Transitioning From Excel To Python: Essential Functions For SEO Data Analysis via @sejournal, @williamjnye

Learning to code, whether with Python, JavaScript, or another programming language, has a whole host of benefits, including the ability to work with larger datasets and automate repetitive tasks.

But despite the benefits, many SEO professionals are yet to make the transition – and I completely understand why! It isn’t an essential skill for SEO, and we’re all busy people.

If you’re pressed for time, and you already know how to accomplish a task within Excel or Google Sheets, then changing tack can feel like reinventing the wheel.

When I first started coding, I initially only used Python for tasks that I couldn’t accomplish in Excel – and it’s taken several years to get to the point where it’s my defacto choice for data processing.

Looking back, I’m incredibly glad that I persisted, but at times it was a frustrating experience, with many an hour spent scanning threads on Stack Overflow.

This post is designed to spare other SEO pros the same fate.

Within it, we’ll cover the Python equivalents of the most commonly used Excel formulas and features for SEO data analysis – all of which are available within a Google Colab notebook linked in the summary.

Specifically, you’ll learn the equivalents of:

• LEN.
• Drop Duplicates.
• Text to Columns.
• SEARCH/FIND.
• CONCATENATE.
• Find and Replace.
• LEFT/MID/RIGHT.
• IF.
• IFS.
• VLOOKUP.
• COUNTIF/SUMIF/AVERAGEIF.
• Pivot Tables.

Amazingly, to accomplish all of this, we’ll primarily be using a singular library – Pandas – with a little help in places from its big brother, NumPy.

Prerequisites

For the sake of brevity, there are a few things we won’t be covering today, including:

• Installing Python.
• Basic Pandas, like importing CSVs, filtering, and previewing dataframes.

If you’re unsure about any of this, then Hamlet’s guide on Python data analysis for SEO is the perfect primer.

Now, without further ado, let’s jump in.

LEN

LEN provides a count of the number of characters within a string of text.

For SEO specifically, a common use case is to measure the length of title tags or meta descriptions to determine whether they’ll be truncated in search results.

Within Excel, if we wanted to count the second cell of column A, we’d enter:

=LEN(A2)

Python isn’t too dissimilar, as we can rely on the inbuilt len function, which can be combined with Pandas’ loc[] to access a specific row of data within a column:

len(df['Title'].loc[0])

In this example, we’re getting the length of the first row in the “Title” column of our dataframe.

Finding the length of a cell isn’t that useful for SEO, though. Normally, we’d want to apply a function to an entire column!

In Excel, this would be achieved by selecting the formula cell on the bottom right-hand corner and either dragging it down or double-clicking.

When working with a Pandas dataframe, we can use str.len to calculate the length of rows within a series, then store the results in a new column:

df['Length'] = df['Title'].str.len()

Str.len is a ‘vectorized’ operation, which is designed to be applied simultaneously to a series of values. We’ll use these operations extensively throughout this article, as they almost universally end up being faster than a loop.

Another common application of LEN is to combine it with SUBSTITUTE to count the number of words in a cell:

=LEN(TRIM(A2))-LEN(SUBSTITUTE(A2," ",""))+1

In Pandas, we can achieve this by combining the str.split and str.len functions together:

df['No. Words'] = df['Title'].str.split().str.len()

We’ll cover str.split in more detail later, but essentially, what we’re doing is splitting our data based upon whitespaces within the string, then counting the number of component parts.

Dropping Duplicates

Excel’s ‘Remove Duplicates’ feature provides an easy way to remove duplicate values within a dataset, either by deleting entirely duplicate rows (when all columns are selected) or removing rows with the same values in specific columns.

In Pandas, this functionality is provided by drop_duplicates.

To drop duplicate rows within a dataframe type:

df.drop_duplicates(inplace=True)

To drop rows based on duplicates within a singular column, include the subset parameter:

df.drop_duplicates(subset='column', inplace=True)

Or specify multiple columns within a list:

df.drop_duplicates(subset=['column','column2'], inplace=True)

One addition above that’s worth calling out is the presence of the inplace parameter. Including inplace=True allows us to overwrite our existing dataframe without needing to create a new one.

