Domainwise Analytics Talk : Insurance Sector

Business Challenges:

·         Increase sales revenue and profitability, while reducing operational costs

·         Improve loss ratios and achieve a higher return on equity

·         Market to customers across channels despite rising campaign costs and dismal response rates

·         Convert pool of data from functional, geographic , channel and other sources across the organization into valuable  intelligence for timely, effective business decisions

Business Intelligence Agenda:

•          Align resource more efficiently

•          Improve response to marketing campaigns

•          Increased Customer Loyalty and Retention

•          Maximize customer  Profitability

•          Know current and prospective policyholders well enough to predict their profitability and risk

•          Realize a rapid ROI

Analytics Techniques:

Agency management

•          Agents, predominant sales channel, it is important to understand the essential distribution dynamics

•          Analyze the cost of the channel and sub-channel

•          Understand overall channel and sub-channel profitability

•          Know which agents sell which insurance products to which customers

•          Asses sales activity, reach, quality of sales/service and channel effectiveness from multiple dimensions

•          Monitor channel performance continuously and intervene as needed with marketing support, training and controls

Risk Based Pricing

Competition and product commoditization have insurers struggling to grow profitability. Risk Based Pricing techniques are useful to analyze the loss experience, and to identify loss behavior influences. It also makes more granular rate structures to grow profitable classes, while charging adequately for other classes

Claims reporting

Claims Reporting is useful to monitor the performance of the P&C claims settlement process.

•          Workload distribution reports across the claims handling organization

•          Claim spend analyses for understanding true cost of settling claims

•          The ability to monitor loss development by product, exposure type, service provider and other dimension to improve reserving accuracy

Customer segmentation

Classifying customers according to their likely behavior and profit potential is crucial for better customer communications and to determine optimum segmentations across customer base and product lines. It also helps in creating more accurate product offers in addition to conveying more effective product communications.

Customer retention

To identify the customers with the highest propensity of lapse and better understanding of their needs is very important. It helps to target high lapse risk customers to improve retention. After all getting new customer is not an easy thing to do, and any retention of customer is as good as getting a new one.

Cross-sell and Up-sell

Cross-sell or up-sell can be done using a score for customer’s cross-sell and up-sell potential. One way to do could be:

•          Identify which customers are most likely to buy

detractors

•          Determine which products they are most likely to buy next

•          Target the right customers at the right time through the right channel

•          Achieve higher conversion rates for cross-selling/up-selling campaigns

Customer lifetime value

CLV can be built by calculating the present value of customers by considering the current holding and then forecasting their future value, by considering cross-sell, loss and lapse probabilities over defined future period.

The business implementation could be to concentrate marketing spend on customers expected to bring future profitability and prioritize servicing levels accordingly.

Campaign management

Effective Campaign Results can be achieved efficiently by using quantitative, customer centric measurements, and strategic segmentation and behavior predictions. One way to do could be to optimize campaigns and channels by automatically tracking each campaign element and implement complex customer interaction strategies efficiently; it could be multichannel, multistage and event triggered campaigns too. This helps maximize customer intelligence to get the most return from your campaigns

Customer Centricity : Future of Marketing Approach

Customer Focused Approach and Customer Centricity seems very similar from outside but they are very distant in principle. Customer focused approach stresses on knowing the profitable and risky customer segments. Then the steps are taken to market accordingly. Generally the importance is given here to the product, price, place (distribution channel) and promotion that appeals to customer. This four Ps approach of Marketing has been very successful when implemented by few of the companies. But this approach has some very serious drawback. This is covered by the centric approach.

The customer centricity is directed towards knowing customers better. Knowing their general behavior and their business value in terms of past purchasing behavior and future potential. It takes the marketeers to make them see from the customers eye. The 4Ps are thus converted to 4Cs here. The product is replaced by Customer wants and needs, upfront price is replaced by Cost to customer, place is replaced by Convenience of purchase and promotion by Communication between the company and the customers. This clearly has certain edge over the customer focused strategy.

The former approach is understanding a demand and pushing the product. The later concept is understanding the demand, knowing the reason behind the immediate demand and predicting chances of the demand movement. Thus shaping the product accordingly. There are some chances of influencing demand shift according to the company's product profile by effective promotion and advertisement.

