Adaptive Right-Time Technologies in Customer Relationship Management
| Autor: | Lukas Grieser, Klaus D. Wilde |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Voice of the customer
Customer retention Process management Computer science Business process Customer relationship management Business relationship management Customer advocacy Customer Service Assurance Supplier relationship management Enterprise relationship management Revenue Marketing Customer to customer Customer intelligence Digital firm Relationship marketing Consumer behaviour Service quality business.industry Business rule Customer reference program New product development Customer satisfaction business Information Systems |
| Zdroj: | Business & Information Systems Engineering. 2:41-44 |
| ISSN: | 1867-0202 |
| DOI: | 10.1007/s12599-009-0084-x |
| Popis: | Customer relationship management (CRM) is highly affected by its dynamic environment. In this context, the symbiotic qualities of adaptability and realtime technologies offer new potentials of process optimization and integration. CRM is about establishing and maintaining profitable long term customer relationships. It involves the coordination of customer directed business processes in marketing, sales, and service (Hippner and Wilde 2002, pp. 6–8). These processes are typically subject to very rapid changes of customer behavior (e.g. due to seasonality, fashion, tests, etc.) and business environment (e.g. new product launches, special prices, commercials, etc.). The implied dynamics necessitate a continuous adaptation to present market conditions. In regard to e.g. target group planning for catalogues, mailings or e-mail newsletters, campaign management in CRM always has to consider up-to-date customer needs while bearing in mind ongoing competitor activities and their effect on customer behavior. In practice, however, target groups for the next month t1 are planned on the basis of the reaction data of similar campaigns from previous months t−1, t−2, . . . , t−n. The actualization of customer-specific response predictions (which are the analytical foundation of the business rules for target group definition) is carried out in month t0. In other words, the actualization is conducted on the basis of out-dated data. In addition, it takes place one month before the actual campaign execution. Accordingly, response prediction for the individual customer does not reflect customer behavior during the campaign execution in month t1, but the behavior within the months of provided data t−1, t−2, . . . , t−n (Berry and Linoff 2000, pp. 206–208). Derived business rules for campaign target groups only insufficiently cover customers with factual strong response probability. Given this diluted decision basis, potential revenues are not realized. A continuous adaptation of business rules in real-time cuts these losses but involves enhancements, both on the system side and on the process side of traditional operations. |
| Databáze: | OpenAIRE |
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