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Not all content is made equal, and not every blog post receives external links.

Getting inbound links for every blog article is unbelievably time-consuming due to the amount of outreach required to secure links to just one article or page.

Instead of establishing a link building program around outreach, having a linkable content asset can earn you links and improve your outreach response rates.

This article will help you get started creating linkable content so you can build a natural link profile in less time.

Let’s start by defining a linkable asset.

What Is A Linkable Asset?

Linkable content assets refer to high-quality content pieces created to attract backlinks from other websites, either via promotional efforts or link building outreach strategies.

These assets can be presented in various formats, such as infographics, written articles, videos, online tools, or downloadable documents.

An optimal linkable asset is a thorough, long-form resource that surpasses top-ranking content in the same niche. Your content should provide value, be unique, and cater to the interests of your target audience.

For an efficient traffic flow to your website, links must direct users to a specific webpage. Therefore, the asset should either be an HTML page, or integrated within a page.

There are a wide variety of linkable assets, one of which is the History of Google Algorithm Updates, published by the Search Engine Journal (SEJ).

Based on data from Ahrefs, this page has successfully gathered nearly 3,000 referring domains.

Well-established brands tend to receive more links compared to new or lesser-known sites.

SEJ has built a solid reputation for producing high-quality SEO content, and its trusted name in the field has played a significant role in the success of this particular asset.

The efficacy of a linkable asset often depends on a site’s proven expertise, experience, authority, and trust (E-E-A-T) within its domain.

Enhancing or establishing E-E-A-T can increase the appeal of your content to publishers. Now, let’s briefly delve into the concept of E-E-A-T.

Applications Of E-E-A-T In Linkable Content

Google’s Search Quality Rater Guidelines introduced the concept of E-E-A-T, which helps users assess content quality.

E-E-A-T has become increasingly significant in SEO, content marketing, and link building. To optimize a linkable asset, ensure it demonstrates each of these elements:

Expertise: Showcase the author’s knowledge and understanding of the topic through education, experience, or publications.

Experience: Highlight the author’s firsthand knowledge of the subject via personal experience, interviews, or research.

Authority: Establish the author’s reputation and credibility through credentials, affiliations, or the quality of their work.

Trustworthiness: Demonstrate the author’s honesty and integrity through transparency, neutrality, or avoiding conflicts of interest.

While the Quality Rater Guidelines and E-E-A-T don’t reveal the algorithm’s workings, Google claims that raters who follow these guidelines achieve similar results. Optimizing an article for E-E-A-T can improve its Google ranking and shareability.

Pro tip: Address questions real people ask in your niche to exhibit expertise and experience.

The emergence of generative AI, such as ChatGPT, has raised questions about demonstrating E-E-A-T in AI-generated content. Next, let’s explore generative AI in content creation.

Harnessing Generative AI Content Ideation

Generative AI is revolutionizing how content marketers conduct research, develop ideas and create content. Tools like ChatGPT, Google Bard, and Bing Chat can significantly reduce research time and provide compelling content topics.

As AI content generation is still emerging, search engine and publisher policies on AI-produced content continue to evolve.

Incorporating subject matter expertise (SME), personal experiences, case studies, and in-depth topic exploration can ensure that AI-generated content remains authentic and avoids plagiarism.

How To Get Content Ideas With ChatGPT

Select a topic or niche, then prompt ChatGPT with “Act as HubSpot’s blog idea generator and provide a list of 20 unique long-form content ideas for [topic/keyword].”

Choose relevant, unique, and searchable ideas, and refine them by comparing them to top-ranking content on Google.

How To Create An Outline With ChatGPT

Prompt ChatGPT with “Act as [industry blog or person] and create a comprehensive outline for an article ‘[title]’, using contemporary content marketing techniques.”

An editor will then refine the AI-generated outline.

Update the outline to include the questions and answers from Bard, and modify the ChatGPT-generated sections from an experienced expert’s perspective, deciding which sections to include or remove.

Pro tips: Use tools like ZeroGPT or ChatGPT AI Classifier to ensure your content doesn’t seem too robotic.

When To Use ChatGPT Vs. Other Generative AI

Bing Chat & Google Bard can access their individual search results and current content, while ChatGPT receives monthly updates and lacks internet access.

Utilize Bing Chat & Bard for research and rely on ChatGPT for titles, outlines, and process lists.

Generative AI has the potential to enhance topic selection and outlines for linkable assets and even address some downsides of linkable content assets.

Next, let’s examine the pros and cons of linkable content assets.

Pros And Cons Of Linkable Assets

While high-quality linkable assets can generate substantial links and traffic, this link-building strategy has drawbacks.


Scalable link acquisition: Using informational content allows link opportunities across numerous topical areas.

Enhanced organic visibility: Long-form informative articles can attract thousands of monthly visits across various keywords.

Relationship building: Linkable content assets help establish relationships with influencers or industry experts, fostering collaboration and trust.

Long-term value: Relevant and valuable content assets can continue to attract backlinks and traffic over time.

Brand exposure and credibility: Linkable assets can boost your brand’s reputation as an industry expert, increasing trust and credibility.


Links not targeting ideal pages: Links typically target the asset, but a commercial page may be preferable for improving ranking.

Time and resource-intensive: Creating high-quality linkable assets can be challenging for smaller businesses or teams due to the required time, effort, and resources.

No guarantee of backlinks: High-quality content does not guarantee backlinks or SEO rewards.

Difficult to measure success: Assessing the impact of linkable assets on SEO can be challenging, as it might take time for search engines to recognize and reward acquired backlinks.

Competition: Increasing competition for backlinks and organic visibility makes it harder to stand out.

Using Linkable Assets For Link Building

Content can be tailored for specific link-building techniques or audiences, or created for a general niche.


This approach focuses on creating content for a specific link-building technique and audience.

Identify sites, forums, bloggers, or influencers interested in a particular topic, create content that appeals to them, and distribute it through email outreach or promotion to earn links.

Examples of technique-first methods include the Skyscraper technique, ranking statistical articles, niche forums, competitor link building, and guest posting.


This strategy involves creating content for an audience or niche, then finding links for that piece. Use existing content or create new content that appeals to an expert audience.

Ideation techniques can involve a subject matter expert (SME) focus group or answering questions without solid answers online.

Next, let’s explore some examples of assets that you can recreate.

