Banking Customer Analytics | Banking Customer Intelligence | Banking Business Intelligence | Banking Product Analytics - FTAI

Customer Behaviour Analytics blog - David McNab

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.

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CUSTOMER CENTRIC BEHAVIOR ANALYSIS FOR BANKS...... focusing on acquisition, attrition, cannibalization

Demonstration videos

CUSTOMER CENTRIC BEHAVIOR ANALYSIS FOR BANKS...

... focusing on acquisition, attrition, cannibalization