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Social Media Marketing – Key Skills and Trends of the Future

Written by: admin Date of published: . Posted in test

Over the years, social media platforms have become the force for progress in the marketing world. Initially created for completely different purposes, these platforms have become a place where businesses can easily market their products. 

Social media marketing is definitely here to stay, and here’s why:

Marketing on social media is also a quick way for your message to get viral. And, with many effective tools for business, marketing on social media has become more effortless. 

Right now, social media marketing is on the rise. But what does the future hold? What skills will be crucial for a social media marketer to have? What trends will define the scope of social media marketing in the future?

Let’s take a look. 

Trends that Shape the Future

The landscape of social media marketing is changing every day, and it’s not an exaggeration. With every update, businesses are introduced to new possibilities, new tools are being created to boost creativity and make social media marketing even more powerful. 

On-the-Spot Content Replacing Pre-Produced Content

The perception of the perfect content for social media is changing as well. Right now, users prefer seeing a storytelling format. 92% of consumers want brands to make more ads that feel like a story. This prompted Instagram and Facebook to come up with the format of Stories. 

Current data shows that stories will remain a preferred type of content both by social media users and businesses:

The reason why users love the format of Stories is that it depicts on-the-spot events rather than preliminary planned and edited content. More reasons why users like this type of content include:

  • Stories don’t require too much attention;
  • Millennials and Gen Z thrive on an authentic and meaningful relationship with brands who let them see behind-the-scenes activities;
  • Editing stories is effortless with many filters at hand. 

Users love in-the-moment, raw content. It makes the product and the brand seem more authentic. Thus, it is expected that this format will shape the content that will dominate social media in the future.

Influencer Marketing

Influencer marketing was introduced to us by social media. And, over the past several years, it has exploded. Reportedly, Google searches for influencer marketing grew 1500% over the last 3 years. 

Social media users adore influencers, even naming them more trustworthy than celebrities. It is reported that 30% of consumers are more likely to buy a product recommended by a non-celebrity blogger, and here’s why:

  • Influencers a relatable. They often communicate with their followers and share honest reviews. 
  • Even though brands pay influencers for promoting a product, most of them are highly unlikely to recommend something they don’t like. 
  • Authenticity is one of the main reasons why social media users trust influencers. They also provide their followers with personalized content. Thus, they remain relevant, catering to the needs of their subscribers. 

Collaborating with a social media influencer brings a ton of benefits to brands. For instance, a collaboration between Scramble Brand Official and YouTube influencers Jenna Marbles and Julien Solomita helped the company sell their limited edition rashguards within several hours. And, due to the increased demand, the company did a restock and sold out within a few days.

Many famous brands, like Daniel Wellington, also often collaborate with influencers, getting immense profits and new customers. Thus, this trend is likely here to stay and impact the purchasing decisions of social media users. 

AI-Powered Personalized Experiences

AI is shaping the future of many industries in the business world, and social media marketing is no exception. With features, introduced by AI, creating personalized experiences and running a successful social media campaign has become easier. 

In the future, social media marketing is expected to see the further development of the following AI-powered features:

  • Using slack bots for customer support. With this feature, it has become easier for customer support service to handle communications with customers. This AI-powered solution uses machine learning to anticipate possible solutions to a problem. 
  • Facial recognition. This feature has already been on Facebook for a long time. It recognizes the faces of your friends on a photo and automatically tags them to make sharing content even faster. 
  • Machine learning is also used for introducing a social media user with ads based on their activity on a certain platform. This is how Facebook ads work and what Instagram uses to build the Discover page. 

It is expected that the influence of AI on social media marketing will become even stronger in the future. Many other features, connected to personalization and anticipation tools, are also expected to be developed in order to improve the whole experience. 

Defining a Perfect SMM Employee: Key Skills 

Now, let’s talk about key skills that will shape the hiring decisions of social media marketers in the future. With social media developing and changing so fast, SMM professionals are expected to adapt to the changing environment. 

Here’s what different HR professionals say about the key SMM skills that will impact hiring decisions in the future. 

Jeff Bullas, a marketing specialist and a famous influencer, as listed by Forbes, calls a future social media marketer a “ninja geek”, saying that he/she will have to tackle many things at once and stay updated 24/7. Thus, the key skills that will help to handle this flow of data and information are strategy planning, community management, and content optimization. 

Professionals at Robert Half, a global human resource consulting firm, agree with Jeff Bullas, adding that essential skills will also include:

  • Adapting the product to the fast-changing world of social media;
  • Employing different analytics tools to receive regularly updated social data;
  • Having excellent communication and leadership skills to delegate tasks and track the performance of the team. 

