Information Management Newsletters, July 28, 2011

Krishna Mehta

As marketing maturity has grown over time, corporations increasingly realize the value of having a 360-degree view of their customers. Traditionally, acquisition, cross-sell/up sell and retention programs have been handled by different groups within a firm.

These groups operate largely independently of one another, and the end result is often suboptimal. The organization stands to gain by focusing on prospects that will stay loyal longer and avoid the ones who are fickle or risky to serve. Similarly, it is better to focus the retention efforts and incentives on customers who are likely to buy more in the future. Therefore, organizations need to understand the lifetime value of their customers. Wikipedia says, “In marketing, customer lifetime value is the net present value of the cash flows attributed to the relationship with a customer.”

In a perfect world, an organization would want to establish a CLTV measure that accounts for the customers’ expected tenure, all likely future purchases (including cross-sell and up sell), the costs of servicing the customer (including product costs), customer service costs and losses in the event of likely default. And this CLTV measure would be available to everybody in the organization, who can then bake it into their decisions about the customer. In this scenario, even the customer service agents’ strategy for handling customer complaints/queries is driven by the CLTV measure.

As appealing as a CLTV framework might be, if not done right, it can quickly become very complex and hard to implement. The right solution depends on the data availability, analytical capabilities of the organization as well as the infrastructural constraints facing it.

Firms have varied capabilities in terms of their ability to capture data, perform analytics on the data and then make it available to their strategists and customer-facing agents. Not recognizing this could lead to a project gone haywire or a set of “cool” solutions that no one can use. For example, if customer service data is not available in the implementation platform, it might be better to use a less ambitious definition of CLTV that still adds a lot of value than to develop a measure that cannot be implemented.

Developing a CLTV framework is very important for an organization, but doing it right is equally important. Here are some best practices to follow to ensure that the CLTV framework is correctly set up.

Define actionable objectives clearly at the outset of the project.

While it is really exciting to have a CLTV score to evaluate the customers and prospects, organizations should clearly lay out at the outset how they plan to use it. This is important because different firms might have varying priorities. I have seen firms with a priority of customer retention that opt to use CLTV to focus on the more valuable customers. Other organizations wish to weed out the risky and fickle customers at the time of acquisition. Determining the desired use of CLTV is important because in an imperfect world where we might not be able to do everything, the objectives can help make the right theoretical and practical compromises to get to a workable solution. For example, if the focus is acquisition of less risky customers, customer service data, though nice to have, is not as important as when one is focusing on retention.

Another reason to have well laid-out objectives is that it makes the evaluation of the program easier. For example, if the ultimate reason for putting in place a CLTV plan is to sell more to existing customers, then evaluation of the program becomes relatively less ambiguous.

This is not to say that an organization should not have multiple or complex objectives while setting up a CLTV measure, but having a clear understanding of the priorities will allow them to make better decisions and add value where it really matters.

Develop a good understanding of business, data and technology constraints.

Once the objectives have been defined, it is important to understand the data and technology constraints that will limit the scope of the CLTV program. Data maturity varies by firms, and the ability to do sophisticated analysis also varies. The data constraints need to be identified, and then the objectives of the project modified if needed to meet the data constraints. The ability to perform sophisticated analysis can be outsourced if the capability does not exist in house, but the technology with which the framework will finally be implemented should be able to handle any solution developed.

The constraints and requirements of the implementation environment need to be well-understood because otherwise the CLTV score developed might not be available for use in strategic and tactical decisions. Very often, data sources are available in the development environment that seem very promising but cannot be integrated with the CRM platform. Such sources need to be avoided upfront, as they will probably show up in the solution and then mess up the implementation. Again, a model might be very good but too complex to be implemented in the CRM platform. A simple model that can be supported in the CRM environment needs to be built. Not planning for this up front will lead to rework and will have consequences for the project budget and timelines.

In addition to these data and technology-driven constraints, there might be some business limitations to what can be accomplished. A CLTV program touches many different groups of an organization, and each of those groups may have some business objectives that cannot be compromised and need to be accommodated. For example, customers in certain prespecified categories might be required to always be offered the highest price discounts, irrespective of what the analytics recommends.

A good strategy is to understand and plan for these constraints in the initial stages of the project.

Design a plan that adds the most value even though it might not be the most highly sophisticated.

It is important to chalk out a plan that is best for the organization, even though it may not be the best plan overall. It is possible that after studying the different constraints, one might come to the conclusion that a comprehensive CLTV measure would be very hard to implement, perhaps due to business strategy reasons, data unavailability or technology-related issues. In this situation, the firm could still devise a plan that takes it toward the goal of including customer value in business decisions.

