Trending March 2024 # The Ultimate Guide To Seznam Seo # Suggested April 2024 # Top 10 Popular

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Seznam.cz is the second largest search engine in the Czech Republic (also known as Czechia).

Speaking with Dušan Janovský of Seznam, the search engine holds around a 25 percent market share, with Google dominating the market.

“Optimization should focus primarily on the user and on the benefit of the content.”

Your approach to optimizing for Seznam should be the same as for Google, putting the user first.

So some modern web techniques such as JavaScript need to be considered and potentially re-engineered when creating an alternate localized version of your website for the Czech Republic.

A Brief History of Seznam

Founded in 1996 by Ivo Lukačovič, Seznam started life as a catalog of some of the best Czech webpages to visit with a basic keyword search function.

By 2010, it was estimated that the entire Internet population of the Czech Republic was visiting the search engine at least once a month.

Eventually, Google gained more dominance within the market at the expense of Seznam.

Seznam has diversified its online offering with a number of other products including:

Novinky.cz (An online news portal)

Lidé.cz (A community dating website)

Sreality.cz (An online real estate portal)

Mixer.cz (A music video site)

Mapy.cz (Maps search engine)

Volnámísta.cz (An online jobs portal)

Firmy.cz (A business directory)

Most recently they have also entered the internet TV market with Zprávy.

Seznam SEO Basics

Like all search engines, Seznam weights certain variables more than others.

This means that despite its now reduced market share, it is still a sizable player in the Czech market and shouldn’t be discounted.

.cz Domains (Myth)

It’s a common misconception that, like with Yandex and Baidu, Seznam has shown a preference to the native ccTLD of the Czech Republic, .cz.

The reasons are similar as to why Seznam shows a preference, too.

The .cz ccTLD show’s strong local relevancy to the Czech market.

When searching in chúng tôi for [seo], the top 3 results I get are:

And if I repeat the same search in Seznam, I get:

This, however, has been confirmed as being a myth, and any data suggesting otherwise is correlative.

JavaScript

Seznam does support some JavaScript elements, however it is not wholly supported.

So it’s important that your Czech alternate version uses a lot of plain HTML for navigational and internal links.

Otherwise, the SeznamBot may have difficulties discovering all URLs on your website and crawling them frequently.

URL Structures & Illegal Characters

While Czech uses Latin characters, it is a Slavic language and uses a number of special characters not found in other Latin alphabets, such as č, š, and ž.

In a URL, č, š and ž need to be included without any accents, so they would become c, s, and z.

Header Tags

H1 tags still hold weight within Seznam rankings.

It’s important that they’re not used for styling purposes and the correct hierarchy of H1 to H6 is used appropriately.

Title Tags & Meta Descriptions

According to Seznam:

“The title is one of the most important tags in the entire page – the search engines put a lot of emphasis on it. Together with the text of the page, they are the basis from which the search engine creates a caption and a search result label.”

Seznam guidelines also place importance on these being unique to each page, not overly verbose, and not to be stuffed with keywords.

Seznam also overrides the specified page title within search results should it feel more appropriate on-page content can be used to better match user intent.

International SEO

Targeting Czechia with Czech, you would use the code:

Canonical URLs

chúng tôi

chúng tôi

chúng tôi

chúng tôi

That being said, Seznam’s guidelines warn that:

“If the robot does not appear to have pages with similar content, the canonical URL is not accepted.”

It is deemed unsuitable use if Page A and Page B aren’t similar in terms of content.

The only time I’ve really seen this is when someone has tried to use the canonical to sculpt PageRank from blog/supporting content pages to primary commercial content pages/user conversion pages.

Frames

Seznam’s guidelines clearly state:

SERP Personalization

Seznam doesn’t personalize search results pages in the same way that Google does and doesn’t take into account previous search history.

That being said, if a user is searching on chúng tôi (infer Google Maps) or chúng tôi (infer a website/business directory), Seznam can detect your location, if enabled, and shows relevant results based on your proximity.

Mapy.cz and chúng tôi snippets also appear within organic search results pages.

How Does Seznam Weight Backlinks?

Studies have shown that citation flow (number of links) outweighs the trust flow (quality of links).

That being said, you can’t simply spam your way to the top.

While quantity and quality are weighted differently, quality shouldn’t be substituted for volume.

Identifying Seznam Crawl Activity

Seznam has two user-agents, one for crawling pages and one for taking screenshots of pages. The primary user agent crawls from the IPV4 range 77.75.74 to 77.75.79 (excluding 77.75.75), and 2a02:598:a::78:x, 2a02:598:a::79:x and 2a02:598:2::x on IPV6.

In your log files, you can identify the primary SeznamBot user agent as:

Whereas the Seznam screenshot user agent can be identified as:

More International SEO Resources:

Image Credits

All screenshots taken by author, September 2023

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Windows Xp: The Ultimate Comparison Guide, Pros And Cons

Windows XP: The Ultimate Comparison Guide, Pros and Cons Why Windows XP is still relevant in the modern world

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Windows XP is an old operating system that has been around since 2001 and has been one of the most popular OSes for many years.

Despite being outdated and no longer a recipient of Microsoft’s security updates, it still has some fans. Let’s find out what’s so special about it.

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Download Fortect and install it on your PC.

Start the tool’s scanning process to look for corrupt files that are the source of your problem

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Windows XP has been around for a long time. It has been the most widely adopted operating system from Microsoft. Since its release in 2001, it has been updated several times. While it had its share of problems like any other OS, people still use it.

Some of the reasons include its simplicity, power, and flexibility. On the downside, Windows XP is no longer supported by Microsoft. The company stopped providing support because it was no longer able to provide security updates for it. Luckily, you can use optimization software to boost its performance.

Some of the reasons why Windows XP is still a fan-favorite include:

What are the main features of Windows XP?

With all the changes made to Windows in recent years, one would wonder, What is the use of Microsoft XP? Windows XP is still being used by millions of people worldwide. It remains popular due to its wide adoption.

Organizations worldwide are still using it because they do not want to upgrade their systems for security reasons or compatibility issues caused by newer versions of Windows. 

1. Support for multiple users

Windows XP includes support for multiple users. Each user has a desktop or personal area to work in. This allows users to have their own settings, programs, and files. 

You can also allow guest accounts that do not require passwords so that people can use your computer without authorization.

2. Start menu 

Users can also pin frequently used applications. The Start menu is also customizable, where users have the option to remove or add links.

3. Taskbar

This bar appears at the bottom of your screen and allows you to see which programs are running, what they’re doing, and how much memory they use. You can also open or close programs from here, as well as minimize, maximize, or restore them.

4. Media features

Expert tip:

It also has a built-in library so users can organize their media collection easily. Windows Movie Maker lets users edit videos and create simple movies with ease. It also includes tools for adding music tracks and captions.

5. Parental controls

Parental controls allow you to restrict access to games, websites, and content based on ratings that you set up yourself. You can set time limits for when your kids can use their computer and what they can do with it while online or playing games. 

You can even lock down certain programs so they can’t be opened without your permission or control what sites they visit while surfing the web.

Why was Windows XP so good?

Here are a few reasons users liked Windows XP:

Fast performance: Windows XP was considered one of the fastest operating systems of the time, and it managed to hold the title for years.

Stability: The OS was fairly stable compared to the other iterations released previously by Microsoft, and provided support to a wide range of apps.

