Generative AI: A Technological Pandora’s Box for Digital Marketers 

With the introduction of Generative AI into mainstream tech, the world is witnessing the resurgence of AI. The harbinger of this revolution is none other than Open AI’s ChatGPT which has driven tech monoliths and competing startups into a frenzy, igniting a rat race to see who can survive the AI Renaissance. Generative AI startups have also started attracting investment as OpenAI gains traction. 

Currently at the forefront of the Generative AI ultramarathon are Open AI’s ChatGPT and image generators like Midjourney. The former, in particular, has racked up more than 100 million users in two months of its debut, garnering over 13 million daily visitors as of 2023.* And social media is being flooded with Midjourneys’ digital creations, demonstrating that generative AI has become the focal point of all digital endeavors. 

ChatGPT in particular has fascinated users globally with its ability to use deep learning and user prompts to generate a seemingly endless amount of content. It does so by drawing on patterns learned while scaling troves of existing human-generated content. 

Small steps for AI, large steps for Marketing?

With generative AI, digital marketers can create content quickly and easily, freeing up time to ideate creative concepts, with capabilities ranging from content creation, automating presentations, video and content editing, and even AIs to optimize website conversion rates.  

The caveat here is that the language models are likely to give the most common answers in relation to user prompts. And with the scores of users accessing ChatGPT, the tone, and content that it generates and that people in turn float on digital streams can seem generic.  

Now while it may appear that the predominant application of Generative AI in Digital Marketing lies in creating posts, articles, blogs, whitepapers, etc., there are some distinctive domains that could benefit by leveraging Generative AI. Let’s look at some of these use cases. 

1. AI-enabled Brand Sentiment Analysis

Deep learning models can use synthetic data to analyze and recognize real-world text data. By using sentiment analysis to generate synthetic text data that categorize sentiments as positive, negative, or neutral, language models can be taught to understand and analyze real-world situations. 

With deep training, AI can then be trained to express or design content meant to convey a specific sentiment that aligns with a brand’s marketing message. Ideally, it would also be able to manage and dish out quick responses to critical or negative brand sentiment on social platforms. Such models can help Digital Marketers realign their marketing strategy in terms of customer responses and offer effective ways to address any problematic issues. 

Take Talkwalker, an AI-powered sentiment technology that allows you to watch platforms where your brand images surface. You can use it to monitor your brand’s impact and performance for a specific demographic, keeping a check on customers’ emotions, tones, and attitudes. Other examples of sentiment analysis AI include Brandwatch and Awario. 

2. Conversational AI for customer service

A 24/7 customer service that handles all kinds of traffic loads, nurtures interest in potential customers, and personalizes customer experience without compromising on quality is a value add to a brand website.  

By using a chatbot software that offers API integration with AI plugins, websites can utilize the power of conversational AI to supplement customer service with a modified chatbot that offers quick responses around the clock along with multilingual support. It would also recommend resources that capitalize on the buyer’s interest areas, helping build a more holistic brand service model.  

Speaking of chatbots, users have been asking for ChatGPT plugins since its launch and OpenAI has complied with those requests and is opening up support for the same. 

3. Generative AI-driven SEO?

Search engines are the intermediaries that most website URLs have to defer to in order to gain recognition. The primary factor that drives said relevance is SEO. 

Using deep learning, Generative AI can help businesses analyze and discern high-performing keywords and phrases for SEO, generate SEO-friendly titles, and outline content structure to boost ranking. It can help fabricate content that is appropriately populated with SEO-centric keywords with an added advantage of content optimization for product and meta descriptions. 

Marketers can analyze search queries and with the help of Natural Language Processing methods, categorize queries based on intent, allowing them to create optimized content as per consumer expectations and requirements. 

Generative AI has applications across industries verticals from audio-visual to code-based to data-centric implementation. As the technology continues to evolve, it is likely that we will see even more innovative and exciting use cases emerge. 

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