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Outreach: Make every email count

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Outreach: Make every email count

30-second summary:

  • Send emails manually to be able to build longer-lasting relationships with your recipients
  • Set up your email signature to make your emails look professional
  • Track email opens to be able to tell which emails were never seen
  • Create an effective follow-up strategy (which includes Twitter)
  • Organize your email campaigns using labels

Email fatigue is real: People get weary of opening yet another email pitch, especially people like bloggers and journalists who are bombarded by emails on a daily hourly basis.

Editors are skeptical of people looking for links, popular bloggers have more offers than they can handle and influencers are too busy to give your email a chance.

Fortunately, there are a few little email tricks you can use to help make things easier and help you get more responses.

Don’t email from an outreach tool

I know the overall industry’s standard is to always use some kind of email outreach platform in order to be able to send hundreds of emails a day. Most outreach managers will tell you that you cannot have a successful email outreach campaign without streamlining it with tools (and they actually told me that).

When I am not a professional outreach manager, and when you do outreach for clients, that’s likely true. But when you reach out to people on behalf of your own business or about your own project, have your team do it manually.

Yes, it will take more time but the reward will be more niche relationships.

Somehow tools make it too quick and faceless. You automate pretty much anything and move on from contact to contact without paying much attention.

When you send manually, you get to know each contact better. You spend some time reading their site or their column. You may even click on their social media links and follow them. You take time to personalize your email with some nice details.

People respond to these emails better. It always feels like there’s a truly personal touch. You just cannot fake it.

Create a detailed email signature

Have your outreach people set up their email signatures which would mention your business, their position, and maybe your social media accounts.

This is a great way to show that you represent a trustworthy brand and can be worked with. It makes it easy for the editor you’re trying to reach to do a little bit of research on you beforehand and know that you’re not hiding anything.

Here’s how to add a signature in Outlook, and here’s how to do that in Gmail. Here’s also a guide for Mac Mail users.

Outreach Make every email count

It is also a good idea to include some kind of soft CTA into your email subject. For example, you can invite your prospects to subscribe to your newsletter. This way there will be an additional conversion funnel for those who didn’t feel like replying right away.

Experiment with your copy

This step can never be perfected because there are no limits to improving your response rate. Just try different subjects and copy ideas to try and get more people to notice your email.

Asking ChatGPT for some email subject and copy ideas is a good way to get inspired!

ChatGPT on email ideas

There are also quite a few templates to experiment with different layouts and wording.

Track your email opens

There are quite a few tools that track certain emails to see whether or not they’ve been opened. You are in control of which emails you want to track so you are not overloaded with information, and those tools work with Gmail, Outlook, and even a few specialty email platforms.

I am using one called Mailtrack, and here’s what it looks like when my email wasn’t read:

Icon when email is unread

The icon changes once your email is opened:

Change email icons

If an email has been opened, you will get a notification at the top of your screen. You can also organize your sent messages to show only unopened emails you are tracking. You can “mute” a conversation whenever you’d like and there are plenty of customizable settings.

This is an excellent way to see where you should spend your time sending follow-up messages. If you know that someone opened your email and did not respond, it means they will likely recognize a second one and may have forgotten to respond. You don’t want to be overbearing, but this helps you see where your opportunities may lie.

Fine-tune your follow-up strategy

Life is busy, so your email may be unnoticed by those who would otherwise find it useful. Following-up is an integral part of any outreach.

Gmail comes with a few nice features helping you follow up manually. For example, it will remind you of unanswered emails automatically after a few days. You can also snooze your emails to be reminded of them once the time comes. To enable snoozing:

  • Select the email you want to Snooze.
  • Click the Snooze button on top of the list
  • Pick a date and time to bring that email back to the top of your inbox.

Snooze emails

You can find your snoozed emails in the Snoozed tab in Gmail.

When it comes to follow-up, a little automation won’t hurt, so you can use one of the many follow-up solutions that work on Gmail or your email client.

It is always a good idea to ping that person on Twitter. This will make you look real and will likely help your lead remember you and find your email in the inbox. Obviously, you can only do that for those contacts that are very important to you.

Use labels to create folders for your pitches

There are certainly different ways you can craft your email pitch, but there are also methods you can use that are directly related to your email interface that can help you stay organized if you use them in the right way. Using labels is one of those methods.

