The future of marketing, in some sense, is going back to the basics with futuristic AI models. Back in the day, a brief would be the first starting point of any campaign creation, be it for media or creative. Today, it doesn’t matter if you have an even more concise brief because you can type what you want into a generative AI-powered tool and get creatives in seconds. Bringing marketing assets to life has become as easy as thinking of an idea and typing it in your natural language.

As a marketer, you can use Dall-E to create interesting images to portray marketing themes and ideas. For instance, I asked Dall-E to “Create an oil painting with the flag of the United States and the people of its country.” Look what it returned:

I asked Jasper.ai to create a headline for this guest post by simply typing a command: “Give me a blog headline for reaching out to the readers of MarTech Series talking about the future of marketing with generative AI.” Look what it returned:

Is it a powerful headline? Maybe, but that’s subjective. But isn’t it something good to work with as a base and put on your editorial hat to create the best ideas using human + AI intelligence?

Marketing Technology News: CDPs Still Critical For Marketers Despite ‘Data Clean Room Trend’

The Practical Use Cases of Leveraging Generative AI in Marketing:

1. High-Volume Text Generation:

The practical applications of generative AI for text generation are the most proven and in-market. This includes two primary categories of natural language generation (NLG):

1. Short-form — Versions of email subject lines, body copy, website content, or other textual material that can be deployed and tested in support of customization and personalization efforts.
2. Long-form — Text generators like GPT-3 that can be used to create marketing copy, news stories, poetry, and resumes.

Generative AI can augment, accelerate, and create new content and experiences. The ability to create original content, synthetic data, models of physical objects, and code to improve response time to customer engagement is providing breakthrough innovation opportunities for marketing.

Rewards:

Increased engagement due to more relevant content. Copywriters and marketing teams can focus their efforts on “big-picture” creative and new campaigns. Generative AI can develop more versions of content to support A/B testing and scale personalization efforts. Long-form text generation can drive more accurate job descriptions and support the hiring of marketers with unique and desired skill sets.

Potential Risks:

Copy generation can lack variation due to shared AI learning models. Creative teams must continue to evaluate copy, even with brand guidelines in place, to ensure that the machine remains on brand as it learns how customers engage. While the implementation of NLG suggests a future of automated message variations tuned to accelerate a customer’s journey, providers with Open LLM models have so far only proven to market for slower, batch-style campaigns and content development for owned channels.

2. Automation of Customer Engagement with Advanced Virtual Assistants:

The conversational AI market is the biggest benefactor of generative AI, as the capability advancements in AI are enabling smarter, more scalable, and specialized solutions. Generative AI is enabling broader adoption of virtual assistants and chatbot automation capabilities, from low-skill tasks to more complex, knowledge-based functions.

In the next few years, advanced chatbots will differentiate from the current chatbots via the inclusion of generative AI and industry domain-specific capabilities to create tailored human-like conversations across use cases of lead generation, commerce, upselling, intelligent recommendations, and more.

1. Conversational Marketing – Natural language processing (NLP), intent recognition, neural real-time machine translation, and synthetic voices will create opportunities for marketers to engage with consumers, backed with customer data persona insights proactively.
2. Conversational Commerce – Generative AI can be used to personalize the shopping experience for individual customers. By analyzing customer data, chatbots, and virtual assistants can make personalized product recommendations and offer promotions that are tailored to each customer’s preferences and purchase history.
3. Conversational Support: Happier customers are stickier customers, and the roles of marketing and customer service are merging to become the custodians of customer experience. Chatbots and virtual assistants can handle a large volume of customer inquiries and provide 24/7 support in the language of the customer’s choice, by understanding natural language and generating human-like responses.

Rewards:

With quicker and more accurate responses to customer queries, advanced virtual assistants can drive better customer satisfaction. For customers, the experience is more personalized thanks to these models leveraging machine learning, thus ensuring better engagement. By freeing up the customer service team for complex issues, they bring efficiency to operations and lead to cost savings. Moreover, businesses can continue to provide high-quality support, as advanced virtual assistants can easily scale to meet increased demands for customer services.

Potential Risks:

In generative AI, “hallucination” refers to the phenomenon where an artificial intelligence system produces outputs that are not based on the input data or task it was trained on. Instead, these outputs seem to be generated from the AI’s own imagination or biases. For example, a generative AI model that has been trained on images of animals might start generating images of creatures that do not exist in the real world, such as a unicorn or a dragon. These images are considered hallucinations because they are not based on any real animal that the model has seen in its training data.

Similarly, a language model might generate sentences or paragraphs that are not logically coherent or relevant to the input prompt or task it was given. These outputs can also be considered hallucinations because they do not reflect the input data or the task at hand.

Hallucinations in generative AI can be both fascinating and problematic. On the one hand, they demonstrate the potential of AI systems to be creative and generate novel ideas. On the other hand, they can lead to errors and bias if the system produces outputs that are not relevant or accurate. Therefore, it is important to leverage fine-tuned Large Language Models for business use cases.

To sum it up, generative AI can significantly enhance how marketers engage with their clients and produce valuable content. Its creative powers, when used in conjunction with data analysis techniques, can help marketers create highly targeted content and automation workflows that cater directly to their target audience.

Marketing Technology News: MarTech Interview with Lisa Arthur, Chief Marketing Officer at Sensedia