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I had the opportunity to interview a few speakers on the MarketingLand track at SMX West this coming March as part of SEJ’s partnership with Third Door Media. Be sure to attend Ric’s sessions on Tuesday (Day 1) and Wednesday (Day 2) and visit the SEJ booth in the exhibit hall!
SMX West Bio: Ric Dragon is the author of Social Marketology and the DragonSearch Online Marketing Manual, both published by Mc-Graw Hill. He is the CEO and co-founder of DragonSearch, with more than 20 years of extensive experience in graphic design, information architecture, web development, and digital marketing. As an artist, Ric has been shown in countless group and solo shows. He is a regular guest columnist for Marketing Land, and Social Media Monthly, and a speaker at many marketing and business conferences.
What are 3 key points or takeaways your SMX West sessions will be focusing on?I have the good fortune, this year, of speaking during four different sessions: two are boot-camp style sessions on Twitter and social media; a clinic session; then on a panel led by Monica Wright on “Top Social Tactics for the Search Marketer.”
My own focus for that last panel is to be on what I call deep content. Deep content is a content marketing play that is not simply about writing yet another blog post, but really investing extensive time and energy into a piece of content that very well might yield content over a long period of time.
Unilever, the parent company to Dove, embarked on a study on how women perceive themselves. That study was not only a source of countless pieces of press, but the underpinning to such classic Dove pieces of content like the recent forensic artist video.
We’ve all heard the refrain, “SEO is dead.” But for me, good SEO is about achieving a better understanding of your customers or audience – understanding how they frame the discussion. Words, phrases, even ideas don’t just exist as singular entities that you should optimize for, but instead, exist in neighborhoods or clouds. With SEO research, you can better understand those clouds and neighborhoods, and hopefully, create better, more meaningful content.
So, in other words, we’re talking about investing in deeper and better researched content, and about obtaining a deeper understanding even of the context of the language we use. Finally, you can research a more granular segmentation of your audience, and endeavor to create content that will exist within the deeper contexts of communities and influencers.
In the past year, how has social media evolved to influence overall marketing strategy?At larger brands, there has been a maturation process wherein different types of businesses are finding those major approaches to social media that make sense for their realm. Airlines, for instance, have been investing even more into what we call the maintenance modality of social media marketing through more comprehensive social media customer service. Others have been finding that the influence approach, or thought-leadership/content marketing approach make more sense.
Brands are creating more sophisticated approaches to understanding the value created by social media. At the smallest business level, a lone entrepreneur or a mom-and-pop asks, “How do we have time to do this? what value will we get from it?” At the larger side of the business spectrum, marketing executives are having to go into the CFO or CEO’s office and explain value or ROI. The organizations that are winning at social have accepted that the value goes far beyond the transaction.
You published “Social Marketology” in 2012. How did the term “marketology” come into fruition and what do marketers need to know to succeed in search and social today?
Towards the end of the book writing process, I invited my social network to come in on a Google Doc and brainstorm the book’s title. The group came up with about 20 potential titles, which I then shared with the editor.
What I was hoping to project with the neologism was the notion that social media is a rich field of study – it encompasses psychology, sociology, ethnography, history, marketing, neuroscience, and more. If you’re moved by the notion of being a polymath, this is paradise. And if we, as marketers, are really going to take our game to the revolutionary place it can be, we need to take on these realms of learning, to better understand the stuff of what we’re dealing with.
Almost every statement made in the book could have been prepended with the phrase, “depending on your business,” or “depending on your industry.” There aren’t any absolutes. In one business case, it may make sense to engage with individual customers on a one-to-one basis. In another, it may be best to provide incredible content, and to simply let your community have at it. The big lesson for marketers in all of that is to suss out what is going to bring your brand or organization to the fulfillment of its purpose – to the place of the big vision.