User-friendly interface: Windows XP had an extremely user-friendly interface, and most actions could be performed seamlessly and within seconds.

Is Windows XP still good to use?

You can still run Windows XP. But the OS lost support from Microsoft in 2014, so you wouldn’t be getting any security or feature updates, which puts the PC at a greater risk of being attacked by malware or virus.

Also, several applications might not run on Windows XP due to compatibility issues. Though if that doesn’t seem like a hassle and the OS is already installed, you can keep using Windows XP, at least for now.

What are the limitations of Windows XP?

It is not free – Windows XP costs money, so it may not be accessible to everyone. Compared to other iterations, users have been able to upgrade free of charge. 

Difficult upgrades – Windows XP is very difficult to upgrade from earlier versions of Windows. If you want to switch from, say Windows 98 to Windows XP, you’ll have to do a clean install that deletes all files on your hard drive.

Complex installation process – It can be difficult to set up a new PC with Windows XP. The installation process can take several hours and requires a CD or DVD drive to install from.

Security risks – Windows XP has known security vulnerabilities that can be exploited by hackers to gain access to your computer system or data files stored on it. Luckily, you can secure your system with Windows XP antivirus software.

Limited RAM – The system memory only supports up to 4GB. You can either upgrade to Windows XP Professional at a cost or a newer Windows version.

Having explored all that Windows XP has to offer, it brings us to the age-old question: Which is better, Windows XP or Windows 10? The two operating systems are very different from each other, but they do share some similarities. 

Ultimately, there are a lot of trade-offs when it comes to upgrading to Windows 10. Some people will consider the upgrade, especially if they use a compatible computer, but others may not even bother. 

It all boils down to the activities you use your PC for. If you are doing basic stuff, then by all means, continue using Windows XP. This just means that you’ll have to take measures such as hiding your IP in Windows XP to minimize the security risks.

However, if you are looking to explore more modern features and overall enhanced performance, Windows 10 is.

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Neural Network 101 – Ultimate Guide For Starters

Date: 03-July-2040

Mission: Project Aries

Destination: Mars

Date of arrival to Mars: 18-Feb-2041

Landing Location: Jezero Crater, Mars 

“Imagine you are on a space mission to go to mars as a part of “Project Aries”. You are in a spaceship along with your crew (8 in total) along with an ASI(Artificial Super Intelligence) let’s called it “HAL9000″. You are drifting through the vast vacuum of the universe millions of miles away from earth. In order to preserve your valuable resources like energy and resources like oxygen and water, you along with your crew enter into a deep sleep state for 4 months. In the meanwhile, your onboard ASI will be monitoring and controlling all operations of your spacecraft. Now, what if HAL9000 considers you and your crew as a threat to its existence and decided to sabotage the mission. Scary isn’t it ?. I am sure you would have figured out which movie this is relating to. That’s right! I am talking about 2001: A Space Odyssey. For those of you who do not know what is HAL9000, well this is HAL9000.” Love that glowing red eye !!

You might ask what is this has to do with neural networks. Well technically HAL9000 is termed as an “Artificial Super Intelligence”, but in a very simple term, it’s a neural network which is the topic of this blog. so let’s dive into the realm of neural networks.

There are several definitions of neural networks. A few of them includes the following:

Neural networks or also known as Artificial Neural Networks (ANN) are networks that utilize complex mathematical models for information processing. They are based on the model of the functioning of neurons and synapses in the brain of human beings. Similar to the human brain, a neural network connects simple nodes, also known as neurons or units. And a collection of such nodes forms a network of nodes, hence the name “neural network.” – hackr.io

Well that’s a lot of stuff to consume

For anyone starting with a neural network, let’s create our own simple definition of neural networks. Let’s split these words into two parts.

Network means it is an interconnection of some sort between something. What is something we will see this later down the road?

Neural means neurons. What are neurons? let me explain this shortly.

So neural network means the network of neurons. That’s it. You might ask “Why are we discussing biology in neural networks?”. Well in the data science realm, when we are discussing neural networks, those are basically inspired by the structure of the human brain hence the name.

Another important thing to consider is that individual neurons themselves cannot do anything. It is the collection of neurons where the real magic happens.

Neural Network in Data Science Universe

You might have a question “Where is neural network stands in the vast Data Science Universe?”.Let’s find this out with the help of a diagram.

In this diagram, what are you seeing? Under Data Science, we have Artificial Intelligence. Consider this as an umbrella. Under this umbrella, we have machine learning( a sub-field of AI). Under this umbrella, we have another umbrella named “Deep Learning” and this is the place where the neural network exists. (Dream inside of another dream 🙂 classical inception stuff )

Basically, deep learning is the sub-field of machine learning that deals with the study of neural networks. Why the study of neural networks called “Deep Learning”?. Well, read this blog further to know more 🙂

Structure of Neural Networks

Since we already said that neural networks are something that is inspired by the human brain let’s first understand the structure of the human brain first.

Each neuron composed of three parts:-

1.Axon

2.Dendrites

3.Body

As I explained earlier, neuron works in association with each other. Each neuron receives signals from another neuron and this is done by Dendrite. Axon is something that is responsible for transmitting output to another neuron. Those Dendrites and Axons are interconnected with the help of the body(simplified term). Now let’s understand its relevance to our neural network with the one used in the data science realm.

In any neural network, there are 3 layers present:

1.Input Layer: It functions similarly to that of dendrites. The purpose of this layer is to accept input from another neuron.

2.Hidden Layer: These are the layers that perform the actual operation

3.Output Layer: It functions similarly to that of axons. The purpose of this layer to transmit the generated output to other neurons.

One thing to be noted here is that in the above diagram we have 2 hidden layers. But there is no limit on how many hidden layers should be here. It can be as low as 1 or as high as 100 or maybe even 1000!

Now it’s time to answer our question. “Why the study of neural networks called Deep Learning”?Well, the answer is right in the figure itself 🙂

It is because of the presence of multiple hidden layers in the neural network hence the name “Deep”. Also after creating the neural network, we have to train it in order to solve the problem hence the name “Learning”. Together these two constitute “Deep Learning”

Ingredients of Neural Network

As Deep Learning is a sub-field of Machine Learning, the core ingredients will be the same. These ingredients include the following:

1.Data:- Information needed by neural network

2.Model:- Neural network itself

3.Objective Function:- Computes how close or far our model’s output from the expected one

4.Optimisation Algorithm:-Improving performance of the model through a loop of trial and error

The first two ingredients are quite self-explanatory. Let’s get familiar with objective functions.

Objective Function

The purpose of the objective function is to calculate the closeness of the model’s output to the expected output. In short, it computes the accuracy of our neural network. In this regard, there are basically two types of objective functions.

1. Loss Function:-

To understand loss function, let me explain this with the help of an example. Imagine you have a Roomba(A rover that cleans your house). For those who do not know what Roomba is, well this is Roomba.

Let’s call our Roomba “Mr.robot”. Mr. robot’s job is to clean the floor when it senses any dirt. Now since Mr.robot is battery-operated, each time it functions, it consumes its battery power. So in this context what is the ideal condition in which Mr.robot should operate? Well by consuming minimum possible energy but at the same time doing its job efficiently. That is the idea behind loss function.

The lower the value of the loss function, the better is the accuracy of our neural network.

2. Reward Function:

Let me explain this with the help of another example.

