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A. 5 Imperatives of Extraction
B.Data Reduction & Profiling
C.i of Discovery-2-way Analytics
D.Large Array & Transform
E. Strategize
I of Discovery
Planning Strategy & Big Data Use

Executive Brief Executive Brief

Executive Brief Executive Brief


5 imperatives of big data use and forward planning


1. extraction of data /volume

2. data integration

3. linear analytics /predictive profili

4. non-linear analytics/disruption control

5. strategizing/implementation/verification


a. softnet/mysql/ibm

b. softnet/qeai

c. spss/pro

d. I of discovery/dis

e. Organization/kpmg/A Anderson/ibm




Applying Predictive Analytics to enhance customer relationship management


Table of contents


Executive summary

The five predictive analysis


Executive summary

Predictive analytics is emerging as critically important in driving customer value and maximizing returns from

customer relationship management (CRM) systems Currently, most CRM systems rely on historical analytics However,

these provide only a “rear-view mirror” of your customer relationships, offering little support for the decisions that shape

the future Meeting your customers’ evolving needs requires forward-looking solutions that anticipate changes in customer

attitudes, preferences, and actions Only predictive analytics can provide this This white paper describes how following a

set of best practices—five linear analysis —can ensure that your company maximizes the value of your customer

relationships and helps sustain higher levels of revenues and profits



Whether you call solving the business problems related to customer profitability “customer relationship management” or

just good business, you know that strengthening customer relationships is imperative for business success for one simple

reason: customers drive profits


In today’s increasingly global and competitive marketplace, customers have more options available to them than ever

before Many analysts and journalists, in fact, are calling this a “customer economy ” Attracting customers cost effectively

and meeting their expectations for selection, price, quality, and service are essential to a customer value strategy It is

equally important, however, to identify and retain profitable customers, and increase their value over time This requires

the ability to anticipate customer needs and present attractive offers in the right way, at the right time The companies

who can do this will be the companies that thrive in the customer economy


Many CRM initiatives are failing to generate expected returns


Businesses in virtually every industry have implemented CRM strategies Some have been massive initiatives, supported

by significant investments in technology and designed to shift a company’s orientation from products to customers Many

of these initiatives, however, are failing to generate the expected returns and deliver significant value This is partly due to

the difficulty of pushing change through established cultures or processes But another factor is that although operational

CRM systems such as sales force automation or call center systems provide the necessary foundation for better customer

relationships, they don’t do much to improve organizations’ ability to maximize customer lifetime value


The lifetime value of a customer is defined as the total profits generated during the time the customer does business with

your company The concept behind maximizing customer lifetime value is simple: Deliver value to customers cost effectively

When your organization delivers what customers need—what’s valuable to them—customers are more likely to remain open

to future marketing efforts, buy more of your products and services and, as a result, become more valuable This is a win-win

relationship for both you and your customer However, to achieve and maintain this type of relationship requires support

from both operational and analytical CRM systems


Analytics drives CRM returns


Until recently, analytics didn’t come up often in CRM conversations Now, though, leading CRM experts are nearly unanimous,

noting that analytics not only improves CRM efforts, but is essential to their success




Maximizing Customer Value




Companies typically start with historical analytics, using

a combination of reporting tools, specialized data warehouses,

and online analytical processing (OLAP) solutions.

As mentioned above, these solutions focus on understanding

and measuring the outcome of past decisions and results,

and can be useful in narrowing the scope of further

investigations. But on their own, they can’t provide your

organization with a clear picture of the future.

Industry leaders, however, are evolving their analytical

capabilities by adding data mining and other predictive

capabilities to their operational CRM systems. Data

mining is the process of discovering meaningful and

previously unknown correlations, patterns, and trends in

large amounts of data. To make these discoveries, data

mining relies on pattern recognition technologies, and

statistical and mathematical techniques. Because it is

forward-looking, data mining enables your organization

to measure the potential of your customer relationships

and develop plans to maximize that potential.

The most evolved analytical CRM solutions continuously apply predictive analytics technologies and deploy the results

enterprise-wide, so that whether customers interact with your organization online, by phone, or face-to-face, they receive

the kind of treatment that meets their present needs and anticipates new ones. This increases their tendency to remain loyal

and make additional purchases, increasing their lifetime value—and your organization’s profits.

