The Customer Journey to Decision Optimization Customer Journ… · The Decision Improvement Lifecycle The decision improvement lifecycle is central to delivering the full value of

The Customer Journey to Decision Optimization

1 © 2020 Decision Management Solutions

The Customer Journey to Decision Optimization Introduction

The balance of profitability and risk is central to success in lending.

Decision Optimization can accelerate success by helping organizations

develop decision-making approaches that better meet their goals and

objectives. Let’s look at a use case that illustrates the effectiveness of

Decision Optimization at a bank that serves the consumer market.

As new lenders and new ways to borrow started to proliferate in the consumer

loan market, the head of retail credit risk at this bank knew his organization

needed to price its loans more competitively in order to reach its core market. But

the bank could not just cut prices—it had to balance competitive pricing against

risk exposure and profitability. They had to balance the whole loan portfolio and

still make loan offers attractive to individual customers. To succeed in a

competitive environment, the bank had to replace its standard policy for consumer

loan amounts and prices with something more sophisticated. Instead of just

offering better prices for better credit, they needed to find a way to consider

other factors, such as price sensitivity among prospective customers.

The retail credit risk department hired a new head of analytics who was

immediately clear on the need to innovate. He did not want to spend a lot of time

fine-tuning existing policy only to get incremental improvement. Job one was to

understand the data and processes around loan approval. This was foundational for

improvement. In particular, he focused in on analyzing decision outcomes. He

needed to see what happens when a particular kind of loan is made to a particular

type of consumer.

By James Taylor CONTENTS

Introduction

The Decision

Improvement

Lifecycle

The Beginning: Codify

Your Current Practice

The Journey: Improve

Your Decision

Strategy

The End Game:

Decision Optimization

The Value of Decision

Optimization

Decision

Optimization: Critical

Success Factors

Getting Started

Sponsored by



This also made him realize that he needed data on loans that had not been offered

previously. This data would help him better understand the tradeoffs and

opportunities. He gathered some of this data experimentally and consulted

internal experts to fill in other blanks. Historical data, experimental results, and

expertise were all working together. Trust and transparency were critical to the

success of this process, along with multiple discussions, cross-functional teams,

and establishing overlapping goals and metrics.

Once he gained an understanding of this, he moved on to making predictions about

the customer behaviors, individual loans, and the portfolio as a whole. Now he

could predict not just how people would respond to different loan offers, but also

how those loans would play out over time and impact profit.

He then introduced the use of mathematical optimization. This provided a

framework to bring different predictions and profit calculations together,

supported scenarios to be compared and identified the best loan decisions: who to

accept, how much to lend, and what price to offer.

Initially, the new strategy was rolled out to consumer loans. As the head of

analytics began to deliver the analytic accuracy required, an iterative refinement

process began. With success came broader usage, and the approach was rolled out

to pre-approved loans and credit card applications. More complex decisions were

optimized in real time to offer consumers the best possible deal for debt

consolidation. Since this new approach was put in place, the bank rolled out more

than a dozen iterations of consumer loan strategies. And the bank also developed

four credit card strategies over four years. Each time, the bank analyzed data, ran

optimization scenarios, developed a strategy, ran it for a while to gather more

data, observed its performance, refined it, and started the process all over again.

This process of change and refinement has become embedded in the company

culture and is critical for long-term success.

What are the results of this new strategic approach? Loan size and approval rates

are up and so is acceptance. Tens of millions of dollars in new revenue were

realized in the first year, and the profitability of the portfolio has risen by over

25%.

This bank, like other organizations that successfully optimize their approach to

decision making, treats decision optimization as a journey with a repetitive

pattern. Every organization experiments, learns, and adapts to systematically

improve their decision making. Smart organizations never stop learning and

recognize that an approach that appeared to be perfect at one time may have to

be revised when market conditions change or new competitors enter the scene.



The Decision Improvement Lifecycle

The decision improvement lifecycle is central to delivering the full value of decision

management. Being able to rapidly and effectively execute a decision strategy,

monitor its effectiveness, learn what works (and what does not), and then improve

your decision making is what makes all the difference. Each iteration moves you

toward increasingly optimal decision making and, ultimately, to true decision

optimization. Figure 1 shows a typical process:

1. Decision Performance Review: Production data about decision outcomes is

reviewed, and the results of ongoing experimentation are considered. What

metrics are unsatisfactory? What are underlying drivers?