Types Of Assets

Creating content to secure links has been a significant focus of link builders since Brian Dean published an article about the SkyScraper technique in 2013 and many before that.

However, there are a lot of content types that work well for securing links. The options for content types seem limitless.

The following is a narrowed-down list of asset types that can improve outreach response rates and earn links under the right circumstance.

Statistical Roundup Lists

This article will aggregate statistics from reputable studies and then organize them into appropriate categories so they are easy to search.

SEJ has a great example of a linkable asset with the roundup of 71 Mind-Blowing Search Engine Optimization Stats.

These are organized into organic traffic, spending, local search, users & search behavior, link building (my favorite topic), Google search, and SEO vs. other marketing channels.

Unique Research Study

These assets are unique studies with accompanying methodology and insights published in a blog article or a general webpage.

These assets are typically surveys, analyzing company data, or compiling & analyzing data from resources like Google Research, chúng tôi or Kaggle.

Ahrefs publishes a lot of data. This study is one example, claiming “90% of content gets no traffic on Google.”

This asset has charts from the study embedded into the article, making both the images and the article linkable.

Listicles Of Companies, Tools, Or People

The term “listicle” is a portmanteau of the words “list” and “article.”

The format of a listicle is easy to skim to find the most relevant information quickly. However, a listicle can oversimplify a complex topic.

The great thing about these articles is that they can be created in almost any industry.

The article 12 Free Logo Makers You Can Try Right Now from a promotional printing company Quality Logo Products, shows that this technique can be used in any niche.

This article provides examples of potential logos along with the pros & cons for each logo maker.

With any listicle, showing examples of using the tool or items is a way to demonstrate experience and build stronger trust with the audience.

Informative Infographics

An infographic is a visual representation of information, data, or knowledge designed to convey complex information quickly and clearly.

Infographics often use a combination of charts, graphs, icons, illustrations, and text to present the information visually appealingly.

The carbon budget infographic by World Resources Institute uses a graphic of the earth to explain how much of the carbon budget (i.e., the amount of carbon the earth can produce before the temperature rises by 2 degrees) will be used by 2045.

Excel & Google Sheet Templates

An example is “Excel budget templates,” which has up to 100,000 average monthly searches.

This technique is very popular with B2B SaaS and project management (PM) software but can be used in any niche. ProjectManager is one of the PM sites producing Excel budgeting templates.

This example is simple but has secured over 100 referring domain links.

More Asset Types Planning Tools

A planner is an online web app that simplifies planning a specific task. The tasks can be for a simple consumer or a business planning task.

Calculators As Content

An online calculator can take the shape of an investment calculator, home improvement, or even a depth calculator.

Checklists Or Cheatsheets

These lists of steps or tasks to complete a specific project can come in the form of an online app, excel template, or pdf.


Creating a linkable asset that publishers, bloggers, and general websites will leverage can reduce outreach time and even earn links without any outreach.

Although these pieces are time-consuming to create, they can reduce the overall time to find links.

An asset can be a complex research study or a simple Excel template to organize planning.

Next time you’re launching a link building campaign, create a piece that is easily linkable.

More resources:

Featured Image: Marynchenko Oleksandr/Shutterstock

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A Guide To Building An End

This article was published as a part of the Data Science Blogathon.

Knock! Knock!

Who’s there?

It’s Natural Language Processing!

Today we will implement a multi-class text classification model on an open-source dataset and explore more about the steps and procedure. Let’s begin.

Table of Contents


Loading the data

Feature Engineering

Text processing

Exploring Multi-classification Models

Compare Model performance



Dataset for Text Classification

The dataset consists of real-world complaints received from the customers regarding financial products and services. The complaints are labeled to a specific product. Hence, we can conclude that this is a supervised problem statement, where we have the input and the target output for that. We will play with different machine learning algorithms and check which algorithm works better.

Our aim is to classify the complaints of the consumer into predefined categories using a suitable classification algorithm. For now, we will be using the following classification algorithms.

Linear Support Vector Machine (LinearSVM)

Random Forest

Multinomial Naive Bayes

Logistic Regression.

Loading the Data

Download the dataset from the link given in the above section. Since I am using Google Colab, if you want to use the same you can use the Google drive link given here and import the dataset from your google drive. The below code will mount the drive and unzip the data to the current working directory in colab.

from google.colab import drive drive.mount('/content/drive') !unzip /content/drive/MyDrive/

First, we will install the required modules.

Pip install numpy

Pip install pandas

Pip install seaborn

Pip install scikit-learn

Pip install scipy

Ones everything successfully installed, we will import required libraries.

import os import pandas as pd import numpy as np from scipy.stats import randint import seaborn as sns # used for plot interactive graph. import matplotlib.pyplot as plt import seaborn as sns from io import StringIO from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.feature_selection import chi2 from IPython.display import display from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import TfidfTransformer from sklearn.naive_bayes import MultinomialNB from sklearn.linear_model import LogisticRegression from sklearn.ensemble import RandomForestClassifier from chúng tôi import LinearSVC from sklearn.model_selection import cross_val_score from sklearn.metrics import confusion_matrix from sklearn import metrics

Now after this let us load the dataset and see the shape of the loaded dataset.

# loading data df = pd.read_csv('/content/rows.csv') print(df.shape)

From the output of the above code, we can say that the dataset is very huge and it has 18 columns. Let us see how the data looks like. Execute the below code.


Now, for our multi-class text classification task, we will be using only two of these columns out of 18, that is the column with the name ‘Product’ and the column ‘Consumer complaint narrative’. Now let us create a new DataFrame to store only these two columns and since we have enough rows, we will remove all the missing (NaN) values. To make it easier to understand we will rename the second column of the new DataFrame as ‘consumer_complaints’.

# Create a new dataframe with two columns df1 = df[['Product', 'Consumer complaint narrative']].copy() # Remove missing values (NaN) df1 = df1[pd.notnull(df1['Consumer complaint narrative'])] # Renaming second column for a simpler name df1.columns = ['Product', 'Consumer_complaint'] print(df1.shape) df1.head(3).T

We can see that after discarding all the missing values, we have around 383k rows and 2 columns, this will be our data for training. Now let us check how many unique products are there.


There are 18 categories in products. To make the training process easier, we will do some changes in the names of the category.

