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Editors Note: This post uses actual examples to draw conclusions. However, nothing is set in stone when it comes to Google and Bing, so keep that in mind when reading this post.

What is the Buzz in Paid Search These Days?

Unless you’ve been living under a rock, you’ve likely noticed a lot of buzz around paid search. People are clamoring about the recent changes Google has rolled out and around Larry Kim’s recent article about rumors of [not provided] for paid search and what is really happening.

But even with all of the firestorm on social media around Google pulling search queries from the referer URLs, there is even more talk and speculation around what might happen on the 22nd of April. In case you missed it, Google has been pushing post after post trying to work everybody up about a new announcement. Larry Kim even hinted today that he knew what was going to be announced on the 22nd –

To the outsider this may look like Chicken Little running around screaming “The sky is falling”, but for paid search professionals this is a big deal. PPC professionals love control! It’s one of the reasons they decide to go into paid search instead of SEO. So losing control is akin to messing with an OCD person’s perfectly organized bookshelf.

There is always a bit of truth in every rumor

Yes it may be funny to think about PPC marketers crying into their pillows at night, but there is always a little bit of truth in every rumor. If you will indulge me for a few minutes I will explain where I think the truth lies, and where the future of paid search is heading.

Google’s first announcement of coming changes happened on March 12th, a full month before the reveal. In that announcement they started with, “It’s an exciting time to be a performance marketer.” They didn’t say search marketer, they didn’t say AdWords marketer they said performance marketer.

If you look at the other announcements including that first one, there is not one mention of keywords! There are numerous mentions of context, devices & content as Google says:

It’s no longer about devices.  Consumers are constantly connected, and the average American today owns four digital devices and spends 60 hours per week consuming content across platforms.* In this multi-device world, the way people use desktops, laptops, notebooks, tablets, and smartphones is blurring. This means people don’t think about which device they’re using, they just expect the right content to appear seamlessly wherever they are ready to engage.

Connecting people to content. With more consumer touchpoints than ever, it is becoming increasingly complex to reach people in the moments that matter. At Google we work hard to develop innovations that let technology do the hard work so that businesses can focus on reaching their customers.

Google also mentioned they were looking at ways technology could simplify, (their word was efficiency) marketing for businesses.

 What do I think the future of paid search could look like?

I think it could look a lot like [not provided], in the sense that it won’t be so much about keywords, but about people, and the context of what they are doing (and have been doing).

Both Melissa Mackey and I saw an interesting blip in different MCC Accounts

It is also a tool similar in nature to Marin, Acquisio, or Kenshoo.

or

Why Would That be a Big Deal?

What better way to connect consumers, devices, context, and content than a tool that runs on all of them and is owned by Google?

That Being Said…

This is more than just a new platform to manage your AdWords or Display campaigns. Google has been trying to transition marketers into a different mind-set for years. Think about The Zero Moment of Truth which they launched in 2011 a whole two years before they pushed out enhanced campaigns.

The future of paid search is not going to be about keywords. In many ways it won’t even be about the SERP as we know it today. Think of the changes with images dominating the SERP, where they used to be an afterthought.

The first place I search is usually on my mobile, because I have it with me all the time. What about you?

Look to more of the announcements Google has made recently around things like:

Search Network with Display Select Campaigns three posts in a week on the +Google Ads page

Universal Analytics out of beta

It’s customers that matter in Analytics

Not to mention Google just announced +Post Ads are available to everyone

And lest we forget, of course [not provided] for paid search

Put them all together and what do you get?

A future for paid search where it doesn’t look like anything we know today. Of course that is just my drop in the rumor mill bucket.

What do you think the future of paid search is going to be?

Image Credits:

Image #1 by Bryant Garvin

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Google Ceo Sundar Pichai Talks Bard & The Future Of Search

In a recent episode of the New York Times “Hard Fork” podcast, hosts Kevin Roose and Casey Newton sat down with Google CEO Sundar Pichai to discuss the company’s latest AI chatbot, Bard, and its potential impact on the digital landscape.

