the journey to automating and optimizing customer engagements · • little automation •...

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WHITE PAPER

The Journey to Automating and Optimizing Customer Engagements

We live and work in a world increasingly defined by data, analytics and digital platforms. Companies aggressively invest in these technologies knowing they are critical to better performance and competitive advantage. Yet many companies struggle to achieve consistently positive results from their data and analytics initiatives. According to Forrester Research, 73% of enterprise architects aspire to help their firms be data-driven enterprises, but only 29% say they are good at translating analytics into action.1

This may be because they have been using the wrong approach. Rather than start with data and analytics, companies are increasingly realizing that the key to translating analytics into action is decision management. Analytics are only useful if they support better and faster decisions.

This has placed a growing focus on decision management technologies and initiatives. Companies that have been using analytics and decision management for years are likely to have mature capabilities while other companies may be racing to implement this technology at scale for the first time. It became clear through hundreds of engagements with our clients that there was a need for an easy-to-understand maturity model and easy-to-use maturity map that provide customer guidance in the technology adoption of analytically powered decision management solutions.

April 2017

1Forrester Research: “Brief: Why Data-Driven Aspirations Fail.”

WHITE PAPERThe FICO® Decision Management Maturity Model and Map


FICO® Decision Management Maturity Map

FICO has developed this Decision Management Maturity Map as a tool to guide users in their journey from novice to advanced users of analytically powered decision management solutions. This map represents a description of best practices developed by FICO and its customers across industries and addresses all possible architectural layers: data, analytics, decision strategies, application design and development, execution, measurement and reporting, and learning loops (machine learned or otherwise).

Valu

e to

Bus

ines

s

Little to No Capability

No Action Plan

Few If Any Predictive Models Action Plan Is Not Clearly Defined

Full Customer Lifecycle Management

Expand to Customer Lifecycle Engagement

Complete Data-DrivenDecisioning

Automated Decision SystemsAcross Most Key AreasNo Data-Driven Decisioning Rudimentary

Predictive Models

Focused on CustomerEngagement Entry Points

Data-Driven DecisioningAcross Multiple Touchpoints Prescriptive Models

Reactive to Customers Little Data-Driven Decisioning Segmentation/PredictiveModels

Proactive, Optimized ActionsAcross Products & Channels

Novice

Intermediate

Advanced

Reactive and Manual Prescriptive and Automated

FICO® Decision Management Maturity Model



NOVICE (Initial, Early User)

INTERMEDIATE (Existing User Ready to Expand)

ADVANCED (Fully Utilizing Decision Management)

DATA

Few formalized data-driven decisioning policies

Data-driven decisioning across several customer touchpoints

Complete data-driven decisioning across customer lifecycle

PRED

ICT

Only rudimentary predictive models available

Moderate to high levels of customer segmentation

Use of transaction, non-financial interaction, social data in models

PLA

N Little or minimal planning Balances various scores, rules and segmentation in a decision strategy

Targeted full customer level lifecycle across all decision areas, products and channels

DEC

IDE Basic scores and rules used in

manual process or basic automated system

Fully automated decision systems across most key decision areas, with some test and learn and estimation

Fully automated decision systems across all products and decision areas, with automated test and learn

ACT

No clearly articulated action plan Individual units manage actions reactively across limited channels

Proactive, coordinated customer actions across products and channels, based on needs, value, risk and regulation

MEA

SURE

Little measurement undertaken Reasonable level of KPI definitions Full set of effectiveness and efficiency KPI metrics employed

GO

VER

N

Incomplete documentation, little governance of decision assets

Basic oversight, version control, audit trail

Connectivity among related decision assets, complete audit trails, well-defined approval processes

We believe this maturity map provides an accurate, easy-to-understand and actionable segmentation of users and businesses against their current capabilities, requirements, skills and end-state objectives. It provides users with guidance about how to plan for and implement customer journeys against a best-practice, standardized maturity curve.



