Traditionally, the merchant and customer data belongs to a service provider who can legally and illegally manipulate it, including selling merchant or consumer information to advertising agencies, or even deciding to turn off particular users. LoyalShopper tokens, which represent loyalty rewards, gift cards, store credits, and promotional discount coupons, belong to their owners - merchants and their customers - which opens enormous opportunities for innovative promotion programs, integrations between different platforms and products, and new ways of managing resources for both businesses and consumers.
Once tokens are obtained by a user, they can be redeemed, traded for other points, or sold. Merchants can set the rules on how their tokens are managed. This way, points never become useless and always have a value, which attracts existing and new customers.
LoyalShopper is an open platform, so everyone can use its API to create their own product or integrate LoyalShopper features into existing products. The same tokens can be used across different ecommerce platforms and point of sale terminals, both online and in brick-and-mortar stores, which makes LoyalShopper an omni-channel solution by design, with a variety of integration features available for users and developers out of the box.
All token transactions and balances are secure and private, so no one can trace the owners or their actions. Most points are fungible, i.e. they are interchangeable and identical to each other regardless of the ownership. Access to balances is anonymous by default (i.e. it does not require user registration); however, it can be simplified by single sign on with other platforms based on user preferences. Therefore, there is no traceable link between the points issuer (merchant) and points holder and user (buyer).
Based on merchant selection, machine learning algorithms are utilized to analyze customer behavior, market conditions, merchant preferences, and many other parameters to automatically select and implement the best promotion strategy and adjust it for better returns. For example, LoyalShopper AI Helper can decide on the amount of reward points granted for every particular purchase to a particular customer, instead of fixed numbers set by a merchant using trial and error strategy. On the other hand, machine learning algorithms help consumers to utilize their points with maximum efficiency. For example, the LoyalShopper AI Helper can make a decision (and implement it) on whether it is better to hold, redeem, trade, or sell particular points based on market situation and customer behaviour and preferences.