Before we start…

Before we go on to “Optimization” lets understand the fundamental difference in how these Answer Engines and Search Engines approach the ‘user problem’ – access to information (or knowledge) .

Answer engines – gives you answers.

Search engines – gives you search results.

There are several commonalities between them. Both are used to find something. Both often need a console (although voice searches can be considered an evolution of answer engines). A mature answer engine gives you answers, while a search engine typically gives you search results.

Where is the focus on?

Traditional search engines are focused on delivering the best search results (a compilation). Although there is an indication that the top results may be the most accurate, there could be more than one correct answer. This also means there is more work to do for the users. They have to skim through each of the available results, go into each link, read, and come to a conclusion themselves. If this involves visiting multiple resources (websites), then the hard task of synthesizing this separate information also falls on the user.

This is useful when you have a well informed, capable user. Most users aren’t that. Imagine billions of individual users repeating this exercise? that where the ‘offering gap’ existed all these years.

On the flip side, answer engines don’t have that luxury; they are expected to give you a single ‘answer,’ not a long list of possible answers. To tackle this challenge, answer engines have to work hard to understand the user’s search intent – thats where our journey begins.

With search engines, the approach was more about matching demand with supply (what was searched vs. what was indexed), leaving the next steps up to the user – the journey is incomplete especially for moderate to complex searches. This is where answer engines come in.

Before we move on further, I have to also mention there is a hidden risk here too. There is too much trust on what the Answer Engine tells you. What if the AE is wrong? (or hallucinating). With Search Engines, you had the option to validate it yourself.

With the reliance on direct answers from answer engines makes trust even more critical for these users. They expect that the answer engine will effectively filter out unreliable content.

It all depends on how good the LLMs are? (thinking brains under the hood) AND how transparent these engines are?

This is a problem (if not appropriately addressed). Many answer engines like perplexity cites every fact it mentions, and also discloses its answering rationale, the steps it took to research etc. etc. More on that soon, but in the interest of time, lets move on.

Key Characteristics of Answer Engine Results (AER)

To understand how AEO differs from SEO, lets understand AEO’s own key characteristics

  • Direct Answers: AEO focuses on providing clear and concise answers to specific questions. This is becoming increasingly important as users shift towards voice and AI-driven searches.
  • Zero-Click Searches: AEO addresses the growing trend of zero-click searches, where users receive immediate answers without needing to navigate away from the search results.
  • Structured Data: Utilizing structured data, such as schema markup, helps search engines understand content better, increasing the chances of being featured as a direct answer.

This table clearly outlines the key differences between AEO (Answer Engine Optimization) and SEO (Search Engine Optimization) across various aspects of their strategies and focus areas.

Aspect AEO SEO
Goals and Focus Aims to provide direct, concise answers to specific user queries. Focuses on optimizing content for AI-powered assistants and answer engines. Concentrates on improving overall website ranking in search results. Aims to increase organic traffic and visibility on traditional search engines.
Content Format and associated optimization needed Emphasizes creating content that can be easily read and understood by AI systems. Optimizes for featured snippets, knowledge panels, and zero-click searches. Involves a broader range of content formats (blogs, infographics, images, videos). Optimizes website content, meta tags, and backlinks for better organic rankings.
Keyword Targeting Utilizes long-tail keywords and natural language processing (NLP). Focuses on matching content with specific user questions. Targets high-volume keywords relevant to the business or industry. Uses a mix of short-tail and long-tail keywords.
User Experience Provides immediate answers without requiring users to visit a website, enhancing user experience. Aims to improve overall website experience and navigation, bringing users to the website for more comprehensive information.
Platform Focus Targets AI-powered platforms, chatbots, and voice assistants (e.g., ChatGPT, Alexa, Siri). Primarily targets traditional search engines like Google and Bing, focusing on improving visibility in standard SERPs.