In marketing research, a lot of research is done to provide insights into how at psychological and market level customers behaviour works. Data Scientists from marketing research domain have been taking about ideas especially, needs, wants and demands in content of recommendation engines.
The understanding needs & wants models to provide a wide angle perspective of Here I just want to give a refresher on types of need with the popular car example. While reading through the example try to think what needs our recommendations engine fulfill by churning volumes of data.
There are five types of needs as per marketing research.
Now, as a Data Scientist I need to ask, which needs are we capturing in recommendation systems and which needs does actually recommendation system fulfils by predicting?
The recommendations engine is built on the purchase history. The recommendation becomes strong by each positive feedback you give to the system. So, if you started browsing shoes of any kind, the system builds a hypothesis that you like shoes. Now every moment you click on shoes or buy, the signal gets stronger and your choices get narrowed by the recommendation engine to shoes. These are psychological cages being created by engines which drift customers away from variety and fulfilling needs except stated needs.
Over period this can eventually cause permanent changes in customer behaviour and market structure;
Data Science is going to change a lot in our daily lives and will give rise to a new civilisation. We have seen these transformations happening in Industrial revolution, Computer revolution and now witnessing in AI revolution. It is important for us to frame new era of AI using our collective knowledge of centuries to create a sustainable and in tandem with nature civilization.
Ethical use of data science for betterment has been at core of sustainable development.
So, what are next steps?
Probyto can work with you to understand how your recommendation engine is working and influencing your users. Limit the adverse effects of recommendation engine and keep happy growing customer base.