As Internet users, we receive each day many offers for multiple products. Robots send them to us through various communication channels. How do advertisers choose which products to show and how to contact us ?
Photo Het Nieuwe Instituut (license Creative Commons)
We generally call these mechanisms “recommendation engines”, and of course, they are based on data!
For end users, the promise of this technology is that “if you liked this, you may like that”, but it is actually fairly more complex. A good recommendation engine is often a mix of different algorithms, corresponding to several basic principles of purchasing behavior.
Remarketing is the idea of pushing to the user some content that he has already viewed, but not yet purchased (he might have forgotten about it, still be hesitating, …)
Remarketing generally delivers good performance, but is limited in number of users for whom it is relevant.
Recommending by similar content is about pushing products that are similar to other ones that the user has liked or bought, ie. that share similar characteristics. For example, if you previously liked a red sweater, you could get suggestions for other red sweaters.
Content similarity is generally the first thing that comes to mind when talking about recommendation. While it is very useful for users who are looking for alternatives, overusing it can lead to always showing the same content to the user instead of expanding its exposure to your products.
Recommending by profile similarity focuses on the actual behavior of users (“who bought what ?”) by comparing their purchase history. We say that products are similar, not if they share the same features, but if they are actually bought by the same people. Therefore, users are “close” if they bought or were interested in the same products.
This type of recommendation is very effective for products without well-defined features, such as cultural goods.
Recommending by short-term purchasing sequence is when you study which products are frequently bought together (same cart , same day) by many different users. Using it, we can find complementary products (a TV and its cable, a console and its games, a pair of earrings and a necklace).
This type of recommendation is generally used when adding items to a shopping cart, to suggest additional related products.
In this kind of recommendation, we look at purchasing sequences over several weeks or months. It helps pushing content corresponding to a change in life: having a baby, moving, switching jobs, etc.
This type of recommendation is particularly interesting to generate repetition in sales for users with the same demographic.
Last but not least, recommendation by popularity consists in pushing products that are most likely to be globally purchased (by brand, trend, etc.).
Like an audience acquisition campaign, which requires a marketing mix, a good recommendation strategy should make good use of the principles mentioned above.
Other criteria should also be taken into account when designing your recommendation:
A good strategy is to use two or three of the principles mentioned above depending on well-understood contexts. For example:
"Recommendation" has multiple meanings, but let's not forget common sense : the customer is not easily fooled. Visitors on a website are more and more aware of the sollicitations. Some users seem already tired of suggestions and start rejecting any advertising presence or are afraid of being tracked.
Once again, technology is bound to evolve. One day, hopefully, recommendation engines may behave like your favorite store salesman, understanding when it is not the right time to bother you, and letting you stroll in peace.
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