This article summarizes the key highlights from their discussion.

Launch Of Bard

Pichai shared that Bard, a lightweight version of Google’s AI model LaMDA, was released to gather user feedback and build trust.

Although the public response has been somewhat muted, Pichai assured listeners that a more capable version of Bard would be released soon.

Generative AI tools like Bard and LaMDA are envisioned to become powerful personal assistants in people’s daily lives.

Pichai recounted his own experiences with LaMDA, describing engaging and anthropomorphic conversations with the AI model.

Integration of Bard Into Gmail

Bard is being tested in Gmail with a limited number of trusted users. Pichai confirms:

“You can go crazy thinking about all the possibilities, because these are very, very powerful technologies. I think, in fact, as we are speaking now, I think today some of those features in Gmail is actually rolling out now externally to trusted testers — a limited number of trusted testers.”

AI Chatbots vs. Traditional Search Queries

When asked about the differences between AI chatbots and traditional search queries, Pichai explained that the technology expands possibilities and that users will likely adjust their behavior based on what the AI models can do.

He anticipates a back-and-forth process with users to refine and improve the AI models.

The AI Race and Competitor OpenAI

Pichai acknowledged that although Google was aware of OpenAI’s progress and the team’s capabilities, the user reception of ChatGPT was surprising.

He commended OpenAI for releasing ChatGPT, as it allows society to adapt to and understand the technology.

Addressing Microsoft’s Challenge In Search

Pichai states:

“I would say we’ve been incorporating AI in search for a long, long time.

When we built transformers here, one of the first use cases of Transformer was birthed, and later, MUM. So we literally took transformer models to help improve language understanding and search deeply. And it’s been one of our biggest quality events for many, many years.

And so I think we’ve been incorporating AI in search for a long time. With LLMs, there is an opportunity to more natively bring them into search in a deeper way, which we will. But search is where people come because they trust it to get information right.”

The Urgency to Innovate With LLMs & Generative AI

Pichai denied issuing a “code red” within Google.

However, he did confirm that he’s encouraging teams to move urgently and harness resources to innovate with large language models (LLMs) and generative AI.

Google’s founders, Larry Page, and Sergey Brin, remain actively involved as board members and are enthusiastic about AI’s potential.

Pichai states:

“I’m laughing, because first of all, I did not issue a code red…

To be very clear, there are people who have probably sent emails saying there is a code red. So I’m not quibbling with — all I’m saying is, did I issue a code red? No.”

Balancing Innovation & Responsibility

Addressing concerns about AI development’s risks and potential dangers, Pichai emphasized that Google aims to be bold yet responsible.

He cited Bard as an example, explaining that they have not yet connected it to their most capable models and plan to do so deliberately.

Pichai stressed the importance of finding a balance between innovation and responsibility.

AI’s Impact on Jobs & The Publisher Ecosystem

Pichai acknowledged that new technologies like AI will require societal adaptation and possible course corrections, including in the job market.

He envisions a future where AI makes programming more enjoyable and accessible, akin to how technology has democratized fields like podcasting.

Regarding the potential impact of AI chatbots on web publishers, Pichai reassured listeners that Google is committed to working with the publisher ecosystem and evolving thoughtfully in this area.

Pichai states:

“Part of the reason we are also being careful with things like Bard, amongst many reasons, we do want to engage with the publisher ecosystem, not presume how things should be done. And so you will see us thoughtfully evolve there as well.”

Future Of Google Search

Discussing the future of Google Search, it was suggested that the search bar could evolve into a more command-line-like interface for users to perform various tasks.

Pichai explained that Google aims to assist users in ways that make sense to them without becoming the ultimate solution for every interaction.

He states:

“I think I want to be careful where Google has always been about helping you the way that makes sense to you. We have never thought of ourselves as the be-all and end-all of how we want people to interact.

