What is CRO?
Conversion Rate Optimisation (CRO) is the process by which we try to improve a website or app to increase the percentage of users who perform an action. That action can range from a click or a lead to a purchase.
However, the incorporation of artificial intelligence has completely transformed the way we understand and optimise the user experience. We no longer analyse and react to what users have done in the past, but rather predict in real time what they will do in the future.
Therefore, we are no longer talking about optimising segments that share similarities in the searches they perform on our site, but rather the trend is to target individual users at the exact moment they interact with our site.
The solution: AI that personalises while the user browses
Now, as you use your Mac cursor to read this blog, artificial intelligence applied to CRO allows us to adapt your digital experience in real time, based on your current behaviour and in a predictive manner. In other words, we can anticipate your next move.
To do this, AI analyses live data such as traffic source, device, page views, interactions, etc., and then detects patterns and anticipates the user’s intention.
For example, if a user has visited several product pages on different devices that are top sellers in our e-commerce store and then has shown interest in the offers section, spending 20 seconds inspecting it, but then quickly moves to the X to close that page, the AI predicts that the user is going to leave the site. And, therefore, it acts. To do this, it can dynamically modify the content with powerful messages and CTAs, through a pop-up with a 10% promotion and a reorganisation of devices with a higher discount margin.
Key benefits of AI applied to CRO
As we see once again, AI has arrived to improve the processes we already had in place in our CRO flow. And now we can:
- Increase conversions: hyper-personalisation reduces friction and increases the likelihood of conversion from the first contact.
- Create unique experiences: each visitor receives an experience that is fully tailored to their needs, interests and stage in the funnel.
- Predict successfully: algorithms are constantly learning and as they collect more and more data, decisions become increasingly accurate.
- Segment predictively: AI can detect micro-segments and tailor specific messages to them.
How to get started with AI-powered CRO
To implement AI in your CRO strategy, you simply need to start with a clear roadmap that transforms your optimisations into predictive personalisation. To do this:
- Audit your CRO strategy: before applying AI, you need to understand where you are. To do this, you will need to critically review your funnel: bottlenecks, pages with high bounce rates, flows… At this stage, qualitative and quantitative analysis tools such as Hotjar and GA4 will be very useful.
- Identify critical moments in the user journey: detect those key points where personalisation really makes sense due to its high impact. For example, adapting messages according to the traffic channel.
- Implement AI tools with predictive personalisation: choose the platform that best suits your needs and start with a pilot test on a key page. For example, if you have a robust e-commerce site and need an advanced personalisation solution, you can use Adobe Target.
- Measure, learn and adjust: the good thing about algorithms is that they are continuously learning, but this does not mean that we should neglect monitoring. It is not a question of automating and leaving it at that, but rather of automating and evaluating.
The change in mindset: from optimising to predicting
CRO is no longer just a matter of trial and error. Adopting AI for CRO is a change of approach and a competitive advantage in this digital environment where attention and relevance are everything.
We are talking about creating unique experiences that adapt in real time and where it is no longer a question of executing A/B tests from our backlog, validating hypotheses one by one, but rather going further and building real-time machine learning systems that anticipate and are increasingly accurate.
The key is to stop optimising what has already happened and start predicting what is going to happen.
