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Assessing and Using User Intent to Improve Ad Recommendations on Mobile Devices Disclosure Number: IPCOM000200894D
Publication Date: 2010-Oct-29
Document File: 2 page(s) / 76K

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The Prior Art Database


Traditional CRM channels are not controlled by the CRM sender, and hence cannot be monitored. Examples of such traditional channels include paper mailings, and email. However, when CRM offers are made through channels within the control of a service provider (e.g., when a mobile service provider sends CRM offers through SMS), activities happening on that channel can be effectively monitored by the service provider. Disclosed herein is a system and method to guage user responses to CRM offers sent through such "in-control" and multi-purpose channels to quantify the recipients response to the CRM offer. Such quantifications would help the service provider to improve his/her targeting of CRM offers in the future.

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Assessing and Using User Intent to Improve Ad Recommendations on Mobile Devices

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Provided above is an example system diagram for a system that is being claimed herein. This illustrates the specific case where the in-control multi-purpose channel is a mobile device.

The major parties involved are:
1. The Service Provider that has the responsibilities of maintaining the coupon database containing coupons that have to be sent to targeted customers. The service provider also maintains the recommendation engine which decides which coupon is relevant for which user.
2. Advertisors who supply coupons to the service provider. The advertiser would pay the service provider using various measures like per impression, per click or any of the standard methods.
3. Users, who periodically receive coupons from the service provider and are free to do whatever they choose to, with the coupons that they get. In particular, this set covers only the set of users who have opted to receive coupons (not all users may want to receive coupons since they may find it intrusive).

The responsibilities for the various pieces of relevance are as follows:

Recommendation Engine: This periodically, by looking up the profile of the users and the coupons available in the coupon database, send coupons to users who are likely to be interested in the coupon. Response Quantifier: Once a user X receives a coupon Y, the response quantifier collects...