So while I think the possibility space is large, for me, it’s important to do it in a way in which users use a lot of things, and we want to help them do things in a way that makes sense to them.”

In Summary

The interview with Sundar Pichai offered valuable insights into the future of AI chatbots, search, and the digital landscape.

Pichai’s cautious yet ambitious approach to AI development reflects Google’s commitment to balancing rapid innovation and responsible implementation.

As large language models become more capable, Pichai emphasizes the importance of vigilance and industry-wide cooperation to ensure that AI development remains beneficial for society.

The revelation that Bard will be upgraded soon leaves us all curious about how the AI chatbot’s capabilities will evolve.

Featured Image: JRdes/Shutterstock

Source: The New York Times

What Is The Best Search Alternative To Google?

Google has dominated the search engine market for most of its 20-year existence. Today, most SEO efforts mainly revolve around the popular search engine.

Google holds a massive 92.74 percent search engine market share worldwide, according to StatCounter, as of October.

While Google is truly a force to be reckoned with, some view its dominance in the internet search space as problematic.

The company, with its large network of Internet-related services and products, owns a vast wealth of information on its users and we don’t exactly know all the ways they are using it.

Privacy concerns are among the top reasons why some people prefer using other search engines instead of Google.

We wanted to know which Google search alternative is favored by marketers, so we asked our Twitter community.

What Is Your Favorite Google Search Alternative?

Here are the results from this #SEJSurveySays poll question.

According to SEJ’s Twitter audience:

36 percent chose DuckDuckGo as their favorite Google search alternative.

32 percent said their top pick is Twitter.

30 percent their favorite alternative search engine is Bing.

2 percent favor Yandex as a Google search alternative.

Here Are a Few Comments from Our Twitter Followers

A few followers explained the reason behind their vote:

DDG hands down, it respects your privacy which is why I use it.

— Denpafighter978VGCP (@DAXISAWINNER) October 29, 2023

But in number of search queries @YouTube is on 2nd position. 🙂

— Digital Prem (@DigitalPrem1) November 1, 2023

For me, Bing is as good as Google. I have started using Bing a lot from last 4 months.

However, I am looking forward to install DuckDuckGo (after seeing the poll result). It’s not prominent in India, so it will be interesting to see what results it gives for Indian search terms.

— Mihir Vedpathak🚀 (@VedpathakMihir) October 29, 2023

I actually don’t use anything other than google

— Imtanan Tech Tips (@ImtananTech) October 30, 2023

Other followers also shared a few other Google search alternatives such as:

Qwant.

Ecosia.

Startpage.

Mojeek.

Which Search Engine Is Right for You?

Whatever your reason is for deciding not to use Google, you have plenty of other search engine options.

Check out the post that inspired our poll, by Chuck Price: 14 Great Search Engines You Can Use Instead of Google.

Learn more about the most popular search engines worldwide with these posts from our SEJ contributors:

Have Your Say

What is your favorite Google search alternative? Tag us on social media to let us know.

Be sure to have your say in the next survey – check out the #SEJSurveySays hashtag on Twitter for future polls and data.

Image Credit

Chart created by Shayne Zalameda

Google Search Allegedly Boosts Youtube Results Ahead Of Competitors

Google prefers ranking content from YouTube over other video sources, according to a new report in the Wall Street Journal.

When Facebook and other competitors host a video that also appears on YouTube, Google will allegedly push the YouTube result ahead of others in search results.

The Wall Street Journal claims tests show YouTube ends up first in the Google Search video carousel more often than not.

Further, YouTube results frequently take up most of the slots in Google’s video carousels.

“The Journal conducted Google searches for a selection of other videos and channels that are available on YouTube as well as on competitors’ platforms. The YouTube versions were significantly more prominent in the results in the vast majority of cases.”

If true, a case can be made that Google is engaging in anti-competitive behavior.

