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In the tightening economy, most companies feel the pain
associated with losing customers. However, it is more
important to focus retention efforts upon preserving
profitability rather than preserving customers. This
is the "Value Attrition" problem. To illustrate
the difference, consider that if a customer who spends
$10 per year leaves, you won't feel a financial
loss. The customer was probably costing you money
anyway. Conversely, if a $1 million relationship gets
cut in half, you'll certainly feel the impact. To
improve the bottom line, companies need to focus on
the value lost as opposed to the number of customers
lost.
Value Attrition
The better alternative to customer attrition models is
Value Attrition. Value Attrition models predict
customer value at risk several months into the future.
For example, if we know which customers have the
highest value at risk in three months, we can approach
these customers with an effective retention offer.
Even better, we can spend more for these offers,
because we are not targeting every customer -- just
those customers at risk for significantly reduced
customer value.
It's Not So Easy
Creating value attrition models is tricky for several
reasons. First, the most natural target variable is
continuous, not binary. In other words, we are
building a model to predict a decrease in dollar
amounts, not whether or not a person is likely to
terminate his or her relationship. This makes some
kinds of models (e.g., neural networks, regression)
better suited than other kinds of models (e.g.,
decision trees).
Second, customer value must be present in the data.
Otherwise, we need to create a suitable derived
variable, and use this variable as a surrogate target
for model building. Fortunately, it is usually easier
to get a good target variable with value attrition
than it is with conventional attrition. With
conventional attrition models, it can be challenging
to define an attriter because the simplest measure of
attrition, account closure, lags too far behind to be
useful for model building. Said differently, a
customer will reduce his or her activity or balances
when attriting, and may not get around to formally
terminating his or her relationship for months or even
years. By then, it is too late for retention
activities.
Time periods present another facet that requires
attention. We need to know how far into the future the
model should predict, in order to allow sufficient
time for effective retention offers. Also, receipt of
data is typically delayed for several weeks. When the
data finally arrives, we want to predict attriters a
month or so further into the future to give retention
efforts sufficient time to be effective. For example,
imagine that the attrition model is built using
December through February data to predict the last
half of March and April, where April is the most
recent data available. Then the resulting model is
applied to the most recent data, February through
April, to predict future value attriters in the last
half of May and June.
There are also subtleties in "rolling up" data
from the appropriate time periods, and in creating
appropriate held-out data for model validation. For
example, we may want to average several months'
worth of balances to smooth the data with respect to
random balance fluctuations of account levels measured
at the end of the month.
Another issue is how to correct for biases in the
sample population. For example, suppose we send a test
solicitation to customers with a predicted high value
attrition score and also send a solicitation to a
smaller random sample of customers. How can we take
into account that these two groups are different
populations when building a revised value attrition
model? The answer, contained in work that won the 2000
Nobel Prize in Economics for James Heckman, allows us
to use response data from both populations in a
principled manner.
Campaign frequency also requires attention. Will the
campaign be run only once, every month, or when
triggered by an event? In the ideal CRM system, the
infrastructure would be in place to immediately
receive and act upon new data from any channel,
including inbound calls. This is a difficult "plumbing"
requirement for an enterprise IS system, and only
recently has this problem been addressed. Achieving
instantaneous all-channel customer knowledge satisfies
the customers' expectations that the company has a
unified view of them across all channels. It also
permits models to be built with a shorter lead time,
predicting less far into the future. Usually we get
best performance by predicting value attrition as
close to the present as possible, yet far enough into
the future to permit effective retention efforts.
We hope these subtleties have engendered sufficient
caution from the reader that he or she will turn a
very skeptical eye toward claims of ready-made,
out-of-the-box attrition solutions. It's not, "Hey,
anybody can build a model!" It's more like, "Don't
try this at home, kids!" Experience and know-how are
indispensable for building value attrition models.
Finally, the big question: Is the retention campaign
worth running at all, and how much do we expect to
realize from the campaign? Answering this question can
require a complex spreadsheet analysis.
The discipline of estimating campaign value is
particularly useful in avoiding some common mistakes.
For example, there was a case where a marketing
department argued, "We need a retention campaign for
these customers, because we spent a lot to acquire
them." In this case, most of the customers in
question were projected to lose money for this
organization, so a classical retention effort would
have been nothing less than throwing good money after
bad.
Do You Care About the Bottom Line?
Value attrition is attrition for those who care about
the bottom line. Although there are subtleties to
building good models and running campaigns, the
returns can be quite large with value attrition
campaigns.
According to Gartner, predictive modeling (such as
value attrition models) shows great promise for
enabling banks to pinpoint real cross-sell and
retention opportunities. Companies that fail to use
information derived from predictive modeling at the
touchpoint will face a competitive disadvantage from
providers that do.
Case in point: Xchange compared the top 5 percent
value attrition customers with standard attriters for
DDA products at a large international bank. The
results were striking. The amount at risk for
attriters averaged $4,125, while the amount at risk
for top Value Attriters was $20,498 -- nearly a
five-fold difference!
Steve Gallant is director of analytic services at
Xchange in Boston.
Exchange Applications, Inc., now doing business as
Xchange, Inc., helps companies focus their resources
on customers who represent the best long-term
profitability.
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After mining through the mountains of information on
this subject, here are a few nuggets of news.
Data integration is one of the most important issues
facing enterprises today. The Gartner Group has
estimated that IT organizations spend about $40
billion annually on integration software, and that the
average enterprise has a median of 14 databases. IT
professionals have realized the need for easier (and
less expensive) access to information resources within
the enterprise.
With the introduction of the Integration Server,
Callixa has developed the first
Enterprise Information Integration platform that helps
companies create a standard data access layer in the
enterprise. The solution allows companies to map data
across multiple disparate data sources, and uses a
common metadata-based data structure that supports any
application in the enterprise. The Integration Server
provides dynamic, bi-directional access to, and
modification of, data located in multiple sources,
enabling users to create unified views of enterprise
data regardless of the data's native source.
Siebel Systems has selected the
Fonix text-to-speech technology to
voice-enable its Siebel eBusiness applications. This
voice interface will allow Siebel eBusiness
applications users to gain real-time access to
corporate information from any telephone.
Sonexis has announced plans to
deliver voice-driven solutions to enterprise customers
utilizing the Jasmine Portal from Computer Associates. These new solutions offer enterprise
customers a single, unified access point to corporate
data. Through its Solution Services Group, Sonexis
will be using its Show N Tel and ActiveCall software
development environments to develop and deploy these
Jasmine Portal solutions.
digiMine has announced its
Enterprise Analytic Services, enabling organizations
to manage, interpret, and act on high volumes of data
from within specific business units or across the
enterprise. The service includes a scalable, hosted
data warehouse architecture, multiple business unit
reporting, digiMine CRA (a marketing campaign analysis
module), advanced data mining, View Builder (an
intuitive analysis tool), digiMine Data Slurper (a
software tool to automatically extract, encrypt, and
deliver data to the digiMine data center), and
professional consulting services.
On the consumer-centric, self-service end of the
market, TroubleTree Software
has
announced its flagship product, TroubleTree, an
interactive solution for inbound contact centers
designed to answer common customer queries. By
targeting the 80 percent of questions asked over and
over, TroubleTree allows the customer to describe the
problem step by step in an interactive manner. A
decision tree is created by this series of diagnostic
questions and responses, and may be scrolled backward
to enter a different response at any time. Patent-pending technology utilizes a relational
database, and TroubleTree is also customized to client
products upon installation and integrates seamlessly
into their Web site.
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