Customer Needing - Conceptualising Industrial Service from a Customer Perspective
Views on industrial service have conceptually progressed from the output of the provider’s production process to the result of an interaction process in which the customer also is involved. Although there are attempts to be customer-oriented, especially when the focus is on solutions, an industrial company’s offering combining goods and services is inherently seller-oriented. There is, however, a need to go beyond the current literature and company practices. We propose that what is needed is a genuinely customer-based parallel concept to offering that takes the customer’s view and put forward a new concept labelled customer needing. A needing is based on the customer’s mental model of their business and strategies which will affect priorities, decisions, and actions. A needing can be modelled as a configuration of three dimensions containing six functions that create realised value for the customer. These dimensions and functions can be used to describe needings which represent starting points for sellers’ creation of successful offerings. When offerings match needings over time the seller should have the potential to form and sustain successful buyer relationships.
|Date of creation:||04 Jul 2008|
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- Johansson, Edvard & Böckerman, Petri & Kiiskinen, Urpo & Heliövaara, Markku, 2007. "The Effect of Obesity on Wages and Employment: The Difference Between Having a High BMI and Being Fat," Working Papers 528, Hanken School of Economics.
- Böckerman, Petri & Johansson, Edvard & Jousilahti, Pekka & Uutela, Antti, 2007. "The Physical Strenuousness of Work is Slightly Associated with an Upward Trend in the Body Mass Index," Working Papers 523, Hanken School of Economics.
- Ahlgren, Niklas & Antell, Jan, 2006.
"Bootstrap and Fast Double Bootstrap Tests of Cointegration Rank with Financial Time Series,"
519, Hanken School of Economics.
- Ahlgren, N. & Antell, J., 2008. "Bootstrap and fast double bootstrap tests of cointegration rank with financial time series," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4754-4767, June.
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