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The 4R Personalisation Framework by Accenture

Updated: Sep 13, 2019

Personalisation can be defined as the action of vigorously planning and designing the experiences of each individual to meet their desired requirements across all available channels. This can include all communication types, such as, marketing, shopping and services experiences, online and offline.

In order to narrow down this topic and help companies identify their personalisation abilities,

Accenture has developed the 4R Personalisation Framework:


Customers wish to be identified by their own name when they arrive to a website, in the same way they would be recognised personally in their favourite brick and mortar clothing shop. It is possible for companies to identify them through CRM databases, social media accounts or DMP's. By welcoming customers by name, the brand can create a bond and feeling of trust, making the customer feel special and increasing the likelihood of them returning to purchase.


Customers not only want for brands to remember their identity, they are also willing for them to know beforehand their predilections without having to state them. This entails not only knowing what customers purchase, but also why these purchases are made. For instance, having a record of customer A purchasing in the last 3 months vegan beauty products, suggests customer A is an 'eco-friendly' consumer who is aware of the impact her/his purchases are having on the environment, and therefore cares about the well-being of living creatures.


Remembering and having a record of what and why consumers have recently purchased, allows brands to offer recommendations guided by this data. They can be reached through marketing by their preferred and most used channel, for instance, social media over email, if their view rate on email has been recently low. They should also be shown the right content which appeals exactly to them, such as an advertisement of the launch of the new vegan skin care product range, for the customer segment which has indicated this category as a shopping preference.


The recommendations made should not only be suggested in terms of feature similarity, but also in terms of relevance regarding the time of the year, location and customer's profile. In other words, the context of the purchasing situation should guide the recommendations. During the Christmas period, for example, a discounted moisturising for ageing skin should be announced to all those consumers that have purchased products for this type of skin concern, or who have previously browsed in the website of anti-ageing products. A relevant and tailored recommendation compared to "what others have viewed/purchased" will guarantee an increase in sales and loyal customers.

At Beauty Matching Engine, we comply with the 4R Framework, thus, we ensure all beauty companies that choose to work with us offer an enhanced and personalised customer journey from beginning to end. Our AI and machine learning driven technology allows beauty brands and retailers to track their customers purchasing behaviour and use this data to personalise landing pages, up sells and cross sells, and triggered online campaigns.

If you are looking to start the personalisation journey for your customers, then book a demo with us today!


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