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Superior Business Decisions Through Better Data Analysis |
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HOW
CAN CABLE COMPANIES DELIGHT THEIR CUSTOMERS? Many customers do not love their cable companies. Advanced
analytics and causal modeling can discover why, and help to figure out
cost-effective ways to boost customer satisfaction, loyalty, and revenues. For
Rogers Communications, Cox Associates developed causal models of customer
satisfaction; identified high-impact interventions for improving customer
satisfaction; and helped to develop achievable targets and strategies for
improving customer experiences in different channels (2011). For Comcast
Cable, we delivered a statistical analysis of the causal drivers of customer
satisfaction to top executives and identified realistic targets and
interventions for improving customer satisfaction (2010-11). HOW TO FORESTALL BAD
DEBT AMONG ENERGY COMPANY CUSTOMERS?
In 2007, Cox
Associates, in partnership with WHAT
IS THE VALUE OF IMPROVING WEB CHANNELS?
In 2006, Cox
Associates, in partnership with North Highland, delivered to a large
telecommunications company a computer simulation model and supporting data
analyses that quantified the economic impact of improving the company's web
channels so that more transactions -- ranging from new serice
orde tracking to repair requests to billing
questions -- could be successfully completed on-line. By
identifying the preferences and behaviuors of
different customer clusters using a combination of transactions data and
survey data, we quantified the number of customers and that would shift
toward the web channel if it were upgraded to work better. The model
also quantified the value of sales rep and service rep time saved, and
studied the economic values from each other channels, ranging from retail
locations and kiosks to call centers. WHO
IS LIKELY TO BECOME BAD DEBT FOR YELLOW PAGES COMPANIES?
In June,
2003, Cox Associates, in partnership with North Highland, developed a new
statistical risk analysis model for predicting which yellow pages customers
are most likely to become bad debts in the next 1, 2, 3, or 4 quarters, based
on currently available information about their business types (yellow pages
headings), open invoices (number, ages, amounts, etc.), account age,
location, billing history, and other readily available information. The new
model outperforms all previous approaches and provides specific
recommendations on which customers are at the greatest risk of defection
(i.e., churn), becoming bad debts, or both. This is the first result from a
new initiative to decide what specific interventions with which customers
when will most increase their values as yellow pages customers. Cox
Associates' causal clustering and customer behavior-prediction technology has
already revealed some striking new insights about which types of customers
are at greatest risk, and programs to reduce churn and bad debt based on
these findings are now being designed. UNDERSTANDING
THE REASONS FOR CHURN AMONG ISP CUSTOMERS
A continuing
goal for many Internet Service Providers is to reduce the rates of churn
(i.e., unplanned loss) of profitable customers. Cox Associates, in
partnership with Adjoined Consulting, has recently begun a data analysis
initiative to understand how the specific reasons for churn affect observed
behaviors (e.g., connect times, number and duration of sessions per month,
number of attempted sessions, called-in service questions and complaints,
etc.). Based on this improved understanding, we are creating new customer
churn risk management algorithms that use changes in observed behaviors to
give early warnings of high-risk customers - those who are most likely to
churn in the next 6 months - and their most likely specific reasons to churn.
These statistical patterns are then used to drive focused, timely
interventions to reduce churn rates among profitable customers. COX
ASSOCIATES MODELS ACHIEVE 40% CHURN REDUCTION FOR A MAJOR ISP
In June,
2002, Cox Associates in partnership with Adjoined Consulting delivered to a
major Internet Service Provider a set of recommendations on how to reduce
churn of high-value customers using predictive algorithms and prescriptive
decision models. The recommendations identified specific combinations of
changes, including more accurately targeting initial offers, early detection
of usage patterns and data that predict probable churn,
and timely intervention with the customers who have the greatest predicted
loss potential. In July through August, the recommendations were tried. The
client's evaluation was that, within 60 days of initial implementation, our
recommended churn reduction program achieved a 40% reduction in churn in the
most at-risk group of customers -- a large percentage of the total customer
base. Based on this highly successful outcome, the modeling results and
recommendations have been extended and deployed to a wide set of the ISP's
customer base. WHO
INVESTS HOW MUCH IN LIFE INSURANCE AND RETIREMENT ACCOUNTS, AND WHEN?
