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The purpose of this paper is twofold: to identify levels of uptake of performance measurement by small and medium‐sized retail companies selling goods online, and to determine key factors, which could explain any variation in use of performance indicators. The study is designed to explore these issues by this type of retailer as currently understanding is fairly limited.

A quantitative mail questionnaire was used to survey UK retailers selling goods online. The questionnaire examined the uptake of performance measurement in conjunction with the business profile of each of the 252 responding companies.

The results show great variation in levels and extent of uptake of performance measurement by online retailers in the UK. Company profile variables: size and operating format help to account for the variation in the number of indicators measured.

The sample frame has some limitations insofar as the study only focused on small and medium‐sized retailers in the UK selling tangible goods. Future research could be extended to include larger and pan‐European retailers selling both tangible and intangible goods. Furthermore, the data collection was cross‐sectional and, whilst this approach was important at this stage in order to provide a picture of how performance measurement is being applied at a given point in time, a longitudinal study would enable greater analysis of strategic impact of performance measurement.

Currently, retailers' performance measurement activities mainly focus on gathering data using financial and Website functionality indicators. From a strategic planning perspective, this could suggest that retailers are adopting a short‐term pragmatic approach towards retailing online. The implications are that performance measurement is being used as a means to ensure that Internet retailing is not having a detrimental effect on business performance rather than driving longer‐term strategy development.

The principal contributions of this paper are that it has provided insight into the current status of performance measurement amongst UK Internet retailers and has identified a useful checklist of performance indicators which retailers can apply to gain a comprehensive view of business performance online.

The purpose of the proposed model is to assist the e-business to predict the churned users using machine learning. This paper aims to monitor the customer behavior and to perform decision-making accordingly.

The proposed model uses the 2-D convolutional neural network (CNN; a technique of deep learning). The proposed model is a layered architecture that comprises two different phases that are data load and preprocessing layer and 2-D CNN layer. In addition, the Apache Spark parallel and distributed framework is used to process the data in a parallel environment. Training data is captured from Kaggle by using Telco Customer Churn.

The proposed model is accurate and has an accuracy score of 0.963 out of 1. In addition, the training and validation loss is extremely less, which is 0.004. The confusion matric results show the true-positive values are 95% and the true-negative values are 94%. However, the false-negative is only 5% and the false-positive is only 6%, which is effective.

This paper highlights an inclusive description of preprocessing required for the CNN model. The data set is addressed more carefully for the successful customer churn prediction.