Head of CRM & Marketing Automation at Mountainise
Certified CRM Consultant with 10+ years of experience in Salesforce, HubSpot, and Marketing Cloud implementations.
Artificial intelligence (AI) has become a transformative force in many industries, including marketing. B2B marketing in particular, the use of generative AI, a subcategory of AI encompassing writing content, ideas, or solutions, is rapidly becoming a necessity. Generative AI is one of the most promising tools. However it is crucial to define its capabilities and limitations in AI for B2B content creation. In this blog, I’ll explain what is possible with generative AI, as well as what is not possible, perhaps guiding companies towards how they might choose to approach AI for their marketing needs.
Probably the most significant use-case of generative AI in B2B marketing is content generation. Tools like GPT-4 from OpenAI or other companies can automate blog posts, whitepapers, emails and even social media posts. It makes B2B marketing highly efficient because it enables this sector to scale up the content production process dramatically. If we talk about blog writing, AI users can have posts written for them in the form of initial drafts given keywords to blog about and specific subject. For example, a marketer could request from AI to create a blog on The Future of AI in B2B Sales and get a rich and well-developed piece that probably only needs slight edits.
In the realm of Social Media, AI can be used to post specials, promotions or general information on social media, time-table the posts and the likely time when it will reach the audiences. In lead nurturing terms, Generative AI marketing tools can write new email content with reference to the recipients and the previous interaction history. In spite of being able to generate lots of content in a short span, generative AI is laid low by the fact that it does not possess the profundity of an experienced professional. AI created content may still need to be checked and reviewed by a human for quality, relevance as well as tone.
Personalization has become an essential concept in B2B marketing, because business people need more close contacts with potential buyers. AI improves personalization to the extent that with generative AI it is possible to generate content for a precise persona or even certain leads. AI can adapt subject lines, body copy and CTAs for a user on the basis of the user’s activity, preferences and past engagement. For instance, a company that specializes in providing its services to organizations can leverage AI solutions for sending emails with content based on the enterprises’ field of activity.
Furthermore, AI-powered lead scoring can bring about fully personalized pages that are different based on the company size of the visitor, or the industry of the visitor, or the level of familiarization of the visitor with the landing page. Even though the above features can be assisted by AI, they key relies heavily on data. For instance, if the data used to personalize the pages is inadequate, flawed or irrelevant, the outcomes are unlikely to be effective, hence a poor user experience. Moreover, personalization may lead to the consumer feeling that their privacy is being violated too much, so the balance has to be applied as well.
While in Personalized B2B campaigns marketing lead generation the goal, not all leads are the same. One of the use-cases of generative AI is lead scoring and qualification due its ability to sort through data from various databases, CRM, website visits, and email interactions. AI models can rank leads depending on the probability to convert, provided by their job title, company size, industry and behavior on the company’s website.
Furthermore, AI can be used to converse with website visitors, ask some questions to filter the leads and get them to the right sales people. These AI lead scoring models can only be as accurate as the data assets used to develop the models. If the data feeds AI with a twisted history or missing pieces, then AI may mis-score leads. Furthermore, though AI chatbots are helpful but less effective in conversations that demand a more personal approach.
B2B marketing still largely depends on search engine optimization (SEO). By using generative AI, different areas of SEO such as content optimisation, keyword research, as well as even technical SEO solutions can be addressed. Search engine trends in any particular business can be analyzed by these AI tools and they can suggest better and more valuable keywords to be incorporated into content. There is the ability of generative AI to optimize further the existing content by such as filling voids, mapping long tail keywords, and making sure the content is friendly to search intent.
Despite the AI’s ability to generate SEO recommendations on its own, it still requires the input of a person. There are also a lot of search engines out there, with varying degrees of sophistication, and while this might be the first generation of AI tools destined for the mass market, some of them could fall behind the eight ball when it comes to providing results that are tuned to the latest Google algorithm update, for example. Complete dependence on AI tools in the formation and optimization of SEO strategies leads to the creation of web content poor in quality.
The capability of the use of AI is the function of processing a huge amount of data in real-time and hence applying efficiency to B2B marketing Services. Within A/B Testing, Predictive Analytics and many other AI tools, generative AI can give the marketer actionable solutions to enhancing marketing. It can also very efficiently evaluate the efficiency of various types of marketing assets and what modifications are probable. For instance, it might suggest which variation of a landing page or an email subject line would be preferred by the target audience. AI can then estimate the probability of leads being converted or customers to churn and update the campaigns in process early enough.
