SEO
AI Advancements Revealed During Latest Earnings Calls
In a rapidly evolving tech landscape, major brands like Amazon, Google, and Microsoft invest heavily in artificial intelligence (AI) to maintain their competitive edge and enhance their services.
These companies leverage AI for coding assistance, advertising optimization, productivity improvements, cybersecurity, and more.
As the race for AI dominance heats up, industry leaders have come forward with ambitious plans to integrate AI-driven solutions, sparking discussions about the potential impact of AI on customers, businesses, and society.
Learn more about the AI advancements companies revealed during or ahead of the first earnings calls of 2023.
Amazon
Andy Jassy, CEO of Amazon, discussed the latest AI for Amazon Web Services.
- Bedrock is a managed service that simplifies building and scaling enterprise-level generative AI applications by providing access to various foundation models, including AI21 Labs, Anthropic, Stability AI, and AWS’s exclusive Titan family.
- Inferentia2 and Trn1n are two next-generation large language models (LLMs) with better price performance and training cost savings.
- CodeWhisperer, an AI coding companion that provides real-time code suggestions, becomes available for AWS developers.
Jassy emphasized that AWS will continue to innovate, citing the recent announcement on LLMs, generative AI, and related chips and managed services in the hopes that the upcoming surge in machine learning drives significant new cloud business.
Chegg
Dan Rosensweig, CEO of Chegg, stated “that generative AI and large language models are going to affect society and business, both positively and negatively. At a faster pace than people are used to.”
After a conversation with Sam Altman about the role of AI in education, Rosensweig swiftly shifted Chegg’s focus to emphasize the integration and adoption of AI within its offerings.
In partnership with OpenAI, Chegg developed CheggMate, a homework helper powered by GPT-4. It combines proprietary data with the knowledge of over 150,000 subject matter experts to improve accuracy.
Rosensweig maintained optimism about plans for the real-time, reliable AI learning companion.
Unfortunately, offering clarity into how ChatGPT may have impacted new Chegg signups led to a 40% drop in share pricing.
Dropbox
Drew Houston, CEO of Dropbox, expressed excitement about AI and a new strategic objective this year to move beyond files and organize all cloud content for customers.
Houston highlighted the new era of augmented knowledge work, where AI and machine learning advancements enable teams to tackle complex tasks and improve productivity. As cloud tools and remote work create chaotic work environments, Dropbox aims to help users organize and simplify their lives with universal search and content suggestions.
Despite economic challenges, Houston remained optimistic, emphasizing Dropbox’s scale, platform neutrality, trusted brand, and strong balance sheet as key strengths for navigating difficult times and investing in the future.
A week before the earnings call, Houston posted a memo to employees. It announced a layoff of 500 employees due to slow company growth and the need to shift resources to AI as the “next stage of growth requires a different mix of skill sets, particularly in AI and early-stage product development. We’ve been bringing in great talent in these areas over the last couple years, and we’ll need even more.”
Duolingo
In the upcoming earnings call, investors will likely hear about Duolingo’s use of AI in education.
Like Chegg, Duolingo partnered with OpenAI to incorporate GPT-4 into its service. Duolingo Max uses generative AI to answer translation-related questions and act as a language partner for conversational practice.
These developments align with thoughts from a TED Talk with Sal Khan, CEO of Khan Academy: AI can give every student (with access) an AI tutor and every teacher (with access) an AI assistant.
Sundar Pichai, CEO of Alphabet and Google, updated investors on recent advancements in AI.
- Bard, the conversational AI service, now utilizes the PaLM model. This allows Bard to assist in coding development projects.
- The PaLM API and MakerSuite tools allow developers to build generative AI applications quickly.
- Organizations can utilize generative AI features in the Google Cloud Platform and Workspace.
- Google Search will harness generative AI to offer new conversational search experiences.
- Google Ads will leverage AI to help advertisers optimize and manage ad campaigns more effectively.
Pichai closed his introduction, excited about “helping people, businesses, and society reach their full potential with AI. ”
Philipp Schindler, SVP and CBO of Google elaborated on AI in Search keyword relevance, Smart Bidding, and Performance Max campaigns. Specifically, Google:
- Updated Search keyword relevance using MUM model-based natural language AI, enhancing ad relevance and performance when multiple overlapping keywords are eligible for an auction.
