When a mother visits www.Pampers.com, the content on the website is filtered and displayed according to her baby’s age group. Similarly, if she has an account with Amazon, the company’s website recommends her products that match her tastes and preferences.
This ability to personalize user experiences is a key advantage that online has to offer. It is also a much-needed capability because websites are often overloaded with content — Amazon, for instance, with about 50 million books (2022) listed on its site, has a very long tail.
Personalization customizes the users’ interaction by bringing to the foreground content that is of greater relevance to them. The goal is to deliver the right content, to the right person, at the right time.
There are broadly two types of personalization — prescriptive personalization and adaptive personalization.
Prescriptive personalization is driven by a set of predefined rules or business logic, which may consider various factors such as the user’s profile, on-site behaviour, landing page, referral source (e.g., search engine), context (e.g., search query, time of day, season), location, transaction details, and more. Users are segregated into profile-based groups or “personas”, and content is tailored for each persona.
Prescriptive personalization is further categorized into two forms — explicit and implicit. Explicit personalization is based on information explicitly provided by the user. For example, in an earlier version of Pampers.com, parents could use a slider scale to set their baby’s age or provide their baby’s birth date when creating an account (as shown in Exhibit 19.11).
Implicit personalization does not require a user to create an account or provide any details. It tracks the user’s behaviours, their clicking activity, to determine the user’s preferences and accordingly filters the content. The current Pampers and Amazon websites take this approach. Based on what the parent is browsing, Pampers can quite easily gauge their baby’s age.
The Amazon website utilizes a type of user basket analysis to display products that were purchased by other customers who shopped for the same items as the user. This approach enables the website to recommend related products that are likely to be of interest to the user, based on the purchasing behaviour of similar customers.
E-commerce sites, including property portals use implicit personalization to keep users interested and engaged. For example, www.prop-gpt.com tracks user online behaviour to identify their preferences and filter out properties (as shown in the similar post section of Exhibit 19.12) that are likely to be of interest to them.
Adaptive personalization does not require set up — the system itself creates the logic and rules that govern what content is to be displayed. Historical behaviour of website users is modelled to categorize users and their preferences, and this data is used to personalize content.
The ability for sites to self-manage customization makes adaptive personalization much easier to implement and maintain than prescriptive customization. It is therefore a compelling option for sites that are seeking the benefits of personalization.
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