Let’s say you are teaching your dog to fetch a stick. Every time when your dog fetches a stick, you award it let’s say a bone. Well that is the concept behind the reward function

Higher the value, the better the accuracy of our neural network.

Optimization Algorithm

Any machine learning algorithm is incomplete without an optimization algorithm. The main goal of an optimization algorithm is to subject our ML model (in this case neural network) to a series of trial and error processes which eventually results in a model having higher accuracy.

In the context of neural networks, we use a specific optimization algorithm called gradient descent. Let’s understand this with the help of an example.

let’s imagine that we are climbing down a hill. With each step, we can feel that we are reaching a flat surface. Once we reach a flat surface, we no longer feel that strain on our fleet. Well, similar is the concept of gradient descent.

In gradient descent, there are few terms that we need to understand. In our previous example, when we climb down the hill we reach a flat surface. In gradient descent, we call this global minimum. Now, what do global minima mean? If you used a loss function, it means the point at which you have a minimum loss and is the preferred one.

Alternatively, if you are going to use a reward function, then our goal is to reach a point where the reward is maximum ( means reaching a global maximum). In that case, we have to use something called gradient ascent. Think of it as an opposite to gradient descent. Meaning that now we need to climb up the hill in order to reach its peak 🙂

Types of Neural Networks

There are many different types of neural networks. Few of the popular one includes following

Let me give you a single liner about where those neural networks are used

1.Convolutional Neural Network(CNN): used in image recognition and classification

2.Artificial Neural Network(ANN): used in image compression

3.Restricted Boltzmann Machine(RBM): used for a variety of tasks including classification, regression, dimensionality reduction

4.Generative Adversarial Network(GAN): used for fake news detection, face detection, etc.

5.Recurrent Neural Network(RNN): used in speech recognition

6.Self Organizing Maps(SOM): used for topology analysis

Applications of Neural Network in Real Life

In this part, let’s get familiar with the application of neural networks

1.Adaptive Battery in Android OS

If you happened to have an android phone running android os 9.0 or above, when you go inside the setting menu under the battery section you will see an option for an adaptive battery. What this feature does is pretty remarkable. This feature basically uses Convolutional Neural Networks(CNN) to identify which apps in your phone are consuming more power and based on that, it will restrict those apps.

2. Live Caption in Android OS

As a part of Android OS 10.0, Google introduced a feature called Live Caption. When enabled this feature uses a combination of CNN and RNN to recognize the video and generate a caption for the same in real-time

3. Face Unlock

Today almost any newly launched android phone is using some sort of face unlock to speed up the unlocking process. Here essentially CNN’s are used to help identify your face. That’s why you can observe that the more you use face unlock, the better it becomes over time.

4.Google Camera Portrait Mode

Do you have google pixel? Wondering why it takes industry-leading bokeh shots. Well, you can thank the integration of CNN into google camera for that 🙂

5.Google Assistant

Wonder how Google assistant wakes after saying “Ok Google”.Don’t say this loudly. You might invoke someone’s google assistant :). It uses RNN for this wake word detection.

Well, this is it. This is all you need to know about neural networks as a starter.

I hope you like this article.

If you like this article please share this with your friends and colleagues.

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How To Use Memoji On Iphone And Ipad Pro (Ultimate Guide)

Memoji lets you enjoy fun-loving messaging and FaceTime calls with camera effects. You can create custom Memoji on your iPhone and iPad with Face ID! It lets you experiment with the looks of your avatar and craft multiple Memoji that get along nicely with different conversations. Let us get started and see how to create, use, and manage Memoji on iPhone and iPad Pro.

What is Memoji?

Memoji is a personalized version of Animoji that you can create according to your liking. You can choose skin tone, hair type, eyebrows, glasses, eye color, and more to create your avatar. Memoji on iOS 13 and iPadOS also become sticker packs that you can use from your keyboard in Messages, Mail, and other apps.

You need an iPhone with Face ID and the latest iOS to create and use animated Memoji. But you can create Memoji stickers on any iPhone or iPad that runs iOS 13 or iPadOS.

Devices that support Memoji are:

iPhone 11 Pro Max

iPhone 11 Pro

iPhone 11

iPhone XS Max

iPhone XS

iPhone XR

iPhone X

iPad Pro 12.9-inch (4th generation)

iPad Pro 12.9-inch (3rd generation)

iPad Pro 11-inch (2nd generation)

iPad Pro 11-inch

How to Create Memoji Stickers on iPhone and iPad Pro

Choose skin tone, freckles, hairstyle, head shape, nose, eyes, lips, ears, and more from several available options. Pick out the one that you think best represents you or the mood you are in!

When you are happy with the result, tap on Done from the top right corner to confirm.

You can make as many Memoji as you want. The process will remain the same.

How to Use Memoji in Messages on iPhone and iPad Pro

Once you have crafted a Memoji, you can use it just the way you use Animoji on your iPhone.

Open Messages app → head to the conversation → tap on the Animoji icon.

Select your created Memoji. You can swipe right to left to see all your Memoji

Hit the record button and then say what you want. Finally, send the message.

You can record up to 30 seconds. Also, after you record, you may choose a different Memoji with the same recording. For this, tap the red stop button, and before sending the Memoji, swipe horizontally to choose a different Memoji. It will have the same audio and facial expressions.

How to Use Memoji in FaceTime

A compelling use case for Memoji is in FaceTime. It lets you use your preferred Memoji instead of your face during a FaceTime video call. It is fun, and you can give it a go. Here is how to use Memoji in FaceTime on iPhone and iPad Pro.

How to Use Camera Effects with Memoji

How to Edit Memoji Stickers

Open Messages app → Conversation → Animoji icon.

Swipe until you find the desired Memoji

Tap on three dots at the bottom left corner

Next up, you have three options:

Edit: It lets you change the looks of your avatar. Tap on it and then fine-tune it to your heart’s liking. In the end, tap on Done.

Duplicate: It allows you to create another similar avatar. But you can design your look from the ground zero if you wished to embrace a complete change. In the end, tap on Done.

Delete: Select this option if you don’t want to use this Memoji anymore and confirm.

Signing Off…

If you have multiple Apple devices and wish to have your Memoji on them, you need to enable two-factor authentication for your Apple ID. Also, you must be signed in to iCloud with the same Apple ID on all your devices. Finally, iCloud Drive should also be turned on. (Settings app → your Apple ID banner → iCloud → enable iCloud Drive)

You may also like to check out:

Author Profile

Ankur

I have been an Apple user for over seven years now. At iGeeksBlog, I love creating how-tos and troubleshooting guides that help people do more with their iPhone, iPad, Mac, AirPods, and Apple Watch. In my free time, I like to watch stand up comedy videos, tech documentaries, news debates, and political speeches.

Python Lists: An Ultimate Guide & Examples (Updated 2023)

In Python, a list is a data type in which you can store multiple items.

To create a list, separate elements with commas in between square brackets.

For example, here is a list of integers:

numbers = [1, 2, 3, 4, 5]

To access list elements, use the square-bracket accessing operator [] with the index of the item. Also notice that the index starts from 0.

For example, let’s get the 1st and the 2nd element of a list:

numbers = [1, 2, 3, 4, 5] first = numbers[0] second = numbers[1] print(first) print(second)

Output:

1 2

To iterate over a list, you can use a for loop.

For example, let’s print each number in a list separately:

numbers = [1, 2, 3, 4, 5] for number in numbers: print(number)

Output:

1 2 3 4 5

This is a complete guide on lists in Python.