- Improved Smart Bidding models for bidding more accurately based on search ad format differences, allowing more effective bidding depending on user engagement preferences.
- Expanded access to Automatically Created Assets (ACA) beta to all English-speaking advertisers, allowing them to generate text assets with responsive search ads and use AI to minimize manual work and maintain fresh, relevant creatives.
- Encouraged advertisers to pair core Search with Performance Max, which reportedly resulted in over 18% more conversions at a similar CPA on average, a 5-point increase in 14 months.
- Made advances in AI underpinning bidding, creatives, search query matching, and new formats like YouTube Shorts, contributing to improved results.
“AI has long been an important driver of our business. Advancements are powering our ability to help businesses, big and small, respond in real-time to rapidly changing market and consumer shifts and deliver measurable ROI when it’s needed most.”
Ruth Porat, CFO of Alphabet and Google, walked investors through using AI in Google Workspace to improve internal productivity. Porat also noted an intentionally slower hiring pace and continued focus on Google DeepMind, acquired in 2014.
Meta
Mark Zuckerberg, CEO of Meta, made AI the key theme of Meta’s earnings call.
- Over 20% of the Facebook & Instagram feed is filled with content chosen by AI from profiles, pages, and groups you don’t follow.
- Useful AI agents could be made available to millions via WhatsApp and Messenger for business messaging, customer support, and more.
- Continued investment in infrastructure will be required to support new LLMs and generative AI product scaling.
Susan Li, CFO of Meta, explained how AI could offer advertisers increased automation through Advantage+ Shopping and that capital expenditures reflected “ongoing build-out of AI capacity to support ads, Feed and Reels, along with increased investment in capacity for our generative AI initiatives.”
During Q&A, in response to a question about hiring, Li noted Meta had been in a hiring freeze for the past six months and, when hiring resumed, it would be for talent in generative AI, ads, infrastructure, and other new products.
Microsoft
During a recent earnings call, Satya Nadella, CEO of Microsoft, highlighted the company’s focus on and future in AI.
- Azure, Microsoft’s cloud computing service, gained market share as customers continued to choose it for AI-powered applications.
- Azure OpenAI Service, which combines advanced models like ChatGPT and GPT-4 with Azure’s capabilities, has seen a tenfold increase in customers’ QoQ.
- GitHub Copilot and Dynamics 365 Copilot for CRM and ERP systems showcased Microsoft’s commitment to enhancing productivity through AI.
- Microsoft 365 Copilot, Viva Sales, and Security Copilot demonstrated AI integration in various aspects of the business.
Amy Hood, CFO of Microsoft, revealed that the company expects healthy revenue growth in the largest quarter of the year due to customer demand for differentiated solutions, such as their AI platform and Microsoft Cloud.
Hood emphasized Microsoft’s focus on delivering long-term financial growth and profitability as they enter the AI era. The company plans to continue investing in cloud infrastructure, especially AI-related spending, to scale with growing demand.
When asked about AI regulation, Nadella said Microsoft proactively addressed unintended consequences of AI rather than waiting for regulation to be implemented with AI principles, internal audits, and a Chief AI Officer responsible for maintaining standards and compliance.
Snapchat
Evan Spiegel, CEO of Snapchat, reviewed its focus on visual communication between friends and family, setting it apart from other platforms with the launch of My AI, an AI-powered chatbot.
The new features include adding My AI to group conversations, providing Place recommendations from Snap Map, and suggesting relevant AR Lenses.
During the Q&A, Spiegel expanded on how Snapchat uses AI in messaging, augmented reality (AR), and content/ads.
- In AR, AI-powered Lenses have driven significant engagement, and the intersection of AR and AI is crucial for future Spectacles.
- On the messaging side, conversational AI plays to Snapchat’s strengths, with users enjoying communication with My AI.
Although no specific stats are shared, Spiegel said Snapchat is cautiously rolling out My AI while being pleased with the engagement so far.
Looking Forward
The growing emphasis on AI-driven solutions by industry giants such as Amazon, Google, Microsoft, and others highlights the potentially transformative impact of AI on businesses and society at large.
As companies increasingly invest in AI infrastructure and talent, they are shaping the future of technology and creating new opportunities for innovation.
However, this rapid shift towards AI integration also raises questions about potential risks, ethical considerations, and the need for regulation to ensure that the advancements in AI benefit everyone.