There are, of course, times when we want to preserve our raw data. In this case, we can assign our deduped dataframe to a different variable:

df2 = df.drop_duplicates(subset='column')

Text To Columns

Another everyday essential, the ‘text to columns’ feature can be used to split a text string based on a delimiter, such as a slash, comma, or whitespace.

As an example, splitting a URL into its domain and individual subfolders.

When dealing with a dataframe, we can use the str.split function, which creates a list for each entry within a series. This can be converted into multiple columns by setting the expand parameter to True:

df['URL'].str.split(pat='/', expand=True)

As is often the case, our URLs in the image above have been broken up into inconsistent columns, because they don’t feature the same number of folders.

This can make things tricky when we want to save our data within an existing dataframe.

Specifying the n parameter limits the number of splits, allowing us to create a specific number of columns:

df[['Domain', 'Folder1', 'Folder2', 'Folder3']] = df['URL'].str.split(pat='/', expand=True, n=3)

Another option is to use pop to remove your column from the dataframe, perform the split, and then re-add it with the join function:

df = df.join(df.pop('Split').str.split(pat='/', expand=True))

Duplicating the URL to a new column before the split allows us to preserve the full URL. We can then rename the new columns:🐆

df['Split'] = df['URL']

df = df.join(df.pop('Split').str.split(pat='/', expand=True))

df.rename(columns = {0:'Domain', 1:'Folder1', 2:'Folder2', 3:'Folder3', 4:'Parameter'}, inplace=True)

CONCATENATE

The CONCAT function allows users to combine multiple strings of text, such as when generating a list of keywords by adding different modifiers.

In this case, we’re adding “mens” and whitespace to column A’s list of product types:

=CONCAT($F$1," ",A2)

Assuming we’re dealing with strings, the same can be achieved in Python using the arithmetic operator:

df['Combined] = 'mens' + ' ' + df['Keyword']

Or specify multiple columns of data:

df['Combined'] = df['Subdomain'] + df['URL']

Pandas has a dedicated concat function, but this is more useful when trying to combine multiple dataframes with the same columns.

For instance, if we had multiple exports from our favorite link analysis tool:

df = pd.read_csv('data.csv')
df2 = pd.read_csv('data2.csv')
df3 = pd.read_csv('data3.csv')

dflist = [df, df2, df3]

df = pd.concat(dflist, ignore_index=True)

SEARCH/FIND

The SEARCH and FIND formulas provide a way of locating a substring within a text string.

These commands are commonly combined with ISNUMBER to create a Boolean column that helps filter down a dataset, which can be extremely helpful when performing tasks like log file analysis, as explained in this guide. E.g.:

=ISNUMBER(SEARCH("searchthis",A2)

The difference between SEARCH and FIND is that find is case-sensitive.

The equivalent Pandas function, str.contains, is case-sensitive by default:

df['Journal'] = df['URL'].str.contains('engine', na=False)

Case insensitivity can be enabled by setting the case parameter to False:

df['Journal'] = df['URL'].str.contains('engine', case=False, na=False)

In either scenario, including na=False will prevent null values from being returned within the Boolean column.

One massive advantage of using Pandas here is that, unlike Excel, regex is natively supported by this function – as it is in Google sheets via REGEXMATCH.

Chain together multiple substrings by using the pipe character, also known as the OR operator:

df['Journal'] = df['URL'].str.contains('engine|search', na=False)

Find And Replace

Excel’s “Find and Replace” feature provides an easy way to individually or bulk replace one substring with another.

When processing data for SEO, we’re most likely to select an entire column and “Replace All.”

The SUBSTITUTE formula provides another option here and is useful if you don’t want to overwrite the existing column.

As an example, we can change the protocol of a URL from HTTP to HTTPS, or remove it by replacing it with nothing.

