Ai For Content Marketing

The AI-powered content marketer will be a crucial factor in shaping the future of successful content marketing. AI can help marketers scrutinize and sort through the mass of data available to them, enabling them to be more efficient and effective.

In this guide, we review the aspects of Ai For Content Marketing, aida model for content marketing, how to use ai for marketing, and How AI will power the future of successful content marketing?

Ai For Content Marketing

Content marketing is one of the most effective ways to build a brand and increase your website traffic, but it’s also one of the most difficult. How do you come up with relevant content that your audience wants to read? And if you can find something they want, how do you keep them coming back for more? Artificial intelligence (Ai) can help solve these problems by generating content ideas based on what readers have already enjoyed in the past. While this sounds like science fiction, it’s actually quite possible—and not just with humans either!

Ai For Content Marketing

AI for content marketing is a new technology that’s being used in many different ways. Some of these include:

  • generating content ideas based on keywords and phrases
  • analyzing images and video for emotions, moods, and other aspects of the content itself (such as how well it’s performing)

Machine Learning Influences How We Search For Information

Machine learning is a type of artificial intelligence that allows machines to learn from data, develop their own rules and make decisions.

Machine learning is used to improve search engine results, by helping us find what we are looking for more efficiently. Machine learning also helps us find the right information when we need it, as well as answers to our questions.

Three Ways to Use Ai To Generate Content Ideas

  • Use Ai To Generate Content Ideas

One way to use AI is by generating content ideas. An algorithm can be trained to scan your website and give you a list of suggested topics, which you can then choose from. This has the benefit of being highly efficient compared with going through everything manually because it’s not necessary for humans to have any expertise in order to create this kind of software.

  • Use Ai To Analyze Content Ideas

If an algorithm is already generating suggestions for you, why not take advantage of it? Once again, this would be extremely time-saving for any business but especially one that writes a lot of content such as an online magazine or blog where there are many people working together at once on different articles (with different levels of experience). The process would be simple: just upload all your existing pieces onto the platform so that it can analyze what works well based off its knowledge about what readers like most often when reading other sites’ content (for example). Then when deciding on future topics or formats for new articles I’d simply look at what was popular among readers before choosing something else instead.”

Machine Learning Is Very Effective At Analyzing Images and Video

This is where machine learning comes in. Machine learning can be used to find patterns or anomalies in images or video. You can train a computer program to recognize images of cats, and then it will be able to identify cats in new photos by itself. This ability makes machine learning very powerful for understanding the content of images and videos, which is important for many applications of AI such as computer vision (the field that deals with digital image processing) and speech recognition (the field that deals with digital audio processing).

A Third Way To Use Ai To Generate Content Ideas

There’s a third way to get ideas for content, and it uses AI. This method is the most advanced, but also the least well-known. Most people don’t even realize it exists because it’s not something that you can use on your own. It requires an AI platform (which has been designed specifically for this purpose).

This type of AI platform generates data from images, video and text as well as data from human behavior, competitor activity or other industries. The goal? To discover new insights about your business or industry that you wouldn’t normally find by reading existing research or reports about the topic.

aida model for content marketing

AIDA is one of the basic marketing concepts known to all specialists in the industry. This model consists of four stages. The aim of the first one is to attract the customers’ attention, whereas the last one is supposed to make them order a product or service. Are you a content creator? If so, benefit from the AIDA model in your work.

What is the AIDA model?

The AIDA model is a solution that can help you effectively optimize your marketing efforts. It was created by E. St. Elmo Lewis. Interestingly, it didn’t happen a year ago or a decade ago, but in 1898. There are also sources that indicate that E.K Strong was the founder of the AIDA model and according to them, he wrote about it in 1925.

Regardless of the origins of the AIDA model, the theory remains valid despite huge market changes.

The acronym AIDA was formed as a combination of the first letters of four words, namely:

The AIDA model and SEO

The AIDA model is applicable in a variety of marketing efforts – including SEO. Top-quality and optimized content that describes your business is one of the important elements of campaigns that aim at improving website visibility for specific keywords. Check out how to use the AIDA model for content creation.