Featured image: iQoncept/Shutterstock
SEO
How to Revive an Old Blog Article for SEO
Quick question: What do you typically do with your old blog posts? Most likely, the answer is: Not much.
If that’s the case, you’re not alone. Many of us in SEO and content marketing tend to focus on continuously creating new content, rather than leveraging our existing blog posts.
However, here’s the reality—Google is becoming increasingly sophisticated in evaluating content quality, and we need to adapt accordingly. Just as it’s easier to encourage existing customers to make repeat purchases, updating old content on your website is a more efficient and sustainable strategy in the long run.
Ways to Optimize Older Content
Some of your old content might not be optimized for SEO very well, rank for irrelevant keywords, or drive no traffic at all. If the quality is still decent, however, you should be able to optimize it properly with little effort.
Refresh Content
If your blog post contains a specific year or mentions current events, it may become outdated over time. If the rest of the content is still relevant (like if it’s targeting an evergreen topic), simply updating the date might be all you need to do.
Rewrite Old Blog Posts
When the content quality is low (you might have greatly improved your writing skills since you’ve written the post) but the potential is still there, there’s not much you can do apart from rewriting an old blog post completely.
This is not a waste—you’re saving time on brainstorming since the basic structure is already in place. Now, focus on improving the quality.
Delete Old Blog Posts
You might find a blog post that just seems unusable. Should you delete your old content? It depends. If it’s completely outdated, of low quality, and irrelevant to any valuable keywords for your website, it’s better to remove it.
Once you decide to delete the post, don’t forget to set up a 301 redirect to a related post or page, or to your homepage.
Promote Old Blog Posts
Sometimes all your content needs is a bit of promotion to start ranking and getting traffic again. Share it on your social media, link to it from a new post – do something to get it discoverable again to your audience. This can give it the boost it needs to attract organic links too.
Which Blog Posts Should You Update?
Deciding when to update or rewrite blog posts is a decision that relies on one important thing: a content audit.
Use your Google Analytics to find out which blog posts used to drive tons of traffic, but no longer have the same reach. You can also use Google Search Console to find out which of your blog posts have lost visibility in comparison to previous months. I have a guide on website analysis using Google Analytics and Google Search Console you can follow.
If you use keyword tracking tools like SE Ranking, you can also use the data it provides to come up with a list of blog posts that have dropped in the rankings.
Make data-driven decisions to identify which blog posts would benefit from these updates – i.e., which ones still have the chance to recover their keyword rankings and organic traffic.
With Google’s helpful content update, which emphasizes better user experiences, it’s crucial to ensure your content remains relevant, valuable, and up-to-date.
How To Update Old Blog Posts for SEO
Updating articles can be an involved process. Here are some tips and tactics to help you get it right.
Author’s Note: I have a Comprehensive On-Page SEO Checklist you might also be interested in following while you’re doing your content audit.
Conduct New Keyword Research
Updating your post without any guide won’t get you far. Always do your keyword research to understand how users are searching for your given topic.
Proper research can also show you relevant questions and sections that can be added to the blog post you’re updating or rewriting. Make sure to take a look at the People Also Ask (PAA) section that shows up when you search for your target keyword. Check out other websites like Answer The Public, Reddit, and Quora to see what users are looking for too.
Look for New Ranking Opportunities
When trying to revive an old blog post for SEO, keep an eye out for new SEO opportunities (e.g., AI Overview, featured snippets, and related search terms) that didn’t exist when you first wrote your blog post. Some of these features can be targeted by the new content you will add to your post, if you write with the aim to be eligible for it.
Rewrite Headlines and Meta Tags
If you want to attract new readers, consider updating your headlines and meta tags.
Your headlines and meta tags should fulfill these three things:
- Reflect the rewritten and new content you’ve added to the blog post.
- Be optimized for the new keywords it’s targeting (if any).
- Appeal to your target audience – who may have changed tastes from when the blog post was originally made.
Remember that your meta tags in particular act like a brief advertisement for your blog post, since this is what the user first sees when your blog post is shown in the search results page.
Take a look at your blog post’s click-through rate on Google Search Console – if it falls below 2%, it’s definitely time for new meta tags.
Replace Outdated Information and Statistics
Updating blog content with current studies and statistics enhances the relevance and credibility of your post. By providing up-to-date information, you help your audience make better, well-informed decisions, while also showing that your content is trustworthy.
