Dave McNab is a Sr. Managing Consultant with IBM Business Analytics and Optimization Strategy practice in Toronto. Dave has 25+ years experience in industry & consulting. He invented the patented "FlowTracker" customer behaviour analysis method for banks. See: Customer behaviour analytics - http://www.flowtrackeranalytics.com Customer value management - http://www.objectivebusiness.com
Each of these factors affects how we measure customer value. The first is important because it means that every client contributes something to overhead - so de-marketing never makes economic sense (okay your chronic loan default and fraud clients should go but that is about it). The second - aggressive discounting - points the finger squarely back at the banker... it is our fault if we give the store away. The third notion, that from time to time we price irrationally is again something that has little to do with the client. in my view cyclically normalized spreads are more relevant than actual spreads for customer valuation. Irrational pricing is quite common, and it happens when competitive pressures make us favour share over margin (for instance in IRA / RSP season, Spring for loans) or when money market rates are in an anomalous state ( yield curve inversion, shocks).
So there are reasons why what looks like lead might really be copper, brass bronze or whatever. But can the value of customers properly identified as being in the bottom ranks be transformed into great customers ? I think the answer is yes to this, again based on global experience.
Customers with decent direct margins suffer from the monolithic cost base of most banks when we look at their value. We have very heavy fixed costs which buries the value of most of the customer base. Is this problem that the customers are no good for your bank ... or is the problem your bank is no good for these customers !?!
In my opinion the problem is not the customer, it is the service model we offer to one and all as if everyone was one of our highest margin accounts. Other banks in less affluent economies have found new and profitable ways of serving these low margin segments. Simplified lending processes enable one bank I worked with to profitably issue loans with face value as low as $5. One bank actually split itself into a "people's bank" and an "afluent" bank with completely separate platforms, services, pricing and distribution processes. These kinds of innovations are portable across markets, and we should be learning from these emerging market innovations.
A lot of good customers appear bad because we don't measure their value properly or we load them up with service costs they don't need. FiIrst true up your metrics so you have a clear line of sight into your real relationship value situation. Then take a look at how you can service lower margin segments with innovative service models that are better suited to the cost base that lower segments can support. I am a firm believe that smart banks can learn how to turn lead into gold.
- Dave McNab
Understanding the motives that drive customer behaviour is increasingly being recognized as essential to relevant customer interaction. Knowing that customers are likely to drop a product or add a new one, or detecting abnormal changes in account use provides only a small part of the information you need to have a meaningful conversation with your customer. Historically our capabilities have addressed identifying what customers are likely to do (see article parts I and II) with little regard to why.
Leading banks are now exploring how to better understand motive by gathering information about what is going on in their customer’s lives, and developing offers and dialogues that are relevant to the individual in the context of their changing circumstances. Detecting “life events” such as birth of a child, cohabitation, going to college, buying a home, renovating, acquiring a vacation property and the like is not easy.
Some of this information must necessarily be solicited directly from your customers, through interviews or online tools that provide the customer with more relevant advice when they share details of their circumstances with the bank. These capabilities have long been used in the investments domain to gather a fuller understanding of customer aspirations and needs, while satisfying “know your client” (KYC) regulatory requirements. There are now many financial planning and customer dialogue scripting tools available to engage your customer and extract vital insights into circumstances and aspirations that can be used to tailor offers and interactions to be more relevant to the individual person you are serving.
Implementation of similar life event dialogue tools is now growing in popularity among leading retail banks. Asking your customers about their life events must involve a meaningful exchange of value to work. Customers must perceive a real benefit to providing information about their plans, aspirations and circumstances, and perhaps more importantly, if they proffer this information they need to see that your bank remembers it, protects it and utilizes it intelligently to improve the relevance of customer dialogue, offers and interventions. Failure to use information you ask for is worse than not asking in the first place. A coordinated customer dialogue requires integration across products, geographies and distribution channels with a consolidated understanding of your customer as the common base.
In addition to revealed motives gleaned from customer dialogue, important insights about how customers use your products and services can be gleaned from traditional information sources. The big challenge is getting past account or transaction based analysis to understand holistically what your customer is doing. Traditional banking systems fragment customer behaviour into sets of transactions that obscure what is really going on. For example when a customer borrows against their home to invest in mutual funds, banks see the two sides of the transaction as unrelated (and even contradictory) events; borrowing and investing. It is only when both sides of the event are seen together that we understand the customer’s real behaviour.
