Natural language generation (NLG), a subset of artificial intelligence(AI), is an important tool for businesses to enhance their digital marketing efforts. NLG technology enables computers to automatically generate human-like natural language from structured data. Using NLG, marketers can quickly and efficiently generate content for their digital marketing campaigns without having to manually write the content.
According to recent reports, the market for "AI in Marketing" is anticipated to reach US$ 48.8 billion by 2030, exhibiting a CAGR of 28.6% throughout the forecast period (2021-2030). This growth is driven by a thorough analysis of the market by application (ad optimization, content curation), technology, vertical (BFSI, government), and deployment (on-cloud).
We'll provide an in-depth explanation of how NLG is helping digital marketers to convert their traditional methods into more advanced ones.
Let us first understand the use cases of NLG in the digital marketing ecosystem and its impact.
NLG in digital marketing is the process of creating written content from structured data. It uses algorithms and AI programs to convert structured data into written or spoken content.
Digital marketers use it to generate automated content such as blog posts, product descriptions, reports, and email campaigns. In short, NLG provides digital marketers with various benefits, along with the ability to quickly and cost-effectively generate personalized content for a wide range of customers.
Businesses are increasingly relying on technology to help them better understand their customers and create more personalized and engaging experiences. NLG quickly and accurately generates large volumes of content based on data. This helps marketers who need to keep up with the ever-changing landscape of digital marketing. NLG can also help marketers better understand their customer's preferences and interests.
NLG helps businesses to better understand their customers by analyzing customer data and generating insights from it. Thus, they can target their marketing efforts and tailor their content to better meet the needs of their target audience.
Digital marketers also use NLG for the following:
Sentiment analysis helps marketers understand how customers perceive their brand and products and help them develop strategies to improve customer experience and increase sales.
For instance, by analyzing customer feedback, marketers can identify customer pain points and areas of improvement, as well as develop better campaigns and strategies to target customers.
In digital marketing, sentiment analysis can be used for the following:
NLG is used for ad targeting by creating personalized ads based on customer data. It is also used to analyze customer data, such as demographics, location, purchase history, and online behavior, and then generate relevant, personalized ads that target specific customers.
Digital marketers use NLG for the following:
Topic extraction is the process of identifying topics and keywords from a large set of digital content (such as web pages, blogs, articles, and other media). Topic extraction techniques can be used to identify the main topics of a website, blog post, or other digital content, as well as to find related keywords and topics.
NLG is used for topic extraction in digital marketing by:
SEO ranking is an important factor in digital marketing because it determines how easily people can find a business online. It is based on various factors, including the quality and relevance of the content, the structure of the website, and the overall online presence.
Using NLG, digital marketers can improve their SEO rankings by:
Automated marketing is popular as businesses look for ways to increase efficiency and reach their target audience most cost-effectively. It helps automate labor-intensive tasks such as creating campaigns, tracking customer behavior, and analyzing data. By eliminating the need for manual labor, automated marketing allows marketers to focus on more important things, such as creating content and nurturing relationships with customers.
For automated marketing, NLG is used for the following:
Content generation and curation create an engaging experience for one's audience and build brand awareness and loyalty. It enables digital marketers to create content that resonates with their target audience while also providing value.
This can help drive conversions and leads and build relationships with customers. NLG-based content creation and curation help digital marketers save time and resources and create more effective campaigns.
NLG creates smart content generation and curation by:
Predictive analytics is a powerful tool that enables businesses to gain valuable insights into customer behavior and anticipate future trends. It can identify potential customer segments, predict customer preferences, and help to plan effective marketing strategies.
In simple words, by leveraging NLG for predictive analytics, marketers can better understand customer needs and behavior and ultimately increase ROI.
NLG can help marketers harness predictive analytics by:
Virtual assistants like chatbots help marketers with various tasks, including scheduling content, responding to customer inquiries, providing customer service, and more. Digital businesses save time and money while still providing quality customer service.
NLG can be used to deploy virtual assistants that assist in a variety of ways, such as:
Personalization allows businesses to form meaningful connections with their customers and prospects. Due to advancements in machine learning algorithms, personalization has become more affordable in recent years.
Personalized messages are more likely to be seen, heard, and acted upon, compared to generic or standard messages. Additionally, personalization can help brands stand out in the increasingly competitive digital marketing landscape.
NLG can be used for personalization in the following ways:
By using NLG, businesses can reduce the time spent on creating and updating their digital marketing content, allowing them to focus their efforts on other aspects of their campaigns.
By using NLG, businesses can make data-driven decisions about their campaigns, identify areas of improvement, and make informed decisions about how to best optimize their campaigns for maximum results.
Since NLG is still developing, some challenges exist in the digital marketing space. These include the following:
For NLG to work properly, it must be trained on a large quantity and quality of data. This data must be accurate and up-to-date to generate content that is both relevant and accurate.
Although NLG can generate content on its own, it still needs to be overseen by a human to ensure that the content is accurate and up-to-date. This means that marketers must be willing to invest in hiring and training staff who can monitor and refine the generated content.
Creating quality content is essential for digital marketing success, but it can be difficult to manage the quality of content when using NLG due to the lack of specific data needed. Digital marketers must be able to monitor the quality of content generated by NLG and be quick to make changes when necessary.
Personalizing content is key in digital marketing, but NLG often relies on generic templates and language, which can make it difficult to create personalized messages for each customer.
NLG is revolutionizing the way marketers create content. Marketers are saving time and money while creating more engaging and personalized content. It is quickly becoming an essential tool in digital marketing and one that all marketers should take advantage of.
NLG software has a broad range of applications across industries. Its strength lies in its ability to adapt to specific brand voices, enabling digital marketers to build relationships with specific audiences.
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