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Five Keys to Out-Pricing (not Under-Pricing) CompetitorsEmbedding customer-centric pricing power into your operational DNA

Number 79

The phrase “competitive pricing” is often used to mean pricing lower than competitors. But a financial

institution that can make offers at higher prices and still win the business is even more competitive.

And if these transactions raise not only margins and profits, but customer satisfaction and share of

wallet, that company is truly a formidable competitor.

This paper examines customer-centric pricing optimization, which encompasses far more than just

price. We look at how financial services can make astute decisions about the entire offer, based not

only on detailed predictions of customer value, sensitivities and behaviors, but also on customer

attitudes and choices revealed at the point of sale.

Today, companies can give frontline staff the flexibility to respond

to and negotiate with customers—including generating a

range of alternative deals—while ensuring all offers meet the

requirements of multiple organizational stakeholders and are

compliant with regulations. They can perform customer-level

optimizations, generating individualized offers of bundled

products and services.

The paper also shares the stories of FICO clients and discusses five keys to out-pricing competitors:

1. Simplify complex real-world pricing problems without “dumbing” them down.

2. Make analytics transparent, understandable and easy to use by business experts.

3. Enable stakeholders to collaboratively drive optimization processes.

4. Integrate backroom policymaking with real-time customer interactions.

5. Be able to turn on a dime for market maneuverability.

Find out how one bank boosted profit by $24 million in 12 months using customer-centric pricing optimization

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August 2014 www.fico.com page 2

In many financial services markets, thin margins, new sources of competition and the ease with

which consumers can compare products and promotional offers are causing downward pressure on

prices. The perceived need to under-price competitors can exert a vortex-like pull, drawing financial

services firms down into a dangerous race to the bottom.

But competitive pricing should not be a race to the bottom. Rather, it should be a race to the “sweet

spot”—an offer fully meeting the needs and objectives of the customer and the business, and

therefore likely to be accepted and profitable. Companies able to identify the sweet spot quickly

have an edge over competitors, even though their prices are often not the lowest.

Increasingly, companies are winning this race with customer-centric pricing optimization. Unlike

traditional methods rooted in price sheets, prices emerge from very granular Big Data-driven analytic

segmentation of customer populations. And with this approach, pricing is not an isolated exercise.

Rather, prices are optimized in the context of the entire offer.

Finding the sweet spot for offers whose characteristics could be combined in millions, sometimes

billions, of possible ways is a complex analytic process requiring mathematical speed and precision.

It’s also a collaborative business process involving multiple internal points of view (product

management, risk management, marketing, finance, etc.). And it’s an interactive process of listening

to and sometimes negotiating with customers.

Customer-centric pricing optimization combines these analytic, collaborative and interactive

processes into flexible, efficient workflows. It delivers answers to mind-boggling pricing questions

in a manner key stakeholders can understand and control. And it helps pricing committees fully

understand the impact of price, not only on demand but on other aspects of customer behavior

that also affect organizational key performance indicators (KPIs).

By using state-of-the-art pricing optimization, business experts have the means, without need

for IT assistance, to rapidly develop and test segment-focused offers as well as bundles aimed at

increasing share of wallet. They can perform frequent, granular re-pricing, driven by automated

feedback from operational results—which all participants can see. Astute pricing becomes an

integral part of day-to-day activities—embedded in the company’s operational DNA.

The following three FICO case studies illustrate customer-centric pricing power at work for auto

loans, credit cards and unsecured loans. The techniques used can be applied to any decision area in

the financial services where there is room for discretionary pricing.

Competitive Pricing Is a Race—But Not to the Bottom

“We estimate that up to 30 percent of the thousands of pricing decisions companies make every year fail to deliver the best price. That’s a lot of lost revenue. ” Using big data to make better pricing decisions

McKinsey & Company, June 2014

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CHALLENGE: The auto finance arm of an Asian automobile

manufacturer was rejecting too many would-

be borrowers. Negative impacts included not

only the high decline rate, but time wasted

rehashing deal structures as sales managers

and credit analysts went back and forth trying

to save the sale.