Besides having the one of the best last names ever (in my opinion), one other cool thing about you is that you’re also an artist. How has art and poetry as a creative outlet influenced your work in search and social? Do you think it’s important for marketers to have a hobby or creative outlet?I hope we all have passions beyond our work: otherwise, we’d be horribly dull to others and ourselves! One of the things about the fine arts is that people in the creative arts work in fields for which there are NO RULE BOOKS; no guides, no owners’ manuals. So, if you do it for a while, you learn to be comfortable without those comforts – you come to understand that there is a natural cycle in creativity.
In the arts, we work to understand the underlying patterns to things. This is true for social media, too: if we understand the design and behavioral patterns underlying all of that technology we’re not taken off-guard with each new social platform feature.
Bonus question: What was last great book you read?A peculiar thing happened to me on the way to being middle-aged: I’ve become extremely promiscuous with books and often have about a dozen or so half-started on the nightstand. There are simply not enough minutes in the day or week to read all of the books I hope to. So, out of the stack of finished books…oh! So many choices.
I did recently finish a dubiously titled book, The Power of Positive Deviance. Positive Deviance (PD) is an approach to problem solving that came out of global nutrition initiatives in the 70’s. In PD, the idea is that a community has the solutions to solving its problems. If the solutions come from an outside authority, they’ll fail, no matter how legitimate they are.
The approach has been applied to business, too. But we live in a world where organizations tend to be based on hierarchies and authority. This thinking, that solutions can come from the bottom up, is really relevant to creating organizations wherein people are wildly passionate about their work, and in which work isn’t cause for you to bang your head on the steering wheel of your car before going into the office.
For this same reason, I do believe that the whole social media phenomenon is part of a revolution in business and society in general. Social media may very well be a solution to changing from that old model of authority to community-based bottom-up solutions. It’s an exciting time we live in!
You may participate in social media casually, but as a marketer it’s crucial to have a deeper understanding of what works, what doesn’t, and who the major players are. You need to know the subtle but key differences between paid, earned, and owned social media channels. And of course, while social media is great for marketing and branding, it can also be an effective channel for PR, customer service, community building, and other areas. This session sets the stage for the rest of the day by equipping you with the core fundamentals of social media marketing.
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Ask anyone – Twitter is “easy.” Just sign up, and tweet your pithy 140 character updates whenever inspiration strikes. That’s true for individuals – but using Twitter for business requires a lot more thought and care. For starters: Why are you using Twitter? For branding? Customer service? Sales? And what type of “corporate image” are you trying to present and maintain? This session shows you how to establish an effective Twitter presence.
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Integrating your search and social media efforts can dramatically improve your marketing results, but where do you start? Speakers in this “speed-round” session will share their favorite authority-building social tips for search, including using Facebook, Open Graph technology, Google+, Twitter, and more.
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Got questions about how to deal with Twitter, Facebook, Pinterest or other social media sites? We’ve got experts on hand ready to take specific questions from the audience.
Register for SMX West here.
photo credit: digitalfemme via photopin cc
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Moz Dropping Followerwonk & Moz Content, Refocusing On Seo Products
Software company Moz is dropping its social media and content marketing tools in an effort to “double down” on core SEO products. The company stated it has not seen the growth it anticipated for Followerwonk and Moz Content, and therefore will no longer be investing in them.
”We’re focusing our efforts on core SEO such as rank tracking, keyword research, local listings, duplicate management, on-page, crawl, and links. In the future, we’ll no longer offer Moz Content or Followerwonk.”
Moz, founded in 2004 as “SEOmoz”, was formerly a consulting firm before shifting toward developing SEO software in 2009. After a round of funding in 2012 the company acquired Followerwonk, which was around the time when it expanded its product offerings to include social media and content marketing software.
In May 2013, the company formally transitioned from “SEOmoz” to just “Moz”, stating it was no longer a purely SEO-based software company. That decision proved to last just a little over three years — here we are today in 2023, where Moz is once again focusing exclusively on SEO software.
