercises specifically to fix weak points and avoid loading injury-prone areas. These programs will change over time as the athlete improves and learns.

For instance, AI tools are now being used by basketball players to track their shot mechanics. The system observes their body posture, arm actions and release point, and provides instant feedback, improving the players’ shooting performance. In team sports such as football, AI systems track muscle fatigue, helping athletes manage their workload to avoid overtraining.

Injury Prevention: From Reactive to Proactive

Historically, injury management has been reactive; some athlete gets hurt, then the medical staff comes in to help. Injury prevention with AI is now, by contrast, largely proactive. Computer vision tools can detect movement patterns that might indicate overtraining or poor form. For example, with a sprinter’s gait, you can evaluate to see if they’re loading their right leg more than their left, which might indicate they’re starting to develop a strength imbalance, which could eventually lead to injury.

AI is also involved in monitoring how players are running during training and on the pitch: if it identifies a slight limp or a question of weight distribution between the two legs, that is picked up by the system and signaled to the coaching and medical teams so that they can try to prevent a serious injury.

Real-World Applications

Consider Catapult, a wearable technology and AI-based performance analytics company, used extensively in both rugby and soccer to track player physical output. The Catapult system, which monitors a player’s movement, velocity, and impact, can identify when the athlete might be at risk of injury after too much training or fatigue. For example, Leicester City Football Club has used precious time between matches to rotate their squad by analyzing player data, and as a result have stayed healthy during long, physically demanding seasons. (Source)

3. Referee Assistance and Decision Making

Referees, however skilled, are human and, as such, prone to error – especially in high-speed, high-stakes conditions such as those present in elite professional sports. Now, computer vision AI is helping to shrink the room for human error in officiating, and thereby securing better outcomes.

Automated Decision Making

One of the best-known examples of AI refereeing is goal-line technology used in soccer. Hawk-Eye cameras around the goal capture the ball from every angle, determining whether it has crossed the line to a millimeter. Once, these calls were left to the split-second judgment of the referee, which has seen its fair share of controversy. Now, thanks to the hard data of goal-line technology, referees can resolve disputed goals in an instant.

Consider, for example, the use of VAR – Video Assistant Referee – in football. VAR is used to review on-pitch decisions such as penalties, offsides and red cards, displaying footage in real-time to the referee to ensure they’re making the correct call. By having access to multiple camera angles and the ability to replay footage frame by frame, a VAR is able to provide the referee with the information they need to make the correct decisions.

Beyond Football

The use of AI in officiating doesn’t end with soccer. In basketball, systems such as Replay Center are used to allow referees to review certain plays in slow motion to determine if an out-of-bounds call, a foul or a basket interference are correct. In tennis, the use of computer vision for determining whether the ball is in or out – once determined by the dubious judgment of human officials – has become standard through computer-vision systems such as Hawk-Eye.

The consequences of these technologies are momentous. As AI assumes the stress of refereeing, the human referee can concentrate more on the rhythm of the game, safe in the knowledge that pivotal decisions can be verified by the most advanced technology. This leads to more equitable outcomes and less contention, allowing everyone to enjoy the game more.

4. Fan Engagement and Broadcast Enhancements

Not only is AI changing life for athletes and coaches, it’s also evolving fan experiences for the better. Visual analytics and augmented reality (AR) overlays developed with the help of AI make today’s sports broadcasts a lot more interactive and engaging than they had been in the past.

Real-Time Insights for Fans

Have you ever watched a live broadcast, and noticed the real-time statistics and graphical overlays next to the field of play? That’s computer vision AI at work. These systems can track the action in a game as it’s happening and give the viewer real-time information such as player speed, shot accuracy and game tactics. In an NFL broadcast, for instance, a fan can see just how fast a quarterback’s throw was, or how much running ground a receiver covered to make a catch.

These real‑time insights are not mere trivia. With access to the tactical reasoning underpinning a play, say and why a team chose a certain formation or how a defender responded to an offensive play, fans are no longer mere spectators. They are immersed, educated and willing to appreciate the strategic dimension of the game.