# Because the computation is time consuming (in terms of CPU), the data was sampled df2 = df1.sample(10000, random_state=1).copy() # Renaming categories df2.replace({'Product': {'Credit reporting, credit repair services, or other personal consumer reports': 'Credit reporting, repair, or other', 'Credit reporting': 'Credit reporting, repair, or other', 'Credit card': 'Credit card or prepaid card', 'Prepaid card': 'Credit card or prepaid card', 'Payday loan': 'Payday loan, title loan, or personal loan', 'Money transfer': 'Money transfer, virtual currency, or money service', 'Virtual currency': 'Money transfer, virtual currency, or money service'}}, inplace= True) pd.DataFrame(df2.Product.unique())

The 18 categories are now reduced to 13, we have combined ‘Credit Card’ and ‘Prepaid card’ to a single class and so on.

Now, we will map each of these categories to a number, so that our model can understand it in a better way and we will save this in a new column named ‘category_id’. Where each of the 12 categories is represented in numerical.

# Create a new column 'category_id' with encoded categories df2['category_id'] = df2['Product'].factorize()[0] category_id_df = df2[['Product', 'category_id']].drop_duplicates() # Dictionaries for future use category_to_id = dict(category_id_df.values) id_to_category = dict(category_id_df[['category_id', 'Product']].values) # New dataframe df2.head()

Let us visualize the data, and see how many numbers of complaints are there per category. We will use Bar chart here.

fig = plt.figure(figsize=(8,6)) colors = ['grey','grey','grey','grey','grey','grey','grey','grey','grey', 'grey','darkblue','darkblue','darkblue'] df2.groupby('Product').Consumer_complaint.count().sort_values().plot.barh( ylim=0, color=colors, title= 'NUMBER OF COMPLAINTS IN EACH PRODUCT CATEGORYn') plt.xlabel('Number of ocurrences', fontsize = 10);

Above graph shows that most of the customers complained regarding:

Credit reporting, repair, or other

Debt collection


Text processing

The text needs to be preprocessed so that we can feed it to the classification algorithm. Here we will transform the texts into vectors using Term Frequency-Inverse Document Frequency (TFIDF) and evaluate how important a particular word is in the collection of words. For this we need to remove punctuations and do lower casing, then the word importance is determined in terms of frequency.

We will be using TfidfVectorizer function with the below parameters:

min_df: remove the words which has occurred in less than ‘min_df’ number of files.

Sublinear_tf: if True, then scale the frequency in logarithmic scale.

Stop_words: it removes stop words which are predefined in ‘english’.

tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, ngram_range=(1, 2), stop_words='english') # We transform each complaint into a vector features = tfidf.fit_transform(df2.Consumer_complaint).toarray() labels = df2.category_id print("Each of the %d complaints is represented by %d features (TF-IDF score of unigrams and bigrams)" %(features.shape))

Now, we will find the most correlated terms with each of the defined product categories. Here we are finding only three most correlated terms.

# Finding the three most correlated terms with each of the product categories N = 3 for Product, category_id in sorted(category_to_id.items()): features_chi2 = chi2(features, labels == category_id) indices = np.argsort(features_chi2[0]) feature_names = np.array(tfidf.get_feature_names())[indices] unigrams = [v for v in feature_names if len(v.split(' ')) == 1] bigrams = [v for v in feature_names if len(v.split(' ')) == 2] print(" * Most Correlated Unigrams are: %s" %(', '.join(unigrams[-N:]))) print(" * Most Correlated Bigrams are: %s" %(', '.join(bigrams[-N:])))

* Most Correlated Unigrams are: overdraft, bank, scottrade * Most Correlated Bigrams are: citigold checking, debit card, checking account * Most Correlated Unigrams are: checking, branch, overdraft * Most Correlated Bigrams are: 00 bonus, overdraft fees, checking account * Most Correlated Unigrams are: dealership, vehicle, car * Most Correlated Bigrams are: car loan, vehicle loan, regional acceptance * Most Correlated Unigrams are: express, citi, card * Most Correlated Bigrams are: balance transfer, american express, credit card * Most Correlated Unigrams are: report, experian, equifax * Most Correlated Bigrams are: credit file, equifax xxxx, credit report * Most Correlated Unigrams are: collect, collection, debt * Most Correlated Bigrams are: debt collector, collect debt, collection agency * Most Correlated Unigrams are: ethereum, bitcoin, coinbase * Most Correlated Bigrams are: account coinbase, coinbase xxxx, coinbase account * Most Correlated Unigrams are: paypal, moneygram, gram * Most Correlated Bigrams are: sending money, western union, money gram * Most Correlated Unigrams are: escrow, modification, mortgage * Most Correlated Bigrams are: short sale, mortgage company, loan modification * Most Correlated Unigrams are: meetings, productive, vast * Most Correlated Bigrams are: insurance check, check payable, face face * Most Correlated Unigrams are: astra, ace, payday * Most Correlated Bigrams are: 00 loan, applied payday, payday loan * Most Correlated Unigrams are: student, loans, navient * Most Correlated Bigrams are: income based, student loan, student loans * Most Correlated Unigrams are: honda, car, vehicle * Most Correlated Bigrams are: used vehicle, total loss, honda financial

Exploring Multi-classification Models

The classification models which we are using:

Random Forest

Linear Support Vector Machine

Multinomial Naive Bayes

Logistic Regression.

For more information regarding each model, you can refer to their official guide.

Now, we will split the data into train and test sets. We will use 75% of the data for training and the rest for testing. Column ‘consumer_complaint’ will be our X or the input and the product is out Y or the output.

X = df2['Consumer_complaint'] # Collection of documents y = df2['Product'] # Target or the labels we want to predict (i.e., the 13 different complaints of products) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state = 0)

We will keep all the using models in a list and loop through the list for each model to get a mean accuracy and standard deviation so that we can calculate and compare the performance for each of these models. Then we can decide with which model we can move further.

models = [ RandomForestClassifier(n_estimators=100, max_depth=5, random_state=0), LinearSVC(), MultinomialNB(), LogisticRegression(random_state=0), ] # 5 Cross-validation CV = 5 cv_df = pd.DataFrame(index=range(CV * len(models))) entries = [] for model in models: model_name = model.__class__.__name__ accuracies = cross_val_score(model, features, labels, scoring='accuracy', cv=CV) for fold_idx, accuracy in enumerate(accuracies): entries.append((model_name, fold_idx, accuracy)) cv_df = pd.DataFrame(entries, columns=['model_name', 'fold_idx', 'accuracy'])

The above code will take sometime to complete its execution.