In this guide, you learn everything you need to know about lists starting from creating one.

Introduction to Lists

A list is one of the most commonly used data types in Python.

It is a mutable (changeable) and ordered sequence of elements.

A list element is commonly referred to as an element, item, value, or object.

These terms are used interchangeably in this tutorial similar to other guides on the internet.

Why Are Lists Useful?

Practically all programs have to deal with a bunch of related values. For instance, a course app might deal with student objects and grades. A weather app can deal with a bunch of locations and weather data.

In Python, you can use lists to store multiple related values in one place for easy access.

Lists in Python are useful for the same reason why pencil cases are useful in real life. You can store related items in the same logical place.

By using lists, your program becomes cleaner and more structured.

Also, lists let you perform all kinds of practical operations to its elements.

For example, you can easily:

Calculate the length of a list.

Sort a list.

Find a specific value.

Add, update, and delete values.

And much more.

To get a first impression of lists in Python, let’s start by creating one.

How to Create a List

To create a list in Python, place the elements inside square brackets and separate them by commas.

For example, here is a list of strings:

names = ["Alice", "Bob", "Charlie"]

This is a list of strings.

Each element in this list is a string that represents the name of a person.

Usually, it is a good idea to store elements of the same data type in a list.

For instance, a list of integers, strings, or booleans.

However, this is not a restriction.

In Python, you can store different types of data into the same list.

For instance, let’s create a list that has integers, strings, and booleans:

mixed = [0, True, "Charlie", 100, False, 9732]

This is a perfectly valid list in Python.

However, as stated before, it is usually a good idea to only store one type of data in the same list.

Length of a List

One of the important properties of a list is its length.

This can be useful for many reasons. For instance, the length of the list reveals how much data you are dealing with.

Later on, you see an example of how to use the length of a list to iterate over its elements.

In Python, there is a built-in function called len(). You can use this function to calculate the length of a list.

As a matter of fact, you can use the len() function on other types of iterables, such as strings or tuples. With strings, the len() function returns the number of letters in the string.

For example, let’s calculate the number of names in a list of strings:

names = ["Alice", "Bob", "Charlie"] length = len(names) print(length)

Output:

3

Good job!

Now you understand how to create a list and count the number of elements in it.

Next, let’s talk about accessing the elements of a list.

How to Access List Items

The reason why you insert elements into a list is to store them for easy access later on.

Without being able to access list elements, a list would be a useless data structure.

In Python, accessing the list elements is possible by using the square brackets accessing operator [].

Here is the syntax:

list[index]

Where:

list is a list of items.

index is the index of the item to access.

You are going to see a bunch of examples in the next sections.

Before that, it is important to learn how indexing works in Python. This is because accessing an element depends on its index.

Indexing in Python

In Python, each element in a list is associated with a unique index.

This index can be used to access that particular element.

Python uses zero-based indexing.

In other words, the indexing starts from 0 and grows from left to right.

This applies to lists as well as other types of iterables.

As a matter of fact, most programming languages use zero-based indexing.

When dealing with lists in Python, zero-based indexing means:

1st element has an index of 0.

2nd element has an index of 1

3rd element has an index of 2.

And so on.

This usually causes headaches, especially for beginners.

Let’s see examples of accessing list elements with the index.

As a first example, let’s create a list of strings and access the 3rd element:

names = ["Alice", "Bob", "Charlie", "David", "Eric"] thirdName = names[2] print(thirdName)

Output:

Charlie

As you can see, this piece of code returns the 3rd name, that is, Charlie.

This is because index 2 refers to item number 3.

Problems with Indexing

Zero-based indexing is commonly a root cause of one of the most common errors in Python, the List Index out of Range error.

This error occurs when you try to access an element with an index that overshoots the list.

Let me show you an example:

numbers = [1, 2, 3, 4, 5] last = numbers[5] print(last)

Output:

Traceback (most recent call last): IndexError: list index out of range

In this piece of code, you try to access the 6th element of the list even though there are only 5 elements.

This causes an error that says the list index is out of the range.

To fix this, you need to recall that Python uses zero-based indexing. You should thus use an index that is one less than the actual position of the element.

Next, let’s talk about negative indexing in Python.

Negative Indexing

Python also supports negative indexing that goes from right to left.

In Python, negative indexing starts at the index of -1 from the right-most element in a list.

In other words:

The 1st element from the right has an index of -1

The 2nd element from the right has an index of -2

The 3rd element from the right has an index of -3

And so on.

Using negative indexing can be helpful if you want to access elements from right to left.

For example, if you are instructed to get the second last element in a list, you can use the index -2.

For example:

names = ["Alice", "Bob", "Charlie", "David", "Eric"] secondLast = names[-2] print(secondLast)

Output:

David

The negative indexing does not start from 0 because the 0th index is reserved for the 1st element in the positive zero-based indexing.

Now you understand how the list indexing works in Python.

The next section teaches you how to access multiple items of a list in one go.

Slicing Lists

In Python, you can access a bigger chunk of a list by using what is called slicing.

For instance, to get the first four items of a list, use slicing instead of manually accessing all four items separately.

The most basic way to use slicing is to access elements from a start index until an end index.

list[start:end]

Where:

start is the zero-based starting index of the slice

end is the exclusive end index of the slice. The item at the index end is not taken into the result.

For example, let’s access the 3 middle-most items in a list:

names = ["Alice", "Bob", "Charlie", "David", "Eric"] firstNames = names[1:4] print(firstNames)

Output:

['Bob', 'Charlie', 'David']

Here the slicing starts at index 1, which is the 2nd element of the list. The slicing continues until it encounters the item at index 4 (5th element) which is excluded.

If you leave out the start parameter when slicing, the slicing automatically starts at the first element of the list.

If you omit the end parameter, the slicing automatically continues to the end of the list.

For example:

numbers = [1, 2, 3, 4, 5] first3 = numbers[:3] last3 = numbers[2:] print(first3) print(last3)

Output:

[1, 2, 3] [3, 4, 5]

Another way to do slicing is by specifying one more parameter, that is, the step size.

list[start:end:step]

Here the start and end parameters work as described previously. The step parameter determines the number of elements to step over in the slice.

For example, let’s access every second element in a list:

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] everySecond = numbers[::2] print(everySecond)

Output:

[1, 3, 5, 7, 9]

Because we omitted the start and end parameters, the slicing starts from the first element and ends at the last one. The step size of 2 makes the slice only include every second element in the list.

The step parameter can also be negative. This inverts the direction of slicing.

For example, let’s reverse a list:

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] rev = numbers[::-1] print(rev)

Output:

[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]

In addition to negative step size, you can also use negative start and end parameters.

For example, let’s grab the last three values of a list:

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] print(numbers[-4:-1])

Output:

[7, 8, 9]

If you are interested, feel free to read an ultimate guide about slicing in Python.

Next, let’s talk about looping through a list. This is one of the most common operations performed on a list.

Looping Through a List

When you have stored items in a list, you usually want to perform an action for each of those.

This operation could mean displaying the values, performing a math operation, checking a condition, or anything of that sort.

In the last chapter, you learned how to access elements of a list.

However, if you have hundreds of items on a list, you do not want manually perform actions on those.

This is where looping comes in useful.

In Python, you can use a loop to iterate over the list of values.

There are two types of loops at your disposal:

For loops.