For example, if customer acquisition is important and the data constraint makes it very difficult to introduce measures of future cross-sell/up sell, a modified plan that introduces some measure of future risk into the acquisition decision might be a good compromise. The important thing is to not get carried away by a grand plan that might be wonderful in a perfect scenario, but rather to build something that works and then build toward a more advanced solution.

Plan to demonstrate tangible outcomes in short to medium run.

Any CLTV implementation can turn out be fairly complex because it touches so many different types of data, requires the development of a large number of complex models, involves so many different groups and then needs to integrate with the CRM platform. This invariably means a very lengthy timeline to put the solution in place. A long, drawn-out project comes with inherent risks of some stakeholders losing interest, and it is important to design it so that tangible returns can be demonstrated along the way. This keeps the different stakeholders engaged and also brings in more supporters as the results start materializing.

For example, one organization embarked on a CLTV project to primarily help with customer retention. The project took more than 18 months to complete. However, they planned for intermediate output to be in place in three months, as simpler customer retention models were first developed, then CLTV components were put in place and finally the integration with CRM was completed. So while the customer service folks did not get to practice CLTV-based retention until the very end, it started being used and incorporated in an increasing number of business decisions at a fairly early stage in the project. This not only allowed for greater organization-wide buy in as people saw the benefits of the pieces that were being put in place, but it also allowed for testing and correction based on the early usage results.

Put in a good measurement plan up front to allow for evaluation and modification.

Finally, it is important to decide up front the metrics that will be used to measure the performance of the program and the criteria that will be used to determine its success. These measures should not just be based on statistical measures of analysis performance but should also include business criteria. In the customer acquisition example, it is important to verify that the statistical measures (like K-S) hold steady over time, but it is equally important to check that the stated business objective (for example, increase the number of multibuyers by 10 percent while increasing the overall base by 5 percent) also holds.

This helps us understand if things are working as planned, and if not, helps identify what needs to be remedied.

Developing a CLTV framework is an important exercise that needs a great deal of planning and commitment. It is important to spend some time upfront defining the goals and understanding the different types of constraints so that a good implementable solution is developed. A good strategy often involves starting with a simpler piece and gradually building the complexity in a planned and phased manner.

Krishna Mehta is co-founder and principal at ThinkAnalytics, an analytics consulting firm based in New Jersey. Krishna has more than a decade of experience in helping corporations and nonprofits apply analytics to make better business decisions.

Probabilities and Tiered Reactions Derived From Predictive Models Align With Company Goals

Information Management Magazine, July/Aug 2011

William McKnight

We make predictions and act on them all the time. I predict that if I jump into the path of a moving bus, I will be hurt – so I won’t jump. I’d conclude that my prediction had been in alignment with my goals, but if I had to, I could only prove it by using the laws of physics or examples of other people’s encounters with moving buses.

If done well, predictive analytics help companies avoid business situations analogous to being struck by a bus. Business situations, however, are usually less dramatic and much more nuanced than avoiding a moving vehicle. And, unlike the bus, a company will often not even know there was a situation worth avoiding.

Even so, business peril requires us to try to stay ahead of trouble. Predictive analytics are key to the prevention of loss by fraud, churn and other bad outcomes. Predictive analytics also help prevent the loss of wasted time and money spent on activities that do not contribute to business goals.

But there are limits to the usefulness of predictive analytics as we have applied them to date. One conclusion we have reached is that it is no longer sufficient to simply try to predict an unimpeded future. We must hedge our predictions with probabilities and be aware that a variety of reactions to those probabilities might be in order.

Many predictive models are tuned to report a binomial result, for example, “likely to churn.” In practice, multiple actions could occur as a result of this discovery, including “do nothing.” Whatever the reaction is (even to an event that has not yet taken place), it must be in alignment with company goals. The predictive models are important unto themselves, but I will focus here on how to support the actions we take when using predictive models, the “next steps” that are often neglected.

Predictive analytics are applied in the process of determining business events that are likely to occur and actionable. The probability threshold of “likely” differs from company to company and risk factor to risk factor. A risk-averse company may decide to prepare for a relatively low probability event that comes with a particularly bad outcome. Companies that are more risk tolerant, unaware or distracted by other projects will be less poised to take action. (See Figure 1.)