Thus, being highly adaptable and always updated are key skills that companies will be looking for in a social media marketer. However, basic skills like communication and planning will play no small part in other key skills needed for social media marketing in the future. 

Final Thoughts

Social media marketing is definitely here to stay as developers invest more and more effort in its development. In the future, it is expected to remain one of the key marketing strategies. The key stipulation is to always remain updated. So, keep up with these trends and key skills that will shape the future of social media marketing to make sure that your campaigns perform successfully.


Adriana is a professional writer and an editor at She also has a Master’s degree in Marketing, having worked several years for major companies in the U.S. Adriana’s articles bring valuable insights to the readers, supported by credible resources and thorough research.

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Boosting Your Link Building Efforts: How to Align Social Media and Content Strategies

Written by: admin Date of published: . Posted in test

Link building is the lifeblood of your ongoing SEO strategy. The benefits are straightforward – high-quality backlinks will help you build authority, widen your reach, boost brand awareness, and even drive referral traffic. Finding the most impactful tactics for link building success, however, is not as crystal-clear as the benefits – and the process itself is continuous, dedicated work. 

From competitive link analysis to third-party outreach, there are many factors to continuously tweak and improve in your link building strategy. Most importantly, generating highly valuable backlinks relies on producing high-quality, valuable content. But that’s not going to be enough because your success depends heavily on strategically promoting that content. 

And what’s the first platform you think of when you hear “strategically promoting content”? That’s right, social media. 

That’s why aligning your content and social media strategies with your link building efforts is a vital approach to a smart SEO strategy. In this article, we’ll give you some useful tactics, tips, and insights to help you integrate all your efforts strategically and harness the power of social media for more successful link building. 

Creating the right type of content

Content and link building efforts need to be aligned from the very beginning. 

In other words, before you actually start producing content, you’ll structure your content strategy in regards to your SEO campaign. That’s how you’ll be able to create content around strategic topics and keyword lists, making it highly relevant to your target audience and able to satisfy searcher intent.

This type of approach sets you up for success because you will be producing content that your key targets will value and want to share with others. This includes your wider audience as well as bloggers, industry influencers, and any third-party website worthy of guest posting. 

That’s why you want to do plenty of research and keep your finger on the industry pulse to come up with link-worthy topic ideas and write content that truly provides value. 

Some worthy tactics include sharing:

  • useful tools and resources. Determine which topics your audience needs help with and then share with them useful tactics, guides, tools they can use, and other resources. The more actionable your piece, the more compelled audiences are to share it with others.
  • research. Creating research-based content is a great addition to your link-building efforts because people can reference it in the future in their posts. Stats are particularly link-worthy, just make sure you’re focusing on a relevant topic to align it perfectly with your SEO efforts. 
  • influencer insights, which we’ll cover in more detail in a moment

It’s important to gain an understanding of your audience, what you can offer them that will meet their needs/expectations, and which type of content they’re most receptive to. With this understanding, you’ll be able to produce valuable content crafted for a specific audience, and that’s really what bloggers and third-party websites want from you. 

Sharing influencer insights

Craft content around the unique ideas promoted by influential people within your industry. These could be certain experts or key industry influencers – the point is that you will be referencing their authority and sharing their insights. 

An interview with an industry influencer is clearly a big boost to your content strategy, but it doesn’t necessarily have to be that. You can feature influencer insights in roundups or by referencing their ideas and quotes in your articles. Get creative – and don’t hesitate to use social media to ask your audience what type of content they would like next from you. 

How does this tactic work?

This type of content is highly valuable and interesting to readers, who are eager to learn from someone that’s well-established as a trusty person within the given industry. Basically, you’re borrowing their influence. Consequently, another thing you’re borrowing with influencer-centered content is their audience, and that’s where social media comes in. 

By reaching out to these people either for collaboration or just to let them know you’ve featured them in your content (which can be done directly or through social media mentions), you’ll encourage them to share your content with their audiences – which are highly targeted and valuable for you. So this is not only valuable and highly shareable content, but it also taps into social media power. 

How will you identify the most valuable industry experts?

Start with your link building targets. You can also use FollowerWonk, which will pick out for you the credible profiles that are relevant to the given topic. This handy tool will sort them by social authority and the number of followers to find great opportunities. 

Get visual

Statistics show that infographics are one of the most often shared types of content – which really comes as no surprise, given their rising popularity over the recent years. Visuals are more digestible than textual content, we learn faster with them, and thus they’re bound for more shares and links. 