In May of
2002, Cox Associates in partnership with Adjoined Consulting completed an
adaptive predictive clustering (APC) system that predicts which customers are
most likely to churn, which are most likely to buy new financial services,
and which are most likely to change their asset investments in existing
products. The system reads in billing records from a major retirement fund,
life insurance, long-term care, and diversified financial services provider
and generates probability scores showing how likely each customer is to
exhibit each behavior in the next month. It calculates the expected economic
values of different intervention strategies (e.g., focusing on retention vs.
additional sales for each customer) and recommends the most appropriate
action to maximize the expected value for each customer. A key finding from
the model is that the current product portfolio, in conjunction with a
customer's age and a few other factors, provide strong, robust predictions
about probable next behaviors. EARLY
IDENTIFICATION OF BIG SPENDERS AND LIKELY DEFECTORS AT A CLEC
In October,
2001, Cox Associates personnel completed a study, in partnership with another
company, to identify ways to predict which competitive local exchange carrier
(CLEC) customers would experience the most revenue growth in the next quarter
and which would be most likely to drop accounts. At a time of chaos in the
telecommunications world, with a huge number of the CLEC's
new customers being acquired in the past year, it appears that our data
mining and modeling technology is still able to make robustly accurate
predictions of the customers who are most and least likely to show near-term
growth. PREDICTIVE
MODELING FOR A LARGE PROPERTY & CASUALTY INSURANCE COMPANY
In August,
2001, Cox Associates completed an analysis of insurance customer data showing
that combining information from homeowner, auto, and other insurance lines
using classification trees and state transition models had the potential to
dramatically improve accurate identification of cross-sell, up-sell, and
retention opportunities. These insights and the quantitative models that
support them may speed the implementation of customer scores and predictive
modeling at a Fortune 100 insurance company. WHO
WATCHES WHAT ON CABLE TV?
In April, 2001, Cox Associates personnel finished a
study of cable company billing records and service records. This study
revealed that customers who are most and least likely to add new services
(e.g., upgrade from basic to extended basic service, or upgrade to digital
service) and those who are most likely to churn can be predicted with useful
accuracy from data contained in recent billing records. Prediction rules were
created and their performance was evaluated by the cable company, which
concluded that they work well. WHAT
DO QWEST'S LARGE BUSINESS CUSTOMERS BUY?
In December,
2000, Cox Associates completed a study of purchasing patterns among large
business customers for Qwest communications. The results show that a few key
products, together with factors such as account age, predict likely stability
or churn of customers. A different small set of products (such as Frame
Relay or business voice messaging), in conjunction with past purchase
histories, can help to predict likely growth potential. This analysis
led to a predictive model of how customer needs and purchase probabilities
evolve over time. This work is continuing in 2001 with larger data sets and
more detailed analyses. HOW
DOES ADVERTISING AFFECT CUSTOMER PERCEPTIONS AND INTENTS?
In 1Q and 2Q,
2000, Cox Associates delivered to U S WEST (now Qwest) statistical analyses
of the effects of U S WEST and competitor advertising and publicity
(including brand/service commercials, direct mail,
and news stories) on customer ratings of value and
loyalty. Applying advanced statistical and causal modeling methods, Cox
Associates identified the ad campaigns and customer characteristics that most
affect outcomes. WHAT AFFECTS STATED AND ACTUAL CUSTOMER LOYALTY?
In January,
2000, Cox Associates, in continuing partnership with the Marketing
Intelligence Decision Support (MIDS) group in U S WEST Communications,
delivered new analyses and causal models of customer survey data that predict
how customer perceptions of value and quality and stated loyalty to U S WEST
will change as different aspects of installation and repair service are
improved. These models are novel in that they explicitly seek to isolate the
causal impacts of U S WEST initiatives on customer survey results. This
contrasts with earlier models, which focused on statistical associations and
predictions, rather than on causal impacts. It also contrasts with previous
models developed by Cox Associates which have emphasized changes in customer
behaviors, rather than changes in how customers will respond to surveys. Also
in January, we completed an analysis quantitatively linking what customers
say they will do on surveys to what they actually do in the months following
the survey. HOW DO ADVERTISING CAMPAIGNS AFFECT DEMAND FOR
LOCAL TOLL SERVICES?
In September,
1999, Cox Associates, in partnership with the Marketing Intelligence Decision
Support (MIDS) group in U S WEST Communications, delivered the first results
from a data mining and modeling project that quantifies the impacts of
television advertising on local toll usage and revenues. The findings
included some unexpected effects of competitor campaigns and of advertising
for other U S products on customer usage of U S WEST toll services. WHICH CUSTOMERS WILL BE MOST VALUABLE?
In June of
1999, Cox Associates, working in partnership with Price-Waterhouse-Coopers,
delivered to the CRMS group in U S WEST Communications a new data analysis
and predictive model for identifying the likely future purchasing,
product-drop, and account disconnect behaviors of individual customers. In
contrast to previous models, this one incorporates more detailed information
about the products that a customer initially acquired, the time since last
transition (product purchase or drop), and the history of product adds and
drops leading up to the current product portfolio. Testing and validation
indicate that the new model has significantly greater predictive power than
previous ones, achieving lifts of several hundred percent on the task of
predicting which 10% of customers are most likely to buy specific products in
the next few months. Several product-specific lifts exceed 300% and some
exceed 500%. The model is based in part on an underlying discrete-event
simulation of customer transitions, with conditional transition intensities
estimated from millions of historical data points. It has been used to
predict the expected change in products and monthly revenues from individual
customers over the next 5 years. (Client Contact: Dr. Jovan Barac, 303-965-3732) The August
30th, 1999 issue of Telephony mentions how these deliverables are now being
applied, in a cover story featuring U S WEST's
successful use of Corporate Data Warehouses, data mining, and modeling
methods (http://www.internettelephony.com/archive/8.30.99/cover/cover.htm).
