Simply everything can be bought online nowadays. From mass production products to specific niche products. However, about a third of all online transactions are returned (1). Buying online can result in certain disadvantages compared to buying in an actual store, one of which is the impossibility to physically hold the product, which can in the case of online clothing retailers, result in product returns. E-commerce clothing retailers like Zalando and Wehkamp.nl use a lot of different kind of recommendations to help the customers make satisfactory choices.
When buying clothes online, a customer can not try the article behind his/her computer screen. A customer will not know if the fabric is what the customer wants or how the product will actually look when he/she wears it. Does it look fancy? Slobby? Casual? Formal? And most of all, a customer will never be sure if the clothing actually fits unless he/she tries it on. This disadvantage has to do with the fact that clothing is an experience product characterized by the attributes that need to be experienced before the purchase, like taste, softness or fit (2). According to Xiao and Benbasat (2007), the use of recommendation agents influences the choice of users to a greater extent in the case of these products. What kind of recommendation agents do Zalando and Wehkamp.nl actually have to help customers choose? Continue reading Experiencing clothes behind a computer screen →
Nowadays, interactions between consumers and firms are increasing in complexity and intensity. Companies need to learn how to deal with it in order to remain competitive.
Moreover, when looking at the world pyramid of ages, we can see that if a 26 years old male is looking forhis optimal female partner (assuming that “optimal” implies the girl is in the same age range and also that there is only one optimal choice), he has only one in 275,001,000 (- the number of females he already met) chance to meet her. His chances fall even lower if we consider that there is no reason why both partners should be in the same age range. Thus unless the guy is very lucky, he might need some help to find the right partner. Continue reading Recommendation System @Match.com →
What if you could learn French for free on your smartphone where and when you want to?
This is exactly what DuoLingo offers. Well, actually if your English is good enough, you could also start learning Spanish, Italian, German or Portuguese. Conversely, if you speak one of these 5 languages, Dutch, Russian, Hungarian or Turkish, you might want to refresh your skills in English. Your lessons will mainly consist in translation exercises adapted to your level of skills in the language of your choice, and hints will be provided to help you when exercises get difficult.
But wait a minute, how can this app offer lessons in 6 languages for free and without any commercials?
Continue reading Parlez-vous Français? →
Netflix was initially an American company in the business of DVD’s rental and was launched in 1997 and headquartered in California. Its initial core business was renting and mailing DVD’s to their clients. They launched their own website in the same year and developed several new rental models such as the online rental model and the monthly subscription model. At that moment, they had already developed video recommendation systems based on the experience of their customers that they cautiously recorded. For instance, they implemented systems for the clients to express concern about movies and to create movies’ ranking.
However, the market significantly changed over the last ten years with the development of the digital movies at the expense of the physical DVD’s. As a result the market was progressively moving away from the physical DVD’s to a more digital business based market. Netflix has subsequently responded to this trend and has changed dramatically over the last ten years. Continue reading Recommendation system at Netflix →