Compare Text Classification Model performance

Here, we will compare the ‘Mean Accuracy’ and ‘Standard Deviation’ for each of the four classification algorithms.

mean_accuracy = cv_df.groupby('model_name').accuracy.mean() std_accuracy = cv_df.groupby('model_name').accuracy.std() acc = pd.concat([mean_accuracy, std_accuracy], axis= 1, ignore_index=True) acc.columns = ['Mean Accuracy', 'Standard deviation'] acc

From the above table, we can clearly say that ‘Linear Support Vector Machine’ outperforms all the other classification algorithms. So, we will use LinearSVC to train model multi-class text classification tasks.

plt.figure(figsize=(8,5)) sns.boxplot(x='model_name', y='accuracy', data=cv_df, color='lightblue', showmeans=True) plt.title("MEAN ACCURACY (cv = 5)n", size=14);

Evaluation of Text Classification Model

Now, let us train our model using ‘Linear Support Vector Machine’, so that we can evaluate and check it performance on unseen data.

X_train, X_test, y_train, y_test,indices_train,indices_test = train_test_split(features, labels, df2.index, test_size=0.25, random_state=1) model = LinearSVC(), y_train) y_pred = model.predict(X_test)

We will generate claasifiaction report, to get more insights on model performance.

# Classification report print('ttttCLASSIFICATIION METRICSn') print(metrics.classification_report(y_test, y_pred, target_names= df2['Product'].unique()))

From the above classification report, we can observe that the classes which have a greater number of occurrences tend to have a good f1-score compared to other classes. The categories which yield better classification results are ‘Student loan’, ‘Mortgage’ and ‘Credit reporting, repair, or other’. The classes like ‘Debt collection’ and ‘credit card or prepaid card’ can also give good results. Now let us plot the confusion matrix to check the miss classified predictions.

conf_mat = confusion_matrix(y_test, y_pred) fig, ax = plt.subplots(figsize=(8,8)) sns.heatmap(conf_mat, annot=True, cmap="Blues", fmt='d', xticklabels=category_id_df.Product.values, yticklabels=category_id_df.Product.values) plt.ylabel('Actual') plt.xlabel('Predicted') plt.title("CONFUSION MATRIX - LinearSVCn", size=16);

From the above confusion matrix, we can say that the model is doing a pretty decent job. It has classified most of the categories accurately.


Let us make some prediction on the unseen data and check the model performance.

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state = 0) tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, ngram_range=(1, 2), stop_words='english') fitted_vectorizer = tfidf_vectorizer_vectors = fitted_vectorizer.transform(X_train) model = LinearSVC().fit(tfidf_vectorizer_vectors, y_train)

Now run the prediction.

complaint = """I have received over 27 emails from XXXX XXXX who is a representative from Midland Funding LLC. From XX/XX/XXXX I received approximately 6 emails. From XX/XX/XXXX I received approximately 6 emails. From XX/XX/XXXX I received approximately 9 emails. From XX/XX/XXXX I received approximately 6 emails. All emails came from the same individual, XXXX XXXX. It is becoming a nonstop issue of harassment.""" print(model.predict(fitted_vectorizer.transform([complaint]))) complaint = """Respected Sir/ Madam, I am exploring the possibilities for financing my daughter 's XXXX education with private loan from bank. I am in the XXXX on XXXX visa. My daughter is on XXXX dependent visa. As a result, she is considered as international student. I am waiting in the Green Card ( Permanent Residency ) line for last several years. I checked with Discover, XXXX XXXX websites. While they allow international students to apply for loan, they need cosigners who are either US citizens or Permanent Residents. I feel that this is unfair. I had been given mortgage and car loans in the past which I closed successfully. I have good financial history. print(model.predict(fitted_vectorizer.transform([complaint]))) complaint = """They make me look like if I was behind on my Mortgage on the month of XX/XX/2024 & XX/XX/XXXX when I was not and never was, when I was even giving extra money to the Principal. The Money Source Web site and the managers started a problem, when my wife was trying to increase the payment, so more money went to the Principal and two payments came out that month and because I reverse one of them thru my Bank as Fraud they took revenge and committed slander against me by reporting me late at the Credit Bureaus, for 45 and 60 days, when it was not thru. Told them to correct that and the accounting department or the company revert that letter from going to the Credit Bureaus to correct their injustice. The manager by the name XXXX requested this for the second time and nothing yet. I am a Senior of XXXX years old and a Retired XXXX Veteran and is a disgraced that Americans treat us that way and do not want to admit their injustice and lies to the Credit Bureau.""" print(model.predict(fitted_vectorizer.transform([complaint])))

The model is not perfect, yet it is performing very good.

The notebook is available here.


We have implemented a basic multi-class text classification model, you can play with other models like Xgboost, or you can try to compare multiple model performance on this dataset using a machine learning framework called AutoML. This is not yet, still there are complex problems associated within the multi-class text classification tasks, you can always explore more and acquire new concepts and ideas about this topic. That’s It!!

Thank you!

All images are created by the author.

My LinkedIn

The media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion


5 Tips To Run A Sustainable Link Building Campaign

Do I want to do link building?

As I wait for the collective sigh of everyone reading this to subside, I think that this is a question that everyone in search asks themselves on a regular basis.

While the answer being overwhelmingly no for the majority of the population, the main reason being the time that needs to be dedicated to the campaign, everyone knows that link equity is a necessary part of the equation for a successful run.

Do I Need to Do Link Building?

The answer to that question depends on your perspective of the industry and where your site falls from an equity standpoint.

To me, sites fall into three buckets which determine how much time and effort need to be dedicated to these types of campaigns:



Big brand.

If your domain authority (yes, we all know this isn’t a real thing, but it’s widely accepted so let’s get over it) is low because you haven’t been paying attention to your search presence (for shame), then you are probably going to need to spend a sizable amount of time here just to get off the ground.

Being an established site means that while you don’t hold big brand equity, you have been building your authority up over time and can compete on terms that are important to your business and probably have some form of link building in the mix.

The three buckets above are not just to characterize sites by equity, it also characterizes them by the challenges they face from a link building perspective:

Unestablished: Have to launch a campaign from scratch, most likely has zero resources to dedicate to this effort and even less budget.

Established: Have established search processes and budget but are most likely paying a third party so they don’t own the process or relationships.