Augmented Reality and Beyond

AI has even made it possible to bring immersive AR technologies into sports broadcasts. Viewers could watch a soccer match with AR graphics that show a player the passing options they have available on the field. AR could also be used to create 3D replays that show plays from multiple perspectives, giving viewers a vantage point that no camera could otherwise offer.

Some of the most rudimentary AI-enhanced experiences have already found a home in the NBA and Premier League, where augmented reality is being used to immerse fans in a whole new, interactive dimension of the action. AR apps allow fans to hold their phones up to watch a live stream of player statistics appear on the screen as they watch the game in person from the stands.

The Rise of AI-Powered Analytics Shows

It doesn’t stop there. AI is also being used to generate analytics-driven TV shows and highlight reels that provide deeper insights into a game. Shows such as ESPN’s Sports Science explain the physics and biomechanics behind some of the best plays of the year, giving fans a peek into what actually causes outcomes on the field. This kind of AI-powered content is also especially appealing to data-driven fans who want a deeper dive into their favorite sports.

5. Future Trends and Ethical Concerns

Given all this, it’s easy to get carried away by where AI is heading in sports, yet equally important to ask where it crosses the line between fair and ethical. How far is too far in AI as it applies to sport? Where is the space for machine judgment, and where do we still need human insight and decision-making?

Enhanced Wearables and Real-Time Analytics

Over the next few years, we can expect these kinds of wearables, with AI-augmented sensory systems, to record every move an athlete makes, along with analytical feedback on strained muscles, depleted hydration, and loss of mental focus. Taken together with computer vision, these wearables will give athletes and coaches information about performance and health that was unimaginable just a generation ago.

Balancing AI with Human Judgment

One of the main concerns in this regard is the increasingly common use of AI to make decisions. Clearly, the use of AI for decision-making can help reduce human errors. But its use raises serious questions about fairness and accountability. Can AI systems remain unbiased in making decisions, or might they develop biases based on the data they are fed? In refereeing, a wrong call can change the course of a game altogether.

Player Privacy and Data Ownership

A third ethical quandary is privacy. Now that AI is capable of monitoring every move an athlete makes, who owns the data, and how should it be used? Can teams and leagues access everything from a player’s scores to their heart rate to the amount of pressure they apply when passing a basketball? That’s a lot of data, and it’s only going to grow as computer vision becomes even more sophisticated. Sports organizations will need to create clear guidelines on how to protect players’ privacy.

The Future Is Bright — and Complicated

No doubt, as computer vision AI continues to advance, it will bring more sophisticated analysis, smarter training routines and deeper fan engagement. The key is to use AI responsibly and develop it as a tool to supplement the human element of sport, not supplant it.

The post The Role of AI and Computer Vision in Sports appeared first on Griffon Webstudios.

Now, there is a new search tool that may require website owners and SEO professionals to consider for search optimization purposes. The tool is called SearchGPT.

What is SearchGPT?

SearchGPT is a new AI-powered search engine prototype from OpenAI, the same company that created ChatGPT. Since Google has dominated the search engine market for decades, OpenAI wanted to develop an alternative search tool that incorporated AI technology into it. That way, it could deliver more personalized search result responses by better understanding what people are looking for in their search queries.

OpenAI has partnered with various publishers to deliver high-quality content with source links to users who want to know more information about a particular topic or concept. The goal is to help users receive faster and more accurate responses to their search queries without having to search a bunch of different terms to find the websites and information they want.

SearchGPT vs ChatGPT

Most people have heard of ChatGPT at this point. It is a revolutionary AI-powered chatbot tool that can understand and communicate with people in natural human-like language. People can use ChatGPT as a virtual assistant for all kinds of digital tasks, such as answering questions, translating foreign languages, creating pictures, solving complex math equations, writing resumes, and so much more.

SearchGPT is not the same as ChatGPT, but it does feature some of the same advanced conversational capabilities to allow users to access real-time web-based information. That is why SearchGPT is considered a “conversational-based search engine” rather than a keyword-based search engine like Google.

