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As the digital world disrupts many formerly tried-and-true marketing channels, content marketing has become increasingly important. But what is it, exactly? And how can you incorporate various types of content into your overall marketing strategy?

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Read on for everything you need to know about content marketing. But first, let's start with the basics.

What is content marketing?

Content marketing encompasses two major components: content creation and the distribution of that content through various channels, like social media, emails, or websites. Pieces of content can include written, spoken, and visually produced materials that often support the goals of a company or nonprofit.

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‍Content marketers basically have the same overall goal: to produce quality information about their company and get it into the hands of current and potential clients. They want to create content, ideally create engaging content, that gets to the right people. So they create a strategic marketing approach to get this content out to guarantee profitable customer action.

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But the way that each content marketing team achieves that result might look significantly different. One content marketer might rely heavily on social media marketing incorporating user-generated content into their strategy. Another might depend on well-researched white papers that they will distribute through an email network.

The content marketing industry

The specifics of your content marketing plan don't really matter, and they'll probably evolve. Newer industry trends might also influence your content marketing strategy. But as long as you understand your message and create material about your message, other people receive, understand, and take action, you're doing it right.

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Nearly 90% of all marketing agencies have dedicated content marketing teams. So the content marketing world can be a pretty vast network of people. And given that most content marketers anticipate their budgets to increase this year, according to a 2021 study by the Content Marketing Institute, this type of marketing will continue to matter in the coming years.[/vc_column_text][vc_column_text css=""]

What is the role of content marketing?

Content marketing efforts provide a meaningful way for brands to interact with their customers through various forms of content. Content ideas and actions will look different depending on the types of customers brands want to reach and where these users are in their customer journey. For example, an eye-catching social post might be ideal for attracting new customers, but it's probably not the type of content you want to use for more established customers who have been depending on your insights for years.

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The benefits of content marketing are pretty numerous, including increased brand awareness, a better understanding of your corporate goals and offerings, and information that compels the readers to take action.

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How do you do content marketing?

There's not necessarily a one-size-fits-all approach to content marketing. But no matter how you do it, there are five steps to help you master the basics of content marketing.

1. Know your business. You need to understand your product or business and determine what your overall goals might be.
2. Know your audience. Who are your potential clients, and how can you reach them?
3. Create the right marketing assets for that audience. You might need different assets and content types for various buyer personas.
4. Distribute those assets through the right channels. You've got to get your compelling content in the places your audience will be.
5. Analyze your results. What's working and what's not?

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It doesn't matter whether you're sending regular newsletters over email or cute reels on Instagram. If your target audience engages with your brand, you're doing it right!

What are some content marketing examples?

While blogs and articles are the most frequently discussed forms of content in "content marketing," content could encompass anything from infographics to videos or even podcasts.

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Here are a few of the most common forms of content marketing, all of which can be highly beneficial marketing tools as part of your overall content strategy. Especially if you're just starting, don't be afraid to try out several forms of content marketing to see what works for your brand and audience.

Blogs

These can be short or long and are a great interactive way for customers to interact with a new potential customer or faithful, loyal customers. Sometimes companies will use blogs to answer customer questions. Other times, they'll write evergreen content related to a critical issue the audience cares about.

Long-form content

You can describe anything over 1,000 words as "long-form content." This data often lives on blogs, articles, and white papers. Long-form content gives readers a more in-depth look at specific insights or goals a company has and has become increasingly important for search engine optimization. It's an excellent way for brands to showcase their unique insights and expertise.

A few tips for content marketing tips for written forms

No matter where you are in the blogging or writing process, here are a few tips to make sure your blogging efforts are effective content marketing for your brand and audience.

• Post consistently on topics related to your business that your audience cares about. You're better off posting a few quality blogs that are well-researched and engaging and posting once a week or so, than posting sub-par information.
• You can post educational content related to your subject area or brand to let your readers know your expertise.
• Optimize your blog for search engines. You want to make sure new users are learning about your blog.
• Your posts should address common questions your readers have regarding your product and industry.
• Aim for quality over quantity. Writing can be such an effective content marketing tool, but bad writing will cripple any content marketing campaign.

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Videos

Whether they're expert interviews or short animated explainer clips, video can be valuable content to have in your arsenal. Be smart about using video as part of your overall content marketing strategy. Video can be a compelling form of content marketing, but a poorly produced or conceived video can backfire. You don't have to invest in ridiculous editing equipment for high production quality, but your videos need to look good and make sense.

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When done well, video has the power to explain complex concepts or showcase products in ways that can be highly appealing and relatable to potential customers.

Infographics

These specific images with details or charts about the company can exist within other types of channels, like a blog or white paper. Other times, they'll stand independently as a post on a social media channel. But regardless of where you find an infographic, it's a powerful way to convey complex data in a simple format. It's also a tremendous business asset that you can place on several forms, from PowerPoint presentations to LinkedIn posts or even within a promotional video.

Content marketing channels

How do you let people know about all of your great content? Through one of many content marketing channels. Most effective content strategies will utilize several of these channels to achieve their business goals.

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If you're not sure where to start, set up a company website and social media account on platforms that are relevant to your business. That way, potential customers can and your content marketing materials online.

Website

Your website doesn't have to be fancy with many videos and graphics. If anything, those bells and whistles might actually slow down your site speed and detract from your overall content marketing work. But a simple and effective website, complete with sections discussing your company and vision, how people can reach you, and even including a blog with more informative pieces, can be an ideal content marketing channel.

Email campaigns

If you have the email addresses of loyal customers or even purchase email lists, you can send your digital marketing materials to that list. Use discretion when sending email and especially if you use a paid list. Many users only feel annoyed to receive an unwanted daily newsletter in their inbox from a brand they might never have heard of. On the other hand, loyal customers who have invested in your brand might really love special offers and announcements from your company.

Direct mail

This form of traditional marketing has been around for years and can be a very effective strategy for marketers. It involves sending letters, catalogs, coupons, or any written and visual content marketing materials to your targeted audience.

Social media

Whether you're sharing an infographic on Instagram, a short video on TikTok, or an article on LinkedIn, social media platforms offer many helpful ways of distributing your valuable content.

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They help you develop a strong relationship with your consumers since social posts tend to be more interactive than other content channels. Not only are customers engaging with your brand, but they're also interacting with each other and building a sense of community. If appropriate, you can also incorporate user-generated content into your social media post. It will give your content a more authentic feel.

Which social channels are the right ones for your business?

Clearly, the right content marketing forms on social media differ depending on the platform, audience, and marketing goals. A B2B financial company will probably have better success running a LinkedIn campaign with quality long-form content and infographics than it would from a campaign with silly TikTok videos trying too hard to be hip.

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When considering social media channels for content marketing, content marketers should first consider these questions.

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• What is the goal of my marketing campaign?
• Do I have a clearly defined audience, and what types of social media is that audience engaging with?
• What's the best fit for my audience?

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There's nothing wrong with trying out several different content marketing strategies. Just be sure you're measuring those results to see where you should spend time and what's not helping you get your message across.

