AI and Personalization: Where Conversion Rate Optimization programs meet tailored, individual experiences (Part-Two)

Intro

AI can deliver personalization at a scale humans never could.

AI can enable personalization in ways that experimentation alone cannot, but let’s be clear, experimentation is still the foundation for making personalization effective.

Experimentation vs Personalization: Like teammates – same team, same goal, different roles.

Experimentation is a controlled, systematic testing to explore, discover and learn.

Personalization is delivering tailored experiences to segments or individuals based on signals, usually found through experimentation and discovery and validated through experimentation and testing.

A common mistake that I find in personalization programs is that they create segments, based on criteria they have, but have not tested to find data that validates that segment.

For example, a company comes up with segment A and segment B, they are very different people in demographics, signals, channels, etc. But when tested, they both behave the same way and respond to the same experiences. If that happens, you don’t have a personalized segment.

Experimentation is necessary to find those patterns, those behaviors, those groupings that behave differently enough to warrant a tailored experience.

The upside is that if you do this right, tailored experiences can deliver higher conversion rates than experiences designed for the general population.

Where AI can amplify Personalization

Machine Learning – this was AI before AI as you know it. Machine learning can identify micro-segments on the fly and deliver one to one personalized experience. It can calculate smaller variables with greater processing than a human can and then predict behavior.

Natural Language Processing – this is where AI in its current buzz exploded. It’s excellent at responding to customer intent in real time, understanding language, nuance and meaning. It can be effective in chat, search and customer interaction centers.

Behavioral Signals – too often we segment and personalize on visitors based on the signals they bring with them. However, actions often speak louder than words, AI can respond to actual site behavior, notice patterns that humans might miss, like browsing depth, hesitation, scroll or others.

Real-Time Decisioning – AI can help deliver content dynamically, instead of waiting around for manual rules. Envision a chat bot that talks with your customer, understands what they are saying and then dynamically points them to a page or dynamically builds a new page on the fly.

AI can enhance the experience by adjusting homepage layouts in real time depending on behavior or personalize email subject lines based on previous engagement and signal or adapt messaging and tone in the chatbot based on customer language sentiment and syntax.

One of the problems with personalization is doing it at scale. AI and other tools have made this easier. Keeping your personalized experiences running, adapting and continuously learning was difficult as you used to use the same software to run tests. They often got in each other’s way. But now, personalization can be run in tandem as experimentation.

The same rules apply, we are looking for exploitable behavior in meaningful cohorts, that will elicit higher conversions and actions. AI can help uncover these segments and help respond to these segments in a scalable, logistically easy fashion.

What Humans Still DO Best in Personalization

As mentioned in Part 1 of this blog post, strategic guardrails are still paramount. Making sure that the delivered experiences follow brand values, stay with what’s ethical and relevant.

One example that I ran into was a company using AI in their chat, but they trained or opened the AI to a large language model that pulls from the whole internet. They quickly had to learn to train it not to mention their competitors features or benefits.

A human strategist might also help coordinate across channels, making sure the AI is consistent. Or better yet, connecting experiences.

Also, a human who manages the AI personalization would be able to avoid awkward scenarios of over-personalization, PII complications, GDPR rules, general creepiness and irrelevant segmentation.

The Experimentation + Personalization Flywheel

Experimentation programs are not a set it and forget it tactic. It is an ongoing system that is constantly learning and adapting.

Experimentation feeds into personalization as you learn what works for different groups and segments.

Personalization then can feed back into experimentation, uncovering new potential segments and signals that need testing.

Together, it’s a learning loop that generates insights for action.

The Future Outlook

AI + ML personalization is going to become increasingly powerful. With experimentation as a backbone to avoid wasted effort on misalignment, it can become a powerful, scalable, fast learning, one to one engagement tool.

Experimentation and Personalization go together like brother and sister. Used together, experimentation-powered personalization is going to grow, where the AI helps learn, test and report on segments, cohorts and behavior.

Still a strategist or customer specialist will be needed to oversee many things like ethics, customer empathy, and sentiment while AI scales execution and delivery.

Conclusion

Personalization and testing should be used as mutually embedded processes. Too often organizations jump straight to personalized delivery without proper data and experiences bloated with assumptions. Experimentation programs are a game changer for companies’ strategy, they allow a way to learn, adapt, and overcome in an ever-changing environment. When used correctly, AI enhances the ability to scale these programs like never before.

To talk to our personalization experts about your team’s use case today: info@softcrylic.com.