customer acquisition and roi white paper

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How Telecom Providers Can Improve Their Customer Acquisition Process

Improvement and Measurable Return on Investment

A Fair Isaac White Paper

July 2004

How Telecom Carriers Can Improve their Customer Acquisition Process

Copyright © 2004 Fair Isaac Corporation. All rights reserved. 1

Table of Contents Where cost, risk and profit start .................................................................................................... 2

Examples of customer acquisiton ROI...................................................................................... 2 Examples of customer acquisition strategies ........................................................................... 2

Customer acquisition best practices ............................................................................................. 3 Achieving significant return on investment in customer acquisition ......................................... 3 Approval rates........................................................................................................................... 4 Risk management ..................................................................................................................... 4 Decision automation ................................................................................................................. 4 Application processing cost ...................................................................................................... 5

Customer acquisition issues ......................................................................................................... 6 Bringing down the cost of good decisions ................................................................................ 6 Using a wider range of decisioning resources cost-effectively................................................. 7 Improving speed, quality and consistency in manual decisioning ............................................ 7 New data sources ..................................................................................................................... 8

Conclusion..................................................................................................................................... 9 About Fair Isaac .......................................................................................................................... 10



Where cost, risk and profit start Acquiring a new customer is a costly decision, affecting 80% of measurable risk over the life of that account and touching many other levers of lifetime customer value. It is also an increasingly complex decision that must be made in decreasing timeframes. Today, telecom service providers must make decisions in unfamiliar markets and often on emerging or less credit-worthy applicants. Yet given margin pressures, it is still essential to accurately price each product and offer.

It is no wonder, given the importance of the decision and all the challenges affecting it, that customer acquisition is one of the best places for service providers to focus analytics and decision automation technology. By adjusting the levers of profitability within the customer acquisition process, providers can achieve significant results. In fact, companies deploying advanced customer acquisition platforms generally see a return on investment (ROI) across multiple performance dimensions.

Examples of customer acquisiton ROI ■ 50% increase in annual postpaid activation rate

■ 35% reduction in manual review rates

■ 15% reduction in application processing costs

■ 15% reduction in risk and fraud rates

■ 25% faster implementation of business policy changes

Results like these are being achieved by service providers of all sizes, including companies with highly automated decisioning processes, as well as those processing applications largely by hand.

Examples of customer acquisition strategies ■ A service provider with a high-volume, fully automated customer acquisition

process. Strategy: Refine the process with additional data sources and analytic models plus the processing intelligence to invoke data and analytics only as necessary, in order to achieve the desired level of decision certainty for each application.

Result: Higher throughput, higher approval rates, lower acquisition cost, lower fraud and bad debt losses.

■ A medium-size service provider with a customer acquisition process that is partly automated. Strategy: Streamline the process by increasing automation and using more precise analytics to reduce the number of applications requiring manual review.

Result: Faster and more consistent decisions, lower manual review costs, lower acquisition cost.

■ A small service provider with manual customer acquisition processes. Strategy: Use real-time analytics to help credit and fraud analysts make better decisions faster.

Result: More competitive decision cycle times, lower fraud and bad debt losses, lower acquisition cost.

This white paper looks at customer acquisition operations—the way leading telecom service providers are applying advanced analytics and automation to application decisioning. We then provide a survey of customer acquisition best practices and explore a variety of ROI scenarios.



Service provider characteristics Number of applications processed annually 1,500,000 % of postpaid approvals 50% Bad rate (60 days or > last 12 months) 3% % of bads that go to loss 75% Operations cost per application $20.00 Decision automation % 50% Current FTEs required to process applications (@$50k fully loaded) 25 Average annual revenue per user (ARPU) $720 Profit margin on good accounts 5%

Customer acquisition best practices How can telecom service providers know what types of improvements in their customer acquisition processes will result in bottom-line gains? By comparing their own current practices with industry wide common practices—and best practices—service providers can assess areas of opportunity.