What This Means for UsersMoz says it will attempt to find a good home for Followerwonk, a service which is used by many but not a revenue driver for the company. Moz Content will be shut down completely, so users of that service would be wise to start looking for an alternative if need be.
With those products gone, Moz will ramp up investments in core SEO features with a particular focus on local search. Here’s what users can look forward to in the future:
”We’ve got updates planned for crawl and rank tracking that we think you’ll love. We know we’re behind in link technology right now, and we’re working on something ambitious.”
The company affirms its passion for developing search products and assures that its customers will see a continued investment in those features.
What This Means for MozWhile the company hasn’t seen the growth it expected with Followerwonk and Moz Content, it is seeing increased interest in its SEO offerings. Moz says churn rates are as low as ever, and its average revenue per user is on the rise.
Reducing its line of product offerings and refocusing on SEO has led to Moz making the tough decision of asking 28% of its staff to leave. The company promises to assist its former employees with the transition in the form of severance, coaching, and helping them find new roles.
The Reaction So FarSarah Bird of Moz says in response: “The Moz community is very special and its a privilege to host it here at Moz. Undoubtedly there will be a few hiccups as we go through this transition in the next couple weeks, but we are committed to nurturing the Moz community.”
17 Amazing Content Research Tools For Seo
Content research is essential to every SEO campaign. Great content research will help you define the keywords, find the content gaps, and create a better strategy to help you achieve better results. Using content research will help you to determine keywords better. You can also find competitor analysis, content gaps, find out which topics are already covered and what topics should be covered. You can find out what topics are most popular and know what is trending on the web. You can also check the key metrics and make better decisions. Here is a list of content research tools that will help you to create a better strategy and get better results
1. BuzzsumoBuzzsumo is a tool that helps you to research content that is trending. It will help analyze the content engagement across all social searches. It helps discover keywords, trending posts, most frequently asked questions and much more. It enables you to feed your social media domain or any other domain that you want to analyze on the website and track the social sharing and engagement of the content.
2. Answer the publicAnswer the public helps get instant and raw search insights from the customers. It enables you to create relevant content, monitor trends, finds hidden niches, carry out meaningful keyword research, and streamline content production.
3. Google TrendsGoogle trends help to analyze the popularity of top search result queries across the globe. You can assort its region and language vice to infer the same.
It also helps compare the relative search volume of two or more items.
4. SoovleSoovle is basically a hub of keywords that are derived from multiple website sources. You can garner keyword ideas from various sources like Google, Yahoo, Bing, Youtube, Amazon and much more.
5. QuoraQuora is a viral platform where you can find a plethora of people feeding in the most common questions that they want to be answered. This will give an idea of the content that is of interest to many people. This helps find the common questions that a multitude of people.
6. Google keyword planner 7. SEMrushSEMrush offers effective research solutions for SEO, content, PPC, social media and other competitive research. To gain marketing insights and to improve online visibility, SEMrush is effectively made use of.
8.Portent’s content idea generatorFrom picking up compelling titles for blogs to preparing content with a flow, portent’s content idea generator can be effectively made use of to research for valuable content that engages a large audience.
9. Google AnalyticsGoogle Analytics is yet another tool like the Google webmaster, which will track the activities of the websites traffic like session duration, bounce rate, and information of the traffic range and much more. This is a valuable content research tool for SEO blogs.
10. RedditReddit is an international platform where people from all over the world discuss niches of topics. Be it any niche; you will be able to see opinions and stands of different topics, which will be a personal space for you to learning and unlearning of many things. This can be a good place for carrying out content research.
11. SpezifySpezify is a visual hub. Be it grabbing content, tweets, music or images; it has all that is trending in the store. Making use of Spezify you can deliver effective content for different domains.
12. UbersuggestYou can do a clear and thorough analysis of the competitor’s website by using the Ubersuggest. To create content using the right set of target keywords, Ubersuggest can be effectively used.
13. AhrefsFor common backlink building and SEO analysis, Ahrefs is used. Through this, effective analysis of well to do content and market analysis can drive better SEO results.