I would like to mention Human Values here. Evaluate the two statements and try to find which is spiritual and which one is material thought.

1. Everybody exists, therefore I exist.
2. I exists, therefore everybody exists.

First one seems spiritual at the first glance. But in actual, second one is the spiritual thought. Consider Gautam Buddha. We all must accept he is spiritual. But he left his mother in poverty and everyone he knew to enrich himself with the truth. Once he got enriched, he came back and enlighten the world with his knowledge.

The principle of Customer Centricity roots from the Human Values. It is a value based approach. It is knowing the present and future market, and preparing company's product portfolio according to it. This is enriching self. The enriched self (here company), thus emit and brightens the world. While customer focused organization tries to focus more on customers current needs and thus play it right, and weakens self. Thus fail in the long run.

While now all of you must have been convinced about the customer centricity being superior to any earlier marketing approaches. Now I will discuss something about this approach and its implementation.

Steps to Implement

1. Define Market
2. Quantify Market needs
3. Segment the market appropriately
4. Profile the segments to make understandable (by business managers and others, non-technical)
5. Determine value proposition of these segments
6. Predict the possible changes in future
7. Communicate the value proposition to all concerned
8. Deliver the value proposition
9. Monitor the value being actually delivered
10. Evolve with time.

The heart of the customer centricity is customer segmentation and profiling, and evolution with time is the key to keep the strategy functional at any specified time.

Economic Crisis to Analytics

Economic Crisis has hit the World. Now it is not only the US Crisis but a Global One and like never before all the World Governments are doing their best to minimize its effect. Financial Institutions are worst hit followed by Real Estate. This has drilled down to other industries, and now from Telecom to Retail every sector is affected. What has been the effect on Analytics Industry?

Analytics as an industry seems to have got boost. I see a lot of Job Vacancies for Marketing Analytics and Risk Management everyday in all medias, be it in Web (Naukri.com, LinkedIn etc.) or Newspaper (I read Times of India and Economic Times). Even more surprising is to see many Analytics and Business Intelligence start-up companies coming up.
I am not sure if this is the trend everywhere or just in India. But it is really surprising (yet true) that Analytics Jobs are everywhere wherever you see.

Does this mean when Economics fall, people realize the importance of Analytics?? I have no answer but I wont be surprised if someone proves it.

Long back I had read a comparison of importance of Three Major Professionals -- Engineers, Doctors and MBAs with Economists and Sociologists. Let me share with you guys here.

When a country is poor and is having lots of social and economic issues, the people who earn the highest are the Doctors (e.g. most Africa, Asian LDCs) followed by Engineers and MBAs. In these Economies, Economists and Sociologists hardly have any role to play.

When country starts growing the importance of Engineers rise, and their salaries too. They overcome the Doctors and at later stage MBAs overcome all (e.g. in India, China etc). Till this stage Economists and Sociologists role is very limited. (I hardly see any economist making a news in India!! Surprised? Well, don't be. Its the reality)

Now comes the developed World. People hardly want to study Technical subjects (got to know from my friends; but not first hand knowledge). And most Technicians are from Underdeveloped and Developing Nations who serve these economies. This is so normal as Doctors and Engineers have very limited role to play here. Infrastructures are there, and system is there. Here managers rule, and Economists and Socialists rule more. Because this is the time when the society needs values with value creaters, and thoughts with philosophers.

When I remember all these stuffs and I see the increased activity of Analytics Industry in the Crisis Time, I feel its the Analysts Time. The time has come, and we have got this rare opportunity and we should capitalize on it.
By the time Crisis ends, Analytics should establish itself as an Important Business Function on the line of Marketing, IT, and Operations. This would mean every big organization having a CAO (Chief Analytics Officer) along the lines of COO and CMO.

Is this possible? I feel, yes. With the Software Biggies (Oracle, Microsoft, IBM) bringing BI Solutions and many specialized BI and Analytics companies bringing great EDM products this is coming to becoming a reality. Not to forget is the contribution of smaller Analytics Consulting Companies that actually uses (and helps clients to use) these tools to bring efficiency to Business.

