SEO

Everything You Need To Know

Published

2 months ago

March 23, 2023

Google has just released Bard, its answer to ChatGPT, and users are getting to know it to see how it compares to OpenAI’s artificial intelligence-powered chatbot.

The name ‘Bard’ is purely marketing-driven, as there are no algorithms named Bard, but we do know that the chatbot is powered by LaMDA.

Here is everything we know about Bard so far and some interesting research that may offer an idea of the kind of algorithms that may power Bard.

What Is Google Bard?

Bard is an experimental Google chatbot that is powered by the LaMDA large language model.

It’s a generative AI that accepts prompts and performs text-based tasks like providing answers and summaries and creating various forms of content.

Bard also assists in exploring topics by summarizing information found on the internet and providing links for exploring websites with more information.

Why Did Google Release Bard?

Google released Bard after the wildly successful launch of OpenAI’s ChatGPT, which created the perception that Google was falling behind technologically.

ChatGPT was perceived as a revolutionary technology with the potential to disrupt the search industry and shift the balance of power away from Google search and the lucrative search advertising business.

On December 21, 2022, three weeks after the launch of ChatGPT, the New York Times reported that Google had declared a “code red” to quickly define its response to the threat posed to its business model.

Forty-seven days after the code red strategy adjustment, Google announced the launch of Bard on February 6, 2023.

What Was The Issue With Google Bard?

The announcement of Bard was a stunning failure because the demo that was meant to showcase Google’s chatbot AI contained a factual error.

The inaccuracy of Google’s AI turned what was meant to be a triumphant return to form into a humbling pie in the face.

Google’s shares subsequently lost a hundred billion dollars in market value in a single day, reflecting a loss of confidence in Google’s ability to navigate the looming era of AI.

How Does Google Bard Work?

Bard is powered by a “lightweight” version of LaMDA.

LaMDA is a large language model that is trained on datasets consisting of public dialogue and web data.

There are two important factors related to the training described in the associated research paper, which you can download as a PDF here: LaMDA: Language Models for Dialog Applications (read the abstract here).

A. Safety: The model achieves a level of safety by tuning it with data that was annotated by crowd workers.
B. Groundedness: LaMDA grounds itself factually with external knowledge sources (through information retrieval, which is search).

The LaMDA research paper states:

“…factual grounding, involves enabling the model to consult external knowledge sources, such as an information retrieval system, a language translator, and a calculator.

We quantify factuality using a groundedness metric, and we find that our approach enables the model to generate responses grounded in known sources, rather than responses that merely sound plausible.”

Google used three metrics to evaluate the LaMDA outputs:

Sensibleness: A measurement of whether an answer makes sense or not.
Specificity: Measures if the answer is the opposite of generic/vague or contextually specific.
Interestingness: This metric measures if LaMDA’s answers are insightful or inspire curiosity.

All three metrics were judged by crowdsourced raters, and that data was fed back into the machine to keep improving it.

The LaMDA research paper concludes by stating that crowdsourced reviews and the system’s ability to fact-check with a search engine were useful techniques.

Google’s researchers wrote:

“We find that crowd-annotated data is an effective tool for driving significant additional gains.

We also find that calling external APIs (such as an information retrieval system) offers a path towards significantly improving groundedness, which we define as the extent to which a generated response contains claims that can be referenced and checked against a known source.”

How Is Google Planning To Use Bard In Search?

The future of Bard is currently envisioned as a feature in search.

Google’s announcement in February was insufficiently specific on how Bard would be implemented.

The key details were buried in a single paragraph close to the end of the blog announcement of Bard, where it was described as an AI feature in search.

That lack of clarity fueled the perception that Bard would be integrated into search, which was never the case.

Google’s February 2023 announcement of Bard states that Google will at some point integrate AI features into search:

“Soon, you’ll see AI-powered features in Search that distill complex information and multiple perspectives into easy-to-digest formats, so you can quickly understand the big picture and learn more from the web: whether that’s seeking out additional perspectives, like blogs from people who play both piano and guitar, or going deeper on a related topic, like steps to get started as a beginner.

These new AI features will begin rolling out on Google Search soon.”

It’s clear that Bard is not search. Rather, it is intended to be a feature in search and not a replacement for search.

