Customer Cash Flows: behaviour to bank on

It is no secret that new money deposits and loans and retention are the keys to portfolio growth. Yet many banks don’t actually measure or manage to these simplest of performance objectives. Are they chasing the wrong goals?

It is actually true…most banks don’t have clear dashboard metrics for the basic drivers of portfolio growth and diminishment. Instead an array of proxies are more common, things like gains and losses in the number of customers, accounts and products and perhaps the balance changes associated with them. Many banks also have good predictive models for these behaviours and use them to guide marketing, sales and retention programs, but there remains one simple problem: more customers, more accounts and more products are not the key drivers of portfolio growth ! What most managers are looking at are changes in things that are correlated to growth, but are once-removed from measuring actual portfolio drivers and results. To get to the core, you need to be measuring flows of dollars.

 

There is a good reason that we use proxies, and that is the reality that the real numbers we need – how much new money flowed in, how much old money flowed out at the account level – are not recorded in legacy information systems. The silo systems architecture has prevented banks from being able to relate flows that occur in one system to those in another, fragmenting understanding of customer relationships, product performance and even basic things like sales management because flow of funds is obfuscated.

 

To right this deficiency in legacy systems is a mammoth task, since the information would need to be captured on virtually every transaction at the time it was created. That kind of infrastructure change, while a meaningful architectural goal, is not going to get funded in any bank we know.

 

This leads us to the next option: analysis. And the good news is you can certainly derive flows of funds at the account level if you have a data warehouse or data mart with a good Customer Information File (CIF). You don’t have to spend tens of millions of dollars to see your key portfolio growth drivers. You don’t have to use statistical proxies or models to approximate what is happening. You can actually derive flows at the account level that are meaningful customer behaviours:

1. Adding new money
2. Moving money from one account to another (incl. across products)
3. Taking money out of the bank

Each of these metrics can be predicted and measured, agreed to portfolio change and analyzed in multiple dimensions: location, product, staff member, etc. Doing so can increase marketing, sales and retention lift by 30%, just by targeting new and lost money instead of product and account substitution (cannibalization).

 

Bankers we talk to seem to understand the power of flow of funds analysis, but very few have actually done anything about it. Perhaps marketing departments are reluctant to see the real cash on the barrel-head results of campaigns. Perhaps sales forces don’t want to give up getting paid to churn deposits and loans. Perhaps product managers don’t want to know how much of their performance has come from shifting flows of customer money inside the bank. Whatever the objections may be, we believe that if your bank wants to outperform the market, you really ought to be driving resources towards the right objectives….and that means getting a handle on flows of funds.

 

Customer Cash Flows: behaviour to bank on

Bank Deposit Services: Undervalued + Misunderstood = Mistake

The outstanding value delivered every day to consumers by core demand deposit account (DDA) services through retail banking operations of consumer banks is getting lost in the currently fashionable cacophony of media bank bashing. As an industry we have been remiss in communicating just how good we are at serving the interest of individuals, businesses and even the government through provision of deposit services. Let's revisit what we should be talking about in addition to fee levels...
First and most visible is the service of efficient, convenient clearing of billions of day-to-day transactions, through conveniently located branches, ATMs, telephone call centers, debit card terminals, cheques, money orders, wire transfers, the internet - just about any way people communicate we facilitate the exchange of value. This service is what freed us from the medieval chains of the barter system, enabling efficient local, regional and international exchange of goods and services. Without retail clearing operations our economy would collapse completely and utterly. Yet when you ask the proverbial "(wo)man on the street" how the cheque they used to buy jewelry in Tokyo got back to the envelope their bank statement (or e-statement) arrived in at the end of the month do you think they know ? The answer is no: people generally have no clue how complex and fantastically efficient clearing operations are. We give this service at nominal cost to millions, and they don't even know what we are doing for them ! The time has come to get this message out there... the value proposition is absolutely fabulous: as an industry we desperately need to improve awareness of it.
The second service we deliver through DDA is a secure haven for safekeeping of the earnings and savings of millions of individuals, with complete recordkeeping services and guaranteed fidelity of custody. In no other situation can you warehouse your assets at such nominal cost. Yet this service is not valued highly by most consumers (or businesses or governments). Without secure repositories for cash every individual in our society would be at far more at risk day and night of being robbed or even killed for the money they are now able to safely store in banks. This service is essential to maintaining law, order and property rights of individuals that are fundamental to society…yet no-one even seems to notice we do it.

The third service embedded in the DDA business is, of course, intermediation between depositors and creditors. Demand deposits are the backbone of the funding base for credit cards, lines of credit and similar loans that are essential to modern living for the vast majority of consumers. Without consumer credit the availability of goods and services to most consumers would be severely reduced. The consumer-driven economy we live in simply could not function.
Despite the extraordinary – in fact unique - value that the retail banking industry delivers every day to every participant in the economy bankers are under siege for the pricing of DDA services today. Consumer resentment over fees for processing NSF cheques and the potential elimination of free checking in the US has become the stuff of politics. In reality the retail banking industry has been undervaluing these essential services for decades, and any of the three value propositions outlined above should easily justify charges sufficient to make these services profitable to banks. The time has come to embrace public enquiry, present the real business case for DDA services to consumers and charge what they are worth.


-DBM

Bank Deposit Services: Undervalued + Misunderstood = Mistake

Understanding customer behaviour: the transition from memory to knowledge

There are essentially three ways a bank can use customer behavioural information to better manage customer relationships.