14. MozMoz makes inbound marketing, link building and content marketing super effective. It is the backbone for a lot of SEO analysis.
15. MarketmuseMarketMuse makes use of AI and machine learning. It is to help build content strategies, accelerate content creation, and inform content decisions.
16. LinkedInTalkwalker helps get real-time insights into the happenings in online media and gives valuable insights into various social channels.
17. AwarioAwario is that tool that receives pieces of information about what is popularly being discussed on social media. The ideas generated from it help get ideas for creating trending content.
The above are a few examples of tools that provide effective solutions for carrying out content research for SEO. By making use of them, you can deliver effective and valuable content for a large audience.
Introductory Note On Deep Learning
Introduction to Deep Learning
Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise you’re going to be a dinosaur within 3 years.
-Mark Cuban
This article was published as a part of the Data Science Blogathon
This statement from Mark Cuban might sound drastic – but its message is spot on! We are in the middle of a revolution – a revolution caused by Big Huge data and a ton of computational power.
For a minute, think how a person would feel in the early 20th century if he/she did not understand electricity. You would have been used to doing things in a particular manner for ages and all of a sudden things around you started changing. Things which required many people can now be done with one person and electricity. We are going through a similar journey with machine learning & deep learning today.
So, if you haven’t explored or understood the power of deep learning – you should start it today. I have written this article to help you understand common terms used in deep learning.
Who should read this article?If you are someone who wants to learn or understand deep learning, this article is meant for you. In this article, I will explain various terms used commonly in deep learning.
If you are wondering why I am writing this article – I am writing it because I want you to start your deep learning journey without hassle or without getting intimidated. When I first began reading about deep learning, there were several terms I had heard about, but it was intimidating when I tried to understand them. There are several words that are recurring when we start reading about any deep learning application.
In this article, I have created something like a deep learning dictionary for you which you can refer to whenever you need the basic definition of the most common terms used. I hope after this article these terms wouldn’t haunt you anymore.
Terms related to Deep LearningTo help you understand various terms, I have broken them into 3 different groups. If you are looking for a specific term, you can skip to that section. If you are new to the domain, I would recommend that you go through them in the order I have written them.
Basics of Neural Networks
Common Activation Functions
Convolutional Neural Networks
Recurrent Neural Networks
Basics of Neural Networks1) Neuron- Just like a neuron forms the basic element of our brain, a neuron forms the basic structure of a neural network. Just think of what we do when we get new information. When we get the information, we process it and then we generate an output. Similarly, in the case of a neural network, a neuron receives an input, processes it and generates an output that is either sent to other neurons for further processing or is the final output.
2) Weights – When input enters the neuron, it is multiplied by a weight. For example, if a neuron has two inputs, then each input will have has an associated weight assigned to it. We initialize the weights randomly and these weights are updated during the model training process. The neural network after training assigns a higher weight to the input it considers more important as compared to the ones which are considered less important. A weight of zero denotes that the particular feature is insignificant.
Let’s assume the input to be a, and the weight associated to be W1. Then after passing through the node the input becomes a*W1
3) Bias – In addition to the weights, another linear component is applied to the input, called the bias. It is added to the result of weight multiplication to the input. The bias is basically added to change the range of the weight multiplied input. After adding the bias, the result would look like a*W1+bias. This is the final linear component of the input transformation.
4) Activation Function – Once the linear component is applied to the input, a non-linear function is applied to it. This is done by applying the activation function to the linear combination. The activation function translates the input signals to output signals. The output after application of the activation function would look something like f(a*W1+b) where f() is the activation function.