Big brand: While links are naturally coming in, most of them are pointing to the home page, which doesn’t help the site as a whole. On top of that, most big brand sites are glacial when it comes to changing processes and development.

So now that we understand how sites stack up against one another from a link perspective, or at least how you can easily place where your site stands, what do you do next?

Getting Started with Sustainable Link Building

These days you simply can’t just “start doing link building” as there are many questions that need answers:

Who is dedicating the time to this?

What’s the process of identifying links?

How do we avoid appearing unnatural?

Can I trust this won’t trigger some type of penalty against my site?

While all of these are important questions that need answers, they still don’t speak to the more important over-arching one, “How do we do link building in a sustainable way?”

Sadly, most folks are still doing link building like it’s 2004.

People are burning calories on these campaigns, but they aren’t burning them in a smart way. Thinking a little bit differently about how you approach this effort will bring much more value in the long-term.

Below are practices and ideas that you can try to run a sustainable link building campaign that won’t burn out in six months or burn your website down.

1. Look Before You Reach…Out

Let’s use data people!

Link building campaigns are typically very myopic in scope as most people think this way:

Look for a relevant site with high domain authority.

Send out prospecting email asking for a link.

Hopefully get a response and work out a deal, rinse, repeat.

2. Get More Specific

Are you focusing on the domain as a whole?

The problem with this approach is that you are only focusing on the overall domain authority, which, as I referenced before, is actually not a real thing even though it is a widely accepted metric.

So why are we basing our efforts on a made-up number?

The solution?

Get more specific.

It makes more sense to dig down and look at the individual pages that make up a domain you would like to link to and what their backlink profiles look like.

If they have strong page authority and you have a piece of content (or can create one) you believe would make sense for them to link back to, that’s a smarter way to find linking opportunities.

If you are selling baseball tickets to a certain game, wouldn’t it make sense to get a link to that page from someone writing a specific piece of content about that game or the teams playing?

This analysis isn’t hard to do if you are looking at the right data. You can set up a simple table and match up URLs:

Your URL # of Backlinks Page Authority / Equity / TrustFlow Topic / Category Their URL # of Backlinks

Page Authority / Equity / TrustFlow

If you want to get crazy (and really don’t want to do any manual work) you can also pull rankings from large indexes such as SEMrush for each URL you discover in your link tools and do some math that calls out which URLs rank for the same keywords, which would indicate they are about the same thing.

It would look something like this:

Your URL Page Authority / Equity / TrustFlow # of Backlinks Their URL Page Authority / Equity / TrustFlow # of Backlinks # Common Keywords Avg. Rank

Total MSV

If you’re worried about where you can get the backlink data, there are many tools for large sets of links to do this type of analysis (e.g., Majestic, Ahrefs, Link Research Tools).

Most of these tools will have the URLs categorized already, but even if you have to roll up your sleeves and look at what the content on the page is, that is better than blindly sending out emails with no data behind them.

This approach can also help with your outreach because you can include the rationalization of why that site should link to you and the shared benefits you two could potentially provide to one another over time.

3. Form Partnerships

There is absolutely nothing wrong with forming alliances with sites and writers that are doing things related to your business.

A lot of negativity clouds around this approach because people start throwing around “guest blogging” and “link networks”, which really isn’t what this approach is about.

First off, there is nothing wrong with guest blogging if you do it the right way.

The problem with guest blogging stemmed from a network of sites that were trading them back and forth in an unnatural way, not because Google felt like too many relevant people were writing for each other’s sites.

If you find like-minded folks who run websites in your industry and you feel there is a mutual benefit to contributing to each other’s sites, then you should do that, just do it in moderation and keep expanding the pool of folks you are partnering with.

4. Work with Influencers

In this world, nothing can be said to be certain except death and that people are going to talk about stuff on the internet.

No matter what product or service you sell, there are people who are considered experts on that product or service – and a lot of other people are listening to these experts.

Your job is to find them.

Most influencers – whether they are writers, vloggers, or speakers – actually aren’t that hard to get a hold of.

Mine YouTube for folks who are reviewing products or services like yours and send them a message to see if they are open to being sponsored.

Most of the time they will partner with you for a free product or a small fee (depending on how popular they are).

They will just have to disclose that they received compensation for the review per Google’s guidelines, which isn’t a big deal.

Or they won’t, but you didn’t hear me say that.

5. One Last Thing: Don’t Buy Links

I can’t believe I still actually have to say this, but buying links shouldn’t even be on your radar.

This is an outdated, time-consuming, soul-crushing methodology.

And let’s be serious – you can do better.

While link [building / earning / whatever you want to call it] isn’t the most fun activity, it’s a necessary evil of our trade and shouldn’t be ignored.


Now that you’ve read this, figure out which bucket your site falls into, pull some data, and start making smarter decisions.

Featured Image Credit: Paulo Bobita

A Beginner’s Guide To Building Your Own Pc

Building your own PC from scratch gives you the freedom to choose the exact specifications you want, and it often saves money as well. However, the idea can be daunting. You have to source the components, stick them all together, troubleshoot problems, ensure everything works, and install an operating system—all of which requires a lot more work than just buying a computer.

Still, once you get started, the process isn’t all that difficult. With the right guidance, anyone can build a custom PC. So we collected everything you’ll need to know. Go ahead, put together your own computer piece by piece.

The building blocks of a computer

Before you start buying components, you need to decide which ones will work best for your needs. Any PC requires a case to hold everything, a motherboard to act as the nervous system of the new machine, a processor and RAM to slot into the motherboard, a power supply unit (PSU) to regulate electricity, a hard drive to store files, and a monitor to interact with your machine.

Choosing a case is as simple as deciding what you want your new PC to look like and how much stuff you want to cram in it. The latter feature will affect the potential size of your other components. For example, more powerful graphics cards need more room, and robust processors require more cooling space, so if you want a seriously fast machine for gaming or video editing, then go big. On the other hand, if you plan to just stream Netflix, you can get away with a smaller case (without a separate graphics card).

On to the motherboard. This part attaches to one of the case’s interior sides, and other pieces (such as the processor) slot into it. Because of that, you’ll need to pick this component’s size based on the case—most cases will list the types of motherboards they can accommodate. You’ll find that the configuration specification called Advanced Technology EXtended (ATX) is the most common choice, while Micro ATX acts as a popular smaller option.

