Are You Ready for Answer Engines?

Content is transforming the search landscape, and search engines are evolving into answer engines. But, what is an answer engine? How is answer engine optimization different from search engine optimization? Let’s find out.

Search has evolved from mere information discovery to prioritizing relevant and useful results to users, i.e., context. But now, search means more and includes predicting the intent and serving relevant results. The search approach is more about understanding intent-driven queries and providing intent-driven answers.

But where does this information come from? And how is it processed? Let’s find out.

Learn More: How to Beat BERT: Combating Google’s Latest BERT Update

What are Answer Engines?

Answer engines are search engines that focus on answering user questions. Answer engines understand the intent of a query and analyze content on websites to provide intent-driven answers. They are powered by Artificial Intelligence (AI) driven neural matching to determine synonyms at a sophisticated level. Additionally, the situational context helps determine the query intent and provides a direct answer.

Image Source: Google Search Results Page

For instance, if you search for ‘weather‘, the result will be the weather status in your region. But, if you query ‘search engines‘, the search results page will return a list of links.

How are Answer Engines Different from Search Engines?

The rise of voice search is transforming search engines into answer engines. Typically a voice query is conversational or in a question-answer format. Therefore, search engines need to give specific answers.

The traditional search focused on what is accessible, how relevant is it, and the authority of the content (for example, how many sites backlink to a page on a website) to serve useful results.

But now, search engines have evolved to:

1. Qualify the query type to understand ‘what‘ information is being requested. For example, a what, who, where, when, or how query. This is also known as primary intent.
2. Then the process determines the secondary intent – the ‘why‘, i.e., the semantic interpretation of the query. For example, the word ‘bank’ has two meanings – a financial institution and a water body. So when a query has multiple meanings, results of all possible interpretations are produced, and the one that closely matches the query is considered the likely intent.

According to the Google Patent on Evaluating Semantic Interpretations of a Search Query, “Using search results to evaluate the different semantic interpretations, other data sources such as click-through data, user-specific data, and others that are utilized when producing the search results are taken into account without the need to perform additional analysis.”

3. The neural matching then analyzes the words on a page, their relation to each other (synonyms), and the situational context to determine ‘how‘ to answer the query.
   
    

The engines will test (and iterate the process to analyze what users do or expect) to learn the intent and yield a high-quality answer (and, of course, some more complex algorithms).

The answer engines understand the query, determine the available results, and respond with the best possible answer. The traditional search is more about the keywords on a page and the backlinks that will lend credibility to that page.

“As a result, the idea of keyword optimization may be unhelpful,” says Ric Rodriguez, SEO Consultant, Yext, in a session on Global Marketing Day, with SEMRush.

Learn More: Yelp Is Taking Personalized Shopping Experiences to the Next Level: AI & ML Tips for Marketers

Are You Ready for Answer Engines?

The most popular search engine, Google, has already taken specific steps toward this initiative. For example, featured snippets (selected search results on the top of Google’s organic results) and the knowledge graph (infobox placed next to the search results) provide direct answers to the searcher’s query without her needing to click through another website.

Google Search Results Page

72% of respondents of the Voice Survey 2019, by Microsoft, have used digital assistants for a voice search. And this led to the search results shifting to a single, immediate, direct answer to be read out by the digital assistants.

Here is how you can get ready for answer engine optimization:

1. Be specific and detailed about your offerings. For example, if you are offering a 20% discount on your product(s) for Memorial Day, let the answer engines know so that your answer (or result) is picked up. It could be presented as Special Offers and Discounts for Memorial Day – 20% off on Product XXX.
2. Optimize for voice search by preparing and aligning your content to answer your target audiences’ key questions. This will improve your chances of showing up in the Google Answer Box. For example, a dedicated FAQs page for your service can solve customers’ queries in natural language, which could be picked by smart home devices and digital assistants.
3. Make your website answer rich to provide ‘quicker’ answers to users. The focus should be on giving them direct answers. For example, if a customer is looking to reach your physical store, share a map with directions rather than merely providing the address of your store.
4. Prepare your content for conversational search as it helps answer engines understand the intent and context and also makes your page( site) more user-friendly and convenient, making your content more valuable. For example, if you have a burger chain, your conversational content could look like this:
   
    

What is today’s special at XYZ Burgers?

Grilled Chicken

How much does a meal for two cost?

$9 for two singles burgers, two small fries, and two soft drinks.

What about an add-on patty?

You can add grilled patty for $2.

Do you have vegan options?

Yes, we have a veggie burger for $5.

and so on.

5. Prepare your business listing on Google Maps. Get listed on Google maps to reach your potential customers and answer all queries there too. Make it a point to answer user queries on sites like Yelp, Quora, or Amazon for purchases, etc. When users ask questions or upvote or flag an answer, they leave a trail of intent that can guide you to prepare ‘better answers‘ based on their intent and context.
   
    

Learn More: How Voice Search Will Revolutionize Your Marketing

Key Takeaways

There is no ‘one’ right answer and the answer engines keep learning based on the volume of queries, clicks, site schema, structure, metadata, etc. It can accordingly gather the ‘right’ intent and context to find possible answers and return the one that is a ‘best’ match according to its algorithms.

Since the answer engines are constantly evolving, you, too, should focus on understanding and anticipating users’ problems and questions to align your content in a way that solves their queries and answers their questions. The idea is to present specific information; fast.

And keep in mind the nuances of voice and conversational search.

 Are you ready for answer engines? Tell us on Twitter, LinkedIn, or Facebook; we’re always listening!