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Acquiring and retaining profitable customers are major concerns of a business. In this paper, the customers of the Restoration and Beauty Clinic of Iran University are segmented using three value types: current value, expected value, and loyalty in a case study to predict the probability of customer churn and future purchase services in the clinic.

This study utilized customers' data records with nine data fields (socio‐demographic and transactional) from three year's transactions of the clinic. Logistic regression as a data mining technique is then used to predict the future behavior of the customers. In addition, the verification and the validation of the models are done using lift charts.

This research segments the customers of the clinic into four categories based on three values (current value, expected value, and loyalty). Then simple marketing strategies that might be adopted are suggested. These strategies might help the shareholders and experts of the clinic to promote relationships with patients and deliver better services to attract and retain their customers.

The results of this research enable public health agencies to evaluate the effectiveness of their policies and detect their shortcomings in order to better serve patients. Also, it will help to increase their profits from the clinics and raise customer satisfaction.

Customer lifetime value (CLV) is one of the key indicators to measure the success or health of an organization. How can an organization assess the organization's customers' lifetime value (LTV) and offer relevant strategies to retain prospective and profitable customers? This study offers an integrated view of different methods for calculating CLVs for both loyalty members and non-membership customers.

This study outlines eleven methods for calculating CLV considering (1) the deterministic aspect of NPV (Net present value) models in both finite and infinite timespans, (2) the geometric pattern and (3) the probabilistic aspect of parameter estimates through simulation modeling along with (4) the migration models for including “the probability that customers will return in the future” as a key input for CLV calculation.

The CLV models are validated in the context of complementary and alternative medicine (CAM)in the healthcare industry. The results show that understanding CLV can help the organization develop strategies to retain valuable customers while maintaining profit margins.

The integrated CLV models provide an overview of the mathematical estimation of LTVs depending on the nature of the customers and the business circumstances and can be applied to other business settings.

This paper attempted a novel approach for system-level modeling and simulation of sigma-delta modulator for low-frequency CMOS integrated analog to digital interfaces. Comparative analysis of various architectures topologies, circuit implementation techniques are described with analytical procedure for effective selection of topologies for targeted specifications.

Virtual instruments are presented in labview environment to analyze the correlation of circuit-level non-ideal effects with key design parameters over sampling ratio, coarse quantizer bits and loop filter order. A fourth-order single-loop sigma-delta modulator is designed and verified in MATLAB simulink environment with careful selection of integrator weights to meet stable desired performance.

The proposed designed achieved SNDR of 122 dB and 20 bit resolution satisfying high-resolution requirements of low-frequency biomedical signal processing applications. Even though the simulation performed at behavioral level, the results obtained are considered as accurate, by including all non-ideal and non-linear circuit errors in simulation process.

Virtual instruments using labview environment used to analyze the correlation of circuit-level non-ideal effects with key design parameters over sampling ratio, coarse quantizer bits and loop filter order for accurate design.