This is another way to stay organized if you’re trying to find different opportunities. As you continue to pitch different editors, you can create a label to sort out all of your emails. You will already have System Labels, such as your Inbox, Starred, Sent, etc., as well as Categories, such as Social, Updates, etc., but you have the opportunity to create custom labels.

If you group your email pitches using campaign-based labels, you can help keep them away from your other work emails and have one specific place to see everyone you’ve tried to reach out to within every campaign; thus helping you know when it’s time for a follow-up email.

Conclusion

Email outreach is still the most effective way to generate backlinks, build niche contacts and create brand awareness. It is becoming harder year by year. Hopefully, the above tips will make yours easier and more productive!


Ann Smarty is the Founder of Viral Content Bee, Brand and Community manager at Internet Marketing Ninjas. She can be found on Twitter @seosmarty.

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Technical SEO Checklist for 2024: A Comprehensive Guide

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Technical SEO Checklist 2024 Comprehensive Strategies

Technical SEO Checklist 2024 Comprehensive Strategies

With Google getting a whopping total of six algorithmic updates and four core updates in 2023, you can bet the search landscape is more complicated (and competitive) to navigate nowadays.

To succeed in SEO this year, you will need to figure out what items to check and optimize to ensure your website stays visible. And if your goal is to not just make your website searchable, but have it rank at the top of search engine results, this technical SEO checklist for 2024 is essential.

Webmaster’s Note: This is part one of our three-part SEO checklist for 2024. I also have a longer guide on advanced technical SEO, which covers best practices and how to troubleshoot and solve common technical issues with your websites.

Technical SEO Essentials for 2024

Technical SEO refers to optimizations that are primarily focused on helping search engines access, crawl, interpret, and index your website without any issues. It lays the foundation for your site to be properly understood and served up by search engines to users.

1. Website Speed Optimization

A site’s loading speed is a significant ranking factor for search engines like Google, which prioritize user experience. Faster websites generally provide a more pleasant user experience, leading to increased engagement and improved conversion rates.

Server Optimization

Often, the reason why your website is loading slowly is because of the server it’s hosted on. It’s important to choose a high-quality server that ensures quick loading times from the get-go so you skip the headache that is server optimization.

Google recommends keeping your server response time under 200ms. To check your server’s response time, you need to know your website’s IP address. Once you have that, use your command prompt.

In the window that appears, type ping, followed by your website’s IP address. Press enter and the window should show how long it took your server to respond. 

If you find that your server goes above the recommended 200ms loading time, here’s what you need to check:

  1. Collect the data from your server and identify what is causing your response time to increase. 
  2. Based on what is causing the problem, you will need to implement server-side optimizations. This guide on how to reduce initial server response times can help you here.
  3. Measure your server response times after optimization to use as a benchmark. 
  4. Monitor any regressions after optimization.

If you work with a hosting service, then you should contact them when you need to improve server response times. A good hosting provider should have the right infrastructure, network connections, server hardware, and support services to accommodate these optimizations. They may also offer hosting options if your website needs more server resources to run smoothly.

Website Optimization

Aside from your server, there are a few other reasons that your website might be loading slowly. 

Here are some practices you can do:

  1. Compressing images to decrease file sizes without sacrificing quality
  2. Minimizing the code, eliminating unnecessary spaces, comments, and indentation.
  3. Using caching to store some data locally in a user’s browser to allow for quicker loading on subsequent visits.
  4. Implementing Content Delivery Networks (CDNs) to distribute the load, speeding up access for users situated far from the server.
  5. Lazy load your web pages to prioritize loading the objects or resources only your users need.

A common tool to evaluate your website speed is Google’s PageSpeed Insights or Google Lighthouse. Both tools can analyze the content of your website and then generate suggestions to improve its overall loading speed, all for free. There are also some third-party tools, like GTMetrix, that you could use as well.

Here’s an example of one of our website’s speeds before optimization. It’s one of the worst I’ve seen, and it was affecting our SEO.

slow site speed score from GTMetrixslow site speed score from GTMetrix

So we followed our technical SEO checklist. After working on the images, removing render-blocking page elements, and minifying code, the score greatly improved — and we saw near-immediate improvements in our page rankings. 

site speed optimization results from GTMetrixsite speed optimization results from GTMetrix

That said, playing around with your server settings, coding, and other parts of your website’s backend can mess it up if you don’t know what you’re doing. I suggest backing up all your files and your database before you start working on your website speed for that reason. 

