data-driven model

This research provides a data-driven model of electronic banking customer experience using digital marketing knowledge. The study is applied-developmental research, and it is a cross-sectional survey research. A semi-structured interview and a Likert scale questionnaire were used to collect data. The statistical population in the qualitative section includes banking industry experts. Using targeted method, 15 experts participated in this section. The statistical population is one million people (active customers of electronic banking) and the sample was calculated based on the Cochran table of 384 people. To analyze the data in the qualitative part, the foundation data analysis method was used in MAXQDA, and for the validation and presentation of the final model, the structural equation modeling method and SMARTPLS software were used. Based on the designed model, 6 categories for causal factors (proper decision-making, time management, digitalization effects, cost management, business trends, and relationship management), 2 categories for background conditions (banking industry and digital economy), 2 categories for intervening conditions (individual factors and environmental factors), 4 categories for strategy (digital tools, trust building and training, digital differentiation and digital platform), 3 categories for outcomes (prosperity of the banking industry, customer satisfaction, and economic productivity) became. Banks are an important pillar of the economy and the strategies they adopt will affect the recovery of the economy after the pandemic. Digitization is one of the important options for banks in order to provide the best and most reliable solutions to customers in their current business with the bank.