Designing a pattern of big data opportunities and challenges for Internet retailing in Iran

EXTENDED ABSTRACT
Introduction: The big data revolution is underway and growing rapidly. Retail actors Internet professionals such as retail managers, retail researchers, as well as public policy investors, retail investors are looking to exploit big data to identify opportunities and challenges ahead. By reviewing the studies and previous researches, the researchers first extracted the relevant concepts, then the experts were used with the help of the interview method. The selection method of 15 experts was based on expertise and availability. Also, by using the thematic qualitative research method, concepts have been categorized. The pattern of big data opportunities and challenges in Iran's internet retailing at the store consists of 15 sub-themes which were categorized into four activity levels: store, company, customer, marke; and it showed that retail companies can gain a competitive advantage by exploiting opportunities and managing challenges, and use it to predict the needs of customers and their buying patterns. Retail actors Internet professionals such as retail managers, retail researchers, as well as public policy investors, retail investors are looking to exploit big data to identify opportunities and challenges ahead. With the expansion of the database, the need to use big data was raised in such a way that big data based on the data collected from the meetings of company managers with each other and customers improves the operational, social, financial and environmental performance of the company, the development of dynamic marketing, Identifying the faulty processes of the system, improving the level of current experiences of retail marketers and creating marketing content with the help of different information sources. Big data offers many new opportunities to create value for retailers give; some of which include price optimization, segmentation of customers, inventory management, analysis of customer expectations; Optimizing marketing processes and managing consumer behavior in the way of customers to create value; product offer; predicting the acceptance rate of the new product; Knowing customers; development of new products and services; Determining the customer's path to create value; creating interest in innovative services; Forecasting the supply chain of needs based on the analysis of expectations can be mentioned.
Methodology: In this research, the qualitative research method of thematic analysis was used to collect data. In the first step, the data was coded in a systematic way in the entire data set, then the data related to each code was collected. In the second step, before drawing the network of themes, the themes should be sorted and presented in the form of a report. The statistical population of the current research are experts in the fields of marketing and business intelligence; Due to the limitation of the population, the sample size will be equal to the population size. The number of expert groups was 15 people (10 men and 5 women), of which 6 people had bachelor's degrees, 6 people had master's degrees, and 3 people had doctorate degrees in business and information technology fields. The researchers, together with the research colleague, coded the number of three interviews and the percentage of agreement within the subject, which is used as the reliability index of the analysis, which finally has a reliability value of 0.86.
Discussion and Results: After collecting and analyzing the content of the interviews from the experts and extracting the key components related to the opportunities and challenges of big data in online retail, in order to reach a consensus regarding the factors extracted from the studies and Delphi method was used to interview experts. At the end of this stage, concepts (codes) that seem to say something specific about the research questions can be organized into broader themes. The themes were mainly descriptive; That is, they described patterns in the data that are related to the research question. Most of the concepts are related to one theme, although some of them are related to more than one theme. Therefore, in the next step, the created sub-themes cover well the concepts under their inclusion, and appropriate main themes can be created with the opinion of experts. After determining the final list of concepts; They were categorized in the four activity levels of store, company, customer and market in the form of a pattern of opportunities and challenges based on big data in online retail.
Conclusion: Store-level online retailers use big data for cross-selling marketing, location-based marketing, in-store behavior analysis, and price comparison. ​Also, at the company level, the opportunities resulting from inventory management can include joint investment in the supply chain and sales channels, cost-effective ordering by evaluating the distribution system, feasibility of customer expectations, and creating emerging markets. Due to the maximum use of big data at the customer level, online retail stores have access to many opportunities by making customer profiles based on their demographic, geographic, and psychological characteristics can achieve a competitive advantage. Big data has been able to point to the opportunity to share knowledge with other industries, the opportunity to use local strategies, the opportunity to maximize value for customers through innovative business models and expand channels by partnering with business partners, as well as business partners through analysis And data analysis by means of big data are used for product development by adapting to customer demands through the supply chain and sharing data with business partners in order to achieve business success.