2. Mobile-First Indexing

Mobile-first Indexing is a method used by Google that primarily uses the mobile version of the content for indexing and ranking. 

It’s no secret that Google places a priority on the mobile users’ experience, what with mobile-first indexing being used. Beyond that, optimizing your website for mobile just makes sense, given that a majority of people now use their phones to search online.

This change signifies that a fundamental shift in your approach to your website development and design is needed, and it should also be part of your technical SEO checklist.

  1. Ensuring the mobile version of your site contains the same high-quality, rich content as the desktop version.
  2. Make sure metadata is present on both versions of your site.
  3. Verify that structured data is present on both versions of your site.

Tools like Google’s mobile-friendly test can help you measure how effectively your mobile site is performing compared to your desktop versions, and to other websites as well.

3. Crawlability & Indexing Check

Always remember that crawlability and Indexing are the cornerstones of SEO. Crawlability refers to a search engine’s ability to access and crawl through a website’s content. Indexing is how search engines organize information after a crawl and before presenting results.

  1. Utilizing a well-structured robots.txt file to communicate with web crawlers about which of your pages should not be processed or scanned.
  2. Using XML sitemaps to guide search engines through your site’s content and ensure that all valuable content is found and indexed. There are several CMS plugins you can use to generate your sitemap.
  3. Ensuring that your website has a logical structure with a clear hierarchy, helps both users and bots navigate to your most important pages easily. 

Google Search Console is the tool you need to use to ensure your pages are crawled and indexed by Google. It also provides reports that identify any problems that prevent crawlers from indexing your pages. 

4. Structured Data Markup

Structured Data Markup is a coding language that communicates website information in a more organized and richer format to search engines. This plays a strategic role in the way search engines interpret and display your content, enabling enhanced search results through “rich snippets” such as stars for reviews, prices for products, or images for recipes.

Doing this allows search engines to understand and display extra information directly in the search results from it.

Key Takeaway

With all the algorithm changes made in 2023, websites need to stay adaptable and strategic to stay at the top of the search results page. Luckily for you, this technical SEO checklist for 2024 can help you do just that. Use this as a guide to site speed optimization, indexing, and ensuring the best experience for mobile and desktop users.

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Why Google Seems To Favor Big Brands & Low-Quality Content

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Why Google Seems To Favor Big Brands & Low-Quality Content

Many people are convinced that Google shows a preference for big brands and ranking low quality content, something that many feel has become progressively worse. This may not be a matter of perception, something is going on, nearly everyone has an anecdote of poor quality search results. The possible reasons for it are actually quite surprising.

Google Has Shown Favoritism In The Past

This isn’t the first time that Google’s search engine results pages (SERPs) have shown a bias that favored big brand websites. During the early years of Google’s algorithm it was obvious that sites with a lot of PageRank ranked for virtually anything they wanted.

For example, I remember a web design company that built a lot of websites, creating a network of backlinks, raising their PageRank to a remarkable level normally seen only in big corporate sites like IBM. As a consequence they ranked for the two-word keyword phrase, Web Design and virtually every other variant like Web Design + [any state in the USA].

Everyone knew that websites with a PageRank of 10, the highest level shown on Google’s toolbar, practically had a free pass in the SERPs, resulting in big brand sites outranking more relevant webpages. It didn’t go unnoticed when Google eventually adjusted their algorithm to fix this issue.

The point of this anecdote is to point out an instance of where Google’s algorithm unintentionally created a bias that favored big brands.

Here are are other  algorithm biases that publishers exploited:

  • Top 10 posts
  • Longtail “how-to” articles
  • Misspellings
  • Free Widgets in footer that contained links (always free to universities!)

Big Brands And Low Quality Content

There are two things that have been a constant for all of Google’s history:

  • Low quality content
  • Big brands crowding out small independent publishers

Anyone that’s ever searched for a recipe knows that the more general the recipe the lower the quality of recipe that gets ranked. Search for something like cream of chicken soup and the main ingredient for nearly every recipe is two cans of chicken soup.

A search for Authentic Mexican Tacos results in recipes with these ingredients:

  • Soy sauce
  • Ground beef
  • “Cooked chicken”
  • Taco shells (from the store!)
  • Beer

Not all recipe SERPs are bad. But some of the more general recipes Google ranks are so basic that a hobo can cook them on a hotplate.

