Improving Retail Branch Networks
The “branch of the future” is a tantalizing concept pursued by nearly every large financial institution. A Google search on the term returns about 258,000 hits and there are over 1,000 articles and white papers on the future of branch distribution.
All of this speculation and analysis is very interesting but few of these commentators address the fact that banks currently operate branch networks at widely different levels of performance. Rather than looking for that elusive “silver bullet” format for the branch or branch network of the future, it might be more productive to take a step-by-step approach to understanding the underpinning causes of these performance gaps and taking remedial steps, if necessary.
Looking at the top 20 retail banks, for example, we can see that some networks dramatically outperform others in terms of organic growth and profitability. The same is true for mid-size and smaller banks. In fact, some of these smaller institutions outperform the best of the larger banks.
A first step for any retail banker to improve distribution performance is to understand the current state of their institution’s network. The following metrics can be calculated for the entire network, each region, and for individual branches:
Branch Fair Share (BFS) Ratio. This measures a branch’s share of retail deposits in its micro-market, relative to its share of retail branches. A branch with a BFS of 100% is achieving its “fair share.” In 2012, the best 10% of the top 50 banks enjoyed an average network BFS of 138% while the worst 10% of networks had an average of only 64%. A network operating at 64% BFS will be struggling to break even, whereas one operating at 138% could be delivering profit margins in excess of 75%.
One important caveat: the power of the so-called “network effect” or “S-Curve” phenomenon is overstated. There are many situations where banks with supposedly powerful dense coverage underperform competitors with notably sparse coverage. Assuming that investing to increase density will cure ills will often end in disappointment.
Branch Close/Consolidation Performance calculates post-consolidation deposit performance in terms of two complementary metrics. First, attrition of deposits from the shuttered branch during the first two years, which ranges from a loss of 5% or less for the best 25th percentile to a 60% loss for the worst. Performance tends to be worse on consolidations after an acquisition, particularly when rates paid on deposits are reduced. However, even when this is not the case, attrition on average is higher than most managers presume.
The second branch close/consolidation performance metric looks at out-year growth of the surviving branch relative to competition in its micro market. Here, most surviving branches lose share, with the worst 25th percentile shrinking quickly, with growth nine percentage points lower than the local market. For example, if the market was growing at 4%, such a branch would be shrinking by 5% per year.
Out-year growth performance is influenced by the size of the deposits held by the surviving branch relative to the amount held by its nearby competitors. Essentially, there is the reversion-to-the-mean phenomenon at work. In other words, if consolidation created an oversized branch without resolving the underpinnings of competitive weakness, the surviving consolidated branch will shrink.
Consequently, when considering close/consolidation actions, banks must be very careful to avoid ending up with a situation where losses in market share eat away the benefits of the cost reduction from shuttering one branch. Such an outcome would not create shareholder value.
Same-Store Deposit Growth (SSDG) is a metric equivalent to that used by retailers, i.e., same-store sales. Rather than retail sales, however, it measures organic deposit growth among the mature, steady-state retail branches of a network. This metric has an over 60% correlation with the typical regional bank’s stock performance. In 2009–2012, for example, the best 10% of the top 50 banks had an average SSDG of 6.1% (lower than the pre-financial crisis period). The worst 10%, meanwhile, had an SSDG average of negative 6.4%.
Head-to-Head Competitive Performance provides insights into a bank’s street-level competitiveness against each of its main competitors. In 2012, the best 10% among the top 50 banks were gaining share at the expense of 90% of their competitors’ networks while the worst 10% were losing share to all of their competitors.
De Novo Performance analyzes the growth performance of de novo branches by age, adjusting for branch type and other factors. We have found that the top 20% of new branches grow to 10 times the size of the bottom 20% by their fifth anniversary. This vast performance gap holds true even when correcting for geographical and market differences, which means that some provide an attractive internal rate of return (IRR) and others are dead-on-arrival sinkholes.
Given the variability in performance, it is not surprising that some banks are shrinking their networks while others are holding steady or even expanding. As banks offer new distribution options, it is likely that there will by a similarly great level of performance difference as the impact of different levels of competency play out.
For some banks, closing gaps in the performance of the branch channel, as opposed to embarking on a transformation, will deliver the highest return on investment. In such cases, determining why the network lags competitors is essential. We have identified 15 possible factors, including service experience, absence of a value proposition, poor training, product deficiencies, excess complexity, problematic incentives, etc.
This is not to say that some investments in newer channels are not important, with mobile being a case in point. Offering at least “table stakes” mobile banking ranked fourth in importance in our recent research where we quantified the utility of 27 dimensions of distribution. Interestingly, mobile was ranked highly by both customers who characterized themselves as more self-service oriented as well as those who preferred person-to-person interactions.
However, despite all the new technology available, consumers and small businesses still consider close proximity to the branch as the top consideration for where to perform their core banking services. Even self-service oriented households ranked branch proximity as the most important distribution feature.
While the performance gaps between top and bottom performers are significant, the right improvement agenda can close gaps and produce large payoffs. The banks that eventually reversed their underperforming trend did so by selecting and delivering on a compelling value proposition that answers a prospect’s question, “Why should I do my business with you?” They also offered proof points to support the value proposition and improved the customer experience.
We close with two facts that should inspire management teams that are trying to cure weaker networks. The first is that stock markets reward trends. What is important is turning the ship around and setting it on the right course. The market can project future outcomes and reward the institution as soon as management, and results, change direction.
Second, when the right set of gap-closing initiatives are deployed, a heretofore weaker network will grow faster than nearby competition, a mathematical probability based on the “reversion-to-the-mean” phenomenon. Banks that move from weak to even-parity performers will eventually claim a fair share of business in each of their markets and thus grow faster while doing so.