Their usage is hard to track or does not reveal useful information about user preferences, or it is difficult and slow to gather informative feedback from users. The reason these feedback loops are so strong is that users reveal clear and unambiguous signals of their preferences in the natural process of consuming the product, which are leveraged to further improve the product or the service for those users.Īt the other end of the spectrum are products with naturally weak data feedback loops. Or Spotify, whose recommender system learns directly from users’ choices of which recommended songs to include in their playlists and how often they listen to those songs. Or Google Maps, where every user’s choice of route and the time taken to reach the destination help the algorithm improve its route recommendations and traffic predictions. Think of smart thermostats, where every temperature adjustment by a user provides a valuable data signal that the device can use to achieve better personalization. Some products have naturally very strong data feedback loops. Not All Data Feedback Loops Are Created Equal But the strength of these data feedback loops can vary greatly, and companies can make deliberate choices in their products or services to strengthen them. Such data feedback loops can help create a sustainable competitive advantage, provided that certain conditions exist. Think, for example, of search engines: the more people search on Google and click on the links provided, the more data Google gathers, which allows its algorithms to provide more accurate and relevant search results, attracting even more users and searches, and so on.
This means that as a firm gathers more customer data, it can feed that data into machine learning algorithms to improve its product or service, thereby attracting more customers, generating even more customer data.
The combination of user data and AI often creates data feedback loops.
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