Thursday, 27 June 2019

How to Boost Your Social Media Marketing Efforts with Content Marketing Tools

Social media marketing has historically been an island of its own: We seldom see it integrated into other digital marketing channels, like SEO or content.

This is unfortunate because your social media strategy can benefit a lot from data and insights that your other teams and tools can contribute. After all, there's one single goal behind all your digital marketing channels: You want to better understand and better serve your target audience to turn one-time visitors and one-time buyers into brand advocates.

The more data you can collect about your target customer's struggles, preferences and interests, the better you can serve them, regardless of the channel. And collecting data from non-social media channels makes a lot of sense – it can help you or your social media manager to better relate to and cater to your audience.

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4 Content Marketing Tools to Improve Your Social Media Strategy

Here are four content marketing tools that can provide valuable insight to inform and direct your social media strategy:

1) Find Topics to Cover on Social Media Using Keyword Research Tools

There's one thing about old-school keyword research (or keyword extensions, as it's traditionally about making your seed term longer): it's terribly under-utilized by marketing teams outside of a purely SEO department. And that's a shame because keyword research provides a goldmine of insight into the interests and struggles of your audience.

What many people fail to grasp is that there's a real breathing human being behind each search query. If you humanize keywords, they become much more useful, giving you lots of ideas on how to better engage and serve your target customer.

The logic is simple: If enough people type a search query into the search box for keyword research tools to make a record of it, this topic is likely to resonate on social media, too. When it comes to crafting social media updates, encourage your team to use keyword research to:

  • Generally learn which topics to cover in social media updates (like creating mini tutorials, posting “tip of the day” updates, etc.)
  • Come up with popular hashtags to increase your social media reach on Instagram and Twitter

The best part is that keyword research doesn't have to be paid. You can use a free tool and find lots of content ideas to implement on both your website and social media.

For example, Kparser is a freemium keyword research tool that generates huge keyword lists for free without even requiring a login. To use Kparser:

  • Type in your basic core term
  • Play with filters to the left to narrow keyword results based on a chosen angle

kparser

The only real problem behind keyword lists is that they are really messy to the point that they are unusable. Even narrow-niche company keyword lists are thousands of lines long because there are so many ways to put the same query into a search box.

This is where keyword clustering (i.e. “grouping”) comes into play. Kparser does some sort of clustering, which allows you to group keywords by a common word inside the phrase. But this is a very basic clustering technique that doesn't give you a good enough overview of the topic. This technique would group hiking shoes and hiking boots into two different categories, while essentially they belong to one.

Serpstat offers a much more effective keyword clustering functionality, which allows you get a better understanding of your niche and target your content and social media updates more effectively:

  • To access the clustering functionality, you'll need to upgrade your Serpstat account to at least Plan B tier ($69 monthly)
  • Proceed to the “clustering” section and upload your keyword list (you can copy/paste the list from your Excel file you generated from Kparser or Google Search Console)
  • Give it some time. Serpstat will go through your keywords, identify those queries that trigger similar/overlapping results in Google and group your keywords into clusters by relevancy:

serpstat clustering related

The beauty of this clustering technique is that you can catch related terms quite easily in order to come up with a more diverse hashtag marketing strategy.

<b>Click here to download your free guide right now!</b>

Dive Deeper: The Content Marketer’s Guide to Keyword Research

2) Use Question Research to Discover What to Ask Your Social Media Audience

While keyword research has been around for ages, the recent voice-search-driven trend toward optimizing for natural language has created a demand for new tools, i.e. those that can be used to research niche questions.

And this is a very fortunate trend for social media marketing because asking questions is one of the best ways to improve social media engagement, including getting more comments. Questions trigger the natural human instinct called “instinctive elaboration”. This instinct forces people to pause and start looking for or formulate an answer.

Adding all types of questions to your social media calendar thus makes a lot of sense and with new SEO and content marketing tools, question research has become much easier and more effective. For example, Campmor, the outdoors gear retailer, asked a simple question on social media and received 139 comments:

social media questions

There are several powerful tools and platforms that make it easy to discover niche questions to include in your marketing editorial calendar. These include:

Google's people also ask

One of the newer tools that allows you to identify and research questions on any topic is Text Optimizer, which uses semantic analysis to extract related and popular questions right from Google's search snippets. You can export the whole list and hand it to your social media manager to include them in your social media calendar.

To find popular questions using Text Optimizer:

  • Head to their content ideas section and enter your seed term
  • The tool will use semantic analysis to identify popular questions on the topic and show you a huge list sorted by popularity:

textoptimizer questions

There are many ways to include popular questions into your social media marketing, such as:

  • Simply ask your audience a question in a new post (you can also include a nicely branded visual version of the question)
  • Create a poll and encourage your users to choose their answers (and comment with more thoughts)
  • Invite an active social media follower or a niche influencer to host a mini-AMA event on your page (or at your Twitter chat) to answer some of those questions

Diversifying your social media content is key to engaging your audience, and incorporating niche questions will make your strategy even more effective.

