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Implementing analytics? You need business rules
 

Implementing analytics? You need business rules

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Delivering the promise of data mining and predictive analytics requires an operational platform that is agile, business-friendly and decision-centric - business rules.

Delivering the promise of data mining and predictive analytics requires an operational platform that is agile, business-friendly and decision-centric - business rules.

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  • 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.

Implementing analytics? You need business rules Implementing analytics? You need business rules Presentation Transcript

  • Implementing Analytics? You need Business Rules
    James Taylor,
    CEO
  • The power of analytics
    Challenges in analytics
    Introducing business rules
    Integrating business rules and analytics
    Decision Management
    Wrap Up
  • ©2009 Decision Management Solutions
    3
    The one slide you need
    Analytics have great potential, especially in improving day to day operations
    Challenges include
    taking action with analytics
    acting rapidly
    engaging the business and IT
    supporting monitoring and compliance
    Business rules and a business rules management system provide an ideal platform for analytics
    Decision Management ties analytics and business rules together in an effective framework
  • The power of analytics
    ©2009 Decision Management Solutions
    4
  • Analytics have power
    CampaignResponse
    AcquisitionRates
    Online
    Conversion
    CustomerChurn
    Fraud
    Risk
  • ©2009 Decision Management Solutions
    6
    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 about “aha” moments
    It’s about making better operational decisions
  • Multiplying the power of analytics
    Type
    Strategy
    Tactics
    Operations
    Economic impact
    Low
    High
    7
    ©2010 Decision Management Solutions
  • ©2010 Decision Management Solutions
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    Case: Telco
    Example: Analytics enabling an individualized decision about the best plan/upgrade offer to make to each customer
  • Challenges in Analytics
    ©2009 Decision Management Solutions
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  • Knowing is not enough
    ©2009 Decision Management Solutions
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    Those who know first, win
    Those who ACT first, win
    Provided they act intelligently
  • Latency in decisions costs you
    ©2010 Decision Management Solutions
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    Business event
    Decision latency
    Action taken
  • Operational decisions are at the center
  • Monitoring and compliance matter
  • 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
  • Case: Trade credit insurance
  • Introducing business rules
  • 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.”
  • Business Rules are everywhere
    Policy Manuals
    Regulations
    Legacy Systems
    Experienced Personnel
    Historical Data
  • ©2009 Decision Management Solutions
    19
    Business rules drive decisions
    Decision
    Policy
    Regulations
    History
    Experience
    Legacy Applications
  • public class Application {private Customer customers[];private Customer goldCustomers[];...public void checkOrder() { for (inti = 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
  • A Business Rules Management System
    RuleRepository
    Operational
    Database
    Testing
    Validation and Verification
    DecisionService
    Deployment
    ProductionApplication
    Rule Engine
    DesignTools
    RuleManagementApplications
    After Smart (Enough) Systems, Prentice Hall June 2007. Fig 6.6
  • Manageable business rules
    Smart (Enough) Systems, Prentice Hall June 2007. Fig 4.3
  • ©2010 Decision Management Solutions
    23
    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
  • ©2010 Decision Management Solutions
    24
    Decision Table
    Set of rules that return a single result
    Look up tables, comparing attribute values
    Sequence does not matter
    Typically exhaustive and exclusive
  • ©2010 Decision Management Solutions
    25
    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
    YN
    YN
    YN
  • ©2010 Decision Management Solutions
    26
    A list of rules representing a rule set
    Smart (Enough) Systems, Prentice Hall June 2007. Fig 6.3
  • Why manage business rules
  • Cross-channel coordination ensures that all relevant offers, including loyalty programs, are combined at the point of sale, with consistent pricing rules applied.
    ©2010 Decision Management Solutions
    28
    Case: International retailer
  • Integrating business rules and analytics
  • 30
    Different kinds of analytics
    ©2010 Decision Management Solutions
    HighIncome
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    Data Mining
    Predictive Analytics
    Business Intelligence
    Who are my best/worst customers? How do I turn my data into rules for better decisions?
    How do I use data to learn about my customers? What has been happening in my business?
    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
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    Your data is a source of insight
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    Insights must drive action
  • HighIncome
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    Descriptive analytics
  • Predictive analytics
  • Scorecards are a powerful tool
    30
    Smart (Enough) Systems, Prentice Hall June 2007. Fig 5.4
  • Building Decision Services
    OperationalData Store
    Business Analytics
    Decision Service
    DataWarehouse
    ProductionProcess
    BusinessRules
    Enterprise IT Infrastructure
    Events
    AdaptiveControl
    Smart (Enough) Systems, Prentice Hall June 2007. Fig 5.1
  • Case: Underwriting
  • Analytics improves operations but…
    Power of analytics is in improving operations
    Specifically operational decisions
    Which means:
  • Decision Management
  • Decision Management
    An approach or business discipline for automating and improving decision-making
    It improves day to day business results by
    Supporting
    Automating and
    Improving operational decisions
    It builds on existing enterprise applications to
    put data to work
    manage uncertainty
    increase transparency
    give the business control
  • ©2009 Decision Management Solutions
    41
    Delivering Decision Management
    3 stages to better operational decisions
    Create a “closed loop” between operations and analytics to measure results and drive improvement
    Design and build independent decision processes to replace decision points embedded in operational systems
    Identify the decisions (usually about customers) that are most important to your operational success
  • ©2009 Decision Management Solutions
    42
    5 core principles of decisioning
    Identify, separate and manage decisions
    Use business rules to define decisions
    Analytics to make decisions smarter
    No answer is static
    Decision-making is a process
  • 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
  • Wrap Up
  • The one slide you need
    Analytics have great potential, especially in improving day to day operations
    Challenges include
    taking action with analytics
    acting rapidly
    engaging the business and IT
    supporting monitoring and compliance
    Business rules and a business rules management system provide an ideal platform for analytics
    Decision Management ties analytics and business rules together in an effective framework
    ©2009 Decision Management Solutions
    45
  • Action Plan
    ©2009 Decision Management Solutions
    46
  • Decision Management Solutions
    Decision Management Solutions can help you
    Focus on the right decisions
    Implement a blueprint
    Define a strategy
    For assistance, to find out more or if you have questions
    decisionmanagementsolutions.com/learnmore
    ©2009 Decision Management Solutions
    47