The reality is that most companies that do predictive analytics do so without attaching a probability. A predictive modeler might produce a result that indicates a customer is likely to churn, yet the model might not indicate how likely or whether the company should care if the customer leaves. Probabilities and commensurate tiers of reactions need to be in place if we wish to fully utilize predictive analytics. Increasingly, predictive analytics users are taking this more-nuanced approach, and analysts are becoming attuned to their environment and their master data, which should also influence the action taken.Let’s examine some of the real decisions that need to be made in the many things predictive analytics can refer to:

Example 1: Customer Lifetime Value

Customer lifetime value is a means to an end. It supports operations as a data point to justify taking other actions, such as whether to market to a person/company, how to support the customer, whether to approve a loan, whether to challenge a transaction as fraudulent, etc. I’m including CLV here to be consistent with prevalent practices of predictive analytics and to note that it should be forward-looking, which is not profit-to-date, but projected profit over the next few years.

Example 2: Clinical Treatment

Care-giving organizations want to provide the best care at the lowest cost. To reach this balance, in principle, multiple procedures for the patient are considered based on probabilities of efficacy. Procedures have subtleties, which shows the need for predictive analytics that start with the customer, not a campaign.

Example 3: Churn Management

When a customer appears likely to churn, companies are increasingly turning to customer lifetime value and other predictive analytics to temper the instinct to rush to the account representative for an attempt to salvage the relationship. The operative term is “churn management,” not “churn prevention.” (See Figure 2.)

And, sorry to say, people like me who just pay their bills every month do not necessarily fall into the highest CLV and highest salvageable category. (This is painfully apparent when I experience exhausting hold times when trying to reach customer service.)

Regardless, proactive intervention to salvage the relationship, if so desired, is a multidimensional decision. Figure 3 shows one of the factors that probability to churn needs to be juxtaposed with: customer lifetime value.

Example 4: Segmenting for Next Best Offer

Descriptive modeling classifies customers into segments that are utilized in a large variety of marketing-related activities. These segments should be formed dynamically in conjunction with campaigns and should correlate to the various activities of the campaign. Rather than marketing to everyone determined “likely to purchase,” a “probability to purchase” should be produced and used with other factors that make the effort worthwhile to the company in the long run. A factor like the customer’s income or another decile might increase the company’s interest in encouraging the customer through a campaign offer.

A related use of predictive analytics is in decision modeling, which might focus on the next customer interaction and whether it should be proactive and driven by the company (like extending an offer) or reactive (like responding to a loan application).

Example 5: Fraud Detection

Predictive analytics is used to determine the potential fraudulent nature of a transaction. Here again, we find that analyzing a transaction without considering how likely it is to be fraudulent and without bringing to bear a customer profile of summarized and recent transactions can lead to false assumptions and actions. Increasingly, a customer profile is required reading for any decision engine performing fraud detection. We find suitably robust profiles primarily in environments that have adopted master data management.

The human element does not disappear in the use of predictive analytics. The trend is to maintain a level of human judgment through self-service predictive analytics, allowing the analyst to consider multiple variables and actions.

But in a larger and broader scale, another trend is bringing more data into the predictions, and that includes Web-scale data in Hadoop and other big-data environments. For example, blogs, now finally being captured in Hadoop, can contribute to CLV by quantifying customer usage burdens on support and other activities that produce probabilities.

Proving the need for multiple dimensions in predictive analytics is like proving I should not have stepped in front of that bus. It’s sometimes hard to demonstrate what you have prevented. These techniques do not necessarily reduce churn, improve results from a given procedure or increase fraud interventions. You will find, however, that they do improve the bottom line, which is the higher calling for all involved.

William McKnight brings process, organizational and architectural focus to building strategies and implementing master data management and data warehousing programs that have consistently, for many years, improved the productivity and performance for his clients including several global corporate giants. McKnight, award-winning consultant and author, can be reached at McKnight Consulting Group.

Few would disagree that third party debt collectors are in the communication business. They communicate with consumers in writing and they communicate with them by phone. In some countries they even communicate with consumers in person.

Historically, the debt collection industry’s approach to consumer communication has been to write scripts and design work flows around the type of debt being collected rather than the characteristics of the consumer from whom the debt is being collected. In other words, the focus has been on whether the debt is a credit card debt, a healthcare debt, a utility debt or a purchased debt, to name just a few, rather than the type of communication used to discuss the debt with the consumer.

This is Part One of an intended series of articles that I hope will present a new way to approach consumers when communicating with them about a past due obligation and asking them for payment. I also intend to challenge regulators and lawmakers to dispel the notion that all consumers demand and require a one size fits all law regarding communication. They do not.

The two most significant generations for us to understand – from a debt collection perspective – are the millennials and the seniors. Each group represents a significant population of people who owe a significant amount of past due debt. Today, I will focus on the millennials.