Dive Deeper:

3) Use On-Page Analytics to Identify the Best Calls-to-Action

Finally, we want some of our social media updates to convert, not just engage. While social media analytics provides some basic insight into how different calls-to-actions perform across different social media updates, we have much more testing and analytics flexibility when dealing with our own website.

On-site analytics provide much deeper insight into what triggers conversions and what drives people away, so it makes perfect sense to use this insight when crafting CTA wording for social media updates. too. Google Analytics offers in-depth goals tracking, but it's a high-level tool, which makes it less useful for social media marketers who are basically looking for very specific answers.

Finteza is a free analytics software that is designed to be straightforward enough for even a non-analytics person to understand. Once you install Finteza's tracking code on your site (much like you install Google analytics code), you can use Finteza's WordPress plugin to add on-page links for event tracking:

finteza plugin

If you are not comfortable with using tracking codes, Finteza allows you to use WordPress's visual editor to create events to monitor.

Once your in-content CTAs are set up for tracking, you can easily put together funnel monitoring inside Finteza. Simply select your URLs and events to visualize which of those lead magnets work best and whether this is something worth promoting via social media ads:

finteza funnels

It is obvious that a “Whitepaper” CTA collects more leads than a “Webinar” CTA, so your social media team may create better-converting ads if they choose the whitepaper asset to promote on social media.

Finteza is one of the most marketing-friendly analytics suits because it's incredibly easy to set up.

Dive Deeper: 

4) How to Put It All Together and Keep Your Team Updated

Finally, with so many data points and (remote) teams working together for the common goal, what's the best way to keep everyone updated?

ContentCal is a collaborating social media management tool that offers a “Campaigns” feature that allows you to schedule content marketing campaigns and create detailed content briefs which can be accessed and edited by other team members:

ContentCal

ContentCal allows you to schedule content + social media marketing campaigns so that your whole company knows what is coming and when, and lets them contribute their own data from their managed tools.

It's a great idea to record all the keywords, questions and best-working content magnets for your social media marketing managers to implement in their strategy:

content cal campaign brief

Each brief can be as detailed or as concise as you choose it to be and your other team members can add their data, too. The end result is that your social media manager knows everything, from lead generation assets to use across ads to hashtags and popular questions to include in social media updates for better organic reach.

<b>Click here to download your free guide right now!</b>

Takeaway: 4 Content Marketing Tools for Social Media

  • Kparser and Serpstat – Use these two keyword research tools for crafting social media updates and coming up with hashtags.
  • Text Optimizer– Research popular questions using this tool and ask those questions on social media for more engagement.
  • Finteza – Use this tool to monitor your social media audience and understand what engages them best.
  • ContentCal – Use this content marketing calendar to put everything together. You can also use Google Timeline to plan your projects.

The post How to Boost Your Social Media Marketing Efforts with Content Marketing Tools appeared first on Single Grain.



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Elizabeth Warren on antitrust: ‘What’s been missing is courage in Washington to take on the giants’

MIAMI, FLORIDA - JUNE 26: Sen. Elizabeth Warren (D-MA) speaks to the media in the spin room following the first night of the Democratic presidential debate on June 26, 2019 in Miami, Florida. A field of 20 Democratic presidential candidates was split into two groups of 10 for the first debate of the 2020 election, taking place over two nights at Knight Concert Hall of the Adrienne Arsht Center for the Performing Arts of Miami-Dade County, hosted by NBC News, MSNBC, and Telemundo.
Sen. Elizabeth Warren (D-MA) defended her position that companies like Amazon should be broken up in the first Democratic presidential candidate debates.Read More

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What is a soft 404 error and what to do about it?

New Search Console Updates Confirm Mobile-first Indexing

Recent changes to Google’s Search Console made it easier to identify whether a site was primarily indexed based on mobile or desktop content and identified which crawler (Googlebot Smartphone or Googlebot Desktop) was primary on a given report or chart.

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How to Run a Strategy-Focused Content Workshop

Document your content marketing strategy with a hands-on, insightful company-wide workshop if you want to achieve big success. Follow these nine steps to lead a session to get everybody on board and working toward the same goals. Continue reading →

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Raise Your Marketing IQ at CTAConf 2019

Wednesday, 26 June 2019

The Effects of Natural Language Processing (NLP) on Digital Marketing

NLP, or natural language processing, is an area of computing that aims to help computers make sense of human (or “natural”) language. It’s on the rise, incredibly powerful and is about to have a life-changing impact on marketing.

