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Implementing
Analytics?
You Need
Decision Modeling
and Business Rules
JamesTaylor,
CEO
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AGENDA
The power of
analytics
Challenges in
analytics
Introducing
business rules
Integrating
business rules
and analytics
Decision
Management
Wrap Up
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©2009-2017 Decision Management Solutions 3
The one slide you need
Analytics have great potential to improve day to
day operations
Challenges include
Business value gap
Engaging the business and IT
Deployment
A business rules management system (BRMS) is
an ideal platform for analytics
Decision Management ties analytics and business
rules together in an effective decision model
framework
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The power of analytics
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Analytics have power
Customer
Churn
Campaign
Response
Acquisition
Rates
Online
Conversion
Fraud Risk
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And that power is operational
How do I…
prevent this customer from churning?
convert this visitor?
acquire this prospect?
make this offer compelling to this person?
identify this claim as fraudulent?
correctly estimate the risk of this loan?
It’s not just about “aha” moments
It’s about making better operational decisions
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Operational Decisions Scale For Big Impact
Strategic
Decision
Tactical
Decision
Operational
Decision
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Case: Telco
Example: Analytics enabling an individualized decision about the best
plan/upgrade offer to make to each customer
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Challenges
in Analytics
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Knowing is not enough
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Those who know first, win
Those who ACT first, win
Provided they act intelligently
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Latency in decisions costs you
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Business
event
Action
taken
Decision latency
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Operational decisions are at the center
Business
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Monitoring and compliance matter
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Analytics improves operations but…
Power of analytics is in improving operations
Specifically operational decisions
Which means :
Focus on actions not predictions
Minimize the time to impact
Engage analytics, IT, and business teams
Handle monitoring and compliance
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Case: Trade credit insurance
New
countries in
weeks not
months
Ongoing
changes in
hours, not
weeks
Immediate
changes in a
crisis
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Introducing
business rules
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What are business rules?
“… a directive intended to influence
behavior.”
“… a formal expression of knowledge or
preference, a guidance system for steering
behavior (a transaction) in a desired
direction.”
“… statements of the actions you should
take when certain business conditions are
true.”
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Business Rules are everywhere
Experienced
Personnel
RegulationsPolicy Manuals
Legacy Systems
Historical Data
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Business rules drive decisions
Decision
History
Experience
Policy
Regulations
Legacy
Applications
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public class Application {
private Customer customers[];
private Customer goldCustomers[];
...
public void checkOrder() {
for (int i = 0; i < numCustomers; i++) {
Customer aCustomer = customers[i];
if (aCustomer.checkIfGold()) {
numGoldCustomers++;
goldCustomers[numGoldCustomers] = aCustomer;
if (aCustomer.getCurrentOrder().getAmount() > 100000)
aCustomer.setSpecialDiscount (0.05);
}
}
}
Unmanageable business rules
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Decision
Service
A Business Rules Management System
After Smart (Enough) Systems, Prentice Hall June 2007. Fig 6.6
Design
Tools
Rule
Management
Applications
Rule Engine
Operational
Database
Rule
Repository
Production
Application
Validation and
Verification
Testing
Deployment
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Manageable business rules
Smart (Enough) Systems, Prentice Hall June 2007. Fig 4.3
If customer is GoldCustomer
and Home_Equity_Loan_Value is more than $100,000
then college_loan_discount = 0.5%
If member has greater than 3 prescriptions
and prescription’s renewal_date is less than 30 days in the future
then set reminder=“email”
If patient’s age is less than 18
and member’s coverage is “standard”
and member’s number_of_claims does not exceed 4
then set patient’s coverage to “standard”
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Rule Sheets
Set of rules that share common action(s)
Sequence does not matter
Typically exhaustive and exclusive
Sometimes called a decision table, rule family
Name
Condition
Attribute 1
Condition
Attribute 2
Condition
Attribute 3
Action
Attribute 4
Rule 1 > 2 < 3 =6
Rule 2 <=2 >=3 =12
Rule 3 <=2 <3 True =13
Rule 4 <=2 <3 False =14
Rule 5 >2 >=3 True =10
Rule 6 >2 >=3 False =2
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Decision Table
Set of rules that return a single result
Look up tables, comparing attribute values
Sequence does not matter
Typically exhaustive and exclusive
Attribute 2
Single Family with no
children
Family with
children
Attribute1
< 10 1 2 3
11 – 20 2 4 6
21 – 40 3 5 7
41 – 99 4 6 8
>= 100 5 6 9
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Decision Tree
Set of rules that select a goal from a set
Divides into increasingly fine grained groups
Often a powerful thinking tool
Typically exhaustive and exclusive
Existing
Customer?
Long term
customer?
