This paper studies the pricing strategies for personalized product bundles. In such problems, a seller provides a variety of products for which customers can construct a personalized bundle and send a request for quote (RFQ) to the seller. The seller, after reviewing the RFQ, has to determine a price based on which the customer either purchases the whole bundle or nothing. Such problems are faced by many companies in practice, and they are very difficult because of the potential unlimited possible configurations of the bundle and the correlations among the individual products. In this paper, we propose a novel top-down and bottom-up approach to solve this problem. In the top-down step, we decompose the bundle into each component and calibrate a value score for each component. In the bottom-up step, we aggregate the components back to the bundle, define important features of the bundle, and segment different RFQs by those bundle features as well as customer attributes. Then we estimate a utility function for each segment based on historical sales data and derive an optimal price for each incoming RFQ. We show that such a model overcomes the aforementioned difficulties and can be implemented efficiently. We test our approach using empirical data from a major information technology service provider and the test result shows that the proposed approach can improve the effectiveness of pricing significantly.