Now, creating infographics would certainly be a phenomenal boost to your link building strategy, but don’t overlook the basics either. Make sure to include compelling as well as useful visuals in your content – everything from featured infographics to charts, graphs, screenshots, and inviting or even humorous (depending on the context) images that pique the reader’s interest. 

Lastly, get visual when promoting your content via social media, especially Twitter. Research has shown that tweets with images get 150% more retweets than those without.

Give before asking for shares in return

Social media marketing runs on this motto. 

Now, social shares may not be a ranking factor, but they are most definitely beneficial to your link building efforts. It’s simple – the more your content is shared, the more people will see it, visit your site, and hopefully stay on it for a while and convert into subscribers. 

But you can’t just churn out post after post about yourself and what you’re doing. Nobody will be listening. Social media is about establishing authority, participating in a community, and building relationships. No matter the size of your enterprise, you need to approach social media as a valuable community member. Be a part of the ecosystem by engaging your audience, sharing helpful content, staying active, facilitating conversations, sharing others’ posts (with credit, of course), etc. 

Then, when you actually do post something promotional, such as your latest guest post, and tell people “hey, check it out and give it a like or a share” – you’ll have a loyal and engaged audience to take you up on your offer. 

Connect with bloggers before pitching

The previous point sums up to one vital tip: build social relationships before building links. The people you especially want to focus on are the ones you hope to collaborate with. Get acquainted with them on social, follow and share their work, and make yourself known to them. Facilitate conversation by mentioning them in your posts and engage with them by replying to their tweets. 

This will open the doors to collaboration and make them more trusting of you so that when you send them a pitch email, you won’t be a complete unknown. If you’re cultivating a genuine and interactive social media presence, they’ll have seen how collaborating with you is beneficial to them even before you send out your pitch. 

Ultimately, by properly aligning your efforts across these fields, you’ll give momentum to your entire digital marketing strategy. And remember – it’s all a continuous effort. None of it is set-and-forget. Focus on building relationships with industry influencers and providing value to your audience, and the tactics you do employ will have a lot more gravity. 


Natasha is a web designer, lady of a keyboard and one hell of a growth-hack geek. She is always happy to collaborate with awesome blogs and share her knowledge about IT, business growth strategies and digital marketing trends. To see what she is up to next, check out her Twitter Dashboard.

The post Boosting Your Link Building Efforts: How to Align Social Media and Content Strategies appeared first on SiteProNews.

Using Python to recover SEO site traffic (Part three)

Written by: admin Date of published: . Posted in test

When you incorporate machine learning techniques to speed up SEO recovery, the results can be amazing.

This is the third and last installment from our series on using Python to speed SEO traffic recovery. In part one, I explained how our unique approach, that we call “winners vs losers” helps us quickly narrow down the pages losing traffic to find the main reason for the drop. In part two, we improved on our initial approach to manually group pages using regular expressions, which is very useful when you have sites with thousands or millions of pages, which is typically the case with ecommerce sites. In part three, we will learn something really exciting. We will learn to automatically group pages using machine learning.

As mentioned before, you can find the code used in part one, two and three in this Google Colab notebook.

Let’s get started.

URL matching vs content matching

When we grouped pages manually in part two, we benefited from the fact the URLs groups had clear patterns (collections, products, and the others) but it is often the case where there are no patterns in the URL. For example, Yahoo Stores’ sites use a flat URL structure with no directory paths. Our manual approach wouldn’t work in this case.

Fortunately, it is possible to group pages by their contents because most page templates have different content structures. They serve different user needs, so that needs to be the case.

How can we organize pages by their content? We can use DOM element selectors for this. We will specifically use XPaths.

Example of using DOM elements to organize pages by their content

For example, I can use the presence of a big product image to know the page is a product detail page. I can grab the product image address in the document (its XPath) by right-clicking on it in Chrome and choosing “Inspect,” then right-clicking to copy the XPath.

We can identify other page groups by finding page elements that are unique to them. However, note that while this would allow us to group Yahoo Store-type sites, it would still be a manual process to create the groups.

A scientist’s bottom-up approach

In order to group pages automatically, we need to use a statistical approach. In other words, we need to find patterns in the data that we can use to cluster similar pages together because they share similar statistics. This is a perfect problem for machine learning algorithms.

BloomReach, a digital experience platform vendor, shared their machine learning solution to this problem. To summarize it, they first manually selected cleaned features from the HTML tags like class IDs, CSS style sheet names, and the others. Then, they automatically grouped pages based on the presence and variability of these features. In their tests, they achieved around 90% accuracy, which is pretty good.