Grandfather
Issue change
Price quoted
Retract
quote
Issue
warning
Y
N
Y
N
Y
N
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A list of rules representing a rule set
AdjustIncome RuleSet (newApplicant)
Name: Six months or less on the job
if newApplicant’s monthsInCurrentJob is less than or equal to 6
then decrement newApplicant’s income by 5600
Name: About 1 year
if newApplication’s monthsInCurrentJob is between 6 and 13
then increment newApplicant’s income by 1000
Name: Greater than 1 year
if newApplicant’s monthsInCurrentJob is greater than 12
then increment newApplicant’s income by 2500
Name: Computer Total Income
if newApplicant is married
and newApplicant’s spousalIncome is greater than 0
then newApplicant’s totalIncome is equal to newApplicant’s income plus newApplicant’s spousalIncome
else newApplicant’s totalIncome is equal to newApplicant’s income
Name: Minimum Income
if newApplicant’s totalIncome is less than 40000
then newApplicant is not eligible
Smart (Enough) Systems, Prentice Hall June 2007. Fig 6.3
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Why manage business rules
Improve Decision Making
• Clear policies and procedures
• Consistently applied across channels, systems
• Increased accuracy from business users participation
Business Agility
• More rapid response to business threats
• Fewer missed opportunities
• Faster time to market
Reduce Costs
• Fewer resources, less time to change decisions
• Lower fines, legal costs from bad decisions
• Reduced IT costs to implement decisions
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Cross-channel
coordination
ensures that all
relevant offers,
including loyalty
programs, are
combined at the
point of sale, with
consistent pricing
rules applied.
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Case: International retailer
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Integrating
business rules
and analytics
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Different kinds of analytics
Data Mining
Who are my
best/worst customers?
How do I turn my
data into rules for
better decisions?
Predictive Analytics
How are those
customers likely to
behave in the future?
How do they react to
the myriad ways I can
“touch” them?
Knowledge - Description Action - Prescription
Business Intelligence
How do I use data to
learn about my
customers?What has
been happening in my
business?
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Your data is a source of insight
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Insight as presentation is disconnected
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Insights as decisions drive action
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Existing
Customer?
Long term
customer?
Grandfather
Issue
change
Price quoted
Retract
quote
Issue
warning
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Low-moderate
income, young
High
Income High income,
low-moderate education
Moderate-high education
low-moderate income
High
Moderate education,
low income, middle-aged
Low education,
low income
Education
High
Descriptive analytics
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Predictive analytics
Attribute 2
Single Family with
no children
Family with
children
Attribute1
< 10 1 2 3
11 – 20 2 4 6
21 – 40 3 5 7
41 – 99 4 6 8
>= 100 5 6 9
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Scorecards are a powerful tool
Years Under Contract
1 0
2 5
More than 2 10
Number of Contract Changes
0 0
1 5
More than 1 10
Value Rating of Current Plan
Poor 0
Good 10
Excellent 20
Score
Smart (Enough) Systems, Prentice Hall June 2007. Fig 5.4
30
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Building Decision Services
Smart (Enough) Systems, Prentice Hall June 2007. Fig 5.1
Production
ProcessData
Warehouse
Operational
Data Store
Business
Rules
Business
Analytics
Decision
Service
Enterprise IT Infrastructure
Adaptive
Control
Events
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Case: Underwriting
Manual reviews Control
Business focus Risk
8 Point Reduction in
Combined Ratio
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Decision Management
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Decision Management
Ties analytics and business rules together in
an effective decision model framework
Models decision-making (DMN standard)
Integrates with execution environment
Builds on existing enterprise architecture
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Delivering Decision Management
3 stages to better operational decisions
Identify the
decisions (usually
about customers)
that are most
important to your
operational success
Design and build
independent decision
services to replace
decision points
embedded in
operational systems
Create a “closed
loop” between
operations and
analytics to
measure results and
drive improvement
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Case: State dept of taxation
Business challenges Solution Benefits
Paper tax returns
increased costs and
slowed responses
Information system
silos
Manual fraud
detection and return
review
Single central
taxpayer database
Integrated system
Sophisticated real-
time predictive
analytics
Recovered millions
of dollars from
dubious tax returns
Increased collection
of unpaid taxes
Decreased number
of questionable
returns
Increased customer
satisfaction
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Implementing Analytics Gets Results
Actions not predictions
Minimum time to impact
Engage IT, Business
Handle monitoring , compliance
Decision Management ties analytics and
business rules together in an effective
decision model framework
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Wrap Up
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©2009-2017 Decision Management Solutions 45
The one slide you need
Analytics have great potential to improve day to
day operations
Challenges include
Business value gap
Engaging the business and IT
Deployment
A business rules management system (BRMS) is
an ideal platform for analytics
Decision Management ties analytics and business
rules together in an effective decision model
framework
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Action Plan
Identify your decisions
before analytics
Adopt business rules to
implement analytics
Bring business, analytic
and IT people together
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Decision Management Solutions
Decision Management Solutions can help you
Focus on the right decisions
Implement a blueprint
Define a strategy
Learn more:
Contact Us
www.decisionmanagementsolutions.com
info@decisionmanagementsolutions.com
©2009-2017 Decision Management Solutions 47
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Thank you!