Tighten or Expand Ideas
Your old content might be too short to provide real value to users – or you might have rambled on and on in your post. It’s important to evaluate whether you need to make your content more concise, or if you need to elaborate more.
Keep the following tips in mind as you refine your blog post’s ideas:
- Evaluate Helpfulness: Measure how well your content addresses your readers’ pain points. Aim to follow the E-E-A-T model (Experience, Expertise, Authoritativeness, Trustworthiness).
- Identify Missing Context: Consider whether your content needs more detail or clarification. View it from your audience’s perspective and ask if the information is complete, or if more information is needed.
- Interview Experts: Speak with industry experts or thought leaders to get fresh insights. This will help support your writing, and provide unique points that enhance the value of your content.
- Use Better Examples: Examples help simplify complex concepts. Add new examples or improve existing ones to strengthen your points.
- Add New Sections if Needed: If your content lacks depth or misses a key point, add new sections to cover these areas more thoroughly.
- Remove Fluff: Every sentence should contribute to the overall narrative. Eliminate unnecessary content to make your post more concise.
- Revise Listicles: Update listicle items based on SEO recommendations and content quality. Add or remove headings to stay competitive with higher-ranking posts.
Improve Visuals and Other Media
No doubt that there are tons of old graphics and photos in your blog posts that can be improved with the tools we have today. Make sure all of the visuals used in your content are appealing and high quality.
Update Internal and External Links
Are your internal and external links up to date? They need to be for your SEO and user experience. Outdated links can lead to broken pages or irrelevant content, frustrating readers and hurting your site’s performance.
You need to check for any broken links on your old blog posts, and update them ASAP. Updating your old blog posts can also lead to new opportunities to link internally to other blog posts and pages, which may not have been available when the post was originally published.
Optimize for Conversions
When updating content, the ultimate goal is often to increase conversions. However, your conversion goals may have changed over the years.
So here’s what you need to check in your updated blog post. First, does the call-to-action (CTA) still link to the products or services you want to promote? If not, update it to direct readers to the current solution or offer.
Second, consider where you can use different conversion strategies. Don’t just add a CTA at the end of the post.
Last, make sure that the blog post leverages product-led content. It’s going to help you mention your products and services in a way that feels natural, without being too pushy. Being subtle can be a high ROI tactic for updated posts.
Key Takeaway
Reviving old blog articles for SEO is a powerful strategy that can breathe new life into your content and boost your website’s visibility. Instead of solely focusing on creating new posts, taking the time to refresh existing content can yield impressive results, both in terms of traffic and conversions.
By implementing these strategies, you can transform old blog posts into valuable resources that attract new readers and retain existing ones. So, roll up your sleeves, dive into your archives, and start updating your content today—your audience and search rankings will thank you!
SEO
How Compression Can Be Used To Detect Low Quality Pages
The concept of Compressibility as a quality signal is not widely known, but SEOs should be aware of it. Search engines can use web page compressibility to identify duplicate pages, doorway pages with similar content, and pages with repetitive keywords, making it useful knowledge for SEO.
Although the following research paper demonstrates a successful use of on-page features for detecting spam, the deliberate lack of transparency by search engines makes it difficult to say with certainty if search engines are applying this or similar techniques.
What Is Compressibility?
In computing, compressibility refers to how much a file (data) can be reduced in size while retaining essential information, typically to maximize storage space or to allow more data to be transmitted over the Internet.
TL/DR Of Compression
Compression replaces repeated words and phrases with shorter references, reducing the file size by significant margins. Search engines typically compress indexed web pages to maximize storage space, reduce bandwidth, and improve retrieval speed, among other reasons.
This is a simplified explanation of how compression works:
- Identify Patterns:
A compression algorithm scans the text to find repeated words, patterns and phrases - Shorter Codes Take Up Less Space:
The codes and symbols use less storage space then the original words and phrases, which results in a smaller file size. - Shorter References Use Less Bits:
The “code” that essentially symbolizes the replaced words and phrases uses less data than the originals.
A bonus effect of using compression is that it can also be used to identify duplicate pages, doorway pages with similar content, and pages with repetitive keywords.
Research Paper About Detecting Spam
This research paper is significant because it was authored by distinguished computer scientists known for breakthroughs in AI, distributed computing, information retrieval, and other fields.
