UX AI Transformation: The Era of Personalization
This post covers one of four transformations and is part of a larger blog series titled 'From Hype to Reality: A Practical Guide to AI-driven UX in SaaS'. You can find the first post of the series here.
Personalization in SaaS: AI's Role in Customized Experiences
While personalization has long been a goal in SaaS, relying on static settings and generalized personas had its limitations. Now, with the advancement of machine learning techniques such as recommendation systems, Natural Language Processing (NLP), and deep learning, the era of personalization has dawned. From Deloitte reporting on the Segment of One, to Jacob Nielsen envisioning a future where everyone will have the perfectly auto generated accessible website the possibilities for personalized experiences are expanding rapidly, ushering in a new era where every interaction feels tailor-made for the individual user.
The pioneers in this realm have been leveraging their vast data sets and emerging technologies to deliver highly personalized experiences for some time. Amazon, for example, utilizes AI-powered recommendation engines to precisely match customers with products they desire. Netflix and Spotify employ similar algorithms to tailor content recommendations and playlists to individual user preferences.
While hyper-personalization, or "Segment of One", isn't yet ubiquitous across SaaS, notable examples of personalisation are emerging:
Canva: Utilizes user behavior data to suggest design templates and layouts that align with the user's previous projects or trending designs among similar users.
Notion: Offers a personalized onboarding experience by segmenting users during signup to showcase the most relevant features based on their needs.
Adobe's Sensei AI: Enables dynamic workflows in creative software that adapt to the user's style and preferences.
Grammarly: Provides personalized feedback tailored to the context of the user's writing and their specific goals, whether professional or casual.
Perplexity is in very early dates of exploring generative UI for its prompt input interface.
Personalized Experiences in Focus: Shaping User Engagement
There's a growing expectation for highly personalized experiences, fueled by the standards set by platforms like YouTube, Netflix, Amazon, and Spotify. Studies have consistently shown that personalization leads to higher engagement rates. However, the landscape of user experience regarding personalization is nuanced:
Privacy Concerns: While users demand personalized experiences, there's a simultaneous concern about data collection, usage, and storage. Overly personalized experiences can sometimes trigger a "creepy factor" when users become uncomfortably aware of extensive data monitoring.
Desire for Transparency and Control: Research indicates that users crave transparency and control over their data. They want to understand what data is being collected and have the ability to influence or opt out of data collection. Regulations like GDPR in Europe are driving companies to provide greater transparency and control to users.
Designing for Personalization: Balancing Expectations and Privacy
To navigate the complexities of personalization, designers should:
Get more familiar with the data and assessing it for biases.
Explore how to shape your static personas to be useful in this new dynamic world
Emphasize user control in personalization efforts such as adjusting what data the system uses to personalize the experience or opting out of certain types of personalization.
Provide transparency into where the experience is being personalised and how AI is being used
Clearly communicate the benefits of personalization and how AI enhances the user experience to build trust and acceptance.
In this era of personalization, balancing user expectations with privacy concerns is paramount to fostering trust and delivering exceptional experiences.