What is your take on it? Are you with me? Or against me? Please share your feedback and comments here.

Global Analytics Preparing to Take on FICO

Fair Isaac ec-CEO Larry Rosenberger is now in the board of Directors for Global Analytics, a San Diego based Analytics firm with major operations center in Chennai, India. This is however not a new news, it has already been about 6 months old.

Interesting fact is that Larry is not the first person to join Global in near past. First Fair Isaac senior fellow Ted Crooks joined Global Analytics as VP for Business Development, who was followed by Chris and Steve. All these together is giving a feeling that Krishna, CEO Global Analytics, is preparing himself with a major battle with FICO. After all he is one of the person responsible for designing and building one of FICO's most successful product, FALCON.

Krishna started Global Analytics with his Falcon Team mate, Steve Biofore and few other folks. He started the company in 2003 and have been quite successful since then. While I joined Global Analytics in 2005, I was the 33rd employee of the company. Now the company has grown to above 120, and most of these people are involved in high end analytics. This makes GA one of the leading player in Analytics.

Global Analytics is heard to be expanding to UK, and have built a Fraud product for Banks. These developments have taken place after its tremendous success in serving Decision Management Services for the Sub-Prime market in US. fair
The two development happened last year i.e. the same year when Ted and Larry were brought in. And it is heard that Krishna and his team is working really hard to take the company to the next level. So, this year and next should be interesting to see on how Global Analytics moves ahead. If it gets the right strike, it would be no wonder to see GA taking FICO head-on in the Decision Management and Analytics Space.

Multiple Levels of Reports

There is a interesting post written by Romakanta in his blog, Datalligence. He has listed and described eight levels of reports that is prepared by BI Folks.
Here is Romakanta's list.
1. STANDARD REPORTS
2. AD HOC REPORTS
3. QUERY DRILLDOWN (OR OLAP)
4. ALERTS
5. STATISTICAL ANALYSIS
6. FORECASTING
7. PREDICTIVE MODELING
8. OPTIMIZATION

The area where I disagree with him is on calling everything reports. I would rather divide these things into various steps of Decision Management. First three comes under reporting, and rest come under Analysis.

Mostly the standard reports and ad-hoc reports are called reports, and the OLAP is more to dashboard than report. It gives a flexibility to play with the data and do some analysis.

Also, I can hardly make out any difference between forecasting and prediction. Prediction Modeling is a technique and forecasting is a business solution. Forecasting can be done in various ways like Predictive Modeling, Time Series (curve fit or waterfall), Multiple Modeling Approach, Business Rules Approach etc.

Optimization is again business solution to a business problem of low efficiency. It can be achieved by various methods including segmentation, Optimization Algorithms (Operations Research Methods like Transportation Problem, Simplex Method etc) etc.

Information 2.0 and OpenI

I have been hearing so much about Web 2.0, Business Intelligence 2.0 etc. But very little has been heard about Information. I have been hearing problems from Business people saying they never get the information or reports they want, and the BI Team and the Analysts feel otherwise. They feel business people don't understand what to expect and what NOT. The issue here is about not understanding each other and the missing link between the two sides.

BI 2.0 has brought huge advantage on it own, but it has forced companies to invest heavily in IT Systems and Software too. This couples with the Salary expenses. But still the systems are not solving the business problems.

These days people have power to send huge information to the mass (by Youtube, blogs, Knol etc), and most people are taking the advantage of it. While few people use it effectively, most are not. People are confused to choose the right medium, and the right information to pass.

The problem in all such is common. It is the non-evolution of Information 2.0. A framework needs to be worked out now for standardization of the Information so that everyone knows what Information they can expect. If not the frame-work, people should start thinking about using BI and Web (or other channels) to directly attack the business issues.

OpenI has made an attempt to rightly address this issue. It is not the platform to build report like JasperSoft or Pentahoo, but it is the solution. OpenI directly delivers the required reports to the end user.
OpenI is Open Source Project and is cheap to implement and use. It has coupled the low cost with good sales and after-sales service. Believe me Sandeep Giri, Lead of OpenI, is a fantastic guy with open mind. Just reach him if you want the service.