While loops.

In the following sections, you are going to learn how to use both types of loops to iterate over lists.

For Loop

In Python, a for loop is used to iterate over an iterable collection of values, such as a list.

Here is the general syntax of a for loop:

for item in list: # actions

The for loop works such that it takes each element in a list at a time and assigns it to a variable called item. The area after the colon is calledthe body of the loop. Here you can run any valid Python code on the list item for example.

To understand how it works, you need to see some examples.

As a first example, let’s create a list of strings and print each string to the console:

names = ["Alice", "Bob", "Charlie", "David", "Eric"] for name in names: print(name)

Output:

Alice Bob Charlie David Eric

Here each string in the list is assigned to a variable called name one by one. Then you use the name to print it into the console.

As another example, let’s square each number in the list and show the result:

numbers = [1, 2, 3, 4, 5] for number in numbers: number = number ** 2 print(number)

Output:

1 4 9 16 25

Notice that this piece of code does not modify the original list.

Instead, each time you call number = number ** 2 you modify the copy of the actual number in the list.

Here is how a for loop works behind the scenes when looping through a list:

The for loop assigns each number in a list to a copy variable called number.

Loop with Index: The enumerate() Function

In the previous examples, you learned how to use a for loop to loop through a list of elements.

But what if you want to know the position of the element too?

In this case, you need to couple each list element with an index.

In Python, there is a built-in function enumerate() that does it.

The enumerate() function takes a list and assigns an index to each element. The result is a collection of item, index pairs.

Here is the general syntax of using the enumerate() function:

for index, item in enumerate(list): # actions

The index is the current element’s position in the list whereas the item is the element itself. You can use both of these variables inside the loop.

The best way to see this in action is by taking a look at an example.

For instance, let’s print the order of people in a line:

names = ["Alice", "Bob", "Charlie", "David", "Eric"] for index, name in enumerate(names): print(f"{index}: {name}")

Output:

0: Alice 1: Bob 2: Charlie 3: David 4: Eric

Using the enumerate() function is useful when you want to loop through a list and know the index of each element.

This prevents you from having to specify a separate variable to keep track of the index. This reduces the amount of code and improves readability.

In case you are curious, you can check a more detailed guide to the enumerate() function in Python.

List Comprehensions: Shorthand For Loops

In the earlier sections, you saw examples of for loops that spread across multiple lines.

But there is a neat little shorthand you can use to compress for loops into one-liner expressions.

This shorthand is called list comprehension.

Here is a visualization of how to turn a regular for loop into a comprehension:

Let me show you an example.

Let’s square a list of numbers using a list comprehension:

numbers = [1, 2, 3, 4, 5] squared = [number ** 2 for number in numbers] print(squared)

Output:

[1, 4, 9, 16, 25]

Here you only needed one line of code to get the job done.

A word of warning: Keeping it short isn’t always good! If you can convert a regular for loop to a list comprehension without sacrificing the code readability, feel free to do so! However, if the code becomes less understandable, it makes no sense to use comprehensions.

It is not wrong to never use comprehensions.

However, list comprehensions are used commonly so you have to understand them.

By the way, there is a lot more to for loops than you saw in this section.

In case you are interested, here is a complete guide to for loops in Python.

Anyway, the next section teaches you about while loops which is another main loop type in Python.

While Loop

While loop is the other basic loop type in Python.

Unlike a for loop, a while loop repeats actions as long as a condition True.

One application of a while loop is to loop through a list.

To loop through a list using a while loop, you need to keep track of the index of the current item. Furthermore, you need to continue the loop as long as the index is less than the length of the list.

Inside the loop, you can use the index to access the list elements.

Here is a blueprint for a while loop with lists:

index = 0 while index < len(list): # actions index = index + 1

The while loop continues as long as the index is less than the length of the list. It is thus important to update the index at each iteration!

For example, let’s print a list of strings using a while loop:

names = ["Alice", "Bob", "Charlie", "David", "Eric"] i = 0 while i < len(names): print(names[i]) i += 1

Output:

Alice Bob Charlie David Eric

This is a really basic example of a while loop.

Here the variable i is used to keeping track of the index of the loop.

The while loop prints out each element with the corresponding index and updates the index for the next round.

A common reason to perform a while loop on a list is to bulk-modify the list elements.

Let’s demonstrate this by squaring a list of numbers:

numbers = [1, 2, 3, 4, 5] i = 0 while i < len(numbers): numbers[i] **= 2 i += 1 print(numbers)

Output:

[1, 4, 9, 16, 25]

Word of warning: Using while loops, it is important not to cause an endless loop (condition is always True). To prevent this, always update the looping condition in each iteration.

To iterate a list, use for loops instead of while loops as much as you can.

This is because the for loop syntax is easier to read and not susceptible to endless loops.

In the next section, you are going to learn a bunch of ways to add an item or items to a list.

How to Add Elements to a List

In Python, a list is a mutable collection of values.

In short, the mutability of a list means you can add elements to a list.

As you can imagine, being able to add items to a list is a must-have feature.

Read more about mutability in Python.

Adding elements to a list can mean one of the following:

Add to the end of a list (to the right-hand side).

Add to the front of a list (to the left-hand side).

Add multiple elements to the end of a list.

Add multiple elements to the front of a list.

The next four sections teach you more about these options.

Add an Element to the End of a List

Perhaps the most common way to add an element to a list is by adding it to the end of the list.

This process is called appending.

In Python, a list has a built-in append() method that lets you add an element to the end of a list.

list.append(item)

The append() method takes an item as an argument and adds it to the end of the original list.

For instance:

numbers = [1, 2, 3] numbers.append(4) print(numbers)

Output:

[1, 2, 3, 4] Add an Element to the Beginning of a List

A less common, but still useful action is to add an element to the beginning of a list.

To do this, you can use the insert() method.

list.insert(index, item)

Where:

index is the index at which you want to add an item.

item is the item to be added to the original list.

This allows you to add an element anywhere in the list. Thus, you can use it to add an element to the beginning of the list.

For example:

numbers = [1, 2, 3] numbers.insert(0, 100) print(numbers)

Output:

[100, 1, 2, 3] Add Elements to the End of a List

In the previous two chapters, you learned how to add a single item to a list.

But sometimes it can be useful to add multiple elements to a list at the same time.

To add elements to the end of a list, use the extend() method.

This method takes a list as an argument and adds each element in the list to the end of the original list.

For example:

numbers = [1, 2, 3] numbers.extend([4, 5, 6, 7]) print(numbers)

Output:

[1, 2, 3, 4, 5, 6, 7]

Notice that the extend() method argument can be any other iterable, such as a tuple or string.

For example:

characters = ["H", "e", "l", "l", "o"] word = "world" characters.extend(word) print(characters)

Output:

['H', 'e', 'l', 'l', 'o', 'w', 'o', 'r', 'l', 'd']

A string is an iterable collection of characters. When you pass a string to the extend() method, it takes each character and adds it to the end of a list.

Add Elements to the Beginning of a List

Last but least, it can be useful to know how to add elements to the beginning of a list.

This is not the most usual operation to perform and there is no dedicated method for doing this.

Instead, you can use the + operator to combine the two lists.

This creates a new list which you can assign back to the original one.