In the below diagram we have “n” inputs given as X1 to Xn and corresponding weights Wk1 to Wkn. We have a bias given as bk. The weights are first multiplied by their corresponding input and are then added together along with the bias. Let this be called as u.
u=∑w*x+b
The activation function is applied to u i.e. f(u) and we receive the final output from the neuron as yk = f(u)
Commonly applied Activation FunctionsThe most commonly applied activation functions are – Sigmoid, ReLU and softmax
a) Sigmoid – One of the most common activation functions used is Sigmoid. It is defined as:
sigmoid(x) = 1/(1+e-x) Source: WikipediaThe sigmoid transformation generates a more smooth range of values between 0 and 1. We might need to observe the changes in the output with slight changes in the input values. Smooth curves allow us to do that and are hence preferred to overstep functions.
b) ReLU(Rectified Linear Units) – Instead of sigmoids, the recent networks prefer using ReLu activation functions for the hidden layers. The function is defined as:
f(x) = max(x,0). source: cs231nThe major benefit of using ReLU is that it has a constant derivative value for all inputs greater than 0. The constant derivative value helps the network to train faster.
c) Softmax – Softmax activation functions are normally used in the output layer for classification problems. It is similar to the sigmoid function, with the only difference being that the outputs are normalized to sum up to 1. The sigmoid function would work in case we have a binary output, however in case we have a multiclass classification problem, softmax makes it really easy to assign values to each class which can be easily interpreted as probabilities.
It’s very easy to see it this way – Suppose you’re trying to identify a 6 which might also look a bit like 8. The function would assign values to each number as below. We can easily see that the highest probability is assigned to 6, with the next highest assigned to 8 and so on…
5) Neural Network – Neural Networks form the backbone of deep learning. The goal of a neural network is to find an approximation of an unknown function. It is formed by interconnected neurons. These neurons have weights, and bias which is updated during the network training depending upon the error. The activation function puts a nonlinear transformation to the linear combination which then generates the output. The combinations of the activated neurons give the output.
A neural network is best defined by “Liping Yang” as –
“Neural networks are made up of numerous interconnected conceptualized artificial neurons, which pass data between themselves, and which have associated weights which are tuned based upon the network’s “experience.” Neurons have activation thresholds which, if met by a combination of their associated weights and data passed to them, are fired; combinations of fired neurons result in “learning”.
6) Input / Output / Hidden Layer – Simply as the name suggests the input layer is the one that receives the input and is essentially the first layer of the network. The output layer is the one that generates the output or is the final layer of the network. The processing layers are the hidden layers within the network. These hidden layers are the ones that perform specific tasks on the incoming data and pass on the output generated by them to the next layer. The input and output layers are the ones visible to us while being the intermediate layers are hidden.
Source: cs231n7) MLP (Multi-Layer perceptron) – A single neuron would not be able to perform highly complex tasks. Therefore, we use stacks of neurons to generate the desired outputs. In the simplest network, we would have an input layer, a hidden layer and an output layer. Each layer has multiple neurons and all the neurons in each layer are connected to all the neurons in the next layer. These networks can also be called fully connected networks.
8) Forward Propagation – Forward Propagation refers to the movement of the input through the hidden layers to the output layers. In forward propagation, the information travels in a single direction FORWARD. The input layer supplies the input to the hidden layers and then the output is generated. There is no backward movement.
9) Cost Function – When we build a network, the network tries to predict the output as close as possible to the actual value. We measure this accuracy of the network using the cost/loss function. The cost or loss function tries to penalize the network when it makes errors.
Our objective while running the network is to increase our prediction accuracy and to reduce the error, hence minimizing the cost function. The most optimized output is the one with the least value of the cost or loss function.
If I define the cost function to be the mean squared error, it can be written as –
C= 1/m ∑(y – a)2 where m is the number of training inputs, a is the predicted value and y is the actual value of that particular example.
The learning process revolves around minimizing the cost.
10) Gradient Descent – Gradient descent is an optimization algorithm for minimizing the cost. To think of it intuitively, while climbing down a hill you should take small steps and walk down instead of just jumping down at once. Therefore, what we do is, if we start from a point x, we move down a little i.e. delta h, and update our position to x-delta h and we keep doing the same till we reach the bottom. Consider bottom to be the minimum cost point.