--
Good follow up by Deep Sherchan.

5 Steps to Build a Predictive Model

I keep on getting inquiries about the right way of building predictive models. I am trying to answer those through this post. But before that let me make one thing clear, there is no definite rules to build a Model. Nor there is any good or better methods. It solely depends on the analyst or modeler.
Here is my way. It is just one among the many ways.

1. Modeling Methodology and Entity
As data comes, first thing an analyst need to do is to study the data. Then understand the business problem for which the model is going to be used. These two things lead to the correct definition of Modeling Methodology and Entity.
Modeling Methodology could be choosing one of among the Linear Regression, Logistic Regression and Poisson Regression. And Entity is to define at what level the Model is going to be built.

For banking while accessing risk, typical model build is a risk model. This model is built with Logistic Regression with Good/Bad Dependent Variable. The entity is usually customer. But if you want to see Risk Involved at each subsequent loan that a person has (and believe more the number of loans to a person risk changes), you could go for a Loan Level.

2. Time-line
Not all data received is usable. The timeline needs to be properly finalized by good understanding of data and the business.
Usually for Credit Cards portfolio of banks, the data taken is for 42 months. It includes 24 months of observation period and 18 months of performance period i.e. all cards taken in first 24 months is taken and their subsequent behavior in next 18 months is analyzed in sliding window basis.

This is quite simple. But things are not simple always. If you need to build a Risk Model for US Sub-Prime Market now, this rule might not hold. The business scenario in the last 12 months have changed a lot and in future it is hard to get similar environment. What to do here?

Might be taking a smaller window helps! Or might have to be conservative in building but solve these issues at the implementation time.

3. DV (Dependent Variable or Outcome Variable) Definition
DV definition has its own challenges. Mostly while deciding on methodology and entity, and on timeline there is a definite thought on DV. But there are areas where we need to go much deeper while defining DV.

For credit card, its the risk in 18 months but how do we define risk. It would be two cheque bounces or missed payments; it could be one; It could be crossing the credit limit and not paying thereafter etc. Each of these have business logic behind them.

For building Response Model for a Email Campaign, DV is response but how do you define response? Is it opening email? or replying email? or taking some action based on the email? It could be any.

4. Sampling
This is slightly different from the other steps. I would prefer to use all data that is available and not sample down or up in most scenarios. But there are certain business issues, or data issues or technical BI tools issues which force to do some sampling.

A case of oversampling could be some cases where the data points are too less. And among them too most of the records are good (could be bad too). Say for e.g. we have 1000 records and 990 are goods. The only way to move forward is to increase bads by taking them multiple times.

Other case could be a place where there are 5 million records. Is it necessary to take whole sample?
I would say yes. But there are certain issues of taking 5 million records. I doubt any tool can handle this huge data apart from SAS. And SAS too will take long time to do any simple process and a iterative process like Modeling will just kill any-body's patience.
Solution: Sample down. Either taken random X% or do stratified sampling by taking Y% of Goods and Y% of bads.

5. IDVs (Independent Variables)
IDVs generation is again a art in itself. It is always good to take as much data as you can get, and create as much derived variables as you can from them. This will open up the window for more advanced statistical tests and helps in capturing performance from all angles.

Some of the data sources for IDVs for Financial Clients would be: internal operations data (like sales, call center usage etc.) , demographic data, personal information of customers, economic data, seasonal factors, etc.

The ways of creating derived variables could be:
a. Log of original variable [log(no. of bank card trade)]
b. Taking exponential with e [e(power No. of Bad Loans)]
c. Difference between two similar variables [Loans in last 5 years - Loans in last 1 year]
d. Interaction Variables using CART
e. Inverse Relations [1/No. of Loans]
f. Series Functions e.g. for No. of Loans 1/(Loans in one yr) + 1/(Loans in 2 yrs)^2 + .........
g. Binned Variables using Single Factor Analysis [Age (<18, 18-30,30-65,65+)]

More readings:
1. Modeling on Censored Data
2. Building Predictive Model
3. Time Series Data

I end my note here. It would be interesting to see how you see my note. There might be places where you agree and other areas where you disagree, would like to know them. Please share your comments and feedback through comment or mail.

