A search engine is a computer program (or platform) that helps customers find answers to their questions. There are three types of search engines: traditional search (or conventional search); modern search (or AI search); and specialized search engines. As search decentralizes from Google and generational trends impact search engine use, brands have to meet customers everywhere they're searching:
Nearly every customer in Gen X is strongly influenced by traditional search engine results
But more than half of Gen Z doesn't use traditional search engines at all
Meanwhile, specialized search platforms like Pinterest can drive brand discovery, even in unbranded searches
Traditional search engines
Traditional search engines (like Google, Microsoft Bing, and Yahoo) crawl the internet. They scan weblinks for keywords, creating a digital "library" organized by indexed pages. When customers ask questions in a traditional search engine, the search engine looks through its library. Then, it surfaces a list of indexed web page links on search engine results pages (SERPs). SERPs contain links featuring snippets of content that match the keywords entered into the search engine. Links are ranked on the SERP by algorithmic factors like link previous popularity, perceived relevance, and source credibility.
Traditional search engines work well for customers who want direct answers to simple questions like, "What time does Wegman's close?"
However, traditional search engines can surface unhelpful, inaccurate information, even when brands use search engine marketing (SEM) and search engine optimization (SEO) best practices. With keyword searches, SERPs look to match word-for-word. So, traditional search engines can fail to answer conversational, nuanced questions from customers like:
"Should my parents get a reverse mortgage if their $400K house is paid for?"
"How do I talk to my 14-year-old son and help him build good hygiene habits without body shaming him?"
"What are the best credit cards for people who love to travel? Include benefits like travel insurance and access to exclusive airport lounges. Call out any and all fees associated with the cards."
Specialized search engines
Specialized search engines give customers the power to narrow their search in a focused, niche platform. They cater to customers with a clear, high intent to buy (even if they're not yet sure which brand to buy from). In part, that's because specialized search engines focus on a particular topic or category. Also, unlike traditional and modern search engine use, customers use specialized search engines at every stage in the customer journey.
For example, customers discover brands offering "moto Boho bags" on Pinterest. Then, they might click through an ad or an organic post and buy one. They consider reviews for the "best breakfast place in Kenwood" on Yelp, then share the link with their friends to finalize plans. They'll book a flight and a hybrid SUV for a trip to Bozeman on Kayak, even if they weren't sure where they wanted to go when they started their search.
AI-powered search engines
Modern, AI-powered search engines (like ChatGPT, Apple Intelligence, and Perplexity) take a different approach. With AI, search engines aren't matching keywords or counting backlinks. Customers don't have to craft a precise query or click through potential links to uncover more detailed information, either.
Instead, AI-powered search engines are designed for contextual communication. They use artificial intelligence and machine learning to read and understand data across digital touchpoints. And they don't stop retrieving information from data sources everywhere. They pull from local listings pages with healthcare provider bios and procedure FAQs. They examine photos, menus, first- and third-party review sites along with NAP data in business directories. They pull from social media captions and even videos on TikTok. Then, they use it to generate new content, in real time, as nuanced, conversational responses to customer queries.
How is that possible? Among other innovations, modern search engines use Natural Language Processing (NLP). NLP models help AI understand complex customer questions. Plus, modern search engines adapt to customer behaviors and interactions with their platform. The more a customer uses a modern search engine, the more the tool learns the customer's preferences. Then it can better personalize responses in search threads.
AI-powered search engines are on the rise. 49% of customers trust AI-generated responses, and these platforms are quickly becoming a go-to tool for brand discovery. Meanwhile, traditional search engine use is declining (as much as 25% by 2026).
Benefits of AI-powered search engines
Modern search engines have four distinct advantages over traditional search engines:
Capability: Today's search engines can handle complex search queries. They also let customers refine their searches with previous questions and answers. This helps customers get layered responses or focus on specific details that interest them.
Personalization: Modern search engines remember customer behavior, preferences, and conversation history. They notice customer clicks, time spent on content, and other digital interactions. Then, modern search engines prioritize similar results in future searches. As search topics shift, modern search engines adapt and update results for the new contexts.
Conversationality: Customers like how easy it is to communicate with modern search engines. They don't have to guess at "the right keywords" or hunt and peck through hyperlinks. Instead, they can conduct a conversational voice search, text-based search, or even a visual search in the search engine. Then, they get a complete response that mirrors their own voice or communication style requests back to them.
Efficiency: AI search engines understand search intent better than traditional ones. Modern search engines surface more complete, accurate, and relevant information at the first go. As a result, customers save time. They don't need to browse multiple pages for answers or endlessly search until they guess the right keywords.
SEO strategies to show up in AI search results
As modern search engines replace traditional ones, search is decentralizing. It's fragmenting into a diverse search landscape — and away from Google. Customers are experimenting with modern search engines. Brands are adding and upgrading their proprietary search platforms. And people are already picking their favorites.
So brands have an opportunity to pull ahead from the competition by adapting their SEO strategies for modern search engines. The key is to invest in strategies that keep your brand discoverable beyond Google. After all, with modern search engines, every customer searches in different places in their own voice. They're also using different search engines at different stages of their customer journey.
Some of those strategies are intuitive and already in the digital marketing playbook. For instance, brands should refresh existing content and keep creating content that reflects how customers talk:
Lower the reading level and simplify the syntax so sentences are shorter and more conversational.
Naturalize crosslinks, so AI-powered search engines and customers can understand and trust that the content is relevant and accurate.
Work with sales and customer experience teams. Together, you can find the exact words and phrases customers use when they ask questions and describe your products, services, staff, and brand.
Some are more data-centric and stretch outside traditional digital marketing strategies. For instance, brands that use a knowledge graph to centralize their data can create content that's easy for modern search engines to find. Also, with customers searching for answers everywhere, brands need to make sure their data management scales. It must be accurate, up-to-date, and consistent on the most diverse publisher network available.
Want a roadmap to build on traditional SEO success and adapt to the new era of modern AI search engines? Keep reading with 3 Keys to SEO Success in Generative Search.