When working with dataframes in Python, we can use str.replace:

df['URL'] = df['URL'].str.replace('http://', 'https://')

Or:

df['URL'] = df['URL'].str.replace('http://', '') # replace with nothing

Again, unlike Excel, regex can be used – like with Google Sheets’ REGEXREPLACE:

df['URL'] = df['URL'].str.replace('http://|https://', '')

Alternatively, if you want to replace multiple substrings with different values, you can use Python’s replace method and provide a list.

This prevents you from having to chain multiple str.replace functions:

df['URL'] = df['URL'].replace(['http://', ' https://'], ['https://www.', 'https://www.’], regex=True)

LEFT/MID/RIGHT

Extracting a substring within Excel requires the usage of the LEFT, MID, or RIGHT functions, depending on where the substring is located within a cell.

Let’s say we want to extract the root domain and subdomain from a URL:

=MID(A2,FIND(":",A2,4)+3,FIND("/",A2,9)-FIND(":",A2,4)-3)

Using a combination of MID and multiple FIND functions, this formula is ugly, to say the least – and things get a lot worse for more complex extractions.

Again, Google Sheets does this better than Excel, because it has REGEXEXTRACT.

What a shame that when you feed it larger datasets, it melts faster than a Babybel on a hot radiator.

Thankfully, Pandas offers str.extract, which works in a similar way:

df['Domain'] = df['URL'].str.extract('.*://?([^/]+)')

Combine with fillna to prevent null values, as you would in Excel with IFERROR:

df['Domain'] = df['URL'].str.extract('.*://?([^/]+)').fillna('-')

If

IF statements allow you to return different values, depending on whether or not a condition is met.

To illustrate, suppose that we want to create a label for keywords that are ranking within the top three positions.

Rather than using Pandas in this instance, we can lean on NumPy and the where function (remember to import NumPy, if you haven’t already):

df['Top 3'] = np.where(df['Position'] <= 3, 'Top 3', 'Not Top 3')

Multiple conditions can be used for the same evaluation by using the AND/OR operators, and enclosing the individual criteria within round brackets:

df['Top 3'] = np.where((df['Position'] <= 3) & (df['Position'] != 0), 'Top 3', 'Not Top 3')

In the above, we’re returning “Top 3” for any keywords with a ranking less than or equal to three, excluding any keywords ranking in position zero.

IFS

Sometimes, rather than specifying multiple conditions for the same evaluation, you may want multiple conditions that return different values.

In this case, the best solution is using IFS:

=IFS(B2<=3,"Top 3",B2<=10,"Top 10",B2<=20,"Top 20")

Again, NumPy provides us with the best solution when working with dataframes, via its select function.

With select, we can create a list of conditions, choices, and an optional value for when all of the conditions are false:

conditions = [df['Position'] <= 3, df['Position'] <= 10, df['Position'] <=20]

choices = ['Top 3', 'Top 10', 'Top 20']

df['Rank'] = np.select(conditions, choices, 'Not Top 20')

It’s also possible to have multiple conditions for each of the evaluations.

Let’s say we’re working with an ecommerce retailer with product listing pages (PLPs) and product display pages (PDPs), and we want to label the type of branded pages ranking within the top 10 results.

The easiest solution here is to look for specific URL patterns, such as a subfolder or extension, but what if competitors have similar patterns?

In this scenario, we could do something like this:

conditions = [(df['URL'].str.contains('/category/')) & (df['Brand Rank'] > 0),
(df['URL'].str.contains('/product/')) & (df['Brand Rank'] > 0),
(~df['URL'].str.contains('/product/')) & (~df['URL'].str.contains('/category/')) & (df['Brand Rank'] > 0)]

choices = ['PLP', 'PDP', 'Other']

df['Brand Page Type'] = np.select(conditions, choices, None)

Above, we’re using str.contains to evaluate whether or not a URL in the top 10 matches our brand’s pattern, then using the “Brand Rank” column to exclude any competitors.

In this example, the tilde sign (~) indicates a negative match. In other words, we’re saying we want every brand URL that doesn’t match the pattern for a “PDP” or “PLP” to match the criteria for ‘Other.’