2. Opportunities Identification: What changes can be made in the data, decision

strategy or supporting analytic models?

3. Experimentation: Updates to the strategy are proposed and simulated using

production data to observe effects of the change.

4. Promote Changes: If the results look promising and do not have unintended,

negative side effects, an approval process pushes those changes into

production.

5. Business-as-Usual Operations: Transactions and decisions are in production.

The new strategy runs for a while, often only impacting a subset of customers

and running in parallel with the old one. This creates new data about decision

outcomes that can be reviewed to start the cycle again.

Decision Management is a set of techniques and business capabilities that enable you to apply artificial

intelligence (AI) technologies—such as business rules, predictive analytics, machine learning, prescriptive

analytics, and mathematical optimization—to automate and manage the day-to-day operational decisions

that are at the heart of your business.



Figure 1. Decision Improvement Lifecycle



The Beginning: Codify Your Current Practice

The most common way most organizations begin this journey is by analyzing and

codifying existing practices. This starts with understanding what is done today,

what works, and what does not. A resulting “best practice” decision strategy is

developed.

Done well, this kind of analysis is still an essential first step. Codifying an existing

approach captures where you are now. Experts like to say that you must know

where you are in order to start designing something better. One of the most

effective techniques for capturing current practices is to build a decision model of

the decision you wish to improve, such as the assignment of an initial marketing

offer to a prospect or a collections strategy. Building a decision model allows for

the effective engagement of domain experts and the graphical representation of a

shared understanding of the current approach.

No matter how well modeled current practices are, they tend to have several

specific limitations.

Current practices often rely on static historical segmentations and

categorizations: Part of this strategy is to assign customers or transactions to

specific segments and categories before using these to select an action. The

static nature of these approaches makes them fragile and likely to be overtaken

by events. For example, many of the policy rules set up to exclude customers

from specific products are not regularly reviewed or challenged.

Decisions involve tradeoffs: Some decision strategies completely ignore the

issue of competing objectives and the resulting tradeoffs, focusing instead on

maximizing one metric (examples: offers accepted or overdue payments

collected). Even when tradeoffs are considered, the mechanisms used are often

not sufficient.

It’s difficult to assess the effectiveness of the current practice: Organizations

often lack complete data, making it hard to assess how the current approach

A Decision Strategy is an integrated set of decisioning artifacts—such as business rules, decision trees,

optimization, and predictive/machine learning models—that assign a specific treatment or approach

to each customer or customer segment.

A Decision Model is graphical representation of a repeatable decision that shows how that decision is

made. It represents the relationships between its sub-decisions, the data it requires to make those

decisions, and the decision-making knowledge required to make them effectively.

Decision Model and Notation (DMN) is an industry standard way to represent decision models.



would apply to all customers, let alone what might happen if changes were

made. Biased data is also common, especially due to selection bias. Most

companies lack data about people who were not selected for consideration and

about the relative performance and results of different decisions or approaches.

A clear understanding of your current practice is essential—but it is just the

beginning.

The Journey: Improve Your Decision Strategy

Every organization’s journey to decision optimization is different. These journeys,

however, have several common elements. Each element can be adopted separately,

and there is a wide range of possible sequences. Some organizations adopt one at a

time, while others tackle several in parallel. Each of the four elements listed below

represents a concrete way to improve your decision strategy:

Decision performance monitoring

Data-driven segmentation

Experimentation framework

Predictive analytics and machine learning

Decision performance tracking

One of the advantages of formalizing your decision strategy is that it improves your

ability to track how you made decisions and how each decision worked out for you.

For instance, if you develop a decision model for your decision strategy, then you

should be able to record how each decision was made each time the model was

applied. Every customer treatment decision can be matched to decisions about risk,

segmentation, lifetime value, and so on.

You already track business performance data. The overall outcome of the decision

strategy—the customer treatment selected—is recorded for each customer, every

time. Now this data can be enhanced with an understanding of why the decision

was made the way it was made. This decision strategy detail can be linked to

business outcomes, creating a continuous improvement feedback loop.

By taking all the customers that were treated a certain way and then segregating

them into a subset based on any of the sub-decisions in your model or strategy, you

can focus on very specific subsets of customers. Seeing how well the treatment

worked for those subsets often identifies obvious improvements. New, more fine-

grained approaches commonly result from this analysis.

