پیش بینی مسیر تبدیل موفقیت آمیز
ترجمه نشده

پیش بینی مسیر تبدیل موفقیت آمیز

عنوان فارسی مقاله: آیا شما هنوز آنلاین یا سیار هستید؟ – پیش بینی مسیر تبدیل موفقیت آمیز در دستگاههای مختلف
عنوان انگلیسی مقاله: Are you still online or are you already mobile? – Predicting the path to successful conversions across different devices
مجله/کنفرانس: مجله خرده فروشی و خدمات مصرف کننده – Journal of Retailing and Consumer Services
رشته های تحصیلی مرتبط: مدیریت
گرایش های تحصیلی مرتبط: بازاریابی
کلمات کلیدی فارسی: مدلسازی نسبی، زنجیره های مارکوف، تجربه مشتری
کلمات کلیدی انگلیسی: Attribution modeling، Markov chains، Customer experience
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.jretconser.2019.04.005
دانشگاه: University of Rostock, Ulmenstraße 69, 18057, Rostock, Germany
صفحات مقاله انگلیسی: 12
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.218 در سال 2018
شاخص H_index: 65 در سال 2019
شاخص SJR: 1.211 در سال 2018
شناسه ISSN: 0969-6989
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
آیا این مقاله مدل مفهومی دارد: دارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E13438
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

1. Introduction

2. Conceptual framework

3. Data collection

4. Results and discussion

5. Conclusion

References

بخشی از مقاله (انگلیسی)

Abstract

As digitalization increases, retail firms must invest in online and mobile commerce to attract customers to their website or mobile store. Since the type of device used to access marketing channels influences conversions, this research examines the different impacts of different devices such as desktop computers, tablets, and smartphones on the success of various marketing channels. We find customer experience (CX) to be important in improving attribution outcomes (e.g., conversion rates) by combining clickstream and survey data to understand consumers’ decision processes. Therefore, this paper also conceptualizes and measures perceptions of CX of clickstream-data participants. We identify the central implications of using each device.

Introduction

Due to rapid evolutions in technology, interactions between customers and retail firms have fundamentally changed (Grewal et al., 2017). Customer nowadays have evolving expectations of retailers because of reduced information asymmetries, past experiences, higher levels of customer orientation, or the multiplication of media outlets (e.g., Kumar, 2018). Retailers are interested in how customer behavior is changing as a result of the adoption of different devices for shopping purposes in both the online and mobile contexts (Kannan and Li, 2017; Souiden et al., 2018). Whereas the online context is mainly represented by desktop computers, customers increasingly use smartphones and tablets as primary devices for shopping in the mobile context (Criteo, 2018). Thus, retail firms are challenged by high levels of diversity and complexity in customer journey configurations across different devices used by customers (Harris et al., 2018). As a consequence, both scholars and practitioners are shifting their primary focus to the allocation of investments to attract customers to online or mobile stores, respectively (e.g., Marketing Science Institute, 2016; 2018). Since the effect of marketing channels (e.g., display advertising) on conversions is expected to differ across different devices, research has been encouraged to include tablets and smartphones in attribution modeling (Kannan and Li, 2017; Lemon and Verhoef, 2016; Souiden et al., 2018). Thus, the first objective of this research is to understand the effectiveness of various marketing channels across desktop, tablet, and smartphone devices in enhancing retailers’ performance of marketing channel budget allocation. Against this background, user-specific clickstream data was collected from a retailer that runs both an online and a mobile store. The paper implements an existing attribution model (Anderl et al., 2016) that tracks customer behavior at device level.