For example, if a user wants to search for something on SearchGPT, they don’t type in keywords and phrases that relate to their topic like they would on Google. Instead, users must engage in a natural conversation with the AI chatbot by asking questions in plain English. Then, based on the AI’s follow-up questions, the user can refine their search queries to pinpoint the information they want to retrieve more thoroughly.

SearchGPT focuses on retrieving and presenting information from the web, while ChatGPT generates responses based on pre-existing knowledge up to its training cut-off date without web access.

As a result, SearchGPT establishes an interactive search experience that you don’t get on any keyword-based search engine. Rather than typing in keyword phrases to get webpage listings, you communicate with the AI chatbot until it understands what you want to find more precisely. The more you communicate with the AI, the more relevant search results it will provide.

How to Get Your Website Ranked on SearchGPT

Are you curious about how to optimize your website ranking on the AI-powered SearchGPT search engine? SearchGPT may not be widely known right now, but there may come a point when you will need to optimize your website for it in a way similar to how you optimize your website for Google.

Website owners need to start learning more about SearchGPT and how to structure their SEO strategies around it because it may become relevant over the next decade. If you can learn about SEO strategies for SearchGPT now, you will be ahead of the competition by getting your website ranked in SearchGPT as early as possible.

You will find many of the SEO strategies for SearchGPT to be similar to how you rank your website on Google and other keyword-based search engines. However, there are some variations in the SEO strategies that you must consider to ensure that SearchGPT can detect your information faster for search users.

Here are four tips to help you get started.

1) Add Schema Markup Coding

Schema markup is a unique webpage code that organizes and structures webpage data to make it easier for search engines to understand and convey it to users. Google already uses schema markup to preview content for users after they search keywords.

SearchGPT heavily depends on schema markup to understand website content. If you have not created schema markup for your website yet, you must do so if you want any chance of SearchGPT detecting your website’s content.

2) Keywords are Still Important

Although SearchGPT is not a keyword-based search tool, it still analyzes keyword phrases in website content and compares them to the words used in people’s conversational content with the AI chatbot.

Therefore, SEO keyword optimization for your website is still an important thing to do to get ranked on SearchGPT. Try to incorporate relevant keywords into all aspects of your website content, including its titles, headers, metadata, and body content.

3) Use a Conversational Tone in Your Content

Original, human-written website content is essential for both Google and SearchGPT ranking. You must also frequently update your website with new original content so that the search tools don’t think you have abandoned the website.

The difference with SearchGPT is that using a conversational tone in your website content is more critical. Since SearchGPT engages in conversations with users, it has a better chance of understanding and conveying your content if written in the same conversational tone.

4) Ensure You Have a Responsive Website Design

A responsive website design is friendly to users on all kinds of platforms, such as smartphones, tablets, laptop computers, and desktop computers. Not only is a responsive website imperative for achieving a high Google ranking, but it is even more essential for SearchGPT ranking purposes.

SearchGPT is more rewarding to websites with mobile-friendly designs. Since many people use their smartphones and tablets to access the SearchGPT tool, it will prioritize all websites that have fast speeds and simple user interfaces on mobile devices.

The Real Question- Will SearchGPT Outperform Google Someday?

It is hard to imagine a world without the Google search engine assisting us in our lives. However, if the SearchGPT prototype becomes successful in what it wants to accomplish, then it could become a major competitor of Google.

SearchGPT is one of the most significant innovations in search engine technology since the Google search engine came out over 20 years ago. Even though Google has incorporated some AI technologies into its search engine, such as Google Assistant, it still doesn’t entirely rely on AI the way SearchGPT does.

Of course, Google has the money and resources to develop similar groundbreaking AI search tools like SearchGPT. But if SearchGPT can build itself as a reputable AI search brand, then Google may want to work with it rather than against it. So, there is no telling which direction Google will go with the rise of SearchGPT. Only time will tell.

The post Will SearchGPT Outperform Google Someday? appeared first on Griffon Webstudios.

