Artificial Intelligence continues to revolutionise marketing, as large language models and machine learning rapidly advance in speed, capability, and complexity.
Unlike traditional AI, which relies on a set of pre-programmed rules and algorithms to make decisions and perform specific tasks, Gen AI can generate prose based on training data and even create visual content from text prompts. This new form of artificial intelligence is untapped, and its potential to change the marketing landscape leaves businesses and marketers with opportunities to gain a competitive edge.
Overview
- What is Gen AI?
- 7 use cases for Gen AI in marketing
- Key differences between search engines and generative engines
- Challenges of Gen AI in marketing
What is Gen AI?
Generative Artificial intelligence (Gen AI) is a form of technology that can perform tasks like learning, reasoning and analysis, using algorithms and machine learning that rely on training data inputs.
Generative Artificial intelligence can string together large data sets and text to perform analysis, or to generate summaries. ChatGPT is one generative AI tool created by Open AI. ChatGPT allows users to enter text prompts. It uses a large language model (LLM) to generate a response using relevant and related available information.
Other examples of generative AI tools include Jasper AI which helps create blogs and ad copy, DALL-E which creates images from text, DreamFusion which generates 3D models from text or Synthesia which helps businesses generate realistic talking avatars for video content.
7 use cases for Gen AI in marketing
Gen AI has the potential to drive efficiency, enhance personalisation, improve customer experience, and provide data-driven insights that can help drive business growth and success.
Here are seven different ways businesses can implement Gen AI for marketing.
1. Creating Audience Personas
Audience personas created with the support of Gen AI tools can help to develop work-in-progress personas that can be refined over time with further testing in the real world. Gen AI can support the creation of digital audience personas that help marketing teams understand their target audience. A well-defined audience persona allows businesses to tailor their tone of voice and align content with the right audience, leading to an increase in engagement and conversions.
Start by gathering audience data from surveys, your CRM, social listening tools and website analytics. Then, there are several different Gen AI tools that you might use to create audience personas.
Generative AI and data analysis tools can analyse customer data and identify trends. CRM systems like HubSpot and Salesforce may have built-in tools, or you can use ChatGPT in combination with data tools like Google Sheets and Python.
Always carefully review persona outputs against real customer feedback to check for, and eliminate, any hallucinations or confusion. Then check that the personas align with the business goals and marketing needs. Test persona-driven marketing campaigns, and adjust the persona accordingly.
2. Idea Creation
Creating new ideas is a constant requirement in modern marketing. Now you can use Gen AI for support in brainstorming — but you will still need to provide a thoughtful and accurate prompt.
An ideation prompt should include:
- The type of marketing ideas you need
- Your target audience
- The platform you are focusing on.
You can also include information about the tone of voice, what you want the customer to feel or experience, and the action you want them to take next.
E-E-A-T best practices give Google signals that your website has the expertise, experience, authority, and trustworthiness to rank higher in Google’s SERP results. These best practices still need to remain at the forefront of any content or idea produced with Gen AI.
Machine learning allows the large language model to understand your prompts, and to create ‘memory’. This means you can teach the Gen AI about tone of voice, or brainstorm ideas that align with E-E-A-T best practices.
3. Image and Video Production
Effective visual content is crucial for businesses to capture attention and drive engagement. While creating images and video content can be time-consuming, Generative AI can help to reduce the time and cost of producing visual content.
Gen AI can produce imagery based on user prompts. Examples of image-generating Gen AI tools include DALL-E, which can produce images from text descriptions; MidJourney, which creates high-quality images from text descriptions; and DreamFusion, which can generate 3D models from text.
Gen AI can also create on-screen captions and transcripts for in-platform YouTube or LinkedIn videos. Of course, it pays to check the captions before publishing to eliminate any mis-spellings or misunderstandings.
4. One-to-one customer connections
Personalised customer interactions can help to build loyalty and offer a one-to-one customer relationship. Going beyond simply ‘personalising’ emails with first names, AI lowers the barriers to allow consumers to participate in the co-creation of a brand story.
Take Coca-Cola’s “Create Real Magic” campaign as an example — winning images generated using a bespoke prose-to-image Gen AI tool were displayed in prominent locations like New York’s Time Square.
AI tools can also help marketers to quickly segment audiences, whether for email marketing, online advertising, or a more traditional campaign. Utilising logic, pattern recognition, and input instructions, Gen AI can segment customer data so marketing efforts are targeted and relevant.
5. Content Personalisation
Gen AI can create insights by analysing user data, past interactions and customer preferences from previous campaign data to identify what consumers are looking for.
Digital advertising is the key to creating awareness for your brand and allows you to direct traffic back to your website. Personalised content has a higher engagement rate and leads to more conversions as the consumers believe that the product or service can directly fix their pain point. Social media platforms like Meta (Facebook and Instagram) have extensive customer insights that allow you to target advertising toward specific audience behaviours and demographics.
Leveraging social media and Gen AI, businesses can learn more about their audience whilst targeting ads more effectively.