Data-driven segmentation

Most decision strategies involve categorizing or segmenting customers so that a

suitable treatment can be selected for customers in that segment. Most



organizations have some rule-of-thumb categories and historical segments that are

baked into the first version of their decision strategy. One of the first steps you can

take to improve your decision strategy is to use your data to improve this

segmentation.

Each element in your strategy can be assessed. Taking the logic in these

segmentation decisions, review your historical data. How alike are customers in the

segment? Is there a clear and distinct difference between the behavior of

customers in one segment versus another? These assessments can be done using

standard business intelligence and guided analytic tools. It’s even more effective to

use analytical techniques to develop data-driven decision trees.

The purpose of the segmentation needs to be clear. For example, in a lending

strategy, one segment could consist of customers who have good credit but are

reluctant to use it. A data-mining or data-driven decision tree tool can be used to

analyze your data and create a set of precise criteria that better differentiates

customers. This might match your expectations, but it is likely to both refine and

occasionally challenge your approach.

Before you integrate this new segmentation into your decision strategy, you can

simulate the impact of the change. Build a version of your decision strategy using

the new, data-driven approach, and run it against a large historical data set.

Compare what you did then with what the new strategy would suggest and analyze

which customers would receive a different treatment. Use the differences to see

how much more value the new approach might have. Invest the time to ensure that

everyone is comfortable with the new approach and agrees that it is more

effective. If it is not completely clear that the new approach is better, consider

using an experimental comparison (see below).

Because your strategy is now based on analysis of data, you need to establish a

rhythm of updates. Data continues to be gathered, so the patterns in your data will

change continually. Regular reviews of your data and an assessment of your data-

driven segmentation is essential to ensure that you do not get out of sync with your

data and, therefore, with the actual behavior of your customers.

Experimentation framework

There are often advantages in assessing different decision-making approaches on

the same subset of customers, so you can identify the relative impact of changes

and which approach works best. Static analysis of the different approaches can

highlight the differences between them. Simulation with historical or synthetic data

can show the likely difference in outcomes. But there is no substitute for real-world

experience. The only way to get good data about what people will do when treated

a certain way is to treat them that way and observe their response.

Running one experiment after another for a period and collecting data about how

that works out works to some extent. In practice, the delay between different

experiments often takes too long and runs the risk of being overtaken by events,



such as a change in market conditions which could render any performance

comparison useless. Creating an experimentation framework to run several

experiments in parallel on random samples is much more effective, and easier to

measure.

Within a decision strategy, you can identify specific elements as the subject of

experiments. Several different ways of making those sub-decisions can then be

defined and used to support various experimental designs. Customers are randomly

allocated to each of several approaches.

Running multiple experiments within the decision strategy itself allows experiments

to be created, tested, evaluated, updated, and retired—without changes to any of

the company’s other systems, as the experiment is completely encapsulated within

the decision-making component.

Predictive analytics and machine learning

Analyzing data to create data-driven segmentation is a powerful tool for improving

decision strategies. Historical data can also be analyzed to make predictions about

likely behavior or the likely impact of a decision. Using predictive analytic

techniques and machine learning, data about past behavior can be turned into

insights that you can use to improve decision making.

For instance, business experts might see that they could change their decision-

making approach if they knew which customers would use the credit they were

offered, buy additional products, default on an existing debt, or promise to pay but

then not stick to it. While machine learning cannot give definitive answers to these

kinds of questions, it can provide probabilities. Often, the decision strategy can

integrate these probabilities into decision making, changing the approach based on

the likelihood of a risk or opportunity.

A decision model or other representation of a decision strategy can be used to

discuss the potential value of such predictions. Business experts can evaluate which

Companies are often reluctant to experiment too widely. Champion/Challenger testing is used so that

most customers get the preferred, or Champion approach, whilst smaller randomly assigned groups are

experimented on—Challengers. The random allocation of customers to different approaches is managed to

ensure statistically significant distributions to all the experiments. Once the experiment has been run

long enough to collect useful data about outcomes, you can compare the differences in outcomes.

Experimental Design is a set of principles and techniques to ensure that the setup and execution of

experiments meets its objectives. Good experimental design allows multiple hypotheses to be tested at

once, helps ensure proper randomization, and supports replication. The experiments might be somewhat

random to fill in gaps in data required for analysis, or they can be more systematic, comparing the

effectiveness of well-defined approaches.



aspects of the decision strategy they might change based on a specific prediction

and then offer guidance on the required level of accuracy. Data scientists can then

analyze available internal data—and perhaps data from outside the company—to

build a predictive model that will integrate into the decision-making.

