Most Marketing teams have someone regularly running Conversion Rate Optimization (CRO) to tweak and improve web pages and the overall web experience. But even with CRO tools, there's still a lot of manual work. Fibr.ai argues that AI agents can automate much of that manual effort and fundamentally change how Marketing teams approach web optimization, argues Ankur Goyal, CEO and co-founder.
Marketers spend a lot of time designing personalized ads, emails, and even social media posts, only for the click to go to a static landing page or website. All that personalized effort wasted. Prospective customers don't get what they expect, lose interest, and drop off the website. Hello, high bounce rates.
Then there's the website itself. Research shows that website visitor traffic is declining, but the traffic that remains is more qualified and comes for very specific purposes. If you aren't giving them the information they want or expected from clicking a link, whether from an ad, email, SMS, or a Large Language Model (LLM) search result, you are wasting your time.
Fibr's goal is to help organizations dynamically optimize experiences for human and machine visitors (LLMS) through personalization and optimization, and it uses AI to do it. Why is that important? Because it allows Marketers to scale their efforts in ways traditional tools can't.
Conversion rate optimization sounds complicated. And for many it is. You have to develop hypotheses to test, define rules around what you want to test, and for whom. Then you have to apply those tests to your website or landing pages, continuously monitor, and adapt. Goyal said there's so much involved that most organizations can conduct only a limited set of experiments each year. Or they pay an agency to do the work for them.
Testing variations of landing pages or web pages is straightforward. A CRO tool scans a page and identifies the page elements to create variants for. For example, you can create variants for headlines, visuals, calls to action (CTAs), and so on. This is also how Fibr works. It has an AI agent that scans the URL, identifies the elements to personalize, and then creates a set of variations to test. The marketer can then review and edit those variants before approving and publishing them. Fibr monitors the tests and sets the winner, archiving the rest.
For those unsure of where to start testing, Fibr has an AI hypotheses generator. Give it a URL, let it connect to your Google Analytics (GA4) account, and it will analyze the page and generate a set of hypotheses you can select from.
Fibr's personalization capabilities span from landing pages for ads, audiences, location, and even entire journeys. Goyal said if you can see it in GA4, they can personalize it.
You can even create personalized pages for large language models (LLMS). Fibr connects with your GA4 account and can see when a visitor is referred by an LLM (using the Referrer attribute). While you can't see what they searched on that led them to the website, you can make some assumptions and create web pages that provide key information on products and services (which is what typically drives a visitor to a website from an LLM).
Connect your ad accounts (e.g., Google, Meta, TikTok, LinkedIn), and Fibr pulls in all your campaign and audience data to help optimize associated landing pages and provide insights into their performance.
Personalizing journeys is another interesting capability. A marketer can select an ad or audience and define a highly personalized journey. If a visitor matches the audience or ad and follows that journey, they will get the personalized experience.
Fibr.ai runs on context graphs, connecting signals from a variety of places, including ads, website behavior, CRM data, and custom machine learning models that determine intent. It works with all your existing systems, from Customer Relationship Management (CRM) and Content Management Systems (CMS), to analytics, and ad and audience platforms.
Another capability Fibr is working on is a landing page generator called Genesis. Right now, landing pages are built using another tool, and Fibr adapts them. With Genesis, marketers can provide sample pages they like and have a landing page created for them that follows brand guidelines.
Some of the conversations around website experiences today involve a chat interface as the first thing a visitor sees when they go to a website. The visitor can ask for specific information, and the chat will provide it, including internal links where appropriate. Some brands are already testing this, and several CMS providers are already thinking about what this means for content management.
The reason I mention this is that when the chat points the visitor to a specific page, that page is still a static, standard web page. But if you connected a platform like Fibr, when that visitor is redirected, Fibr can ensure they get an experience contextualized to the conversation. Goyal and I talked about this, and he believes tools like Fibr will become even more critical when we get to this point.
And we are getting there. But even if the first experience isn't an AI chat, visitors come from somewhere, and marketers should be using that knowledge to deliver a more personalized experience. Or, if the visitor matches a key audience, the experience can be tailored to that audience rather than providing the generic messaging that web pages typically offer.
Marketers can't sit back and assume a standard static website is enough. And they can't assume the website isn't important anymore because it's basic. Customers and prospective customers are still coming to the website, and they are expecting to find the information they need without having to click three, four, or five times.
Tools like Fibr are becoming increasingly critical, and they can't be complicated or manual. AI can help scale testing and personalization, with a human-in-the-loop guiding strategy, ensuring the brand is maintained, and providing oversight on what's happening, so visitors get the experience they deserve.
If you are researching a CRO tool, make sure you understand how it works and how much effort it requires, compared to what AI can provide. Half a dozen experiments a year isn't enough; you have to continually test and optimize.