However, these allegations are difficult to prove.

Here are more highlights from WSJ’s report, including how it arrived at these conclusions.

Does YouTube Have An Unfair Advantage in Google Search?

The Wall Street Journal is clear about what it’s trying to prove in its report.

“Engineers at Google have made changes that effectively preference YouTube over other video sources, according to people familiar with the matter.

Google executives in recent years made decisions to prioritize YouTube on the first page of search results, in part to drive traffic to YouTube rather than to competitors…”

Here’s what was found to show up most frequently in the first spot of Google’s video carousel.

YouTube vs. Facebook Watch: YouTube is first 95% of the time versus 5% of the time for Facebook.

YouTube vs. Twitch: YouTube is first 86% of the time versus 14% of the time for Twitch.

YouTube vs. Dailymotion: YouTube is first 82% of the time versus 18% of the time for Dailymotion.

Videos on YouTube will even rank first when they receive more views and engagement on other platforms, WSJ finds.

In fact, WSJ deliberately chose content with more engagement on other platforms in an effort to detected “skewed” results in Google search.

“The tests found that video results in a large majority of cases featured YouTube videos ahead of the same or very similar versions of the videos available on competitor sites, even when the actual views and followers were higher on the competitor sites.”

The disparity was particularly evident in comparing results from YouTube against Twitch.

When searching Google for seven of the most popular streamers on Twitch, the video carousel would rank YouTube results in the first position most often.

“Out of a total of 69 carousel slots, Twitch occupied only four,” the report states.

What Does This Mean For Marketers?

Again, these claims are all alleged, and Google’s official stance is that it does not give preference to YouTube in search results.

Going strictly off of data gathered by the Wall Street Journal – marketers should strongly consider uploading all videos to YouTube in order to have the best chance of getting them surfaced in search results.

That means if you have a video that went viral on Facebook, for example, also upload it to YouTube if you haven’t already.

Regardless of how much engagement a video receives on other platforms, this report indicates the video will underperform in Google Search unless it’s uploaded to YouTube.

It’s up to video publishers themselves to drive traffic where it would best serve them.

If Facebook is your priority, then it may be helpful adding a disclaimer on YouTube saying: “This video first appeared on our Facebook page. Follow now for similar videos.”

Twitch streamers are exceptionally good at this. It’s rare to find a YouTube video from a Twitch streamer without several calls-to-action to follow them on other platforms.

That’s a sound strategy for all publishers right now, unless Google acknowledges there’s a problem with the video carousel and changes it.

Methodology

Of the searches, 82 returned a video carousel. The relative placement of results were then compared.

For the Twitch tests specifically, the WSJ searched Google for the handles of the seven most-followed still active on Twitch to bring up their videos.

To carry out these tests, WSJ created computers in the cloud that presented unique IP addresses for each search.

For more details about these tests and how they were conducted, see the full report here.

Different Search Results For The Same Term On Google

Needless to say I went right out and picked up a copy. The article alone is worth the price of the magazine (about $5 US). One of Wired magazine’s senior writers interviewed a few members of Google’s team and a member of Bing was also interviewed. This is a really great piece, I recommend reading it. One of the points mentioned in the article that Brad brought up is the varying results for the same term when searching on Google. Since I’m not giving away anything Brad hasn’t already from the article, I feel okay expounding on my opinion about one revelation of the many revealed in Wired.

Google revealed they are placing users in two different algorithms when performing searches. One is the normal or “control” algorithm and the other is the “test”. If a user gets different results than normal the chances are he/she just got to experience the test algorithm. I was sharing this news with a client who asked me days earlier why he got different results in Google from time to time. So I called him back and explained what he might be experiencing. He responded, “What? You mean they are intentionally skewing the results? That’s not fun.” And I have to admit for a split second I agreed with him.

How are these results different from the usual?

Are these results better than what I normally find? If so how?

Would these new alerts benefit my clients? What challenges would they present?