Analytics Grows at the Cost of?

Analytics Industry is growing very fast across the World. It could be because of various reasons, one of them could be poor economic scenario. When Business is in problem, everyone turns to find the reason behind it while in the other case people chase growth. Its simple.

There could be other reason too. Analytics and Reporting Companies are being acquired by Software Biggies and they are integrating the Analytics and Reporting Module into their ERP packages.

The third could be the fall of Market Research and Management Consulting! Well, I don't say this but Google Trend says it. Hard to believe but Graph shows it!
My Take:
Management Consulting and Market Research both have started using Analytics, and Analytics is growing as the heart of the Strategy Consulting and Decision Management Space.

Metamorphosis of Decision Management Process

There are numerous players in the market providing consulting services that mainly focus on solving company’s problems and help the senior executives to take optimal decisions. These consulting services within the Decision Management space fall in one of three categories:

1. Analytics Consulting
2. Market Research Consulting
3. Management Consulting

There was a time when Consulting mean Management Consulting. Consultants were heavily paid individual from MC companies like Mckinsey and BCG. Companies and big Business groups often called them and asked to their suggestions.

But Consulting was not that alone. Fair Isaac has been providing Analytics based Consulting services for the last 50 years and they have helped companies grow in their own way. Analytics Consultant helped business managers to understand the existing operations, the pros and cons of the decisions made etc.

The third too existed, Market Research Consulting. This is somewhere between the first two. This is about knowing Market and competitors, and planning ahead to catch the opportunity that Market throws. TNS and AC Nielson are the major players in this area.

All three have existed and done well. They did not compete but worked together to help companies grow. But they never talk with each other. This has created a huge problem for the long term prospects of the company.

Every company waits for the issue to come and then call for the decision management service. There is however no sustainable solution. And each time one issue gets over, another problem is awaited.

If a sustainable solution is to be found, it needs to be started right from knowing the existing business scenario with good MIS Reporting System. Even though most of the companies have good reporting system, it lacks efficiency. It is better to automate such services so that little time and manpower resources are wasted.

Next comes knowing the operations in a better way and identifying the major decision points. This follows replicating the profitable decisions and eliminating the unprofitable ones.

Once this phase is overcome, the stage is ready to understand the market and the competitors to come up with highly optimized decisions.

At the end, when everything is streamlined and understood. The forward looking thought needs to be formed with all the necessary information ready. This is the stage for calling the Management Consulting services.

Until this process is not implemented by the companies today. Every year they will face a new problem in their business and will have to call Management Consultants to find the escape route. And the Management Consultants go away with a huge fee showing companies a quick solution, which in itself is based on lack of actual information of the existing business.

Analytics for Payday Lenders

Loan Amount Sensitivity Analysis and Interest Rate Sensitivity Analysis

(If there is availability of relevant data)

a. See the effect of one factor at a time to each relevance factors (removing effect of other factors).

b. Form a strategy by combining all factors

c. Aim will be to increase the portfolio amount and thus increase revenue, keeping acquisition cost and risk low.

Bureau Data Use Optimization

In the current scenario very high acquisition cost is expected as they inquire many sources.

Business Rules

• Based on segmentation
• Based on Business Intuition

Predictive Models

FPD Model, Revenue Model, Conversion Model, Attrition Model, and Risk Model (like by behavior up to cycle 6)

a. Leads approved but not converted will affect the performance as it is resource intensive (like have to call to verify, do verid checks etc).

b. Leads which generate income less than Acquisition plus Operations cost are actually the loss making leads (hence can be called Attritors).

c. Good Profit comes from people

i. who actually go bad in the first pay but pay later with the late fee

ii. who renews cycle over cycle and stays long

d. Risk based pricing to decrease withdrawals

i. Lower interest rate for reactivations.

ii. Higher loan amount and low income for reactivations.

iii. Low interest rate for high score candidates (who score high in all scores – Revenue, FPD and 6 Cycle Risk).

