Making 'Customer profitability' Mark.

Customer Profitability. Much has been written about this financial-services industry issue over the past five to 10 years. Many myths exist about both the process and the uses of the results. Despite all this discussion, however, efficient execution of account and customer profitability is certainly not yet a standard in the banking industry.

We do know that the issue is important. Institutions that prevail and focus on the appropriate process steps and translate the output into strategies and initiatives for their sales forces can achieve as much as a 30 percent improvement in average relationship value in a 12-month period.

This article's purpose is to provide some guidelines about what is considered to be important in an account and customer profitability valuation. Your bank can use these guidelines to compare its efforts to date. And, the guidelines can serve as a template for the bank's next steps.

Although there are still more institutions talking about customer profitability than actually have it, more and more banks are moving in the direction of obtaining this information. Unfortunately many institutions have spent millions of dollars in their quest, only to be unsatisfied with the results.

Obtaining accurate profitability information is harder than it seems at first. Here are some of the major barriers to getting this data:

Not knowing what components truly drive better profitability valuations. This means you don't know where you can make compromises without diluting the value of the output.

Partnering with an inexperienced vendor. Some of them don't have many customers actively using their profitability modules--let alone clients that are pleased with the process and outcomes.

Lock of commitment within the organization to get to the "right" data. The devil is truly in the details. It's worth spending a little time to "get it right."

Thinking you can make a list of fields needed to calculate profitability and be done with it. Every institution is dealing with its own universe of available information. It's far more productive to start with what you have than to ask for something that might not exist.

Understanding that it is a "process" and an ongoing effort and not a onetime project. This impacts not only the full-time employees within the organization necessary to support such an endeavor, but also the amount of support you can count on from other areas of the bank that will be affected.

Thinking you can simply publish the information and that it will find its way into the sales efforts of contact staff. There's no point in sharing the information unless you are prepared to answer questions and to instruct people in how they should be using it.

The important thing in beginning this process is to gather as much information at the account level as you can. Every distinction you can make, instead of allocating things in an ordinal fashion across groups of accounts, will enhance your ability to make better distinctions at the account level. The table (this page) details some key items you want to gather into your database/data warehouse for input into the profitability valuation.

It is also important to understand how you might use the information. The best way to maximize the uses of the information is to include fields by which you will review the profitability output--"slice and dice" fields--if you will. Listed in the second table (opposite page) are basic data elements you would also want to include in your database/data warehouse to facilitate analysis.

Banks that have begun this process know that gathering the information is not necessarily a guarantee of success. To assist you with avoiding some of the barriers discussed earlier and to support your efforts, here is a template for the successful implementation of account and customer profitability.

Team approach and sponsorship. The project must have a champion This may not necessarily be the CEO, but it should involve at least the head of retail or commercial banking. Additionally, there must be someone within the organization whose primary job will be the successful completion of your first conversion as well as the monitoring of ongoing efforts. The team should include representatives from the area where the output information will reside, as well as finance, systems, line management and marketing.

Data manipulation. There are several components to data manipulation. These components include but are not limited to: data sources, data extracts, data quality review, formula development, formula maintenance, default values and output needs--both electronic and hard copy. As valuations become more sophisticated, the number of data feeds and extracts increases, as does the expertise required to manage the data. There are no easy answers here--but focus on data-quality testing improves the process.

Transaction files. Most state-of-the art valuations today include transaction files for both assets and liabilities. While the asset transactions have been less important until recent years, they are extremely important as a source of fee income and the use of various channels for payments. Additionally, they provide much more detailed information for the creation of a higher level valuation for mortgage customers. It is also important to know the source of each transaction in addition to the account that has the transaction. In this manner, branches that service more customers than others can be identified.

Most institutions also mine this transaction data to discern key customer behaviors. Most often transaction data is also used to support efforts in finance. This provides a high degree of synergy whereby the volumes captured by the profitability project can be used to support finance, and the unit costs developed in finance can support the profitability project.

Account-level data assumptions. The best valuations today include the following all executed at the account level: marginal maintenance or servicing costs, transaction costs by channel, fees and fee reversals, funds transfer pricing assignment, opening/origination costs, multiple levels of risk (interest rate, credit and operating), capital and liquidity assignments, fixed costs, and overhead. This should translate into having multiple "levels" of profitability available for use. This is important because different types of post-calculation projects require different views of profitability. At a minimum, your valuation should include:

Net Contribution. This level of profitability includes spread income, fee income and marginal expenses. It may or may not include the cost of opening an account. Banks treat account opening differently--some exclude it, some amortize it, others front-load the expenses. You will also have to make this decision in keeping with your organizations philosophy.

Net Income Before Taxes (NIBT). This is basically net contribution less fixed expenses.

Net Income After Taxes (NIAT). This is NIBT less taxes.

There are other levels of profitability that also subtract overhead expenses and/or capital allocations, but this article will limit discussion to the three mentioned above.

Data quality and reconciliation. State-of-the art projects include multiple data quality review checkpoints. It's far easier to check the data being supplied to the valuations than it is to find the anomalies after profitability has been calculated. It is not unusual for as much as 15 percent of the data elements to "shift" in a given month. Most of these shifts necessitate formula revisions in the current month, as well as returning formulas to their original state for the next valuation. Most anomalies are not permanent; however, most anomalies do not involve the same fields from month to month.

Additionally, the valuation must reconcile to the known institution financials. Most institutions set an acceptable target with a plus (+) or minus (-) range. If the output of basic number of account, balance, income and expense information falls within this range, the valuation is considered to be reconciled.