Houston-based Continental Airlines, which had $8.4 billion in revenues in 2002, uses an array of information technologies

to optimize revenue for each flight, improve service for its best customers, and increase customer loyalty. By using PASW®

Modeler* from SPSS Inc. for predictive modeling, as part of a solution that includes a data warehouse from Teradata® and

business intelligence software from Oracle,® Continental agents and flight staff are able to identify high-value customers and

ensure that their needs are met efficiently. Increased customer satisfaction resulted in increased annual revenues—an average

of $200 each for its “valuable” customers and $800 each for its “most profitable” customers. Total revenues for the year

increased by $40 million. In addition, Continental saved $31 million in operational costs in 2002, with this system.

* PASW Modeler, formerly called Clementine®, is part of SPSS Inc.’s

Predictive Analytics Software portfolio.

Five Predictive Imperatives for Maximizing Customer Value 3

Every organization has data about its customers. Reporting and OLAP

provide information about past customer interactions. Data mining and

real-time personalization are forward-looking and can be used to guide

future interactions. Over time, as companies move toward these predictive

technologies, they increase the business value of their CRM information.



The five linear analytic 


1 Base your customer strategy on global profiles

2 Predict the best way to win the right customers

3 Predict the best way to grow customer relationships

4 Predict the best way to keep the right customers longer

5 Use predictive intelligence at every customer touch-point



1: Base your customer strategy on analysed profiles

Detailed, accurate predictive profiles are the essential foundation of any customer strategy and CRM initiative To

understand your customers better, use analytical tools to create customer segments, and then create predictive profiles of

each segment These profiles, when deployed enterprise-wide, enable your entire organization to focus on activities that are

most likely to generate the highest returns


Identify key customer segments


You can define customer segments based on behavioral information drawn from operational systems and on attitudinal

information obtained through market research The two approaches complement each other, enabling you to gain a more

accurate customer understanding and develop more effective strategies for each customer segment


You can segment customers and prospective customers according to a number of different criteria For example, you can

analyze customers by the amount they spend with you, by their payment pattern, by the length of the relationship, and

many other factors You can split customer segments into smaller sub-segments, even reaching the ultimate one-to-one

relationship, in which you understand each individual’s needs and preferences By understanding which customers are

most likely to purchase certain products or services, you can focus your marketing programs to obtain the highest possible

response on your marketing investment You can segment customers by value, behavior, demographics, and even by attitude





Segmentation by value builds an understanding of who your most valuable customers are


Segmentation by behavior helps you know who is most likely to purchase your products or services, so you can use

marketing funds more effectively


Segmentation by demographic and other supplemental data provides additional information that can be used in

predicting customer behavior


Segmentation by attitude adds another dimension to your customer understanding One of the best ways to understand

customers’ attitudes is to ask them through survey research


Create predictive profiles of each segment


Once you’ve identified the segments of customers who use and value your products and services, the next step is to


understand what products or services customers in each segment are likely to want next Adding this predictive element

makes your customer relationship significantly more productive and profitable






Zurich-based Credit Suisse Group, case study.


financial services companies, initiated a customer loyalty

program in 1997 Credit Suisse used PASW Modeler, a data

mining solution from SPSS Inc , to analyze its data warehouse

of 2 5 million customers according to 400 different attributes

By defining a number of different customer segments, Credit

Suisse can focus its marketing campaigns on the one percent

who are not only “extremely likely” to purchase a product or

service, but also have the credit rating to do so Credit Suisse

recouped its investment in the project within two years



2: Predict the best way to win the right customers

beginning by allowing companies to acquire customers more cost

effectively, as this graphic shows. Then, throughout the customer life

Acquiring customers is costly but necessary Paying too high


cycle, predictive analytics helps companies design more attractive offers

a price to attract customers, however, or acquiring the wrong and conduct more effective marketing campaigns, leading to increased


sales. Predictive analytics also helps companies retain customers,

types of customers, can have a significant negative impact on increasing the revenues and profits they obtain from these relationships.


your profits


Using inefficient methods to attract customers will result in higher costs and profits that are lower than they should be