Mapping the Decision Management Journey

This paper will explore in more detail each phase of the maturity map as defined by the user and business profile and then depict the complete maturity curve so users can obtain a clear view and direction about how to reach their decision management goals.

To successfully move up the maturity curve it is important to implement solutions that are successful across each of the seven key dimensions of decision management maturity: Data, Predict, Plan, Decide, Act, Measure and Govern. Focusing on a single dimension of maturity is not sufficient to meet goals. Success in each dimension supports and reinforces success in other dimensions, resulting in the deliberate and predictable advancement along the decision management maturity curve.

The Three Levels of Decision Management Maturity

Novice: Users or businesses at an early or initial stage of adopting technologies such as analytics, decision modeling or optimization to solve customer entry points in the engagement process.

Intermediate: Users or businesses looking to expand upon and gain greater use of analytics and decision logic to further penetrate and uniquely engage with customers in a profound and consistent manner through an entire buyers’ journey or lifecycle.

Advanced: Users or businesses that have fully utilized decision management and integrated prescriptive analytics into the customer engagement lifecycle and business processes to create optimal and consistent business decisions and customer offers.



The Seven Key Dimensions of Decision Management Maturity

1. Data: Connecting to, accessing and wrangling data to identify relevance, value and leverage. This category includes ETL, data management, privacy and security, and oversight, as well as access to streaming data sources.

2. Predict: Identifying value, trends and predictions in data. Creating mathematical models and statistical analysis of data to identify future trends and likelihood, as well as outliers and anomalies in data.

3. Plan: Develop an understanding of decision-making processes, analytics, key stakeholders and regulatory or compliance issues, as well as business goals and priorities for decision-making. This may include the top-level development of a decision-making process such as a DMN model, as well as identifying the best way of combining rules, segmentation and predictive models together as decision strategies, in order to meet KPIs against specific objectives and constraints. This can also include using a decision impact model.

4. Decide: Creating a strategy flow for a decision-making process that includes the relevant connections to executable decision logic, data and business processes, and optimization services to create, simulate, modify and ultimately execute a decision service.

5. Act: Development and execution of applications that leverage decision, analytic and optimization services, delivered in end-user-specific ways that interact with and convert end users and customers in an optimal and delightful manner.

6. Measure: Define, track and report decision and campaign effectiveness in a way that’s actionable and easy to understand. The ability to quickly communicate areas of improvement and campaign efficacy.

7. Govern: Define, codify and monitor adherence to the business processes that produce, update and execute decision assets. Create and enforce review cycles to assure quality and maintain clear audit trails. Formalize connections among decision assets to sustain agility and quality.



NOVICE (Initial, Early User)

DATA

• Few formalized data-driven decisioning policies

• External data is limited to demographic, and possibly credit/external bureau data

• Internal data includes product holdings of the customer and potentially pooled segmentations and scores

• Difficulty incorporating new data sources

PRED

ICT • Only rudimentary predictive models available

• Judgmental assessment with insufficient empirical support

• Only basic segmentation of customers

• Uses basic expert or BI-based analysis, bureau scores, or very basic internal models, at an account level

PLA

N

• Little or minimal planning

• Manual or basic auto decision-making

• Manual or business intelligence–based decision strategy development

• Decision strategy development planned using anecdotal feedback and some data analysis

• Single decision area and/or profit driver focus

• Limited changes can be made

DEC

IDE

• Basic scores and rules used in manual process or basic automated system

• Little flexibility to amend logic

• Number of scores combined with rules

• Manual test and learn

• Basic decisions made in minutes

• Changes to logic can be made easily, mostly by technical users

ACT • No clearly defined or articulated action plan

• Little automation

• Individual units manage the actions reactively

• Little targeting

MEA

SURE

• Little measurement undertaken

• No agreed-upon standard reports or analyses

• Limited KPIs defined

• Rule-of-thumb measurement—with estimations or perceived values on a limited set of KPIs

• Often only ad hoc or reactive basis

• No consistent location for persisting reports and analyses

GO

VER

N

• Few business processes defined to cover the ideation, development, implementation and ongoing maintenance of decision assets

• If any inventory of decision assets exists, it exists in a silo, and may be incomplete or out of date

• Development and implementation quality of individual assets is acceptable, though documentation about the lifecycle of decision assets is sparse and scattered

• No central view of historical activities

The complete FICO® Decision Management Maturity Map is included in the appendix.

