Becoming a User-centric Company: Leveraging AI for Improving Customer Experience

AI can help companies to become more user-centric. It's a transformation process from data quality to personalization, to customer acquisition and retention, and beyond. Every and each company should start this journey now, since AI will drive the customer experience of the future. This blog post will give a short overview about applications and the needed data.

What can artificial intelligence (AI) and machine learning (ML) do to improve customer experience? It is already in use with online shopping: you cannot use any shopping service (like Amazon, for instance) without getting recommendations, which are often personalized based on the vendor’s understanding of your traits: your purchase history, your browsing history, and possibly much more.

Data quality: Everything starts with better data

Who are your customers? Do you really know who they are? All customers leave behind a data trail, but that data trail is a series of fragments, and it’s hard to relate those fragments to each other. Customer experience starts with knowing exactly who your customers are and how they’re related. Scrubbing your customer lists to eliminate duplicates is called entity resolution; it used to be the domain of large companies that could afford substantial data teams. Then, you have to ask how well you know your customers. Getting a holistic view of a customer’s activities is central to understanding their needs. ML and AI are now being used as tools in data gathering. But gathering customer data can be intrusive and ethically questionable; as you build your understanding of your customers, make sure you have their consent and that you aren’t compromising their privacy. As the number of data sources grows, the number of potential data fields and variables increases, along with the potential for error: transcription errors, typographic errors, and so on.

Common applications

One common application of machine learning and AI to customer experience is in personalization and recommendation systems. In recent years, hybrid recommender systems - applications that combine multiple recommender strategies - have become much more common. ML and AI are automating many different enterprise tasks and workflows, including customer interactions with automated chatbots. As our ability to build sophisticated language models improves, we will see chatbots progress through a number of stages: from providing notifications, to managing simple question and answer scenarios, to understanding context and participating in simple dialogs, and finally to personal assistants that are “aware” of their users’ needs.

Fraud detection is another technology that is now digesting machine learning. Fraud is no longer person-to-person: it is automated, as in a bot that buys up all the tickets to events so they can be resold by scalpers. It is much harder to discover those bots and block them in real time. That’s only possible with machine learning, and even then, it’s a difficult problem that’s only partially solved; however, solving it is a critical part of re-building an online world in which people feel safe and respected.

Multi-modal models that combine different kinds of inputs (audio, text, vision) will make it easier to respond to customers appropriately; customers might be able to show you what they want or send a live video of a problem they’re facing. But if we’re going to get customers through the human and robot interaction barrier, we also have to respect what they value. AI and ML applications that affect customers will have to respect privacy; they will have to be secure; and they will have to be fair and unbiased.


Customer experience is an important driver for customer retention and so traffic. Amazon made it to a core component of  its fly-wheel and leverages AI for to continuously improve it.  Every user-centric company of the future has to consider  how it can leverage and process its data by AI to continuously improve its services like Amazon has done.