Talk:Coupon/Archives/2018
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Semi-protected edit request on 25 April 2016
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Please change the following section to read as follows:
Mobile app coupons
A mobile app coupon can be a regular coupon for redemption (discount value or discount percentage) used at checkout distributed by the app-makers, a rebate coupon from a mobile app, or unique, personal sharing codes owned by each user to recruit new users with referral benefits (e.g. Airbnb, Uber). The latter form requires personal sharing on behalf of users to their personal networks. Mobile app coupons that offer a rebate, such as ibotta, savingstar, and Checkout 51,and GroceryiQ, can be used in conjunction with manufacturer and store coupons to enhance your savings and often have overlapping savings. These apps require that you select the offer prior to making your purchase and then submit a copy of your receipt to receive a rebate typically deposited into your paypal account.
Gamachel (talk) 17:07, 25 April 2016 (UTC)
- Not done. Request includes inappropriate use of external links, and is promotional in content. Scr★pIronIV 17:18, 25 April 2016 (UTC)
Not done per above. flipping switch. — Andy W. (talk · contrib) 18:02, 26 April 2016 (UTC)
Digital Coupons
The article refers to retailers providing digital coupon systems. This is akin to writing that retailers provide credit card payments. In actuality, Digital Coupons ride competing networks similarly to credit cards. The three main companies in the space are Quotient (fna Coupons Inc.), YouTech and Inmar. These companies all offer entry points for digital coupon content and retailers sign up for (generally) one network to subscribe to to get their content and receive remuneration for display, selection and redemption thereof.
The key difference in the distribution methods might be left out for concision. The interesting thing about digital couponing is the general requirement for "basket data" (the list of items purchased) to be matched against a digital wallet (selected discounts). This inherently brings with it nuances in targeting, collaborative filtering, bounce-back behaviour and future preference management all based on the analysis of an identity (loyalty or phone # for example) with purchase data. This makes such systems or networks very interesting to companies interested in treating the network like a digital ad network with all of its constituent components.