After posting on the 10 analytic truths/myths earlier today I was reminded that I had not posted about Fern Halper’s post: Is it Possible to Make Predictive Analytics Pervasive?. I enjoyed Fern’s post and meant to blog about it but then got distracted. She makes the key points well – we increasingly do not need PhD mathematicians to build models (as Neil Raden cogently argued on his blog) as models are becoming easier to build and we do not need people to use them explicitly if they are embedded in operational processes – or in operational decisions, to use my terminology.

Alison Bolen posted a nice list of analytic truths, or perhaps myths, on the SAS blog today and asked what people thought. I was, of course, unable to resist:

1. To make analytics successful, the CEO has to have a personal interest in it. MYTH
   While it is true that the only companies I see who have made it to what Tom Davenport called “analytic competitor” are those that have CEOs who are involved with the analytics, I do not believe that CEO involvement is central to all analytics success. Line of business managers and other executives can successfully drive analytic projects, just don’t think you are going to get company-wide adoption without the CEO.
2. Analytical organizations have to be positioned in a central high-power position. MYTH
   I think that centralized analytics are a consequence of success not a pre-requisite for it. As you get some localized success you will want to bring it together to drive more success but I don’t believe a central group is needed or event desirable to start.
3. Every company in a competitive environment needs analytics to be successful. TRUTH
   As I have said before, your data is your one truly defensible competitive edge and if you are not using it (which takes analytics) then you are stupid foolish incompetent missing out.
4. Analytical expertise can be out-sourced/in-sourced/off-shored. TRUTH BUT…
   While you can and should bring in outside expertise you need to have a basic understanding of the power of analytics in house. Someone must grasp the potential for analytics and understand the business, even if they cannot develop the models.
5. Getting data and technology in place is a long and cumbersome process. TRUTH
   It also cannot be rushed and should be done incrementally with each stage developing additional capability that is put to work adding value. Don’t build all the data and technology infrastructure before you start delivering value. And start with the decision in mind – build what you need to improve a specific decision.
6. Without data and technology you cannot do analytics. TRUTH and axiomatic
7. Analytics is a thing mainly insiders and experts understand, and vice versa. TRUTH
   And this is a challenge, see #8
8. Communication of analytics is more important than analytical people think. TRUTH with bells on
   This is so true it is hard to over-emphasize. Analytics people are often terrible at this – talking about statistical measures not business measures, over-explaining the approach and under-explaining the consequences etc etc. If you can improve the skills of your analytic team in only one area, this is it.
9. Analytics only should do things which have a measurable impact. TRUTH
   And measurable in business terms, not mathematical or statistical ones. Business people don’t care about lift curves, they care about results. Remember this.
10. Analytics mainly is applicable in retail/standardized environments. MYTHish
    Any business that has large numbers of repeatable decisions – operational decisions – can and should be using analytics to improve them. This implies a repeatable environment and one with lots of participants so retail or B2C environments are more common for sure. But companies can have many thousands of partners or locations, parts and suppliers so decisions about these things can be analytically enhanced also even in B2B environments.

Great list. Thanks Alison.

Tom Davenport published a new article recently in the Harvard Business Review titled Make Better Decisions. In it he gives some examples of bad decisions and asks why this decision-making disorder?

First, because decisions have generally been viewed as the prerogative of individuals—usually senior executives. The process employed, the information used, the logic relied on, have been left up to them, in something of a black box. Information goes in, decisions come out—and who knows what happens in between?

This is, of course, a critical issue and one of the reasons I push organizations to adopt decisioning technology. The ability to log exactly how a decision was made, the steps that were taken, the analytic models considered is something that comes with the use of technology like business rules management systems. Beginning to create a history of how and why decisions were made puts you in a dramatically improved position when it comes to conducting systematic analysis. Tom’s focus in the article is on ways in which organizations can make manual decision making more explicit, but the potential for decisioning systems to play a role should not be forgotten.

Second, unlike other business processes, decision making has rarely been the focus of systematic analysis inside the firm. Very few organizations have “reengineered” their decisions. Yet there are just as many opportunities to improve decision making as to improve any other process.

Absolutely. Like Tom I believe organizations should conduct some kind of decision discovery – indeed this is the first step in my Decision Management methodology. Decision Discovery helps organizations to identify decisions and see how they impact strategy, balanced scorecards, KPIs or other operational measures. Identifying the decisions that will make the most difference and then classifying, understanding and prioritizing them puts organizations in a better position when it comes to improving manual decision making as well as adopting decisioning technology. And just like other re-engineering opportunities the power of technology to maximize the value of re-engineering is real with organizations that adopt decisioning technologies as well as a thoughtful approach to decision making seeing tremendous results.