Source
Mathematically, to find the local minimum of a function one takes steps proportional to the negative of the gradient of the function.
You can go through this article for a detailed understanding of gradient descent.
11) Learning Rate – The learning rate is defined as the amount of minimization in the cost function in each iteration. In simple terms, the rate at which we descend towards the minima of the cost function is the learning rate. We should choose the learning rate very carefully since it should neither be very large that the optimal solution is missed nor should be very low that it takes forever for the network to converge.
Source
12) Backpropagation – When we define a neural network, we assign random weights and bias values to our nodes. Once we have received the output for a single iteration, we can calculate the error of the network. This error is then fed back to the network along with the gradient of the cost function to update the weights of the network. These weights are then updated so that the errors in the subsequent iterations is reduced. This updating of weights using the gradient of the cost function is known as back-propagation.
In back-propagation the movement of the network is backwards, the error along with the gradient flows back from the out layer through the hidden layers and the weights are updated.
13) Batches – While training a neural network, instead of sending the entire input in one go, we divide in input into several chunks of equal size randomly. Training the data on batches makes the model more generalized as compared to the model built when the entire data set is fed to the network in one go.
14) Epochs – An epoch is defined as a single training iteration of all batches in both forward and backpropagation. This means 1 epoch is a single forward and backwards pass of the entire input data.
The number of epochs you would use to train your network can be chosen by you. It’s highly likely that more number epochs would show higher accuracy of the network, however, it would also take longer for the network to converge. Also, you must take care that if the number of epochs is too high, the network might be over-fit.
15) Dropout – Dropout is a regularization technique that prevents over-fitting of the network. As the name suggests, during training a certain number of neurons in the hidden layer is randomly dropped. This means that the training happens on several architectures of the neural network on different combinations of neurons. You can think of drop out as an ensemble technique, where the output of multiple networks is then used to produce the final output.
16) Batch Normalization – As a concept, batch normalization can be considered as a dam we have set as specific checkpoints in a river. This is done to ensure that the distribution of data is the same as the next layer hoped to get. When we are training the neural network, the weights are changed after each step of gradient descent. This changes how the shape of data is sent to the next layer.
But the next layer was expecting a distribution similar to what it had previously seen. So we explicitly normalize the data before sending it to the next layer.
Convolutional Neural Networks in Deep Learning17) Filters – A filter in a CNN is like a weight matrix with which we multiply a part of the input image to generate a convoluted output. Let’s assume we have an image of size 28*28. We randomly assign a filter of size 3*3, which is then multiplied with different 3*3 sections of the image to form what is known as a convoluted output. The filter size is generally smaller than the original image size. The filter values are updated like weight values during backpropagation for cost minimization.
Consider the below image. Here filter is a 3*3 matrix which is multiplied with each 3*3 section of the image to form the convolved feature.
18) CNN (Convolutional neural network) – Convolutional neural networks are basically applied to image data. Suppose we have an input of size (28*28*3), If we use a normal neural network, there would be 2352(28*28*3) parameters. And as the size of the image increases the number of parameters becomes very large. We “convolve” the images to reduce the number of parameters (as shown above in filter definition). As we slide the filter over the width and height of the input volume we will produce a 2-dimensional activation map that gives the output of that filter at every position. We will stack these activation maps along the depth dimension and produce the output volume.
You can see the below diagram for a clearer picture.
Source: cs231n19) Pooling – It is common to periodically introduce pooling layers in between the convolution layers. This is basically done to reduce the number of parameters and prevent over-fitting. The most common type of pooling is a pooling layer of filter size(2,2) using the MAX operation. What it would do is, it would take the maximum of each 4*4 matrix of the original image.
Source: cs231nYou can also pool using other operations like Average pooling, but max-pooling has shown to work better in practice.
20) Padding – Padding refers to adding an extra layer of zeros across the images so that the output image has the same size as the input. This is known as the same padding.
