Customer Analytics and Data-Led Omnichannel Commerce Minitrack
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The continued development of e-commerce, social media, online information search and business intelligence have fundamentally changed the conducting of business in all fields and industries. Virtually all businesses are now facing an omnichannel world, in which presence and activity in a number of different channels determines business success. Increasingly, cutting edge business practices are ones that combine arrangements in different channels, helping the more traditional sales, marketing, communication and distribution channels become a part of the digital revolution.
The rise of digital communications and data has changed many things in traditional industries as more and more industries are able to understand, track, serve and sell their offerings digitally. Passing customers smoothly from one channel to another is increasingly important, and many businesses that have excelled in the digital space are turning to more traditional channels to embrace the combination opportunities. Simultaneously new media and applications, e.g. cutting edge moving image social media applications, commoditized high-definition virtual reality and algorithm-based automated procurement systems, keep the scene lively. Combining the old and the new has become a competence and a sustainable source of competitive advantage.
One of the primary sources of this competitive advantage is the ability to extract timely and relevant customer data for serving customers better. Customer analytics comes in various forms, but the cutting edge of such analytics is one that starts from practical and acute needs of the customer. Such customer analytics is not legacy database-led, but deals with customer interaction, engagement and service design, and is linked to protocols of how to react to, target, automate and optimize.
We seek empirical and conceptual research papers, methodological papers, quantitative analyses, case studies, reviews and practitioner reports related to contemporary and cutting edge customer analytics and omnichannel marketing. Also contributions about brand new technologies in this vein are appreciated. The degree of novelty in both the technology employed and innovativeness of business implementation is given considerable weight in the evaluation of the papers.
Relevant topics include (not limited to):
- Customer analytics
- Customer tracking
- Cross-media efficiency
- Business intelligence
- Using databases (e.g. loyaltycard data)
- Privacy concerns and ad for blockers
- Predictive modelling
- Cross-channel and data-led marketing
- Channel collaboration
- Marketing automation
- Dynamic pricing and advertising
- Targeting and retargeting
- Customization and website morphing
- In-store digital media
- Technology assisted personal selling
- Second screen
- Lead nurturing
- Customization and personalization
- Mobile app based business models
- Virtual reality applications
- Sharing economy applications
- Cutting edge cross-media technologies and methodologies, e.g.:
- Algorithm development
- Artificial intelligence
- Persuasive systems
- Semantic web
- Internet of things
- (Contextual) Multi Armed
- Bandit problems
- Sequential experimentation with online media
Petri Parvinen (primary)
University of Helsinki
University of Tampere
ItemWhat’s the difference between work and fun? Explaining the difference between utilitarian and hedonic IT use( 2017-01-04)Information systems theory tells us that the deepest going difference between utilitarian and hedonic information technology use is that different sets of motivational factors direct the two types of use. However, recent advances in social psychology and consumer behavior research suggest that there is an even more profound difference: Only utilitarian IT use depends on the self-control mechanism and the limited resources consumed bµ exercise of self-control. This causes the daily and weekly rhythms of utilitarian and hedonic use to be different. Utilitarian information technology use decreases throughout the day and the week while hedonic information technology use does not. In this paper, we test for the first time whether the daily consumption pattern of utilitarian information technology use indeed reflects the hypothesized patterns at the aggregate level. Our data suggests that it does, which means that the self-control mechanism should be integrated in the information systems models that seek to explain information technology use.
ItemPersonalized Product Recommendations: Evidence from the Field( 2017-01-04)Targeting personalized product recommendations to individual customers has become a mainstream activity in online stores as it has been shown to increase click-through rate and sales. However, as personalization becomes increasingly commonplace, customers may feel personalized content intrusive and therefore not responding or even avoiding them. Many studies have investigated advertising intrusiveness and avoidance but a research gap on the effect of degree of personalization on customer responses based on field evidence exists. In this paper, 27,175 recommendation displays from five different online stores are analyzed. The results show that the further the customer is in the purchasing process, the more effective personalization is if it is based on information about the present rather than past browsing session. Moreover, recommendations in passive form are more effective than recommendations in active form suggesting the need to dispel the perception of intrusiveness.
ItemAn Explorative Study on Sales Distribution in M-commerce( 2017-01-04)Despite the proliferation of studies on the sales distribution in e-commerce, little research has been conducted on the sales distribution in the m-commerce channel. This study empirically examines the sales distribution of various product categories in the mobile channel, using the large transaction data from a leading e-marketplace in Korea. Overall, transactions in the mobile channel are more concentrated to head products compared to the PC channel sales, but the pattern is inconsistent across product categories. Transactions in product categories of high average price (e.g., computers) and low purchase frequency rate (e.g., health care products) are less concentrated to head products in the mobile channel than the PC channel. The revenue distribution, however, shows the opposite. Head products generate relatively less revenue in the mobile channel than the PC channel. We provide explanations why the mixing results appear across product categories and between the distribution types.