It is generally best to evaluate new strategies utilizing predictions as experiments

first and compare the results achieved to decision strategies that do not use them.

Data bias and selection bias need to be considered. Companies have data about the

kinds of customers they have historically selected and not about those they have

historically rejected. This needs to be considered when analyzing data to make

better, more informed predictions that impact decisions.

Useful Tool: Influence Diagrams

No matter which of the above techniques you utilize, a good starting point is to build an influence

diagram focused on the decision in question, to see how the various elements fit together. An influence

diagram is an intuitive visual display of the key elements of a decision problem, illustrating the influences

among them as arrows. Often undertaken as a whiteboard exercise, just drawing out an influence diagram

of your business problem can be a great help in better understanding the key profit drivers and how they

are connected. If you can predict or measure the outcome of these drivers for different potential

decisions, you can start to make better decisions.

Figure 2 shows that three types of data (application, credit bureau, and customer data) are used to make

three key decisions: who to accept, how much to loan them, and what rate to charge them. How those

decisions are made affects some critical measures, like take-up rate (Are the people who are lent money

spending it?), charge-off (Do they fail to pay it back?) and early pay-off rates.

Figure 2. Influence Diagram

These elements can materially

improve your decision strategy.

Eventually your strategy will have

multiple drivers of profitability,

several key predictions about

customer behavior or risk, and

multiple experiments running in

parallel. The trade-offs between

these may not be clear—and

manually choosing among them will

lead to sub-optimal results. At this

point, it’s time to think about

optimizing your decision strategy

mathematically.



The End Game: Decision Optimization

Decision Optimization applies mathematical processes to identify the best decision

strategy, given an organization’s data, constraints, and objectives. Most

organizations have multiple constraints on how they make decisions based on

regulatory frameworks, budgets or policies. They also have competing objectives,

such as profitability, attrition, and bad debt. By working through the incremental

improvements described above, you can generate a wide range of data about your

customers and the impact of decisions. Decision Optimization consumes all this and

finds the best possible decision strategy.

Data

To optimize a decision, you ideally need to have data about all the possibilities.

The first step is to ensure that the journey so far has filled in the data you need.

Mathematical Optimization uses mathematical formulas to identify the best or most plausible solution to

a complex problem. Optimization generally maximizes or minimizes something, such as profit, while

considering various inputs and constraints on the possible solutions. The intent is to find an optimal

answer to a well-defined problem.

http://www.fico.com/decision-optimizer



You may need to run experiments to deliberately offer products or prices to those

not normally eligible to see what happens. You will almost certainly need to rely on

experts to extrapolate and fill in some of your blanks. Tracking the confidence of

your data in various areas will help you focus optimization where the data is robust

enough to get a good outcome.

Decision Impact Models

Your understanding of the elements of the decision (shown in Figure 2) now needs

to be modeled more formally. To get started building such a model, your influence

diagram can be extended into a more detailed and precise model, detailing the

inputs, decisions, component models, calculations and the influences between each

element. FICO, for instance, use Decision Impact Models to provide the framework

for a model specification, such as that shown in Figure 5.

It’s important to ensure that the predicted outcomes of each decision are as

accurate as possible. You may need to build prescriptive, causal or action-effect

models that consider the impact of different decisions. Such models make

predictions about the likely effect of each action by analyzing your historical data

to find the things that determine who does what in response. For instance, you

need to predict who will take up to a loan offer and drawdown the loan. These

models are a mathematical representation of your understanding of how customers

respond to your decisions, as illustrated in Figure 3.

Some of the actions customers take are variable, and the variability also needs to

be predicted. For example, you can predict that a customer will stop paying for a

loan before it is paid off. But to calculate profitability, you will need to know how

Figure 3. Action Effect Models



many payments they are likely to make before they stop. These additional

predictive analytic models predict key measures or metrics about a customer or

account in specific circumstances and are combined with the action-effect models

that predict how likely those circumstances are.

You also need to ensure you have considered all the different outcomes in the

decision problem. For example, the potential outcomes to a Loan Origination

decision are shown in Figure 4.

When the customer applies, you can accept or reject them. For accepted

applications there are three possible outcomes:

1. Customer accepts the loan but does not use it – there is no take-up.

2. Customer use the loan but then defaults – they fail to make all the scheduled

payments.