We are all aware of the technological advancements in today’s world that are making day-to-day tasks more manageable for us, one of which is the rise of Chatbots. Powered by AI software, chatbots, also called conversational robots, are hugely important to the online marketing industry. 

Due to their super fast learning capabilities and 24/7 availability, Chatbots are helping brands rebuild how they interact with customers. In today’s modern world, where effective communication and time management are essential, Chatbots help us fulfill these factors with their advanced features. 

Chatbots are of different types; brands can use chatbots that follow simple rule-based systems with a set of defined instructions ranging from advanced systems that can learn and improve from customer interactions with time, providing us with valuable and personalized information.

As we all know, one of the most critical factors that signifies the growth of an organization is having an excellent online presence, serving your customers spontaneously, and having good engagement with your potential and existing customers. But handling too many clients can be difficult, right? Here’s where Chatbot comes to the rescue.

This article will discuss the importance of Chatbots and customer engagement in online marketing.

Definition and evolution:

Chatbot marketing uses computer programs to automate conversations with prospects and consumers on your website or app to generate sales.

Basic Chatbot: 

This is the very first Chatbot, which was introduced in the year 1966. Today, this chatbox is still being used by a few organizations. They lack NLP, so they work with predefined questions and target keywords to which they can give solutions based on user interactions.

Conversational agents:

The introduction to advanced machine language (ML) paved the way for this to come to use in 2010. Conversational agents are advanced compared to the previous type. With the help of NLP and ML, they can learn from previous conversations and perform voice- and text-based tasks. They can also answer your customer’s questions more accurately than the primary chatbot type.

Generative AI Chatbots:

This Chatbot came into existence with the advancement of Transformer neural networks and large language models (LLMs). Jasper AI, Chat GPT, and Bard are Generative AI Chatbots. Advancements in ML and NLP led to the development of this Chatbot, which can help them understand and process customer interactions more efficiently based on their past interactions.

Many help desk and customer support use this Chatbot to curate emails according to customer preferences. Chatbots are generally very helpful, but this type of Chatbot has been helping brands get innovative and more personalized with how they connect with their clients.

Key role in Customer Engagement:

We all know engaging with your customers has been essential since traditional advertising days. However, digital marketing widely differs from conventional advertising methods. 

One of the most important strategies is to gain customer engagement in online marketing platforms. Does Chatbot fulfill that criterion? Let us have a look. 

Let’s assume you buy a makeup product from an online website. You later face issues with the product you purchased or would like to return the product and repurchase a new one. You can’t get them when you try to contact the customer executive due to the unavailability of time slots. Isn’t it frustrating from a customer perspective? 

On the other hand, it can be challenging for brands to get in touch with their customers as soon as they receive a complaint because sometimes brands may have to answer many customer queries.

Here’s where the Chatbot comes to the rescue. 

With the help of the Chatbot as a customer, you can get instant customized replies according to your interaction; the Chatbot will communicate with you. You can get instant recommendations and feedback from other customers on the products you are looking to purchase. You can also get help from the Chatbot with troubleshooting errors you have been facing while on the website. 

Installing precisely detailed information is one of the best ways to gain customer engagement in any online marketplace. Additionally, it’s a plus since generative Chatbots can have human-like interactions, which makes the conversation even better.

Benefits of chatbots in online marketing:

We all know chatbots are very helpful for all of us, whether you are a student, an employee, or an employer. We have listed the three most essential benefits of chatbots in online marketing.

With the advancement of technology in NLP and ML, chatbots can adapt how they communicate with each customer according to their interactions, making it more personalized and easier.

Cost-effectiveness: 

Business can save their cost by hiring more employees for customer support. Providing solutions to multiple customers accurately throughout the day can be a hectic task for human beings. This can be eliminated with the help of Chatbots.

Scalability: 

We can all admit that answering precisely and quickly can be very difficult. Chatbots provide flexibility and scalability to brands by operating quickly and efficiently with the growing customer base. 

Language: 

Nowadays, users can communicate with Chatbots in their preferred language, breaking the communication barrier. During the beginning of the conversation, users should select their desired language to proceed further, and Chatbot will communicate via text or voice based on users’ preferences.