The other consideration: What do you want to slot into the motherboard? Specific models will house specific central processing units (also called CPUs or processors). Because this is a key spec, every motherboard prominently displays the types of CPU it can accommodate. You do have some leeway—a motherboard will support a particular line or family, rather than just a single one. Once you plug your chosen CPU into the large square slot near the center of the motherboard, you’ll need to dissipate heat by slapping a heatsink or sometimes a cooling fan on top of that (the faster the processor, the bigger the cooling setup). Luckily, most CPUs come with standard heatsinks, so you should find everything you need in the box.

In addition to the CPU, you’ll have to plug in some random access memory (RAM), which gives the computer room to think and handles open applications. Plug in more RAM, and you can work on more files simultaneously, access applications more quickly, run games at higher resolutions, and keep more browser tabs open at once—all without slowing your computer to a crawl. Again, you’ll need to buy the right RAM for your chosen motherboard, but you don’t need to be as particular about this component as you were about the CPU. Just make sure your motherboard has enough RAM slots for your needs. Look for two or four long slots in the motherboard—the manual will tell you the precise location.

You can also give the motherboard a graphics card. As we’ve explained in our separate guide, this component is optional. Today’s CPUs come with what’s known as integrated graphics, enough to power your PC’s display. A separate card only proves its worth when you’re trying to put a lot of fast-moving pixels on your computer’s screen for top-end gaming or you have your machine make graphics-related calculations for video editing. It slots into one of the PCI Express slots on your motherboard, which are usually on the other side of the CPU socket.

A graphics card can improve your gaming, as well as image and video editing. Gigabyte

The most powerful graphics cards need an extra power connection to the power supply unit, which brings us back to the PSU. The key spec you should pay attention to here is the wattage, how much power it can provide to the system. Most PSUs on the market will cover a basic setup of CPU, RAM, and hard drive—but if you’re installing a separate graphics card or an extra hard drive, then you might need more. Cooler Master has a very useful PSU calculator you should use to work out the wattage you’ll need.

Speaking of the hard drive, you’ll need this long-term digital storage to hold your files and applications. The physical component sits in a separate cage inside the case. Then you connect it via cables to the motherboard (for data) and the PSU (for power). When you’re shopping, you can opt for an older hard disk drive (HDD), which gives you more capacity for a cheaper price, or a newer Solid State Drive (SSD), which is much faster but more expensive. You can also choose a hard drive with a greater or lesser storage capacity.

Your new PC will also need a monitor, so pick one based on the amount of screen space you want. Just be aware that the larger the monitor’s size, the more you’ll have to pay. Almost all of the products you’ll find on the market will use HDMI as the connection standard. This lets you plug the video output from your motherboard or graphics card into the monitor’s input.

One component we haven’t mentioned is a DVD or Blu-ray drive. By all means buy one if you think you’re going to use it. But if you do, make sure to purchase a case that has an optical disc drive bay. Once you do, the internal connections are the same as for the hard drive: one to the PSU for power, and one to the motherboard for transmitting data.

Shopping for components

There’s no exact formula for working out the components you’ll need, and there are almost an infinite number of combinations to choose, but don’t panic. As you start to browse around, you’ll soon get comfortable using the common terms and brand names.

You should start with your processor. In this case, you’ve got a choice between two brands: Intel (usually best for performance) and AMD (usually best for value-for-money). Intel offers several generations of i3, i5, and i7 processors, rising in power and price as you go up that list. The newest versions of these processors are the 8th-generation chips, but if you want to go for more affordable option and don’t mind a slight performance trade-off, look for older-generation CPUs still on sale. As for AMD, the second-generation Ryzen processors are the newest on the market, and like Intel, it has a rising scale of performance and price: Ryzen 3, 5, and 7. We don’t have room to give you a complete buying guide here, but benchmarking and comparison tools like CPU Benchmarks can help.

Broadly speaking, an Intel Core i5 processor (or the AMD equivalent, Ryzen 5) and 8GB of RAM will give you a decent mid-range machine. If you want to save money and don’t mind budget-level performance, then downgrade an Intel Core i3 chip and 4GB of RAM. For the fastest, most powerful machine, you’ll want to bump up to an Intel Core i7 chip and 16GB (or more) of RAM.

Picking hard drive storage is a little easier than sifting through the dozens of graphics cards on the market: 1TB is a good size for a capable PC. Get more if you’ll be installing a lot of games or working with a lot of 4K videos; get less if you’ll be mostly working on the web and storing a lot of your data in the cloud.

Once you’ve decided on CPU and RAM, these will guide your choice of motherboard and case. The PSU and hard drive are more independent because most models of these components will fit most motherboards. Still, you should double-check the specs sheet to make sure that they’ll function well together. If you can’t immediately figure out the compatibility, a quick web search or a chat with a customer service representative should help you.

NewEgg is one of the best-known PC component retailers. David Nield

After you double-check your choices, you’re ready to buy. Dedicated electronics retailers such as NewEgg, OutletPC, and Micro Center are good places to start your search. These sites are easy to navigate—computer parts are clearly categorized, so you can jump straight to the type of motherboard or RAM that you require. You’ll also find plenty of PC components on Amazon, but the retail giant doesn’t have the same variety that the dedicated retailers do.

Put together the build

You’ve picked your components, checked their compatibility, and ordered them. Now you’re ready to actually build your computer. You can easily do this within a couple hours—though you should avoid rushing the process if you’ve never put together your own PC before. And if you can enlist the help of a reasonably tech-savvy friend, all the better.

First, set up in a good environment. A hard, flat table is the perfect place for assembly. Avoid carpets, which are uneven and tend to generate static electricity that can damage the components.

Speaking of static electricity, before you touch any components, ground yourself by touching a metal part of the computer case, or by wearing an anti-static wrist strap. As for other tools, a lot of modern cases let you slot in components without them. Still, we’d recommend keeping a Phillips screwdriver on hand, just in case. That’s just about the only equipment you’ll need.

The PC parts you buy should ship with just about everything you need—for example, the PSU will come with its own power cable. Handle all of these components carefully by the edges. When you’re not using them, place them on top of the anti-static bags they came in.

Now you’re ready for assembly. First, fit the PSU into the case, then screw in the motherboard. Next, add the CPU, RAM, hard drive, and graphics card (if you’ve bought one).

Unsure about where to put everything? The instructions supplied with the motherboard and other components should tell you. If they’re confusing or incomplete, an online search should help—make sure your search terms include the exact model names and numbers of your components, or you won’t get the right results.

