Archive for the ‘Marketing Analytics’ Category

…….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

By Hari Prasad Kafley

Market Basket Analysis based on affinity algorithms is one among the most popular and commonly used techniques to analyze market basket data mostly applicable to consumer package goods. The association rules tend to identify a basket of products which are likely to be bought together. With increasing complexity and competitiveness in consumer package goods industry due to information revolution, mass merchandising is on decline. Consumers are increasingly recognized into large number of distinct segments. Diverse items are available for sale. Products category contain hundreds of competing products. High degree of differentiation is being achieved leading to strong brand names offering strong competitive advantage. Temporary price reductions, temporary product assortments and placement at stores are not sufficient for inducing customer loyalty. Due to greater degree of information access, consumers are highly mobile and knowledgeable to make purchase decisions based on price and differentiation of products. Eventually, retailers having greater ability to negotiate favorable terms with suppliers and manufacturers reap the advantage.

With maturity of consumer package goods industry, mere opening of new stores to sell to larger number customers hardly ensures profitability. Essentially, it important to retain existing customers with increased product portfolio. Retailers those are able to reduce their operating costs, manufacturers achieving economies of scale increase profitability. Operational efficiency can typically be increased with introduction of customer loyalty program leveraging on information technology.

Present day demands great deal of collaboration among retailers, suppliers and manufacturers. Actionable recommendations from analysis should cater to the needs of all partners in the chain. Market Basket Analysis tends to give biased recommendations profitable to retailers. Though statistically the results are credible but in this competitive economic environment and informational open market results may not translate into actionable strategy.

Problem takes origin from affinity algorithms in Market Basket Analysis which takes into account individual and isolated rules. Current need is to generate rules to predict the association among group of products. Complex algorithms being practiced can be used to include product categories, but it leads to increase in rules exponentially. The rules appear to be complex leading to difficulty in interpreting the rules. Furthermore, affinity algorithm in itself does not assume causal assumptions whereas the output of the algorithm suggests causal relationship among the products. Hence there is a need to introduce either a new and causal form of affinity analysis. Extension of algorithm to produce rules to be applicable across larger groups of transactions needs to be explored.

……… 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” at www.analyticsbhups.blogspot.com and “Global Thougtz India” at http://india.globalthoughtz.com/.

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

www.marketelligent.com

Please send us your feedback at info@marketelligent.com