It’s a great article and there’s lots I agree with. For instance several times in the article Tom talks about institutionalizing better decisions. One way to do this is to embed these decisions in decisioning technology so that it the right decision is available to everyone – right down to front line staff – and yet still determined by those with the relevant expertise and experience. He also talks about formalizing the consideration of decision alternatives and the use of adaptive control techniques – part of a phase I call Decision Analysis – is critical in both designing and then executing and learning from experiments. His warnings to ensure that the assumptions behind models are understood and his push for managers to have enough analytic/mathematical understanding to use such models are equally valid. Finally I really like his closing comment to the effect that if you are not measuring the impact of your decisions, of your choices, you are most unlikely to get any better at it. And you need to.

Tom is a little too negative on automated decisioning for my taste. He describes automated decisioning systems as hard to develop and implies they can be hard to change. My experience is that it is getting easier and easier to develop automated decisioning – easier than Tom thinks  – and that the use of flexible business-centric technologies like a Business Rules Management System makes it easy to change and evolve the decision criteria even when these are embedded in automated decisioning systems. I also happen to think the book Neil and I wrote, Smart (Enough) Systems, has some good stuff about decisioning in it – though I like all the books he lists too (Competing on Analytics, Blink and Super Crunchers are all ones I have reviewed).

I recommend the article both as a general one on decisioning and for those of you thinking about how to improve decision making  in your executive and management teams.

My partners over at Marketelligence have launched a new blog – marketelligent.blogspot.com/ – that should be worth following. Anunay and I are giving a webinar on getting started with your first predictive analytic model – you can register here. I also met Michael Berry, author of some fabulous books on data mining including Data Mining Techniques and his company, Data Miners, has a blog too. Enjoy.

I hosted a panel last week on predictive analytics at the Business Analytics Summit. I was joined by Richard Boire of the Boire-Filler Group, Jean-Paul Isson of Monster.com and Michael Berry of Data Miners (and author of Data Mining Techniques, one of my favorite Data mining books). I asked a series of questions and we got some great answers from the experts:

• How is your organization using predictive analytics and what has been the business value of doing so?
  • Richard’s customers use predictive analytics for acquisition models that target not only high responding prospects but also prospects that will be of high value to that organization once they become customers, retention Models that target high value, high risk customers and upsell Models that allow organizations to target customers most likely to become higher-value type customers among others.
  • Monster.com uses predictive analytics for customer intimacy, customer satisfaction, customer retention, customer up sell and wallet share growth, customer acquisition, pricing, sales coverage optimization and product development.
  • Michael focused on some more general points. He made the great point, if basic, that all predictive analytics focus on the future because that’s the only place you can have an effect and also pointed out that the business definition is critical. For instance, predicting which acquisition channel has the highest trial subscription sign up likelihood is potentially much less useful that predicting which channel is most likely to acquire customers that will keep a subscription beyond a trial period.
• What are the challenges you have faced implementing Predictive analytics in your business?
  • Michael emphasized cultural and educational challenges – that this is a new way of doing things and companies often resist things that are not “our way”. The inability to find appropriate data in the right format was another big issue.
  • Richard talked about obtaining buy-in and engagement from key stakeholders, the challenges of data and the value of having the right team to effectively implement predictive analytics. The absence of numeracy, of basic understanding of the power and limitations of the models was another big challenge.
  • Jean-Paul also emphasized data quality and availability, especially because different countries and systems define things differently. A lack for application systems integration and standardization and of effective change management across regions can also be a problem, though the recent recession has helped with the second by making people more receptive to anything that might help.
• How did you sell predictive analytics – how do you demonstrate the value of predictive analytics to the various stakeholders within your business?
  • Richard suggested conducting sensitivity and business analysis to demonstrate monetary potential of project as well as identifying stakeholders who are engaged with the data and working with them to prove your case. A project that is a quick win in terms of ROI and implementation also really helps.
  • Jean-Paul emphasized taking baby steps – starting with the basics and always have something meaningful to deliver. Showing the ROI of a model on a small group of customers (a smaller country or region for instance) also really helped.
  • Michael said to focus on showing how the model will help them do what they do and made the point that he often finds he is the first to look at the data, putting him solidly into discovery mode. Like Richard and Jean-Paul he emphasized the importance of linking everything to real monetary measures.
• With predictive analytics being such a hot topic, what do you think holds companies back from embracing and exploiting these techniques?
  • Richard felt that a lack of knowledge combined with a discomfort around mathematics and numbers was a big problem. Change management and adopting a new approach also cause problems.
  • Michael emphasized a lack of executive support and the need to get enough support to overcome organizational inertia. He also had a great example where existing measures can make adopting a model hard because the model will drive better overall results while driving a critical measure in the “wrong” direction.
  • Jean-Paul talked about the lack of understanding/knowledge of the real value of predictive analytics also. The attitude of old school management that they are already successful so why do they need to change and spend more money. Bad experience with IT solutions over the years and the communication skills of those proposing the idea sometimes don’t help either.
• What skills set are required to achieve success with predictive analytics?
  • Richard emphasized importance of learning about the business domain, both so that effective models can be developed and so that the models can be related to measures that matter to business executives. Obviously strong quantitative/mathematical background and an ability to work easily with numbers as well as good communication and interpersonal skills were also needed.
  • Jean-Paul said that a wide variety of skills are required with programmers, statisticians and data miners, business analysts, and web developers needed to deliver the solution to end users.
  • Michael pointed out that intuition and creativity – an ability to see what’s important – is necessary also.
• We wrapped up with the question what does it take to operationalize predictive analytics, to integrate predictive analytics as a regular business discipline? What are the pitfalls?
  • Richard talked about discipline, repeatability, as well as tracking and performance management. A ruthless focus on the business implementation model is also key.
  • Michael reminded us that actionability is critical – if we cannot act and act effectively on a prediction then it does us little good.
  • Jean-Paul said it takes a clear vision, human capital, collaboration, people/process/technology and a focus on the customer/user experience.

