Whisk can make food experiences more personal, enjoyable, and intuitive by learning about a user’s food preferences — everything from diets and allergies, favourite cuisines, preferred store brands, nutrition goals, and more.
Whisk’s personalisation system builds a deep understanding of a user (their preferences, behaviour, and context) by collecting a wide range of data from both explicit and implicit behaviour.
Whisk can provide more relevant content (such as recipe recommendations) and make it quicker and easier to use by intuitively acting based on user preferences (such as which store items they want based on brand or budget).
How it works
Hard Constraints vs. Soft Influences
Whisk personalisation applies two basic principles to the user experience:
Whisk uses a combination of data in three categories of hard constraints and soft influence: preferences, context, and user behaviour.
Preferences are data that the user has explicitly and intentionally entered in the system. Preferences can be broken down into three sections:
1.1 Constraints (Hard)
1.2 Interests (Soft)
Contexts are external factors and circumstances surrounding a user’s interactions with the system. These all affect personalisations and recommendations throughout the system.
Whisk collects large amounts of data from a user to algorithmically learn about tastes and preferences. Data is collected automatically from user interactions with the system, including:
You can read more about how best practices in integrating personalisation into your app in our separate article here.