What Is A Search Feature?

A feature is something like Google’s Knowledge Panel, which provides knowledge information about notable people, places, and things.

Google’s “How Search Works” webpage about features explains:

“Google’s search features ensure that you get the right information at the right time in the format that’s most useful to your query.

Sometimes it’s a webpage, and sometimes it’s real-world information like a map or inventory at a local store.”

In an internal meeting at Google (reported by CNBC), employees questioned the use of Bard in search.

One employee pointed out that large language models like ChatGPT and Bard are not fact-based sources of information.

The Google employee asked:

“Why do we think the big first application should be search, which at its heart is about finding true information?”

Jack Krawczyk, the product lead for Google Bard, answered:

“I just want to be very clear: Bard is not search.”

At the same internal event, Google’s Vice President of Engineering for Search, Elizabeth Reid, reiterated that Bard is not search.

She said:

“Bard is really separate from search…”

What we can confidently conclude is that Bard is not a new iteration of Google search. It is a feature.

Bard Is An Interactive Method For Exploring Topics

Google’s announcement of Bard was fairly explicit that Bard is not search. This means that, while search surfaces links to answers, Bard helps users investigate knowledge.

The announcement explains:

“When people think of Google, they often think of turning to us for quick factual answers, like ‘how many keys does a piano have?’

But increasingly, people are turning to Google for deeper insights and understanding – like, ‘is the piano or guitar easier to learn, and how much practice does each need?’

Learning about a topic like this can take a lot of effort to figure out what you really need to know, and people often want to explore a diverse range of opinions or perspectives.”

It may be helpful to think of Bard as an interactive method for accessing knowledge about topics.

Bard Samples Web Information

The problem with large language models is that they mimic answers, which can lead to factual errors.

The researchers who created LaMDA state that approaches like increasing the size of the model can help it gain more factual information.

But they noted that this approach fails in areas where facts are constantly changing during the course of time, which researchers refer to as the “temporal generalization problem.”

Freshness in the sense of timely information cannot be trained with a static language model.

The solution that LaMDA pursued was to query information retrieval systems. An information retrieval system is a search engine, so LaMDA checks search results.

This feature from LaMDA appears to be a feature of Bard.

The Google Bard announcement explains:

“Bard seeks to combine the breadth of the world’s knowledge with the power, intelligence, and creativity of our large language models.

It draws on information from the web to provide fresh, high-quality responses.”

Screenshot of a Google Bard Chat, March 2023

LaMDA and (possibly by extension) Bard achieve this with what is called the toolset (TS).

The toolset is explained in the LaMDA researcher paper:

“We create a toolset (TS) that includes an information retrieval system, a calculator, and a translator.

TS takes a single string as input and outputs a list of one or more strings. Each tool in TS expects a string and returns a list of strings.

For example, the calculator takes “135+7721”, and outputs a list containing [“7856”]. Similarly, the translator can take “hello in French” and output [‘Bonjour’].

Finally, the information retrieval system can take ‘How old is Rafael Nadal?’, and output [‘Rafael Nadal / Age / 35’].

The information retrieval system is also capable of returning snippets of content from the open web, with their corresponding URLs.

The TS tries an input string on all of its tools, and produces a final output list of strings by concatenating the output lists from every tool in the following order: calculator, translator, and information retrieval system.

A tool will return an empty list of results if it can’t parse the input (e.g., the calculator cannot parse ‘How old is Rafael Nadal?’), and therefore does not contribute to the final output list.”

Here’s a Bard response with a snippet from the open web:

Google Bard: Everything You Need To Know

Screenshot of a Google Bard Chat, March 2023

Conversational Question-Answering Systems

There are no research papers that mention the name “Bard.”

However, there is quite a bit of recent research related to AI, including by scientists associated with LaMDA, that may have an impact on Bard.

The following doesn’t claim that Google is using these algorithms. We can’t say for certain that any of these technologies are used in Bard.

The value in knowing about these research papers is in knowing what is possible.

The following are algorithms relevant to AI-based question-answering systems.

One of the authors of LaMDA worked on a project that’s about creating training data for a conversational information retrieval system.