Attracting the wrong customers impacts profits, too For example, if you attract customers who are likely to leave or “churn,”

you may incur the acquisition cost without ever seeing a profit from the customer relationship Other customers may be loyal,

but cost so much to serve that they are only marginally profitable


With predictive analytics, your organization can minimize costs by directing programs toward the people most likely to

respond You can further boost profits by focusing on the types of prospects most likely to become profitable customers


Create a prediction-based customer attraction strategy


Use predictive profiles to determine what types of customers you want to attract Then create a cost-effective attraction

strategy that includes separate plans for each customer segment


Most companies will want to focus their attraction efforts on winning over prospects that fit the profile of their most profitable

customers But other, less-profitable customer segments may have more room to grow over the long term, or may be more

cost-effective to attract—so marketing to these segments may be an attractive option when marketing budgets are tight


Optimize your customer attraction strategy with response modeling


Fine-tune your customer attraction plans by using response modeling to predict which marketing programs will generate

the highest response This benefits your organization in two ways: you attain the results you want, while avoiding the high

costs associated with unproductive marketing efforts In this way, you see higher profits for the money you invest


Belgium-based insurer Corona Direct, with $25 8 million in revenues in 2002, sells auto, fire, and property insurance through

multiple channels The company’s profit margin was narrowed and its growth strategy threatened because the cost of adding

new customers exceeded revenues from first-year premiums by almost 50 percent Using PredictiveMarketing from SPSS Inc ,

Corona Direct first identifies groups most likely to respond to a campaign, and then performs a sophisticated profit-cost

analysis Using this information enabled Corona Direct to reduce its direct marketing costs by 30 percent, resulting in


Five Predictive Imperatives for Maximizing Customer Value






Customer value analytical architecture


acquisition campaigns that are profitable in the first year In addition,


long-term customer profitability increased by 20 percent


Improve conversion rates with prospect surveys


Market research can be used to improve customer acquisition both before

and after your campaigns Beforehand, surveys of groups identified as likely

prospects can clarify their reasons for buying your products or services,

enabling you to refine your campaign offers Afterwards, by surveying

prospects that did convert and those that did not, you can learn what worked,

and what you need to change, to earn prospects’ business in the future

By using this type of predictive intelligence to guide your customer attraction

strategy, you can improve the conversion rate for your best prospects


3: Predict the best way to grow customer relationships

To maximize customer growth and increase customer lifetime value, your

organization needs to know not only what customers are most likely to want,

but also when and how they will want it delivered With predictive analytics,

you can achieve this level of customer knowledge

Build predictive analytics into your customer

Create a prediction-based customer growth strategy relationships and learn from the data generated

By using predictive profiles, product-affinity models, segment-migration models,

by every interaction. Use your data to know

customers better, create predictive intelligence on

response models, and even survey research, you can generate predictive each customer segment, and use that intelligence

to improve customer interactions and maximize

intelligence about your customers As a result, your customers will be more customer lifetime value.


satisfied with your service, and reinforce their decision to buy from you again


Discover product affinities


Customers often purchase products and services together, or in certain sequences By analyzing their “market baskets”—

products and services purchased at the same time—you can offer customers appropriate additional products at just the right

time Understanding what products your customers buy together can lead to improved product placement in retail stores,

attractive “bundling” of products in both direct marketing and online offers, and more timely offers Not only does this increase

revenues, it generally improves customer satisfaction and contributes to maximizing customer lifetime value


Sofmap, a leading computer retailer in Japan, used PASW Modeler from SPSS Inc to build a recommendation engine that

suggests products to visitors to its Web site Recommendations are based on customer profiles and information about prior

purchases contained in the company’s database The first year it was implemented, the recommendation engine resulted in a

sales increase of 18 percent, and a profit increase of 200 percent


Predict segment migration


Applying data mining techniques to your historical sales data shows you who buys what By combining this information

with other data, you can also make other kinds of predictions, such as which customer segments will become more valuable

and which less valuable, and by what amount Predictive segmentation modeling shows you which characteristics are linked

to migration between customer value segments Adding this kind of predictive intelligence to your customer growth strategy

enables you to realistically plan growth for each segment






Optimize your customer growth strategy with

response modeling Models useful in analytical CRM


Fine-tune your customer growth plans by using response n

Response models predict which customers are

modeling to predict which types of marketing programs likely to respond to a new offer