How to use the AIDA model in content creation

When writing content for your website, company website, or landing page, you can benefit from the AIDA model. If you apply it in content marketing, you take the recipients on a journey during which you attract their attention, keep them interested, arouse desire, and inspire action.

When creating content, you should prepare materials that are understandable to the reader. Don’t surprise users with concepts and terms they don’t understand. Take into account the target audience. When creating texts for your page, categorize them according to the AIDA model and objectives they’re supposed to achieve.

A – attract the attention

Internet users are surrounded by a huge amount of content and attracting their attention isn’t a piece of cake. Headers are one of the elements that can help you attract new page visitors.

Internet users usually search for specific information, therefore, they analyze headers and try to find the ones that are of interest to them. Headers should concisely present what can be found in a given part of the article. They should invite users to read the entry and briefly show them what to expect.

Answer the questions: Who are the recipients of your offer? What are their problems and what information do they need? Tailor your writing style and terminology to the readers. Content for a clothing store for teenagers will require a completely different style than content for a law company.

The first paragraph of the entry should be your priority, just after the header. Don’t wait until the last sentence to attract the user’s attention – provide the important information earlier. This part of the article shouldn’t be long – convince users that they should continue reading your entry. Don’t forget to highlight the most important parts of the content, use bold or italics.

The table of contents is also important – it’ll help the reader verify whether the article will meet their expectations.

What kind of content can you use to get the attention of internet users? These can be tutorials that solve customer problems with the help of the products you offer. If you run a hardware store, provide information concerning the rules of using a given solution or applications of a given product. Alternatively – show your recipients articles that will convince them to make a purchase decision: “You have USD 40 for headphones but you don’t know which model to choose? Check out our ranking.” In this case, the right title is the key to success.

I – arouse interest

If you’ve managed to attract the customer’s attention, your goal now is to encourage them to stay on your website and continue reading your content. Here, focus mainly on your content layout – it shouldn’t be a long block of text.

The article must be transparent and divided into paragraphs. Think about bullet points, images (e.g. infographics), or videos. These elements will help you attract attention.

Each paragraph should include specific information related to the header. This means that the user should find exactly what he/she is looking for and what is suggested in the header (e.g. if your header is a question, you should clearly provide the answer in the paragraph below). It’s also worth marking the most important pieces of information in bold.

The quality of content is another important element. Users may have different opinions on the same subject. Emphasize that the article was written by an expert in the field, add an author bio with a short note about the author’s career, knowledge and experience. This applies especially to content related to law or health. Alternatively, add information that the article was prepared by the editorial team.

The next important issue is the data in the article. Show that you use verified sources – legal acts, analyses conducted by state institutions, independent studies, or reports. The reader will pay attention to this information. Thanks to it, they’ll know that they aren’t just reading a random analysis, but a text-based on a valuable source.

What type of content attracts the user’s attention? For example video materials with storytelling elements. A great example here is IKEA and their “Ask a designer” series.

D – create desire

At this stage, you should make people desire your products or services. But what does it actually mean? Your goal is to make the recipient believe that your service or product is what they need. How to use the AIDA model for content creation to achieve this objective?

First of all, don’t focus on listing key features and benefits of the product – such information doesn’t bring any benefits to the recipient. Instead, focus on specific advantages that result from these benefits. Show how a given product or service can solve your customers’ problems, or present a case study explaining how a specific product helped another customer.

You can also achieve this goal through “before” and “after” content – this way you simultaneously inspire desire and trust. This is a technique used by e.g. companies offering auto detailing services. They prepare a photo gallery and a video where they show what the vehicle looked like before their service and what were the results. Such a step can make people want to take advantage of the offer, as potential clients also want such results.

A – call to action

It’s time for the final step – now users are supposed to make a purchase or send an inquiry. You should encourage potential customers to act, and for this purpose, you can place CTAs in the text. These CTAs should be clear, not too long and transparent. Inform users what they need to do to order the product or service. Make sure that your CTAs are visible on the page (proper placement, size, color, text).

