With the rise of Generative AI, the future of search has been a buzzword over the past year. ChatGPT, the fastest-growing platform in history, has become the go-to place for users to ask questions and get quick answers to their hyper-specific search queries. According to a study conducted by UBS, ChatGPT has set a record for the fastest-growing user base for a consumer application, reaching 100 million users in just 2 months after its initial launch in November of 2022. As of May 2023, ChatGPT website reached 2 billion monthly visits.
Following the meteoric rise of ChatGPT, we’re beginning to see the emergence of what we call “answer engines” — large language models, AI systems or voice search technologies that provide a singular response to users’ questions without requiring them to click on a blue link. Consumers are no longer scrolling through search engine results pages to look for the right articles or websites that might answer their original search query; they can simply ask ChatGPT or Bard and get a response within a few seconds.
Despite search being the principal traffic driver for brands online (claiming around 50% of traffic for most companies), the landscape is changing. Traditional search is morphing into LLM powered answer engines. The likes of ChatGPT and Bard are now the preferred alternatives for many, including myself, offering a zero-click, personalized response rather than impersonal multiple choices. (we covered this here before).
This rapid evolution in search has left many marketing strategies lagging. Brands are yet to pivot their strategies to answer engine optimization.
Enter the era of AEO!
What is AEO (Answer Engine Optimization)?
AEO, or Answer Engine Optimization, refers to the practice of optimizing content to provide direct and relevant zero click answers to user queries. This can be on a large language model powered Chatbots like Gemini, Claude or ChatGPT, conventional search engine results pages like Google or Bing or voice search assistants like Alexa or Siri.
AEO is sometimes also known as LLMO, or Large Language Model Optimization, the process of optimizing large language models (LLMs) and influencing its learning and training data and feedback loop to ensure that your brand is present intentionally where it needs to be. It’s arguably a subfield or an evolution of SEO (search engine optimization), with a similar principle of needing to understand the user’s search intent and how best to answer it. While AEO shares a common goal with SEO to provide highly useful content that answers users’ queries as accurately and concisely as possible, AEO specifically focuses on creating content that is a direct answer to the question, is easily readable and understood by AI answer engines like Bard or ChatGPT.
We are therefore witnessing the shift from search engines (e.g. Google, Bing) to answer engines (e.g. Bard, ChatGPT) as the primary source information on the web. With the move to answer engines, brands have to leverage AEO and think about how their story is being told by these answer engines, as well as how they can optimize the LLM for the optimal brand result.
Examples of Popular Answer Engines:
- ChatGPT by Open AI
- Bard by Google
- Bing (powered by ChatGPT)
- Siri by Apple (Voice)
- Alexa by Amazon (Voice)
- Google Assistant (Voice)
- Poe by Quora
Answer engines essentially eliminate the need to click on blue links, open tabs, and filter through a wealth of information in order to find the right answer to your question. The future of search no longer lies in blue links and SERPs; to understand the future of search is to understand how answer engines are being used and how you can effectively optimize for them.
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Truly forward-thinking! Your breakdown of Answer Engine Optimization opens up a whole new approach to the future of search. Thank you for the insightful post!
well written article