Contact centers, as the organization’s touch point, have a considerable effect on customer experience and retention. It has been shown that 70% of all business interactions are handled in contact centers. A framework is proposed in this conceptual paper to build cleaned interactive customer recognition framework (CICRF) in CCs. CICRF consists of two integrated modules: cleansing and ICRF. The first module focuses on the detection and resolution of duplicate records to improve the effectiveness and efficiency of customer recognition. The second module focuses on interactive customer recognition in a customer database when there are multiple records with the same name. Cleansing module uses Semi-Automatic deduplication process by incorporating three main functions in its design, namely: DedupCrowd, DedupNN and DedupCSR. DedupCrowd is a function that provides training pairs of records for DeduppNN which is a deduplication based neural network. Researchers suggest leveraging human computing power in managing duplicate data which is scalable top the large size of contact centers data. However completion of crowdsourcing tasks is an error-prone process that affects the overall performance of the crowd. Thus, controlling the quality of workers is an essential step for crowdsourcing systems and for that I propose OSQC, an online statistical quality control framework, to monitor the performance of workers. DeduppNN is a neural network based deduplication method that uses output of DedupCrowd for the training purposes. DeduppNN has two features: first is that it is an online deduplication method which is essential for the purposes of customer recognition. Second is that in terms of costs it is much lower in comparison with DedupCrowd. The last function is designed for providing label to pairs when DedupNN is not sure about their label. The intuition behind this function is similar with active learning area which selects appropriate data for labeling. ICRF consists of three integrated sub-modules. The first sub-module (DedupNNSelect) focuses on the detection and resolution of duplicate records to improve the effectiveness and efficiency of customer recognition. The second sub-module determines the level of ambiguity in the recognition of an individual customer in a customer database when there are multiple records with the same name. The third submodule, depending on the level of determined ambiguity from the second module, recommends to the CSR the series of feature related questions that need to be asked of the customer for his/her recognition.