The first is to develop a corporate memory and understanding of who each customer is, based on historical information. Databases of historical service and account data organized by customer identity provide a basis for analyzing customer value (profitability), channel preferences product affinities, geographic and demographic data all of which are useful for segmenting customers and developing customer management strategy. Using historical customer information capably is table stakes in today’s relationship managed banks.

The second way to leverage customer behaviour data has a more responsive orientation. It involves parsing through transactions and service contact data in near-real time to know what customers are doing. Analyses can be automated to identify exceptions that prompt an intervention by sales and service staff. Ideally, identification of exceptional customer activity enables timely responsive customer interaction. There is certainly value in responsive behavioural analytics provided the process can work quickly and accurately enough to provide leads to sales and service staff that are credible, timely and relevant. False leads delivered to the front lines can foment resistance in the field quickly stalling responsive programs with inadequate business rules.

The third way to leverage customer data is more proactive. Models are developed to predict what customers are likely to do and allocate resources using this knowledge. Most banks already use predictive credit scores to adjudicate loans and to evaluate likelihood of default for credit loss provisioning. Similar predictive scores can be developed to identify customers at risk and those most likely to accept an offer. Predicting behaviour enables proactive customer management programs to be developed for acquisition, cross-selling and retention. If you can predict what customers are going to do, you can improve sales and service performance. The keys to program effectiveness are precision in scoring coupled with effective customer engagement by sales and service staff.

In all three cases what matters most of all is relevance. There is no point in identifying or predicting something that does not matter with a high degree of precision. Or worse, identifying / predicting the wrong thing.
Unfortunately this is exactly what happens a lot of the time in bank customer intelligence analytics. Models are created that identify “significant deposits” or predict “probability of account closure”, for example. Neither of these things is the right target behaviour of interest. Significant deposits may or may not reflect a significant source of new money to the bank. Similarly account closure may not bear any relation to the withdrawal of funds from an account.

We need to remember that the retail banking business is about flow of funds, and managing their cash flow is what customers do in real life. We need to understand the types of cash flow behaviours from a customer perspective rather than a transactional or data driven perspective. Focusing on what customers do with their money and the patterns of these behaviours offers a sharper and more effective basis for understanding historical behaviour, predicting future behaviour and reacting to current activity. The essential thing is to know what customer behaviour really is, then measure it, then model it.

- David McNab

Understanding customer behaviour: the transition from memory to knowledge

Events versus transactions in marketing analytics

Information management strategy lags the development of technology in most industries. We have seen the slow march of progress towards customer intelligence progress from initial customer identity management in the late 1980s through consolidated customer position snapshots (Customer Information Files or CIFs), crude velocity metrics (recency, frequency monetary or RFM), behaviour based customer value | profitability metrics, to monitoring customer behaviour.

During this procession of learning there have been many diversions of effort into unfruitful investment because the technology was pushing ahead of management thinking. Striking examples are not hard to find: database technologies drove investments in ERP, SCM and similar technology-enabled management methods, rarely with any discernable return to shareholders. Similarly the introduction of Enterprise scaled data warehouses enabled development of the CRM boom, first in the form of contact management later as customer experience based interaction engagement models.

Most projects failed to deliver promised benefits to customers or shareholders and for good reason: the rarified atmosphere of an overheated economy allowed managers to buy into the visionary states being promoted by vendors of technology. Unfortunately technology vendors are mainly interested in selling data storage, processing and analysis tools rather than actually managing your business. They don[t really know what will work for you and why, because they don't know enough about your business - which is perfectly reasonable.

One of the later entrants on the scene has been Transaction Trigger Analytics. First champinoed by banks in Australia (notably NAB,) it was discovered that parsing through transaction files overnight could result in identification of significant changes in customer accounts which, when acted on within 24 hours, could change customer behaviour. By detecting a significant deposit, for example, the bank could contact the customer to ensure all is well and offer any new services that the customer might require, such as investment advice. This technique is used primarily to keep new money in the bank or to keep old money from leaving.

Their experience proved the businesscase for transaction trigger detection - ROI was very high. The technology vendors were delighted - now banks had a good reason to store all their trnasaction files and load their databases up with new data every day instead of periodically as had been the norm. This meant lots of new extract, transformation and loading processes, lots of new storage requirements and lots of new processing power requirements to grind through massess of data every night.... a vendor's dream if there ever was one !

The only problem is that transactions are not a good representation of customer behaviour. Yes they are what what changes accounts, but this is from a company perspective (or more accurately an account management system perspective) which is not the same as customer perspective. Customer behaviour can bve far more comples than "significant deposit" showing in a transaction file. For example, that transaction could arise from a tax refund; sale of a property or business; transfer of an investment account; liquidation of investments; relocation of an account between locations and the list goes on. We have discovered that over 1/4 of banking balance changes result from internal flows of money within a customer's existing relationship.

This means that the transaction triggers will be false positives nearly half the time. Why ? because for every significant internal "plus" there is a corresponding "minus" so each side of an internal transaction appears to be a signirficant transaction event trigger. Transaction triggers can generate false leads about half of the time, draining staff time, program credibility and, worst of all, annoying customers with pointless dialogue.

What banks and other organizations need is to better define customer behaviours in the context of their business relationships. Know what customers really do and model these customer behavioural events . Then aply detection mechanisms to find and route real customer behaviour changes to your customer service staff. Better quality leads to improvements in efficiency, effectiveness and satisfaction for customers, employees and shareholders simultaneously. Stop wasting time with transaction detection - it is too primitive a tool to be relevant in today's customer management environment.

- David B. McNab

Events versus transactions in marketing analytics