For example, let’s add a list of numbers from 1 to 3 to the beginning of a list from 4 to 6:

numbers = [4, 5, 6] oneToThree = [1, 2, 3] numbers = oneToThree + numbers print(numbers)

Output:

[1, 2, 3, 4, 5, 6] How to Modify List Items

In Python, you can modify list elements by accessing an element with an index and assigning a new value to it.

As you can imagine, being able to modify list elements is a crucial task because it allows you to change data.

For example, let’s change the first number of a list of numbers:

numbers = [1, 2, 3] numbers[0] = 50 print(numbers)

Output:

[50, 2, 3] How to Remove List Items

Similar to adding elements to a list, you can remove elements from a list.

Removing list elements can refer to any one of these:

Remove the last item.

Remove an item with a specific index.

Remove an item with a specific value.

Remove all items with a specific value.

Remove all items.

The next five sections teach you how to handle each of these situations.

Remove the Last Item from a List

In Python, you can remove the last item of a list by using a built-in pop() method.

For example, let’s remove the last number in a list of numbers:

numbers = [1, 2, 3] numbers.pop() print(numbers)

Output:

[1, 2]

Notice that this method returns the removed element. This can be useful if you want to operate on the removed element.

For example, let’s remove a value and show it in the console:

numbers = [1, 2, 3] last = numbers.pop() print(last) print(numbers)

Output:

3 [1, 2] Remove an Item with a Specific Index

In Python, the pop() method can also be used to remove any element with a given index in a list.

The pop() method takes an optional index argument that removes the element corresponding to that index.

For example, let’s remove the first element of a list:

numbers = [1, 2, 3] numbers.pop(0) print(numbers)

Output:

[2, 3] Remove an Item with a Specific Value

In the previous sections, you learned how to remove values from a list by index.

However, sometimes you want to remove an item based on its value instead of the index.

To remove an element with a specific value, use the remove() method.

list.remove(value)

The remove() method removes the first occurrence of the value in a list.

For instance, let’s remove the name “Bob” from a list of strings:

names = ["Bob", "Alice", "Charlie", "Eric", "David"] names.remove("Charlie") print(names)

Output:

['Bob', 'Alice', 'Eric', 'David']

If you have multiple items with the same value in a list, the remove() method only removes the first one!

For example:

names = ["Bob", "Bob", "Alice"] names.remove("Bob") print(names)

Output:

['Bob', 'Alice']

As you can see, there is still one “Bob” on the list.

To remove all occurrences, you need to use a different strategy as explained in the next section.

Remove All Items with a Specific Value

You cannot use the remove() method to remove multiple elements of the same value from a list.

But there are many other options you can use.

The simplest way is by using a loop.

For example, here is a list comprehension approach:

numbers = [1, 4, 4, 26, 4, 4, 8, 0, 4] target = 4 numbers = [number for number in numbers if number != target] print(numbers)

Output:

[1, 26, 8, 0]

This approach creates a new list in which the specific values are filtered out. Then the new list is assigned back to the original one.

If you want to modify the original list directly, then you can use a while loop.

How to Empty a List

Last but not least, sometimes you may want to clean the entire list.

To do this, you can assign the original list to an empty list:

numbers = [1, 2, 3] numbers = [] print(numbers)

Output:

[]

But you can also use the built-in clear() method:

numbers = [1, 2, 3] numbers.clear() print(numbers)

Output:

[]

Next, let’s talk about finding elements in a list.

How to Find an Element in a List

When you are dealing with big lists of data, you commonly want to find items with a specific value.

This can mean:

You want to check if an element exists in a list.

You want to access the index of the specific value.

Let’s take a closer look at each operation.

How to Check If a Value Exists in a List

If you only want to know if a list contains at least one element with a specific value, use the in operator.

For example, let’s check if numbers 3 and 100 exist in a list:

numbers = [1, 2, 3] print(3 in numbers) print(100 in numbers)

Output:

True False How to Get the Index of a Specific Value in a List

Usually, you also care about the position of the specific item in addition to knowing it exists.

To get the first index of a specific value in a list, use the list.index() method.

For example, let’s find the index of “Bob” in a list of names:

names = ["Alice", "Bob", "Charlie", "David"] bobIndex = names.index("Bob") print(bobIndex)

Output:

1

If you have multiple items with the same value, the index() method returns the first index:

names = ["Alice", "Bob", "Bob", "Bob"] bobIndex = names.index("Bob") print(bobIndex)

Output:

1

In the above list, there are multiple “Bob” strings. However, only the index of the first one is returned by the index() method.

In the next section, you learn a technique to find all the indexes of a specific value.

Find All Indexes of a Specific Value in a List

To get all the indexes of a specific value in a list, you can use a loop or a list comprehension.

For example, here is a list comprehension to find all indexes of “Bob” in a list of names:

names = ["Alice", "Charlie", "Bob", "Bob", "Bob", "Charlie"] bobIndexes = [idx for idx, name in enumerate(names) if name == "Bob"] print(bobIndexes)

Output:

[2, 3, 4]

If the enumerate() part confuses you, scroll up to see what it does.

How to Merge Two Lists

In Python, you can merge two lists together by using the addition (+) operator.

This is convenient because it is syntactically clear as to what you are trying to accomplish.

list1 + list2

When you use the + operator to merge two lists, you are creating a new list that is a combination of the lists.

For example, let’s merge two lists of numbers:

part1 = [1, 2, 3] part2 = [4, 5, 6] combined = part1 + part2 print(combined)

Output:

[1, 2, 3, 4, 5, 6]

If you want to modify the original list directly, use the extend() method you learned earlier.

This method adds a list to the end of another.

For example:

part1 = [1, 2, 3] part2 = [4, 5, 6] part1.extend(part2) print(part1)

Output:

[1, 2, 3, 4, 5, 6]

This piece of code modified the original list, instead of creating a new list.

How to Copy a List

It is quite common for you to want to clone a Python list.

In Python, copying lists (and other objects) is not possible by using the assignment operator (=):

a = [1, 2, 3] aCopy = a

Instead, you have to use the copy.deepcopy() function.

The assignment operator (=) creates a new reference to the same object in memory.

This is a more complex topic and is out of the scope of this guide.

You can read more about copying lists in Python here.

The next section teaches you the very basics of copying lists in Python.

Why = Operator Does Not Work?

In Python, the assignment operator (=) creates a new reference to an existing object.

Here is a great illustration of what happens under the hood:

You would think an assignment like this creates a copy but it doesn’t.

In the above scenario, you end up having two variables that refer to the same object in memory.

In other words, if you modify the elements of one of the lists, the other one changes too.

For example, let’s create a copy of a list and modify the original one:

a = [1, 2, 3] b = a a[0] = 10000 print(a) print(b)

Output:

[10000, 2, 3] [10000, 2, 3]

As you can see, both lists a and b changed. This is because both a and b refer to the same object.

This proves copying this way is not possible.

In the next section, you learn how to create an independent copy of a list in Python.

The deepcopy() Method

In Python, there is a specific built-in copy module that can be used to create true copies of objects.

To create a completely independent copy of a Python object, use the copy.deepcopy() module.

For example:

import copy a = [1, 2, 3] b = copy.deepcopy(a) a[0] = 10000 print(a) print(b)

Output:

[10000, 2, 3] [1, 2, 3]

As you can see, the copied list did not change. This means it is a truly independent copy of the original list.

Next, let’s go through useful list methods and functions.

Useful List Methods

An efficient programmer reuses existing code as much as possible.