After the application of filters, the convolved layer in the case of the same padding has a size equal to the actual image.
Valid padding refers to keeping the image as such and having all the pixels of the image which are actual or “valid”. In this case, after the application of filters, the size of the length and the width of the output keeps getting reduced at each convolutional layer.
21) Data Augmentation – Data Augmentation refers to the addition of new data derived from the given data, which might prove to be beneficial for prediction. For example, it might be easier to view the cat in a dark image if you brighten it, or for instance, a 9 in the digit recognition might be slightly tilted or rotated. In this case, the rotation would solve the problem and increase the accuracy of our model. By rotating or brightening we’re improving the quality of our data. This is known as Data augmentation.
Recurrent Neural Network in Deep Learning Source: cs231n23) RNN(Recurrent Neural Network) – Recurrent neural networks are used especially for sequential data where the previous output is used to predict the next one. In this case, the networks have loops within them. The loops within the hidden neuron give them the capability to store information about the previous words for some time to be able to predict the output. The output of the hidden layer is sent again to the hidden layer for t time stamps. The unfolded neuron looks like the above diagram. The output of the recurrent neuron goes to the next layer only after completing all the timestamps. The output sent is more generalized and the previous information is retained for a longer period.
The error is then backpropagated according to the unfolded network to update the weights. This is known as backpropagation through time(BPTT).
24) Vanishing Gradient Problem – Vanishing gradient problem arises in cases where the gradient of the activation function is very small. During backpropagation when the weights are multiplied with these low gradients, they tend to become very small and “vanish” as they go further deep in the network. This makes the neural network forget the long-range dependency. This generally becomes a problem in cases of recurrent neural networks where long term dependencies are very important for the network to remember.
This can be solved by using activation functions like ReLu which do not have small gradients.
25) Exploding Gradient Problem – This is the exact opposite of the vanishing gradient problem, where the gradient of the activation function is too large. During backpropagation, it makes the weight of a particular node very high with respect to the others rendering them insignificant. This can be easily solved by clipping the gradient so that it doesn’t exceed a certain value.
End NotesThe media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion.
Site Audits: Evaluating The Seo, Content & Social For 3 Websites
The SEJ ThinkTank hosted our second live site audit webinar on March 4.
This time, we decided to cover just three sites (instead of four like we did in our first audit), which would allow the panel to spend more time drilling down into issues the sites might have.
This article will cover the tools and recommendations we covered.
The audit was moderated by SEJ’s Chief Social Media Strategist Brent Csutoras, and the SEJ panel included the expertise of our:
Executive Editor Kelsey Jones.
Lead News Writer Matt Southern.
Social Media Manager Debbie Miller.
For this audit, we choose two sites to audit. The team spent time before the audit researching the sites and noting recommendations:
AllstateFoundation.orgThis site was particularly interesting because we really enjoy working with nonprofits and this particular foundation is tied to such a big brand. The site owner said they have been performing their own site audits, but were looking for tips. A few of the issues noted by the panel included missing chúng tôi files, minimum social presence, and a lack of CTAs on the home page.
MeasurITThis Irish-based B2B company was chosen because many B2B businesses struggle with online marketing. The site owner was wondering if the website was providing enough information about their product Tideflex Duckbill Valves. A few recommendations from the panel included including a location page, moving the social buttons above the fold, and increasing activity on the blog.
Wild Card Site: Olympus-ToursOne of the most exciting parts of doing the live site audit is our Wild Card site! This is a site that is chosen live from the webinar audience – which none of the panelist have looked at before. No prep, no rehearsals, just an audit in real-time!
Watch the full webinar here:
Tools of the TradeThrough out the site audit, the panel mentioned several tools that can be used to check different aspects of your site.
Screaming FrogThis is a free program allows you to swiftly analyze and audit your site from and onsite SEO perspective. Matt uses it to uncover a wide variety of SEO issues, including duplicate content, missing chúng tôi files, and missing H1 tags.