Novice: Decision Management Maturity Map

Early users of analytically powered decision management technology often face significant challenges across multiple dimensions of maturity. By focusing on addressing each dimension, Novices will be able to implement a solution that will allow them to meet their objectives and advance up the maturity curve.



Novice Case Study: Wonga.comWonga is one of over 100 payday lenders operating in Europe’s markets. The UK regulatory authority, the Financial Conduct Authority (FCA), intends to reduce this number to less than 10. Those that remain must operate at a higher level with more rigorous governance and smaller margins.

Objective

Rapidly develop and deploy a new decision management originations application for high-risk markets in less than six months that must:

• Be cloud-based to minimize infrastructure investment and administrative overhead

• Give business user policy ownership so high-risk credit experts can directly manage originations and underwriting policies and turn them over to IT for deployment

• Provide rich web and mobile UI functionality

Solution

FICO® Decision Management Suite, including the following components:

• FICO® Application Studio (UI/service orchestration)

• FICO® Decision Modeler

• FICO® Blaze Advisor® decision rules management system

• FICO® Model Builder

Results

Leveraging the FICO® Analytic Cloud, development started immediately and the solution was deployed as a cloud service in less than four months:

• Originations policies were represented and automated in decision rules

• Manual intervention on loan decisions dramatically reduced

• Credit experts now control origination policy rules

• Policy rules utilized in originations are governed and auditable

• Wonga can develop and deploy predictive models



INTERMEDIATE (Existing User Ready to Expand)

DATA

• Data-driven decisioning across several customer touchpoints• In-use data includes historical usage of current products and

services, and a range of custom predictions, e.g., account-level behavior risk, purchase propensity, etc.

• Decisions also draw from internal shared data references, such as blacklists for fraudulent usage, abusive purchase returns and repeat policy offense

• Can incorporate new data streams, but only with medium/high cost and effort

• Capture and use of recent dialog between customer and company• Some use of transaction data for more precise and timely

reactions to customer trends• Use of multiple external data references, such as credit bureaus

PRED

ICT • Moderate to high levels of customer segmentation

• Performance inference and model engineering techniques applied to overcome bias from historical decisions

• Some transaction-level data incorporated into predictions

• Ability to quickly refresh and deploy predictive models • Use a wide range of customer-level models for propensity,

response, risk and customer value that can be combined for use in decision strategies

PLA

N

• Look to balance various scores, rules and segmentation in a decision strategy

• Some x-decision area / x=product analysis at customer level• Parameterized changes to decision logic

• Customer lifecycle management across decision areas, products and channels

• Consider all key profit drivers and provide customers with targeted options

• Test and learn used to inform development

DEC

IDE

• Fully automated decision systems across most key decision areas, with some test and learn and estimation

• Decisions made in minutes• Full range of models, rules and segmentation across multiple

decisions in each decision area• Changes are easily made with well controlled approval workflow

• Some decisions made in real time with triggers• Some simulation used, mostly for basic what-if analysis• Easy to make changes to decision logic by business users• Decision performance is tracked

ACT

• Individual units manage actions reactively across limited channels• Majority of decisions and actions are automated• Some targeting

• Either individual units manage actions reactively across limited channels or actions are coordinated across products and channels with some proactive elements

• Majority of decisions and actions are automated• Any data capture is verified• Some actions targeted and resources considered

MEA

SURE

• Reasonable level of KPI definitions • Measured at regular intervals (weekly/monthly/quarterly)• Defined guidelines for assessing when additional scrutiny required• Champion/challenger assessments• Periodic assessment of post-conversion behavior, but not always

linked back to activities

• Track detailed KPI performance, revenue and costs for each customer segment and activity individually