SOLUTION: The company implemented decision

modeling with real-time optimization

of alternative deals. Initially, a role-based

interface enabled credit analysts to perform

optimizations in response to dealer input.

Later, an interface was also provided to dealers

so they could do their own optimizations

during negotiations with customers.

Seamlessly integrated with existing loan

origination systems, the solution generates

at least 10 optimized alternative deal

structures and financial ratios within

30 seconds. Dealers are thus able to offer

customers a range of choices that flexibly

respond to their needs and preferences.

They can bring these deals to the customer

with confidence that every alternative fully

complies with all lender and regulatory

requirements. In fact, these real-time

optimizations can also factor in and balance

dealership objectives and business

agreements between the dealership and

lender. And they can incorporate portfolio,

segment and customer-level constraints

(e.g., recent purchasers receive an additional

discount).

CASE STUDY IN AUTO FINANCE: Empowering the frontline to make better deals faster

RESULTS:

• Projected $12 million revenue increase

• Projected $3 million annual labor cost savings

• Consistent lending decisions with clear, demonstrable regulatory compliance

• higher dealer and customer satisfaction

CHALLENGE: A North American bank sought to counteract

the negative impacts of new regulations on

its bottom line by making more profitable

origination decisions on its credit card

portfolio. It needed a scientific way of

pinpointing where it could raise prices

without also raising attrition.

SOLUTION: The bank optimized price and initial credit line.

Simulation tools enabled managers to explore

the impact on loss and profit of adjusting

constraints on risk exposure and attrition. The

resulting range (efficient frontier) of potential

optimized strategies, shown in the chart,

demonstrated that there was room to raise

prices without lowering profit.

The strategy selected makes the most of

limited exposure by shifting it from low-

risk/low-revenue consumers to low-risk/

moderate-revenue consumers. It also gives

higher prices (APRs) to riskier accounts with

less revenue potential (lower balances and

utilization).

CASE STUDY IN CREDIT CARDS: Pinpointing where to raise prices

RESULTS:

• 17% decrease in loss while maintaining revenue

• $20 increase in profit per account

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Strong performance gains, like those seen in the FICO case studies, are powered by very flexible

analytic and decision management technologies that can be used in different ways to solve different

kinds of problems. There are some fundamental principles, however. Here are the five keys to out-

pricing competitors.

1. Simplify complex real-world pricing problems without “dumbing” them down

Out-pricing competitors means tackling the real complexity of the pricing problems financial

services face in today’s crowded, dynamic, highly regulated markets. That requires moving from a

price-sheet approach to one where optimal pricing emerges from very granular analytics-based

segmentation of customer populations. State-of-the-art optimization supports segments as small as

one individual—for customer-level pricing.

CHALLENGE: A South African bank wanted to improve the

performance of its portfolio of unsecured

personal loans for new and repeat business.

It needed to make better originations

decisions that would increase take-up

rates while reducing bad debt exposure. To

maximize loan lifetime profitability, the bank

also wanted to reduce prepay rates and know

when to target customers with promotions

for repeat business.

SOLUTION: The bank optimized price and loan amount.

Among the many inputs to the optimized

decision strategy, analytics predict when

customer behaviors (like early repayment) will

occur, helping the bank time marketing to

existing customers.

CASE STUDY IN UNSECURED LOANS: Making loans and customer relationships more profitable

RESULTS:

• increased take-up rate

• 12% increase in average loan amount

• 14% increase in profit per application

• Projected 1-year incremental profit of more than $24 million

Five Keys to Out- Pricing Competitors

“The flood of data now available provides companies with an opportunity to make significantly better pricing decisions. For those able to bring order to big data’s complexity, the value is

substantial. ... Time-consuming, manual practices for setting prices make it virtually impossible to see the pricing patterns that can unlock (that) value. ” Using big data to make better pricing decisions McKinsey & Company, June 2014

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To perform this fine-grained segmentation and accurately target offers to segments, financial

services need to consider a wide range of data and customer behavioral predictions. For instance,

price sensitivity (demand elasticity) models are widely used, but it’s less commonly understood that

pricing sensitivity affects multiple dimensions of customer behavior. So, in credit card originations,

we need a demand elasticity component in multiple models predicting not only offer take-up, but

usage level, revolver or transactor patterns, delinquency risk, account profitability, and perhaps

customer lifetime value.

