Customer 2.0, Operational BI and $20M

A series of articles and posts caught my eye tonight. First I read Mike Murphy's article on Meeting the Needs of Customer 2.0: Intelligence All Around (DM Review). Mike discussed the issues and difficulties of serving a

new customer, one with high expectations and little patience -"Customer 2.0."

He goes on to say how product quality is no more important than customer experience to these customers. Mike's focus is on knowledge management and the need to treat knowledgeable customers. Customer 2.0 (great phrase) is not just knowledgeable though, they are also impatient and intolerant of "let me refer you to someone else". Now Mike walks through an example of dealing with this kind of customer. Using the example of call center agents, he makes the point that "agents don't have time to search through too much information". Indeed. In fact they often don't have time to look at any data to make a decision, they need to be presented with the decision itself - "the best answer in the shortest amount of time" to use Mike's phrase. When customer 2.0 is looking for pricing, for a refund, for activation - for a business decision - the agent needs the answer not more information. No matter how "operational" the business intelligence is, it will not really help. And that brings me to the second article, one by Barry Wilderman on Operational Business Intelligence: A New Approach (also DM Review).  Barry is focused on the "next horizon for BI" which he says

involves deploying applications along with specific operational solutions and processes, thus enabling "right-time" decision support to a broader base of users at all levels of the organization

Well, perhaps, but is Operational BI an oxymoron? Continuing with our scenario of treating and dealing with customer 2.0, it seems to me that decision support is not going to cut it. Instead the need is for decision automation and decision management. Don't get me wrong, he is right when he says that "even the most finely tuned transaction-based processes come to a stand-still when non-routine events occur". But he misses the fact that the transaction-based process itself must make decisions. This integrates "the transaction world with the analytical world " not "by linking transactions to executives and analysts and then linking the actions they take back into the transaction systems" but by analytically enhancing the transactions themselves. Why have the order entry person recognize the problem, when the system can? Why send someone to a dashboard when rules, enhanced with analytic predictions can be executed more rapidly? If something occurs even somewhat frequently, a decision can and should be built into the system and not rely on an alerted knowledge worker. Barry says:

Automated decision-making functionality ... features proactive notifications with actionable links, role-based dashboards at all levels, and benchmarks and best practices

But surely "automated decision-making" should feature automated actions taken based on policies, regulations, best practices and analytically derived insight? Enterprise decision management, in other words. Which leads me to Seth Godin's post on How to spend $20 million. Seth says:

So yes, treat different customers differently. The more the better, actually. But do it consistently and in a way that your customers respect and understand.

It seems to me that not only does this matter even more for Customer 2.0 (who will find out how others are treated and want to understand why), it means deciding on the right way to treat customers even when they are using self-service applications, websites, IVR systems or talking to an agent. You should use data mining and predictive analytics to create markets of 1. You cannot use operational BI for this and you must be able to control and improve the customer experience. A need for EDM to meet these challenges and use what you know about your customers runs through all three stories, even if the authors cannot see it.

Technorati Tags: BI, BI 2.0, call center, CRM, customer decisions, customer experience, Customer Service, data mining, decision management, EDM, enterprise decision management, extreme personalization, knowledge management, operational BI, personalization, predictive analytics, Seth Godin