When dealing with lists, there are lots of operations that you perform countless times.

Calculating the length of a list is one of those.

Even though you could implement a loop that counts the number of elements in a list, you should use the built-in len() function instead.

This saves your time and allows you to write less code.

A Python list comes with a bunch of practical methods you can use to perform some common tasks.

In this guide, you have seen a bunch of them already, such as the list.pop() or list.index() methods.

To top it off, here are two useful list methods we did not cover yet.

sort()

To sort a list in Python, use the sort() method.

By default, the sort() method sorts numbers in increasing order.

If you sort a list of strings, the strings are sorted in alphabetical order.

For example:

names = ["Charlie", "Alice", "Bob"] names.sort() print(names)

Output:

['Alice', 'Bob', 'Charlie']

Let’s also see an example of sorting numbers.

For example:

numbers = [3, 1, 2, 8, 0, 23] numbers.sort() print(numbers)

Output:

[0, 1, 2, 3, 8, 23]

Read more about sorting in Python.

reverse()

In Python, you can reverse the ordering of a list by using the reverse() method.

For example, let’s reverse the ordering of a list of numbers:

numbers = [1, 2, 3, 4, 5] numbers.reverse() print(numbers)

Output:

[5, 4, 3, 2, 1]

In addition to list methods, there are also useful built-in functions you can use to make your life easier when working with lists.

Built-In List Functions

In addition to calling the methods of a list to perform useful operations, you can use built-in functions.

By the way, these functions are not restricted to working with lists. Instead, they can be called on other types of iterables, such as tuples as well.

In this section, you learn about the most useful built-in functions:

min()

max()

sum()

all()

any()

min()

To find the smallest value in a list, you could use a for loop to iterate over each element and find the smallest item.

But there is a built-in function, min(), you can call on a list to get the job done.

The min() function takes a list as an argument. It then returns the smallest element in the list.

For example, let’s figure out the smallest number in a list:

numbers = [10, 2, -100, 4, 3, 19, 7] smallest = min(numbers) print(smallest)

Output:

-100 max()

Can you already guess what this function does?

In Python, you can use the built-in max() function to figure out the greatest element in a list.

For example, let’s find the biggest integer in a list of integers:

numbers = [10, 2, -100, 4, 3, 19, 7] biggest = max(numbers) print(biggest)

Output:

19 sum()

The sum() function calculates the sum of the list elements.

For example, let’s sum up all the integers of a list:

numbers = [10, 2, -100, 4, 3, 19, 7] total = sum(numbers) print(total)

Output:

-55 all()

In Python, the all() function checks if all the values of a list are True in a boolean context.

For example:

bools = [True, False, True] allTrue = all(bools) print(allTrue)

Output:

False

The result is False because one of the booleans is False. In other words, not all booleans in the list are True.

In Python, every data type has a corresponding boolean value.

For example, an integer of 0 is considered False, whereas 1 or any other integers are True.

This means you can call the all() function on a list of values other than booleans.

For example:

bools = [1, 0, 4, 7, 19] allZeros = all(bools) print(allZeros)

Output:

False

The result is False because there is one 0 in the list. In other words, there is one value that translates to False in a boolean context. Thus, not all the values are True as bools and the result is False.

any()

In Python, the built-in any() function checks if at least one of the list elements are True.

For example:

bools = [True, False, True] someTrue = any(bools) print(someTrue)

Output:

True

The result is True because there is at least one True in the list.

Similar to the all() function, the any() function can be called on a list with non-boolean elements. This is because all non-boolean values have a corresponding boolean value, as described in the previous section.

For example:

bools = [1, 0, 4, 7, 19] someNotZero = any(bools) print(someNotZero)

Output:

True

The result is True because there is at least one integer whose corresponding boolean value is True.

How to Find All List Functions and Methods in Python?

Thus far you have seen a bunch of list methods and functions in Python.

These are more than enough for you to work with lists efficiently.

However, it is good to understand there are more functions you can call on lists.

To see a list of all the list methods and functions, call the dir() function on a list:

If you take a look at the last bunch of elements in the list above, you can see some familiar names such as pop, append, and index. Those are all the built-in methods of a list.

But what are those methods with underscores?

Those are called double-underscore methods (dunder methods for short). They are methods that specify what happens when you call a corresponding function on the list.

For example, in the above list, there is a function called __len__. This is a special method implemented by a list that specifies what happens when someone calls the len() function on a list. With lists, it returns the length of the list.

You can call these special methods directly instead of using the built-in functions as an intermediary.

For example, let’s call both len() function and the __len__ method of a list:

numbers = [1, 2, 3] len1 = len(numbers) len2 = numbers.__len__() print(len1) print(len2)

Output:

3 3

Behind the scenes, the len() function runs the __len__ method of the list. Thus, they produce the exact same result.

The dunder methods are not a list-specific thing.

Other data types implement the same dunder methods, and you can implement them in your custom classes as well.

If you are confused by the dunder methods, I recommend watching this video. Even though it is an old video, the information is still relevant.

In addition to the dunder methods, there are other built-in functions you can call on lists and other iterables.

Here is a complete list of all the built-in functions in Python.

Conclusion

That is a lot of information about lists!

To recap, a list is one of the most commonly used data types in Python.

A list can be used to store data for later access. For example, a course application could store each student’s grades on a list.

You can easily add, update, and remove list elements.

Also, you can perform useful operations on lists, such as counting the length, finding a specific value, looping, and much more.

Thanks for reading.

Happy coding!

Further Reading

For Loops in Python

A Guide To Enterprise Seo Strategy For Saas Brands

Software-as-a-service (SaaS) is a highly unique but profitable business model when combined with a successful marketing strategy.

Since the cost of hosting cloud networking and applications tends to be reduced with additional customers, SaaS companies need to grow their subscriber base quickly to thrive in a competitive market.

Over the years, I’ve found that many SaaS companies tend to focus more on paid acquisition for steady traffic flow and conversions. While this strategy certainly has short-term profitability, once you turn the faucet off, the traffic doesn’t come back.

For this reason, I recommend that most SaaS companies invest more into SEO as an all-encompassing strategy for growth.

Furthermore, the SEO strategies I list below will only improve your existing marketing efforts, whether you market your company using PPC, email, or social media.

With this in mind, I’d like to discuss some of the unique challenges SaaS companies face in the digital space and ways SEO can be used to overcome these challenges.

Then, I’ll provide nine actionable tips to help you improve your online presence and grow your business.

5 Unique Digital Challenges For SaaS Companies 1. Economies Of Scale

As I stated in the introduction, SaaS marketers face a tough challenge in scaling SaaS businesses to a comfortable degree in order to offset the cost of hosting their cloud applications.

To achieve a lower cost of total ownership (TCO), SaaS companies need to build an effective network scale that:

Acquires new customers constantly.

Retains existing ones.

Entices customers to communicate with one another using the software to build a full-fledged network.

Instead, what’s needed is an omnichannel strategy that builds awareness organically through multiple channels.

2. Levels Of Service

Many SaaS providers use varying business models, including self-service, managed service, and automated service models for customer support.

These models relate to the amount of support the SaaS vendor provides, which greatly affects the cost of managing and running their platforms.

In some ways, a managed or automated troubleshooting model could be a positive piece of marketing material.

But if your SaaS platform has a notoriously high learning curve, such as Salesforce, and you use a self-service model for customer support, you may need to invest heavily in educational materials and tutorials to assist customers as they learn about your products.