You can download the 2022 research paper as a PDF here: Dialog Inpainting: Turning Documents into Dialogs (and read the abstract here).

The problem with training a system like Bard is that question-and-answer datasets (like datasets comprised of questions and answers found on Reddit) are limited to how people on Reddit behave.

It doesn’t encompass how people outside of that environment behave and the kinds of questions they would ask, and what the correct answers to those questions would be.

The researchers explored creating a system read webpages, then used a “dialog inpainter” to predict what questions would be answered by any given passage within what the machine was reading.

A passage in a trustworthy Wikipedia webpage that says, “The sky is blue,” could be turned into the question, “What color is the sky?”

The researchers created their own dataset of questions and answers using Wikipedia and other webpages. They called the datasets WikiDialog and WebDialog.

WikiDialog is a set of questions and answers derived from Wikipedia data.
WebDialog is a dataset derived from webpage dialog on the internet.

These new datasets are 1,000 times larger than existing datasets. The importance of that is it gives conversational language models an opportunity to learn more.

The researchers reported that this new dataset helped to improve conversational question-answering systems by over 40%.

The research paper describes the success of this approach:

“Importantly, we find that our inpainted datasets are powerful sources of training data for ConvQA systems…

When used to pre-train standard retriever and reranker architectures, they advance state-of-the-art across three different ConvQA retrieval benchmarks (QRECC, OR-QUAC, TREC-CAST), delivering up to 40% relative gains on standard evaluation metrics…

Remarkably, we find that just pre-training on WikiDialog enables strong zero-shot retrieval performance—up to 95% of a finetuned retriever’s performance—without using any in-domain ConvQA data. “

Is it possible that Google Bard was trained using the WikiDialog and WebDialog datasets?

It’s difficult to imagine a scenario where Google would pass on training a conversational AI on a dataset that is over 1,000 times larger.

But we don’t know for certain because Google doesn’t often comment on its underlying technologies in detail, except on rare occasions like for Bard or LaMDA.

Large Language Models That Link To Sources

Google recently published an interesting research paper about a way to make large language models cite the sources for their information. The initial version of the paper was published in December 2022, and the second version was updated in February 2023.

This technology is referred to as experimental as of December 2022.

You can download the PDF of the paper here: Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models (read the Google abstract here).

The research paper states the intent of the technology:

“Large language models (LLMs) have shown impressive results while requiring little or no direct supervision.

Further, there is mounting evidence that LLMs may have potential in information-seeking scenarios.

We believe the ability of an LLM to attribute the text that it generates is likely to be crucial in this setting.

We formulate and study Attributed QA as a key first step in the development of attributed LLMs.

We propose a reproducible evaluation framework for the task and benchmark a broad set of architectures.

We take human annotations as a gold standard and show that a correlated automatic metric is suitable for development.

Our experimental work gives concrete answers to two key questions (How to measure attribution?, and How well do current state-of-the-art methods perform on attribution?), and give some hints as to how to address a third (How to build LLMs with attribution?).”

This kind of large language model can train a system that can answer with supporting documentation that, theoretically, assures that the response is based on something.

The research paper explains:

“To explore these questions, we propose Attributed Question Answering (QA). In our formulation, the input to the model/system is a question, and the output is an (answer, attribution) pair where answer is an answer string, and attribution is a pointer into a fixed corpus, e.g., of paragraphs.

The returned attribution should give supporting evidence for the answer.”

This technology is specifically for question-answering tasks.

The goal is to create better answers – something that Google would understandably want for Bard.

Attribution allows users and developers to assess the “trustworthiness and nuance” of the answers.
Attribution allows developers to quickly review the quality of the answers since the sources are provided.

One interesting note is a new technology called AutoAIS that strongly correlates with human raters.

In other words, this technology can automate the work of human raters and scale the process of rating the answers given by a large language model (like Bard).

The researchers share:

“We consider human rating to be the gold standard for system evaluation, but find that AutoAIS correlates well with human judgment at the system level, offering promise as a development metric where human rating is infeasible, or even as a noisy training signal. “

This technology is experimental; it’s probably not in use. But it does show one of the directions that Google is exploring for producing trustworthy answers.