• Monitor trends over time• Model actual vs. expected performance measures• Some used to guide experimental design• Each team has agreed-upon standard for persisting reports and

analysis

GO

VER

N

• Business processes defined in easily discovered documentation, with tasks clearly mapped to key stakeholders (e.g., RACI assignments)

• At least a partial centralized inventory of key decision assets, focused on pertinent documentation (e.g., design documents and test reviews)

• Little, if any, connectivity relations (e.g., uses, used-by) across decision assets

• An established body of documentation that serves as a reasonable audit trail of reviews and dispositions

• Limited ability to quickly view and explore a history of governance activities

Intermediate: Decision Management Maturity Map

Intermediate users of analytically powered decision management have successfully adopted decision management technology and best practices in key areas of their business, but have yet to consistently apply it across the full customer engagement lifecycle. The key for Intermediate users is to identify and address the gaps in each maturity dimension—from Data to Measurement and Governance—and build those capabilities that will move them to the advanced phase of the maturity curve.



Intermediate Case Study: VantivVantiv, Inc. is a leading payment processor differentiated by an integrated technology platform. Vantiv offers a comprehensive suite of traditional and innovative payment processing and technology solutions to merchants and financial institutions of all sizes, processing over 17 billion payments per year.

Objective

Vantiv wanted to speed up the merchant onboarding process, which was being done manually and could take up to nine days. Vantiv had the following objectives:

• Make faster onboarding decisions

• Mitigate risk by onboarding the best vendors

• Configure policies quickly

• Make the appropriate pricing choices

Solution

FICO® Decision Management Suite

• FICO deployed its merchant onboarding solutions, which include the following FICO Decision Management Suite components:

• FICO® Decision Modeler

• FICO® Application Studio

• FICO® Data Orchestrator

• FICO® Decision Management Platform

Results

• Automating application evaluation cut decision time from days to minutes, helping keep merchants happy and reducing costs

• Increased accuracy reduced portfolio risk, cuts costs and helped minimize fraud

• Faster application process improved the customer experience and facilitated business growth through increased revenue

• Streamlined rule and strategy development helped Vantiv quickly adapt to market or regulatory changes

• Purpose-built dashboards offer easy insight into all databases and business systems

“Working with FICO helps us automate our processes and make better credit decisions. We can process more applications and better manage our risk as well as the customer experience.”Ravi Shah, Senior Vice President, Enterprise Architecture and Strategy at Vantiv



Advanced: Decision Management Maturity Map

Advanced users and businesses are typically market leaders, innovators and customer-centric enterprises that have made analytically powered decision management a core competency and key driver of competitive advantage.

ADVANCED (Fully Utilizing Decision Management)

DATA

• Complete data-driven decisioning across customer lifecycle• Decisions driven by data available in real time, at customer- and

transaction-level detail, including both financial and non-financial events

• Extensive data on historical usage of current products and services, plus multiple external data sources (financial, non-financial, social media, external references, positive and negative bureau data, etc.)

• Wide range of custom customer-level predictive models, on all profit and value drivers

• Running regular data capture experiments• Full customer profile, including relationships with competing

providers• Full flexibility to incorporate new data streams

PRED

ICT

• Use of transaction, non-financial interaction, social data in models

• Use of uplift/action-effect models to assess impact of alternate decisions

• Use of multi-dimensional objectives for segmentation, prediction, ensemble modeling, machine learning

• Actively introduces updated predictive models based on automated insights learned from recent and long-running experience

• Constraint-based optimization used in conjunction with propensity, response, risk and value models to create optimized decision strategies that can run whenever required

PLA

N

• Targeted full customer-level lifecycle management across all decision areas, products, channels and preferences—both current and potential

• Use of simulation, forecasting and constraint-based optimization to drive what-if simulation scenarios and stress- test analysis to anticipate future changes in decision strategy

• Goal-orientated strategy development and decision making, with clearly stated objectives and constraints