Lastly, None is included because we want non-brand results to return a null value.

VLOOKUP

VLOOKUP is an essential tool for joining together two distinct datasets on a common column.

In this case, adding the URLs within column N to the keyword, position, and search volume data in columns A-C, using the shared “Keyword” column:

=VLOOKUP(A2,M:N,2,FALSE)

To do something similar with Pandas, we can use merge.

Replicating the functionality of an SQL join, merge is an incredibly powerful function that supports a variety of different join types.

For our purposes, we want to use a left join, which will maintain our first dataframe and only merge in matching values from our second dataframe:

mergeddf = df.merge(df2, how='left', on='Keyword')

One added advantage of performing a merge over a VLOOKUP, is that you don’t have to have the shared data in the first column of the second dataset, as with the newer XLOOKUP.

It will also pull in multiple rows of data rather than the first match in finds.

One common issue when using the function is for unwanted columns to be duplicated. This occurs when multiple shared columns exist, but you attempt to match using one.

To prevent this – and improve the accuracy of your matches – you can specify a list of columns:

mergeddf = df.merge(df2, how='left', on=['Keyword', 'Search Volume'])

In certain scenarios, you may actively want these columns to be included. For instance, when attempting to merge multiple monthly ranking reports:

mergeddf = df.merge(df2, on='Keyword', how='left', suffixes=('', '_october'))
    .merge(df3, on='Keyword', how='left', suffixes=('', '_september'))

The above code snippet executes two merges to join together three dataframes with the same columns – which are our rankings for November, October, and September.

By labeling the months within the suffix parameters, we end up with a much cleaner dataframe that clearly displays the month, as opposed to the defaults of _x and _y seen in the earlier example.

COUNTIF/SUMIF/AVERAGEIF

In Excel, if you want to perform a statistical function based on a condition, you’re likely to use either COUNTIF, SUMIF, or AVERAGEIF.

Commonly, COUNTIF is used to determine how many times a specific string appears within a dataset, such as a URL.

We can accomplish this by declaring the ‘URL’ column as our range, then the URL within an individual cell as our criteria:

=COUNTIF(D:D,D2)

In Pandas, we can achieve the same outcome by using the groupby function:

df.groupby('URL')['URL'].count()

Here, the column declared within the round brackets indicates the individual groups, and the column listed in the square brackets is where the aggregation (i.e., the count) is performed.

The output we’re receiving isn’t perfect for this use case, though, because it’s consolidated the data.

Typically, when using Excel, we’d have the URL count inline within our dataset. Then we can use it to filter to the most frequently listed URLs.

To do this, use transform and store the output in a column:

df['URL Count'] = df.groupby('URL')['URL'].transform('count')

You can also apply custom functions to groups of data by using a lambda (anonymous) function:

df['Google Count'] = df.groupby(['URL'])['URL'].transform(lambda x: x[x.str.contains('google')].count())

In our examples so far, we’ve been using the same column for our grouping and aggregations, but we don’t have to. Similarly to COUNTIFS/SUMIFS/AVERAGEIFS in Excel, it’s possible to group using one column, then apply our statistical function to another.

Going back to the earlier search engine results page (SERP) example, we may want to count all ranking PDPs on a per-keyword basis and return this number alongside our existing data:

df['PDP Count'] = df.groupby(['Keyword'])['URL'].transform(lambda x: x[x.str.contains('/product/|/prd/|/pd/')].count())

=SUM(COUNTIFS(A:A,[@Keyword],D:D,{"*/product/*","*/prd/*","*/pd/*"}))

Pivot Tables

Last, but by no means least, it’s time to talk pivot tables.

In Excel, a pivot table is likely to be our first port of call if we want to summarise a large dataset.

For instance, when working with ranking data, we may want to identify which URLs appear most frequently, and their average ranking position.

Again, Pandas has its own pivot tables equivalent – but if all you want is a count of unique values within a column, this can be accomplished using the value_counts function:

count = df['URL'].value_counts()

Using groupby is also an option.

