AI in marketing

AI marketing refers to the use of artificial intelligence and machine learning technologies to enhance various aspects of marketing activities. There are multiple opportunities to streamline, automate, optimize, and increase efficiencies within any marketing department and examples of AI platforms that can help with each one of them.

Which technologies a team chooses to incorporate into their processes will be as unique as each brand and the challenges and opportunities they face.

One of the splashiest uses of AI in marketing comes in the form of generative AI tools, like ChatGPT-4. Generative AI broadly refers to any AI technology that ‘generates’ a unique product, and text-based platforms like ChatGPT-3 and ChatGPT-4 are popular because of their increasing ability to create contextually relevant answers to specific questions.

Marketing teams are increasingly integrating the use of generative AI tools in their content cycles. From content marketing to email marketing and personalization, content is the force behind many marketing efforts. But content creation is often a stumbling block for many brands precisely because it takes so much time and effort from start to finish. The use of a generative AI tool can cut that time sharply by giving content creators a head start.

It’s important to note that there are ethical concerns with this approach. Brands should carefully consider where generative AI fits into their strategy before getting started. Knowing how the algorithm is trained and on what materials is critical. As is familiarizing teams with the limitations of AI content, such as the importance of carefully fact-checking said content.

It’s also important to keep track of how heavily AI was used for content generation in a final draft and to disclose to the audience that AI was part of the content creation process.

Social media management tools are increasingly making investments in AI technologies and becoming vital in helping brands understand the voice of their customers and increase value with their target audiences.

Natural language processing (NLP) is especially useful in this regard because it is focused on teaching machines to both better understand and accurately interpret language. This functionality is necessary for analyzing social media posts on platforms, such as LinkedIn and customer review sites.

With AI tools that can identify keywords and triggers in audience conversations on social media platforms, brands gain the ability to determine what topics customers are most interested in, better target their content creation efforts, and increase the opportunity for brand engagement.

AI and machine learning have brought critical advancement to the field of online search. In addition to looking for contextual relevance between the query and the content, AI-powered search learns from search intention and leverages predictive functionality to deliver suggestions in real time. This provides an excellent opportunity for reaching customers on a brand website.

Most AI search tools available today use APIs, allowing development teams to easily embed content search onto websites. Marketing teams can then leverage the predictive technologies that users have come to expect from modern websites and gain the ability to curate a search experience powered by their internal content marketing taxonomies.

Brands who take advantage of AI-powered onsite search can also access other features to improve the customer experience and enrich the customer journey, including Q&A, recommendation widgets, and real-time personalization.

Search engine optimization (SEO) is unquestionably a critical part of the search discussion; AI tools can help content creators optimize their content for SEO algorithms, which in turn makes the content easier for both visitors and search engines to find.

This is one of the key areas where artificial intelligence and machine learning dovetail seamlessly with the goals of many marketing teams. The need for data-driven decision-making has grown considerably in the last several years, and the path to becoming a data-driven organization has been steeper and rockier than many organizations may have expected. Having the data is one thing – analyzing it and making it actionable is another.

Data has always been key to analytics analysis in marketing. For AI technologies to be effective, they require large amounts of data. So it’s no wonder that they are useful for extracting key insights, metrics, and trends for brands. When AI is combined with business intelligence, brands can gain a more comprehensive view of their ecosystems, trends in the market, and customer behavior.

AI can also automate processes, freeing teams to dive into the results of their initiatives, make strategic decisions more quickly, and build longer-lasting relationships with their customers by providing value in exchange for customer data.

A key piece of ecommerce marketing strategy, predictive analytics has been popularized and leveraged to great effect by companies like Amazon and Netflix. Predictive analytics uses historical and real-time data to make predictions about future customer engagements; they can be used to serve personalized marketing messages and specifically shape customer journeys to build trust and relationships with customers.

This is very useful when considering how best to meet customer needs, as predictive analytics offer opportunities to suggest products that customers might enjoy or curate content that audiences might find interesting based on previous content they’ve consumed. These kind of helpful experiences contribute to customer retention.

Conversational AI is perhaps the most ubiquitous and useful tool in the modern marketing AI toolkit, largely because of its utility. A well-deployed and high-quality chatbot can build customer loyalty by ensuring that customers have access to the brand 24 hours a day and can quickly reach needed information and teams.

Powered by natural language processing (NLP), which focuses on enabling machines to not only process human language but understand the nuance it contains, chatbots and bots alike can boost marketing messages for specific segments and provide quick and easy assistance for the target audience.

From content creation and search engines to marketing automation and A/B testing, each individual technology represents opportunities to increase engagement and customer satisfaction as AI marketing tools continue to evolve and grow.

For many marketing teams, a phased approach toward AI adoption will be most beneficial, such as automating tasks and using individual products while moving toward integrating AI into their existing systems. Many teams are already using machine learning systems and constantly learning about and evolving their marketing strategies.

The next step is to use artificial intelligence and machine learning to build a better experience for both visitors and employees, improve conversion rates and workflows, and create opportunities for innovation within digital marketing teams.

To learn more about how you can use generative AI to tailor the web experience using personalization, visit here.