E-commerce Prediction Models

E-commerce and prediction is a match made in heaven! You can make use of our predicition models to learn more about your customers, and drive your sales using this knowledge.

There is a wide variety of possible usages of predicition models in e-commerce. The most commonly known predicition models are recommendations. You can recommend items to a user based on his known preferences and based on the behaviour of similar users. This way, you don't recommend items at random, and your users will be more inclined to buy your recommended products, because you know that he or she will like them.

But this is just the start. You can for example make a model predicting wether a customer will buy a certain item while visiting its product page. Or just if he will or will not buy multiple products during his visit to your site. If so, you might want to take him back to the shop after putting something in his shopping cart. If not, you might want to send him to payment page. Or you can make him a special offer when the likeliness to buy drops below a certain point, to tip the scales in your favor.

You might even make predictions about how likely it is that a customer will buy an item that he has put on his wishlist. With this knowledge in mind, you could retarget him in order to persuade him to really buy your item. After your customer has bought your item, you may even build a model predicting if he will cancel his order. You could use this information in your order picking.

But our prediction models don't have to be solely targeted to your clients. You can also put them to use to predict server load in the future, so you can proactively prepare your servers. You can do stock predicition to restock at the ideal time, or sales prediction that takes seasonality into account to prepare for summer or winter sales.

The possibilities for using Data Science in e-commerce are pretty much limitless. Did this get you interested? We're always ready to talk!

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