Paid marketing campaigns

You can run a paid marketing campaign across any of these channels, and they can be a fantastic way to supplement your content marketing efforts. Whether you boost your blog articles on Facebook, promote your app in an app store, or sponsor an ad in a podcast, paid advertising campaigns can help to supplement your existing content marketing efforts.

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While these tactics can be effective, especially on search advertising, organic results almost always outperform paid search. That's not to say paid searches can't be extremely helpful at increasing your distribution, but the overwhelming majority of searchers click on the first organic link.

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What do you share for a paid campaign? Start by looking at what's already performing well among your loyal supporters. What worked on social? Which emails have high click-through rates? Then refine that and bring it to a broad audience through your paid campaign.

What is good content marketing?

Any content marketing that connects with your target audience counts as "good content marketing."But while several types of content marketing can be effective, quality content marketing efforts generally include the following.

1. Fresh content posted on a regular basis. It's not enough to write one blog post and hope it goes viral. Your brand and content should stay fresh in your prospective and existing customers' minds.
2. Incorporates editorial calendars. Don't just haphazardly add articles to your blog. Set aside some time each month to make a monthly content calendar that allows you to create relevant content across strategic topic areas.
3. Utilizes an effective email marketing strategy. When used strategically, email lists can be a fantastic way to reach your target audience.
4. Effective use of visuals. A picture is worth 1,000 words, and visual content can be an extremely effective marketing technique. Just remember to add relevant titles and labels to your photos for search engine optimization.
5. SEO-Optimized content. Organic search can be a wonderful way to get users to your site, and your content marketing strategy needs to consider technical elements of SEO that support your great writing and articles.
6. Evaluated consistently with analytics. There's no shortage of tools or analytical metrics for digital marketing efforts. Check out how many people are opening your emails, and which ones have a better open rate. Which piece of content is getting to the top of SERPs, and how are users responding after coming to your website? Are you getting strong conversions from inbound marketing efforts?

Extra content marketing resources

Creating content can be a struggle for even the most seasoned content marketers. Making consistent content that resonates with your target audience and has the highest reach across digital media channels can be even more challenging.

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But content marketing success is possible, especially if you have the right help! If you need additional resources to create high-quality content that gets seen on search engines and connects with your audience, give the experts at rellify a call. They combine the best of human knowledge with machine learning that's specifically tailored for your business so that you can take your content marketing efforts to the next level.

What should content teams focus on to see the results they're looking for? Alan Edgett, Founder and CEO of The Gig Agency, recently addressed this topic and others in his recent rellify webinar, "How to Use Data to Drive Your Content Marketing Performance.

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"After his initial presentation, Edgett held a Q&A session and discussed data tracking, AB testing, focus areas and best practices for content teams, and more! This transcript has been edited from the original video.

Stats that matter for content teams

What is the most important stat that people should be looking for [in long-form content] in terms of whether they're succeeding or failing in their content marketing efforts?

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Alan Edgett: If you can't easily repurpose and slice and dice your content, then in effect, you're creating more work for everybody else; your paid acquisition marketers have to make their own ad, and your SEO marketers may not be happy, etc. The ability to utilize one piece of content for the organization in multiple areas helps alleviate some of that pressure.

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But it depends on the primary goal. I would either focus on impact. Or, (if I was being judged on lead generation performance and I was the content marketer), I would look at some of those engagement metrics. For example, time spent, scroll rate, or trying to find keys for why or how or where my content was being impactful. If it's a video, it's a little more obvious because you get the viewing metrics. But I would focus on content that is performing well and engaging its audience.

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‍Finding the right strategy for content teams

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‍And again, it doesn't even have to be on-platform; it could be off-platform: if I take this video over to LinkedIn, does it get any engagement at all? Are there any likes or shares? Try to quickly sort through and try to figure out why a particular piece of content is getting more engagement than others, and then focus your efforts. That is one way to alleviate the burden of producing content. In digital marketing, we're all under the demands of, "I need to fill this entire content calendar." But the truth of the matter is, is we're trying to create a good content strategy, not just a high amount of content.

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Make sure that you are contributing to your goal and you're not just producing content for the sake of producing. I see a lot of people that have hundreds of blog posts, and they still rank poorly in SEO. That should not happen. So, what does that mean? You are not accomplishing your goal, and you need to revisit it.

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Quantity is not helping you, and you need to slow down and focus more. On the other hand, I see people all the time that produce landing pages that don't generate the lead. So again, if the goal of that content is lead generation, you need to learn why that landing page isn't working. Test, test, test - three, four, five, six, seven different versions - so that you can find one that works rather than just continuing to do what you've done.

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UTM tagging and source data

If UTMs (Urchin Tracking Modules) are being used to capture the original lead source, will UTM tagging on-site links overwrite the original source data?

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Alan Edgett: Yes. Generally, if your website's constructed in a certain way, the last UTM will probably be stored. I try not to do that. I think it is important knowledge to capture the UTMs where the traffic came from. Like Facebook - let's get all those UTMs and store them. Then if they go through internal processes that add UTMs, we'd like to store that in a separate area. So, that's just a little bit of configuration for your web, HubSpot, or CRM marketers to think about.

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The same phenomenon happens when users come from LinkedIn: they browse, they go away, then they Google you, then they come back to your site. Oftentimes in B2B, they'll fill out a form from Google, go away for two weeks, and go cold. You might resuscitate that lead with a LinkedIn ad, then they come back and fill out the form again. And usually, CRM/marketing automation programs always store that last UTM. But, that does not give credit to the first UTM that sourced them, so we like to try to store our UTMs in an array.

Avoiding content marketing pitfalls

What are some common pitfalls content marketers can fall into if they have a very lean content team, i.e., they're the only content person?

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Alan Edgett: Not knowing the goal or target audience of your content creation is probably the biggest problem. I see sometimes content marketers are told, "Hey, we need a newsletter." What is the goal of the newsletter? Is it re-engaging current users or getting conversions? Am I just trying to get engagement, or is it trying to lead gen a user who's not quite filled out a form, but they gave us their email? Where are they in the funnel? Those are all totally different newsletters.

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Try to define and choose your goal wisely because then you can choose your key performance indicators that go with it. I see a lot of content people who are advised to just do the best they can. Or maybe they're told, "Here are the ten topics that the CEO wants us to talk about." They don't know why, and they don't know how they're being judged. So they just go about producing these ten pieces of content (that takes them quite a long time). Then three months later, everyone yells at them. "We're not getting leads," and they weren't even told that they were supposed to be generating leads from that content.

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So, the biggest pitfall I see with a content team is they hurry up and produce without knowing why or what they're getting judged on. Make sure everyone agrees on the KPI and the judgment of that particular piece of content.

Competitor tips for content teams

If you want to look at your competition and say, "How are they doing in content marketing? What is working for them, and what isn't?" What are some basic things people can do?

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Alan Edgett: Participate in your competitors' social points of presence, whether it's Twitter, LinkedIn, Facebook, etc. There is a lot to learn from what your competitors are doing in their social media marketing. If they're getting engagement and you're not getting engagement, start there. Potentially their topics are more interesting, or the way they're writing is more interesting. It could be that they're the largest established competitor, or they have a bigger audience. Try to sort through that.

