Headline - Using implied s telegram user database ocial proof and herd mentality with a headline of "Best Sellers"
Reviews and Ratings - Second, they showcase a products ratings, giving further security that it is a high quality product.
Displayed # of Reviews - Third, they underline a products popularity and give more credence to the ratings by displaying the number of reviews a product receives.
Trust Icons - Lastly, they display their Prime Icon, which has built up reputation on automatic two day delivery and Amazon backed fulfillment/customer service.
9. Aid decision making with comparison widgets
Product recommendations can also be used to help customers make the best decisions. Again, Amazon provides an instructive example of how to combine product recommendations and product attribute data in an easy to digest format.
When combined with merchandizing strategies, brands can control what context they are comparing the current product, and help influence the ultimate purchase decision.
How do Product Recommendation Engines Work?
The purpose of product recommendations is twofold: first, to improve the shopping experience, and second to increase revenues.
Product recommendation systems do this by presenting shoppers offers they are most likely to want.
Engines sift through the tens, hundreds, or thousands of items a store carries, and decide which one best suits this particular user.
Generally speaking, there are three broad techniques engines use to filter through SKUs.
1. Collaborative Filtering Technique
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Collaborative filtering uses other user’s actions to predict what another user will like.
For example, if one user bought dresses, but ultimately bought a purse, the software would begin to draw a correlation between these two categories. As more and more users confirmed this association, it would begin influencing which products were being recommended.