FlowTracker Analytics Inc. - bank customer behaviour analytics  

Flowtracker Bank Analytics


Bank customer behaviour analytics: deposit and loan product attrition, acquisition and cannibalization

CUSTOMER CENTRIC BEHAVIOR ANALYSIS FOR BANKS...

... focusing on acquisition, attrition, cannibalization

Executives of full-service consumer banks know the difficulty of focusing resources on acquiring new money and curbing attrition of balances. Internal competition for balances among product and geographic silos within a financial institution diverts resources from actions that grow your deposit and loan portfolios. Cannibalization also introduces false positives when targeting growth and attrition prospects. The solution to these problems is performance metrics that separate new and lost money from money moving between business unit, branch and product silos across all financial services products within the bank and its affiliates.

FlowTracker is a repeatable process that derives and classifies flow of funds information from account balances precisely and efficiently, with a minimum of set up and maintenance cost. We have assembled an optimal business solution consisting of a proprietary analysis method, platform independent software, business rules that are readily customized to work with your data and the banking business expertise to solve the problems of inter-silo competition and false positive targets efficiently.
Bank deposit and loan product attrition, acquisition and cannibalzation - click to enlarge This diagram shows results of a study we conducted on 1 million banking customer households over three months. Fully 30% of all account balance changes arise from internal flows between accounts" i.e. "old money".

Internal flows mask key performance results by mixing new money sales with product substitution and lost money with product cannibalization. Distorted analysis of customer behavior results in excess contact cost, inappropriate customer contact, employee frustration, diminished customer satisfaction and ultimately lost business.

The FlowTracker analysis method is patented in the USA and is patent pending in Canada. We can improve your business analytics and competitive advantage in ways no-one else can.

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Customer Analytics | Business Intelligence | Management blog
End-to-End Revenue Management in Commercial Banking - IBM white paper - Sept 2013
For those community members in the Commercial Banking space, this white paper may help you envision a future state where pricing, billing, deal compliance, client profitability and variable compensation capabilities are brought together to align business strategy, account manager behaviour and...
Consumer loyalty... some lessons learned.
Consumer loyalty can directed towards your brand, your products and services, your people or your loyalty program itself. Loyalty can also be emotional (attitude, beliefs) or behavioral (purchasing, recommending). Leading practice companies seek to create both emotional and behavioral loyalty bonds...
Analytics in Banking: Using Customer Profitability Analytics to Enhance Financial Performance [videocast]
IBM Customer Profitability Analytics in Banking Date: Wednesday, March 20, 2013 at 12 pm ET Click here to register Customer Profitability Analytics Videocast By understanding who your most valuable customers are, what they want, and how they will behave in the future, banks can better and more...
Information governance evolution
Perhaps 2013 will be a year in which we start managing information governance as a strategic weapon instead of a regulatory burden? Fact is, good information management strategy and information quality are imperatives to competitive advantage in marketing, pricing and risk management. Bad data, bad...
Channel Profitability - really?
As non-traditional channels grow the desire to measure profitability to justify and sustain investment quickly follows. Does it make sense to treat channels as profit centers? If so what is the real meaning of a channel "bottom line"? In many banks a P&L empowers executives among their peers....
RFM ... is it really a Customer Profitability metric ?
Over the past 3 to 4 decades Banking has borrowed a lot from the Retail and Consumer Packaged Goods Industries. The notions of product management, channel management and merchandising are among the more notable management philosophies that have crossed over into Financial Institutions. The concept...
Shouldn't core banking be profitable ? Time for a rethink, America !
All the hullabaloo in Washington about the Volker Rule and Glass-Steagall seems to be missing the main point - bank ROI is insufficient to satisfy investors unless trading profits supplement the core business of retail and commercial financial services. As long as core banking services do not...
Turning Lead Into Gold: Marketing Alchemy or Fiction ?
Not literally... that is sadly the stuff of fiction. What I am talking about is your lowest value client segment, which I heard a marketing SVP at a money center bank once refer to as their "Lead" clients (true story). The question is, can they be transformed into profitable client relationships ?...
Customer Analytics Evolution a look down the road - Part IV
In my earlier three posts in this series I outlined the history of customer analytics evolution, from response to regulatory pressures (see Part I and Part II ) through to the leading practices of today (see Part III ), where banks are seeking to understand, react to and anticipate customer...
Customer Analytics Evolution: what can we learn from the rear-view mirror? PART III
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...

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Customer Behaviour Analytics blog - David McNab
Analytics in Banking: Using Customer Profitability Analytics to Enhance Financial Performance [videocast]
IBM Customer Profitability Analytics in Banking
Date: Wednesday, March 20, 2013 at 12 pm ET
Click here to register Customer Profitability Analytics Videocast


By understanding who your most valuable customers are, what they want, and how they will behave in the future, banks can better and more cost-effectively meet their needs. More importantly, you can use those insights to better measure and manage profitability.

Frank McKeon, Banking and Financial Markets Industry Executive, IBM and I will be discussing Customer Profitability Analytics with Farhana Alarakhiya, Director of Industry Solutions, IBM Business Analytics in this session.

Join us on Wednesday, March 20th at 12:00 PM ET, during this one hour video-cast where banking industry experts will discuss new approaches for optimizing customer relationships and engagements to drive profitability. Using illustrative and case study examples, the panel of experts will help you answer questions such as:

  • Who are my best customers, and how to I attract others like them?
  • How do you measure profitability?
  • How do profitability insights help to improve pricing, cross-selling and other point of impact decisions?

Registrants are invited to submit questions regarding customer profitability analytics and the panel will attempt to answer as many as possible during the conversation.