Earlier in the article, performing a groupby that aggregated our data wasn’t what we wanted – but it’s precisely what’s required here:

grouped = df.groupby('URL').agg(
     url_frequency=('Keyword', 'count'),
     avg_position=('Position', 'mean'),
     )

grouped.reset_index(inplace=True)

Two aggregate functions have been applied in the example above, but this could easily be expanded upon, and 13 different types are available.

There are, of course, times when we do want to use pivot_table, such as when performing multi-dimensional operations.

To illustrate what this means, let’s reuse the ranking groupings we made using conditional statements and attempt to display the number of times a URL ranks within each group.

ranking_groupings = df.groupby(['URL', 'Grouping']).agg(
     url_frequency=('Keyword', 'count'),
     )

This isn’t the best format to use, as multiple rows have been created for each URL.

Instead, we can use pivot_table, which will display the data in different columns:

pivot = pd.pivot_table(df,
index=['URL'],
columns=['Grouping'],
aggfunc='size',
fill_value=0,
)

Final Thoughts

Whether you’re looking for inspiration to start learning Python, or are already leveraging it in your SEO workflows, I hope that the above examples help you along on your journey.

As promised, you can find a Google Colab notebook with all of the code snippets here.

In truth, we’ve barely scratched the surface of what’s possible, but understanding the basics of Python data analysis will give you a solid base upon which to build.

More resources:

---

Featured Image: mapo_japan/Shutterstock

10 Dos And Don’ts For Building A Successful Travel Website via @sejournal, @seo_travel

When it comes to search engine optimization (SEO), your website is the foundation on which all of your hard work will rest.

It doesn’t matter how many keywords you add, meta descriptions you tweak, or plugins you remove: If the building blocks of your tourism website aren’t properly assembled, you’re always going to struggle to get the site to rank as well as it should.

On top of the technical benefits of a well-built website, as a travel brand, your website is one of your most valuable assets.

Here, potential customers can browse available packages, read testimonials from previous guests, and get a taste of the experiences on offer through engaging imagery and text – along with actually booking a holiday directly with your company.

Creating a travel website from scratch is always a significant project, but the benefits of designing a site from the ground up are widespread when it comes to ranking as high as possible on search engines.

So whether you’re a new travel brand that needs a website to start your business journey, or are redesigning your site to reap the benefits of a custom design, here are 10 dos and don’ts for building a successful travel website.

Do: Start With SEO Research

Every well-ranking website design starts with SEO research.

Before you decide on any of the pages or content that’s going to be included on the site, you need to understand which factors will influence how your site ranks on search engines.

High-ranking websites are structured based on keyword research and target phrases, as these influence the topics that landing pages are focused on and the kinds of questions that content needs to answer.

They’re also built to optimize page speed and minimize unnecessary plugins while making it as easy as possible for search engines to understand their intent.

This is one of the key reasons why using a website builder template or trying to cut corners with website design can be such a big problem.

Unless the website for travel is specifically designed with SEO in mind, you’re shooting yourself in the foot right from the start.

Don’t: Opt For A Bad Hosting Platform

If you’re trying to quickly get a new travel website off the ground, it can be tempting to choose a generic hosting platform.

These are often advertised as simple ways to build a functional website, and whilst this is true on the surface, there are plenty of problems that lie beneath.

When you use ‘drag-and-drop’ website builders to put together a website using existing templates, you often end up with a lot of unnecessary code and dependencies in the site’s backend.

This massively impacts how long it takes for pages to load and how smoothly the whole site runs, which will stop the site from ranking highly on search engines, and potentially frustrate users that want to book a holiday or make a purchase.

Using bad platforms to build your site can also limit the features you’re able to add to it later down the line.

Not only can this stifle creativity when you’re trying to come up with the best travel website design, but it can also come back to haunt you in the future when you’re unable to expand your site.

Do: Make Use Of Stock Image Databases

Opting for a custom-built design for your travel website is the route I recommend to ensure that all of your specifications are catered for – but this can be an expensive and time-consuming venture.

