Abstract: Recommendations for purchase are made based on customer behavior across multiple sessions. Correlations used for recommendations include: buy-to-buy (cross-session), view-to-view (same-session), view-to-buy (same-session), and abandon-to-buy (same-session) actions. A preview display allows a merchant to adjust recommendation algorithm weightings to achieve a desired result. A closed-loop system is provided with real-time feedback. The recommendations can be based on various segments of other users, including users of the same search engine.

Abstract: Recommendations for purchase are made based on customer behavior across multiple sessions. Correlations used for recommendations include: buy-to-buy (cross-session), view-to-view (same-session), view-to-buy (same-session), and abandon-to-buy (same-session) actions. A preview display allows a merchant to adjust recommendation algorithm weightings to achieve a desired result. A closed-loop system is provided with real-time feedback. The recommendations can be based on various segments of other users, including users of the same search engine.

Abstract: A process for tracking a consumer's behavior based on his or her entries into a number of cybernetic device sources such as personal computers, automated goods and services dispensing kiosks, automated teller machines and cell phones does not depend exclusively upon the presence of cookies implanted into such devices by vendors or upon a consumer's having provided a positive identification form. The process uses a pairing algorithm to comb elements of information from the source device visitation records in search of one or more common characteristics that can be presumptively attributed to a single consumer. Characteristics such as source device identifiers, phone numbers, street addresses or email addresses are each weighted by a weighting factor that is used to identify the consumer and attribute the visitation record and its associated activity to that consumer at a calculated confidence level. The attributed information can analyzed to track that consumer's behavior.

Abstract: A process for tracking a consumer's behavior based on his or her entries into a number of cybernetic device sources such as personal computers, automated goods and services dispensing kiosks, automated teller machines and cell phones does not depend exclusively upon the presence of cookies implanted into such devices by vendors or upon a consumer's having provided a positive identification form. The process uses a pairing algorithm to comb elements of information from the source device visitation records in search of one or more common characteristics that can be presumptively attributed to a single consumer. Characteristics such as source device identifiers, phone numbers, street addresses or email addresses are each weighted by a weighting factor that is used to identify the consumer and attribute the visitation record and its associated activity to that consumer at a calculated confidence level. The attributed information can analyzed to track that consumer's behavior.

Abstract: Recommendations for purchase are made based on customer behavior across multiple sessions. Correlations used for recommendations include: buy-to-buy (cross-session), view-to-view (same-session), view-to-buy (same-session), and abandon-to-buy (same-session) actions. A preview display allows a merchant to adjust recommendation algorithm weightings to achieve a desired result. A closed-loop system is provided with real-time feedback. The recommendations can be based on various segments of other users, including users of the same search engine.

Abstract: Recommendations for purchase are made based on customer behavior across multiple sessions. Correlations used for recommendations include: buy-to-buy (cross-session), view-to-view (same-session), view-to-buy (same-session), and abandon-to-buy (same-session) actions. A preview display allows a merchant to adjust recommendation algorithm weightings to achieve a desired result. A closed-loop system is provided with real-time feedback. The recommendations can be based on various segments of other users, including users of the same search engine.