Even though AI is capable of operating on unstructured data at a speed that is much faster than its human counterpart, it lacks context (Wagner, 2016). It remains relevant to interpreting the insights generated by AI and applying it to broader business objectives.
ABM is a special approach in the marketing of B2B organizations that zeroes on high-value accounts. ABM programs can benefit greatly from generative AI, which can therefore be applied to automate creation of personalized content and improve patterns of engagement. AI can develop extremely individualized messages for influential decision-makers within desired accounts, or target marketing prospect audiences that can be entered by marketing and sales organizations. With the help of collected data, AI can find out the most suitable prospects to approach according to firmographics and intent data.
What sets ABM apart is that it is a very specific process that must consider the pains and wants of the target account. Although AI can help in getting different ABM tasks done in automation, it cannot provide the required emotional intelligence and interpersonal interaction skills which is very important in account based marketing.
Generative AI in B2B Marketing is excellent for creating insights and great content material but misses out on the creativity and instinct that makes marketing ‘amazing’. This form of marketing does involve the establishment of thought leadership content as well as an understanding of complex business issues and relationship building, all of which are not strong suits of AI. While value can be created through idea generation, AI cannot unearth campaigns that need non-conventional thinking for success in marketing.
Human marketers are necessary to bring emotion, sarcasm and adapt marketing to the culture. AI tools can learn to write in a certain writing tone that is associated with a particular brand, but it cannot own the identity. Consistency in the brand messaging and its tone need to be supervised by humans, not machines.
In B2B marketing, relationship and trust are of the essence, as well as customer loyalty throughout the business partnerships’ lifetime. Generative AI in B2B Marketing can help with communication but it cannot establish trusting relationships with clients or partners. AI can ideally automate emails and alert the user with the right content of the article, but do not build the level of trust needed to seal the big deals.
It is for this reason that the human element is crucial in fostering relationships and especially to facilitate negotiation processes. What AI cannot do is empathize, which is required a lot of the time when dealing with customers or clients. B2B buyers remain curious about talking to a real person, particularly when they are making a big ticket item, a capital expenditure or when they will be committing a long-term business with a supplier.
AI can assist when it comes to recommendation and has almost no capability when it comes to decision making that involves judgment, risk taking abilities, and moral obligations. Though, AI can provide recommended tactics based on certain data but it cannot decide strategies based on organizational goals and objectives, or on factors that are outside the data domain; such as the market condition, organizational culture, and others.
But in matters which concern; a very bad public image or a time that a company comes across a very crucial decision on the future of its business then here AI is very poor in empathetic ability coupled with strategic planning.
Usually, AI requires well-ordered data while a lot of the information in B2B marketing lacks a clear structure, for example, consumer opinions, feedback on social networks, trends in the market. While the AI tools can work on unstructured data to some extent the big picture and the shades of gray are lost on the machine. While beliefs can be learned from social media posts using AI or even form feedback, it can only understand negative polarity of comments or jokes, not the actual fun behind it. Fundamentally, generative AI utilizes data to execute its tasks regardless of the quality or the quantity of data collected, incomplete and erroneous data at the input end lead to wrong output.
Legal and ethical issues are on the list of critical factors that should be done and addressed while conducting any business across the globe. However, the research also reveals new legal and ethical concerns that are surrounding the application of Generative AI in B2B marketing. It presents challenges connected with Intellectual Property Rights, Data protection and transparency on the origin of the AI content. Generative AI relies on large datasets to churn out new content, as in who actually owns the rights to that material when much of it is recycled from copyrighted material?
Since most AI models deal with personal data, they have to follow data privacy laws like GDPR. Failing to use safeguards properly while leveraging AI for customer data analysis may result in legal penalty. Deep learning models are not immune to this problem and may even amplify the existing prejudice inherent in the data set. In B2B marketing, this could lead to practices that are considered unethical or unfair to other groups or industries.
Generative AI in B2B Marketing is a versatile technology that can be used to improve multiple areas of B2B marketing that includes content generation and delivery, personalization, lead scoring and analysis. However, its drawbacks, including relationship management, unstructured data, or decision-making, make it clear that artificial intelligence is more appropriate for being an additional tool for marketers rather than a sole contractor.
B2B organizations that get AI right will do so with the best qualities of AI and the best qualities of human talent. Knowledge of what current generative AI is capable of, as well as its limitations, is vital so that marketers can incorporate it into strategies and improve outcomes while maintaining empathy, ethics, and customer-centrism. In other words, generative AI is a great friend in the growing context of B2B marketing, but the human element is always needed.
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