Common practices Best practices Benefits of improvement FICO® score only based credit decisions

Sophisticated credit risk models that employ credit bureau, application and other data sources

Reduced credit risk and bad debt

Thin-file and no-hit applicant denials

Access expanded data sources to improve assessment of credit worthiness

Increased approval rates without increasing risk

Simple and standard product offers

Advanced analytics used to optimize offers and pricing

Increased approval and activation rates, higher customer net-present-value, reduced bad debt risk, and improved retention

Achieving significant return on investment in customer acquisition How much of an impact can improving customer acquisition decisioning make on the bottom line? Below is a sample scenario, based on the kinds of improvements being achieved today by companies deploying advanced customer acquisition solutions.

Let’s imagine a provider with the characteristics shown in the box at right. This organization has many opportunities to improve its profitability by investing in its customer acquisition process. The question the company needs to answer is “Where do I spend my money—what type of investment would be most beneficial?”



Approval rates The ability to approve more accounts without increasing risk is key to portfolio growth and market penetration. The expansion of a provider’s universe of creditworthy customers leads to higher revenues without placing undue risk on an account. Advanced customer acquisition models allow providers to be more proficient in identifying “good” customers, and leads to immediate account growth and improved profitability.

Metric Previous

rate New rate

Annual revenue increase

Postpaid approval rate 50% 60% $1,305,000

As this provider increases its postpaid approval by 20% (from 50% of applications approved for postpaid service to 60%), the impact to the bottom line is immediate and significant. Over $1,300,000 in additional revenue would be gained, without a corresponding increase in losses. For each 1% improvement in approval rate, an additional $60,000 in annual revenue is realized. Advanced customer acquisition models are able to identify good credit risks on a more consistent basis than traditional scores.

Risk management A significant area of opportunity for many providers is in risk management. The ability to reduce losses has a major effect on the profitability of their accounts. Analytics, above and beyond traditional credit bureau and FICO scores, give providers the ability to more accurately assess risk, and create a portfolio that is more stable and more profitable.

Metric Previous

rate New rate

Annual savings

Bad rate 3% 2.75% $480,000

In this example, the provider was able to reduce its bad rate by approximately 10%, reducing the rate of account charge-off from 3% to 2.75%. This improved level of risk management would result in direct savings of $480,000 annually. Each 1% reduction in bad rate results in nearly $50,000 in lowered charge-off expense.

Decision automation The benefits realized through the automation of decisioning are a direct result of savings on manual labor. With fewer credit applications that need to be reviewed by analysts, providers are able to reduce costs and assign resources more efficiently while ensuring consistency in decision processes.

Metric Previous

level New level

Annual savings

Decision automation % 50% 60% $250,000

In the example above, an increase of decision automation by 20% (from 50% of decisions automated to 60%) results in annual savings of $250,000. For each 1% improvement, this service provider would realize $12,500 in annual savings. These savings are also augmented by the fact that credit policies would be applied more uniformly, resulting in accounts that are more consistent with the intended strategy.



Application processing cost Another metric to consider when assessing the cost of your customer acquisition process is by looking at the per application processing cost. By analyzing the costs that go into each application, from hardware and software to the various costs of data and products, providers can determine if they are getting a reasonable return on each application they process.

Metric Previous

level New level

Annual savings/revenue

Application processing cost $20.00 $17.00 $150,000

In this example, the provider is able to achieve a 15% improvement in its per application processing cost, lowering it from $20 per application to $17. This leads to an annual savings of $150,000. For each 1% reduction in cost per application, the corresponding savings is $8,500

Other factors Which investments are best for this provider will depend on a number of factors in addition to ROI. These include the provider’s current level of automation. Generally, most organizations will experience incremental benefits for every additional 1% of applications processed automatically, with a target goal of about 90% of customer acquisition volume.

Other factors include the provider’s current approval rate and level of risk tolerance. As the table above shows, even a 10% increase in approval rate will produce significant additional revenue. This frontier of opportunity is not infinite, however. The objective is to find the “efficient frontier”—the point at which the approval rate produces maximum profits and beyond which a higher bad rate results in diminishing returns.

