When a mother visits www.Pampers.com (Exhibit 13.9), 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 overloaded with content — Amazon, for instance, with over 50 million books listed on its site, has a very long tail.
Personalization customizes the user’s interaction by bringing to the foreground content that is of greater relevance to her. 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 based on a set of predefined rules or business logic. The logic may be based on a number of factors including profile, on-site behaviour, landing page, where the user came from (e.g. search engine), context (e.g. search query, time of day, season), location, transaction details and so on. Users are segregated into profile-based groups or “personas”, and content is tailored for each persona.
Prescriptive personalization is further divided into two forms — explicit and implicit. With explicit personalization the users’ profiles are often based on details provided by the users. For example, in an earlier versions of Pampers.com, parents used a slider scale to set their baby’s age, or if they maintained an account, they provided their baby’s birth date while creating the account (Exhibit 13.9).
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 he or she is browsing, Pampers can quite easily gauge the customer’s baby’s age. At Amazon, the site employs a form of user basket analysis to display products that other customers, who shopped for the same product, also shopped for.
Exhibit 13.10 provides a typical example of implicit personalization. Here the property site www.mas-HomeFinder.com tracks online behaviours to determine the users’ preferences, and filter out properties (similar post) that they might be interested in.
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.
Note: To find content on MarketingMind type the acronym ‘MM’ followed by your query into the search bar. For example, if you enter ‘mm consumer analytics’ into Chrome’s search bar, relevant pages from MarketingMind will appear in Google’s result pages.
Two-day hands-on coaching on Digital Marketing and Advertising, to train participants in developing and executing effective digital marketing strategies.