Copyright © 2009 James Taylor. Visit the original article at When the customer knocks you need decision management not operational BI.Syndicated from ebizQ
A friend passed on an article titled “When the customer knocks” in which Scott Arnett of Pitney Bowes discussed the power of data to improve customer interactions. Nothing there to cause me to […]
Copyright © 2009 James Taylor. Visit the original article at When the customer knocks you need decision management not operational BI.
Syndicated from ebizQ
A friend passed on an article titled “When the customer knocks” in which Scott Arnett of Pitney Bowes discussed the power of data to improve customer interactions. Nothing there to cause me to blog you would think. Except that Scott, like too many in the Business Intelligence community, fails to acknowledge that using data to improve customer interactions is qualitatively different from BI. It’s not just a question of operationalizing existing BI tools and practices (so-called Operational BI) but one of taking systematic control of the customer interaction decision itself – Decision Management in other words.
Scott begins his article with a story about getting preferential treatment from an airline agent because he was a high-status traveler. As he said
In this case, the agent knew what to do and, more importantly, had the most up-to-date information from which to act.
But I think he misses two critical points in this story. Firstly it is only because the agent decides to treat him differently that the information is useful (the decision is what matters to his customer experience, the data is necessary but not sufficient) and secondly that while Operational BI might be enough to help the agent process him appropriately, what if he had used an automated terminal? Would that have treated him appropriately? Not if Operational BI was all there was.
The power of Decision Management in this kind of scenario is threefold. Firstly it focuses on the decisions themselves – what decisions matter to the customer interaction. This ensures that the data being collected and used is that which will make a difference. Beginning with the decision in mind in this way focuses analytics and data gathering. Secondly it allows the decision to be made consistently across channels so that customers get the same service from the agent at the gate, the call center, the service center or the kiosk. Operational BI assumes there is a person to make the decision and so cannot deliver this true cross-channel consistency. Thirdly, Decision Management recognizes that policies and regulations matter as much, sometimes more, than data. Presenting the data and even its analysis to someone who then fails to follow procedure is not helpful. Decision Management combines the policy aspects of a decision with the analytic aspects in a way Operational BI does not.
Scott does make valid points about the need to do analysis on data that is up to the minute (in case the most recent transactions or conversation are critical) and he emphaisizes the importance of being able to rapidly and effectively update predictive models once they are in production. Without the framework of Decision Management to bring put predictive anlaytics to work in operational systems, however, I think those trying to use data to improve every customer interaction will be doomed to disappointment. Operational BI is just not enough.
For more on this, check out:
- To Hell with Business Intelligence, try Decision Management.
- A Decision is a prerequisite for success with predictive analytics
- Decision Management focuses on Microdecisions for Macro Impact
- Decision management and automated recommendations
- Here’s how decisions and rules relate (and how to manage them)