• Library of potential scenarios developed for anticipated conditions

• Easy to make logic changes and switch between strategies

DEC

IDE

• Fully automated decision systems across all products and decision areas, with automated test and learn

• Decisions made when needed (batch, stream, real time) or when new data is received or event occurs

• Advanced simulation using decision impact modeling

• Optimization techniques are adopted to systematically optimize decisions with or without user supervision

• Decision performance is tracked automatically—creating a feedback loop

• Repository of decision assets with governance• Decision strategies and assets can be updated dynamically

ACT

• Proactive actions coordinated on customers across products and multiple channels, based on needs, value, risk and regulation

• 100% of decisions and actions are automated with full guidance available

• Codified process and best practices actively enforced and governed from central repository, providing a clear command center over all decision assets

• All data capture is verified• Actions optimized to available resources

MEA

SURE

• Full set of effectiveness and efficiency KPI metrics employed• Performance measured during activities (in real time) and at

regular intervals• Performance monitoring automated with alerts triggered when

KPIs/metrics drift• Well defined, managed process to investigate and resolve

issues• Continuous customer surveys measure satisfaction

• Post-conversion behavior measures product usage, customer risk, customer value and attrition

• Shapes model development, experimental design, champion/challenger and product development

• Performance compared against expectations and targets, and between champion and challenger strategies

• Continuously monitors decision performance with self-updating alerting thresholds to distinguish breakout anomalies from long-running drift

GO

VER

N

• Business processes fully codified into software workflows, with tasks clearly mapped to key stakeholders (e.g., RACI assignments)

• Complete centralized inventory of decision assets, housing activity and documentation across the lifecycle

• Clear connectivity relations (uses, used-by) between decision assets

• Comprehensive audit trails in a searchable chronology of approvals and dispositions

• Configurable business intelligence to surface, explore and monitor governance activities across the enterprise



“FICO impressed us with its experience in credit optimization, as well as its acquisition of big data analytic technologies. This complements our strategic investment in big data to improve our understanding of customers’ needs and risk.”Ivan Cavinato, Head of Credit Risk Management at UniCredit

Advanced Case Study: UniCreditUniCredit is Italy’s largest bank. It operates in 17 countries, has over 7,500 branches, 142,000 employees and an international network that spans more than 40 markets.

Objective

Build a new decision management infrastructure across the whole credit lifecycle, including:

• Agile and high-performing rules management system definable by business users

• Powerful optimization solvers and intuitive business user environment

• Seamless architectural integration with existing bank systems

Solution

FICO® Decision Management Suite

Results

UniCredit can now:

• Easily develop and deploy strategies into the decision engine without IT intervention

• Keep the acceptance rate under control

• Reduce manual intervention and over-reliance on “ judgment calls”

• Perform champion/challenger and simulation

• Optimize strategies according to goals and constraints to find the best and most profitable action or offer



FICO® Decision Management Suite 2.0

Available on-premises or in the cloud, the FICO® Decision Management Suite provides the basis for easy and powerful creation, customization, development and deployment of analytically powered decision applications. It allows customers at any maturity level, from Novice to Advanced, to quickly integrate management tools and components from FICO and others with their own components to rapidly and easily develop, test, deploy and scale new decision management solutions.

It is a complete solution to ingest and explore data (streaming or at rest), discover analytic insights, execute analytics in operational decisions, create and deliver engaging web and mobile applications, and track and improve analytic models and decision strategies.

The FICO Decision Management Suite supports clients in all phases, dimensions and levels of the maturity curve. The platform provides the capabilities to successfully meet client objectives and use cases at all levels of maturity.

Its integrated, modular approach means that organizations can deploy those components that will support their objectives and phase of the maturity curve, from a product-specific use case all the way up to enterprise-wide Advanced maturity use cases.

