Here’s how to incorporate generative AI into your keyword research, content creation and data analysis efforts.
As technology and automation evolve, B2B marketers can access tools and information faster than ever. With the rapid adoption of generative AI, that evolution is happening in real time. As B2B marketers, we must embrace and use this technology to our advantage.
This article will cover three ways to use generative AI: keyword research, content creation and data analysis. Doing so will completely change your approach to how you market products and services across the digital ecosystem, leaving competitors who are not up to speed in the dust.
Unleashing the power of generative AI in keyword research
Traditional keyword research includes many methods, but they all have one thing in common: It’s a manual process. Some paid tools, free tools and plug-ins can help marketers analyze keywords, but this takes time and effort. It can also be costly when outsourcing this work to an agency. Even so, keyword research is an integral part of marketing. It should never be skipped or overlooked.
Some of the tools marketers use for keyword research include Google Keyword Planner, Google Search Console, Semrush and Surfer SEO which integrate AI into the platform. Browser plugins like MozBar and Keyword Research have also come a long way and continue to add value to B2B marketers.
Up to 44.5% of marketers use generative AI for keyword research. Platforms like ChatGPT can help marketers be more efficient with keyword research. SEO automation speeds up the process and makes it easier to find keywords, but humans are still required to ensure that generated keywords are relevant, make sense and fit the context. While AI outputs are improving daily, smart prompt engineering is now becoming a critical skill marketers need to learn to achieve better results.
Using generative AI for keyword research has many benefits, such as improving efficiency and accuracy and finding keywords that have yet to be used before. They speed up research and give users a competitive edge by letting them respond quickly to changes in search behavior.
These models also develop more specific and valuable keywords, ensuring marketing efforts reach the right people. Generative AI models can find low-volume or long-tail keywords that make it easier to rank content.
Even though generative AI models for keyword research have plenty of potential, a few challenges should be addressed. For example, if you rely too much on AI, you might optimize content with keywords that could be taken out of context. The accidental spread of biases in the AI data could lead to keywords that could harm your brand reputation.
The biggest challenge with generative AI is that it lacks cultural context. Global multinational companies with markets everywhere could have an issue with using AI to optimize for local languages and ensure that all the content aligns culturally, considering slang and other local issues.
To overcome these challenges, finding a balance between AI-generated results and human oversight is essential.
Integrating generative AI models into content development
The significance of content in digital marketing cannot be overstated. It enables B2B and technology companies to engage with target audiences, elevate brand recognition and establish an integrated marketing program deployed across all channels.
High-quality and relevant content that delivers value results in customer trust and loyalty. Companies must always prioritize content to thrive in the highly competitive digital landscape.
Like keyword research, content creation is a labor-intensive process. Marketers frequently invest considerable effort into writing long-form content like blogs, white papers, ebooks and reports. They also write short-form content for social media, headlines and other ad copy.
It’s also common for marketers to outsource content production to agencies, freelancers or copywriting platforms like Compose.ly. This increases expenses and complicates communication. Consequently, traditional content generation methods consume substantial time and resources.
ChatGPT and similar platforms offer marketers unprecedented opportunities to enhance all content creation and production. These models can generate content that appears to be handcrafted, ensuring consistency in the brand’s voice and simplifying the creation of diverse, engaging and contextually relevant content.
However, marketers must always balance AI with an added layer of human supervision when employing generative AI in content development. While these models can expedite content production, human context remains necessary to ensure coherence, accuracy and cultural relevance. By incorporating feedback loops and refining procedures, marketers can achieve an equilibrium between AI-generated content and human expertise, ultimately enhancing content quality and efficacy.
The advantages of generative AI for content production include accelerated processes, increased precision and the capacity to generate substantial volumes of content. These models can rapidly create high-quality material, allowing marketers to respond to market fluctuations and seize real-time engagement opportunities.
Additionally, generative AI can generate accurate and relevant content tailored to specific audiences, ensuring the success of digital marketing campaigns. Producing high volumes of content allows marketers to think more strategically instead of writing a blog post.
Despite the transformative potential of generative AI, specific challenges exist. For instance, current AI technology can not fully grasp the cultural or business context, which could result in superficial or nonsensical content.
Ownership and copyright concerns may emerge as AI-generated content obscures the distinction between human and machine authorship. Transparency is vital in AI-generated content to preserve audience trust and mitigate misinformation.
Businesses must proceed cautiously when incorporating generative AI in content creation, ensuring that human oversight and transparency remain indispensable components.
Using generative AI in data analysis
Generative AI models bring in a new era of advanced data visualization. These methods enable real-time data tracking and dashboard creation, complex network visualization and various data display options. As a result, organizations may obtain the most up-to-date information, make informed decisions and quickly adjust to market shifts by leveraging real-time monitoring.
Detailed network visualization reveals the complicated connections between data points, providing crucial insights into the interactions between different data points. This multidimensional data representation allows businesses to understand each component of their marketing campaign performance.
AI models can likewise help marketers extract actionable insights from data. With the right prompts, AI outputs can find anomalies and outliers, assess feelings and emotions, segment markets and develop buyer personas.
Anomaly detection identifies unusual variances that may indicate possible problems or possibilities. This is extremely helpful when managing large paid media campaigns across paid search and display ads.
When analyzing large conversational data sets, AI outputs can find the emotional impact of the content through sentiment analysis and emotion recognition. Market segmentation and consumer profiling help organizations focus their marketing efforts by allowing them to modify their strategy accordingly.
Generative AI models can also improve predictive analytics. For example, time series forecasting uses historical data to predict future trends and events. Machine learning algorithms are critical in generating data-driven predictive models. Generative AI models lead to more accurate forecasts by developing these methodologies, which can help predict campaign performance.
Text analytics has also advanced significantly. Topic modeling and document clustering, network analysis, named entity recognition and relationship extraction, text summarization and content production are all tasks that use these models.
Topic modeling identifies fundamental topics in large data sets like social media mentions, call center transcripts or media coverage. It can help find patterns of hidden context and narratives.
Network analysis reveals the connections between diverse communities, named entity identification and relationship extraction, on the other hand, reveal connections between separate entities. These text analyses can help marketers identify higher-authority influencers and content creators.
Generative AI is also making social media analysis more efficient. Social network analysis and community detection reveal the links between people in online communities, revealing user behavior and interests.
Trend analysis and hashtag monitoring measure the popularity of specific subjects and discussions, allowing marketers to keep up with industry developments and trending topics. Influencer identification and interaction make finding notable industry individuals and future collaboration opportunities easier.
Making the most of generative AI in your B2B marketing efforts
As the digital marketing landscape changes, B2B marketers must use cutting-edge technologies to stay ahead of the curve. The good news is several generative AI statistics show marketers are starting to adopt this new technology, and for a good reason.
Generative AI can potentially change keyword research, content creation and data analysis in ways that have never been seen before. This will usher in a new era of data-driven and integrated marketing strategies. Even though there are still challenges and limits, generative AI models can lead to incredible results when used wisely and with human expertise and oversight.
The post 3 ways B2B marketers can use generative AI appeared first on MarTech.
MarTechAbout the author
Michael Brito has been building brands online since Al Gore invented the Internet. He’s currently the Head of Global Analytics for Zeno Group. He’s active on LinkedIn and Twitter and frequently shares his insights on his digital marketing blog.
(5)
