{"id":455,"date":"2017-04-12T12:23:54","date_gmt":"2017-04-12T09:23:54","guid":{"rendered":"https:\/\/rees46.com\/blog-en\/?p=455"},"modified":"2017-04-28T17:56:43","modified_gmt":"2017-04-28T14:56:43","slug":"progressive-personalization-in-cosmetics-beauty-ecommerce","status":"publish","type":"post","link":"https:\/\/rees46.com\/blog-en\/2017\/04\/12\/progressive-personalization-in-cosmetics-beauty-ecommerce\/","title":{"rendered":"Progressive Personalization in Cosmetics & Beauty eCommerce"},"content":{"rendered":"

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It is a widely known fact that personalized product recommendations are one of the main marketing tools of any online store. They help you sell more to every customer. However, sometimes, recommendation engines fail to give relevant recommendations in cosmetics and recommend a wrong type of hair conditioner or hand cream. Sometimes it\u2019s even worse when a person allergic to a certain component is recommended items with that component.<\/span><\/p>\n

To solve this problem, the market had to come up with the Progressive Personalization technology. Every one of us, as a customer, has a set of unique key parameters a recommendation system must include into its computations to eliminate any chance of error. What it is and how it works you\u2019ll have known reading this post.<\/span><\/p>\n

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Why Regular Personalization Fails for Cosmetics & Beauty Stores<\/span><\/h2>\n

Personalization can rely on different types of approaches. One of the most popular is the Big Data approach. A Big Data-based personalization predicts the consumer demand based on a collective behavior. For example:<\/span><\/p>\n

A customer is reading the product details of a hand cream by Clinique. Big Data tells the recommendation system that 5000 other consumers bought this cream visiting this product detail page, and 1000 of them also bought a Wella shampoo, at least once. Based on that, the system recommends this shampoo to the given consumer.<\/span><\/em><\/p>\n

Here is where Big Data-based personalization fails. The consumer has oily hair, while the shampoo is designed for people with dry hair. Although, statistically, that recommending decision was not incorrect, as a lot of consumers from this group bought the shampoo, this case shows the major flaw of Big Data-based approaches. The recommendation has no value either to the store or customer.<\/span><\/p>\n

There is another aggravating factor in this situation: the nature of Cosmetics & Beauty does not allow universal recommending algorithms, as the purchase decision in this retail segment is influenced by a very particular set of key parameters. A man would almost never buy a shampoo designed for women. Dry skin requires specific products, making normal and oily skin products irrelevant to the user. A consumer allergic to certain components would never purchase any products based on any of those components. The list goes on.<\/span><\/p>\n

The otherwise strong sides of Big Data-based types of personalization, such as statistical analysis and CF (Collaborative Filtering), are useless for individual personalization failing to form the right kind of links and see the relationship between the events.<\/span><\/p>\n

Key Parameters in Cosmetics & Beauty<\/span><\/h2>\n

A distinctive feature of Cosmetics & Beauty that differentiates it from any other retail segment is highly individual purchase decisions that are in\ufb02uenced by a great variety of user parameters:<\/span><\/p>\n