24/7 customer support:

People often face issues related to waiting time, globally differing time zones, and fast replies at all times. With the help of Chatbots, we can eliminate all these issues. 

They provide 24/7 support, and with their help, customers get instant solutions and customized interactions with the chatbots based on their conversations. Advanced chatbots like Generative can adapt and learn more about client interactions with the help of NLP, the leading technology behind it.

 Brands can reply to consumers spontaneously, which can be a hectic manual task. Customers worldwide can reach out anytime according to their preferred timings, and Chatbot handles it all, providing 24/7 customer support.

Personalization and experience:

Customers may need help with various issues when they are purchasing online. It could be related to finding recommendations from the items available on your website, troubleshooting a problem, or simply requiring more information about a specific product.

Using the analysis techniques based on a client’s browsing history, previous purchases, interactions, and provided feedback, Chatbot adapts and solves the user query in no time. 

Chatbots also interact using the consumers’ names, helping them feel more connected to the brand.

Lead generation and data collection:

Lead-generation Chatbots are highly efficient in holding fluent conversations, enabling the customers to stay connected without leaving the site. We know that the conversational approach is constructive when it comes to understanding customers’ requirements; this can be achieved with the help of lead-generation chatbots.

They offer many flexible options through which the customers can navigate and select their desired choices, which will help the Chatbot collect valuable customer data. Through the gathered information from the visitors, the marketing team can approach their prospective and potential customers, create more strategies that will help them achieve more sales, and recognize areas that require more improvement.

With the help of lead-generation chatbots, your website visitors can also be converted into customers. 

Social media integration:

As we all know, social media is vital in the e-commerce market. Brands on various social media platforms like Instagram, FaceBook, and YouTube have used chatbots. Integrating social media has its benefits, such as:

• • Chatbots send live updates/notifications to your customers.
  • It is more straightforward to connect with more clients from different social media platforms
  • Builds community
  • Creates social media campaigns that are creative and attractive
  • Users can easily purchase through social media platforms
  • Targets trends, curates creative content, and schedules posts, making it easier for the social media team

Analytics and insights:

Last, we cannot wrap the blog without discussing this critical factor: Chatbot’s Analytics and Insights.

Yes, chatbots can be very beneficial to the sales and marketing team because they collect user data and essential information from the customer using an interactive conversational method. Every time a customer reaches out, the Chatbot gathers information such as their name, products they are interested in, geographic location, etc.

In the analytics and insights section, you’ll also see customer ratings based on their satisfaction, feedback, the interactive flow of the conversation, the frequency of visitors, and gender.

69% of consumers were satisfied with their last interaction with a chatbot, with 21% remaining neutral and 10% unsatisfied.

Some brands develop a predefined set of questions that will be asked at the end of each conversation. Users have to choose and click their answers from the list of questions or options that appear. 

Marketers and sales teams can identify and plot strategies with the data collected from this section.

Challenges, ethics, and future trends:

Although chatbots have significantly grown over the past few years, one of the most important abilities it lacks is having a human-like conversation, which is one of the biggest challenges.

Don’t get us wrong, Chatbots have been advancing with the help of new technologies and advancements. It’s still growing and has yet to fulfill its main requirement: to converse with a consumer, a human being. But we must admit that Chatbot’s latest version is still better than the basic one.

Now let’s discuss ethics; in the previous section, we discussed Analytics and Insights, which make it clear that Chatbot stores your data, such as interactions, personal information, and feedback, yesterday. Although this data is essential for the brand, customers should know where it will be stored.

A pop-up box should appear, stating that their data will be collected before they interact with the chatbox. This way, they can either choose to proceed further or not. This transparency is essential between a brand and its consumers, especially in this generation where privacy plays an important role.

Chatbot is developing day by day, and it’s still in the learning and growing phase. Innovations should be introduced, and with the help of NLP, chatbots are already getting better at understanding complex information. 2024 will have chatbots better at initiating conversations more naturally with even better adaptability. 

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