An effective CTA should clearly inform users what to expect when they click on it. The recipient needs to know where they’ll be redirected after clicking the “contact us” button.

Near the CTA, you can add information that may be relevant from the perspective of the interested buyer. You can mention the return policy or link the privacy policy, which can resolve the doubts of those who are worried about their personal data.

The AIDA model and content creation – the takeaway

Creating content based on the AIDA model is definitely a good step. If you see that your content doesn’t bring the intended results, then take action to improve the situation. Analyze your texts, keeping the AIDA model principles in mind, implement changes and monitor if they affect the conversion. Step by step, you’ll achieve the optimal version of your content.

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how to use ai for marketing

Many companies – and the marketing teams that support them – are rapidly adopting intelligent technology solutions to encourage operational efficiency while improving the customer experience. These intelligent solutions often come in the form of Artificial Intelligence (AI) marketing platforms. Through these platforms, marketers can gain a more nuanced, comprehensive understanding of their target audiences. The artificial intelligence-driven insights gathered through this optimization process can then be used to drive conversions, while also easing the workload for marketing teams.

What is AI (Artificial Intelligence) Marketing?

AI marketing uses artificial intelligence technologies to make automated decisions based on data collection, data analysis, and additional observations of audience or economic trends that may impact marketing efforts. AI is often used in digital marketing efforts where speed is essential. AI marketing tools use data and customer profiles to learn how to best communicate with customers, then serve them tailored messages at the right time without intervention from marketing team members, ensuring maximum efficiency. For many of today’s digital marketers, AI is used to augment marketing teams or to perform more tactical tasks that require less human nuance.

AI Marketing Use Cases Include:

Types of AI Marketing Solutions

It’s clear that artificial intelligence holds a vital role in helping marketers connect with consumers. The following components of AI marketing make up today’s leading solutions that are helping to bridge the gap between the vast  amounts of customer data being collected and the actionable next steps digital marketers can take in their  future campaigns:

Machine Learning

Machine learning is driven by artificial intelligence, which involves computer algorithms that can analyze information and improve digital marketing campaigns automatically through experience. Devices that leverage machine learning analyze new information in the context of relevant historical data, which can inform digital marketing campaigns based on what has or hasn’t worked in the past.

Big Data and Analytics

The emergence of digital media has brought on an influx of “big data”, which has provided opportunities for digital marketers to understand their efforts and accurately attribute value across channels. This has also led to an over-saturation of data, as many digital marketers struggle to determine which data sets are worth collecting. AI marketing can help parse through all that data at lightspeed, filtering it down to its essentials and not only analyzing it but also recommending the best elements of future digital marketing campaigns.

AI Marketing Platforms & Tools

Effective AI-powered marketing solutions provide digital marketers with a central platform for managing the huge amounts of data being collected. These AI marketing platforms have the ability to glean insightful marketing intelligence from your target audience so you can make data-driven decisions about how to best reach them. For example, frameworks such as Bayesian Learning and Forgetting can help marketers gain a clearer understanding of how receptive a customer is to a specific digital marketing effort.

Challenges for AI Marketing

Modern marketing relies on an in-depth understanding of customer needs and preferences, and the ability to act on that knowledge quickly and effectively. The ability to make real-time, data-driven decisions has brought AI marketing solutions to the forefront for marketing stakeholders. However, marketing teams must be discerning when deciding how to best integrate AI into their campaigns and operations. The development and use of AI marketing tools is still in its early stages. Therefore, there are a few challenges to be aware of when implementing AI into your digital marketing strategy.

Training Time and Data Quality

AI marketing tools do not automatically know which actions to take to achieve marketing goals. They require time and training, just as humans do, to learn organizational goals, customer preferences, historical trends, understand overall context, and establish expertise.  This learning process also requires data quality assurances. If your  AI marketing tools are not trained with high-quality data that is accurate, timely, and representative, you’ll end up with inaccurate data decisions that don’t truly reflect consumer desires, making your shiny new AI marketing tool nothing more than a toy.

