تقسیم بندی مشتری با کانال های خرید و نقاط ارتباط رسانه ای
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تقسیم بندی مشتری با کانال های خرید و نقاط ارتباط رسانه ای

عنوان فارسی مقاله: تقسیم بندی مشتری با کانال های خرید و نقاط ارتباط رسانه ای با استفاده از داده پانل تک منبع
عنوان انگلیسی مقاله: Customer segmentation with purchase channels and media touchpoints using single source panel data
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: رفتار خرید چند کاناله، مراحل خرید، تحلیل خوشه ای مکون-کلاس، داده های رفتاری
کلمات کلیدی انگلیسی: Multichannel shopping behavior، Purchase stages، Latent-class cluster analysis، Behavioral data
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journal List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.jretconser.2017.11.012
دانشگاه: University of Tsukuba – 1-1-1 Tennoudai – Tsukuba-shi – Ibaraki – Japan
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2018
ایمپکت فاکتور: ۲٫۹۱۹ در سال ۲۰۱۷
شاخص H_index: ۵۷ در سال ۲۰۱۹
شاخص SJR: ۱٫۲۱۶ در سال ۲۰۱۹
شناسه ISSN: 0969-6989
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 11
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
کد محصول: E10626
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Multichannel and multimedia customer segmentation


3- Method


4- Results


5- Contributions and implications


References

نمونه متن انگلیسی مقاله

Abstract


This study examines how customers use multiple channels and media in modern retail environments. It segments customers by using Latent-Class Cluster Analysis, which focuses on the purchase channels of bricks-and-mortar and online stores, media touchpoints of PC, mobile, and social media, and psychographic and demographic characteristics. It extends the framework of prior research by analyzing 2595 Japanese single source panelists’ data in which purchase scan panel data on low-involvement, more frequently purchased categories, media contact log data, and survey data are tied to the same ID. The analyses reveal seven segments including the properties of research shoppers and multichannel enthusiasts.


Introduction


The multichannel retail environment has developed over the past decade. As the number of firms selling their products both online and offline has increased, achieving synergy by integrating sales and communication channels has assumed greater importance (Zhang et al., 2010). The development of communication channels based on multimedia such as mobile devices and social media has enabled the firm to build a direct relationship with the customer (Ganesan et al., 2009; Van Bruggen et al., 2010). The firm can now build an interactive relationship by providing product information through its own website or through social media platforms, such as Facebook and Twitter, in addition to traditional communication channels. Conversely, from the point of view of customers, opportunities for customers to select information are increasing. They can now obtain the information they need whenever and wherever they want without visiting bricks-andmortar stores, since they can purchase anything online. Subsequently, it has become more important for firms to understand how customers utilize multiple channels and media and how to integrate them seamlessly (Dholakia et al., 2010; Verhoef et al., 2015). One effective way is to design channel strategies based on customer segmentation (Neslin et al., 2006). A number of studies have examined the characteristics of multichannel customers (e.g., Kushwaha and Shankar, 2013; Chintagunta et al., 2012; Gensler et al., 2012; Valentini et al., 2011; Ansari et al., 2008; Thomas and Sullivan, 2005). In addition to this background, studies have begun to emerge that do not simply focus on customers’ purchase channels, but instead reference their purchase stages including information search stage and purchase stage. (Konus et al.,2008) proposed a Latent-Class Cluster Analysis scheme based on customers’ channel use that considers the information search and purchase stages as well as the individual differences in their psychographic and demographic covariates. This scheme has become a universal scheme adopted in several studies (Wang et al., 2014; Keyser et al., 2015; Sands et al., 2016). Sands et al. (2016) extended the scheme to evaluate communication channels more precisely by taking into account the influence of mobile devices and social media.

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