As AI makes its way into every corner of our lives, one place we will no doubt be most aware of its impact is in the experiences we have with companies, products, and services. AI systems are seeing widespread implementation from AI chatbots for customer interaction to hyperpersonalized recommendations of organizational offerings. But even moreso, we’re seeing AI changing the very nature of the way people interact with the products and services they buy and use on a daily basis.
Hyperpersonalizing the Customer Experience
The concept of “hyperpersonalization” is the idea that we can use data to narrowly customize and tailor a specific offering to each individual user. Using this approach, companies and government agencies no longer would need to bucket users into groups or categories to most effectively serve them and deliver the solutions they are most interested in. AI systems are able to analyze individual customer data and then provide recommended and personalized products or tailored services based on those individual customer needs and behaviors. These AI systems can use past and current behavior, preferences, engagement activity, and use that to spot patterns or trends that might suggest different products or services, or further customize those offerings.
While we might have direct experience with this sort of hyperpersonalization in our ecommerce and online retail experiences, or with media and entertainment services such as Netflix, this sort of hyperpersonalization is being implemented much more widely from insurance to banking, healthcare to government services. AI can analyze customer data to enhance customer satisfaction and engagement.
Everything Will Be Conversational
One of the biggest impacts of generative AI is the growth of conversational interfaces, whether spoken or typed, as user interfaces to products. Many systems are often difficult to navigate, with cumbersome user interfaces and features hidden behind opaque menus or hidden in system settings and preferences. Sometimes you need to go online to search for how to do things because you can’t figure out how to do it in the increasingly complicated and changing products you use. Conversational systems help users get what they want out of products by bypassing these UI elements and get what they want through direct interaction. These GenAI powered tools can let you describe what you want the tool or service to do, and the systems will either execute the task that you’re looking to do, or navigate you to the right place.
Conversational assistants are now being used to create slide decks, images, and text of all sorts. Increasingly, conversational features are getting embedded directly into the tools that people are using on a daily basis, with a “magic sparkles” icon or emoji indicating where AI is powering the solution. Increasingly, you’re going to start to see a lot more of those AI-enabled features making their way into your everyday products, whether or not you want to use them. These AI powered chatbots and virtual assistants enhance the quality and value that you’re getting with many products, especially as user interfaces may not be intuitive.
AI-Powered Sentiment Analysis Improving our Product and Service Experiences
AI tools are also being put to good use to understand how customers and users are interacting with products and services. AI systems can analyze customer feedback, social media posts and online reviews, to gauge customer feelings and perception, and then suggest ways to improve the overall customer experience. With the help of AI, these companies can look at that customer feedback and combine that data with customer behavior to understand potential churn or renewal behavior, or improve the use of features, upsell, or cross-sell of different products and understand different engagement levels.
Companies use this information to understand customer satisfaction and to tailor their responses, improving overall customer relations. AI predicts customer behavior, such as potential churn, by analyzing past interactions, purchase history, and engagement levels. These systems can then proactively engage at-risk customers to offer assistance and provide more personalized incentives to help retain their product usage or upsell them.
AI systems can even help optimize the purchasing and pricing process by tailoring products to the specific needs of users. Dynamic pricing, which includes the ability to do demand pricing, competitive pricing, even usage based pricing is relevant for many products that require constant price changes due to supply and demand. We’re familiar with that sort of dynamic pricing in cloud based services, or ride sharing services, or airplanes or hotels in which prices can change on a minute-by-minute basis. AI systems are able to more accurately gauge customer interest, demand, and available supply to optimize pricing for that moment. Pricing can be very complicated and AI systems can help, as well as ensuring that the customers are not getting stuck at various parts of the purchasing experience.
Improving Translation and Localization
One of the other major impacts of the widespread use of generative AI and large language models is that they can provide more out-of-the-box ability for users to engage with products in their native language. It used to be that products required significant labor and effort to translate user interface, instructions, manuals, websites, and all the various different interaction points to a variety of languages. As such, companies would have to make choices about which languages they would support and the labor needed to support those translations.
Now with the power of multilingual LLMs, translation and localizations are significantly simpler and lower effort. AI is superpowering localization and translation capabilities, making it such that even small developers and companies with very limited products or websites or any application can get the translation capabilities done very easily, quickly, and hopefully accurately. Accuracy is always the challenge with translation, but editing and tweaking translations are significant factors of time and cost more efficient than sourcing from scratch. In addition, users themselves are empowered to interact with conversational agents to correct their language usage. As LLMs themselves continue to improve and become more widespread in usage, systems that make use of those LLMs will gain those improved capabilities automatically over time.
So we’re really seeing AI used in the entire customer journey from customer onboarding, to customer purchase, usage, retention, upsell, and ongoing engagement. AI is helping to make for a seamless customer experience, to enable customers to get the value they want from products and services and become engaged, happy, and repeat purchasers.