3. Customer Acquisition Vs. Retention

While we focus heavily on customer acquisition to grow the network of a SaaS provider, keeping customers on the network is equally important.

Whether you rely on a one-time purchase or a subscription model, constantly iterating with new products, releases, and continual customer support is critical for maintaining steady growth for your business.

For this reason, SaaS companies need to invest in a wide-range marketing strategy that appeals to new and existing customers in different ways.

4. Competing For Branded Keywords

Most of your keywords may be branded, which can be difficult to scale if no one is aware of your software or brand.

For this reason, a mix of PPC, link building, and high-level content will be critical to growing your brand’s name and people’s affiliation with your products.

5. Optimizing For Search Intent

Finally, when you’re dealing with branded products and multiple keywords, it can be difficult to decipher intent.

As we’ll discuss, optimizing your funnel and content strategically around intent will be important for your overall SEO strategy.

Benefits Of SEO For Sustainable SaaS Growth

Since SaaS companies rely on building economies of scale to reduce costs and increase profit, a long-term strategy like organic SEO makes the most sense for SaaS businesses.

Some of the benefits of SaaS SEO include:

Generating sustainable growth through steady customer acquisition.

Reducing the cost-per-acquisition (CPA) of each new customer.

Creating widespread brand awareness for your products.

Educating and retaining customers through highly authoritative content.

Improving overall omnichannel marketing performance.

The last point is interesting because most SaaS companies will typically use email marketing and paid media to attract and retain customers.

As a final point, increasing brand visibility around your software is perhaps the most important aspect of SEO.

Many products like Microsoft Office and G-Suite benefit from having more users on the platform because it reduces friction for people trying to communicate through two different products.

So by establishing yourself as a thought leader and building a loyal customer base using a mix of content and SEO, you can build out a wide-scale network of users that reduce hosting costs and accelerate your growth.

To get started, let’s discuss seven actionable SEO strategies for SaaS businesses.

7 Actionable Ways To Scale SaaS Businesses With SEO 1. Establish The Fundamentals

First and foremost, you need to build a user-friendly site for people to download your products, contact customer support, and just read content.

Some technical fundamentals your website needs include:

HTTPS protocol.

Mobile optimization.

Fast page speed.

Optimized images (quality and size).

Clear web structure.

Strategic keyword usage.

Clear calls-to-action (CTAs).

A sizeable crawl budget.

An XML sitemap.

No duplicate content issues.

Hreflang tags for international or multilingual users.

Once established, it will be easier to rank your website for authoritative content and keep users dwelling on it once they visit.

2. Create Your Buyer Persona

Next, your team should develop a list of buyer personas you will pursue using multiple conversion tools. Input for buyer personas could be based on the following sources:

Sales and marketing teams.

Existing analytics sources (e.g., Google Analytics, Google Search Console, or Paid Media Channels).

Customer service representatives.

Direct feedback from customer surveys and interviews.

Now, your buyer personas or avatars will differ whether you’re targeting a B2C or B2B space.

In a B2C space, your buyer persona will be based on several demographic and psychographic inputs, including:

Location.

Age.

Interests.

Occupation.

Education level.

For example, if you were selling photo editing software, you would likely create separate avatars for professional/freelance photographers and also hobbyists.

On the other hand, your B2B persona will likely target specific people in an organization, such as managers, founders, or daily users.

For example, one marketing campaign and persona may focus on a software solution for sales teams and sales managers. At the same time, another campaign in the SEO space may target SEO managers looking to switch from existing products.

Once you have a list of buyer personas and avatars, you can create strategic campaigns with actionable solutions that appeal to these personas on both paid and organic channels.

3. Optimize Content For All Stages of the Funnel

As a SaaS provider, you will likely need to create separate content for separate buyer’s personas, but also for new and existing customers.

In terms of acquisition, creating specific content at each stage of your individual sales funnel will increase your chances of conversion.

Awareness

Create awareness that the user has a problem and that your software can solve it. Common marketing materials include:

Blog posts.

Guest posts.

Press releases.

Boosted social media posts.

Interest

Build interest in your products and find ways to engage with users.

For example, encouraging users to sign up for your newsletter or email service can be a great way to engage with users over time.

Evaluation/Decision

Engage with users further to push them closer to a conversion. Some common tactics include:

Free trials.

Limited consultations.

Free demos.

Free beta testing.

Purchase And Loyalty

Once a user has purchased one of your products, continue to engage them with special offers or educational content that improves their user experience and delivers satisfaction.

4. Focus On The Right Keywords

Since the acquisition cost for early-stage SaaS providers is incredibly high, it’s important to curate a strategic organic keyword strategy that brings in qualified traffic to your website.

Some strategies to generate high-converting keywords and to use them appropriately include:

Target a list of your highest-converting PPC keywords.

Analyze what keywords competitors are bidding on and targeting organically.

Optimize for informational keywords (e.g., photo editing software: “How to enhance a photo”).

Leverage “integration” related terms if your software works with other products.

Focus on benefits (e.g., increase, improvement, automation, etc.).

List features (e.g., photo editing, red-eye removal, cropping, etc.).

Segment target keywords by intent across your sales funnel (e.g., informational keywords at the top of the funnel and keywords about features/benefits for mid-funnel content).

Optimize for lower volume, niche keywords with less competition to carve out market share.

5. Build Out Topic Clusters For Authority

Once you have a list of keywords and an actionable content strategy for your funnel put in place, it’s time to execute.

Since SaaS products are fairly sophisticated and highly competitive, it’s ideal to follow Google’s E-A-T guidelines (Expertise, Authority, and Trustworthiness) to craft your content.

In addition, I also recommend creating topic clusters around topics with similar content that reinforces the main topic to generate authority and answer as many user questions as possible.

HubSpot is a good example of a blog and SaaS platform that creates highly sophisticated content clusters around its main products, including blogs and user tutorials.

To create a topic cluster, start with a seed keyword that serves as the main topic, such as “Photography,” and create a series of related topics.

For example, Adobe provides a series of photography tips designed to educate users about and sell their products, such as Photoshop.

As a bonus, leverage community forums to further engage and educate users with common troubleshooting concerns with your products.

6. Don’t Forget About Links

While backlinks are still a valuable ranking signal, I view backlinks as a more valuable promotion strategy.

If you follow my content tips above, you will create many linkable assets that naturally accrue backlinks and can be used for promotion to earn more.

For example, white papers, ebooks, surveys, studies, and tutorials provide great resources to educate people and cite information for their own research.

However, to gain early exposure and build links to content, follow these actionable tips below:

Guest post on popular blogs and websites to generate buzz.

Promote educational content on paid channels, such as Facebook and Google.

Email educational content to relevant people in your industry to build awareness.

Contact resource pages for links to your software.

Conduct roundup interviews with industry professionals.

Promote surveys and studies through press releases or paid channels.

7. Tie Everything Together Across Multiple Channels

Finally, combine all of these strategies into an omnichannel strategy.

Using a mix of PPC for brand exposure, content to build authority, and organic SEO to scale customer acquisition will provide the best strategy to scale an early-stage SaaS business.

Combine your PPC and SEO keyword research to optimize your funnel and create a consistent marketing strategy that nurtures users from awareness to the decision stage.

In Conclusion

SEO and SaaS don’t just sound alike, but they truly do go together.

More resources: 

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