Multiple Objective Decision System

a. Strategy Table and Joint Odds Approach

i. This technique is used if the number of dimensions is less. Up to four relevant factors (like Generic Score, Revenue Score, FPD Score, and Conversion Score) can be combined using this approach.

ii. Tackling more than four factors becomes extremely difficult.

b. Joint Score Approach

i. All Risk Related Scores are combined to form a single score (Joint Score).

ii. This approach can handle any number of scores.

iii. All decisions are based on this one score only

iv. There are ways to give more weight to one Relevance Factor over other (like giving more weight to Revenue than FPD).

Other General Analysis

a. Fraud Check (few examples below)

i. People who write name in improper capitalization are more likely to be fraud.

ii. When Home Phone and Work Phone is the same.

b. Miscellaneous Analysis (examples below)

i. Behavior in Military Population

ii. Absolute Income Behavior

iii. Loan to Income Ratio Sensitivity Analysis

iv. Lead Aggregator Performance Analysis

v. Lead Position Performance Analysis

News & Views ... Marketelligent Q2 2008 Newsletter

…….from the CEO’s desk
After getting an encouraging feedback on our first edition we are happy to bring out the second edition of our news letter – News and Views. This quarter the world has been gripped with the ever worsening US (and UK?) housing crisis, rising crude oil prices and global inflationary pressures.

Whether the oil prices are speculative - only time will tell, but inflation is likely to stay high for some time forcing companies to adopt ruthless cost cutting measures to stay competitive. However in this environment there are a breed of managers thriving through smart decisioning by relying more on ‘Measurability’ and ‘Efficiency’.

Talking about ‘Smart Decisions’, in this issue we have an article by our partner James Taylor. He focuses on how analytics empowers business managers with the knowledge and capability to make smarter customer decisions. In an era of massive data warehouses and information overload; extracting meaningful insights, understanding Customers better and taking proactive actions (before your competition does) is key.

We would also like to introduce Sweta, who is one of the early associates of Marketelligent. As you will see, she has accomplished a lot in a relatively short professional span.
As always we are on a constant improvement process and would like your feedback on this issue.

Roy K Cherian

Meet some of our associates..... Sweta Sharma

Sweta Sharma is involved in Analytics Consulting and Business Development for Marketelligent. She has rich experience in Pharmaceuticals Analytics, Risk Analytics and Retail Analytics, including Segmentation, Forecasting, and Predictive Modeling.

She specializes in Time to Event Analysis, Multivariate rank Analysis, Multiple Comparison tests using ANOVA, ANCOVA models, Predictive Modeling, Customer Segmentation & Financial Forecasting.
Her achievements include building default model for US sub prime lending client and strategising retail analytics for one of the leading beverage manufacturer in India. She is also a recipient of Bronze award for Excellence for her work in one of the Phase Four Trials in GlaxoSmithKline.

Prior to joining Marketelligent, she was with GlaxoSmithKline Pharmaceutical Ltd. She holds a Masters in Statistics from Bangalore University.

Her interests range from hiking to reading philosophy and fiction, traveling & cooking. She occasionally paints and is a Gold Medallist in Tae-kwan-do.

Smarter Customer Decisions, by James Taylor
James is the Founding Principal of Smart Enough Systems. He is also an authority on Decision Management Systems, a prominent blogger (Smart Enough Systems), and is a co-author of the book “Smart (Enough) Systems”. Previously, James was VP of Product Marketing
at Fair Isaac.

When you interact with your customers, the most likely scenario is that the point of contact is one of three types:
• A customer service representative or other junior member of staff (driver, store clerk etc).
• An automated system (IVR, ATM, website)
• Someone who works for a third party (store-in-store clerk, outsourced CSR)

If you want to deliver a good customer experience, you need to ensure that all these different touch points are making good decisions about how to treat your customers. In reality they are probably not doing so. Most of the decisions are being made by people who don’t know this specific customer or how to treat them, who have little or no real support and who are expected to remember a large manual of policies and procedures. The decision-making in the systems involved is either non-existent or was delegated to programmers rather than driven by customer-facing business people. When the customers interact with systems directly, they probably use a system that lacks any real sense of personalization – it is the same for everyone.