(All registrants will receive the "Banking for Success: Using Analytics to Grow Wallet Share" white paper from IDC Financial Insights).

I hope you will join us. (And yes, there is no cost to register.)

- David McNab, CPA, CA
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.

 
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
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
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
If...
IF  you could save 25% of your marketing costs....
IF you could save 25% of irrelevant marketing communications...
IF you could save 25% of your sales commission costs...
IF you could align people to do the right thing for your customers and your business...

What would it be worth to your company ?

The reality is that most consumer banking / financial services companies have a waste factor of at least 25% of resources which is targeted at capturing money that is already in the bank or retaining money that isn't at risk of leaving. That is the startling finding of the fact-based bank customer behaviour research we did over the last several years using our own proprietary methods.

The key to unlocking these savings is pretty obvious: simply stop chasing money that is churning among products and branches in the bank and focus on real dollars won and lost.  The tricky bit is being able to distinguish and measure money flows in the same dimensions that are used to manage the bank - product, location, business unit, legal entity - because money flows are inherently a customer driven behaviour.

Fortunately there are solutions at hand. The simplest is to simply manage sales and lost business at the aggregate customer level - if their balances go up, you have sales, if they go down you have lost business. This works, but is pretty crude. We have developed a better way that quantifies the flow of funds at the account level, which enables reporting and analysis in the same dimensions as are used for management. This bridges the gap between customer and business, enabling management to synchronize resource allocation to customer behaviour objectives.

All this should be old hat, but sadly it's not. Most banks are still struggling under the old paradigms of driving branch and product balance, revenue and profit targets instead of adding the customer dimension.  How to move the mountain of management intertia ?

David McNab
Unprofitable Customers: Naughty or Nice ?
Customer value helps identify the top tier customers we need to focus retention activity on, but what of the other 80% of customers who generate nominal or even negative contribution? Are they naughty or are they nice to have in your portfolio?


This question has been troubling strategists and marketers since customer profitability measurement first became viable in the early 1990s. And with good reason: depending on what you are measuring and what your goals are customer value can suggest very different actions. It is imperative to measure customer values using a model appropriate to the decisions you want to make. More often than not you will discover that a variety of measurement models are needed to support different kinds of decisions. (See CMA article How should we measure customer profitability). Even if you’ve got the models you need, however, it is inevitable that 60% of your clients are going to be somewhere in the middle and 20% at each of the top and tail of your list.

Let’s consider the bottom 20%. Are they “bad” customers that we should de-market? Are they “abusers” of our services? Customers in the bottom quintile of value rarely have an “average” customer profile. In this tier you will find customers with a wide variety of business relationships with your bank, most of them fairly substantial in terms of balances and activity. If you dig deep enough into the numbers, you are likely to find pricing at the root of their negative value. Some will have their value depressed by shrewd negotiation of rates and fees, others by strategic discounting and others still by irrational market pricing conditions.

Negotiated discounts in fees and rates certainly need to be taken into account when assessing customer value. But pricing anomalies driven by market conditions or strategic discounting have little to do with the customer, and should not be included in customer value. The extreme case of this is when the market prices entire business lines at negative spreads, which happens from time to time in periods of crisis. Whole segments of customer values can turn from gold to brass in a matter of months when this happens.

Obviously one cannot switch customer relationship strategies with these shifting winds of chance. You need to look past the numbers to manage customer strategy effectively. Clearly we need to reprice relationships where excessive discounting is negotiated. It is equally clear we should not penalize customers for aberrations in market or strategic conditions. There is no substitute for wisdom and understanding when working with customer value !

Best holiday wishes to all.
David McNab
Customer behaviour = real intelligence !
Nothing artificial about it... real custoemr intelligence comes from understanding your business, and how customer behaviour gets reflected in the data you capture about customer interactions.

Many data mining experts today come from one of two schools - first the traditionalists who are dyed in the wool statisticians. These folks are plenty smart, but are first and foremost mathematicians and they draw their insights from interpretation of mathematical modeling techniques. They can glean powerful insights about the correlations among your data.. but is that really meaningful ? Is not customer behaviour - and it's dynamics not more to the point ?

The second school of data miners are those who use current generation data mining tools and advocate the concept of  self-service data mining. Tools have become much easier to use, with GUI interfaces and process logic that manages things - like covariance - that it used to require a mathematician to control when designing models. These tools are great - they reduce the mathematical knowledge required to create models and accelerate model production. But are they measuring things that really matter ? Is this more data or more information ?

Our sense is that it is imperative to apply deep business knowledge to the process. Understanding who your customers are, why they buy / use your products, what they like and dislike, and how they behave under varying circumstances is crucial to obtaining real inferential insights about customer behaviour. Unless you know what is an aberration and can separate real from false positive behaviours models are essentially just more data that has limited value for business decisions.

Liberate yourself from the tyranny of the math ! Model your customer interactions and search out the menaingful indicators of relationship change. Getting these basics right can save you a ton of money and improve service to boot.-DBM
Analytics - key to efficiency
In discussions with members of the Bank Administration Institute community, we have been finding that though analytics are known to be valueable, they are also under-resourced, preventing banks from realizing the potential to make real headway on efficiency, effectiveness and relevance of customer interaction. The latest blog article at BAI has the details.
What is going on in customer inteligence
Finally our blog is here. Thanks for checking out the news and content we offer. There is a rich opportunity for improvement of data mining and analytics throught the use of customer intelligence, and especially techniques that deduce customr behaviour from raw data as an interim step in the creation of behaviour prediction models.

(c) David McNab - FlowTracker Analytics Inc.


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