Many people will opt to choose a template on a site like WordPress to build their travel website, and the good news is that there are plenty of ways you can customize this to make it look unique.

One of these is the photos that you use on your travel website, which are an incredibly important visual element. One of the key ways you will engage a user is by encouraging them to picture themselves on a trip or in your accommodation, and eye-catching, immersive images are a great way to do that.

Having unique photographs on your website is ideal, but it’s not always possible when building a website, especially if your company is based in a different location. Thankfully, there are plenty of stock image databases you can utilize, offering both free and paid images.

If you’re looking for beautiful free stock photos, Unsplash is a fantastic option. Pixabay and Pexels are also good, but often only have a limited selection of images on offer.

If you’re looking for a greater variety, paid sites like iStock and Shutterstock offer reasonable prices for their images.

Don’t: Try To Cut Costs With A Bad Booking System

Plenty of travel companies have a booking system as part of their website. This element needs to be carefully considered, as some booking platforms can negatively impact a site’s SEO.

It’s worth properly doing your research into the best kind of booking system for your travel website, as many can be difficult to use, time-consuming to update, or are just built with a lot of unnecessary code that can really slow down this section of the website.

Here are some of the best WordPress booking plugins that cater especially to travel businesses:

In some cases, you may be better off building a custom booking system that is much easier to edit and clearly lets customers book what they want without any misunderstanding.

Yes, building a bespoke booking system is another cost if you’re already investing in a unique travel website design. But it is likely to save you a lot of time and money in the future by making this booking system simple and optimized specifically for your brand, minimizing issues later on.

Do: Plan In Time For Content Creation

If you’re working with an agency or a designer, content creation might be included in the website design package that you’re paying for.

But many brands prefer to write their content themselves in order to capture the right tone of voice, so it’s important to factor in how long this is going to take.

You can start writing content for your website once you have a clear plan of the site structure and how many pages are going to be built.

It might be useful to see template designs for different landing pages to know how much text is needed – or your designer might ask you to write the content first, so they can design the pages around this.

If you want your travel website to rank highly when your customers search for your offering, you need to complete keyword research first.

This gives you a list of popular, relevant phrases that you can include in your travel website content that will help the site to rank higher and ensure that genuinely interested web users are arriving on the pages they’re looking for.

There are several different paid and free keyword research databases that you can use for this, with my favorite being Ahrefs.

These databases often have a tool that allows you to enter a word or phrase and then generate a list of related keywords, showing search volume and other useful data.

For travel keyword research, you want to focus on keyword phrases that indicate the user is looking to book a trip, visit a location, or get a recommendation.

For example, ‘family safari in africa’ is a general phrase with broad intent, whereas ‘where is the best place for a family safari?’ or ‘what animals can you see in south africa?’ are likely being searched by someone planning a holiday.

Once you’ve compiled a list of relevant phrases, you can either write landing page content that includes these phrases, or create pages of blog posts that specifically answer questions or focus on a topic with high search volume.

As well as keyword databases like Ahrefs, you can use Google Trends to identify popular travel topics and create content targeting query spikes, capitalizing on seasonal traffic.

Don’t: Forget Headers, Title Tags, And Meta Descriptions

It’s not just chunks of text for landing pages that you need to think about when it comes to website content creation.

Headers, title tags, and meta descriptions need to be carefully considered and written, as they are a key aspect of a website’s SEO.

Every landing page needs a unique heading structure, and you ideally want to fit target keyword phrases into each of these headings. This structure makes it easier for users to navigate a page, along with helping search engines to rank each page more easily, which will boost how well each page ranks.

To create a header structure, start with the page’s title (H1), which will usually contain the target keyword phrase. You can then use keyword research to find similar or related phrases that can be used as headings further down the page.

For example, a page might have the title ‘Luxury Family Safari Holidays’with subheadings including ‘Family Safaris in South Africa,’ ‘How to Book a Family Safari,’ and ‘What to Expect on a Safari Holiday.’

