Web Based Email Marketing Based Recommendation
Email marketing is a powerful tool which is very rapidly used by new organizations to promote their business.
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 and also city wise graph is generated to know in which location which product is used or
accessed more which in turn helps in deciding business strategy. 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. Also
on customer’s birthday special offer is given before 2 days to him/her. 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.
Keywords— Web Browsing, K-Means Algorithm, Filter, Sorted, Browsing History, Personal Information, City Wise