Research Paper On Editing Responses For Factuality

Lastly, there’s a remarkable technology developed at Cornell University (also dating from the end of 2022) that explores a different way to source attribution for what a large language model outputs and can even edit an answer to correct itself.

Cornell University (like Stanford University) licenses technology related to search and other areas, earning millions of dollars per year.

It’s good to keep up with university research because it shows what is possible and what is cutting-edge.

You can download a PDF of the paper here: RARR: Researching and Revising What Language Models Say, Using Language Models (and read the abstract here).

The abstract explains the technology:

“Language models (LMs) now excel at many tasks such as few-shot learning, question answering, reasoning, and dialog.

However, they sometimes generate unsupported or misleading content.

A user cannot easily determine whether their outputs are trustworthy or not, because most LMs do not have any built-in mechanism for attribution to external evidence.

To enable attribution while still preserving all the powerful advantages of recent generation models, we propose RARR (Retrofit Attribution using Research and Revision), a system that 1) automatically finds attribution for the output of any text generation model and 2) post-edits the output to fix unsupported content while preserving the original output as much as possible.

…we find that RARR significantly improves attribution while otherwise preserving the original input to a much greater degree than previously explored edit models.

Furthermore, the implementation of RARR requires only a handful of training examples, a large language model, and standard web search.”

How Do I Get Access To Google Bard?

Google is currently accepting new users to test Bard, which is currently labeled as experimental. Google is rolling out access for Bard here.

Screenshot from bard.google.com, March 2023

Google is on the record saying that Bard is not search, which should reassure those who feel anxiety about the dawn of AI.

We are at a turning point that is unlike any we’ve seen in, perhaps, a decade.

Understanding Bard is helpful to anyone who publishes on the web or practices SEO because it’s helpful to know the limits of what is possible and the future of what can be achieved.

More Resources:

Featured Image: Whyredphotographor/Shutterstock

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SEO

What You Need To Know In 2023

Published

6 hours ago

May 30, 2023

EntirewebNewsBot

In a recent interview, Rene Ritchie, YouTube’s creator liaison, sat down with Todd Beaupre, YouTube’s growth and discovery lead, to discuss the algorithm’s future and its implications for creators in 2023.

Beaupre shares many insights that can help content creators understand and navigate YouTube.

This candid Q&A uncovers vital details, such as:

The importance of focusing on audience satisfaction over algorithmic manipulation.
The role of audience feedback and survey responses in refining YouTube’s recommendation system.
Strategies for creators to build long-term relationships with their audiences for sustained success.
YouTube’s dedicated efforts to support new or smaller creators.
Advice on managing multi-format, multi-language content and the advantages of channel experimentation.
The future of content discovery on YouTube, including the potential of emerging technologies and user interface enhancements.

This article overviews their enlightening conversation, with all the details on optimizing your YouTube content in 2023.

From Algorithm To Audience: A New Perspective

Q: What’s the main thing creators should focus on for the YouTube algorithm?

Beaupre emphasizes the importance of not thinking about algorithms but audiences. Creators are often asked about the best time or frequency to upload videos to optimize algorithm favorability.

Beaupre encourages a shift in perspective:

“Creators often ask about optimizing their upload time or frequency for the algorithm. But we want creators to shift their thinking. Rather than focusing on the algorithm, they should focus on the audience. Replace the word “algorithm” in their questions with the word “audience.” We design the algorithm to serve the audience, so understanding audience preferences will help the algorithm favor their content.”

The Satisfaction Metric: A Holistic View Of Engagement

Q: Can you explain the significance of the satisfaction metric in the YouTube algorithm?

Beaupre addresses an essential aspect of YouTube’s algorithm: audience satisfaction.

While watch time is a commonly known factor the algorithm considers, Beaupre says that not all watch time is equal:

“Everyone knows that watch time is one of the factors we look at. But we’ve realized that not all watch time is equal. We also need to understand the value an audience derives from a video. To do this, we run surveys about recommendations and specific videos, feeding those responses into the recommendation system. This helps the algorithm identify patterns of satisfying content, looking at various signals like likes, dislikes, watch time, and survey responses.”

A Long-term Strategy: The Key To Creator Success

Q: What kind of strategy should creators adopt for success on YouTube?

Beaupre says creators who prioritize long-term audience value over immediate views stand to benefit more long-term.

