Shopping is inherently linked with making purchasing decisions. Which product to choose from the many offered? Have you ever wondered how does a customer make this choice?
In traditional stores a shop assistant will approach customers, enquire about their needs and help pick the right product. Such a business model calls for at least two assistants, so works best in high-margin domains e.g. luxury goods.
In the self-service model of the online store, the customers rely on their own search – filtering and comparing offers, not knowing if their choice was right. Filters work in binary fashion and very good products might end up filtered out, just by missing one criterion (e.g. price) by a tiny amount. This model is practically cost-free to the store, and the hidden cost, passed on to the customers, is the time they have to spend on choosing the right product and making a decision. Often before it happens, customers leave, undecided or frustrated.
Data Driven Artificial Intelligence
Data driven AI-based solutions come to the rescue. They present automatically generated recommendations based on similarities to other customers or purchasing history. Although easy and cheap to implement, they have one drawback – they need relatively large datasets to make accurate recommendations.
What makes feeCOMPASS®
stand out from the competition?
feeCOMPASS combines the advantages of an experience shop assistant from a traditional store with automatic recommendation given by AI solutions, while eliminating their disadvantages. Our system uses:
information about the client’s needs and preferences, expressed in non-specialist, easy-to-understand language,
an intelligent algorithm that creates personalised rankings for each client, based on knowledge acquired from expert salespeople
We guarantee better performance than AI algorithms with similar operating costs. And with many additional useful features.