James Taylor, CEO
james@decisionmanagementsolutions.com
www.decisionmangementsolutions.com
Putting business analytics to work is top of mind for organizations like yours. Business agility and operational responsiveness are more important than ever. There is a real opportunity to use analytics – especially predictive analytics – to seek out increasingly small margins and understand your customers, products, channels, partners and more. But predictive analytics is only part of the solution – you must put these analytic insights to work making better decisions every day. Business rules offer the agile, business-centric platform you need to manage decisions and effectively deploy predictive analytics. Putting them together requires a new conceptual framework – Decision Management.
Applying analytics to acquire, retain and grow 100M customersBusiness challenge:100M customers and 3Bn calls / day200TB of customer information1.3M Retail partnersRural and urban consumers, large and small companiesSolution:Integrated data warehouse across all channels, all productsReal-time analytics for micro-segmentation, offer targetingWeb, retail, call-center and mobile channelsBenefits:Rapid growth with 2-3M new customers/monthGrowing and accelerating Revenue Market Share
Models make predictions but predictions alone will not help much – you must ACT based on those predictions.When you are thinking about smarter systems, taking action means having the system take action in a way that uses the predictions you made. You need to make a decision based on those predictions and this means combining the models with rules about how and when to act.Let’s take our retention example from earlier. Knowing that a customer is a retention risk is interesting, acting appropriately and in time to prevent them leaving is usefulGrovel index story
Remember – decisions are where the business, analytics and IT all come together
Once deployed analytics cannot be a “black box”, we must understand analytic performanceObviously you need a 'hold out sample' or business as usual random group to compare to.You need to understand what's working and what's the next challenge – which segments are being retained, for instanceYou must understand operational negation.You need to track input variables, scores, decisions or actions taken (classic example is in collections where a strategy may dictate a 'do nothing' strategy, but the collections manager overrides the decision and puts the accounts into a calling queue) and operational data that fed the decisionBoth analysts and business users must think about what they can do to improve decision making, which is the foundation of adaptive controlIn our retention example I need to have some customers I don’t attempt to retain or that I don’t spend any money retaining. I have to capture what the call center representative ACTUALLY offered and what was actually accepted (if anything), not just what SHOULD have been offered and I have to be able to show the results to my business users in terms they understand.
Actions not predictions - Business rules add actions to analytic insightTime to impact - Externalized decisions, rapid deploymentBusiness results - Decisions impact KPIs, implement strategyEngage IT, Business - Rules for the business, Decision services for ITMonitoring , compliance - Rules and explicit models expose decision making
Sometimes the ROI is discussed in terms of keeping a company in business, eliminating those company killing risks.This company offered trade credit insurance and a decision service provides trade credit calculations, combining business rules and algorithms developed by using predictive analytic techniques. Business experts interact directly with trade credit rules by using rule templates to ensure that rules match the underlying object model, without business users having to understand the object model’s technicalities.They got some classic BRE ROI:A country can be added in a few weeks rather than months, so the organization went from 2 to 16 covered countries in just 3 months.Ongoing changes can be made in hours rather than weeks.Most importantly though the system allows immediate changes to rules in a crisis, preventing the possibility of liability or other legal exposure for the organization.
So making decisions correctly will be hard unless we can pull all these rules together.Given this is how rules often look to start with, this is clearly going to be hard.But rules also change…Because your business policies doBecause your competitors doBecause the law doesBecause stock levels doBecause your services and products doBecause your customers doBecause your customers’ needs doSo we need something that will let us collect, manage and update the business rules that drive our decisions
The most basic representation is a list of rules or a rule set. These are simple atomic rules grouped into a logical set for execution and storage. Rule sets are often shared between decisions.
A big part of the benefit companies get from managing rules comes from putting the business in charge more directly. Having business users manage business rules reduces costs by eliminating a step – that of having the business tell IT what they want so that IT can do code it – and improves accuracy by eliminating the impedance of this step. It also increases business agility by making it easier for a company to respond to changes – after all the business folks notice the changes firstIn my experience, the use of business rules and a BRMS to manage high-volume, operational decisions have a proven track record in reducing application development costs and application maintenance. It takes fewer developers and less time to specify how a system or service should behave using a BRMS thanks to the increased expressive power of business rules and the improved verification and testing offered by BRMS. Maintenance of these rules is easier, often dramatically easier, than the maintenance of the equivalent code. Not only are can the business rules be changed independently and safely; business users can participate directly in the maintenance process for the first time. Domain expertise is applied more directly and less time and money are spent making changes.