The best way to explore the various options, trade-offs and value is to work with an experienced provider of predictive analytics and decision automation. Each provider’s business is different, and therefore the customer acquisition improvements that will produce the greatest benefits will vary in each instance. Yet there is one common denominator: no matter what the circumstances, every provider’s customer acquisition process can be improved in some way, and these improvements will produce more profit.



Customer acquisition issues

Bringing down the cost of good decisions Automating customer acquisition processes simultaneously reduces decisioning time and labor costs. Providers that have achieved these benefits in application customer acquisition are now moving to deploy automated platforms in other lines of business. In addition, where disparate and nonintegrated solutions are being used in different lines of business and channels, moving to a single integrated customer acquisition platform produces not only savings across these functions, but very attractive enterprise-wide efficiencies and economies of scale.

Today’s advanced customer acquisition platforms automate application processing end-to-end. They can simultaneously orchestrate any number of unique decisioning processes for products, marketing campaigns, lines of business and channels.

They also provide user tools for creating and modifying these customer acquisition processes. Without requiring assistance from IT programmers, business analysts are able to specify the steps in a process, the conditions controlling routing across steps and the data sources, predictive models, decisioning logic and computations to be executed within each step.

Fair Isaac—the leader in smart, fast decisions Fair Isaac offers powerful, flexible customer acquisition solutions for telecom service providers of all sizes. We provide comprehensive platforms that can encompass any number and variety of data sources, scorecards and other types of predictive models, as well as complex decision strategies and workflows. We also offer companies the ability to bring sophisticated analytics into their decision processes, using ASP-based services accessible from a standard browser.

Today’s solutions are built on top of 50 years of experience helping financial institutions and telecom service providers all over the world reduce risk and improve profitability by applying data, analytics and automation to their decisions. Fair Isaac invented credit scoring in the 1960s, and our FICO score has long been the bedrock of credit decisioning. Adaptive control, real-time transactional fraud and risk detection, and Strategy Science have followed in a continual stream of innovations. As a result, leaders in the telecommunications industry trust us to power billions of mission-critical customer decisions a year.



MINI-CASE STUDY One of the top 10 US converged telecom service providers cut rising costs and losses with an improvement in application processing and decisioning technology.

Reduced wireless customer acquisition costs by 30%

Reduced credit and fraud manual review costs by more than 15%

Lowered bad debt by 38%

Reduced wireline bureau data costs by 20% and improved segmentation and treatment strategies using more sophisticated strategy design and champion/challenger testing

Using a wider range of decisioning resources cost-effectively Even in lines of business where the customer acquisition process has already been automated and predictive scorecards are in place, service providers can improve results by inserting additional analytics into the decisioning process.

Additional data sources also improve decision precision. Internal data calls can be made, for example, not only to the customer master file and negative files, but also to retention and collection data, enabling customer acquisition decisions to take into account the characteristics of profitable and unprofitable customers. External data sources include not only multiple credit reporting agencies, but identity verification and asset valuation systems. Debit bureau data can improve and accelerate decisioning on thin-file or subprime applicants. Decisions about services to small business can be facilitated by analyzing not only commercial credit bureau information and scores, but also consumer credit data on company principals.

Supplemental decisioning resources must be employed selectively, however. The key to cost-effective use is building plenty of intelligence into the decisioning process. Automated customer acquisition processes should determine at each step of processing whether decision precision on a particular application could be improved by additional data or analysis—and invoke them only as needed. In other words, customer acquisition solutions processing high volumes of applications at very high speeds should be capable of treating each one in an individualized manner, so as to arrive at the best decision in every instance.

The outcome of an end-to-end customer acquisition solution is a decision, delivered in the form of a score or a recommendation directly to customer-facing applications. The decision is not an isolated end point, however. State-of-the-art platforms capture all of the historical application data, decisioning criteria and performance outcomes involved in a decision, and store them in a data mart. This wealth of data can subsequently be mined to develop sophisticated risk-based customer acquisition strategies. It can also be used at other parts of the customer lifecycle: account management systems, for example, could analyze it to improve retention treatments and credit limit strategies. Captured data, along with audit tracking, is also helpful for documenting regulatory compliance.