An Intel CPU inside a motherboard socket. Alexandru-Bogdan Ghita via Unsplash

The processor has perhaps the most involved installation process, but it should also come with step-by-step instructions to help. When you drop it into the motherboard slot, you should see some form of clip or bracket you can use to fix it in place. Apply a thin layer of thermal paste, if it doesn’t come pre-applied on the cooler, then fix the heatsink and cooling fan on top. These typically screw straight into the motherboard.

Once you’ve installed all these pieces, the last hardware requirement is to connect the power cable and actually switch on the machine. You do this via a button on the PSU or the case. When you hit it, you should hear the reassuring sounds of the motherboard and storage drive starting up…that is, if you’ve connected everything successfully. If not, don’t panic. Switch the power back off, double-check all the connections and slots, and then try again.

Troubleshooting problems is a whole new article in itself, but one way to work out what’s going on is if the motherboard emits a beep or two. To translate those noises, Computer Hope offers a comprehensive beep code list. In fact, your motherboard’s manual might include its own decoder. For example, on a Dell machine, two beeps indicates that the motherboard can’t detect any installed RAM. If the motherboard doesn’t offer any tell-tale noises, you’ll have to go methodically through each component, one by one, making sure they’re all correctly seated and connected. Are data and power cables hooked up to the hard drive? Is the CPU heatsink firmly attached on top of the processor? The connections must be solid for the system to work.

Install the operating system

When the hardware warms up, your computer will need an operating system, either Windows or Linux. The best option is to use a different computer to set up a USB drive that holds the necessary installation files. Microsoft has instructions for doing this with Windows, and you can follow these instructions to do the same thing for Ubuntu Linux.

Although Linux is free, Windows 10 isn’t: You’ll need to pay $139 for the direct download, and then you can transfer it to your new PC via USB.

To get your new machine to recognize the USB stick and the software on it, you may need to adjust the way the hardware boots up. Watch the screen for a message about entering the BIOS, which stands for Basic Input/Output System. This is the software on the motherboard, which handles communications between all the different parts of the computer. The motherboard user manual should come with a shortcut key to help you get into the BIOS. You should see a boot order option somewhere, where you can tell the BIOS to load from the USB drive rather than the hard drive or the optical drive. While you’re here, you can check that the motherboard is correctly recognizing the drives, RAM, processor, and all the other components.

After you install your operating system, you should be ready to go! The whole process may take some time, but it’s also a lot of fun. And in the end, you’ll have a PC tailored to your exact specifications.

Social Media Marketing For Link Building: Top Tactics & Strategies

The role of social media marketing in SEO isn’t that obvious. Some people feel skeptical about social signals affecting Google’s rankings in any way. Some separate social media marketing and SEO as fields with different goals and methods. Yet these people constitute a minority among SEO experts, as it has recently turned out.

In the largest industry survey on how SEO professionals do link building in 2023, the results showed that most specialists use social media for link building. Namely:

88 percent of SEOs include links in social media profiles.

81 percent share their social media content.

53 percent have experimented with viral campaigns (social media contests, etc).

Here’s a chart that shows the efficiency level by category:

That is how we know link building with social media is a widely used tactic and an effective one, but overseen by many specialists of the field. If you’re one of them, keep reading. This article will give you an overview of tested methods that are worth trying out.

Include Links in Your Social Profiles

Social media is often thought as pointless for SEO for one simple reason: links from social media platforms are unfollowed. Even Google Plus, YouTube, Pinterest, Tumblr, LinkedIn – platforms that either used to be exceptions to this rule, or thought by some to be – have at one or other points of their existence gone down the same road.

Today, Reddit is the only exception. However, marketing on Reddit is barely possible. Your product has to be extremely relevant to some subcommunity for your promotion and link building to work.

For example, on Facebook, make sure to add:

Your website link in the website field under Contact and Basic Info on your About tab;

Your website URL anytime you post something on your wall, including images and videos;

A link to your website to your group’s pinned post.

Similarly to what you’ve done on Facebook, go through your other social media pages and include links to cover photos, bios, images, descriptions, and so on. Even if you won’t keep up working with these social media pages, there’s no harm in having a rich description with links – only potential traffic!

Get People to See and Read Your Content

One of the main struggles of SEO and content marketing is getting people to see content. It’s no surprise that given the plethora of content online, it’s hard to find the audience that your articles will be relevant to. So here are some essential tactics for content promotion.

1. Make Your Text Readable

It should go without saying, but many people still write badly. Your content should be engaging, mistake-free, and concise if you want it to be seen. Even if it exists for SEO purposes only.

2. Research Competitors

3. Aim for Evergreen Content

Easier said than done, but evergreen content should be your ultimate goal. Also, focus on content that can be rediscovered regularly. For example, an article about Halloween has a good chance of attracting new traffic every October.

4. Consider Translating Your Existing Content

If your audience is international, you’ll get more exposure and so much more links if you simply translate the content you already have.

5. Submit to Niche News Sites and Content Communities

Not every industry has a niche news site or content community, but most do. For example, in digital marketing, there’s chúng tôi chúng tôi and chúng tôi chúng tôi features blog posts about almost anything. Look for similar websites in your industry, and submit your content for more exposure.

6. Submit to Communities on Social Networks

Communities on social networks are no less important. Depending on the niche, they can be active and therefore valuable to you. There are Facebook Groups, LinkedIn Groups, and Google+ Communities, but you can also attempt marketing in the relevant Reddit or Tumblr communities.

Don’t forget about niche or local social platforms as well! There are some of them dedicated specifically to books or music, some are popular in specific countries only.

Create Viral Content

Going viral is the quickest and hardest way to earn links. When social media campaigns or even social media crisis cases go viral, the links appear on popular resources and news websites with no further effort from you.

While getting into a social media crisis to end up on The Guardian is risky, creating viral content is the dream many marketers have. But not many even attempt to do that.

While some videos go viral purely by chance or due to their outstanding content, many are created by marketers that know the key to viral marketing. Let’s try to reverse-engineer the process by looking at companies whose campaign go viral quite regularly.

1. Aim Your Social Media Marketing at Teens & Young Adults

Why do brands aim at this part of their potential buyers when creating their social media strategy?

Perhaps, they realize that this is precisely the audience that makes something go viral.

As a result, simple conversations between these brands and clients, or these brands and other brands go viral simply because they are funny.

