CRM through Mining of Customer Online Reviews and Touch Points

Merchants selling products on the web often ask their customers to give review about the products that they have purchased and the associated services. As e-commerce is becoming so popular, the number of customer reviews for a product grows rapidly. For a famous product, the number of reviews can be in hundreds or even thousands. This makes it difficult for a potential customer to read them and to make decision on whether to purchase the product or not. It also makes it difficult for the manufacturer of the product to keep track and to manage customer opinions are transferred into a feature-opinion pair and then mining is performed on the reviews utilizing the algorithm of association classification. For the manufacturer, there are additional difficulties because many ECommerce sites may sell the same product and the manufacturer normally produces many kinds of products. In this research, I have used mining to summarize all the customer reviews of a product. There have been several approaches developed based on the concept of discovering different change patterns in patents, news and consumer purchase data. This summarization task is different from traditional text summarization because we only mine the features of the product on which the customers have showed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a subset or rewrite some of the original sentences from the reviews to get the main points as in the classic text summarization. Our task is performed in following steps: (1) mining product features that have been commented on by customers; (2) discovering the opinion sentences in each review and deciding whether each opinion sentence is positive or negative; (3) summarizing the results. This paper proposes CRM strategic techniques to perform these tasks. Our experimental results using reviews written for the number of products sold online demonstrate the effectiveness of the techniques. The summarized results can help consumers and marketing managers to make decision.