Robin Donovan (Instagram), a cookbook author and online recipe blogger observed:

“I think the problem with google search rankings for recipes these days (post HCU) are much bigger than them being too simple.

The biggest problem is that you get a bunch of Reddit threads or sites with untested user-generated recipes, or scraper sites that are stealing recipes from hardworking bloggers.

In other words, content that is anything but “helpful” if what you want is a tested and well written recipe that you can use to make something delicious.”

Explanations For Why Google’s SERPs Are Broken

It’s hard not to get away from the perception that Google’s rankings for a variety of topics always seem to default to big brand websites and low quality webpages.

Small sites grow to become big brands that dominate the SERPs, it happens. But that’s the thing, even when a small site gets big, it’s now another big brand dominating the SERPs.

Typical explanations for poor SERPs:

  • It’s a conspiracy to increase ad clicks
  • Content itself these days are low quality across the board
  • Google doesn’t have anything else to rank
  • It’s the fault of SEOs
  • Affiliates
  • Poor SERPs is Google’s scheme to drive more ad clicks
  • Google promotes big brands because [insert your conspiracy]

So what’s going on?

People Love Big Brands & Garbage Content

The recent Google anti-trust lawsuit exposed the importance of the Navboost algorithm signals as a major ranking factor. Navboost is an algorithm that interprets user engagement signals to understand what topics a webpage is relevant for, among other things.

The idea of using engagement signals as an indicator of what users expect to see makes sense. After all, Google is user-centric and who better to decide what’s best for users than the users themselves, right?

Well, consider that arguably the the biggest and most important song of 1991, Smells Like Teen Spirt by Nirvana, didn’t make the Billboard top 100 for that year. Michael Bolton and Rod Stewart made the list twice, with Rod Stewart top ranked for a song called “The Motown Song” (anyone remember that one?)

Nirvana didn’t make the charts until the next year…

My opinion, given that we know that user interactions are a strong ranking signal, is that Google’s search rankings follow a similar pattern related to users’ biases.

People tend to choose what they know. It’s called a Familiarity Bias.

Consumers have a habit of choosing things that are familiar over those that are unfamiliar. This preference shows up in product choices that prefer brands, for example.

Behavioral scientist, Jason Hreha, defines Familiarity Bias like this:

“The familiarity bias is a phenomenon in which people tend to prefer familiar options over unfamiliar ones, even when the unfamiliar options may be better. This bias is often explained in terms of cognitive ease, which is the feeling of fluency or ease that people experience when they are processing familiar information. When people encounter familiar options, they are more likely to experience cognitive ease, which can make those options seem more appealing.”

Except for certain queries (like those related to health), I don’t think Google makes an editorial decision to certain kinds of websites, like brands.

Google uses many signals for ranking. But Google is strongly user focused.

I believe it’s possible that strong user preferences can carry a more substantial weight than Reviews System signals. How else to explain why Google seemingly has a bias for big brand websites with fake reviews rank better than honest independent review sites?

It’s not like Google’s algorithms haven’t created poor search results in the past.

  • Google’s Panda algorithm was designed to get rid of a bias for cookie cutter content.
  • The Reviews System is a patch to fix Google’s bias for content that’s about reviews but aren’t necessarily reviews.

If Google has systems for catching low quality sites that their core algorithm would otherwise rank, why do big brands and poor quality content still rank?

I believe the answer is that is what users prefer to see those sites, as indicated by user interaction signals.

The big question to ask is whether Google will continue to rank what users biases and inexperience trigger user satisfaction signals.  Or will Google continue serving the sugar-frosted bon-bons that users crave?

Should Google make the choice to rank quality content at the risk that users find it too hard to understand?

Or should publishers give up and focus on creating for the lowest common denominator like the biggest popstars do?



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Google Announces Gemma: Laptop-Friendly Open Source AI

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Google Announces Gemma: Laptop-Friendly Open Source AI

Google released an open source large language model based on the technology used to create Gemini that is powerful yet lightweight, optimized to be used in environments with limited resources like on a laptop or cloud infrastructure.

Gemma can be used to create a chatbot, content generation tool and pretty much anything else that a language model can do. This is the tool that SEOs have been waiting for.

