Use of Machine Learning is now essential to make it as a successful company involved in retail and eCommerce business. Every big label is known to use it nowadays. There is a good reason why – it provides countless methods to improve the whole selling system and due to that, it is proven that ML can increase sales even up to 300%. What are those methods?
Retail vs. e-commerce
It is hard to track all the actions of all the customers in stationery stores. Though you can attempt to track all the purchases by offering a customer a loyalty card which one should use every time it comes to payment, its power is limited. It is not possible for example to track how much time a person spends in a store, how long one decides to buy something and does not, etc. That is why eCommerce gives more possibilities than retail in stationery stores. There is simply more data to process which leads to more conclusions that can become reality. Such details of one’s shopping habits can provide information on what products to recommend.
Recommendations by definition anticipate demand because they have to be made before the purchase. If recommendations are accurate, we will make the sale which we probably would not make if we had not recommended the product in the first place. Maybe a customer would choose to purchase the product in the competition’s store or one simply would not realize that there even is a need for this product. Of course, we have to be careful with anticipating and recommending purchases to not creep people out, which the infamous Target story is all about. In a nutshell, Target knew about the pregnancy before the rest of the family did, this assumption was made based only on earlier purchases made by the girl. It turned out to be true. But we must not be completely scared of recommending anything to costumers, because when doing it right we can build up customer loyalty thanks to those individualized recommendations.
So, recommendations can be made to someone based on purchases of the individual but also on purchases of other people that may have something in common. The decision on matching people with products they would likely buy can be made either on collaborative or content-based filtering The first one refers to analyzing what else other people that also bought the thing you bought, bought, and the second one is largely based on characteristics of a client (for example gender or age) or on features of a product they like.
Of course, there would be some inconsistencies, especially if we have a lot of data to process. Detecting anomalies can be a good thing because it makes tracking abusive behavior easier. Many frauds could have remained undetected if not for the AI.
Automatization of customer service
Machine Learning is on such a high level now that AI can even answer phone calls. It could have provided problems in the past but now it is in our reach. Of course, AI can also write e-mail responses and recommendations, and provide support via chat as a chat-bot.
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