Taking a holistic view of customer use of products and services enables you to gain insight into customer intent. 30% of account level growth and diminishment occurs because your customer is switching money from one product or service to another. Transaction and account analyses fail to see these important flows of funds – everything is seen as a win or a loss of business. This distortion leads to false targeting, false triggers and irrelevant customer dialogue (see blog entires What is a cross-sale? and Event detection). Let’s take a look at how customers move money around inside the bank:
Accounts of the same product Features, location
Deposit or lending products Rate, features, location
Deposit and lending products Borrow to invest / pay down loans
You can refine these insights by knowing which products are involved. For instance borrowing from a HELOC to invest in short term mutual funds may indicate an impending renovation, whereas borrowing from a HELOC to invest in equities or long term CDs likely indicates a change in investment strategy. Knowing what your customer is doing holistically reveals – at least partially – their intent, which should inform your customer dialogue.
Switches between deposit and lending products are another interesting case. These substitutions – often called cannibalization – illuminate customer preferences. Within deposits, for example a customer can move money between products with different liquidity characteristics (short term, long term, demand) and with different rates of return and risk. Changes in preferences reflect changing customer needs which you should be aware of.
Analysis of this type reveals a lot about customer behaviour. When you combine this data with other information acquired through tools designed to gather life event data you can sharpen the relevance of your dialogue significantly. Moving beyond the analysis of individuals, statistical techniques can be applied to your entire portfolio of customers to better understand, predict and optimize your customer interactions by first understanding why customers do what they do.
Let’s now layer on the introduction of analytics enabling capabilities with the growth of customer insight over the same period. When we do distinct phases of advancement in business insight over these three decades emerge.
Before the 80s banks ran on volume metrics in branches, with practically no technology enabled insights into customer relationships. This was followed by a product-centric management trend (thanks partly to FTP, partly to the influx of consumer packaged goods marketers in the 80s) and the evolution of the geography-product management matrix that still prevails in most banks today.
With Basel I we got better at assessing credit exposure and that information, coupled with FTP and ABC costing gave us the basic ingredients for understanding customer "profitability" (more properly Customer Value). There was a big push in the 90s to develop a 360 degree view of each customer relationship (the Customer Information File or CIF) and start leveraging FTP, credit loss exposure and ABC cost to measure customer value to the bank. The insights produced were profound, enabling us to understand the value dimension of the customer base for the first time, revealing huge disparities in contribution of different customer groups. These observations spurned development of target marketing and marketing strategies that are still dominant today.
Leading firms started to look at transaction streams when AML requirements (and card fraud losses) forced investment in streaming data analysis. Banks started to apply business rules to identify anomalous transactions – statistically outside of a customer’s normal volume, frequency, location etc. – in an attempt to identify appropriate interventions in response. These efforts were fruitful, enabling retention and cross selling interventions to be identified in real time, dramatically increasing the relevance of the response to events detected by the monitoring tools.
In parallel predictive analytics – forecasting future events based on history – has grown in importance. Originally used to predict loan defaults (credit scoring) the same statistical techniques have extended to identify next likely sale, probability of offer conversion, probability of account and customer defection and the like. More sophisticated methods of analysis such as price optimization based on price elasticity of demand at the micro-segment and individual customer level have been meeting with significant success in recent years.
All of these techniques have led us to better understand our customers better. The new frontier in Financial Institution customer analytics is an extension of these insights to get past understanding the probability of a customer doing something or reacting quickly to new or unusual transactions, to understanding why the customer is doing what they are doing.
Understanding “why” is the key to being relevant. This is the domain of customer behaviour analysis, which requires a new way of thinking about our data, our tools and our objectives in using analytical tools.... and this time advances are being driven not by regulators, but by the need to create competitive advantage.
NEXT: Beyond What: understanding customer behaviour
Let’s start by laying out two key timelines in parallel - major events affecting our industry and major regulatory changes - as a baseline. There is a distinct pattern of new regulatory regimes following closely after major industry events, which should come as no surprise to the members of this community.
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Early in the new century 9/11 brought us the Patriot Act, which spawned the required Anti Money Laundering (AML) transaction analytics capabilities needed to comply with Homeland Security (and fraud detection) needs. At the same time the Basel Accords expanded to include understanding (and measuring) capital requirements associated with non-credit risks. Most recently liquidity has moved to the foreground as a consequence of the 2008 global financial liquidity crunch. Basil III and balance sheet “stress tests” are now in the spotlight.
What can we learn from this? First and foremost it appears that both management and regulation of our industry have been largely reactive rather than proactive in the advancement of the analytics side of banking business intelligence. Core capabilities that enable modeling of customer value and other key insights have trickled in over the years driven not by our quest for insight, but by capabilities demanded by regulatory requirements. We have not been brilliant business leaders exploring new frontiers of insight for business advantage: we have actually been slow to exploit technology to uncover business fundamentals.
