In this post I want to focus on a specific implication of the service-dominant logic: Complexity. Adhering to the service-dominant view of exchanges requires a significantly more complex view of the world, and this is definitely true for customer advocates. You can no longer focus on just one customer transaction or one type of customer interaction point without accounting for a great number of other partners and relationships, many of which do not directly interact with either you or the customer (e.g., supply chain, R&D, economic policy, etc.).
This is not a new problem; nearly everyone recognizes the complexity of modern markets. The problem is understanding this complexity and optimizing the right pieces to ensure customers' needs are met. The only way to do this is through advanced analytics and comprehensive databases. Standard business analytic tools like correlation and even simple, multivariate regression are not going to cut it. Analyzing data from one source is not going to suffice. We need tools that can understand moderating effects and the complex, multi-level, cross-boundary interactions implied by service-dominant logic.
There is good news, though. First, there are tools available and being developed that can help us. Second, complex models don't have to be complicated to understand. About a month ago my colleague, Leslie Pagel, had a post about embracing complexity, which linked to a great TED video by Eric Barlow titled, "How complexity leads to simplicity" (it's only 3-minutes long). This type of approach has to be the future for customer feedback analysis if we expect to capitalize on our customer relationships and our customer understanding as a sustained competitive advantage.
"Simplicity does not precede complexity, but follows it."
- Alan Perlis
Troy Powell, Ph.D.
VP, Statistical Solutions