He explains that a video’s potential to leave a lasting impression and foster a long-term relationship with the audience would correlate well with satisfaction.

“I would advise creators to think about the long-term value for their audience. Rather than focusing on getting a lot of views in a week, think about creating a lasting impression with your audience. This could mean they’ll want to return to your channel in the future.”

Supporting Smaller Channels

Q: How does YouTube support new or smaller creators who don’t have a large audience?

For creators with smaller audiences, Beaupre reveals that YouTube has a team focused on helping them identify their audience, using various approaches like assessing video titles and descriptions.

“We have a team that focuses on this exact challenge. They use different approaches, like assessing video titles and descriptions, to help these creators identify their audience. We track the success of new creators on the platform, and we’re committed to helping them succeed.”

Multi-format, Multi-language Content:

Q: How should creators manage their channels with the rise of multi-format, multi-language content?

Beaupre touches on the evolving content landscape, including long-form videos, Live, Shorts, and podcasts.

His advice to creators navigating this space is:

“My advice to creators is simple: “Same audience, same channel, different audience, different channel.” We’re looking for ways to make it easier for creators to manage their channels in this multi-format, multi-language world. We encourage creators to experiment with different formats on the same channel and see how their audience reacts.”

The Future Of Discovery On YouTube

Q: What’s the future of discovery on YouTube?

Speaking about the challenges and opportunities ahead, Beaupre highlights several focus areas.

These include leveraging emerging technology, such as large language models, and making the discovery experience more enjoyable.

“We have several areas of focus. We’re excited about emerging technology like large language models, which could improve recommendation quality. We’re also working on enabling seamless user journeys across various formats. Another challenge is to make the discovery experience more enjoyable for users. We’re exploring opportunities to make the interface more entertaining and less overwhelming.”

Final Words

Beaupre signs off with the message that YouTube’s algorithm prioritizes the audience’s satisfaction.

By creating long-term value for your audience, understanding their needs, and experimenting with different formats, you can better align with the platform’s goals and succeed.

Source: YouTube

Featured image generated by the author using Midjourney.

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SEO

TikTok Dominates Short-Form Content, Instagram Reels Not Far Behind

Published

13 hours ago

May 30, 2023

EntirewebNewsBot

TikTok Dominates Short-Form Content, Instagram Reels Not Far Behind

Three platforms dominate short-form video content: TikTok, Instagram Reels, and YouTube Shorts.

A recent study conducted by Social Insider dives into the performance stats of these platforms, analyzing key metrics to determine which comes out on top.

In this article, we’ll examine these key insights:

TikTok holds the crown for the most engagement.
Comments pour in twice as much on TikTok as on Instagram Reels and YouTube Shorts.
Brands post twice as much content on TikTok as on Instagram Reels and YouTube Shorts.
Instagram Reels leads the highest watch rate, while YouTube Shorts lags.
Each platform’s algorithm plays a role in how content performs.
Each platform caters to specific audiences and marketing objectives.

Keep reading as we unpack these findings and explore what they mean for users and marketers alike.

TikTok Reigns Supreme In Engagement

TikTok, widely recognized as the forerunner of the short-form video trend, claims the engagement rate crown.

The study finds TikTok outperforms Instagram Reels and YouTube Shorts in interaction, scoring double the comments of its competitors.

“From an engagement rate perspective, in this TikTok vs. Reels vs. Shorts performance comparison, TikTok sets itself apart as the undisputable winner,” the study notes.

The study compares engagement rates, revealing that YouTube Shorts averages around 3.80%, Reels hits an average of 4.36%, and TikTok boasts a significantly higher rate of 5.53%.

The Power Of TikTok’s Virality

TikTok’s success is partly due to users’ ability to harness viral trends, enabling explosive follower growth.

The study mentions that a social media strategy focusing on authenticity and humanized approaches can lead to rapid success.

Brands post twice as much content on TikTok as they do on Reels and Shorts, further emphasizing TikTok’s dominance in this space.

Reels & Shorts: Not To Be Overlooked

While TikTok may lead in engagement and content volume, Instagram’s Reels and YouTube’s Shorts have their strengths.

Reels, for instance, records the highest watch rate among the three platforms.