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‍Analyze the competition

‍Draft off this and see which of their pieces are failing and which of their pieces are getting some engagement on the social side. Also, on the paid side, make sure you understand how your competitors are using their Google search ads. Understand what landing pages and what content they're driving into. Don't always just look at their main website because performance pages sometimes are different, and they're testing things over there. Identify where they like to test and what they're testing.

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A landing page does not survive the month if it sucks. So, if your competitors are still using a landing page multiple months in a row, either they're total idiots, or it's a good landing page, and you should learn from it. Especially if they're a bigger brand, it's probably working. I always look at competitive landing pages or even just the big boys in the industry of all industries. It doesn't matter what [industry] you are – go find the big players. Look at their landing pages and see what kind of taglines they're using, how they're describing their benefits, and pay attention to their user experience because you'll learn a lot.

Best analytic tools for content teams

Do you have any favorite analytics tools for AB testing? Are there any tips for doing it faster than normal?

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Alan Edgett: I do! If you have money, I like AB Tasty. If you don't have money, use Google Optimize. It's free, so you can't go wrong, and it's not a bad tool! If you have a lot of money, Optimize is fantastic, but AB Tasty is about half the cost and does just the job. For testing, keep it simple. People forget all the time that testing takes time and traffic. If you're testing your home page and your home page gets fifty thousand visitors a month or so, you can do a test or two every week. But if you're testing a lower-level blog page that gets three hundred visitors in a month, you can do one test every two months. Be realistic on what you're going to test, and don't test multiple things on a page.

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Understand the difference between multivariate testing and AB testing. If you're going to do AB testing, you're at a high level; you're trying to determine different macro approaches. "Does macro approach A work better than macro approach B?" You are not able to determine whether the single line of text was better because you're trying to keep it at a high level. For example, I've got two landing pages competing with my Medicare client: one with a doctor on it and a whole bunch of text, and one with a regular consumer. I'm not judging it on the lower-level pieces of text. To do that, I would need to do a multivariate test. But AB is important here. And so, what is my hypothesis? "Doctor" is better than "consumer." People trust the doctor.

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‍A/B testingIt's that kind of high-level learning. Once you get through the test, of course, now you can take "doctor." Put her on all the pages, and test the next thing, the tagline. And so that's how you keep the variables simple so that you don't get confused. You do not want to try to test the new tagline with the doctor and a different tagline with a consumer. That violates the Google principle, right?

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You don't know which of those two was pulling. Keep your test simple and high-level if you're doing AB. If you have a lot of traffic and you can get over into multivariate with a tool like AB Tasty or Google Optimize, then you can set up cells where you are testing different taglines and different pictures, all at the same time and it will sort that out for you. Understand how complex you're capable of going based on what level of traffic and kind of what tools you have.

What's a good starting point for content teams?

Take someone that's just getting started. Let's say they've been focused on the content side of things and not the data side of things, and now they want to balance it out and focus on the analytics data a little bit more. Where is the best place to start?

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Alan Edgett: I would start with your target audience and their persona; Where does your persona want to consume your content? Make sure your content is there. How does that persona want to consume your content? Video, text, long-form vs. short-form content? What marketing channels is your target audience on? For starters, try to figure out where your persona would like to engage with you. Then look into how they would like to engage with you and steal from your competitors' knowledge. See what they're doing because they might already know the answer. That might start to help you start to narrow down where and how best to serve them the best customer experience with relevance.

More resources for content teams

As the most successful content teams know, you don't have to go it alone. If you need more assistance crafting your content strategy, be sure to contact the experts at Rellify. They have lots of experience helping content teams across various industries create meaningful content that connects with the target audience.

By Dan Duke – Understanding natural language processing and the way it informs machine learning can dramatically improve your content marketing efforts. Read on to learn all about this tool and how it can help drive a greater audience to your company.

What is Natural Language Processing?

Natural Language Processing (NLP) is a tool that uses computer science, AI concepts, linguistics, and data to allow computers to understand human language – both written and verbal. It converts written or spoken words into data and makes it possible for devices to communicate with each other.

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These connected devices have changed our world – think of the "smart home," for example – allowing us to use voice-based or text-based solutions (from our phones, typically) to complete actions from a distance, whether that's turning on the lights, adding to your grocery list, or setting the temperature on the hot tub. Verbal command capability and chatbots (query management) have joined the scene, along with climate control devices (thermostats) and home monitoring devices (security systems).

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We may not be aware that the response to our inquiry is generated by an algorithm, the driving force behind NLP, but it often is. NLP offers the ability to program computers to analyze vast amounts of data from natural language. It allows us to use data from chatbots, social media posts, documents, and website pages. This tool is finding its way into marketing as well, and that’s what we’re going to discuss.

The importance of natural language processing

NLP allows machines to understand human language through complex comprehension. It bridges the human-to-machine gap by using Artificial Intelligence (AI) to process text and the spoken word. It’s a form of AI that manipulates and interprets human language using computational linguistics (the parsing out of parts of speech and words into data).

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NLP consists of two branches. First is Natural Language Understanding (NLU). This extracts the meanings of words by studying the relationships between them. Then, Natural Language Generation (NLG) transforms data into understandable language, writing sentences and even paragraphs that are appropriate, well-constructed, and often personalized.

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NLP allows computers to create responses for chatbots and virtual assistants, write subject lines for emails, and even compose advertising copy and marketing tools. Here’s how to think about it: NLU focuses on your computer’s ability to read and listen (speech-to-text processing), and NLG allows it to write and speak (text-to-speech). Both are part of NLP.

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NLP is everywhere:

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• Intelligent Personal Assistants (IPAs) answer customer questions.
• Voice assistants like Siri respond to commands.
• Marketers use it to create custom content, personalize or push specific promotions, and personalize offerings to the taste of a particular shopper.
• Auto-complete and auto-correct functions in texting help you write and sometimes drive you nuts.
• Machine translation tools clue you into words from other languages.

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Even brick-and-mortar stores can take advantage by customizing individual stores’ landing pages to show local hours of operation, addresses, directions, and additional information.

How does NLP work?

How in the world can a machine understand the spoken or written word? Well, it’s all about linguistic analysis. Natural language processing allows a computer to translate spoken or written words into data, including all of our colloquialisms, regionalisms, misspellings, and abbreviations. It’s astonishing, really, that this can be done, given all of our personalizations of speech. This is where computational linguistics comes into play. Programs can break down any text into four parts: tokens, semantic information, syntactic information, and context information. The computer deals with each piece separately.

Tokenization

This is where it all starts. Segmentation is the first step of breaking down human language into units that a computer can understand. Tokens are typically words, subwords, or characters. Tokenization is a fundamental pre-processing step, enabling computers to understand and analyze human language.

Semantic information

This is the actual meaning of a specific word.  You can interpret a sentence like “He is enjoying the date” in different ways, depending on the meaning of “date.” Is the gentleman out on the town with a friend or chewing fruit from a palm tree? Knowing which form of the word “date” to use — knowing the relevant definition in this specific instance — is critical in understanding the meaning of that sentence.