I really enjoyed the panel and I hope I have captured its essence here. If predictive analytics interests you, and it probably should, check out this white paper I wrote on Putting Predictive Analytics to Work and this webinar I recorded with Eric Seigel on Optimizing Business Decisions with Predictive Analytics.

Cross-posted to BeyeNetwork and ebizQ, both of whom were media sponsors of the event.

I am hosting a panel on Predictive Analytics at the Business Analytics Summit and I got a chance to attend a session beforehand where Dave Stodder presented on performance management and Key Performance Indicators.

Dave began by emphasizing that performance management is both a business and IT issue and that it needs to link people, process and technology. Performance management is focused on how we are doing, why are these things happening and what should we be doing. KPIs are designed to track and measure within a performance management framework. At the end of the day it comes down to Drucker’s comment that you cannot manage what you don’t measure. As a result Performance management is driven by everything from Balanced Scorecards (Kaplan and Norton), Six Sigma, TQM and more.

Balanced KPIs keep people focused on what they should be doing, what they can do with their information, as well as providing balance between conflicting goals. They should also be based on multiple measures not just financial ones. Performance Management joins Process Management and Decision Management as a “higher power”, one of the levers of improved enterprise performance. Performance management also helps bring BI from departmental usage, focused on reporting, to enterprise-driven metrics and best practices.

Three business imperatives drive demand for BI, Performance Management and KPIs:

• Establish value – find, increase and retain value
• Manage risk – identify, predict and protect
• Rebuild worker productivity – measure, manage and enhance by focusing workers on core issues

Performance Management has some overlap with operational BI. The move to an operational focus, from traditional BI to more operational BI, is often focused on improving efficiency and customer service. Getting rapid access to accurate information improves efficiency and improves customer service. This pushes more BI functionality out to front-line employees, business processes. The use of performance management and a focus on metrics can help focus this and simplify it by allowing IT to deliver simple metrics rather than complex reports.

At the same time the focus is moving to centrally managed approaches, where departmental systems were more common in the past. The move to operational BI and metric-driven performance management is driving centralization. This delivers consistency, cheaper/faster integration and makes it easier to implement data mining and predictive analytics.

Improving customer service, focusing on customer metrics, is the most important objective for 60% of folks surveyed by Ventana Research. This focus on customers means more sources (because customer data is scattered) and on more real-time data (because customers keep doing things). Interestingly customer contact centers are emblematic of the challenges with KPIs. For instance, though the agents are measured and managed very tightly, their supervisors are not. Supervisors don’t feel they can impact customer service directly so they don’t see the metrics as relevant to them. KPIs must be designed to match what the person can impact (and, I would add, give them the ability to change the systems that affect the metric when this is necessary).