It is released in two versions, one with two billion parameters (2B) and another one with seven billion parameters (7B). The number of parameters indicates the model’s complexity and potential capability. Models with more parameters can achieve a better understanding of language and generate more sophisticated responses, but they also require more resources to train and run.

The purpose of releasing Gemma is to democratize access to state of the art Artificial Intelligence that is trained to be safe and responsible out of the box, with a toolkit to further optimize it for safety.

Gemma By DeepMind

The model is developed to be lightweight and efficient which makes it ideal for getting it into the hands of more end users.

Google’s official announcement noted the following key points:

  • “We’re releasing model weights in two sizes: Gemma 2B and Gemma 7B. Each size is released with pre-trained and instruction-tuned variants.
  • A new Responsible Generative AI Toolkit provides guidance and essential tools for creating safer AI applications with Gemma.
  • We’re providing toolchains for inference and supervised fine-tuning (SFT) across all major frameworks: JAX, PyTorch, and TensorFlow through native Keras 3.0.
  • Ready-to-use Colab and Kaggle notebooks, alongside integration with popular tools such as Hugging Face, MaxText, NVIDIA NeMo and TensorRT-LLM, make it easy to get started with Gemma.
  • Pre-trained and instruction-tuned Gemma models can run on your laptop, workstation, or Google Cloud with easy deployment on Vertex AI and Google Kubernetes Engine (GKE).
  • Optimization across multiple AI hardware platforms ensures industry-leading performance, including NVIDIA GPUs and Google Cloud TPUs.
  • Terms of use permit responsible commercial usage and distribution for all organizations, regardless of size.”

Analysis Of Gemma

According to an analysis by an Awni Hannun, a machine learning research scientist at Apple, Gemma is optimized to be highly efficient in a way that makes it suitable for use in low-resource environments.

Hannun observed that Gemma has a vocabulary of 250,000 (250k) tokens versus 32k for comparable models. The importance of that is that Gemma can recognize and process a wider variety of words, allowing it to handle tasks with complex language. His analysis suggests that this extensive vocabulary enhances the model’s versatility across different types of content. He also believes that it may help with math, code and other modalities.

It was also noted that the “embedding weights” are massive (750 million). The embedding weights are a reference to the parameters that help in mapping words to representations of their meanings and relationships.

An important feature he called out is that the embedding weights, which encode detailed information about word meanings and relationships, are used not just in processing input part but also in generating the model’s output. This sharing improves the efficiency of the model by allowing it to better leverage its understanding of language when producing text.

For end users, this means more accurate, relevant, and contextually appropriate responses (content) from the model, which improves its use in conetent generation as well as for chatbots and translations.

He tweeted:

“The vocab is massive compared to other open source models: 250K vs 32k for Mistral 7B

Maybe helps a lot with math / code / other modalities with a heavy tail of symbols.

Also the embedding weights are big (~750M params), so they get shared with the output head.”

In a follow-up tweet he also noted an optimization in training that translates into potentially more accurate and refined model responses, as it enables the model to learn and adapt more effectively during the training phase.

He tweeted:

“The RMS norm weight has a unit offset.

Instead of “x * weight” they do “x * (1 + weight)”.

I assume this is a training optimization. Usually the weight is initialized to 1 but likely they initialize close to 0. Similar to every other parameter.”

He followed up that there are more optimizations in data and training but that those two factors are what especially stood out.

Designed To Be Safe And Responsible

An important key feature is that it is designed from the ground up to be safe which makes it ideal for deploying for use. Training data was filtered to remove personal and sensitive information. Google also used reinforcement learning from human feedback (RLHF) to train the model for responsible behavior.

It was further debugged with manual re-teaming, automated testing and checked for capabilities for unwanted and dangerous activities.

Google also released a toolkit for helping end-users further improve safety:

“We’re also releasing a new Responsible Generative AI Toolkit together with Gemma to help developers and researchers prioritize building safe and responsible AI applications. The toolkit includes:

  • Safety classification: We provide a novel methodology for building robust safety classifiers with minimal examples.
  • Debugging: A model debugging tool helps you investigate Gemma’s behavior and address potential issues.
  • Guidance: You can access best practices for model builders based on Google’s experience in developing and deploying large language models.”

Read Google’s official announcement:

Gemma: Introducing new state-of-the-art open models

Featured Image by Shutterstock/Photo For Everything



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