Product School

Can Generative AI Automate Customer Delight?

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Author: David Geffen

February 16, 2024 - 4 min read

Updated: March 1, 2024 - 4 min read

AI can be tricky. Customer delight—and loyalty—require a deep understanding of their needs, translated into a seamless product experience. While AI can significantly improve these experiences, it’s important not to leave personalization behind. Otherwise, interactions can feel hollow and robotic to your customers. 

Achieving the right balance isn’t always easy, especially considering the rapid pace of AI’s evolution. Sometimes, the changes feel more overwhelming than exciting—like when ChatGPT clobbered everyone’s news feeds back in 2022.

Admittedly, it was for good reason. Unlike its predecessors, generative AI can create new outputs, from poetry to programming code. But can it create world-class product experiences? Can you actually put customer delight on autopilot? 

Let’s find out! Here are three ways product teams are leveraging generative AI to boost customer-centricity—and where we predict this technology is taking us next.

1. Analyzing customer behavior

Learning about your customers is often a cumbersome process. This is especially true when it is difficult to track down the information in the first place. Even the most seasoned PM can end up buried under a pile of tools and dashboards.

Sure, you can see what’s happening inside your product. But do you have any idea why?

Generative AI is dramatically transforming how product teams connect the dots to uncover the root causes of behavioral patterns and trends. The key is to consolidate and process data from multiple sources. In particular, from sources that are not easily accessible to product teams. 

An error log, for example, is normally DevOps’ domain. However, a single glitch can impact customer behavior just as much as the interface. In fact, product analytics and technical performance data are often closely related. By effectively aggregating data sources, AI can boost analytics capabilities by affording product teams a more comprehensive view of their customer’s product experience.

2. Personalizing customer experiences

Generative AI doesn’t just process data more effectively. Its real superpower is the ability to act on that data. For example, generative AI can automatically segment customers by any combination of characteristics, including:

  • Preferences

  • Browsing behavior

  • Purchase history

  • Usage patterns

These insights make it easier to tailor product offerings and deliver more relevant experiences at scale. Some examples of generative AI personalization in the wild include: 

  • Netflix: Suggests new content to viewers based on previous content consumption. This helps users discover new shows and movies they’ll actually enjoy (most of the time).

  • Amazon: Recommends the most relevant products to users based on real-time interactions with different product pages.

  • Spotify: Suggests songs and playlists to listeners offering a continuous stream of music that aligns with individual tastes. 

When used effectively, generative AI can make products feel more personalized and less robotic. 

3. Collecting (and responding to) feedback

Generative AI also improves how product teams gather, process, and respond to customer feedback. A few examples:

  • Suggesting product and feature improvements based on customer feedback, usage patterns, and performance metrics

  • Predicting customer behavior based on historical data and even market trends

  • Forecasting customer churn, enabling product teams to address churn risks and boost their retention efforts proactively

What's next: Autonomous customer experiences

You’re thoughtfully measuring customer interactions, with the ability to refine every tape, click and swipe to perfection. But, let’s be honest, actually making product improvements can take forever. Optimization efforts frequently get stuck in silos, roadblocks, and data gaps, leading to frustrated product teams and increasingly impatient customers.

While AI as a product enhancement is nothing new, generative AI is pushing the envelope even further, enabling products to self-improve

The best example is Autonomous CX, which leverages generative AI to analyze, prioritize, redesign, test, and deploy digital customer experiences in response to real-time user behavior. 

The idea is to identify and correct sources of friction as they occur. This is the future for forward-thinking product teams.

So what does all this mean for customer delight?

No form of AI can automate true customer delight. That comes from deeply knowing and understanding your customers—their needs, wishes, preferences, behaviors—the whole enchilada. 

What generative AI can do, however, is break down those hefty roadblocks standing in your way that prolong discovery and hinder your ability to act. The time-consuming reports. The missing information. The weeks of testing and planning before you can deploy a real feature enhancement.

Generative AI’s biggest contribution to product teams is absorbing interference. It clears the way so that you can understand your customers faster and build them better products.

Learn more about AI-powered digital experiences at glassbox.com or dig into the topic with us in person at ProductCon London on February 20, 2024.

Updated: March 1, 2024

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