ANALYSIS

DECISION

DATA

ACTIONDecision ProcessDefinition

Optimization

• Scorecards, decision trees• Predictive analytics• Insights, enrichment

Analytics

• Declarative definition• Decision analytics• Decision optimization

• Solvers• Optimization models

Digital Asset Management• Government processes• Performance monitoring

Decision Insights

• Application hosting environment• Scalable, elastic execution platform• Request-response, batch and streaming

FICO Decision Management Suite Benefits• More than 90% reduction in time

to deploy any type of models

• 70% reduction in time to develop decision management applications

• 30%–50% increase decision performance

• 10%–40% increase in model performance

• More than 60% reduction in maintenance costs

• Reduce internal resources required to onboard new customers by 65%



Start moving up the decision management maturity curve: For more information on the FICO® Decision Management Suite, go to:

https://www.ficoanalyticcloud.com/decision-management-suite/

To start using a free trial of the software, go to: https://community.fico.com/community/fico-blaze-advisor-and-decision-modeler-community

https://community.fico.com/community/fico-optimization-community

To join the FICO Decision Management Community, go to: https://community.fico.com

For further reading on decision management topics, go to:1. Why a Decision-First Approach Is Critical for Competitive Advantage

2. Decision Management for the Masses

3. Tapping Unstructured Data for Better Predictions and Decisions

4. The Business Value of Prescriptive Analytics by Butler Analytics

5. Big Data Opens the Door for Prescriptive Analytics

6. Big Data Analytics for Every Organization

7. Delivering Customer Value Faster with Big Data

8. A New Application Development Approach for Today’s Speed of Business

9. High Performance Scorecard

10. Solving the Unsolvable

https://www.ficoanalyticcloud.com/decision-management-suite/

https://community.fico.com/community/fico-blaze-advisor-and-decision-modeler-community

https://community.fico.com/community/fico-blaze-advisor-and-decision-modeler-community

https://community.fico.com/community/fico-optimization-community

https://community.fico.com

http://www.fico.com/en/latest-thinking/whitepapers/why-a-decision-first-approach-is-critical-for-competitive-advantage

http://www.fico.com/en/latest-thinking/white-papers/decision-management-for-the-masses-white-paper

http://www.fico.com/en/latest-thinking/ebook/tapping-unstructured-data

http://www.fico.com/en/latest-thinking/white-papers/prescriptive-analytics

http://www.fico.com/en/latest-thinking/white-papers/big-data-opens-the-door-for-prescriptive-analytics

http://www.fico.com/en/latest-thinking/ebook/big-data-analytics-for-every-organization

http://www.fico.com/en/latest-thinking/white-papers/delivering-customer-value-faster-with-big-data-white-paper

http://www.fico.com/en/latest-thinking/executive-brief/a-new-application-development-approach-for-todays-speed-of-business

http://www.fico.com/en/latest-thinking/ebook/high-performance-scorecard

http://www.fico.com/en/latest-thinking/white-papers/solving-the-unsolvable1


Appendix

NO

VICE (Initial, Early User)

INTERM

EDIATE (Existing User Ready to Expand)

ADVA

NCED

(Fully U

tilizing Decision M

anagement)

DATA

• Few

formalized data-driven decisioning

policies•

External data is limited to dem

ographic and possibly credit/external bureau data

• Internal data includes product holdings of the custom

er and potentially pooled segm

entations and scores•

Difficulty incorporating new

data sources

• D

ata-driven decisioning across several customer touchpoints

• In-use data includes historical usage of current products and services, and a range of custom

predictions, e.g., account-level behavior risk, purchase propensity, etc.

• D

ecisions also draw from

internal shared data references, such as blacklists for fraudulent usage, abusive purchase returns and repeat policy offense

• C

an incorporate new data stream

s, but only with m

edium/high cost

and effort•

Capture and use of recent dialog betw

een customer and com

pany•

Some use of transaction data for m

ore precise and timely reactions to

customer trends

• U

se of multiple external data references, such as credit bureaus

• C

omplete data-driven decisioning across custom

er lifecycle•

Decisions driven by data available in real tim

e, at customer- and

transaction-level detail including both financial and non-financial events

• Extensive data on historical usage of current products and services, plus m

ultiple external data sources (financial, non-financial, social m

edia, external references, positive and negative bureau data, etc.)

