Putting the customer back into the customer relations management paradigm

Abstract In retailing, customer relationship management techniques developed rapidly in the mid 1990’s with the help of applied IT and have become known as customer relationship management, i.e. CRM (Minami & Dawson, 2008). Modern CRM is often based on data mining techniques and has proven useful to innovations and efficiency (Ngai et al., 2009) but also important for effective customer care, retaining customer, identifying both valuable and non-valuable customers and building loyalty programs. When studying strategies on customer relationship management (CRM), research literature show that much of the operations have been carried out with a short-term perspective of the customer (Karlicek, Chytková, Tyll & Mohelská, 2014) extracting knowledge about customers with a focus on the exchange of goods (Vargo & Lusch, 2004), using a customer-facing level (Reinartz, Krafft & Hover, 2004). When CRM was introduced the opportunity to build better relationships with customers was the motivator, and with the help of database creation, analysis, selection, and targeting, organizations welcomed the information technology revolution (Winer, 2001). It seemed like CRM primarily was viewed as investments in technology and software, providing organizations with formalized CRM processes in terms of relationship initiation, maintenance, and termination (Reinartz, Krafft & Hover, 2004). In contemporary consumer markets with retailers becoming global, and online actors growing by the minute, the competition have made many firms leave the prioritized work with building long-term relationships (maintenance) in exchange for winning customers (initiation). A vast majority of today’s consumer-based-firms use CRM-program and the market for software programs is endless, growing more than 3200% since 2011 reaching about 5000 different platforms within marketing technology and CRM in 2018 (Cues, 2018). Key performance indexes on CRM-metrics are often focused on number of customers, conversion rate, frequency, time-to-purchase, cost per order, and campaign outcome. Retail chain stores are known to use this kind of customer metrics more than individual retailers (Bjorn & Matteo, 2016). Customer relationship managers also have a tendency to focus on measuring the measurable, seeking to bond customers to their brands by offering an additional incentive pose (Dowling & Uncles, 1997). All of the above-mentioned CRM metrics are believed to be the right tools to a more loyal customer but there are few evidences proving their effectiveness (Sansone & Colamatteo, 2017). However, some firm managers have seen the potential in using CRM challenged by a changing consumer pattern and an increase of touch-points stimulating omni-channel strategies i.e. product recommendations on web sites, product discussions on social medias, sharing of photos etc (Chen and Popovich 2003). Further development within the field of Internet of Things and Artificial intelligence adds to the complexity of data processing as connected everyday object are becoming online and autonomous, and generating information accessible for companies to process and include in the bigger picture that is CRM. To marketing professionals today, the challenge is not about collecting more data, but to use data and insight that is relevant in regards to their overall strategic goals. It seems that the retail sector are facing serious challenges on how to understand and to manage customer relationships in an evolving technological everyday “consumer culture” (Arnould & Thompson, 2005), and we suggest that marketers still have a lot to learn by understanding and using existing knowledge on data analysis and in-depth customer insight. In this paper we have the purpose to identify what in the narratives of a large Swedish online retailing company is missing in terms of CRM in the strive of both initiating new customer relationships as well as maintaining old ones. The academic/theoretical purpose is to elaborate on the profound need for connecting consumer cultural knowledge with data-driven analytic skills, applied professional skills along side with strategic company goals within CRM. Together, this may lead to better data and consumer insights, and thus improved performance of the retailer. The structure of the paper starts with a literature framework, followed by a methodological description of how data from the case study was collected, ending with the case study and a concluding discussion. INSiD