I realize that this may seem trivial, attempting to look at what Google is testing in order to predict what may come. Ultimately no one knows what Google will do but Google. But come on, you can’t tell me a chance to see what is being tested isn’t intriguing. Part of SEO is staying on top of trends and changes. Google is offering us a chance to look at what is being considered. I think this is incredible. What do you think?

Joshua Titsworth is a Digital Marketing Specialist at Chemidex. Josh maintains the SEO and SMM in addition to assisting with the PPC and Google Analytics reporting. While off the clock he volunteers as a SEO consult to his church in Olathe, KS, as well as to other non-profits in the area. When M.I.A. online he can be found roaming golf courses in search of his shanked golf balls. You can touch base with Josh on his twitter account @joshuatitsworth.

Neural Architecture Search: The Process Of Automating Architecture

Neural Architecture Search (NAS) has become a popular subject in the area of machine-learning science

Handcrafting neural networks to find the best performing structure has always been a tedious and time-consuming task. Besides, as humans, we naturally tend towards structures that make sense in our point of view, although the most intuitive structures are not always the most performant ones.

Neural Architecture Search

is a subfield of

AutoML

that aims at replacing such manual designs with something more automatic. Having a way to make

neural networks

design themselves would provide a significant time gain, and would let us discover novel, good performing architectures that would be more adapted to their use-case than the ones we design as humans.

NAS is the process of automating architecture engineering i.e. finding the design of a

machine learning model

. Where it is needed to provide a NAS system with a dataset and a task (classification, regression, etc), it will come up with an architecture. And this architecture will perform best among all other architectures for that given task when trained by the dataset provided. NAS can be seen as a subfield of AutoML and has a significant overlap with hyperparameter optimization. 

Neural architecture search is an aspect of

AutoML

, along with feature engineering, transfer learning, and hyperparameter optimization. It’s probably the hardest

machine learning

problem currently under active research; even the evaluation of neural architecture search methods is hard. Neural architecture search research can also be expensive and time-consuming. The metric for the search and training time is often given in GPU-days, sometimes thousands of GPU-days. 

Modern deep neural networks sometimes contain several layers of numerous types. Skip connections and sub-modules are also being used to promote model convergence. There is no limit to the space of possible model architectures. Most of the deep neural network structures are currently created based on human experience, requiring a long and tedious trial and error process. NAS tries to detect effective architectures for a specific deep learning problem without human intervention.

Generally, NAS can be categorized into three dimensions- search space, a search strategy, and a performance estimation strategy.

Search Space:

The search space determines which neural architectures to be assessed. Better search space may reduce the complexity of searching for suitable neural architectures. In general, not only a constrained but also flexible search space is needed. Constraints eliminate non-intuitive neural architecture to create a finite space for searching. The search space contains every architecture design (often an infinite number) that can be originated from the NAS approaches.

Performance Estimation Strategy:

It will provide a number that reflects the efficiency of all architectures in the search space. It is usually the accuracy of a model architecture when a reference dataset is trained over a predefined number of epochs followed by testing. The performance estimation technique can also often consider some factors such as the computational difficulty of training or inference. In any case, it’s computationally expensive to assess the performance of architecture.

Search Strategy:

NAS relies on search strategies. It should identify promising architectures for estimating performance and avoid testing of bad architectures. Throughout the following article, we discuss numerous search strategies, including random and grid search, gradient-based strategies, evolutionary algorithms, and reinforcement learning strategies.

There is a need for a way to design controllers that could navigate the search space more intelligently.

Designing the Search Strategy

Most of the work that has gone into neural architecture search has been innovations for this part of the problem that is finding out which optimization methods work best, and how they can be changed or tweaked to make the search process churn out better results faster and with consistent stability. There have been several approaches attempted, including Bayesian optimization, reinforcement learning, neuroevolution, network morphing, and game theory. We will look at all of these approaches one by one.

Reinforcement Learning

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