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Rendering In The Era Of AI

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Adapting rendering strategies to the age of AI-driven user engagements & conversational search

This blog was originally published on medium.com, March 20, 2024.

In the dynamic ecosystem of technology, understanding web page rendering and its impact on SEO is essential for businesses to thrive.This is particularly crucial for eCommerce websites, as over 40% of traffic originates from organic search and paid search ads. Just as nature constantly adapts to its environment, the tech industry must keep pace with the changing tides of innovation. In this article, we will explore the various rendering techniques, their pros and cons, and how they adapt to the evolving landscape of search and user interaction.

SEO is the practice of optimizing websites to improve their visibility and ranking in search engine results pages (SERPs). Much like how a well-adapted species thrives in its habitat, a website optimized for SEO flourishes in search results, securing top positions within its niche. This prime placement is crucial, as companies that achieve the highest rankings on their SERPcan achieve click-through rates (CTR) of 39.8%. Furthermore, even a slight improvement in search result position can have a significant impact, with a single spot advancement potentially boosting CTR by 2.8%.

A critical factor in SEO is the search crawl budget, which refers to the limited time and resources search engines allocate to crawling and indexing websites. Efficient page rendering plays a vital role in maximizing the value of the crawl budget, ensuring that web pages load quickly and provide meaningful content, making it easier for search engine bots to navigate and understand the site.

The landscape of rendering techniques

Web page rendering has evolved significantly over the years, with each technique having its own strengths and weaknesses. Let's explore the different rendering methods:

1. Server-Side Rendering (SSR):

SSR is the traditional approach where the server generates and sends a complete HTML page to the browser. Like a sturdy and reliable oak tree, SSR ensures fast initial page loads and better SEO, as search engine bots can easily crawl and index the content. However, SSR may lack the interactivity and dynamism of modern web experiences.

2. Client-Side Rendering (CSR):

CSR shifts the rendering process to the user's device, enabling highly interactive and dynamic web pages. The browser receives a minimal HTML file and renders the content using JavaScript. Akin to the agile and adaptable hummingbird, CSR offers a seamless user experience but can pose challenges for SEO, as search engine bots may struggle to crawl and index the content efficiently.

3. Incremental Static Regeneration (ISR):

ISR is a hybrid approach that combines the benefits of SSR and CSR. It generates static HTML files at build time, similar to SSR, but allows for incremental updates without rebuilding the entire site. Like a resilient and adaptable coral reef, ISR provides fast initial page loads and good SEO, while enabling dynamic content updates.

4. Prerendering:

Prerendering is a technique that generates static HTML files ahead of time, similar to SSR, but does so asynchronously. It improves page load times and SEO by providing fully rendered pages to search engine bots and users. Prerendering, like a diligent ant colony storing food for the future, can be particularly useful for content-heavy websites or those with frequently accessed pages.

Simulating user interactions in rendering

User interactions on web pages, such as clicks, hovers, and scrolls, play a crucial role in the overall user experience and engagement. Traditional page rendering methods may fail to capture the visible information that is revealed only after user interaction.

For eCommerce vendors, simulating user interactions during the rendering process is particularly important. Let's consider a few examples:

1. Product variants:

Many eCommerce websites display product variants (e.g., different colors or sizes) when a user clicks on a specific option. Without simulating this interaction, search engine bots may not discover and index the full range of available variants, limiting the product's visibility in search results.

2. User reviews and ratings:

Customer reviews and ratings often play a significant role in influencing purchase decisions. If these elements are loaded dynamically upon user interaction (e.g., clicking a "Read Reviews" button), failing to simulate this interaction during rendering can prevent search engine bots from indexing this valuable user-generated content.

3. Infinite scroll and pagination:

Ecommerce sites often implement infinite scroll or pagination to load additional products as the user scrolls or clicks through pages. Simulating these interactions ensures that search engine bots can discover and index products beyond the initial page load, improving their discoverability.

4. Faceted navigation and filters:

Many eCommerce websites offer faceted navigation and filters to help users refine their product search. Simulating user interactions with these filters allows search engine bots to crawl and index the various product combinations, making them more easily discoverable for specific user queries.

By simulating synthetic user interactions, such as clicking on product images, hovering over descriptions, or scrolling through reviews, eCommerce websites can ensure that search engine bots access and index the full extent of their content. This comprehensive indexing improves SEO scoring, making products more easily discoverable and increasing the likelihood of attracting relevant traffic and conversions.