The solution to these and other problems is to adopt a new approach to building information systems - to adopt a new approach to automating decisions using information technology.
This approach is known as Enterprise Decision Management.
Enterprise Decision Management (EDM), or Business Decision Management as it is sometimes known, is an approach for automating and improving high-volume operational decisions.
Focusing on operational decisions, it develops decision services using business rules to automate those decisions, adds analytic insight to these services using predictive analytics and
allows for the ongoing improvement of decision-making through adaptive control and optimization:
• A decision service is a service in an Service Oriented Architecture (SOA) that answers a business question. Decision Services allow critical business decisions to be externalized from
your applications, managed once and shared between channels, processes and systems.
• Business rules, especially when managed using a Business Rules Management System or BRMS, allow you to take a declarative rather than procedural approach to decisions. This
means you can state how you want your customers should be treated rather than having to program it.
• Analytic insight is added into decisions using predictive analytic techniques to derive more statistically valid rules or to derive predictive insights into your customers that can be
represented as executable models.
• Adaptive Control deals with the reality that customer decisions do not remain static. You need an infrastructure for adapting those decisions to changing needs and strategy in a
controlled many. This is sometimes known as champion/challenger where new “challenger” approaches are constantly developed to challenge the existing “champion”.
You can take some simple steps to get started in making your customer decisions smarter and adopting EDM:
• Identify some key decisions. You might target some decisions you know you already take that affect your customers or you might use the hidden decision categories to find some.
Pick those decisions about which customers complain or that competitors do well.
• Automate using rules and embed decision services in your systems. Using a business rules management system to design decision services is an effective way to start delivering
smarter decisions. Your business staff can set the policies and regulations that drive the decisions and the decision service allows the right decision to be shared across systems,
processes and channels.
• Enhance with analytics as you can. Decision Services are ideal targets for analytic improvement. As you develop insight about your customers – what are the right segments to
target or what predicts future profitability for a customer – you can easily embed this insight into your decision services and update the rules to take advantage of it.
• Prepare and plan for change. No customer decision is static for long. Getting out of the mindset that changes to systems are “bad” and mean that someone made a mistake is
important. Only then can you continuously review and enhance the way you treat customers. Your competitors change, your customers change, your products change and your
decisions should too.
There is no need to tolerate dumb customer decisions. You should figure out how to make them smarter before your customers realize they don’t have to tolerate it and find someone
who will treat them better.

Q2 2008 Highlights

• Engaged with a leading Middle-East bank to implement Strategic MIS across their Credit
Cards Business; including Business P&L, Segment P&L, Credit Control and Collections.
• Partnered with a Direct Marketing Company to evaluate Customer buying behavior for a
leading India-based manufacturer of consumer watches.
Sponsored in association with Salford Systems USA the first ever Analytics summit in India at NIT Durgapur

www.marketelligent.com
please send us your feedback at info@marketelligent.com

News and Views -- Marketelligent Q1 2008 Newsletter

......... from the CEO's Desk

We are happy to introduce “News and Views” our quarterly News Letter. Through this news letter we hope to update you on trends and happenings in the Analytics industry. In this edition we have an article by our COO Anunay Gupta “Catching the new wave of Globalization” published in American Banker. The article captures how outsourcing plays a key role in globalization and how analytics is emerging as the new area for outsourcing. We are also introducing you to our Team by putting them in focus in the news letter. This issue we introduce Bhupendra, who is the first associate of Marketelligent. Quite a personality as you will see.....

Going forward we hope to bring you interesting news and views from the world of analytics. Analytics has become the key differentiator in an increasingly competetive world and we want to work with you to bring best practices to your business. As always, we believe in continuous improvement and look forward to your feedback on this issue.

Roy K Cherian

Catching the New Wave of Globalization
American Banker | Friday, September 28, 2007
By Anunay Gupta, COO,Marketelligent

Globalization and offshoring are two words that basically mean the same thing but have very different perceptions, depending on what part of the world you are in. Having worked in the United States for over 20 years and in India for the last three, I have seen firsthand the changes taking place in both nations. And I think "globalization" is the appropriate term to describe the offshoring phenomena occurring around the world today.