Title tags are an element of a webpage’s HTML that explains the title of the page and appears in the tab at the top of a screen when a website is loaded up on a browser (and on the search engine results page).

Not only do these need to be clear so that the user understands the purpose of the page, but they also impact a page’s SEO, so should contain the target keyword phrase.

Title tags also often contain the brand’s name and occasionally a key phrase that summarises the brand’s offering. To build on the previous example, the title tag might be ‘Luxury Family Safaris | South Africa Safari Holidays | M+J Safari Experiences.’ 

Finally, a meta description is another HTML element that summarizes the content of a page and, again, influences how well it ranks for its target phrase. Meta descriptions should be under 200 characters, so need to be carefully written to provide a concise description of a landing page and include relevant keyword phrases if possible.

An example might be ‘Witness a side of South Africa you’ve never seen before with a luxury family safari holiday from M+J Safari Experiences.’

Do: Implement A Sitemap

A sitemap does what it says on the tin: it maps out where each of the landing pages is on your website and provides a navigation point that directs how to get to each of these.

No matter how you build a travel website, you should implement a sitemap to provide search engines with a way of quickly understanding what the website contains.

Without a sitemap, bots crawling your site to decide where to rank it on search engines will try and interpret your internal linking instead. This can impact your ranking, so it’s best to have a sitemap established in the early stages of building a travel website.

You also need to consider user experience when you’re building a sitemap.

Potential customers on a travel website are likely going to want to browse the experiences you have on offer, see pictures and videos of your service offering, read testimonials to gain social proof, and then look at how to book a trip.

You should design your website so that this journey is easy to follow, with internal links and calls to action (CTAs) guiding the user toward making a purchase.

Don’t: Avoid Internal Linking

Speaking of internal linking, whilst it’s not a substitute for a sitemap, it’s definitely not something that you should avoid.

Internally linking landing pages to one another helps to direct users to different pages and keep them on your site for longer, ideally moving them towards making a booking.

Having lots of internal links throughout your travel website also helps it to rank better, as it helps search engines to identify the key pages on a site.

It also helps new pages to start ranking for phrases faster by associating them with existing pages that are already ranking well.

It’s very straightforward to put internal links in the header and footer of a landing page. But if you really want to reap the benefits of this approach, you should include internal linking in the content on each landing page as well.

Consider the anchor text that you’re adding the link to each time; it needs to be related to the page that it’s linking to, otherwise, the link will be seen as spammy.

Do: Optimize Your Images

It’s not just written content that needs to be optimized.

Travel websites tend to have lots of images on them, but the size of these can affect how quickly a landing page loads, which in turn can affect how well it ranks.

You shouldn’t compromise on image quality, but you should resize and compress each image before it’s uploaded to save space.

Images can also be optimized by ensuring that they have a title and alt text that describes what the image contains. Images should be relevant to each landing page, so this alt text can be used to insert more keyword phrases – or at least phrases that are semantically similar.

Don’t: Overuse Plugins And JavaScript Elements

Finally, whilst we’re talking about elements that impact loading speed, plugins and JavaScript can both cause problems with how fast a landing page loads.

Both can be useful, but both need to be chosen carefully so that your website’s SEO isn’t affected.

Plugins can be used to add a variety of exciting features to a travel website, but overuse will make it impossible to load a page in under a couple of seconds.

Consider the benefits of each plugin before you add them to your website, and regularly review these to ensure you’re still getting what you want.

JavaScript, similarly, can make a website more dynamic; these elements are resource-heavy dependencies that will also impact how quickly a page loads.

Some browsers also have JavaScript disabled, so by using a lot of these elements on a page, you face losing functionality if JavaScript isn’t enabled.

Summary

A new travel website build is often a lengthy process that can come with a hefty price tag if you don’t know what you should be asking for.

The above advice should give you a clear set of guidelines of what to prioritize and avoid when it comes to designing a new website for your travel company, helping to ensure you’re left with a beautiful and functional site for your brand that ranks well on search engines and brings in plenty of business.

More resources:

---

Featured Image: REDPIXEL.PL/Shutterstock