Faster, easier, independent changes to decision logic Coordination of decisions across channels and products Higher employee productivity and resource utilization a leading French retailer of cosmetics, faced the challenge of multiple channels and overlapping marketing and loyalty offers. A customer might be eligible for a loyalty offer, have downloaded a web coupon and heard a “discount word” on the radio. This made it hard for retail staff to ensure the price was handled correctly at the point of sale. In addition, they needed a better way to get loyalty offers to the customer. Yves Rocher replaced their POS devices with Linux-based terminals and developed a rules-based system that allowed all the pricing rules to be defined by the marketing department and then downloaded into the terminals. All relevant offers are correctly combined at the point of sale. This system also takes the customer’s loyalty card and applies loyalty offers. Using purchases and loyalty history, it prints an incentive offer designed to bring the customer back to the store on the card itself—the cards are re-printable so the customer sees the offer that will be made when they return.
the classic sources of rules – policies, regulations, best practices, expertise. Many, most, of your rules will come from these sources. But your data is also rich source of rulesYou can analyze the data you have to find exactly which thresholds you should use in your rules – is it really customers above 21 years old or would 20 or 19 be a more meaningful cut off?You can use data mining techniques to actually find the rules – association rules such as if someone is buying this product try and sell them that product or segmentation rules – using your historical data to find out what were the most successful rules in the pastYou can use this historical data to create new insights into customers – predicting things about them that extend the data you can work with and against which you can write rules.
Descriptive analytics can be used to categorize customers into different categories – to find the relationships between customers - which can be useful in setting strategies and targeting treatment. But this analysis must be delivered not just to your analysts, also to your systems. Analysis is generally done offline, but the results can be used in automated decisions – such as offering a given product to a specific customer – often by developing rules that embody the analytics.For instance a decision tree can be created where each branch, each end node, identifies the segment for a particular member.Data mining can also create rules with less effort and with a quicker time to market in certain circumstances
Predictive analytics often rank-order individuals. For example, rank-order members by their likelihood of renewing – the higher the score, the more “completers” for every “non-completer”. The risk or opportunity is assessed in the context of a single customer or transaction and these models are not an overall pattern, even if they are predictive. Models are called by a business rules engine to “score” an individual or transaction, often in real time, though the analysis is done offline.These models are often represented by a scorecard where each characteristic of a member adds to the score and where the total score can then be returned.
All these pieces contribute to ever-more sophisticated decision services that support your business processes.Decision Services externalize and manage the decisions production processes and systems needBusiness rules allow business users to collaborate in the declarative definition of decisionsAnalytics can create better more data-driven business rulesAnd ultimately additional predictive analyticsAdaptive control allows test and learn to become part of a continuous improvement loop
Here’s another example, this time of an insurance company with about 750,000 policies that implemented a risk-based underwriting decision service for use across its systems. In the first year an eight-point reduction in combined ratio – a big deal for an insurance companyThey got this improvement from all the areas I see when clients apply decision managementThey reduced costs by eliminating many manual reviews and by putting underwriters and actuaries in charge of the rules behind the decision – they eliminated or reduced many of their IT costs.They boosted revenue, the second major area, by improving risk management (far more tiers and more fine grained decisioning) and by focusing their staff on the book of business and helping agents improve it rather than on transactional approvalsThe third area does not show up in the specifics but when I talked to them it was clearly the most powerful aspect of the whole thing. They gained true strategic control over their underwriting decisions.
Actions not predictions - Business rules add actions to analytic insightTime to impact - Externalized decisions, rapid deploymentBusiness results - Decisions impact KPIs, implement strategyEngage IT, Business - Rules for the business, Decision services for ITMonitoring , compliance - Rules and explicit models expose decision making
Little decisions add up so focus on operational or front-line decision makingThe purpose of information is to decide so put your data and analytics to workYou cannot afford to lock up your logic so externalize it as business rulesNo answer, no matter how good, is static so experiment, challenge, simulate, learnDecision Making is a process to be managed
Begin!Identify your decisionsHidden decisions, transactional decisions, customer decisionsDecisions buried in complex processesDecisions that are the difference between two processesConsiderWho takes them nowWhat drives changes in themAssess Change ReadinessConsider Organizational changeAdopt decisioning technologyAdopt business rules approach and technologyInvestigate data mining and predictive analyticsThink about adaptive control
Decision Management Solutions can help youFind the right decisions to apply business rules, analyticsImplement a decision management blueprintDefine a strategy for business rule or analytic adoptionYou are welcome to email me directly, james at decision management solutions.com or you can go to decision management solutions.com / learn more. There you’ll find links to contact me, check out the blog and find more resources for learning about Decision Management.