Rendering for conversational search and LLM bots

The rise of large language models (LLMs) like ChatGPT, Claude, and Arc Browser is transforming how users interact with the web. These advanced AI systems, much like the intricate communication networks found in nature, enable users to engage with information in a more intuitive and personalized manner. As a result, rendering strategies must evolve to cater to the unique requirements of LLM bots.

Photo by Mohamed Nohassi on Unsplash.

Failing to optimize rendering for LLM bots can lead to frustration and a subpar user experience, potentially driving customers away to competitors who have optimized their sites for conversational search.  Let's explore a few scenarios:

1. Product comparisons:

Imagine a user asking an LLM bot to compare two specific products based on their features, specifications, or customer reviews. If the website hasn't properly rendered and structured its content for LLM bots, the AI system may struggle to extract and present the relevant information accurately, leading to an unsatisfactory user experience.

2. Personalized recommendations:

For instance, consider a user interacting with an LLM bot to find the best hiking shoes for their upcoming trek. They might ask, "What are the top-rated waterproof hiking shoes for men size 10?" If the eCommerce website hasn't optimized its rendering for LLM bots, the AI system may struggle to parse the product information, specifications, and customer reviews to provide an accurate recommendation. This could result in the user receiving irrelevant or incomplete information, eroding their trust in the website and the LLM bot.

3. Answering FAQs and support queries:

Many eCommerce websites have extensive FAQ sections and support documentation. Say a user is seeking advice on setting up a smart home system. They might ask an LLM bot, "How do I connect my Wi-Fi enabled smart lock to my home security system?" If the website selling smart home devices hasn't properly structured its content for LLM bots, the AI system may have difficulty understanding the compatibility and integration steps, leading to confusing or inaccurate guidance. This poor user experience could deter the user from making a purchase and damage the website's reputation. Also this can lead to frustration and a higher volume of support tickets, straining the company's resources.

4. Understanding product compatibility:

In certain industries, such as electronics or automotive, product compatibility is crucial. A user might ask an LLM bot if a specific accessory is compatible with their device or vehicle. If the website hasn't optimized its rendering process to expose compatibility information to LLM bots, the AI system may provide inaccurate or incomplete answers, potentially leading to customer dissatisfaction and returns.

In short, failing to optimize rendering for LLM/AI bots can lead to frustration and a subpar user experience, potentially driving customers away to competitors who have optimized their sites for conversational search.

To address this, rendering services should focus on generating structured and semantically rich data specifically tailored for LLM bots. This involves using techniques like schema markup, entity extraction, and natural language processing to present information in a format that is easily understandable and queryable by AI systems.

By providing comprehensive and well-structured data, websites can ensure that LLM bots can accurately answer user queries, enhance the conversational search experience, and ultimately drive more traffic and engagement.

PhotonIQ Prerendering: pioneering the future

PhotonIQ Prerendering is at the forefront of innovation, offering a comprehensive solution for businesses to navigate the evolving landscape of web page rendering. Like a skilled gardener tending to a flourishing garden, PhotonIQ Prerendering optimizes websites for success in the modern digital ecosystem.

By combining traditional rendering techniques with the ability to simulate synthetic user interactions and render pages specifically for LLMs, PhotonIQ Prerendering delivers significant value to customers. This enhances your website's visibility and drives measurable SEO score improvements by enabling more pages to be crawled and more high-quality data can be captured.  It ensures that websites are not only discoverable and indexable by search engines but also optimized for the emerging world of conversational search and AI-driven interactions. See it in action in this demo!

Navigating the future of web page rendering

The evolution of web page rendering is a complex and ongoing process, driven by the ever-changing needs of users and the advancements in search technologies. As businesses navigate this landscape, understanding the various rendering techniques, the importance of SEO and search crawl budgets, and the impact of user interactions becomes paramount.

Just as nature finds a way to adapt and flourish, the tech industry must remain agile and responsive to the winds of change. As the industry continues to evolve, adaptability and a deep understanding of the technical intricacies will be key to thriving in the digital ecosystem.

By embracing innovative solutions like PhotonIQ Prerendering, businesses can stay ahead of the curve and optimize their websites for success in the era of conversational search and AI-driven interactions. Learn more today by chatting with an Enterprise Solution Architect.

First photo by Unsplash+In collaboration with Philip Oroni.

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