In this article, our client, Dr. Jovan Barac,
speaks of the SAS scoring models developed and delivered by Cox Associates
and the value that these models have brought to U S WEST's
database marketing efforts. IMPROVING CUSTOMER PERCEPTIONS AND LOYALTY
In July of
1999, Cox Associates, in partnership with U S WEST's
Marketing Intelligence and Decision Support (MIDS) group, completed a first
version of a causal simulation model that quantifies the likely impacts of
different U S WEST initiatives on customer perceptions of U S WEST quality
and value. The simulator uses causal models based on survey data to predict
the sensitivity of customer perceptions and stated willingness to drop U S
WEST to changes in pricing, service performance levels, service rep training
and actions, and other factors. (Client Contact: Dr. B.J. Deering,
206-345-7932) PREDICTING CONSUMER PURCHASES MORE ACCURATELY
In January,
1999, Cox Associates delivered to U S WEST Communications a breakthrough
predictive model for identifying which specific product(s) each Premium
customer is most likely to purchase next. The new model, implemented in SAS,
is based on data mining algorithms and causal models, including classification
tree analysis and detailed consumer state transition modeling. Initial tests
indicate that the new Next Optimal Bundle model nearly doubles the expected
increase in sales due to targeted marketing compared to older approaches such
as logistic regression. ANTICIPATING NETWORK IMPACTS OF MARKETING
DECISIONS
On December
16th and 17th, 1998, Tony Cox and Warren Kuehner of
Cox Associates, in partnership with the new national Business Planning unit
of AT&T Wireless, gave a two-day workshop on the design of an Integrated
Forecasting, Planning, and Optimization system at AT&T Wireless in
Redmond, Washington. The workshop documented the ways in which marketing,
finance, RF planning, network planning, and switch planning currently work
together to forecast network load growth and capacity expansion requirements.
It identified gaps in the current process, data, and algorithms and
recommended both quick fixes and longer-term improvements. A key theme was
that causal forecasting based on predictive models of customer responses,
rather than historical trending, is essential to anticipate the traffic load
effects of new price plans and other marketing initiatives. Approved clients
may click here to download the background
materials and final report from the workshop. PREDICTING CHURN
In November,
1998, Cox Associates delivered to U S WEST Communications' Small Business
Group (SBG) a new model for predicting which small business customers are
most likely to drop specific U S WEST products and services. The initial
version of the model was based on data mining algorithms applied to
behavioral data (product add and drop histories) only. In December, the model
was refined by including firmographic and event
data. It appears to offer significant power for identifying customers at
relatively high risk of churn several months or quarters ahead of the event. WHO BUYS DIGITAL CABLE?
In December,
1998, Cox Associates delivered initial analyses of market survey data to TCI
identifying the types of telephone customers (based on patterns of
residential and long-distance telephone use) who are most likely to subscribe
to digital cable. Our data mining technology revealed some surprisingly
strong relations between the types of telephone services and features that customers purchase and the types of cable services that
they will subscribe to. These data suggest some new ways to bundle telephone and
cable services to stimulate demand for both. Approved TCI and AT&T
clients may send e-mail to us to obtain copies of these results. A BETTER WAY TO
PREDICT CUSTOMER PURCHASES?
Cox
Associates, in partnership with SystemView, has
recently been experimenting with a novel idea: applying advanced algorithms
developed for speech recognition and signal processing to predict future
purchase behaviors for customers based on their observed purchase histories.
Initial results, reported in October at the Fall INFORMS conference in ECONOMIC VALUE OF DATA ITEMS
In late June
1998, Cox Associates delivered to U S WEST Communications a method to
quantify the dollar value of specific data elements, such as information on
subscription to online services, ownership of PCs, age of account, household
demographics, and recent customer purchase histories. The method quantifies
the economic values of these data elements for specific business decisions,
such as deciding whom to target for mass mailings as part of new product
promotion campaigns. Working with data specialists from Computer Sciences
Corporation (CSC), Cox Associates applied the new method to the example of
targeting mailings to stimulate sales of Additional Lines. METHODS: Our
new technique integrates traditional decision-analytic principles for calculating
the Expected Value of Sample Information with the data mining technique of
classification tree analysis for estimating conditional probabilities of
customer responses, given real (possibly incomplete, imprecise, or incorrect)
data about them. RESULTS: Our
valuation methods shows that the values of specific
data elements are greatest in improving mailing decisions among customers who
have roughly a 20% probability of purchasing. The dollar value may exceed $2
per customer for some data elements and some customer groups. CLIENT
CONTACT: Dr. Jovan Barac, U S WEST Communications,
303-965-3732. |