will generate the highest response n

Product-affinity models predict which sets of



products customers are likely to purchase together

Center Parcs Europe, a leader in the field of short break n

Segment-migration models predict which groups

holidays, operates 15 vacation parks that welcome of customers are likely to become more or

more than three million visitors per year, generating less valuable

revenue of more than $500 million Customers can book n

Attrition models predict which customers are

online, through the company’s call center, or through likely to leave

a travel agent Center Parcs’ growth strategy included

improving its already high occupancy rate of 90 percent

and cross-selling more sports and leisure activities to guests Using PredictiveMarketing from SPSS Inc , Center Parcs added

smaller, targeted mailings to its marketing strategy This reduced direct marketing costs by close to $1 5 million in a single

year, while increasing revenues by $1 65 million


Grow relationships by asking customers what they want


Using the data you already have to predict customer needs is a powerful way to improve interactions and lifetime value

But it is also important to systematically ask customers what they want Surveying your customers and gaining a better

understanding of their needs, and why they buy from you, enables your organization to improve your customer growth

strategy and maximize customer lifetime value


4: Predict the best way to keep the right customers longer

Studies have shown that customer acquisition can cost five to 12 times more than retention, and that improving its

customer retention rate by just five percent can increase an organization’s profitability by from 25 to 100 percent

Obviously, improving customer retention can have a big impact on profits


Customer attrition is particularly challenging for online retailers and companies in financial services, telecommunications,

and other industries where customers can change vendors relatively easily


Create a prediction-based customer retention strategy


Keep your best customers longer by creating attrition models, and then use these models to determine which customers are

at risk of defecting You can enrich these models through survey research that adds valuable attitudinal information


Create predictive attrition models


Understand which customers are most likely to leave for competitors and, more importantly, why By applying data mining

techniques to data about your customers, you can develop profiles of customers who are valuable and customers who have

previously defected Then you can develop strategies to keep your valuable customers from leaving


Banco Espírito Santo (BES) fights customer attrition with data mining Founded in 1880, BES serves more than one million

customers in Portugal, Spain, and 12 other countries Using PASW Modeler from SPSS Inc , BES identified key behaviors


Five Predictive Imperatives for Maximizing Customer Value






of customers likely to leave the bank, so it could do what was needed to keep them By focusing retention efforts on their

most valuable customers, BES reduced attrition by 15 to 20 percent and increased profits by 10 to 20 percent


“SPSS Inc empowered us to know ahead of time when a customer is at risk of leaving Now we can take the appropriate

action to keep them,” says Jorge Portugal, Banco Espírito Santo’s Strategic Marketing Director


A European telecommunications company also uses PASW Modeler to identify customers likely to leave or “churn ” By

discovering what types of customers were likely to leave, the company was able to make targeted offers that reduced churn

by 20 percent, compared to a similar group that did not receive the offer


A U.S.-based telecommunications company combines SPSS Inc ’s text mining technologies with PASW Modeler to predict

and prevent churn It increased the effectiveness of its attrition model by 10 percent, saving hundreds of thousands of

dollars and putting itself in a stronger competitive position in its industry


Conduct and analyze satisfaction surveys


Satisfaction surveys are invaluable in determining not just whether customers are satisfied, but why, and in uncovering


issues that may affect their future loyalty in time to take corrective action Even customers that you aren’t able to retain

have potential value to your organization By surveying customers that you failed to retain, you can better understand what you

need to do to keep customers like them


The Hospital of Walcheren, a general hospital located in Vissingen, Netherlands, serves nearly 120,000 patients annually The

hospital, already committed to quality healthcare, also needed to comply with the requirements of national quality legislation

In addition to monitoring mandated quality standards, the hospital also wanted to monitor patients’ opinions and preferences

The Hospital of Walcheren chose components of the Dimensions™ survey research suite from SPSS Inc , to collect and save

information This information is then analyzed using PASW Statistics* Survey results pointed to several areas in need of

improvement—for instance, a majority of patients indicated they were insufficiently informed about where they could turn for

emotional support The hospital has made changes, as a result of its findings, and is extending its evaluation process to its

nursing and outpatient departments


5: Use predictive intelligence to drive customer interactions at every touch-point

Monitor and manage customer value


Management gurus tell us that we cannot manage what we do not measure This is certainly true of customer relationship

management Profitable customer relationship management requires precise, timely measurement of the factors that affect

customer success, and your bottom line This effort requires a combination of historical and predictive technologies: predictive

analytics, to identify customer targets for acquisition, up-selling, or cross-selling; and historical analysis, to monitor the results

of marketing campaigns and sales programs


* PASW Statistics, formerly called SPSS Statistics, is part of SPSS Inc.’s Predictive Analytics Software portfolio.