6. Customer Support
On websites, a consumer may search for basic troubleshooting, customer support or a quick answer that the FAQ’s may not provide. Gen AI can automate 24/7 customer service processes through the use of chatbots.
Chatbots use the large language model features to understand the website user’s prompts and generate a conversational response to get more information or provide a solution to the customer’s problems.
Chatbots can create natural-sounding responses and problem-solve live to fix a user’s issues, passing only more complex queries to the customer service support team. Chatbots can scan the website’s publicly available database or FAQs to find answers to the user prompts and can improve responses over time based on customer feedback.
7. Automate Manual Tasks
Gen AI can save time and human resources by accelerating tasks like data entry or analysis of campaign performance. By automating repetitive tasks, marketers can focus more time on creating data-driven high-level strategies for their next campaigns.
For example, gen AI can be used to improve email tone quickly, which means faster communication with stakeholders and more positive relationships. Marketers may use Gen AI to assist with analyses of A/B tests to gain insights quickly.
There are many ways to automate everyday tasks to improve content quality and productivity — while the first time you try a new task using AI it may seem time consuming, your processes and prompts will improve over time.
Key differences between search engines and generative engines
Traditional search engines like Google or Bing use algorithms and machine learning to understand a search query and provide a ranked list of websites that may answer the queries. Generative engines will generate a written prose response to a query that aims to contain the answer within the platform, rather than directing the enquirer out of the platform.
Generative Search Engines (GEs) are Large Language Models (LLMs) that can respond to prose prompts with human-like prose responses. Some generative engines include citations in the response to the sources it refers to.
SEO and GEO compared
Search Engine Optimisation (SEO) optimises websites to rank higher on search engine results pages (SERPs). SEO usually incorporates keyword strategies, backlink building, internal and external linking, and creating content that incorporates E-E-A-T best practices, as described by the Google search engine guides.
Generative Engine Optimisation (GEO) focuses on building content to answer user prompts. Factors such as length, position, uniqueness, and presentation of cited content contribute to visibility in Generative Engine responses. GEO is different from SEO as it summarises responses rather than providing a list of websites that may answer the query or prompt. Generative engines can provide a prose answer that draws on multiple sources, combining information from different websites, while search engines favour an answer from a single website.
GEO strategies for enhanced visibility
Clear, concise, well-organised answers to commonly asked questions are most likely to show up in generative engine responses. Citations, quotations and statistics can also help to provide authority to the content.
Increasing your visibility with GEO strategies may drive AI traffic to your website, or potentially result in referral traffic from the Generative Engine if your site is provided as a reference for an answering response. In addition, creating an effective GEO strategy can increase your trust and credibility by building content that is highly relevant to your audience’s queries.
Challenges of using Gen AI in Marketing
Hallucinations and false narratives
Gen AI has the potential to create false narratives and produce inaccurate content. This can lead to severe consequences both for businesses if they publish incorrect information and for consumers if they rely on incorrect information. The responses generated by Gen AI need to be checked for accuracy to make sure your business is not spreading misinformation.
Copyright information may be included in responses
Because Gen AI engines like Chat GPT sources its information from various sources, including copyrighted information on the internet, the owner of the information is unclear. Gen AI has the potential to use information that is copyrighted which can lead to issues for businesses unaware that use this information. Marketers need to be cautious when using information generated by Gen AI to prevent potential intellectual property infringement concerns.
Data security and privacy considerations
The final challenge of Gen AI is whether sensitive information and data entered in prompts can show up as information for other user prompts. It is unclear whether or not this information is used for other prompts, so be sure to read the privacy policy of the tool you are using and be cautious to enter only de-identified data.
Integrating Generative AI Into Your Marketing Strategy
Understanding Gen AI and how your business can use this technology can give you a competitive advantage and transform your business. At Kwasi, we don’t just follow new marketing trends, we are leaders in helping your business integrate them into your digital strategy.
Whether you’re searching for AI-powered solutions, search or generative engine optimisation, contact our team of expert digital marketers.
FAQ
What is generative AI for marketing?
Generative AI is a form of AI that can create something new, using information from a large training database. Gen AI learns the underlying patterns and generates new data that mirrors the training data set. Gen AI can create text outputs, images, music, and even computer code. Generative AI can help marketers speed up their brainstorming and content creation processes.
How is GenAI changing Marketing?
Generative AI increases efficiency for tasks like content creation, campaign optimisation, and customer segmentation so marketing teams can focus on high-value strategic initiatives rather than repetitive manual tasks.
What is generative AI examples?
Examples of Gen AI tools that can generate text, images, videos, code or music, based on training data, include:
- ChatGPT for text responses, customer support and content writing
- Jasper AI for content creation, blogs and ad copy
- DALL-E creates images from text descriptions
- MidJourney creates high-quality AI-generated images from prompts
- Synthesia generates realistic talking avatars for business videos
- AIVA composes music for films, ads and video games
- OpenAI Codex translates natural language into code
- DreamFusion generates 3D models from text.
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