Balancing Hyper-personalisation And Consumer Choice
You’re walking into a store where the shelves are stocked with products tailored to your exact preferences, and the sales associate greets you by name, knowing exactly what you're looking for. Sounds like a dream, right? This is the promise of hyperpersonalisation – using AI to deliver experiences that are tailored to individual users' needs and preferences. But, as we'll explore, there's a fine line between personalisation and pushiness.
How Hyperpersonalisation is Implemented
Hyperpersonalisation uses machine learning algorithms to analyse user data, such as browsing history, search queries, and purchase behaviour. This data is then used to create detailed user profiles, which are used to deliver targeted content, recommendations, and offers. For example, Netflix uses hyperpersonalisation to suggest TV shows and movies based on your viewing history, while Amazon uses it to recommend products based on your browsing and purchasing behaviour.
Real-World Examples
Spotify's Discover Weekly playlist uses hyperpersonalisation to create a unique playlist for each user, based on their listening habits.
Sephora's Virtual Artist uses AI-powered chatbots to offer personalised makeup recommendations based on users' skin types, tones, and preferences.
Domino's Pizza uses hyperpersonalisation to offer customers tailored deals and promotions based on their ordering history.
The Fine Line between Personalisation and Pushiness
While hyperpersonalisation can be incredibly effective in delivering relevant experiences, there's a risk of crossing the line into pushiness. When users feel like they're being bombarded with irrelevant or intrusive messages, they're likely to become annoyed and disengage. For example, if a user receives a constant stream of emails from a retailer, even after they've unsubscribed, they're likely to view the brand as spammy and untrustworthy.
Why Providing Users with Choice is Crucial
To avoid the pitfalls of pushiness, it's essential to provide users with choice and control over their experiences. This means giving them the ability to opt-out of certain types of content or communications, and allowing them to customise their preferences. For example, Netflix allows users to create multiple profiles, each with its own set of preferences and viewing history. This way, users can choose to watch content that's tailored to their individual tastes, without feeling like they're being forced into a particular mould.
Implementing User Choice in AI Systems
So, how can we implement user choice in the AI systems we build? Here are a few strategies:
Transparency: Be transparent about the data you're collecting and how it's being used. Give users clear options for opting-out of certain types of data collection or communication.
Customisation: Allow users to customise their preferences and settings. This could include creating multiple profiles, setting content filters, or choosing the types of communications they receive.
Feedback mechanisms: Provide users with feedback mechanisms, such as ratings or reviews, to help them influence the content they receive.
Explainability: Provide users with explanations for why they're receiving certain types of content or recommendations. This can help build trust and transparency.
Hyperpersonalisation has the potential to revolutionise the way we interact with brands and services. But, to avoid the pitfalls of pushiness, we need to provide users with choice and control over their experiences.
By implementing transparency, customisation, feedback mechanisms, and explainability, we can build AI systems that deliver relevant and respectful experiences. By putting users in the driver's seat, we can create experiences that are both personal and epowering.