In this fast-paced, highly competitive world, businesses should focus on their customers if they are to achieve differentiated competition. They should try to understand how the customers feel while interacting with their products, apps, and websites, i.e., user experience (UX) should be the center of attention in this competitive economy. Artificial Intelligence (AI) has redefined how businesses approach this concept. A strategic UX design can go a long way in creating a positive experience for the customer. Thanks to the recent advancements in AI and machine learning (ML), businesses now have a powerful tool in their hands to deliver a robust experience for their customers. In this post, let us find out how AI and UX work hand in hand to enhance a customer’s experience.
AI and UX, are they complementary?
The concept of UX came into being in the 90s, and since then, its framework has expanded (Basri. et al., 2016). It refers to the “non-utility aspect of human-computer interaction and focuses on the user’s emotions, feeling, and the significance and value of such interaction in daily life” (Yang et al., 2020). Simply put, it focuses on the individual experience of a user concerning a product. With better UX comes greater loyalty, leading to better business (Zare & Mahmoudi, 2020). Now let us delve into how AI fits into the equation.
For years, businesses have relied on quantitative survey methods such as customer satisfaction and net promoter scores to understand the customer (Yang & Jiang, 2020). However, a significant drawback of such strategies is their inability to recognize emotional responses, crucial feedback for businesses. Quantitative surveys are easy to perform, whereas qualitative surveys are much more labor-intensive. However, thanks to AI, businesses can now quickly analyze qualitative data that gives a good picture of the customer’s experience in real time.
With the assistance of AI, UX designers can now collect and analyze massive data sets and come up with practical solutions to enhance the UX and thereby draw sales. What makes AI different from traditional technology is how it makes use of the data collected. For example, in the case of an e-commerce company, AI technology would aid it in keeping track of its user’s activities and analyze the data generated to develop a better idea about their preferences and thereby customize the UX in resonance with it. This would, in turn, generate more leads and sales for the company. AI algorithms also can predict customer behavior based on their analysis of the otherwise less explored comments section, where users are more likely to reveal their true thoughts and feelings. It uses the linguistics-based natural language processing (NLP) approach to extract and map the customers’ vocabulary in which they express their feelings, combined with the traditional rating scales to gather insights (Zaki et al., 2021). Such insights will take the front seat in determining the actions to be taken to attract and retain customers.
AI’s impact on UX
AI has an increased effect on how businesses are approaching UX nowadays. The qualitative analysis undertaken by AI has had many profound effects on UX. Often, businesses fail to recognize what the customer needs as, in most instances, they approach issues from a business perspective. One reason for it could be that their decisions are mainly based on quantitative data without considering the customer’s perspective. AI with its ability to capture emotional and cognitive responses (Yang et al., 2020)
could help in filling that gap. It can inform the business of the issues faced from a customer’s perspective. Moreover, for a problem to be fixed, you need to understand the underlying cause. Insights gathered from the data analyzed by AI identify not only the problems but also the causes (Zaki et al., 2021). It is only if the solutions are directed toward the causes problems can be rectified conclusively.
Another AI-driven capability that has had a significant impact in facilitating more outstanding UX is personalization. A personalized UX can result in greater user efficiency and correspondingly enhanced UX (Cheng & Jiang, 2020). A popular form of AI that is being widely used nowadays in reshaping the digital eCommerce sector is chatbots. Chatbots are computer programs that simulate human conversations through text, voice commands, or both (Duijst, 2017). Bots are connected to hundreds of different applications through open APIs and, backed by AI and ML, can identify and solve customers’ problems faster and easier than ever using the data collected at the source, thereby removing the multiple mediators. They are otherwise involved in the usual user feedback process. ML algorithms can automatically detect outliers and thereby help in saving a significant amount of time spent to identify issues through emails and calls (PK, 2018). Using NLPs, these bots can mimic human conversations and understand the intention behind a text and respond accordingly (Huang & Rust, 2018). It provides quick customer support, and now with its ability to learn from past conversations, it can respond more effectively(Illescas-Manzano et al., 2021). Bots are also contextually aware-because they track the user’s activities, such as their google search and landing pages. The conversations can be tailored to ensure that it is relevant to the user’s interest from beginning to end. This feature equips the bots to deliver messages that grab the user’s attention right away while also addressing his needs. Bots can also learn from the interactions and identify user patterns which in turn would aid in anticipating potential issues and thereby address it proactively (Illescas-Manzano, M.D. et al., 2021). Such personalization enabled by chatbots brings a human touch to the whole interaction while simplifying the whole process.
AI is here to stay!
Although the combination of AI and UX is relatively new, it appears to be a winning one. Placing the customer at the center facilitates closer engagement, quicker processing, customized response, innovative insights, and more intuitive interfaces. Overall, it boosts interaction, achieves customer loyalty, and long-term growth. So, we will undoubtedly find more applications of this combo in our daily lives.
References
Basri, N.H., Noor, N.L.Md., Adnan, W.A.W, & Saman, F.M. (2016, August). Conceptualizing and understanding user experience. In Proceedings of the international conference on user science & engineering. (i-USEr)
Cheng, Y. & Jiang, H. (2020). How do AI-driven chatbots impact user experience? Examining gratifications, perceived privacy risk, satisfaction, loyalty, and continued use. Journal of Broadcasting and Electronic Media, 64(4), 592-614.
Duijst, D. (2017). Can we improve the user experience of chatbots with personalisation?. https://www.researchgate.net/publication/318404775_Can_we_Improve_the_User_Experience_of_Chatbots_with_Personalisation
Huang, M.H., & Rust, RT (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155-172
Illescas-Manzano, M.D, Lopez, N.V., Gonzales, N.A., & Rodriguez, C.C. (2021, May 5). Implementation of chatbot in online commerce and open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 1-20.
PK, S. (2018, February). Enhancing user experience using machine learning. International Journal of Engineering Research & Technology, 7(2), 353-358
Yang, B., Wei, L., & Pu, Z. (2020, November 19). Measuring and improving user experience through artificial intelligence-aided design. https://www.frontiersin.org/articles/10.3389/fpsyg.2020.595374/full
Zaki, M., McColl-Kennedy, J.R., & Neely, A. (2021, May 4). Using AI to track how customers feel-In real time. https://hbr.org/2021/05/using-ai-to-track-how-customers-feel-in-real-time?registration=success
Zare, M. & Mahmoudi, R. (2020). The effects of the online customer experience on customer loyalty in e-retailers. International Journal of Advanced Engineering, Management and Science, 6(5), 20-214.