Improving speed, quality and consistency in manual decisioning Some mid-size and small providers do not have the application volume to justify automation, but they still need to make increasingly complex decisions in shorter timeframes. Even the most experienced credit and fraud analysts are hard-pressed to meet these new requirements without assistance.



Analytics, ranging from simple scorecards to sophisticated custom predictive models, help credit and fraud analysts make good credit decisions faster. They increase the amount of data and depth of analysis that can be brought to bear on rapid decisions, enabling human experts to do a better job of predicting risk and protecting profit margins. They also enable smaller telecom providers to adopt more competitive application decisioning cycles while increasing approval rates and lowering processing costs.

The bottom line is that risk and decision speed are no longer inversely proportionate. Improvements in decisioning automation do not imply loss of decisioning control. Analytics simply provide human decision makers with more information and insight. In addition, they improve institutional control of decisioning processes by reducing subjectivity and supporting consistent lending practices.

Nor do analytics increase the workload of decision makers by inundating them with lengthy reports full of complicated information. State-of-the-art analytics can deliver insights to human experts in the simple, actionable form of scores accompanied by reason codes. Scores with codes help underwriters work efficiently, and they can also be helpful in responding to customer service inquiries and handling legal or regulatory matters.

New data sources Adding new data sources to decisioning processes is another measure that should be evaluated carefully from the point of view of cost-effectiveness. To make optimal use of supplemental data sources, the software controlling the decisioning workflow must have the intelligence to make on-the-fly decisions about when it’s necessary to make a data call and when it’s not. The sequence with which data calls are made can also affect the value derived from them.

The level of funds available to invest will also determine how a provider goes about improving its customer acquisition process. Fully custom predictive analytics with the power to boost approval rates while holding risk constant require an upfront investment, for which they produce very substantial returns year after year. On the other hand, pre-packaged (empirical) models, as well as models based on data from a consortium of providers, are available in many cases and will also produce significant returns. In addition, a variety of predictive analytics and automated decisioning processes can be implemented through ASP-based services that involve very little upfront cost.



Conclusion There are few places in operations where improvements affect the bottom line as clearly and immediately as customer acquisition. The customer acquisition process touches many of the levers of account profitability. Changes—from major transformations to lever tweaking—predictably produce measurable results.

Customer acquisition excellence should, in itself, be viewed as a process. Along with expanding automated methods to gradually encompass an increasingly larger share of the business, providers can incorporate new data sources and analytics into their decisioning processes over time, achieving continual performance improvements, and incremental cost savings and revenue gains.

Given that the complexity of decisions required at account customer acquisition will certainly continue to increase and the time allowed will decrease, the important thing is to keep moving. From whatever level of analytics and decision automation a company finds itself today, it should have a plan for taking the next step.


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Fair Isaac and FICO are trademarks or registered trademarks of Fair Isaac Corporation, in the United States and/or in other countries. Other product and company names herein may be trademarks or registered trademarks of their respective owners. Copyright © 2004 Fair Isaac Corporation. All rights reserved. 1711WP 07/04 PDF

About Fair Isaac Fair Isaac Corporation (NYSE:FIC) is the preeminent provider of creative analytics that unlock value for people, businesses and industries. The company’s predictive modeling, decision analysis, intelligence management, decision management systems and consulting services power billions of mission-critical customer decisions a year. Founded in 1956, Fair Isaac helps thousands of companies in over 60 countries acquire customers more efficiently, increase customer value, reduce fraud and credit losses, lower operating expenses and enter new markets more profitably. Most leading banks and credit card issuers rely on Fair Isaac solutions, as do insurers, retailers, telecommunications providers, healthcare organizations and government agencies. Through the www.myfico.com Web site, consumers use the company’s FICO® scores, the standard measure of credit risk, to manage their financial health. For more information, visit www.fairisaac.com.

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