Marc Najork
One of the co-authors of the research paper is Marc Najork, a prominent research scientist who currently holds the title of Distinguished Research Scientist at Google DeepMind. He’s a co-author of the papers for TW-BERT, has contributed research for increasing the accuracy of using implicit user feedback like clicks, and worked on creating improved AI-based information retrieval (DSI++: Updating Transformer Memory with New Documents), among many other major breakthroughs in information retrieval.
Dennis Fetterly
Another of the co-authors is Dennis Fetterly, currently a software engineer at Google. He is listed as a co-inventor in a patent for a ranking algorithm that uses links, and is known for his research in distributed computing and information retrieval.
Those are just two of the distinguished researchers listed as co-authors of the 2006 Microsoft research paper about identifying spam through on-page content features. Among the several on-page content features the research paper analyzes is compressibility, which they discovered can be used as a classifier for indicating that a web page is spammy.
Detecting Spam Web Pages Through Content Analysis
Although the research paper was authored in 2006, its findings remain relevant to today.
Then, as now, people attempted to rank hundreds or thousands of location-based web pages that were essentially duplicate content aside from city, region, or state names. Then, as now, SEOs often created web pages for search engines by excessively repeating keywords within titles, meta descriptions, headings, internal anchor text, and within the content to improve rankings.
Section 4.6 of the research paper explains:
“Some search engines give higher weight to pages containing the query keywords several times. For example, for a given query term, a page that contains it ten times may be higher ranked than a page that contains it only once. To take advantage of such engines, some spam pages replicate their content several times in an attempt to rank higher.”
The research paper explains that search engines compress web pages and use the compressed version to reference the original web page. They note that excessive amounts of redundant words results in a higher level of compressibility. So they set about testing if there’s a correlation between a high level of compressibility and spam.
They write:
“Our approach in this section to locating redundant content within a page is to compress the page; to save space and disk time, search engines often compress web pages after indexing them, but before adding them to a page cache.
…We measure the redundancy of web pages by the compression ratio, the size of the uncompressed page divided by the size of the compressed page. We used GZIP …to compress pages, a fast and effective compression algorithm.”
High Compressibility Correlates To Spam
The results of the research showed that web pages with at least a compression ratio of 4.0 tended to be low quality web pages, spam. However, the highest rates of compressibility became less consistent because there were fewer data points, making it harder to interpret.
Figure 9: Prevalence of spam relative to compressibility of page.
The researchers concluded:
“70% of all sampled pages with a compression ratio of at least 4.0 were judged to be spam.”
But they also discovered that using the compression ratio by itself still resulted in false positives, where non-spam pages were incorrectly identified as spam:
“The compression ratio heuristic described in Section 4.6 fared best, correctly identifying 660 (27.9%) of the spam pages in our collection, while misidentifying 2, 068 (12.0%) of all judged pages.
Using all of the aforementioned features, the classification accuracy after the ten-fold cross validation process is encouraging:
95.4% of our judged pages were classified correctly, while 4.6% were classified incorrectly.
More specifically, for the spam class 1, 940 out of the 2, 364 pages, were classified correctly. For the non-spam class, 14, 440 out of the 14,804 pages were classified correctly. Consequently, 788 pages were classified incorrectly.”
The next section describes an interesting discovery about how to increase the accuracy of using on-page signals for identifying spam.
Insight Into Quality Rankings
The research paper examined multiple on-page signals, including compressibility. They discovered that each individual signal (classifier) was able to find some spam but that relying on any one signal on its own resulted in flagging non-spam pages for spam, which are commonly referred to as false positive.
The researchers made an important discovery that everyone interested in SEO should know, which is that using multiple classifiers increased the accuracy of detecting spam and decreased the likelihood of false positives. Just as important, the compressibility signal only identifies one kind of spam but not the full range of spam.
The takeaway is that compressibility is a good way to identify one kind of spam but there are other kinds of spam that aren’t caught with this one signal. Other kinds of spam were not caught with the compressibility signal.
This is the part that every SEO and publisher should be aware of:
“In the previous section, we presented a number of heuristics for assaying spam web pages. That is, we measured several characteristics of web pages, and found ranges of those characteristics which correlated with a page being spam. Nevertheless, when used individually, no technique uncovers most of the spam in our data set without flagging many non-spam pages as spam.