Accountability is critical for KPIs. Is it reasonable to hold someone accountable to a particular metric? Are the people who come up with the metrics held accountable for their implementation? Who needs to see what – are you keeping people focused on metrics that fall into their area of responsibility?

Proliferation is a second issue. Just like reports, too many KPIs can be distracting rather than useful. Vague implementation without real accountability and control will result in metrics that are just noise.

A central focus on well defined metrics can also really help after mergers and acquisitions – it can be easier to develop integrated metrics than integrated reporting. A focus on metrics also tends to deliver a top-down view not the bottom-up view typical of reporting, and improve alignment.

Human psychology is critical in performance management. Will people focus only on achieving the goal or will they be more thoughtful about what is really needed? Will people understand why those metrics matter? Do people want their performance to be transparent and at what level?

Keep the number of KPIs reasonable, make sure people understand what is driving the KPIs and think continuous change.

Cross-posted to ebizQ and BeyeNetwork as both were media sponsors of the Business Analytics Summit

Syndicated from BeyeNetwork

I participated in BeyeNetwork’s Radio Show this morning and we had some interesting discussions around analytics and in-memory analytics. Check out the recording of our discussion on business analytics, in-memory databases and the BI maturity curve: http://www.b-eye-network.com/listen/12070

Syndicated from ebizQ

In a press release just over a month ago, Gartner said that Context-Aware Computing will provide significant competitive advantage. As the press releases says:

Gartner defines context-aware computing as the concept of leveraging information about the end user to improve the quality of the interaction. Emerging context-enriched services will use location, presence, social attributes, and other environmental information to anticipate an end user’s immediate needs, offering more-sophisticated, situation-aware and usable functions.

Why is this interesting? Well I think that context-aware computing will have to be decision-centric. Think about it – if you are not able to make decisions with this context, what good will it be? You need to have a decision in your system or process into which you can feed this context. A decision, for instance, about what coupon or offer to make that uses the consumers current location and environmental factors to target them more effectively. A retention decision that takes their social interactions into account when deciding how valuable they are as a customer (like SingTel Optus do, for instance).

Gartner go on to say:

new business opportunities will emerge, by virtue of knowing the customer more intimately; and productivity will improve as systems eliminate complexity for the user.

Knowing more about your customer won’t help if you cannot act on that knowledge – if you can’t make decisions using it – and system that don’t make decisions, don’t act cannot eliminate complexity for the user. Decisions and decision management are necessary for context-aware computing.

I gave a keynote at the Business Rules Forum today on Smarter systems for uncertain times.  I gave the presentation without slides and had planned to use my notes as a post but, as the notes ran to 5,000 words, I have decided to write a white paper based on them instead!

To keep you going until I get around to that, here are the high level points I made in the presentation:

• The business climate requires smarter systems
  • Change is constant and increasing with most executives, for instance, saying they face more competition than they did 5 years ago
  • The competitive landscape is changing with lower barriers to entry and the flattening effect of the Internet making for uncertainty and new competitors in unexpected places
  • The volume of transactions involved in running a modern business means you need systems that can deal with this climate not just an organization that can
  • More and more actions are required in real time and cannot be delayed until tomorrow, next week
  • Businesses are evolving to use complex business webs with outsourcing, partners and more replacing centrally managed corporations
• Smarter systems have four key characteristics
  • They are action-oriented
    They make decisions so they can take appropriate actions on your behalf rather than waiting
  • They are flexible
    In business terms, not IT terms, because these decisions are managed using business rules that bring business people in and put them in control
  • They are forward looking
    Because they embed predictive analytics that turn uncertainty about the future into usable probabilities
  • They learn
    Because they support adaptive control/champion-challenger testing and because they use analytic models that learn and adapt
• Getting there requires Decision Management
  • A management discipline focused on decisions not a technology stack
  • First step is to discover the decisions that matter to your business, understand them and separate them from the processes and systems that hide them
  • Second step is to build decision services or decision agents, components that can make decisions for other components
  • Third step is to implement decision analysis so you can monitor, improve and learn about decision making

You can also check out these posts from Sandy Kemsley and Eric Charpentier who were in the audience:

• Smarter Systems for Uncertain Times #brf
• #BRF Keynote: Smarter Systems for Uncertain Times

The second full day of the Business Rules Forum/Enterprise Decision Management Summit is over and once again I have been taking notes rather than blogging live. Once again there were some great sessions – today I heard Steve Hendrick of IDC, Sandeep Gupta of Equifax, Chaitan Sharma of DAASL, Zach Springborn of OneData and Mo Masud of Deloitte. I have to confess to skipping Eric Siegel’s session as I have heard it recently (check out 5 ways to reduce cost with predictive analytics or watch the webinar Eric and I did on Optimizing business decisions with predictive analytics).