When you give problems like this to scientists and engineers with no domain expertise, they will generally come up with complicated, bottom-up solutions. The scientist will say, “Here is the data I have, let me try different computer science ideas I know until I find a good solution.”

One of the reasons I advocate practitioners learn programming is that you can start solving problems using your domain expertise and find shortcuts like the one I will share next.

Hamlet’s observation and a simpler solution

For most ecommerce sites, most page templates include images (and input elements), and those generally change in quantity and size.

Hamlet's observation for a simpler approach based on domain-level observationsHamlet's observation for a simpler approach by testing the quantity and size of images

I decided to test the quantity and size of images, and the number of input elements as my features set. We were able to achieve 97.5% accuracy in our tests. This is a much simpler and effective approach for this specific problem. All of this is possible because I didn’t start with the data I could access, but with a simpler domain-level observation.

I am not trying to say my approach is superior, as they have tested theirs in millions of pages and I’ve only tested this on a few thousand. My point is that as a practitioner you should learn this stuff so you can contribute your own expertise and creativity.

Now let’s get to the fun part and get to code some machine learning code in Python!

Collecting training data

We need training data to build a model. This training data needs to come pre-labeled with “correct” answers so that the model can learn from the correct answers and make its own predictions on unseen data.

In our case, as discussed above, we’ll use our intuition that most product pages have one or more large images on the page, and most category type pages have many smaller images on the page.

What’s more, product pages typically have more form elements than category pages (for filling in quantity, color, and more).

Unfortunately, crawling a web page for this data requires knowledge of web browser automation, and image manipulation, which are outside the scope of this post. Feel free to study this GitHub gist we put together to learn more.

Here we load the raw data already collected.

Feature engineering

Each row of the form_counts data frame above corresponds to a single URL and provides a count of both form elements, and input elements contained on that page.

Meanwhile, in the img_counts data frame, each row corresponds to a single image from a particular page. Each image has an associated file size, height, and width. Pages are more than likely to have multiple images on each page, and so there are many rows corresponding to each URL.

It is often the case that HTML documents don’t include explicit image dimensions. We are using a little trick to compensate for this. We are capturing the size of the image files, which would be proportional to the multiplication of the width and the length of the images.

We want our image counts and image file sizes to be treated as categorical features, not numerical ones. When a numerical feature, say new visitors, increases it generally implies improvement, but we don’t want bigger images to imply improvement. A common technique to do this is called one-hot encoding.

Most site pages can have an arbitrary number of images. We are going to further process our dataset by bucketing images into 50 groups. This technique is called “binning”.

Here is what our processed data set looks like.

Example view of processed data for "binning"

Adding ground truth labels

As we already have correct labels from our manual regex approach, we can use them to create the correct labels to feed the model.

We also need to split our dataset randomly into a training set and a test set. This allows us to train the machine learning model on one set of data, and test it on another set that it’s never seen before. We do this to prevent our model from simply “memorizing” the training data and doing terribly on new, unseen data. You can check it out at the link given below:

Model training and grid search

Finally, the good stuff!

All the steps above, the data collection and preparation, are generally the hardest part to code. The machine learning code is generally quite simple.

We’re using the well-known Scikitlearn python library to train a number of popular models using a bunch of standard hyperparameters (settings for fine-tuning a model). Scikitlearn will run through all of them to find the best one, we simply need to feed in the X variables (our feature engineering parameters above) and the Y variables (the correct labels) to each model, and perform the .fit() function and voila!

Evaluating performance

Graph for evaluating image performances through a linear pattern

After running the grid search, we find our winning model to be the Linear SVM (0.974) and Logistic regression (0.968) coming at a close second. Even with such high accuracy, a machine learning model will make mistakes. If it doesn’t make any mistakes, then there is definitely something wrong with the code.

In order to understand where the model performs best and worst, we will use another useful machine learning tool, the confusion matrix.

Graph of the confusion matrix to evaluate image performance

When looking at a confusion matrix, focus on the diagonal squares. The counts there are correct predictions and the counts outside are failures. In the confusion matrix above we can quickly see that the model does really well-labeling products, but terribly labeling pages that are not product or categories. Intuitively, we can assume that such pages would not have consistent image usage.

Here is the code to put together the confusion matrix:

Finally, here is the code to plot the model evaluation:

Resources to learn more

You might be thinking that this is a lot of work to just tell page groups, and you are right!

Screenshot of a query on custom PageTypes and DataLayer

Mirko Obkircher commented in my article for part two that there is a much simpler approach, which is to have your client set up a Google Analytics data layer with the page group type. Very smart recommendation, Mirko!