When you bring segmentation based on these detailed insights together with all the objectives of

the financial service (and perhaps its dealers or partners), applying business constraints at portfolio,

segment and customer levels, decisions become quite complex. The combinatorial possibilities

for the actions the company could take grow very large—and expand even more if cross-selling

or up-selling opportunities are added. Plus, for each possible action, you need to predict how the

customer is likely to react and what the impact will be on the company’s KPIs.

Such complexity is beyond what the human brain can grasp and beyond what can be effectively

managed with business rules alone. To capture such complex pricing problems—without dumbing

them down—analytic techniques are used to model the decision. Modelers work backward from the

business goal to identify the important decision factors and codify relationships between them in

mathematical equations (represented by the blue arrows in Figure 1).

Decision models can comprise any number of other models, including descriptive models

identifying similarities (e.g., demographic, behavioral) between customers and predictive models

forecasting their future behavior. At the heart of the decision model is a network of action-effect

models, predicting likely customer reactions to the company’s possible actions (e.g., pricing and

other aspects of an offer) and the resulting impact on KPIs.

FIGURE 1: DECISION MODELS CAPTURE COMPLEXITY (OFTEN FAR MORE THAN IN THIS DIAGRAM)

Volume

Margin

Profit

Revenue

Loss

Cost

Take-up

Earlyrepayment

Bad/charge-off

Lifetime value

Time topurchase

Time toearly repay

Time tocharge-off

Application data& scores

Credit bureauinfo & scores

Customer andother data

Down payment

Other inputs

Who to accept

Loan amount

Term

Price

Optimization goal(maximize withinbusiness constraints)

Single goalor balancemultiple goals

Component modelspredict customer reactionsand impact on KPIsInputs Decisions

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The model diagrammed in Figure 1

highlights these action-effect models

as orange ovals—but we’re looking

at an abridged view. Decision models

usually comprise far more models and

calculations than can be represented

in a diagram like this. Case in point:

For one client, FICO recently built an

optimization model with several

dozen component models and about

30 billion calculations.

The accuracy and completeness of the

decision model in large part determines

the accuracy and reliability of the

optimization result. (See Figure 2.) The

quality of the result also depends on the

optimization engine itself. It must also

be able to handle non-linear systems.

Some equations in the decision model

may describe relationships where a change in one variable does not produce a directly proportional

change in another variable (linear), but rather more complex effects (non-linear) across multiple

variables. It also performs optimizations requiring discrete price points ($2.99, $3.19, $3.39, etc.).

2. Make analytics transparent, understandable and easy to use by business experts

Can financial services companies trust the answers output by pricing optimization? Yes, provided

the input data, decision model and component models are all of high quality. But trust shouldn’t be

blind. Unlike “black box” analytic solutions, FICO’s approach to customer-centric pricing optimization

is transparent. A fundamental principle is that business experts can expose and examine any part

of the modeling process. Where their role allows, they may make adjustments, injecting domain

expertise and business judgment into the mathematical process.

Imagine a pricing manager has just received a proposed optimized pricing strategy from a pricing

analyst. He’s concerned about the accuracy of the profitability predictions it’s based on. How well

does the cost of funds in the historical data used for modeling align with current and projected

cost of funds? To find out, the pricing manager can drill down into components of the optimized

strategy to examine the values for this variable and the time period from which the historical data

was taken. He can also adjust the time period, and immediately see the results in a simulation of the

re-optimized strategy.