Privacy

Consumers and regulating bodies alike are cracking down on how organizations use their data. Digital marketing teams need to ensure they are using consumer data ethically and in compliance with standards such as GDPR while implementing an AI marketing strategy, or risk heavy penalties and reputation damage. Unless your AI marketing tools are specifically programmed to observe specific legal guidelines, they may overstep in what is considered acceptable in terms of using consumer data for marketing personalization.

Getting Buy-In

It can be difficult for digital marketing teams to demonstrate the value of AI marketing investments to business stakeholders. While KPIs such as ROI and efficiency are easily quantifiable, showing how AI marketing has improved customer experience or brand reputation can be less obvious. With this in mind, digital marketing teams need to ensure they have the right measurement tools to attribute these qualitative gains to AI investments.

Deployment Best Practices

Because AI marketing is a newer tool in the digital marketer’s toolbox, definitive best practices haven’t yet been established to guide marketing teams’ initial deployments. Every digital marketing team should think about the long-term effects of implementing an AI marketing strategy, not just the short-term benefits they could reap.

Adapting to a Changing Marketing Landscape

With the emergence of AI marketing comes a disruption in the day-to-day marketing operations. Marketers must evaluate which jobs will be replaced and which jobs will be created. One study suggested that nearly 6 out of every 10 current marketing specialist and analyst jobs will be replaced with marketing technology.

How to Use AI in Marketing

It’s important to begin with a thorough plan when leveraging AI in marketing campaigns and operations. This will ensure marketing teams minimize costly challenges and achieve the most value from their AI investment in the least amount of time. 

Before implementing any AI marketing tools, there are a few key factors for digital marketers to consider:

Establish Goals

As with any marketing program, it is important that clear goals and marketing analytics are established for your AI marketing program from the outset. Start by identifying areas within campaigns or operations that could stand to be improved, such as segmentation. Then establish clear KPIs that will help illuminate how successful the AI-augmented marketing campaign has been – this is especially important for qualitative goals such as “improve customer experience.”

Data Privacy Standards

At the outset of your new marketing program, be sure that your AI marketing platform will not cross the line of acceptable data use in the name of data personalization. Be sure privacy standards are established and programmed into your AI marketing platforms as needed to maintain compliance and consumer trust.

Data Quantity and Sources

In order to get started with AI marketing, digital marketers typically need to have a vast amount of data at their disposal. This data will train the AI marketing tool in customer preferences, external trends, and other factors that will impact the success of AI-enabled marketing campaigns. This data can be taken from the organization’s own CRM, previous marketing campaigns, and website data. Additionally, marketers may supplement this with second and third-party data, which can include location data, weather data, and other external factors that may contribute to a purchasing decision.

Acquire Data Science Talent

Many marketing teams lack employees with the necessary data science and AI expertise, making it difficult to work with vast amounts of data and deliver insights. To get AI marketing programs off the ground, organizations should work with third-party organizations that can assist in the collection and analysis of data to train their tools for optimal performance and facilitate ongoing maintenance.

Maintain Data Quality

As machine learning and AI marketing programs consume more data, they will learn how to make accurate, effective decisions. However, if the data being fed to your AI marketing program is not standardized and error-free, the insights will not be useful and can actually cause decisions that hurt your bottom line rather than help it. Prior to implementing any AI marketing programs, marketing teams must coordinate with data management teams and other lines of business to establish processes for data cleansing and data maintenance. When doing so, consider the seven essential data dimensions:

Create an Integrated AI Marketing Strategy

Most digital marketers find their AI marketing tools are especially effective when integrated with their existing marketing strategy, rather than being used as a stand-alone tactic. AI marketing tools create opportunities to optimize steps in a marketing strategy that might currently be labor-intensive, such as data analysis, or have a risk of inaccuracy, such as attribution. 

By incorporating AI to fill in these “blind spots”, digital marketers can not only leverage these powerful forward-thinking methods to take their marketing to the next level but also build on the foundation of effective digital marketing methods they’ve used in the past.

Selecting an AI Platform

Selecting the right platform or platforms is a crucial step in getting an AI marketing program off the ground. Marketers should be discerning in identifying the gaps that the platform is trying to fill and select solutions based on capabilities. This will revolve around the goal marketers are trying to achieve – for example, speed, and productivity goals will require different functionality than tools used to improve overall customer satisfaction with AI. 