This could be credited to Instagram’s follower-based model, with Reels serving as a potent content type for brands with a large audience.

On the other hand, YouTube Shorts functions more as a discovery tool.

Most Shorts’ views originate from the homepage. From there, YouTube starts recommending long-form content.

This recommendation system can increase a channel’s subscribers, views, and traction on long-form videos.

A Multifaceted Approach for Marketers

Given each platform’s different strengths and audiences, the study suggests a diversified approach for brands.

“Using TikTok, Reels, and Shorts complementarily and creating unique content for each, aligned with the individual’s platform audience and design, is the best approach marketers and brands alike could have,” the study concludes.

Ultimately, TikTok leads in engagement and content volume, Instagram’s Reels has the highest watch rate, and YouTube’s Shorts is the most effective discovery tool.

Each platform has a unique role in the short-form video landscape. The key for brands and marketers is understanding these roles and crafting strategies around them.

Featured image generated by the author using Midjourney.

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SEO

20+ Blogging Tools to Improve Your Workflow

Published

19 hours ago

May 30, 2023

EntirewebNewsBot

20+ Blogging Tools to Improve Your Workflow

If you want to make the most of your time blogging, choose the right tools because they’ll save you a lot of money and effort.

But there are a lot of tools out there—and not all of them are worth it.

Well, I’ve been blogging for over a decade and have used hundreds of tools in that time. To help you sort the wheat from the chaff, I’ll list all the tools I’ve used to grow several blogs to a six-figure income—and what each of them is useful for.

Just want the best blogging tool stack? Here are my 10 most-used tools for blogging:

WordPress – Best content management system (CMS) to manage your blog.
Google Docs – Best word document editor to collaborate with your team.
Wordable – Easily upload your articles from Google Docs to WordPress with one click.
Ahrefs – Best all-around SEO tool for ranking high on Google.
Notion – Best task management and content planning software.
Google Search Console – Best tool for making informed SEO decisions.
Canva – Best graphic creation tool.
Snagit – Best screenshot capture and editing tool.
Loom – Best screen recording tool.
ConvertKit – Best email marketing software.

Want more? Keep reading.

Let’s kick things off with a list of tools you can use to research, write, edit, and publish your blog posts.

WordPress

What it is: A content management system (CMS) to build your website and publish blog posts.

Why I love it: It is the most robust, SEO-friendly CMS on the market. Because it’s open source and so popular, you can do almost anything with WordPress with the right plugins or a good web developer. I’ve been using it since I started my first blog at 15. It’s not the easiest blogging tool for beginners, but it’s much more powerful than “beginner-friendly” website editors like Wix or Squarespace once you learn how to use it.

Price: Free.

Google Docs

What it is: A document editor.

Why I love it: Google Docs is an easy-to-use free document editor that makes collaborating with writers and editors a breeze. I’ve been using it to write my blog articles for over 10 years. It just works.

Price: Free.

Wordable

What it does: Uploads articles from Google Docs to WordPress in one click.

Why I love it: Google Docs has hidden code that’s brought over when you copy-paste content to WordPress. Wordable fixes that by uploading the document to WordPress while keeping the formatting but removing the extra code. It also cleans up and optimizes your images.

Price: Free up to five exports per month (then $50/month).

CoSchedule Headline Studio

What it does: Scores your headlines clickability and SEO.

Why I love it: It helps me write killer headlines—which is important for both clickability and SEO. Better headlines mean more clicks in the SERPs which, in turn, can help your content rank higher on Google. The tool gives you ideas of power words and uncommon words to use to make your headline more interesting, and its AI can write and suggest headlines for you.

Price: Free ($19/month or $99/year for premium).

Grammarly

What it does: Makes suggestions to improve your writing and fixes grammar and spelling mistakes.

Why I love it: Sometimes when you’re in the flow of writing, the words just pour out of you—and you don’t want to interrupt that flow by fixing typos or grammar mistakes. I use Grammarly post-draft to fix my mistakes after the bulk of the article is finished. It’s also compatible with Google Docs and WordPress if you get the Grammarly browser plugin, which is nice.

Price: Free.

ChatGPT

What it is: An AI chatbot.

