LoFrumento set about assembling a CRM team with the necessary skills, which includes Chief Statistician Dr. Tom Tao and Business Intelligence Analyst Laura Bartels. To accomplish its objective of implementing and supporting a CRM infrastructure, the CRM team works closely with teams from market research and client technology services to provide the necessary analytics that allow Morgan Stanley to become a client-centric organization.
A Holistic Customer View
The CEO's directive essentially guaranteed that those responsible for implementing a CRM program at Morgan Stanley had the senior management support critical to the success of any CRM initiative. The CRM team's next challenge was to create a holistic view of Morgan Stanley's customers. This required the financial services company to literally change the way it did business.
Traditionally, customer information within financial services organizations is scattered among product silos. No collective view of the customer exists.
LoFrumento firmly believes in the crawl, walk, run method of building a CRM infrastructure and environment. In this case, it meant gathering the data, filling in the gaps, mining and analyzing the data, deriving customer knowledge from it and then getting it into the hands of those who could use it.
So the CRM team began by gathering and assessing all available information. LoFrumento's philosophy: "You must first gather data, transform data into customer knowledge, and make this customer knowledge available throughout the organization."
The CRM Team Vision
A successful CRM initiative starts with the end goal in mind: to understand clients and prospects from all perspectives.
Morgan Stanley's vision: to have such a robust set of information about clients and prospects that it can predict the next product a client is likely to buy. "That's what we're working toward," affirms Justine Metz, executive director, strategic market research and Web site marketing. "We haven't gotten there yet, but that's the coup: getting the data set in place to then be able to analyze different trends within the data, different types of behaviors and build predictive models."