One thing to keep in mind when selecting a tool is the level of visibility you will need regarding why an AI marketing platform made a certain decision. Depending on the algorithm in use, digital marketing teams may get a clear report on why a certain decision was made and which data influenced the decision, while algorithms working on a more advanced level with deep learning may not be able to give as definitive reasoning. 

Benefits of Leveraging AI (Artificial Intelligence) in Marketing

There are a myriad of use cases for incorporating AI marketing into your digital marketing programs, and each of these use cases yields different benefits. For example, AI marketing can be an asset in areas such as risk reduction, increased speed, greater customer satisfaction, increased revenue, and more. Benefits may be quantifiable (number of sales) or not quantifiable (user satisfaction). There are a few overarching benefits that can be applied across AI marketing use cases:

Increased Campaign ROI

If leveraged correctly, marketers can use AI marketing to transform their entire marketing program by extracting the most valuable insights from their datasets and acting on them in real-time. AI platforms can make fast decisions on how to best allocate funds across media channels or analyze the most effective ad placements to more consistently engage customers, getting the most value out of campaigns.

Better Customer Relationships & Real-Time Personalization

AI marketing can help you deliver personalized messages to customers at appropriate points in the consumer lifecycle. It can also help digital marketers identify at-risk customers and target them with information that will get them to re-engage with the brand.

Enhanced Marketing Measurement

Many organizations have trouble keeping pace with all of the data digital marketing campaigns produce, making it difficult to tie success back to specific campaigns. Dashboards that leverage AI marketing allow for a more comprehensive view into what is working so that it can be replicated across channels and budgets allocated accordingly.

Make Decisions Faster

AI marketing is able to conduct tactical data analysis faster than its human counterparts and use machine learning to come to fast conclusions based on campaign and customer context. This gives team members time to focus on strategic initiatives that can then inform AI-enabled campaigns. With AI marketing, digital marketers no longer have to wait until the end of a campaign to make decisions, but can use real-time analytics to make better media choices.

7 Examples of AI (Artificial Intelligence) in Marketing

AI marketing is being used in digital marketing initiatives in a multitude of use cases, across a broad array of industries. Industries leveraging AI marketing and its optimization capabilities include financial services, government, entertainment, healthcare, retail, and more. Each AI marketing use case offers different results, from customer retention improvements to campaign performance, to enhanced customer experience, or greater efficiency in marketing operations. 

There are numerous ways businesses can take advantage of AI marketing and machine learning to create a more comprehensive marketing plan. Consider the following:

 1. Bidding on Programmatic Media Buys

A problem that marketing teams often encounter is deciding where to place advertisements and messaging. Marketing teams can create informed plans based on user preferences, but these teams are often not flexible or agile enough to alter the plan in real-time based on the latest consumer information. AI marketing is being used by digital marketers to mitigate this challenge through programmatic advertising. 

Programmatic platforms leverage machine learning to bid on ad space relevant to target audiences in real-time. The bid is informed by data such as interests, location, purchase history, buyer intent, and more. This enables digital marketing teams to leverage AI marketing to target the right channels at the correct time, for a competitive price. Programmatic buying exemplifies how machine learning can increase marketing flexibility to meet customers as their needs and interests evolve.

2. Select the Right Message

Across channels, different consumers respond to different messages – some may resonate with an emotional appeal, some humor, others logic. Machine learning and AI marketing can track which messaging consumers have responded to and create a more complete user profile. From there, marketing teams can serve more customized messages to users based on their preferences. 

For example, Netflix uses machine learning to understand the genres a certain user is interested in. It then customizes the artwork that the user sees to match up with these interests. On the Netflix Tech Blog, the author explains how Netflix  leverages AI tools and algorithms to determine which artwork will most entice a viewer to watch a certain title, saying:

“Let us consider trying to personalize the image we use to depict the movie Good Will Hunting. Here we might personalize this decision based on how much a member prefers different genres and themes. Someone who has watched many romantic movies may be interested in Good Will Hunting if we show the artwork containing Matt Damon and Minnie Driver, whereas, a member who has watched many comedies might be drawn to the movie if we use the artwork containing Robin Williams, a well-known comedian.”