Five Predictive Imperatives for Maximizing Customer Value




Power CRM systems with prediction


By deploying the results of predictive analytics to every customer touch-point from your branch offices to your call center to

your Web site, you can achieve greater effectiveness and profitability Build predictive results into your Web site, and visitors

will be automatically presented with the offer most likely to result in a sale Or build predictive results into your call center, so

that sales representatives know what products or offers are most likely to suit a particular customer’s needs Every bit of data

you have coming in from these systems becomes fuel for driving future customer interactions and realizing higher returns



This is an example of predictive intelligence

deployed to a call center application. The

call center rep has information about the

customer, including their lifetime value (1),

risk of churning (2) and the recommendation

most likely to satisfy them (3). That recommendation

can even be refined in real time—

while the rep is talking to the customer—by

conducting a brief “needs assessment”

survey. The survey results are fed into the

call center application, which generates

a new recommendation, based on the

application’s predictive model.


SPSS Inc. products and services for maximizing customer value


Interacting with customers profitably requires sophisticated analytical techniques and powerful deployment capabilities

SPSS Inc offers such techniques and capabilities through a broad range of predictive analytics products and applications

These offerings provide your organization with the analytical capabilities needed to maximize customer lifetime value


Deployment family:


PredictiveMarketing™ — A campaign optimization and execution solution PredictiveMarketing helps you reduce marketing

costs and increase revenues by sending the right offers to the right customers at the right time, through the right channel


PredictiveCallCenter™ — A real-time solution that enables you to turn your call center into a profit center by accurately

predicting sales opportunities and retention risks PredictiveCallCenter instantly determines which inbound callers are the

best candidates for up-sell, cross-sell, or retention offers, and suggests which offers to make


Modeling family:


AnswerTree® — Decision-tree software for data mining Enables users to segment customers, create profiles, and predict

response rates using the widest range of decision-tree algorithms available


PASW Modeler — A scalable data mining workbench for quickly developing predictive models PASW Modeler offers an

intuitive, graphical interface that enables users to incorporate valuable business expertise as they develop models Models

can then be deployed in several different ways to solve business problems







PASW® Text Analytics—A powerful text mining workbench PASW Text Analytics enables you to extract key concepts,

sentiments, and relationships from textual or “unstructured” data and convert them to a structured format that can be used

to create predictive models


Data Collection family:


Dimensions™ — A full-service technology platform that supports the entire survey research process Dimensions components

enable organizations to create surveys in any language, for any collection format; then collect data through any medium,

store data centrally for easy access, and create an information portal to provide results to colleagues and clients in real time


SPSS Data Entry™ — Survey design and data collection software SPSS Data Entry helps companies quickly and securely

gather clean, complete data on paper, by phone, or over the Web


Statistics family:


PASW Statistics — A proven, enterprise-strength statistical analysis solution PASW Statistics includes a broad range

of statistical techniques to support predictive modeling and reporting Additional modules offer other advanced capabilities


Amos™—Powerful and easy-to-use structural equation modeling (SEM) software With Amos, you can specify, estimate,

assess, and present your model in an intuitive path diagram to show hypothesized relationships among variables This

enables you to test and confirm the validity of claims such as “value drives loyalty” in minutes, not hours


Consulting and training services:


SPSS Inc has more than 40 years’ experience in delivering predictive analytics Our expertise is available to extend your

own in-house expertise and achieve the highest return on your investment We also offer training, to transfer to your

employees the knowledge and skills they need in order to use our analytical software most effectively for business

problem solving






To learn more, please visit For SPSS office locations

and telephone numbers, go to


SPSS is a registered trademark and the other SPSS Inc products named are trademarks of SPSS Inc .

All other names are trademarks of their respective owners .

© 2009 SPSS Inc All rights reserved FPIWP-0209





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