This does not obviate you from testing outbound account and relationship values. While many items look OK "in total" there may be quite a bit of distortion at the account level. Due diligence must be done on this output, especially if you are sharing account level output with customer contact areas.

Smoothing information. It is important that account and customer profitability information be "smoothed" for more than one time period before it is used. Several years ago, and even today, many consulting firms support a 12-month period as being the best. However, this time period is too long to support a useful understanding of customer relationship values. Especially in the case of deterioration, the customer is long gone before the institution has the opportunity to re-establish the previous relationship. Today most progressive institutions smooth the information over a three-month period. They may then utilize four three-month periods at the account level--providing them with the same 12-month view, but allowing them to capture and respond to relationship shifts in a timely fashion.

Most importantly you should understand that a revenue model, such as rate times balance, risks misclassifying customers by 40 percent to 50 percent--nearly a random event!

The net contribution score

Most banks today score their customers at a level that best meets the needs of the intended users. If you are going to send customer scores to sales people, calling centers and relationship managers, most banks score the customer base at the net contribution level. This doesn't overburden the customers with fixed expenses or overhead. This does not prevent you from using NIBT or NIAT at a product level for product design and pricing decisions. This allows you to use different levels of profitability for different purposes.

So now, you have a profit value assigned to every account and customer and you have a score associated with every customer as well. Most U.S. banks are not passing actual dollars of profitability to their sales staff-but rather a score 1, 2, 3 . . . or A, B, C. . . . That means you have to decide how many customer groups you will divide your base into.

Selecting a scoring scheme

In the past, many banks chose to divide their customer bases into deciles. Conventional thought now is that this distinction is not meaningful. It falls short for the following seasons:

* It's a mathematical distinction, nothing more.

* Ten levels of distinction imply that you're going to differentiate actions (pricing, sales contact, etc.) 10 different ways. Ten is too many.

* Ten levels of distinction are difficult to communicate to a sales force i.e. what's the difference between a 6 and an 8 for example.

* Shifts in a customer score are not directly related to customer behavior.

For these reasons, most banks are now dividing their customer bases into three to five groups. Let's look at a five-group scenario with group 1 being the most profitable and group 5 being the least profitable. Once you know what your average relationship value is, you can arrange the groups. Group 3 is typically those customers whose profitability is above zero but less than average. Group 2 customers are above average but below some level you designate as "wildly" profitable. Groups 4 and 5 are both unprofitable--with 5s being worse than 4s. It is not unusual for the group 1 customers to be four or five times more profitable than average.

Using the information

The fastest way to gain acceptance for this information within the organization is to know how you intend to use it. Many will be waiting with great anticipation to incorporate customer profitability into their initiatives--others however, will not be so receptive. It is important to not only understand the "personality" of your organization but also to understand the "perceptions" of your potential audiences. Relationship managers and loan officers will love having more accurate ways to evaluate the pricing of current and future bank relationships, but only if they believe the value includes "everything."

Product managers will have access to better information than they've ever had, but it has to be timely and easy to get to. Call centers and branch personnel will be more likely to use the information if it's kept simple and straightforward. Additionally, they will need to understand how they can use the information if it is to be helpful to them. Don't give incentives for volume increases or cross-sell, for example, if what you really want is increased relationship profitability.

Aggressive integration into strategic initiatives

Most institutions have two to three immediate applications of customer profitability information. These initiatives typically include:

* Product realignment evaluations (especially in organizations growing through acquisition).

* Product design or retooling.

* Relationship product development and relationship pricing efforts.

* Preferential customer efforts.

* Segmentation and target marketing.

* Direct mail target selection.

* Promotional review (comparing new business to the overall portfolio).

* Line of business or customer segment evaluations (small business, middle market, etc.).

* Branch closing and opening analyses.

Account and customer profitability didn't receive much attention until the late 1980s and early 1990s with the advent of customer databases, formerly known as MCIF (marketing customer information file) systems. The notion that some customers were contributing more than others to the bottom line (perhaps regardless of their balances) seemed intuitively correct. Additionally, the desire to target marketing to households rather than to individuals supported the notion

However, the ability to accurately create information at the account and customer level that could be supported by bank financials has lagged far behind the desire to have such information.

Hopefully, some of the concepts covered in this article will help you launch or improve the profitability efforts at your own institution.

This is the first of two articles on the topic of customer profitability. The second, which will discuss bow to use profitability information to make better marketing decisions, will appear in the September 2001 issue.

Kim Sutherland is director and chief operating officer for Market Line Associates (MLA) of Atlanta. MLA is a consulting firm specializing in profitability, costing, customer relationship management and risk management.

Sample Account-Level Data Elements
Income Related     Cost Related          Product Specific
                                         Characteristics
Balances           Transaction Counts    Statements
                   by Channel
Fees by Category   Maturity Dates        Maturity Dates
Service Charges    Level of Delinquency  Delinquency Notices
Interest Received  Open and Close Dates  Open and Close Dates
Interest Rates     Account Status        Renewal Dates
                                         Term
Sample Account-Level Analysis Elements
Type of Analysis      Type of Data Element
By Product            Account Type, Product Type
By Branch Trade Area  Branch Numbers, Trade Areas
or Region             Codes and Region Numbers
By Segement           Segement Codes
By Officer            Officer Numbers
By Cost Center        Cost Center Distinctions
Risk of Inaccurate Valuation
Customers Misclassified
Account-Level Transaction Data  26% - 28%
Smoothed Value                  20% - 22%
Account-Level FTP Assignment    18% - 20%