Nowadays, when we need a product, the first thing we do is search it online. Even when we decide on one of the e-commerce websites, we come across different companies selling the product we need, or even different vendors selling the same product. These choices are so many sometimes that we can’t decide which one and we have left the website. Various filters are used to reduce selections on websites to help the decision and in order to prevent them from being unable to decide with the majority of the choice.
The most important factor among the factors that buy a product is the concept of Social Proof. Social proof is trying to get an opinion about a product by trying to understand how society reacts to the product. Reviews and ratings of the product are checked.
So social proof allows us to choose between many options more easily with the wisdom of crowds.
If social proof is the most important factor influencing purchasing, then we understand how important it is to rank product reviews and calculate ratings for products (for example, a rating by increasing percentages of recent ratings).
As a data scientist, at this point
- calculation of product scores
- sorting of products
- sorting of user comments
- design of the page, process, and interaction areas (for example, does the simplification of the page affect my purchase)
- feature trials (does a change applied to the website lead to more time on the site) These factors are the conditions that affect the purchase and need to be worked on.
https://medium.com/@didemmparlak/what-makes-a-product-buy-measurement-problems-a2c29650b4af