Keynote: BRMS at a Cross Roads: The Next Five Years

Steve Hendrick thinks that the business rules market is at a crossroad thanks to increased visibility by virtue of the big players joining the market, the growing acceptance of decision services as an implementation approach, the availability of open source alternatives and more. Nevertheless the market is currently small – just $285M for the BRMS market itself out of a total software market of more than $280B. The market is continuing to grow but from a fairly small base. Steve sees no reason for this growth to stop and identified market momentum, growing awareness and companies’ desire to improve Governance Risk and Compliance or GRC as reasons for this growth to continue.

Steve, like me, sees the future not in terms of BRMS products as a standalone market, however. Instead he is talking about a Decision Management Platform and his description of it would resonate with any regular reader of this blog – business rules, predictive analytics and event processing. BRMS is good for categorical or data-driven decisioning while analytics can improve rule relevance and quality, make the data more readily consumable and refine the outcomes of rules-based decisioning to find optimal solutions to decisioning problems. The combination is thus particularly powerful.

Steve adds event processing and the handling of state because he believes that an always-on decisioning platform that listens for events, analyzes the information available when the event happens and then decides what actions/processes are appropriate is the best way to handle decisioning in an increasingly real-time world. He sees a decision management platform as enabling a move away from process-centric to information-centric decision making.

Steve advised vendors to focus on analytics as a complement to business rules, to think about how to migrate to the decision management platform he outlined and to track developments in CEP, BRMS, Analytics and event-driven architecture more generally. For customers he again advised a focus on analytics, especially segmentation (like me he thinks this is the best way for companies using rules to get started with analytics), and urged them to think about real-time decisioning going forward.

Maturing with Business Rules and Business Intelligence – The Combined Power

Sandeep gave an interesting presentation on the use of rules with business intelligence. He was talking not about using BI in the context of a transactional decision, the major use of business rules, but the possibility of using business rules as part of the strategic or management decisioning that relies on BI. Equifax is a user of business rules in a number of its solutions, but these use rules to handle decisions at a micro level – is this transaction fraudulent, is this customer a good risk etc. The work he was discussing in this presentation was attempting to bring the power of rules to increase transparency and business control as well as the action-orientation, to a BI environment.

Clearly the two environments have a significantly different focus – transactions on one hand, summaries and analysis on the other. Yet both are built on a business object model and in both cases there is a move to empower business people to write their own rules or build their own dashboards. If the business object model could be fused between the two environments, perhaps the rules approach could be used to make the dashboards and reports more actionable. This would require that the rules could have conditions that took advantage of the statistical analysis and summary capabilities of the BI environment as trying to handle this kind of data crunching in a rules engine would perform poorly.

Equifax has been conducting some research work to allow a user to specify the query, the WHERE clause if you like, in the condition of a rule using the business-friendly and English-like syntax of their rule engine (they use IBM ILOG). The rule can then be executed in the rule engine and can reach into the BI environment to get the data analysis it needs. Actions are taken using the rules engine’s usual ability to execute the THEN side of a rule. While this research is interesting, there are challenges with the impedance mismatch of the two environments as well as the difficulty of turning intuition (based on this graph I think I should do this) into an explicit rule.

An interesting topic – almost the reverse of the usual rules+analytics story in that it is bring rules to an analytic summary rather than analytics to a rules-based transactional decision.

Decision Automation – Implementation Challenges and Productivity Improvements

Chaitan presented on his experience working with ICICI Lombard, India’s largest general insurer, on a legacy modernization effort using FICO Blaze Advisor. The driver to modernize these applications was a change from fixed to variable pricing in the insurance market along with an influx of new customers about which very little is known – the information business is less developed in India so many of these customers must be taken on with an expectation that a different approach will be required once their behavior becomes known. It was a classic legacy modernization challenge in that SMEs were in short supply, the code was not documented and the system was in use while it was being reengineered.

To manage the process, DAASL focused on a couple of things. They classified business logic so they could prioritize the use of business rules based on the likely degree of change and the degree of reuse. They built a pilot by observing the current system so they could show business users how it would look. They did code walkthroughs to draw out rules and then matched these up with the output of business user interviews to see where the inconsistencies were. And they delayed integration/governance functionality until the business had experience with several decisions and with several iterations so that the requirements for these elements would be grounded in actual experience with the BRMS. All good advice for anyone approaching rules for the first time.