• W

ide range of custom custom

er-level predictive models, on all

profit and value drivers •

Running regular data capture experiments

• Full custom

er profile, including relationships with com

peting providers

• Full flexibility to incorporate new

data streams

PREDICT

• O

nly rudimentary predictive m

odels available

• Judgm

ental assessment w

ith insufficient em

pirical support•

Only basic segm

entation of customers

• U

ses basic expert or BI-based analysis, bureau scores, or very basic internal m

odels, at an account level

• M

oderate to high levels of customer segm

entation•

Performance inference and m

odel engineering techniques applied to overcom

e bias from historical decisions

• Som

e transaction-level data incorporated into predictions•

Ability to quickly refresh and deploy predictive models

• U

se a wide range of custom

er-level models for propensity, response,

risk and customer value that can be com

bined for use in decision strategies

• U

se of transaction, non-financial interaction, social data in models

• U

se of uplift/action-effect models to assess im

pact of alternate decisions

• U

se of multi-dim

ensional objectives for segmentation, prediction,

ensemble m

odeling, machine learning

• Actively introduces updated predictive m

odels based on autom

ated insights learned from recent and long-running

experience •

Constraint-based optim

ization used in conjunction with propensity,

response, risk and value models to create optim

ized decision strategies that can run w

henever required

PLAN

• Little or m

inimal planning

• M

anual or basic auto decision-making

• M

anual- or business intelligence–based decision strategy developm

ent•

Decision strategy developm

ent planned using anecdotal feedback and som

e data analysis

• Single decision area and/or profit driver focus

• Lim

ited changes can be made

• Look to balance various scores, rules and segm

entation in a decision strategy

• Som

e x-decision area / x=product analysis at customer level

• Param

eterized changes to decision logic•

Customer lifecycle m

anagement across decision areas, products and

channels•

Consider all key profit drivers and provide custom

ers with targeted

options•

Test and learn used to inform developm

ent

• Targeted full custom

er-level lifecycle managem

ent across all decision areas, products, channels and preferences—

both current and potential

• U

se of simulation, forecasting and constraint-based optim

ization to drive w

hat-if simulation scenarios and stress-test analysis to

anticipate future changes in decision strategy•

Goal-oriented strategy developm

ent and decision-making, w

ith clearly stated objectives and constraints

• Library of potential scenarios developed for anticipated conditions

• Easy to m

ake logic changes and switch betw

een strategies

DECIDE

• Basic scores and rules used in m

anual process or basic autom

ated system•

Little flexibility to amend logic

• N

umber of scores com

bined with rules

• M

anual test and learn•

Basic decisions made in m

inutes•

Changes to logic can be m

ade easily, mostly

by technical users

• Fully autom

ated decision system

s across most key

decision areas, with som

e test and learn and estim

ation•

Decisions m

ade in minutes

• Full range of m

odels, rules and segm

entation across multiple

decisions in each decision area•

Changes are easily m

ade w

ith well controlled approval

workflow

• Som

e decisions made in real

time w

ith triggers•

Some sim

ulation used, mostly

for basic what-if analysis

• Easy to m

ake changes to decision logic by business users

• D

ecision performance is

tracked

• Fully autom

ated decision system

s across all products and decision areas, w

ith autom

ated test and learn•

Decisions m

ade when needed

(batch, stream, real tim

e) or w

hen new data is received or

event occurs•

Advanced simulation using

decision impact m

odeling

• O

ptimization techniques are

adopted to systematically

optimize decisions w

ith or w

ithout user supervision•

Decision perform

ance is tracked autom

atically—creating a feedback loop

• Repository of decision assets w

ith governance•

Decision strategies and

assets can be updated dynam

ically

FICO and Blaze Advisor 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. © 2017 Fair Isaac Corporation. All rights reserved. 4393WP_EN 4/17 PDF

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NO

VICE (Initial, Early User)