Why I love it: I use ChatGPT for researching and outlining a lot of my content. It can help you identify topics or ideas you didn’t think about in your initial content outline. I have also started playing with it to improve my content and my life in general, such as using it to help me come up with road trip itineraries for specific needs—like finding the best routes based on weather and areas with free camping—then turn that trip into a blog post and social media videos.

Price: Free ($20/month for premium access).

Hemingway App

What it is: A text editor that grades your writing using the Flesch-Kincaid Grade Level.

Why I love it: As someone who performs and writes about fairly complex SEO tasks on a near-daily basis, it can be hard to know if my writing is easy to understand for the complete beginner. While the Hemingway App won’t necessarily help me break down these complex tasks, it will tell me if my writing is overly complex from a readability standpoint. If it is, I can simplify things more so that nearly anyone can read it. It’s up to me to make sure it still makes sense, though.

Price: Free.

Next up, let’s look at tools to help you create a content calendar and keep your content organized.

Notion

What it is: A productivity software.

Why I love it: Notion is one of the coolest software I’ve ever used. You can customize it to do almost anything: manage your to-do list, plan a content calendar, collaborate with your team, and much more. I use it to keep track of my projects and goals (both personal and business), plan out my content, journal, take notes about new things I’m learning, and more. I use Thomas Frank’s Ultimate Brain template, which has a steep learning curve but totally transformed how I plan my life and business.

Price: Free (premium plans start at $8/month).

Slack

What it is: A messaging app.

Why I love it: I used to use Slack to stay in touch with my team. However, now that I use Notion, we mostly just communicate via that app. Now, I use Slack to join communities with other bloggers and content marketers and keep in touch with masterminds I’ve met over the years. Check out some of these Slack communities for SEOs.

Price: Free (premium plans start at $7.25/month).

Google Analytics

What it is: An analytics dashboard for your website.

Why I love it: Seeing analytics data (e.g., what pages people are visiting on your site, how long they’re staying on your site, and where that traffic came from) is important to make informed decisions about what types of content to produce and where to promote your content. I check the analytics at least once a week to see which pages are performing best.

Price: Free.

Google Trends

What it does: Shows search trends for topics over time.

Why I love it: It’s super useful for finding trending and breakout topics. For example, I was recently looking for new topics for my camping website and found that searches for “lake berryessa camping” have risen 70% in the last 12 months.

Price: Free.

Search engine optimization and blogging are two peas in a pod. If you want to grow your blog organically, you need to learn SEO. Here are a few tools to help you rank higher on Google:

Ahrefs

What it is: An all-in-one SEO tool suite.

Why I love it: I use Ahrefs for a lot of things. For me, it’s been most useful for keyword research and finding backlink opportunities. My most-used feature of Ahrefs is the Content Gap report within the Site Explorer tool, as shown in the screenshot above. I like to spy on my competitors’ keywords to see what they’re ranking for that I’m not. Of course, that’s just scratching the surface of what Ahrefs can do.

Price: $99+ per month (free tools available).

SurferSEO

What it is: A software that analyzes current Google search results to score your content based on how well optimized it is compared to competing results.

Why I love it: I use SurferSEO to help me outline my content in a way that is more likely to rank well on Google. It has a content editor with a content score system that goes from 0 to 100 to grade how well optimized your content is for a given keyword (100 being the most well-optimized article). It also has a Chrome extension that lets you use it in combination with Google Docs.

Price: $49+/month (depending on tier).

Yoast SEO

What it is: A WordPress plugin that helps you better optimize your pages for search engines.

Why I love it: Yoast makes it easy to set your page’s meta tags, add robots.txt and sitemap files to help Google more easily crawl your site, and quickly see what pages aren’t optimized for search.

Price: Free ($99/year for premium).

Google Search Console

What it is: A tool to monitor and troubleshoot your website’s appearance in search results.

Why I love it: I use Google Search Console to help me find pages that are losing traction on the SERPs over time. Just go to the “Performance” report; then for the date, compare the last three months to the previous three months (or longer). Anything that’s dropped in traffic should be considered for a content refresh. I also use it to keep tabs on my site’s performance in general and see if it has any issues like not passing Core Web Vitals.

Price: Free.