Syntax information

This refers to the sentence structure. We’ve moved from the word level to the phrase level. “Sarah died peacefully with her family on September 4.” Oh my – how many people died?  The whole family, or just Sarah? That’s an important distinction! How can a computer figure that out?

Context information

The relationship of words, phrases, or sentences to each other is the key here. How should it be understood if someone says, “Man, that’s hot!” Is the item’s temperature high? Or is the speaker describing something fashionable or something to be desired? So how can a machine be taught to analyze these distinctions? It’s not hard to teach a system to understand the basic rules and patterns of a language — that’s the science of linguistic computation. It just takes a lot of time. And (as we saw in the “hot” example above), the rules can’t always keep up with the evolution and revisions of language.

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The earliest text-mining systems were based entirely on patterns and rules, but as NLP and machine learning evolved, hybrid machine learning grew. Hybrid machine learning uses those same rules and patterns in both supervised and unsupervised models, and there are several different versions, using low-, mid-, and high-level text functioning. At the low level are the first processes that consider any input text. They turn the unstructured text into data. At the middle level are the text analytics that extract the content (Who’s speaking? What’s being said? What is being talked about?). At the high level is the sentiment analysis.

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As we’ve seen, the meanings of words change with the speaker’s intent and the listener’s expectations.  Machine learning and NLP offer solid solutions for analyzing words, but each system must be tuned or trained to match the user’s needs.

Growing from GPT-3 to GPT-4

OpenAI, an AI research and deployment company, has made significant advancements in the field. Its GPT-1 and GPT-2, the first and second versions of the Generative Pre-Trained Transformer, were the original forays. GPT-3 represented a giant leap and is still available for free. It can:

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• Use internet data to generate text.
• Take a small bit of input text and generate a large amount of sophisticated machine-generated text.
• Analyze and input a piece of language and predict what the writer or speaker meant to infer.

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GPT-3 can be trained, for example, to compose tweets or press releases or even computer code. GPT-3 uses (NLG) to create easily understood responses. Chatbots often use this. Some businesses use it to develop copy for headlines, scripts, and summarizations. And some online content marketing services use it to generate keywords automatically. GPT-3 has brought deep learning, a form of machine learning, and other artificial intelligence tools into everyday use, fueling social media copywriting and content generation.

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GPT-4, available with a monthly subscription, has further broadened and strengthened the use of NLP. This version is not “just a language model.” It also can consider the visual world by including images as an additional input type. This means that Chat GPT-4 can generate text outputs based on combined text and image inputs.

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The implications of this shift are substantial. Chat GPT-4 can generate captions for images, classify visible elements within images, and even analyze the content of images.

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Another one of the most significant distinctions between GPT 4 is that it's both more creative and reliable. It also can respond accurately to more nuanced prompting, and its multilingual capabilities make it more versatile and inclusive.

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It's also able to process text on a much larger scale. Chat GPT-3 had a maximum context length of 4,096 tokens, while the Chat GPT-4-32K variant can handle 32,768 tokens.

Content marketing and NLP

Natural language processing can be a vital component of a content marketing plan, given its many uses. Highlights include:

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• Analyzing content for sentiment
• Helping to determine which keywords will be most relevant
• Writing product descriptions for digital commerce sites
• Helping to develop a marketing strategy by assessing the content of a particular client or by performing a content audit
• Refining chatbot functions to enable the gathering of solid leads.

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And it is a growing science. A Fortune Business Insights report estimates that the value of the global NLP market will grow from $29.71 billion in 2024 to $158.04 billion in 2032.

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‍The e-commerce company Alibaba’s digital marketing wing (Alimama) offers an AI-powered tool for copywriting that uses NLP from millions of language samples to generate copy. The tool is easy to use. An advertiser inserts a link to a product page and clicks the button (Produce Smart Copy) to receive content ideas. The data scientists at Alimama report that the system can produce up to 20,000 lines of copy per second.

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The ability to "Produce Smart Copy" — across a variety of ad formats for posters, web banners, headlines, and product pages — can make copywriting more efficient. Brands and companies using the service can even decide the tone of their copy. Alimama reports that users can choose between long or short, “promotional, fun, poetic, or heartwarming” pieces of copy. Here are some other ways that you can use AI tools to strengthen your business’ reach:

Content sentiment and content analysis

How can a computer determine a customer’s sentiment? Well, it’s a two-pronged process that uses AI for both content analysis and sentiment analysis.

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‍Content analysis is the objective and quantitative assessment of a text-based, visual, or aural message. Researchers analyze each message scientifically.

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‍Sentiment analysis is the science of interpreting and classifying a user’s emotions about a specific brand, product, or service by using text analysis. Generally, the analysis determines whether the sentiment is positive, negative, or neutral.

The power of the internet and its reach into our lives has offered a vast amount of analyzable content gleaned from blogs, social media, YouTube, and websites. The software can recognize patterns without human input.

Determine your keywords

For the strongest search engine optimization (SEO), the right keywords are essential. NLP tools, like the ones Rellify uses, can help you choose keywords that increase your site’s traffic and attract just the right audience — readers who will turn into leads and customers.

Develop your content marketing strategy

Using NLP-based AI tools, you can perform a content audit to review all of the content on your site.  Repeat this practice regularly — every two months or so — to ensure that your content is up-to-date. Review your posts on social media and web pages to determine how your target audience perceives your customer service.

Use a lead-generator chatbot

Familiarize yourself with this NLP/NLG tool; you may find that it’s a helpful utility. For example, a chatbot can help you identify potential prospects and garner their interest in your products or services by asking questions, conducting surveys, and offering quizzes.  When a user visits your site, the bot can learn why. Some businesses use the information users give to chatbots to complement their email efforts, strengthen the customer experience, and qualify leads by asking specific questions or offering specific tools, all of which can increase their return on investment (ROI).

Text classification

Text classification, in the context of NLP, refers to the task of automatically categorizing text documents or phrases into predefined categories or classes. This type of is technique used in NLP applications, including:

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• Sentiment analysis
• Spam detection
• Content tagging
• Topic categorization

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All of these are valuable use cases, but let's take a closer at a specific type of text classification employed by NLPs — topic categorization.

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Rule-based classification in topic categorization

This can be a super helpful tool, especially if your business deals with large volumes of data that would take a human much longer to comb through.

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Rule-based techniques use a set of manually constructed language rules to categorize text into groups. These rules tell the system to classify text into a particular category based on the content of a text by using semantically relevant text elements. An antecedent or pattern and a projected category make up each rule.

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For example, imagine you have tons of new articles, and your goal is to assign them to relevant categories such as Parenting, Health, School, etc.

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With a rule-based classification system, you will do a human review of a couple of documents to come up with linguistic rules like this one:

• If the document contains words such as doctor, wellness, remedies, or medicine, it belongs to the Health group (class).

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Rule-based systems can be refined over time and are understandable to humans. However, there are certain drawbacks to this strategy.