Page Speed InsightsThis Google Developer tool offers insights into how your website could run faster. This is another free tool that looks at how fast your site load on both mobile and desktop computers. It also rates your site’s problems in order of urgency.
Mobile-Friendly TestThis is another free tool from the Google development team. This will look at how mobile friendly your site is, which will be a big deal in the coming year according to Matt (and Google!).
Visit our #SEJThinkTank archive to listen to other SEJ Marketing ThinkTank webinars.
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How Content Delivery Networks (Cdns) Can Impact Seo
It is no secret that a site’s load time impacts its search ranking. SEOs everywhere have stressed page load time and page speed must be considered, and Google has even admitted that page speed does matter. CDNs play a huge part in optimizing page speed and many people aren’t familiar with CDNs or the process in which they can improve page speed.
What is a Content Delivery Network?Contact Delivery Networks (CDNs) are a system of servers distributed throughout the world delivering web pages, and other web content (like video streaming) based on the users geographical location. This means that large amounts of content can be delivered quickly and without any interruptions.
For instance, if your website is based in Los Angeles, the people who are accessing it in San Francisco will receive the content faster than people accessing it from Shanghai. The farther away the person accessing the website or application is, the longer the load time and the more frustrating the user experience becomes.
CDNs have been a hot topic for businesses wanting to expand by reaching Internet users globally. It gives businesses the opportunity to reach out to millions of web users anywhere in the world in just seconds. Being able to put your company out in front of as many people as possible and quickly should be a priority for any business looking to grow, and when it comes to SEO, it must be a priority.
How do CDNs Work?CDNs are made up of a network of servers referred to as “points of presence” or POPs. Each one of these servers (POPs) are spread out in locations all over the world. The CDN server that is located closest to where the user accesses it is called an “edge server.” When a user requests content from a website or application through a CDN, they will be connected to the closest edge server. This ensures the user will receive the best experience possible.
According to Incapsula, some of the benefits of using a CDN include:
Faster load times for content on your site (this is especially useful for increasing performance of your mobile site)
Image compression improves performance by reducing the size of images sent to the user
Session optimization reduces the number of open connections to your web server
Scaling quickly when there is increased or heavy traffic to your website
Ensuring the stability of your website by minimizing the risk of traffic spikes at point of origin
Better site performance and customer experience
CDNs allow you to cache (temporarily store) your websites content on a CDN so it is delivered from an edge server to the user faster than if it were to be delivered from the origin. This allows the content requested to travel from the nearest POP and back, instead of having to travel all the way from the website’s origin server and back.
Another benefit of using a CDN is that CDNs will remove and update (purge) your content regularly, to ensure the most relevant and current content is being delivered, even if the website content is cached. This process is referred to as content invalidation.
How do CDNs Help SEO?CDNs improve the speed and quality of content that is delivered to the user. CDNs should be seen as part of the solution for search ranking as it is applied to page speed and efficient content delivery, but it is not the only thing that needs to be done to increase search ranking. Think of CDNs as a way to improve upon the technical ranking factors for SEO.
How CDNs Make Your Site Safer?Having the site load from several servers and from several locations will not only improve your site’s health, but again it is a huge benefit for SEO by increasing the load time of your website.
The risk of attack on your site will also decrease, because of the multiple servers located in multiple geographical locations. Having multiple servers will also help prevent your website from crashing by dividing the load time up among the various servers.
Should You be Using a CDN?CDNs can benefit anyone with a website or mobile application that could be requested by multiple users at a time. CDNs are especially useful for websites that have a lot of content and varying content types, and complex websites with users from multiple geographical locations. Most importantly, CDNs will increase site load speed, which will increase your search ranking.
On the other hand, if you are going to implement a CDN, make sure that you implement it correctly. Contact a professional to help you implement your CDN, because an incorrectly implemented CDN can compromise your site’s SEO and usability.
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