Even though language is second nature for the vast majority of humans, it’s very difficult for computers to interpret and use correctly. The rigid, rule-bound format of spreadsheets and databases are perfect for software, but the random, context-bound and seemingly rule-less nature of human languages makes AI want to reboot!

NLP might not ring a bell for you right now, but it’s been around for the last 30 years or more – and it still has a long way to go. Experts believe that some of NLP’s next steps will be huge, centering around the move from structured data (databases) to unstructured data (text), as well as an increased ability to “understand” humans as they speak normally.

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Why Should Marketers Care About NLP?

As a marketer, you might be thinking, “That’s nice, but what has it got to do with me?” Well, if experts are to be believed, some of the biggest, most revolutionary uses of NLP center in and around its applications on marketing.

Now, bearing in mind that NLP is a scientific discipline (and one that’s not necessarily easy to understand in a single, 2,000-word article), let’s start by running through the main types NLP you may encounter on a regular basis:

  • Optical Character Recognition: Converting written or printed text into data a computer can read. Ever tried to edit a non-editable PDF? If you have, you have my sympathy. OCR is the tech that “helped” the process.
  • Speech Recognition: Converting spoken words into data a computer can understand. This is the NLP technology you use every time you ask Siri, Cortana, Echo or Google Voice a question.
  • Machine Translation: Translating text from one language to another. This is the tech that underlies translation apps like Google Translate.
  • Natural Language Generation: Outputting information as a human language. This is the tech you use every time Siri or Cortana answers your question.
  • Sentiment Analysis: Extracting data from topics being discussed (often “big text”) and assessing whether that data is negative or positive (or if it can detect something else).
  • Semantic Search: Closely linked to speech recognition, as above, this allows you to ask natural questions of an app like Siri, rather than having to formulate your question in a particular, unnatural way.
  • Machine Learning: Machine learning is a whole other topic, but essentially, it uses the data that NLP interprets to “teach” itself about future actions.
  • Natural Language Programming: These are tools that allow users to make apps and software using natural language commands (instead of programming in the traditional, computer-friendly way).
  • Affective Computing: Using NLP and other technologies to understand and replicate human emotions (this is the one that most people are scared of).

These definitions may seem high-level but, in fact, you already use them. You might have even used them today if you’ve consulted:

  • A spellcheck app
  • Google Translate
  • Siri, Cortana, Echo or Google Voice
  • A chatbot:

SG - Why Chatbots Are a Must-Have for Businesses (and How to Build One!)

All these apps – and many more – use NLP so that you can interact with them and they can interact with you. Have more ideas for NLP occurred to you? If you’re the creative type, the answer should be “yes!” Let’s walk through some of the uses of NLP in marketing that aren’t just a reality; they’re highly successful as well.

Dive Deeper:

How NLP Is Shaking Up Marketing

The one use of NLP that you may already have heard of is big text sentiment analysis. You’ve heard of big data, right? Well, meet its cousin, big text.

sentiment analysis

Sentiment analysis is becoming sufficiently advanced these days so as to be able to give us not just an insight into what people are saying about our brand online, but also how they feel about it. As all marketers know, mentions do not equal positive mentions. With NLP, we have the power to prove it.

With NLP employing sentiment analysis, we can mine big text to find those negative mentions and reach out to try and mitigate the consequences. Likewise, sentiment analysis can help brands find instances of people with a clear intention to purchase so that you can make the moves necessary to ensure that your brand appears before their eyes.

If you’re in e-commerce, you’ll enjoy this one: Other aspects of NLP can be used to sift through product descriptions and automatically amend the HTML to include attributes that may not have been added when the product was originally uploaded. Not only does this cut down on grunt work for you, it adds context and detail to the listing, meaning that Google is even better informed when it comes to ranking your beautifully descriptive products in search.

Our final example is the use of NLP to improve the performance of chatbots. Not only can NLP help improve their usability – and their customer experience as a result – but it can also be combined with marketing psychology and targeting to actually increase conversions and sales.

As an example, last year retailer Asos reported a 300% increase in orders by using their new “fashion bot” Enki. The company used to have a chatbot (the boring-sounding “gift assistant”) and, by all accounts, it was pretty underwhelming. Using the new, all-improved Facebook Messenger chatbot, they saw a 250% return on spend while reaching 3.5x more people. Impressive, right?

ASOS

Cosmetics giant Sephora has also jumped on the chatbot wagon, with not one, but three automatic assistants:

  • Sephora Reservation Assistant (Facebook)
  • Sephora Virtual Assist (Facebook)
  • Sephora's Kik bot

The Facebook booking bot has an 11% better conversion rate compared to any other method of booking a makeover.

