Some servers even record the page that referred you to them, websites you visit after them, which ads you see and which ads you click on. Online advertising uses cookies, a tool used specifically to identify users, as a means of delivering targeted advertising by monitoring the actions of a user on the website. For this purpose, the cookies used are called tracking cookies. An ad network company such as Google uses cookies to deliver advertisements that are relevant to the interests of the user, control the number of times that the user sees an ad and "measure" whether they are advertising the specific product to the customer's preferences. This data is collected without attaching the people's names, address, email address or telephone number, but it may include device identifying information such as the IP address, MAC address, cookie or other device-specific unique alphanumerical ID of your computer, but some stores may create guest IDs to go along with the data. Cookies are used to control displayed ads and to track browsing activity and usage patterns on sites. This data is used by companies to infer people's age, gender, and possible purchase interests so that they could make customized ads that you would be more likely to click on. An example would be a user seen on football sites, business sites, and male fashion sites. A reasonable guess would be to assume the user is male. Demographic analyses of individual sites provided either internally (user surveys) or externally (Comscore \ Netratings) allow the networks to sell audiences rather than sites. Although advertising networks were used to sell this product, this was based on picking the sites where the audiences were. Behavioral targeting allows them to be slightly more specific about this. In the work titled "An Economic Analysis of Online Advertising Using Behavioral Targeting," Chen and Stallaert (2014) study the economic implications when an online publisher engages in behavioral targeting. They consider that the publisher auctions off an advertising slot and are paid on a cost-per-click basis. Chen and Stallaert (2014) identify the factors that affect the publisher's revenue, the advertisers' payoffs, and social welfare. They show that revenue for the online publisher in some circumstances can double when behavioral targeting is used. Increased revenue for the publisher is not guaranteed: in some cases, the prices of advertising and hence the publisher's revenue can be lower, depending on the degree of competition and the advertisers' valuations. They identify two effects associated with behavioral targeting: a "competitive effect" and a "propensity effect." The relative strength of the two effects determines whether the publisher's revenue is positively or negatively affected.