. We can manipulate the order of products in the listing so that users find many "new" ones.

. We can advise users on additional purchases using machine analysis. For example, we may use statistics and behavioral patterns of similar users to suggest “best purchases” ().

. We can create a category of “goods with the best price-quality ratio." For example, "the highest quality products under $ 9.99".

. We can speed up product/service selection by adding an "unfavorable option" to the pages where users stay (think) the longest.

. We can create "offer-forks," where there is an opportunity to get some product at a good discount "right now." Or we can let them get a bigger discount on the item sometime later.

. We can appeal to the novelty of a proposal in order to show that it is "better than everything that came before."

. We can experiment with the product wrapper - description, meaning, accents, etc. The fewer services/goods we have, the more critical is the perfection of the wrapper.

. We can create a fundamentally new product category where users will be willing to pay extra money for. It can be a seasonal offer or something like "Limited time offers of July." The idea is for a person to consider their spending on our project not as a whole but break them down into categories. Example: "I spent $190 shopping at Amazon and only $29.50 on monthly sale items."

. We can push users to spend more time and effort on a particular part of the product, with a consequent increase in its cost. Many companies are guided by this logic, transferring their most popular features into higher service plans.

If we operate with many price tags or different products, we can effectively use .