3. Customer uses the loan and then pays if off completely.

Each outcome needs to be considered. The probability of each outcome can be

calculated using the model in Figure 4.

1. The probability of Non-Take up is 1-a, the inverse of the probability of the

customer taking up the loan.

2. The probability that the customer will take up the loan and then default is a * b

– the likelihood of take-up multiplied by the probability of default.

3. The probability of the loan being taken up and then being paid off completely is

a*(1-b).

Because these represent all the possible outcomes, the sum of these probabilities –

(1-a) + (a*b) + a*(1-b) should be 1.

Models such as FICO’s

Decision Impact

Model tie all this

together with the

calculations of your

metrics and

objectives. The

resulting combined

model shows how

likely the possible

customer responses

are for a given

decision and what

the result of those

responses will be on

metrics and

Figure 4. Decision Outcomes



objectives. Fixed costs can be included too, so that profit and loss can be

accurately modeled. A mathematical model has been developed to predict the

value of each possible decision.

Decision Impact Models can be executed against your customer portfolio. Decisions

can be compared, applying two different decision strategies to each customer and

then calculating the value of each decision-making approach. More interestingly,

though, the models can be used to optimize the decision strategy using a solver.

Optimization

The possible decision strategies are fed into the model, along with portfolio-level

constraints, such as total amount of credit available or bad debt tolerance. The

constraints limit the available strategies at both the customer and portfolio level.

The objectives are used to give the solver an objective function. The solver

compares possible outcomes for each customer to find the best possible decision for

each customer, given your objectives and your constraints. These results can be

used directly to treat each customer or can be analyzed to come up with an

overarching decision strategy that selects the best of the available decisions for

each customer.

To support mathematical optimization, the relationships shown in a Decision Impact

Model are expressed as formulas that can be calculated and variables available to

the solver. The example in Figure 6 below shows a section of the profit calculation

for the example and includes formulas for probabilities, costs, losses etc. The

Figure 5. Decision Impact Model for Loan Origination



formulas can be used in multiple scenarios. These can be simulated with different

constraints or decision strategies. These scenarios can be combined to find the best

way to manage within your current constraints and show the value and tradeoffs of

changing those constraints.

It should be noted that getting value from Decision Optimization does not require

an immediate jump to a fully realized solution. Several simplifications can be made

to get started quickly while still benefitting from decision optimization. You can

begin at the segment level. Because you’re not trying to assess behavior at the

individual level, there’s less need for action-effect models at this stage. You can

also consider just the initial decision and have generic calculations for subsequent

predictions and outcomes like offer acceptance and usage. A simpler decision

impact model design can be used, such as that in Figure 7 below.

Moving on to consumer-level assessments, you can minimize complexity by defining

less sophisticated action-effect models and by considering fewer drivers of

profitability or success. Being consumer-focused and not segment-focused will

improve accuracy, even if you do not add all the possible details to action-effect

models.

These additional models and the use of optimization enhance but do not change the

decision improvement lifecycle. As Figure 8 below shows, the added sophistication

and precision enhances how you improve decisions in your decision improvement

lifecycle.

Figure 6. Profit Calculation

Solvers are mathematical software tools that take problem descriptions in a mathematical form and

calculate the best and most practical solution to that problem. Business problems are turned into a

mathematical representation defining objectives or business goals, variables, and constraints. Solvers use

various techniques to find the best solution out of a potentially large number of possible solutions.



Specifically:

Influence diagrams enhance stage

two—opportunity identification—by

making it clearer what improvements

will be beneficial.

Action Effect models can also bring a

lot more focus and accuracy to the

Impact Analysis in stage 2.

Decision Impact Models and

Optimization can be added to stage

three, finding the best outcome

among those being considered.

Optimization also allows many more

simulation attempts to be

considered, mathematically picking

between all the simulated scenarios.

Figure 7. Basic Loan Origination Decision Impact Model

Figure 8. Decision Optimization and Improvement Lifecycle



The Value of Decision Optimization

Adopting Decision Optimization can bring many business benefits. In summary, key

areas highlighted by users include:

Focuses on the outcomes of different decisions, the things that truly impact

business success.

Identifies decisions that meet your goals and constraints – whether its

meeting a regulation our making the most out of scarce resources.

Clarifies the business problem as being able to quickly compare scenarios

identifies the key profit drivers, shows how to respond to potential threats, and

reveals opportunities.

Generates a significant return on Investment. Published case studies show

annual profit improvements of 5% to 30% or more, with ROIs of over 10:1. these

are the kinds of numbers that can transform a business.