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These systems, to begin with, demand in-depth expertise in the field. They take a lot of time since creating rules for a complicated system can be difficult and frequently requires extensive study and testing.

The Power of Natural Language Processing

A digital transformation is taking place on a global scale, and you can use these NLP tools to strengthen your bottom line. Rellify seamlessly uses NLP and other Artificial Intelligence tools within its platform, allowing you to find topics and keywords that will resonate best with your target audience. Our AI capabilities can then help you write content that will naturally rank well on search engines. With an exclusive and custom Relliverse™, we use our NLP tools to crawl huge volumes of data specific to you, your competitors, and your market. Through machine learning and NLP, we'll help you create your own content or generate it automatically. The platform even uses AI to provide helpful guidance in SEO. Our "R-score" changes in real time as you write and edit to help you create relevant content.

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Wondering how it all works? Schedule a consultation or demo with a Rellify expert, free of charge, and find out exactly how Rellify can supercharge your content marketing efforts.

By Dan Duke — Let’s face it; search engine optimization has a language all its own. Understanding and using these SEO terms effectively, however, empowers businesses to create content that connects with potential clients and customers.

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So how can you unlock this unique vocabulary and recharge your online content marketing? In this guide, we'll review the terms, their meanings, and the power of the tools and practices they describe. We'll focus on essential SEO terms that can lead to a successful content strategy.

Essential SEO terms

Keywords

Keywords are the words or phrases that people type into a search engine to find what they are looking for. (Or, for voice search, the words they speak into a smartphone or smart speaker.) If a website wants to boost traffic, it needs to use the right keywords in the right spots in its content. Some examples of where to place them are:

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• Early in the text, to indicate the relevance of the content.
• The meta description, to give users a quick summary.
• The page title and headings, to help readers and search engines quickly assess the content.
• Any alt text, which is placed with photos to describe what they show for the benefit of sight-impaired users as well as search engines.

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Keywords are essential to SEO strategy because they signal to search engines that the content will address a searcher’s question. Content marketers employ a variation called long-tail keywords. These are phrases people use when searching for specific information. Long-tail keywords have lower search volume, but the searchers using them tend to be more intent on taking action based on the search results.

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Content marketing managers, editors, and writers strive to create keyword-rich content that will be relevant in search engine algorithms. They must not overdo it, however.

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A blog post with too much keyword density might be flagged as keyword stuffing or spamming and can result in punishment by Google or lower rankings in Google SERPs. This is an example of black hat SEO.

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Thorough keyword research will identify a focus keyword (possibly a long-tail keyword), keyword phrases, keyword questions, and a list of must-have keywords. Using platforms and services such as Rellify helps a business post SEO-friendly content that will rank high in organic search results.

SERPs

A Search Engine Results Pages (SERP) is what appears on your screen when you do an online search. On Google, the top of the page might offer a paid ad, sponsored content, or a featured snippet. These SERP features are the search engine’s attempt to answer the query before the searcher clicks into a web page. Common features are:

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• Knowledge cards. A box that answers a simple question, such as “Who won the Super Bowl in 1979?”
• Featured snippets. An excerpt from one of the top organic results that succinctly answers a query.
• Shopping results. A roundup of paid ads.
• People also asked. A list of questions related to the query with organic links to answers.

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The first organic search result — the content with the best ranking — shows a web page that should best answer the user's question. It also gets the most clicks. One of the main goals of content creation is to claim one of the first three spots for the right keywords. About 75% of all clicks go to the top three results. Web pages that appear on the second page or beyond receive relatively few clicks, if any.

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That’s why relevance is so important in creating content.

Ranking factors

Ranking factors are the criteria applied by search engines when indexing web pages to decide where a page will fall in the SERPs. Google uses algorithms, which are complex formulas or sets of rules, to crawl web pages. These crawlers, or bots, constantly go from site to site, and revisit sites, using algorithms to analyze websites and decide which ones provide relevant content.

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Google doesn't provide a comprehensive list of the ranking factors built into its algorithms. SEO experts agree, however, that ranking factors include the quality of content, backlinks, user friendliness, technical SEO, mobile optimization, social media links, and keyword optimization.

One of the best things you can focus on with your content strategy is having relevant content that helps answer searchers’ questions and stands the test of time. And remember, Google and other search engines are constantly updating their algorithms, which affects the ranking factors. This is why it is good to stay in tune with future-facing SEO tools and update content regularly.

Meta description

A meta description summarizes a web page’s content in one or two sentences — about the length of a tweet. It usually appears on SERPs along with the URL and the title.

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It's not a direct ranking factor, but a well-crafted meta description can improve the chances that someone will click on the link to your web page. That, in turn, can boost your rankings. Google might also use a meta description as a featured snippet, which also will boost your traffic. These summarizations are marked with HTML coding to make it easy for web crawlers to find and assess them.

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A content marketer can improve the click-through rate by writing a meta description that:

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• Includes keywords. Using the focus keyword at the beginning of the text helps, too.
• Uses active, engaging language.
• Emphasizes the relevance of the content.

User experience

User experience, or UX, refers to users’ interactions and impressions during their time on a web page. This is an element of on-page SEO.

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Website developers must keep this in mind, because Google certainly does. Some crucial factors when creating a quality user experience are:

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• Easy-to-read text
• Clear call-to-action buttons or designs
• High-quality images and graphics
• Pages that load quickly
• A home page that's easy to navigate
• A mobile-first website

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A website owner should dig deep into the mindset of its target customers when considering what bells and whistles — if any — to include on a site.

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A satisfying user experience can have long-term positive effects on the success of a website in search results. Essential signals that indicate a positive user experience are click-through rate, visit duration, return-to-SERP rate, and the highly coveted conversion rate.

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You don’t want a clunky website to overshadow your great content. Instead, your great work should make the audience feel welcomed and comfortable.

On-page SEO

We just mention page speed as part of a good user experience. That's also an element of on-page SEO — the optimization of individual web pages to improve their search engine rankings and attract the target audience. It also involves things like:

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• Relevant, authoritative content
• Keyword usage
• Meta tags
• Alt tags
• Internal linking  

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Its effectiveness can be measured through metrics such as keyword rankings, organic traffic, and  the bounce rate — the percentage of visitors who leave the site after viewing just one page.

Off-page SEO

Off-page SEO involves factors outside of the website itself that affect its search engine rankings. It primarily focuses on building high-quality backlinks (also called inbound links) from other reputable sites. When web crawlers see that reputable sites link to pages on your site, it gives you more credibility for providing good answers. Social media marketing and online brand mentions are other important factors. The effectiveness of off-page SEO can be measured through metrics like link building, domain authority, referral traffic, and social signals, which collectively indicate the website’s credibility and popularity on the internet.

Local SEO

Local SEO is part of off-page SEO. It focuses on search results for location-specific queries, helping businesses attract customers from their geographic area. It involves strategies like optimizing Google My Business listings, using local keywords, and acquiring customer reviews. How do you measure the effectiveness of local SEO? Through metrics such as local search rankings, online reviews and ratings, local citation volume, and the amount of traffic from local searches.