INTERM

EDIATE (Existing User Ready to Expand)

ADVA

NCED

(Fully U

tilizing Decision M

anagement)

ACT

• N

o clearly defined or articulated action plan•

Little automation

• Individual units m

anage the actions reactively

• Little targeting

• Individual units m

anage actions reactively across lim

ited channels

• M

ajority of decisions and actions are autom

ated•

Some targeting

• Either individual units m

anage actions reactively across lim

ited channels or actions are coordinated across products and channels w

ith some

proactive elements

• M

ajority of decisions and actions are autom

ated•

Any data capture is verified•

Some actions targeted and

resources considered

• Pro-active actions coordinated on custom

ers across products and m

ultiple channels, based on needs, value, risk and regulation

• 100%

of decisions and actions are automated w

ith full guidance available

• C

odified process and best practices, actively enforced and governed from

central repository, providing a clear com

mand center over all decision

assets•

All data capture is verified•

Actions optimized to available resources

MEASURE

• Little m

easurement undertaken

• N

o agreed-upon standard reports or analyses

• Lim

ited KPIs defined•

Rule of thumb m

easurement—

with

estimations or perceived values on a lim

ited set of KPIs

• O

ften only ad hoc or reactive basis•

No consistent location for persisting reports

and analyses

• Reasonable level of KPI definitions

• M

easured at regular intervals (w

eekly/monthly/quarterly)

• D

efined guidelines for assessing w

hen additional scrutiny required

• C

hampion/challenger

assessments

• Periodic assessm

ent of post-conversion behavior, but not alw

ays linked back to activities

• Track detailed KPI perform

ance—revenue and

costs for each customer

segment and activity

individually•

Monitor trends over tim

e•

Model actual vs. expected

performance m

easures•

Some used to guide

experimental design

• Each team

has agreed-upon standard for persisting reports and analysis

• Full set of effectiveness and efficiency KPI m

etrics em

ployed•

Performance m

easured during activities (in real tim

e) and at regular intervals•

Performance m

onitoring automated w

ith alerts triggered w

hen KPIs/metrics drift

• W

ell defined, managed process to investigate and

resolve issues•

Continuous custom

er surveys measure satisfaction

• Post-conversion behavior m

easures product usage, custom

er risk, customer value and attrition

• Shapes m

odel development, experim

ental design, cham

pion/challenger and product development

• Perform

ance compared against expectations and

targets, and between cham

pion and challenger strategies

• C

ontinuously monitors decision perform

ance with

self-updating alerting thresholds to distinguish breakout anom

alies from long-running drift

GOVERN

• Few

business processes defined to cover the ideation, developm

ent, implem

entation and ongoing m

aintenance of decision assets

• If an inventory of decision assets exists, it exists in a silo, and m

ay be incomplete or

out of date•

Developm

ent and implem

entation quality of individual assets is acceptable, though docum

entation about the lifecycle of decision assets is sparse and scattered

• N

o central view of historical activities

• Business processes defined in easily discovered docum

entation, with tasks

clearly mapped to key

stakeholders (e.g., RACI

assignments)

• At least a partial centralized inventory of key decision assets, focused on pertinent docum

entation (e.g., design docum

ents and test reviews)

• Little, if any, connectivity relations (e.g., uses, used-by) across decision assets

• An established body of docum

entation that serves as a reasonable audit trail of review

s and dispositions

• Lim

ited ability to quickly view

and explore a history of governance activities

• Business processes fully codified into softw

are w

orkflows, w

ith tasks clearly mapped to key

stakeholders (e.g., RACI assignm

ents) •

Com

plete centralized inventory of decision assets, housing activity and docum

entation across the lifecycle

• Clear connectivity relations (uses, used-by) betw

een decision assets•

Com

prehensive audit trails in a searchable chronology of approvals and dispositions

• C

onfigurable business intelligence to surface, explore and m

onitor governance activities across the enterprise

the journey to automating and optimizing customer engagements · • little automation •...

Documents