NEXT: Out of distress.... insights
Sometimes it pays to keep things simple. Over 30 years in the industry I have had the opportunity to be involved in many think tanks working on KPIs, reporting, scorecards and dashboards. Throughout it all, some fundamental metrics consistently emerge as essential, since they describe manageable dimensions of the business in simple yet effective terms. They may sound too simple (and they certainly are not new), but simple works... and if these metrics aren't working in your shop, your bank isn't operating optimally. Of course there are lots of other metrics that add value to management - I do not suggest abandoning these insights - but if I could only look at one page of one dashboard every day this is what I'd want to see. I'd also like to hear what your favorites are... this could get interesting if you participate in the conversation. Without further ado, here they are, my top 5 KPIs for a Retail Banking Sales & Service organization: 1. Average balance per product . Market positioning. All other things being equal, if you have higher balances per account than your competitor you will have a lower efficiency ratio since 2/3 of costs in the business are fixed. 2. Average total margin% per product. Price positioning. The margin / volume tradeoff is captured by monitoring this in conjunction with average balances. Successful positioning optimizes price / volume tradeoff which can be achieved with price optimization analytics. 3. Number of (active) products per customer. Sales effectiveness. Penetrating the existing customer base is imperative. This is a barometer of relationship depth and is highly correlated to share of wallet and retention. All other things being equal more products per customer will drive higher lifetime value per customer. 4. Number of customers per front line FTE.
Case load. It is often revealing to see how many hours per customer staff have... an RM with 2000 customers is going to spend less than 10 minutes a quarter with each of them. Depending on your positioning in hte classic 2x2 strategy matrix (high volume, low margin v. low volume, high margin etc.) maybe that is okay ... or not. 5. Cost per front line FTE. Staff mix. How you align staff to customers is crucial to profitability and customer experience. High touch, high dollars here must be justified by high balances, prices and penetration. Now the fun begins... these five simple metrics combine into the single most important ratio in the Financial Services sales and service business - revenue per staff dollar. The proof is simple: Balances per product X Margin per product = Product Net Interest Margin (add fees if relevant) X Products per customer = Revenue per customer X Customers per FTE X 1/(cost per FTE) = Revenue per staff dollar If you are maximizing revenue per staff dollar on the front line you are running a good sales & service organization, in my view. The beauty of these five little KPIs lies in their ability to reveal the levers you can pull to make a difference. It's nice some things in life really are simple. Agree ? - Dave McNab
Of course the right answer is both, but it is important that the vaue of client information is protected by managing the risks of improper information management.
Over the course of the past few months there has been increasing attention gathering around the subject of information governance, particularly since people have see the disastrous effects on share prices at compaines like Sony when a catastropnic security breach occurs.
Traditionally the business case for investing in enterprise information management stewardship functions has been a tough sell to anyone outside IT departments; the risks seemed remote and the process of controlling information seemed arcane. With increased attention comes better literacy and awareness about this important aspect of possessing data. Recent events have demonstrated just how important managing information properly is ... what should be one of your strategic assets can quickly and unforgivingly inflict catastrophic damage to your brand, share price and your business franchise in total.
One of the key things to realize is that we are not just talking about information you might have in a data mart or data warehouse containing client data. It extends much further than that ... documents, emails, paper records, telephne recordings, website history - all of these forms of unstructured data exist in your bank and they may contain 80% or more of the specific client information that needs to be protected from hackers, competitors and even espionage efforts. The scope is vast and growing exponentially, yet few are yet aware of the extent of the business and operational risk exposure maintaining client information entails.
There is an upside to this story, and it is well worth considering before labelling information management as a risk containment exercise. One part of the upside lies in the value of information as an asset to be mined - well understood by marketing departments - to increase revenue through more relevant, timely and selective communications which reduce attrition and increase acquisition per dollar of spend. Similarly high quality, trusted and controlled information is critical to support operational decision making and deploymnet of the increasingly practical analytical optimization of business processes via statistical process control and measurement - what we called Operations Research back when I was at University.
The other lesser known opportunity lies in the hard dollar savings that can be realized from getting rid of information that should not be retained. Eliminating redundancy and complying with disposition regulations can be a big money saver, as it reduces not only hardware and processing costs but reduces all of the other data management functions such as backup and retention processes as well. This aspect alone can make Governance a self-funding investment for your bank.
Information about your clients can be an asset or a liability. Good management practices can reduce cost and increase effectiveness of management decision making. Poor management leaves you exposed to catastrophic risks that could empty some C-level offices. The time to acti is now, the choice is yours.
If you would like to do a self-assessment of the state of information governance in your organization I recommend you join the global Information Governance Community which is available at no cost (IBM sponsors it).
- Dave
It is no secret that new money deposits and loans and retention of blaances 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 metrics for 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 growth and diminishment activity 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 certainly can 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:
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 understand the power of flow of funds analysis as a concept, 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.