When AI marketing tools and machine learning are used, these platforms can gather valuable data on customers that allow marketing teams to increase conversion rates and improve the customer’s experience. Marketing teams can then analyze all of this data to create a more nuanced view of the customer, even considering additional factors such as if a user would have watched a title regardless of the image, and how that plays into future messaging. 

3. Granular Personalization

A highly granular level of personalization is expected by today’s consumers. Marketing messages should be informed by a user’s interests, purchase history, location, past brand interactions, and a host of other data points. AI marketing helps marketing teams go beyond standard demographic data to learn about consumer preferences on a granular, individual level. This helps brands create curated experiences based on a customer’s unique tastes. 

For example, Spotify uses AI marketing tools to create customized playlists based on what a customer has listened to in the past, current hits across genres, and which music is being talked about. It uses these datasets to create customized playlists for users and to create genre playlists based on artists that appear in conversation, in articles, etc. This data has helped Spotify to become a top streaming service and emphasize customer experience through personalization.

Another trend based on AI-enabled personalization is atomic content. Here, AI learns customer preferences and pulls pieces from a library of content to create a customized email or offer for a client featuring relevant images, videos, or articles.

4. Chatbots and Conversational Experiences

With the development of natural language processing through AI, chatbots are now being used to augment customer service agents. Customers with more basic queries can refer to chatbots which will give immediate, accurate answers. They will be able to leverage past questions and historical data to deliver personalized results. This gives time back to customer service agents to work on complicated requests that need more human nuance.

5. Predictive Marketing Analytics

With so much data flowing into their organization every day, marketing teams are having a hard time actually deriving insights from it. AI marketing tools allow marketing teams to make the most of this data using predictive analytics, which leverages a combination of machine learning, algorithms, models, and datasets to predict future behavior. This can help digital marketing teams understand the types of products a consumer will be looking for and when – allowing them to position campaigns more accurately.

For example, Amazon uses predictive analytics to suggest products to consumers based on past purchases and behaviors, increasing conversions and customer satisfaction.  AI marketing tools can also be used to help marketing teams more accurately track attribution, allowing teams to see which campaigns contributed most to ROI.

6. Marketing Operations

Another key use case for AI marketing tools is to increase efficiency across various processes.  AI can help to automate tactical processes such as the sorting of marketing data, answering common customer questions, and conducting security authorizations. This allows marketing teams more time to work on strategic and analytical work. 

7. Dynamic Pricing

AI marketing can help make brands more competitive by enabling dynamic pricing. AI platforms can suggest optimal prices for products in real-time by evaluating huge quantities of historical and competitive data. This strategy has been especially effective in retail. It allows brands to adjust prices to reflect demand for certain products, boost sales, and edge out the competition.

How AI will power the future of successful content marketing

These statistics won’t surprise many marketers. AI is vital to enhance the customer experience and become data-driven.

Embracing AI allows marketers to gain better insights into their competitors and their audience, personalize the content experiences they provide and become more efficient.

In simple terms, AI helps content marketers to achieve their goals faster and easier. If your company is looking to gain an advantage, you need to look at how AI can augment your content marketing strategy and processes.

5 Ways AI Can Be Used in Content Marketing

Content Profiling

AI-powered content profiling is the process of auditing and analyzing your content to see how it impacts your audience and the role it plays in the buyer’s journey at your company.

You can gain a complete overview of your existing content and web pages to see how your visitors engage with the content and what steps they take next. Understanding your content asset performance at this detailed level would be unachievable without the use of AI.

AI can also assist in content management, helping you quickly crawl and analyze your website to identify content that needs updating, expanding, or changing to appeal to your audience and increase the chances of ranking high in search engines.

With these audit and profiling insights, marketers can better understand content performance and see which posts contribute most to progress the buyer’s journey.