Rapid developments in telecommunications and the adoption of Internet technology have enabled fast-moving companies to leverage globalization to their strategic advantage.
Many of the largest U.S. banking companies have worked with an Infosys or a Wipro on software. The bankers can discuss their business requirements and software specifications in the United States, the development takes place in India, and the delivery and execution occur across the globe.

This model affords two significant advantages: It's a 24/7 working model, allowing for development to occur seamlessly around the clock; and there are significant cost advantages in using a competent, low-cost talent pool that is well versed in engineering and technology. Companies that do not take advantage of this phenomenon will slowly but surely find it difficult to survive in an increasingly competitive global economy.

Consider the amazing growth of Infosys Technologies, a Bangalore, India, company that provides consulting and information technology services to clients around the world. Established in 1981, it needed almost 25 years to reach $2 billion of annual revenue, a level it reached in 2005. It then took just one year to cross the $3 billion threshold. This growth is being generated from a global set of clients, 60% of which are based in the United States.

As the globalization of software-related services grows and matures, companies will look for the next area where they can improve their productivity. I predict that will mean adopting the globalization model in analytics operations. From a consumer banking perspective, analytics encompasses any functional area that requires leveraging available data and information, along with talent well versed in statistical and econometric skills, to make better business decisions. These functions are usually called risk management, decision management, customer relationship marketing, database marketing, etc.

A few U.S. financial services companies have ventured down this path. General Electric Co. was one of the first movers, setting up its back-office company, GE Capital International Services, in India 10 years ago with operations that initially focused on "business processes." A few other large firms have since followed, including American Express Co. in 2001 and Citigroup Inc. three years later. GECIS also started working on many analytic functions, including risk and decision management for GE's consumer and commercial lending operations.

Globalizing analytics will be possible essentially for the same reason Infosys has done so well in technology: the global availability of affordable analytical talent well versed in statistics and econometrics, leadership talent with deep domain knowledge, and a business model that provides round-the-clock delivery.

But working on information-related processes requires a set of controls: selective access to sensitive customer level information as well as tight data security. In addition, a significant part of analytics involves understanding the local economy and its consumer mindset. Although this issue is more challenging than data security ones, companies in India are tackling this by sending employees to host countries for two or three months for "immersion" assignments. Moreover, the ready availability of global news across television and the Internet, as well as the significant number of expatriates, are helping bridge the gap between the data analyst working in Bangalore and the U.S. consumers shopping their local malls.

We are in the initial stages of a renewed wave of globalization. The success and rapid adoption of technology, combined with the globalization of software and business process-related services, will help companies move up the value chain of information-based services.

Meet Some of Our Associates -- Bhupendra Khanal

Bhupendra Khanal is involved in Analytics Consulting and Business Development for Marketelligent. He has rich experience in Analytics including Customer Segmentation,
Predictive Modeling, Competitor Analysis and Business Restructuring, Marketing Campaign Design, Capital Planning and Financial Forecasting, and Decision Control System Design and Implementation.

He specializes in designing Business Rules and Predictive Models based Marketing Strategy. He has been involved in various Analytic Consulting projects from different sectors
like Marketing and Branding, Risk Management, Collections, Human Resources etc. and has worked in variety of Industries including Banking, Insurance, Retail, Gaming, Debt Management and Charity.

His achievements include designing and implementing Decision Control System for the Sub-Prime Loans Market in US, and building Multiple Objective Decision System
for a Banking Client.

Prior to joining Marketelligent, he has been with Global Analytics and Fair Isaac Corporation. He holds a B. Tech. (Computer Science and Engineering) degree from NIT Durgapur, India.

His hobbies include Biking, Trekking, Mountaineering, Adventure Sports and Photography. He is interested in travelling new places, knowing new cultures, exploring nature’s beauty and capturing it in camera. He is also a passionate blogger and maintains blog “Business Analytics" and “Global Thougtz India”.


Q1 2008 Highlights

• Designed & developed a first pay default model for a large US Consumer Finance company
• Partnered with a leading Indian Beverages Manufacturer to put in place an on-demand market
share& industry sales tracking mechanism
• Initiated a transportation optimization study for the Indian operations of a global Chocolates
manufacturer
• Delivered on an unique CRM initiative for a leading Middle-East based Database Marketing
Company

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