Email marketing is a powerful tool through which it is possible to talk directly to the customers about today's goods and future successes. Every business today require lots of marketing so that their product or business outcome will give them loads of profit and increase their progress graph. Many online business do their marketing through emails. But they are not aware of whether customer really liked that product. Web browsing is very popular activity till date wherein consumers not only purchase product online but also search information related to products and services before they purchase any product. We reduce the task of marketer's by introducing web recommender in which depending on customer's likes and dislikes of product they will be recommended their required product along with some product related to it. Along with likes and dislikes location wise clustering is followed. The logic we described constructs a data about history of user's web access data, habit and behavior, which in turn provides personal recommendation to users in timely manner. The Marketer need not be always online for sending e-mails, this is achieved by allowing automatic mail sending. This model also increases scalability and needs of market. This approach we introduced is build based upon path analysis, K-means algorithm which filter and provide sorted recommendation of resources based on user's browsing history, personal information through email marketing.

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Web Browsing K-means Algorithm Location Wise Filter Sorted Browsing History Personal Information Automatic Mail Sending