Dive Deeper:

The Future of NLP in Marketing

One thing that might hamper your understanding of NLP and its possibilities for the future of marketing is that even though it’s not hard to understand how it works (it helps computers understand human speech and text), it can be hard to imagine the full breadth of the applications it might be used for.

One of the major challenges – and advantages – of NLP-powered systems is that they can process a HUGE amount of data. What’s more, much of this will be unstructured data that we’ve never been able to process on a large scale before. The result, from our point of view, is that we now have unimaginable amounts of data from which we can draw conclusions and influence strategy.

The problem lies in the fact that we must be able to actually draw these conclusions. In other words, we have to be able to use the data in a meaningful way. If we don’t, it’s effectively the same as not having any data at all. That’s why the first requirement and challenge of using NLP is the need to have systems in place that can take advantage of the data, in addition to systems that pass that data onto even more systems that can actually take action with it.

Many of the world’s newest NLP-enabled apps are just that: tools that take actionable data and use it to achieve a goal. The degree to which companies manage to do this is the key challenge influencing how NLP will affect the world of marketing in 2020 and beyond.

Here are some of the biggest challenges with NLP.

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Challenge #1: Presenting Raw Data Attractively

Much of the use of NLP in marketing centers around social media – using the technology to sift through the millions of casual mentions of a given topic and pull out both the most important ones and the overall “feeling” about the topic. Sometimes these apps focus on a certain social media platform like Twitter, while others are built into social media management apps, like Hootsuite:

Screenshot 2019 06 03T112731.918

Either way, the challenge here is to analyze the growing amount of big text. And grow it will – big data market revenues are projected to increase from $42B in 2018 to $103B in 2027 (and big text is part of big data). As the data increases, tools will need to hustle even harder to make sure such vast knowledge can actually be understood and used by humans.

Dive Deeper:

Challenge #2: Presenting Raw Data in a Way that Saves Humans Time

Likewise, this avalanche of data will be much more usable if apps find a way of “triaging” the information it provides, making it not only easier to understand, but also making inroads into how much of it is left to humans to deal with after automated processes have started the job.

Apps like MonkeyLearn, for example, analyze customer support tickets and then automatically tag and categorize tickets based on – you guessed it – sentiment analysis. Once employees interact with the data, it’s incorporated into their normal workflow, reducing the amount of effort required to get it support ready.

MonkeyLearn

Challenge #3: Presenting Raw Data in a Format that Can Be Used in Real Time

The idea of having to let a computer “do its work” while you wait is an old-fashioned one. We live in a society that expects things now. Even so, getting that NLP-enhanced info to you on the fly is in the early days, and it’s definitely still got some distance to go.

We’ve already seen a great real-time use of NLP for the writer as they’re writing: the ability to examine content as it’s being written and to communicate suggestions for improvement as learned through machine learning and big text. This helps writers make decisions that will take an article from average to highly optimized, helping them spot missed opportunities.

It’s a fascinating topic, and we’re already seeing progress in this area. One app that attempts to perform this task is MarketMuse:

MarketMuse

In exchange for an email address, they’ll take a peek at a piece of your content and suggest how it might be improved. That’s the power of NLP.

Challenge #4: Making It Easier to Interact with Tools that Use NLP

Although marketing and customer experience aren’t the same, they are related, and we’ve seen already how improving automated bot experiences can offer major marketing benefits in terms of conversions and sales.

Chatbots, knowledge bases, and customer support resources can all be optimized by helping people access the information they need more quickly (data mining), allowing them a more natural state of interaction with the tools that can help them (natural language processing), and by streamlining the human-led section of the customer support process (by automatically categorizing, tagging or triaging inquiries).

Stress-free technology interactions are a key to happy customers, and, as we all know, happy customers make the whole company smile.

Dive Deeper:

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The Future of NLP in Marketing

If you’re in marketing, you should be very excited about the possibilities of NLP. If the sheer opportunities it presents aren’t enough, then at the very least, the journey that it’s already made and its possibilities for the future – many of which haven’t yet been discovered – should get you excited. If you ever tried using Google Translate back in 2006 when it was first launched, I’m sure you’d more than agree!

As we move towards the future of NLP in marketing, keep an eye on the evolution of the NLP-powered tools that will be made available. No matter what you’re marketing and no matter if you’re a big business or a small player, you’ll be able to make use of some of the most exciting and practical uses of big data we’ve ever seen.

Since one of the keys to modern marketing seems to be the analysis and application of big data insights, anything that helps us better manage this big data should be welcomed. NLP may be one of the best tools we have to do this in a sustainable, scalable and real-time way, making it one tech buzzword you can’t afford to ignore.

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