Besides the general improvement in agility and accuracy from using business rules, the system also gave the business the power to generate explanations and reason codes for the decisions as they were made and to log exactly how each decision was made. These were important as it enabled some real analysis on the part of the business into what works and what does not.

One final piece of advice from the team was to keep on top of the technology/platform deployment issues. Incompatible product versions, sudden changes in the platform and other technology-centric problems could have derailed the project even though the rules piece was going well. Keeping the project on track meant dealing with these issues and not allowing them to derail the main effort to extract, document and implement the business rules.

Decisioning: Managing Counterparty Risk for Financial Markets

Zach gave an interesting presentation on the use of business rules and extract-transform-load tools in delivering better counterparty risk management. OneData helps companies manage risk by providing information to them and, in this case, the focus was on counterparties. In particular the challenge of managing the final counterparty – who owns the company that owns the company that owns the company that you are considering taking a position in? And what, therefore, is your exposure to a risk that the parent company gets into trouble. He gave a great illustration of how complex companies like ABN AMRO or AIG get with a wonderful snowflake diagram with ABN AMRO at the center and all its layers of subsidiaries fanning out.

Anyway, the project combined TALEND (open source ETL tool) with IDIOM’s Decision Manager to bring multiple data sources together and largely automate the process of matching securities that were being bought and sold with the ultimate counterparty. About 90% of the securities could be matched using the decisioning engine and the remainder had between 2 and 15 options that had to be manually considered. This is complicated by the fact that different customers might have different rules for who counts as a counterparty – some consider 25% ownership enough, for instance, while others consider 51%.

The end result was a counterparty hierarchy matched to the individual securities that was fed into the Algorithmics risk management platform so that companies could consider the risk of their portfolio in terms of the ultimate counterparties involved in their positions. Traders and desk managers could see what their total exposure was to a particular parent company or what the impact of a particular trade would be on their risk exposure.

A somewhat unusual use of rules but a fun example of how two technologies, ETL and business rules, can be used together to deliver real business value.

Integrating Predictive Analytics and Business Rules Management to Enhance Insurance Marketing Strategies

Last session for me today was Mo Masud talking about the role of predictive analytics and business rules in commercial insurance. Deloitte has been doing a lot of work in this area, focusing on expanding the use of predictive analytics beyond its usual home in underwriting and pricing to marketing, agent selection, customer service and claims. This is important as the insurance industry has been losing money, as an industry, for a most of the last 10 years. Combined ratios over 100 (meaning they spend more on claims and admin than they take in in premiums), poor investment returns and some bad years for catastrophes have all contributed. But the net effect is that insurance companies must get better at their core business so they can drive down their combined ratio – and rules/analytics/decisioning has been shown to do just that.

The use of rules and analytics to automate decisions and so drive out subjectivity, identify profitable opportunities and improve efficiency is critical. In particular, an enterprise view of the use of these approaches so they are not applied only in underwriting, say, but broadly and systematically works wonders for the bottom line.

Deloitte has found that companies need to focus on data and data sources because these drive the kinds of models they can develop and on business rules to make these models actionable. Mo gave a great example of using a predictive analytic model to rank-order those applying to be agents in terms of how likely they were to become profitable for the carrier. This model was combined with rules to allow many applicants to be accepted or rejected automatically while those in the middle were referred to marketing managers for further analysis. Rules considered things like geography and coverage as well as the likely profitability of the agent. Using this approach, and focusing on signing up the agents in the top 7 deciles of the model, one carrier shaved 2 points off its loss ratio and saved 10-15% of its marketing/administration fees for agency management.

A good illustration of the power of analytics and rules in areas of the business not traditionally associated with them.

Here are other posts on today’s sessions:

• Sandy Kemsley
  • Business Rules Management: The Misunderstood Partner to Process #brf
  • BRMS at a Crossroads #brf
  • Business Rules Governance and Management #brf
  • Comprehensive Decision Management: Leveraging Business Process, Case Management and CEP #brf
  • Business Rules and Business Events: Where CEP Helps Decisions #brf
  • Rapid Change: The New Decision Dilemma
• Jim Sinur
  • Business Rules Forum 2009
• Eric Charpentier
  • #BRF Difference between CEP and Business Rules
  • #BRF Keynote BRMS at Cross Roads
  • #BRF Introducing a rules methodology part 2 Presentation
  • #BRF First hundred days of a BPM effort
  • #BRF Practioners Panel
  • #BRF Business Rules and Business Events