Simulation is a powerful way for business experts to explore pricing optimizations, make strategy

adjustments and evaluate forecasted results prior to deployment. By tightening or loosening

constraints, for instance, they can explore trade-offs between multiple, sometimes conflicting

objectives to better understand performance drivers.

FIGURE 2: SOLVING REAL-WORLD PRICING PROBLEMS

Fuzzy problem resolution deliversapproximate answers

Packaged solutions require problems to be “dumbed down” to fit limitations of modeling tools and optimization engine

Sharp problem resolution deliversaccurate, reliable answers

No limits on data types/ quantities or predictive model inputs

True customer-level optimization (segments as small as 1)

Encompasses linear and non-linear relationships

Optimizes for discrete price points ($2.99, $3.19, etc.)

Constraints applied at any level (portfolio, segment, customer)

Balances competing objectives

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As shown in the earlier case study on bank credit cards (page 3), this kind of exploration generates

an efficient frontier of possible optimized strategies from which the company selects the best

operating point for its business. It’s a quick process, so a large number of “what if?” scenarios can be

considered in a small amount of time—by individual users, or in a pricing committee meeting to

focus discussion and aid consensus building.

3. Enable stakeholders to collaboratively drive optimization processes

The various stakeholders in financial services pricing have different points of view, and all need

an easy way to participate in the optimization process. As depicted in Figure 3, they can input

objectives and constraints from their own management perspective into role-specific, workflow-

driven interfaces. They can also review proposed pricing strategies and view optimization process

details, results and reports as appropriate for their job.

Pricing processes vary widely among financial institutions, so how this works must be completely

open and configurable. A state-of-the-art optimization solution will enable any workflow involving

any number of role-based interfaces to be driven from shared “single source of truth” repositories

for data, analytics and business rules. Such comprehensive, highly customized solutions can be

developed and deployed in a fraction of the time usually required for interactive, collaborative data-

driven applications.

FIGURE 3: CUSTOMER-CENTRIC PRICE OPTIMIZATION BECOMES AN INTEGRAL PART OF OPERATIONS

DA

TA

REP

OR

TS

Optimization Exploration and adjustment Re-optimization

Analyst adjusts strategy

Option of real-time re-optimization during point-of-sale negotiation

Publishes new pricing

Pricing manager reviews and obtains executive approval

CFO adjusts ratios for return on risk-weighted assets

Product manager adds rules for new bundling options

Pricing responds to customer needs and POS choices while meeting all company requirements, including regulatory compliance

Risk manageradds constraint limiting number >60 days loans

Office, branch or dealership

Immediate, automatic distribution

Call center

Online self-serve

Pricing committee members input objectives and constraints, and review proposed pricing strategies

Pricing strategy

Selects an optimal operating point and submits proposed new pricing

Pricing analyst explores efficient frontier of optimized pricing strategies

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4. Integrate backroom policymaking with real-time customer interactions

The collaborative process can be extended as far as needed into the frontline so that pricing

optimization takes into account not only on what is known about the customer, but what is learned

at the point of sale.

Auto dealers, mortgage brokers and bank branch managers, for example, can be provided with

interfaces that enable them to not only input data, but also adjust the relative importance (“weight”) of

various aspects of the deal based on the customer’s stated preferences. In seconds, they can run a real-

time optimization and see a range of alternative offers that reflect each customer’s needs and attitudes.

In this way, agents on the frontline can have meaningful conversations at the point of sale. They can

respond flexibly to what they’re hearing from the customer to restructure and re-price offers, while

staying within the parameters of the creditor’s policies.

The advantages of this approach include fewer exception pricing requests and higher offer take-up

rates. Faster decisions also improve cost of sales. And customers experiencing efficiency, as well as

responsiveness, to their individual needs are more satisfied with the process.

Another advantage is that real-time optimization enforces consistency in point-of-sale actions.

Companies ensure their policies are driving and circumscribing frontline interactions, and can

demonstrate to regulators that consumers are being treated equitably.

