Profitability & Pricing Analysis
Whether your profitability analysis will be accomplished using a Profitability Analysis Module within an MCIF, or whether your model will be developed in another software environment, using MCIF data in your analysis will be invaluable.
The MCIF Profitability Module approach is suitable if the module is flexible enough in its logic structures to support non-standard or unusual pricing scenarios. More importantly, this approach works best if the purpose of your analysis is to:
A weakness and important limitation of these modules is that they generally calculate static contribution margins, which don't consider the life of an account or the unevenness of contribution over its term. Externally-built profitability models using MCIF data will be more suitable if your needs include estimating the actual economic value of an account, relationship, customer, or household; or applying profitability results to the development of cost/benefit analysis or return on investment for a specific marketing program or other capital investment.
The fact that most customers only have one or two services with a given financial institution was proven with the arrival of MCIF systems.
Through profitability analysis, we have learned that multiple service relationships provide a better, more predictable, more stable revenue stream for the financial institution. Also, multiple service relationships in core products lower the Cost of Funds.
We also know that retaining customers who have few services is a hit-and-miss proposition, and that successful cross-selling and customer retention are highly correlated. For most institutions we have worked with, about 50% of single-service checking households are lost each year. The addition of a savings relationship improves retention to about 67%; and adding a loan relationship as well improves retention to 90% or more.
MCIF systems guide you to cross-sell what makes sense to cross-sell, and what makes money to cross-sell. Studying the profitable multiple-service relationships you do have establishes the foundation for your face-to-face and direct cross-selling efforts. Adding demographic data to your MCIF records refines the process further -- allowing you to target your efforts based on both customer profile and financial behavior.VIP Program
Everyone has heard about the 80-20 rule, that 20% or fewer of your customers are responsible for 80% of your deposits (and perhaps 20% of your borrowers represent 60% or more of the loan dollars). MCIF analysis often shows that the effect is even more pronounced than that. The customer relationships that carry the most weight need to be acknowledged if they are to be retained and developed -- and how do you do that when some of your most important customers rarely or never come into your branches?
MCIF analysis can identify which of your customer relationships are responsible for most of your deposit and loan dollars. Lists of these customers can be generated for special retention efforts using database targeted mail, telemarketing and face-to-face contact.Geographic Analysis
MCIF systems can be "geocoded" based on where customer households live. These geocodes represent standard U.S. Census Geography and Postal Geography -- from very small units containing less than a hundred households like Zip+4, to intermediate levels like Census Tracts, to large units like Counties or an institution's custom sales geography.
A geocoded MCIF allows an institution to analyze its customer (and prospect) locations -- in the aggregate, by specific product, by profitability contribution, etc. -- relative to the institution's delivery network and to the networks of competitors. Among other applications, a geocoded MCIF shows how far customers are willing to travel for a given product or service; and identifies the relationship between profitable customer relationships and delivery systems.
With the addition of demographic information based on where customers reside to the geocoded MCIF, customers of high potential but little relationship can be cultivated; target marketing based on age and income can be developed; branch managers can be shown their trade areas and which geographic area to concentrate on for a particular product or service; realistic sales goals can be developed; and so on.
The addition of geographic analysis and presentation mapping software to MCIF makes it easy for even the smallest institution to apply sophisticated geographic analysis previously available only to the largest financial institutions, real estate developers and urban planners. The ability to make geographic queries of your MCIF databases, as well as more traditional logic-statement queries, unlocks the true value and real meaning of your data.
CRA compliance applications are an offshoot of Geographic Analysis applications. For institutions without MCIF, CRA compliance can be a nightmare. Institutions are required to identify their residential loan applications, fundings and denials by Census Tract, relative to the income of the tracts that compose their communities. Unfortunately, many institutions -- especially community banks, savings institutions and credit unions -- have not geocoded their residential lending activity. Without an MCIF, this geocoding requires a lot of data processing expertise and represents a "hit" on departments responsible for Operations Systems.
The MCIF approach to CRA compliance provides the Compliance Officer, the Marketing Department and the rest of the CRA team with their own tool for compliance. Again, the addition of geographic analysis and presentation mapping software to MCIF makes it easy for even the smallest institution to create CRA maps and apply sophisticated geographic analysis to compliance activities -- or to have CRA maps and analysis created for them by a service bureau.