This paper presents the task of recognizing product information (PI) (i.e., product names, prices, materials, etc.) mentioned in customer statements. This is one of the key components in developing artificial intelligence products to enable businesses to listen to their customers, adapt to market dynamics, continuously improve their products and services, and improve customer engagement by enhancing effectiveness of a chatbot. To this end, natural language processing (NLP) tools are commonly used to formulate the task as a traditional sequence labeling problem. However, in this paper, we bring the power of machine reading comprehension (MRC) tasks to propose another, alternative approach. In this setting, determining product information types is the same as asking “Which PI types are referenced in the statement?” For example, extracting product names (which corresponds to the label PRO_NAME) is cast as retrieving answer spans to the question “Which instances of product names are mentioned here?” We perform extensive experiments on a Vietnamese public dataset. The experimental results show the robustness of the proposed alternative method. It boosts the performance of the recognition model over the two robust baselines, giving a significant improvement. We achieved 92.87% in the F1 score on recognizing product descriptions at Level 1. At Level 2, the model yielded 93.34% in the F1 score on recognizing each product information type.