I am using this example for illustration purposes. What if the issue requires a deeper exploratory investigation? If you already started the analysis using Python, your creativity and knowledge are the only limits.

If you want to jump onto the machine learning bandwagon, here are some resources I recommend to learn more:

Got any tips or queries? Share it in the comments.

Hamlet Batista is the CEO and founder of RankSense, an agile SEO platform for online retailers and manufacturers. He can be found on Twitter @hamletbatista.

The post Using Python to recover SEO site traffic (Part three) appeared first on Search Engine Watch.

Not Getting Any Feedback at Work? Here’s How to Seek it Out

Written by: admin Date of published: . Posted in test

Are you the kind of employee who gives 110% to a work project and expects your supervising managers will notice and acknowledge your effort? Or are you the kind who needed additional resources to complete a project and wondered why your VP did not come to you with a recommendation of how she could provide the needed assistance? If you said yes then you are probably also the kind who will quit without having the situation resolved.  Gallup reported that voluntary turnover (read: employees who quit) can cost U.S. companies 1 trillion dollars.

One trillion dollars saved could finance a lot of resources for a lot of new projects and probably stop a lot of existing employees from leaving.  If 52% of “the exiting employees say their manager or organization could have done something to prevent them from leaving,” it is time to find out what that “something” is and make it a part of a manager’s performance criteria.  For that to happen managers and leaders need to do better at inquiring about an employees’ job satisfaction throughout the course of a year.  

Leaders of all forms need to stop using the ‘garbage can approach’ to annual reviews, using one time a year to cover all the good, the bad and the ugly.  Informal, but consistent, casual conversations focused on an employee’s personal satisfaction and well-being about their job and the organization may go a long way in reducing unwanted employee exits.  When only 51% of existing employees had such a conversation in the 3 months before they left, there is a lot of room for leadership improvement. 

Here is the really bad news. Whether or not you end up with a great manager or leader who values your satisfaction and well-being is outside your control.  

Sure, managers and leaders can prevent these losses by checking in with their staff, but the unhappy employee could also have also taken the responsibility to speak up, share feedback, and initiate the conversation about their job satisfaction. The problem is, as much as we think we want to be asked how we are and how we are doing, we really only want neutral to good information.  We know how to have a ‘Facebook’ conversation.  A ‘Facebook’ conversation is the equivalent of answering ‘fine’ when someone asks how you are.   More than one research study has shown we avoid feedback because we’re afraid of feedback and feedback does not work anyway. A recent Harvard Business Review cover went so far as to declare feedback a failure.  We don’t like getting feedback and we avoid delivering negative information because we don’t want to be seen as unsupportive of the organization’s goals.  

Now for the very good news.  When those 52% of voluntarily exits are leaving, they are not leaving because they did not get feedback.  They are leaving because they did not feel supported.  They, their satisfaction and well-being were systemically ignored.  Being ignored is something you can control and in so doing breakdown your fear of feedback

You can take control and actually take the fear out of feedback and have the conversations you want to have with your managers by initiating the conversations yourself.  Here are five steps to getting started.   

Step 1:  Know thyself.  Knowing what brings you satisfaction and enhances your well-being and performance is an internal job.  Start by knowing your top strengths.  A quick trick to satisfaction on the job is making sure you get to use your top strengths at work.

Step 2: Initiate it. Armed with the knowledge of your strengths, use them often in conversation with your manager.  Being specific such as, “I am really challenged with the project because I am not getting to use my natural leadership skills,” gives information she may be able to help correct.

Step 3:  Don’t wait for your manager to come to you with a criticism. Instead, set a personal calendar to initiate feedback sessions with peers and supervisors once a quarter.

Step 4: Structure it. Tailor the session to something specific, like your work on the current project, your ability to collaborate within a team, or your capacity to think creatively.

Step 5: Acknowledge It. The key to making feedback sessions work is to acknowledge the advice, develop a plan to improve, and schedule a follow up to track your progress.

Remember the grass will not necessarily be greener at another organization because the same person (YOU) with the same habits will be mowing the grass.  No matter where you go your grass goes with you so consider adding a little fertilizer before you go looking for greener pastures.


Often called a “Success Sherpa” Dr. Andrea Goeglein is a Workplace & Career Psychologist specializing in Positive Psychology. Andrea is the Founder of ServingSuccess and helps individuals, entrepreneurs, & CEOs reach their goals while increasing their levels of happiness, productivity, and satisfaction. She’s been interviewed by The Rachel Ray Show, CBS News, Forbes, Quartz, The Huffington Post, Brit + Co, Recruiter, The Ladders, and many others.

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