This approach—when extended beyond the frontline, right to the consumer—can drive

personalized, choice-based pricing. For instance, FICO is helping a North American bank deploy

a pilot project in customer-centric price optimization for its retail lending products (secured and

unsecured loans, credit cards, home equity lines, etc.). The aim is to meet each customer’s need

for new credit or debt consolidation by proposing an individualized bundle of products. The bank

intends to deploy the solution for interactions with loan officers in its branches, as well as for

self-serve interactions at its website.

5. Be able to turn on a dime for market maneuverability

Given the dynamic nature of today’s markets, pricing collaboration and optimization need to be

asynchronous and ongoing. Participants must be able to make changes to their inputs and choices

at any time. Submitted changes (subject to business-rules-driven workflows and approvals) would

then immediately affect all subsequent optimizations.

For instance, a risk manager, concerned that the company is writing too many 72-month loans,

could adjust pricing parameters to make such loans more expensive or harder to quality for. Based

on current inventories and promotions, a sales manager might allow or disallow offers that include

up-selling of higher-value products or bundled cross-selling of accessories. Similarly, companies

can implement new promotional campaigns and agreements with business partners without delay.

They can adeptly respond to new regulatory requirements, or to sudden promotional moves or

pricing parries by competitors.

Financial services companies can also constantly look ahead at what to do next because customer-

centric pricing optimization incorporates an endless feedback loop. All participants in the process

can very quickly evaluate the operational outcomes of current strategies. They can see how well

actual and simulated results align—where gaps indicate need for improvements and point to

opportunities for further learning about customer behavior.

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For more information North America Latin America & Caribbean Europe, Middle East & Africa Asia Pacificwww.fico.com +1 888 342 6336 +55 11 5189 8222 +44 (0) 207 940 8718 +65 6422 7700 info@fico.com LAC_info@fico.com emeainfo@fico.com infoasia@fico.com

FICO and “Make every decision count” are trademarks or registered trademarks of Fair Isaac Corporation in the United States and in other countries. Other product and company names herein may be trademarks of their respective owners. © 2014 Fair Isaac Corporation. All rights reserved.

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the insights white paper series provides briefings on research findings and product development directions from FiCO. to subscribe, go to www.fico.com/insights.

To out-price competitors, financial services companies must be quick at identifying the sweet spot

where not only price but all aspects of the offer meet the needs and objectives of both customer

and business. The only way to do that—given Big Data, the complexity of factors to be weighed,

the number of internal stakeholders and the need to consider real-time customer inputs—is with

customer-centric pricing optimization.

Winners in this race no longer focus on calculating price sheets but on using analytics to finely

segment customer populations and deeply understand their behavior. For them, pricing may start

with backroom policymaking—but it extends through all aspects of operations and into real-time

interactions with customers.

To learn more about the latest analytic advances and best practices for financial services, visit the

FICO Banking Analytics Blog and read these Insights white papers:

•  Cloud Democratizes Access to Big Data Analytics (No. 74)

•  When Is Big Data the Way to Customer Centricity? (No. 67)

•  10 Questions to Ask Before Buying an Optimization Solution (No. 47)

Conclusion— Win the Pricing Race

FICO pricing optimization is powered by the FICO® Xpress Optimization Suite, the world’s premier mathematical modeling and optimization

solution. Banks, retailers, airlines, car rental companies, telecoms and other companies use it to explore the universe of pricing possibilities and zero

in on prices that increase value for all parties. FICO’s state-of-the-art approach includes:

•   Powerful analytically derived pricing, including optimizations 

involving linear, non-linear and discrete pricing problems.

•   Pricing based on granular population segmentation—down to 

segments of one—enabling true customer-level pricing.

•   Business controls enabling domain experts to adjust the pricing 

process as needed without requiring IT assistance and to inject their

judgment into the mathematical process.

•   Rapid generation of fully custom role-based user interfaces and 

pricing workflows to support collaborative, interactive participation

by organizational stakeholders.

•  Immediate deployment of approved prices across all channels.

•   Real-time optimization and generation of alternative deal structures 

at the point of sale.

FICO PRICING OPTIMIZATION