For example, considering the compression ratio heuristic described in Section 4.6, one of our most promising methods, the average probability of spam for ratios of 4.2 and higher is 72%. But only about 1.5% of all pages fall in this range. This number is far below the 13.8% of spam pages that we identified in our data set.”
So, even though compressibility was one of the better signals for identifying spam, it still was unable to uncover the full range of spam within the dataset the researchers used to test the signals.
Combining Multiple Signals
The above results indicated that individual signals of low quality are less accurate. So they tested using multiple signals. What they discovered was that combining multiple on-page signals for detecting spam resulted in a better accuracy rate with less pages misclassified as spam.
The researchers explained that they tested the use of multiple signals:
“One way of combining our heuristic methods is to view the spam detection problem as a classification problem. In this case, we want to create a classification model (or classifier) which, given a web page, will use the page’s features jointly in order to (correctly, we hope) classify it in one of two classes: spam and non-spam.”
These are their conclusions about using multiple signals:
“We have studied various aspects of content-based spam on the web using a real-world data set from the MSNSearch crawler. We have presented a number of heuristic methods for detecting content based spam. Some of our spam detection methods are more effective than others, however when used in isolation our methods may not identify all of the spam pages. For this reason, we combined our spam-detection methods to create a highly accurate C4.5 classifier. Our classifier can correctly identify 86.2% of all spam pages, while flagging very few legitimate pages as spam.”
Key Insight:
Misidentifying “very few legitimate pages as spam” was a significant breakthrough. The important insight that everyone involved with SEO should take away from this is that one signal by itself can result in false positives. Using multiple signals increases the accuracy.
What this means is that SEO tests of isolated ranking or quality signals will not yield reliable results that can be trusted for making strategy or business decisions.
Takeaways
We don’t know for certain if compressibility is used at the search engines but it’s an easy to use signal that combined with others could be used to catch simple kinds of spam like thousands of city name doorway pages with similar content. Yet even if the search engines don’t use this signal, it does show how easy it is to catch that kind of search engine manipulation and that it’s something search engines are well able to handle today.
Here are the key points of this article to keep in mind:
- Doorway pages with duplicate content is easy to catch because they compress at a higher ratio than normal web pages.
- Groups of web pages with a compression ratio above 4.0 were predominantly spam.
- Negative quality signals used by themselves to catch spam can lead to false positives.
- In this particular test, they discovered that on-page negative quality signals only catch specific types of spam.
- When used alone, the compressibility signal only catches redundancy-type spam, fails to detect other forms of spam, and leads to false positives.
- Combing quality signals improves spam detection accuracy and reduces false positives.
- Search engines today have a higher accuracy of spam detection with the use of AI like Spam Brain.
Read the research paper, which is linked from the Google Scholar page of Marc Najork:
Detecting spam web pages through content analysis
Featured Image by Shutterstock/pathdoc
SEO
New Google Trends SEO Documentation
Google Search Central published new documentation on Google Trends, explaining how to use it for search marketing. This guide serves as an easy to understand introduction for newcomers and a helpful refresher for experienced search marketers and publishers.
The new guide has six sections:
- About Google Trends
- Tutorial on monitoring trends
- How to do keyword research with the tool
- How to prioritize content with Trends data
- How to use Google Trends for competitor research
- How to use Google Trends for analyzing brand awareness and sentiment
The section about monitoring trends advises there are two kinds of rising trends, general and specific trends, which can be useful for developing content to publish on a site.
Using the Explore tool, you can leave the search box empty and view the current rising trends worldwide or use a drop down menu to focus on trends in a specific country. Users can further filter rising trends by time periods, categories and the type of search. The results show rising trends by topic and by keywords.
To search for specific trends users just need to enter the specific queries and then filter them by country, time, categories and type of search.
The section called Content Calendar describes how to use Google Trends to understand which content topics to prioritize.
Google explains:
“Google Trends can be helpful not only to get ideas on what to write, but also to prioritize when to publish it. To help you better prioritize which topics to focus on, try to find seasonal trends in the data. With that information, you can plan ahead to have high quality content available on your site a little before people are searching for it, so that when they do, your content is ready for them.”
Read the new Google Trends documentation:
Get started with Google Trends
Featured Image by Shutterstock/Luis Molinero