You can see which types of content generate the most engagement and contribute to your strategy’s success. This allows you to improve your existing content assets and dedicate more resources to producing the types of content and topics you know will resonate with your audience.

Content Creation & Production

Content creation is one of the most time-intensive marketing activities, and it’s no surprise that many organizations outsource this task. According to the Content Marketing Institutes’s 2021 B2C Content Marketing Benchmarks, Budgets, and Trends report, 75% of B2C companies outsource content creation.

AI is making the content creation process much more efficient and streamlined. Companies can utilize the technology to create more engaging, relevant, and error-free content that builds an audience.

Tools like Grammarly can augment content writing and provide helpful suggestions during the writing process. Grammarly uses an AI algorithm that can offer suggestions to improve the text based on the content’s context and style.

Natural Language Generation (NLG) is the next step for many marketers looking to leverage AI for content creation. NLG can process data to quickly provide a written narrative without the need for human input. Many publishing organizations are already using NLG to create content on a wide variety of topics. The Washington Post uses NLG to create articles for publication, with over 850 AI written stories in 2017.

With AI-powered NLG becoming more sophisticated, these tools will soon be able to produce more in-depth, long-form content.

Content Strategy and Intelligence

Only 42% of B2C marketers have a documented content marketing strategy. This is an area where AI can make a significant impact. There is a range of AI-powered tools to help marketers create and optimize their content strategy.

For example, AI can analyze your content’s current performance, compare it to your competitors, identify content gaps, and offer recommendations based on predictive analytics.

Many companies use competitive analysis to inform their content strategy. Tools like SEMrush collect data from across your site, social media, and your competitors to help you identify topics you can target, keywords you may have overlooked, and provide insights into your competitors’ strategy.

Utilizing AI to create your content strategy allows you to collect and process vast amounts of data online and from your customers to build a data-driven strategy for engagement and traffic.

HubSpot uses AI to help you identify topics clusters to help you publish content that ranks well in search engine results and fulfils your audience’s needs. You can use the tool to track your content’s performance and optimize your strategy based on your analytics.

SEO & Content

Google has long used AI as part of its search engine algorithm, with AI becoming more influential with every algorithm update. Just as search engines use AI to understand search intent and serve relevant content to their users, you can use AI to inform your SEO strategy and optimize your content. To achieve higher rankings, businesses need to use AI and Big Data in their SEO strategy.

Understanding search intent is vital for SEO optimized content and providing content that matches your audience’s intent and needs. You can use AI to identify patterns, analyze searchers’ behavior, and make data-driven predictions to inform SEO strategy and keyword selection.

Clear scope is an AI-powered SEO tool that helps marketers create content optimized to rank well in search results. After entering a keyword, the tool will analyze the top results in the SERP to provide guidelines for achieving a high ranking. Content can then be analyzed and assigned a grade based on a comparison with the content that currently ranks in the top spots of the SERP.

Selecting topics for content is another task that can be augmented with AI. You can use predictive analytics to determine predicted audience engagement and help marketers decide if a potential content topic offers a ranking opportunity and high ROI.

With AI powering your SEO efforts, you can create content that answers your customers’ search queries more authoritatively than your competitors and achieve a higher ranking in search results.

Content Personalization & Recommendations

Personalization is key to delivering a valuable customer experience that distinguishes your brand from your competitors. When it comes to personalized content, 87% of consumers state that highly relevant content positively impacts their feelings about a brand.

But delivering personalized content experiences is a challenge for marketers. To deliver on consumer demand for personalization, marketers need to collect and analyze a huge amount of customer data.

AI is the only way for content marketers to achieve this goal. Machine learning captures relevant data from multiple touchpoints throughout the buyer’s journey to create specific audience segments.

Content can then be created for each buyer persona and audience segment to create tailored customer experiences.

One of the most exciting applications of AI is the automated customization of landing pages. Personyze is an AI-powered tool that utilizes historical user behavior, traffic source, demographic data, and other data points to present dynamic, optimized landing pages in real-time.

Utilizing detailed customer profiles based on website interactions, social media engagement, and page visits, elements of the landing page can be changed to match user preferences and increase conversion rates.

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