Technical SEO

Technical SEO refers to the optimization of a website’s infrastructure to enable search engines to crawl and index it effectively, boosting overall performance and user experience. Key elements include:

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• Configuring the robots.txt file to guide search engine bots.
• Creating and submitting an XML sitemap to outline the website’s structure.
• Implementing canonical tags to avoid duplicate content issues.
• Setting up 301 redirects to the primary version of a given URL. If, for example, your preferred version is https://www.abc.com, any other versions should 301 redirect to that version.
• Migrating your website to HTTPS protocol. Hypertext transfer protocol secure (HTTPS) protects your visitors’ data in the exchange between their web browser and your website. 
• Using structured data to give bots information about your pages and their content. One of the most common types of structured data is called schema markup.

Domain authority

Domain authority (DA) is a search engine ranking score developed by Moz, a provider of SEO tools. It indicates how likely a website is to rank on search engine result pages (SERPs). A Domain Authority score can range from 1 to 100, with higher scores corresponding to a greater ability to rank. For example, Wikipedia.org has a domain authority of 94 and Facebook.com is scored at 96.

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In general terms, it is an assessment of the overall authority and trustworthiness of a domain. It's heavily influenced by backlinks. However, things like age, history, formatting, and quality of content count, too.

SEO tools

Dozens of web analytics tools are available. Many of them have a free, beginner's level. Look for the right balance of price, features, and usability. Here are a few examples:

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• Ahrefs. A comprehensive SEO toolset that includes features such as site audits, keyword research, backlink analysis, and rank tracking.
• SEMrush. Another all-in-one website analytics tool that offers features like keyword research, competitor analysis, site audits, rank tracking, backlink analysis and content optimization.
• Google Search Console. A free tool provided by Google that helps monitor and maintain your site’s presence in Google Search results. Its dashboard provides insights into search volume, indexing status, and keyword performance.
• Google Analytics. While primarily a web analytics tool, Google Analytics offers valuable insights for SEO, including organic search traffic analytics, user behavior data, and conversion tracking.

Integrate more SEO terms into your strategy

SEO covers multitudes of terms and tactics. Some businesses find it overwhelming to keep up. To make the most of your marketing resources, you can rely on Rellify.

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We remove the guesswork from content creation and enable you to decide what topics to cover by furnishing you with a Relliverse™. Your custom-AI subject-matter expert is generated by deep machine learning that's attuned to your business sector and target audience.

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And you can use our detailed AI guidance to optimize your content for search engine results. That, along with our built-in monitoring dashboard, means you never miss an opportunity to improve your content.

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Curious about how Rellify can revolutionize your content strategy, production, and monitoring? Schedule a free demo with one of our experts today!

Would you believe us if we said NLP, NLU, and NLG are already a part of your daily life and probably part of your business as well?

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These acronyms stand for "natural language processing," "natural language understanding," and "natural language generation," respectively, and they're all used within the context of AI (Artificial Intelligence) technology that you encounter daily.

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These processes are used everywhere: in the results of your online searches, in voice assistants like Amazon’s Alexa and Apple’s Siri, and in chatbot conversations that offer a personal assistant to help answer questions. And it’s a strong ally for businesses that need to respond to a variety of customers, all at once, with personalized information.

What is natural language processing?

Natural language processing (NLP) is a branch of AI that focuses on enabling computers to understand, interpret, and generate human language in a way that is both natural and meaningful. NLP involves the interaction between computers and humans using natural language. It encompasses a wide range of tasks, including:

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1. Text understanding. This involves tasks such as parsing, sentiment analysis, named entity recognition, and text classification. The goal is to extract meaning from text data.
2. Text generation. NLP can also be used to generate human-like text, such as in chatbots, language translation, and summarization systems.
3. Speech recognition. NLP techniques are applied to convert spoken language into text, enabling systems like virtual assistants to understand and respond to spoken commands.
4. Language translation. NLP is used extensively in machine translation systems, allowing the translation of text from one language to another.
5. Question answering. NLP techniques are applied to understand questions posed in natural language and provide accurate answers from a given dataset or knowledge base.

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NLP systems typically involve techniques from computational linguistics, machine learning, and deep learning, among other disciplines. These systems learn from large amounts of annotated data and are trained to perform specific tasks related to understanding and generating natural language.

What is natural language understanding?

Natural language understanding (NLU) is a subfield of NLP that focuses on the comprehension of human language by computers. While NLP broadly covers the entire spectrum of tasks related to processing natural language, NLU specifically deals with the understanding aspect.

Here's a breakdown of what AI text analysis with NLU involves:

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• Semantic understanding. NLU is used to extract meaning from text or speech. This includes tasks such as identifying entities (people, places, organizations), recognizing relationships between entities, understanding sentiment, and grasping the overall context of a piece of text.
• Syntactic analysis. NLU involves analyzing the structure of sentences to understand the relationships between words, phrases, and clauses. This includes tasks such as parsing sentences to identify grammatical structure, understanding word order, and recognizing parts of speech.
• Pragmatic understanding. NLU considers the pragmatic aspects of language, such as understanding implied meaning, recognizing metaphors, and interpreting context-dependent language.
• Disambiguation. NLU systems must be able to recognize and resolve ambiguities in language. This includes clarifying between words with multiple meanings, interpreting pronouns correctly, and understanding the intended meaning of ambiguous phrases.
• Contextual understanding. These systems aim to understand language in context. This involves considering the broader context of a conversation or document to accurately interpret meaning and make informed decisions.

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NLU is a crucial component of many applications, including virtual AI assistants, chatbots, sentiment analysis systems, machine translation, and information retrieval systems. This is the type of technology that would be valuable to a college professor interested in using an AI content detector tool to scan students' work. It's looking for patterns in language that indicate a student has used an AI generator to instantly produce an essay they waited until 11:30 p.m. to start.

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By enabling computers to understand human language at a deeper level, NLU systems can provide more natural and effective interactions between humans and machines, and even help combat AI misuse.

What is natural language generation?

Natural language generation (NLG) is another key aspect of NLP, focusing on the generation of human-like text or speech by computers. Unlike NLU, which involves extracting meaning from human language, NLG involves the generation of language output.

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Here's a look at what NLG entails:

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• Content planning. NLG systems typically start by planning the content they need to generate. This may involve selecting relevant information from a database, organizing the information into a coherent structure, and determining the main points to convey in the generated text.
• Text structuring. NLG systems then structure the generated text according to linguistic conventions. This involves organizing sentences and paragraphs in a logical order, providing coherence and cohesion, and applying appropriate style.
• Linguistic realization. NLG systems convert the planned content into natural language text or speech. This involves selecting words and phrases, inflecting verbs, conjugating nouns, and applying grammatical rules to generate grammatically correct and fluent output.
• Stylistic variation. NLG systems may also incorporate stylistic variation to produce text with different tones, registers, or styles.
• Personalization. Some NLG systems can personalize generated content based on individual preferences or characteristics. This may involve incorporating user-specific information, adapting the language to match the user's profile, or tailoring the content to suit the user's needs or interests. For example, they may generate formal language for in-house business memos, conversational language for chatbots, or persuasive language for marketing materials.