AnswerThePublic

What it is: A software that finds common questions people ask on Google that are relevant to a given search query.

Why I love it: I use ATP to ensure I’m addressing all the common frequently asked questions people have around a given topic when I’m writing an article. It also helps me to show up as the answer for People Also Ask questions on Google.

Price: Free (premium plans start at $9 per month).

To The Web Title Tag Preview Tool

To The Web's title tag and meta description preview tool

What it is: A tool to see at a glance if your meta title and description are too short, too long, or just right.

Why I love it: I use it to make sure my title tags won’t be truncated (cut off) in the Google search results. If your title tag is too long (or too short), you can see it with this tool and adjust it accordingly.

Price: Free.

Let’s face it—there are only 24 hours in a day. You don’t want to waste your time manually sharing every blog post on social media or sending individual emails, do you? These tools will automate some of those time-consuming tasks for you.

Buffer

What it is: A social media scheduling tool.

Why I love it: Posting the same thing to four different social media channels is both a pain and a waste of time. Buffer makes it so that I can push updates across channels from one dashboard and see how they’re performing without logging in and posting from each channel separately. I also hired a freelancer and trained her to schedule all my blog posts to all my channels on Buffer for a double-whammy of time-saving awesomeness.

Price: Free (premium plans start at $6/month per channel).

ConvertKit

What it is: An email newsletter automation tool.

Why I love it: I love the fact that ConvertKit is extremely intuitive and easy to use. I’ve tried Mailchimp, BirdSend, and a handful of other email marketing tools—none of them were as simple as ConvertKit. I also love that its powerful automation features allow me to set up custom email drip campaigns that are tailored to individual segments of my audience, which increases engagement rates by allowing me to create highly personalized emails.

Price: Free up to 300 subscribers (then starts at $9/month).

Zapier

What it is: An automation API.

Why I love it: What’s not to love about automation? I use Zapier to send me an email when someone fills out one of my Typeforms to inquire about my services, but you can do a lot with it. Check out its list of use-case examples to get ideas.

Price: Free up to 100 tasks/month (starts at $19.99/month for paid plans).

Last but not least, I have a suite of tools I use to create images, videos, screenshots, and more.

Canva

What it is: A drag-and-drop image editor.

Why I love it: I use Canva to create featured images and social media share images for all of my blog posts. It’s incredibly easy and intuitive for a non-designer like myself to create high-quality graphics that look professionally made. It also has an AI photo editor to automate—or at least assist in—the process.

Price: Free ($12.99/month or $119.99/year for premium).

Snagit

What it is: A screen capturing and editing tool.

Why I love it: I’ve used a lot of screen capture tools, and this one has been by far the best. We use it at Ahrefs for all of our screen captures to show you how to use our tools. I also use it in my blogs to add extra information when needed and in my standard operating procedures for freelancers.

Price: $62.99 (free trial available).

Loom

What it is: A screen recording tool.

Why I love it: It’s simply the easiest way to record your screen and share it with your team, blog readers, or whoever. I use it to record tutorials, how-tos, and other videos to help my employees learn SOPs and to show exactly how to do certain tasks in my YouTube videos.

Price: Free (unlimited video starts at $8/month).

Descript

What it is: An audio and video editor.

Why I love it: Descript is much more powerful than a simple audio or video editor—it has cutting-edge AI software that allows you to do things like “greenscreen” any background, remove all the “ums” and “uhs” from your audio, and easily rearrange clips. It even has an AI tool that can clone your voice in case you forget to say something. Pretty cool if you ask me.

Price: Free (paid plans start at $12/month).

Fotor AI Image Generator

What it is: An AI image generator. Duh.

Why I love it: It’s fun to create totally unique custom images for your blog posts based on text prompts using AI. While it’s not great yet, it’s come a long way and soon will be able to create photo-realistic images to use in your content. I also use it to occasionally give me fun things to share on Instagram.

Price: Free ($12.99/month or $119.99/year for premium).

Unsplash

What it is: A free-to-use, high-quality stock image library.

Why I love it: I use Unsplash to find high-quality photos to use in the Canva graphics I create for my blog posts—such as featured images and photos to visually show what I’m discussing in the text. I used an image of a truck on my page about truck driver statistics, for example.

Price: Free.