2. React to Whatever Is Going on in Social Media in Real-Time

As the former Digital Marketing and Social Media lead at Taco Bell, Nicholas Tran said:

“The main difference in strategy now vs. before is that what we are doing today in social media is real-time, and we listen and engage all the time.”

A rare campaign will go viral without a social media manager adding fuel to the fire. The key is keeping up a conversation that seems to attract more attention than usual and engaging with any mentions of the campaign.

Another tactic is reacting to whatever is already trending on social media. To bring this idea to life, marketers should be on the lookout for what’s trending and jump on the trend at the right moment.

Keep in mind that brands should always check what the trend is actually about, and consider the risks that any sensitive trends can bring about. While jumping on some social media trends is purely unethical, some cases can be risky but worth it.

For example, DiGiorno Pizza jumping, apparently (and hopefully) by accident, on the trending hashtag #WhyIStayed campaign that aimed at bringing light to domestic violence cases, definitely belongs to the first category.

3. Be Creative

That is undoubtedly another area when it’s easier said than done. However, you can’t hope to go viral by simply creating the kind of content that everyone is creating, even if it’s better, deeper or more colorful.

You have to stand out to go viral.

One tactic for that is looking at your competitors and doing something absolutely opposite.


In any case, have a go at trying most tactics and strategies described here, and see what works best for you.

Image Credits

Why Link Building Will Not Die, Just Evolve

“Link building is dead” is a phrase I’ve heard from a number of marketing consultants over the past few years.

It wasn’t too long ago, following the emergence of social media, and then Google+, that I heard many social media experts predicting that the importance of links would die off eminently.

These opinions have since been shredded as link building maintained its position as one of the most important marketing activities to increase search visibility.

From my perspective, consistently delivering great results in search has primarily been due to the impact of high quality backlinks that I’ve built for my clients over the last nine years.

Link Building Has Evolved

The role of a link builder has developed over the years, to the point where “link building” perhaps isn’t the best name for the services many agencies now offer.

Typically, the modern link Builder gets involved in PR and brand building a lot more often – forcing link builders into working much more closely with the SEO and social media teams. Because of this, many link building specialists are having to learn a broader range of skills, and develop their knowledge of marketing, more than ever before. Data capture, content creation, email marketing, and social all play into developing a successful link building strategy.

This need for an expansion of skills began after Google began to clean up their search results – resulting in penalties for sites with unethical link building strategies and backlink profiles, both manually and algorithmically.

The shortcuts a number of link builders used are no longer a path to success, whether it be short-term or long-term. And the introduction of the Google Penguin algorithm update has certainly forced an increase in the skills of the average link builder from two years ago and pushed them towards building a clean backlink profile for their clients and/or businesses.

Matt Cutts Says Links Have Many Years Left

If we rewind our tapes all the way back to June 2012, Google’s head of web spam, Matt Cutts, spoke to Search Engine Land Editor-in-Chief Danny Sullivan, Mr. Cutts said:

“There’s this perception that everything will go social or that links will go obsolete, and I think it’s premature to reach that conclusion. I don’t doubt that in ten years things will be more social and those will be more powerful signals, but I wouldn’t write the epitaph for links quite yet.”

Just this past week however, Matt Cutts said that “over time there will be less emphasis on links” – referring to the Google search results. He did conclude that part of his speech with:

“I still think backlinks have many, many years left in them, but inevitably what we’re trying to do is figure out how an expert user would say, this particular page matched their information needs. Sometimes backlinks matter for that.

So I think over time backlinks will become a little less important.

Inevitably over time there will be a little less emphasis on links, but I would expect that for the next few years we will continue to use links in order to assess the basic reputation of pages and of sites.”

Why Link Building is Still Valuable

Working with numerous e-commerce clients, organic search is still the biggest referer of traffic and sales for the majority of these.

When the majority of your traffic is coming from organic search, it is important to maintain and improve your position in the search results. And if you’re not ranking in the search results you’re missing out on a wealth of traffic – it’s important for you to be there.

Getting to the top of Google still requires both SEO and link building – there have been a lot of changes in Google’s search algorithm over the past couple of years. There’s been a number of warnings for the type of links to avoid in order to keep within the guidelines. But links remain core to Google’s algorithm.

The goalposts haven’t really changed much over the years though. The shortcuts some SEOs and webmasters were taking – ie paid links, bulk directory submission, and mass anchor text links are now out of the equation. If anything, this makes it a fair playing field. The company who can make their brand the most popular and who produces the most successful campaigns rises to the top.

There’s no getting away from link building if you want to rank in Google. At least not in the near future. Link building isn’t about directory submissions etc. – it’s about building a brand, creating exposure, and ultimately, building a following.

Google Search Results Without Backlink Relevance

In February this year, Matt Cutts said in one of his videos on the Google Webmasters YouTube channel that Google has internally tested their search engine results without links and it was a disaster:

“We don’t have a version like that [search engine that excludes any backlink relevance] that is exposed to the public. But we have run experiments like that internally and the quality looks much, much worse. It turns out backlinks, even though there’s some noise and certainly a lot of spam, for the most part are still a really, really big win in terms of quality for search results.

So we’ve played around with the idea of turning off backlink relevance. And at least for now backlink relevance still really helps in terms of making sure that we return the best, most relevant, most topical set of search results.”

The Internet Relies on Links

Simply put, Google and internet users in general have always relied on links. If you’re old enough to remember the internet before Google and search engines, links were navigational tools to get from one place to another.

Directories were created and became popular for being a list of links or resources to help you find what you were looking for.

Google results are essentially a list of links. You search for a product, place, or ask a question and you’ll be shown a list of relevant links (hopefully they’re relevant) to what you’re looking for.

Your Business Relies on Links

Links are still the number one factor is getting your website visible in Google.

Perhaps it’s because I don’t work exclusively on social media, but all of my clients receive more traffic and conversions from search engines and referrals than from social. Here are a couple of examples:

Screenshot taken 13/05/2014 from Google Analytics

While links influence search rankings, they remain important. And even Matt Cutts has said they will be around for many more years.

Follow Google’s guidelines and stay clear of link schemes. Keep your link building clean, develop your brand, and I promise you you’ll reap the benefits in the end – just don’t stop building links. And be sure to mix this up with PR, SEO, social, and email marketing to build a proper online marketing strategy for your business.

Featured image credit: chúng tôi Used under license.

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