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But how can you best make use of these new tools?

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What are some examples of natural language generation?

NLG has plenty of applications, including:

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• automated report generation
• automated chatbots
• chats with virtual assistants
• content generation for websites and marketing materials
• language translation
• accessibility tools for generating alternative formats of text or speech.

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By enabling computers to generate human-like language, NLG systems can automate the creation of content, streamline communication processes, and enhance user experiences in human-computer interaction

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When Siri, Alexa, or Cortana answer your questions, they’re using Natural Language Generation and other programming to translate text into a spoken form.

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When “Sam, your personal assistant” responds to your written query (perhaps as you’re waiting for a live person to respond), the company's software is using chatbot technology to interpret what you have typed and then to respond with an appropriate message through NLG technology. The software searches for keywords in your questions, and then uses specific applications to generate pre-written answers based on the frequency of their usage.

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That’s a lot to consider, sure, but there’s an easy way to understand the distinctions between these various forms of AI. It’s so much more than Robotic Processing Automation, a form of business process automation technology used to do repetitive, low-value work.

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Thanks to the data scientists who’ve done all the research and much of the work for us, NLG is revolutionary for marketers hoping to personalize responses using natural language to clients.

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You may have heard of (or used) one of the most prolific generators of AI content, ChatGPT. OpenAI, a San Francisco-based research lab, created GPT-4, the latest version. The most sophisticated NLG model, GPT-4, or Generative Pre-trained Transformer 4, can write poetry, prose, and even computer coding that is hard to distinguish from that created by humans. The fourth version of this AI tool is 10 times more advanced than GPT-3. It has huge improvements in data processing speed, language comprehension, and its ability to process visual and audio input.

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How can businesses use NLP, NLU, and NLG for marketing?

We are likely to see more and more Natural Language Processing as AI technology is integrated into every aspect of marketing and business. Businesses in many sectors benefit from the sophistication that NLP, NLU, and NLG allow them to offer clients and customers:

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NLP in content marketing

This is one of Rellify's specialties. Its state-of-the-art platform uses NLP in all three of these aspects of your content pipeline, ensuring the best and most efficient use of this technology.

1. Competitive analysis. NLP can analyze competitors' content to identify gaps, uncover emerging trends, and benchmark performance. This insight can inform content strategy and help businesses differentiate their offerings.
2. Content optimization. NLP systems can optimize content for search engines by analyzing keyword usage, readability, and semantic relevance. This includes tasks such as keyword extraction, entity recognition, and sentiment analysis to ensure that content aligns with SEO best practices.
3. Content creation. NLP can assist in generating content ideas by analyzing trending topics, keyword research, and competitor analysis. It can also automate tasks such as content summarization, topic clustering, and content curation, helping marketers identify relevant content opportunities.

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NLU in content marketing

This branch of NLP focuses on the nuances of understanding human language.

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1. Audience understanding. NLU enables marketers to better understand their target audience by analyzing social media conversations, customer feedback, and online reviews. By understanding audience sentiments, preferences, and pain points, marketers can create more relevant and engaging content.
2. Content personalization. NLU techniques can personalize content for different audience segments based on their interests, demographics, and behavior. This includes dynamically generating content variations, such as email subject lines, article headlines, and product recommendations, to resonate with specific audiences.
3. Content distribution. NLU can optimize content distribution strategies by analyzing audience engagement metrics and identifying the most effective channels and timing for content delivery. This helps marketers reach their target audience more effectively and maximize the impact of their content.

NLG in content marketing

This is the aspect of NLP we're most familiar with — the conversational chatbots and ChatGPTs. But it's so much more than just a fun tool, it's a game-changer for online marketing.

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1. Content generation. NLG can automate the creation of various content formats, including blog posts, articles, social media posts, and product descriptions. Marketers can use NLG to scale content production, generate content variations, and maintain consistency across channels.
2. Email marketing. NLG can personalize email marketing campaigns by generating personalized email subject lines, body copy, and product recommendations. This helps marketers increase open rates, click-through rates, and conversions by delivering more relevant and engaging content to subscribers.
3. Content localization. NLG can assist in translating and localizing content for different markets and languages. By generating localized versions of content, marketers can expand their reach and connect with global audiences more effectively.

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By leveraging NLP, NLU, and NLG in content marketing, businesses can create more impactful content, better understand their audience, and drive engagement and conversions across various digital channels.

Put NLP, NLU, and NLG to work for your content marketing

Rellify has strategically leveraged these types of AI within all aspects of the content marketing pipeline, from keyword and topic research to content generation and monitoring. With the latest offering — a custom Relliverse, you can use NLP to crawl large volumes of content throughout the web, and use its expertise to find topics and keywords that best match your target customers' niche search engine queries. Then, you'll be ready to knock your competition out of the water with relevant, optimized, and well-written (or generated) content.

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Contact a Rellify expert today to find out how it uses Intelligence Augmentation, machine learning, and NLP to create uniquely optimized content that naturally ranks high in search engine results. We'll even give you a free demo of the platform and the unparalleled capabilities of a custom Relliverse. What are you waiting for? Revolutionize your content strategy and processes today!

By Dan Duke - If none of the 5,000 new Oreo flavors are exactly your speed, you can build your own Oreos to celebrate your next special occasion. Don’t like running? Take a quick quiz to discover the perfect fitness routine for you. And if your daughter’s been dying for an American Girl doll, why not get your money's worth and design a one-of-a-kind doll that looks just like her? Hyper-personalization is the name of the game, especially for large household brands.

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The trend is gaining momentum as more sophisticated algorithms, ready-made platforms, and AI technology make it possible for start-ups and boutique brands to get in on the action, too.

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Fifty years ago, a content strategist would likely tell you the entire purpose of branding was to ensure the customer could recognize your company. That still matters, but now it’s just as important to show your customers that you recognize them. That’s where hyper-personalization comes in.

What is hyper-personalization?

Hyper-personalization is the practice of tailoring marketing messages, products, services, and experiences to individual consumers based on their preferences, behaviors, and demographics. It goes beyond traditional personalization by leveraging real-time advanced data analytics and artificial intelligence (AI) algorithms to create customized and relevant interactions with customers. Businesses use this marketing strategy to deliver curated content, customized products, and tailor-made product recommendations and services. They also can use it for targeted customer support through an AI chatbot. Think high-touch high tech.

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This type of marketing aims for higher conversion rates and greater customer engagement, loyalty, and satisfaction. Consumers want hyper-personalization throughout the customer journey, and they’re usually willing to give up some data and personal information to get it. But are companies are actually using hyper-personalization correctly to get the best results? An Ascend2 survey of top-level marketing executives reveals that only 9% of marketers have completed a hyper-personalization strategy for their company. Sometimes, with the increased pace of both technology and advertising, we see how hyper-personalization can backfire if users feel their privacy has been violated or if ads are overly-personalized but not presented to a target who's ready or able to buy.

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Clearly, there’s still a gap between the expectations of the target audience and business marketing strategy — and in that gap there’s a lot of opportunity.