Decision Optimization: Critical Success Factors

Decision Optimization is a powerful technique that builds on your work in decision

management, experimentation, machine learning, and data quality. Here are some

critical success factors shared by customers who have had positive experiences.

Be willing to experiment: You will need to experiment to fill the gaps in your data. Your optimal decision strategies may be quite different from your current approach. A willingness to experiment and run experiments in production will help people feel comfortable that the new approach is both better and safe.

A mixed team brings together a wide range of knowledge and skills: Besides data science and technical skills, you will need domain expertise. Domain experts are essential to creating your initial strategies, designing meaningful experiments, and reviewing business impacts.

Decision optimization requires flexible and powerful software: You will need tools that handle rules, machine learning, and optimization in a coherent way. These tools need to be integrated with the software you currently use to deploy decision services into production.

Use a proven methodology, ideally one based on models, not requirements documents: Decision models built using standard notations like DMN and decision-impact models that show how the pieces fit together are easier to validate and for business owners to accept.

Finally, if you are inexperienced in this realm, get advice: Seek some expert guidance and support, especially for experimental design and some of the more advanced analytic models.



Decision Optimization: Where Next?

Many approaches and techniques for decision optimization have been tried and tested over recent years.

Several recent developments offer the potential to significantly improve the development, management,

and improvement of decision strategies.

Enhanced machine learning and artificial intelligence models

Machine learning and artificial intelligence techniques are evolving to deliver more accurate predictions

more rapidly. These predictions can be used to improve decision optimization, though risks around data

bias, overfitting, and bad model design must be mitigated. An increasing number of these developments

focus on causal inference. These techniques answer the question of what would happen if we did X or Y or

Z? Predicting this is fundamental when calculating the outcomes of different potential decisions. Such

techniques may reduce the need for controlled experiments and data collection.

Global tree optimization

Decision trees are used extensively in Decision Management. Standard decision tree algorithms assess the

predictive power of different variable splits at each level of the tree, one node at a time. This can lead to

overfitting and locally optimal splits. Research by institutions such as MIT, Microsoft, IBM, and FICO is

looking into ways to create the entire decision tree at once and achieve a globally optimal result. These

will result in smaller, easier-to-use decision trees that have more predictive power and that can be easily

assessed and understood by business reviewers.

Automated business learning

Decision Management generally relies on specialists who understand how to design, develop, configure,

and manage the various business rules, analytic models, and optimization components. User demand and

new technology increasingly allow business domain experts to manage these components. Tools are

evolving that allow domain experts to define their goals and objectives and automatically consider

alternative scenarios for achieving them. These solutions seamlessly combine machine learning,

decisioning, and optimization technologies. A high degree of automation ensures key models and

components are kept updated.


Getting Started

It’s time to embark on your own journey to decision optimization. These three

steps will get you on the right path.

1. Begin with the decision in mind: Be explicit about the decisions you want to

improve or optimize. Identify, understand, and model your current approach to

making these decisions. Put in place the decision management technology you

need to manage these decisions.

2. Work with your business partners to ask “if only”: Explicitly define these

conditions: “if only we could predict this,” “if only we knew how customers

would respond,” “if only we could balance these conflicting objectives.” This

will help you understand what is preventing you from making better decisions

and will guide the data you collect and where you need to focus your energies.

3. Plan for the journey at the start: If you want to optimize decisions, make that

your objective. Find the help and support you will need for the whole journey,

not just for the first step. You need a goal for your project that is compelling

enough to get people excited.

To help you get started, the following page has a template for designing Decision

Impact Models. List your inputs, decisions, predictions, outcomes, and objectives.

Then think through the connections between these elements so you can develop a

more effective, more optimal decision strategy.

CONTACT US

Decision Management Solutions helps large organizations harness data-driven decisions by applying

decision management, business rules, and advanced analytics to solve their most pressing business

challenges.

www.decisionmanagementsolutions.com Email : [email protected]

http://www.decisionmanagementsolutions.com/

mailto:[email protected]

https://twitter.com/decisionmgt

http://www.slideshare.net/jamet123

https://www.youtube.com/user/DecisionManagement

https://www.linkedin.com/company/decision-management-solutions

Documents

The Customer Journey to Decision Optimization Customer Journ… · The Decision Improvement Lifecycle The decision improvement lifecycle is central to delivering the full value of