The Payoff from Profitability Analytics
The Payoff from Profitability Analytics
Posted June 1, 2011 I by Mary Beth Sullivan
Customer profitability information needs to be applied where it counts – on the frontlines.
Many financial services companies today have some understanding of the profitability of their customers or are working to build this information. While creating robust customer profitability analytics is a difficult process, it can be made easier if there is some clarity around the primary ways in which the information will be put to use. Determining these priorities is critical to ensuring the process changes required to use the information are defined and engineered. As Albert Einstein once said, “Information is not knowledge.”
In the late 1980s, I managed the customer profitability development effort at Chemical Bank in New York. Our job was to build customer-level profitability information, make it useful and get it to those who needed to use it when and where they needed it. Building the data analytics wasn’t easy, but it was easier than engineering change in the way we communicated with customers, queued them for servicing, created marketing treatments, targeted our sales efforts and priced our services. Armed with customer profitability information, we built a more knowledgeable business model. This type of intelligence will be indispensible in the future as profit margins remain under pressure. Analytics on customer profitability will facilitate better decisions regarding the optimal allocation of capital and the expected return on incremental investments.
Working with banks of varying sizes across the country, I see a good deal of room for improvement in the level of customer intelligence applied throughout the business model. Some examples:
- Intelligent exception pricing. I am still amazed at the rather random application of fee waivers at many banks. Frontline staff members are either permitted to waive fees or not – but these two extremes miss the mark. What frontline staff members need is information that tells them for whom to waive fees and by how much, not some simple flag that indicates a profitable customer relationship. With customer profitability information and analytics that incorporate hurdle rates of return, bank analysts can identify customers that qualify for waivers and the amount of waivers that can be offered and still keep the overall relationship profitable.
- Rationalized loan pricing. Properly pricing commercial loans so that risk is balanced with return also requires profitability information at the relationship level. In today’s competitive environment, banks that are able to factor into their pricing decisions the value of the full set of services a customer uses and understand the risk of both the borrower and the facility (a two-factor risk rating) will be positioned to offer the best prices to the best customers while ensuring the total relationship remains profitable and the bank is compensated adequately for the risk it assumes. It is likely that the purview of the new Consumer Financial Protection Bureau will encompass micro businesses; therefore, it will be incumbent upon management to establish an objective basis for employing differentiated pricing.
- Better product design. Many banks are redesigning their deposit products to simplify the range of options offered to customers, encourage multiple product purchases and eliminate products that are no longer profitable. In this process, estimating the profitability impact of product/pricing changes to specific account holders is relatively easy but again, it misses the mark. Understanding how individual product changes impact the overall customer relationship is critical. Building into this equation overall customer relationship profitability and additional customer level analytics such as price sensitivities, propensity to purchase additional products, and cost-to-serve metrics can ensure that product line and price changes result in more relationships that are profitable over the long term.
Many banks allocate significant portions of back office costs using a standard metric such as the number of checking accounts. However, delivery and service preferences among consumers vary dramatically and standard allocations increasingly don’t make sense. Cost allocation methodologies should be reexamined and adjusted to provide a more accurate view of customer profitability.
Finance, marketing, and business line experts have to work together to build robust customer-level information and then transform it into applied knowledge. When done right, this knowledge can be transformational for the organization.