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Personalization vs. hyper-personalization

Hyper-personalization is often described as an extreme version of personalization, but there’s more to it than that. They differ in degree, but there are important differences between the two, as marketing strategies, in scale, speed, and results.

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Scale: Segments vs. individuals

While personalized marketing tends to focus on profiles within market segments or “persona-based marketing,” hyper-personalization thinks in terms of individuals. Gone are the days of generic push notifications or segmented email campaigns. Hyper-personalization gives us a way to offer unique experiences to every website visitor and tailor suggestions just for them.

Speed: Follow-ups vs. real time

In a personalized marketing campaign, marketers are generally limited to follow-up emails, text messages, and phone calls based on aggregated data. Hyper-personalization also uses follow-up advertising tactics. (We’re all familiar with those product recommendations that follow us around social media after we’ve abandoned our cart!)However, it is also capable of using big data, AI, and machine learning to deliver unique user experiences in the moment. This makes potential customers feel like their needs are being anticipated and their satisfaction is increased.

Results: Good vs. great

Personalized marketing campaigns have been around forever, from the legendary Staff Picks at Blockbuster to restaurants offering free appetizers on your birthday. These tried-and-true strategies work, but they don’t go far enough. Customers, especially in the context of digital marketing, now expect more.

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When it comes to the difference in results between the two strategies, the numbers speak for themselves. According to the 2023 State of Personalization Report produced by Segment, over half of consumers (56%) say they will become repeat buyers after a hyper-personalized experience, a 7% increase from 2022. Hyper-personalization provides a better ROI than regular personalization.

The limitations and frustrations of hyper-personalization

Of course, nothing as revolutionary as hyper-personalization comes without headaches and frustrations. Typically, companies that are trying to hyper-personalize face challenges in three core areas:

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• Data collection, analysis and management. How will you collect customer data and who will manage and analyze that data for you? This question tends to be the biggest hurdle most businesses face when starting this marketing strategy. Big companies like Amazon, Spotify, and Netflix have the resources to implement sophisticated hyper-personalization at scale, but what about smaller companies? Developing a plan for data collection and analysis is the first step. You can’t customize a user experience if you don’t know anything about your potential customer.
• Privacy concerns. Even though millennials, Gen Z, (and yes, even Gen Alpha) consumers are usually more than willing to swap their data for personalized experiences, you still have a responsibility to keep that data secure and private. Fortunately, many turnkey web platforms offer excellent data security with built-in algorithms for hyper-personalization.
• Silos in the workplace. Implementing this type of marketing plan makes it even more imperative that company departments learn to work together, specifically the marketing, sales, IT, and customer service departments. Many CEOs find it simpler and more efficient to hire an outside marketing and data firm to manage their hyper-personalization campaigns and serve as a single point of contact for all involved departments.

An example of hyper-personalization in action

Let’s look at personalized vitamin packs as just one example. Companies like Persona Vitamins took a common problem, applied hyper-personalization to it, and turned personalized nutrition and medicine into nearly a $600 billion industry.

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What was the problem? Every adult knows they should be taking vitamins, but it’s such a pain. Does this sound familiar? "Did I take them today? How do I know what to take? Why are there so many bottles to open and close every day? Do I take this in the morning or the evening?

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"Instead, a hyper-personalized vitamin service asks users to take a simple assessment and then ships easy-to-open, easy-to-remember, custom-designed vitamin packs on a monthly schedule.

3 factors driving consumer demand for hyper-personalization

Even with data and privacy concerns, most consumers are still willing (and eager) to engage with companies that use hyper-personalization. After all, it saves users time, delivers more relevant results, and creates a better overall use experience.

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Three other factors are boosting demand for hyper-personalization.

Time spent online is increasing

Our online lives have been steadily increasing over the past decade. More importantly, our time on mobile devices has quintupled since 2011, far outpacing time spent on desktops or laptops. Younger generations are particularly prone to spending more time online. It's factored into their education, leisure, and social obligations. Young people expect algorithmic technology to provide ad personalization, because it takes the guesswork out of searching for a product that they'd like. According to Salesforce, 74% of Gen Zers are interested in personalized products compared to 67% of Millennials, 61% of Gen Xers and 57% of Baby Boomers. We want our favorite companies to put relevant results at our fingertips.

Online shopping is here to stay — but it's changing

The convenience of online shopping is unbeatable, and like working from home, it seems it is here to stay. However, there has been a proliferation of drop-shipping pop-up shops and low-quality products using hyper-personalization to net unwitting consumers.

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Another challenge: Consumers are getting good (and will get even better) at filtering out online ads that are overtly promotional or blatantly targeted. One of the ways hyper-personalization can help circumvent this trend is by tailoring long-form content to answer questions your target customers are asking. This still uses hyper-personalization, but just in a more effective and long-term strategy while avoiding the pitfalls of rapidly-changing consumer habits.

Customers are busy

Everyone is so busy these days. Or, at least we feel really busy. Choice fatigue is part of the problem, and hyper-personalization can help solve it. Delivering the right offer to the right target at just the right moment prevents them from spending hours scrolling, searching, comparison shopping, and deciding. And they’ll love you for it.

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Since hyper-personalization is essentially one-to-one marketing, there are thousands of examples of it at work. It is so powerful precisely because of its versatility and ability to address a wide range of specific concerns.

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Are too many customers returning purchases? Use hyper-personalization to deliver more relevant product recommendations. Losing customers to a new competitor? Personalize an experience to increase brand loyalty. Missing out on an entire market segment because of a language barrier? Try a multilingual chatbot plug-in.

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Perhaps the best way to make hyper-personalization work for you is to dig down into your company’s primary pain points and figure out if there’s an automated, AI-driven solution that has the added benefit of approaching your customers like individuals.

AI has changed everything

Artificial intelligence has provided a unique way for hyper-personalization to become even more commonplace. One unique aspect of artificial intelligence is its ability to adapt and scale along with consumer expectations. This capability of blending AI with hyper-personalization efforts makes it a contender for adapting to future trends in marketing, whether that's augmented/virtual reality, voice-based chatbots, or something entirely new!

Make hyper-personalization work for your brand with a Relliverse™

At Rellify, we've done a lot of testing, re-testing, and fine-tuning to learn how artificial intelligence and natural language processing models can improve long-form content creation. We've found that when it comes to boosting organic SEO, the content you produce should reflect the user profile of someone who's in need of your product. This is where a customized Relliverse™ comes in. By crawling large volumes of data from websites and industries that are similar to yours (and ranking well), a custom Relliverse™ is able to tell you exactly what topics and keywords your company needs to focus on in order to blow your competition out of the water. It's a type of hyper-personalization, but instead of simply collecting user data, it's putting context to statistics surrounding search engines and the content that they rank well.

Know Your Audience. Know Your Competition. Get Results.

The increased demand for hyper-personalization reflects consumers’ interest in receiving results that are relevant to their unique interests. The groundbreaking Relliverse™ provides individual solutions to clients by using company-specific research to craft content that is relevant to potential leads — the ones who are most likely to become customers. Contact a Rellify expert today to see how it can help your business extend its reach and relevance.