New technology lets you know what your customers want — before they do
Recommendation Engines are an integral part of e-commerce. But do they always hit the bullseye? Yes, but only if all customer-relevant data and their relationships to each other are evaluated in real time. When Google Ads recommends you hotels in a place you visited two weeks ago, this isn’t very helpful.
One step ahead of the customer
Product recommendations created via so-called recommendation engines are among the most important building blocks in e-commerce. Content-based systems analyze features of products and compare them with the customer’s online behavior. If the user has already ordered a book through the online shop, the website using a standard recommendation service will recommend other titles of the same genre or author.
However, it is possible to do much more. In collaborative filtering, for instance, the patterns of behaviour of user groups are evaluated in order to deduce the interests of individuals. If user X rates a purchased item positively, it will be recommended to the ‘similar’ user Y as well. In the previous case it is the characteristics of a product, and in the second it is the relationships with other buyers that form the basis of recommendations.
Such complex networks of relationships are difficult to map in relational databases. Also, the query would take too long for the recommendations to be made in real-time. Not infrequently recommendations, therefore, are based on outdated data. This is especially annoying when recommending a product that has just been bought, returned or even badly valued. Such “wrong” recommendations can lead to customer complaints or even resentment, rather than real added value.
Really relevant recommendations take into account comprehensive additional information and provide a personalized experience to each customer by showing them content that suits their age, gender, education, interests and other characteristics. For the proposals to actually hit the bull’s eye and trigger a deal, all of these individual factors must be included in the recommendations.
Profiles enable a real-time customization of websites
A tech startup is taking recommendations a step forward and developed a system, that can adjust whole websites to individual visitors.
BehaviourExchange uses artificial intelligence to analyze a broad array of big data to create virtual user profiles. The system can ‘recognize’ visitors by their profile the moment they enter a website, and automatically adjust its content to suit each visitor’s characteristics, needs, and wishes. Users who decide to allow the platform to process their data remain in the possession of their blockchain-based profiles at all times – they can, at any time, decide not to reveal it to the platform.
To achieve transparency, data protection and fair distribution of profits among all stakeholders, the BehaviourExchange system depends on a utility token (BEX) economy. Users are rewarded with tokens for sharing their profile information with the system and can use those for shopping, while B2C businesses can pay for the recommendation services with BEX tokens at discounts.
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