Profile of the Millennials

To complicate their situation, 26.9% of the millennials are uninsured. The second most likely age group to be uninsured is made up of 26-34 year olds. Of this age group, 25.9% are uninsured. Together these two age groups represent the highest percentage of uninsured in the U.S.

The millennials are experiencing a dramatic rise in student loan debt. For the first time in recent history, outstanding consumer loan debt hit $829 billion, exceeding credit card debt ($826 billion) for the first time. Student loan debt is also growing faster than credit card debt within the past three months.

The millennials debt load is rising in general as compared to previous generations.  Families with heads of households under the age of 35 have the highest leveraged ratio of debt compared to all age groups.

Americans in their early 20s have a notoriously short attention span, and this certainly applies to millennials. Their preferred methods of communication include text messaging, social media, and mobile phones. They abhor email communications unless the emails are pushed to their smart phones and even if they are pushed to their smart phones, the millennials will generally not respond to emails. Email communication is actually cumbersome to the millennials. They want immediate access to information and immediate responses to their communications. For this reason, text messaging serves them well.

To put this into the current recovery context as an immediate action item, I think collection letters targeted to millennials need to be short, simple, and to the point.

The millennnials are community-influenced. This means they seek information from their peers before making many decisions. And they seek their information almost exclusively online. They are not likely to first seek information from a single source or a traditional authority figure such as a parent, a doctor, or a family friend. Instead they will seek information from social media sources that represent the collective wisdom of many.

This demographic is also highly transient and difficult to find. They are more likely to be located by way of their phone than at a permanent residence. Not surprisingly, the millennials are perfectly comfortable being tracked and targeted. But as a consequence, they are fiercely protective of their mobile phone number and will go to extraordinary lengths to maintain their mobile phone number over time regardless of expense or inconvenience.

Of critical importance to lawmakers and regulators should be the fact the millennial generation has very few privacy concerns. They care about access and convenience – not privacy. They will tell you when, where and how to reach them if they want you to do so. If not, they will be difficult to contact.

The constraints presented by the Telephone Consumer Protection Act (TCPA) regarding the use of auto dialers are archaic. The millennials are perfectly capable of informing the debt collector whether it is convenient for them to receive calls or text messages on their wireless phones as already permitted by the Fair Debt Collection Practices Act (FDCPA). In short, the consumer should control the decision as to who and how they may be contacted on their wireless phone/computer rather than the government and the law needs to catch up with technology.

Takeaways About the Millennials

• If you leave them a voice mail message they will probably not listen to it. If they do, it is unlikely they will call you back. If you try to make them feel guilty – you will fail. They are fiercely independent.

• They don’t really care about buying a home – especially right now — but will go to extremes to protect their mobile phone service.

• They will Google your company’s name before they pay you, and if they want to find out how you operate and treat people, they will follow the blogs.

• If they have some money to pay you, they would prefer to do so electronically. Recurring payments don’t particularly bother them as long as they can pay you via online banking.

• If you give them an app to help them pay by phone they will appreciate you. Don’t ever expect them to mail you a check.

• If they tell you they don’t have health insurance, they probably don’t.

• If they tell you they are unemployed, they probably are.

• If they tell you they live at home, they probably do.

Sources for this article include: Department of Health and Human Services, National Center for Health statistics, May 2009; CTIA The Wireless Association, 2010 Year End Figures; Forrester. If you would like additional information about the research and supporting resources used to prepare this article please feel free to contact the author Rozanne M. Andersen, Chief Compliance Officer and Vice President of Government Affairs, Ontario Systems LLC, a leading software company for the ARM industry at rozanne.andersen@ontariosystems.com or Melissa Jenkins, Senior Director, Strategy & Business Development, Ontario Systems LLC at Melissa.jenkins@ontariosystems.com.

InfoManagement Direct, April 7, 2011

Gary Cokins

Do you ever wonder why it takes so long for organizations to adopt modern management methods than can help them make better decisions on a daily basis?

Methods (and the software technologies that support them) such as business intelligence, analytics and balanced scorecards have proven benefits when it comes to improving an organization’s ability to quickly and accurately make decisions. However, organizations seem hesitant to adopt them. Is it analysis paralysis or brain freeze or just resistance to change?

In our personal lives, many of us have no problem making everyday decisions, such as whether or not to purchase a smartphone or join a social network. How can individuals make decisions so quickly, while organizations often struggle and are slow to react?

What’s the Difference: Early Adopters vs. Laggards

The field of marketing scientifically examines influences on the rate of adoption of products, services and technology. Everett Rogers, a business researcher, developed his “Diffusion of Innovations” model with five categories of adoption: innovators, early adopters, early majority, late majority and laggards.

Which category best describes many organizations’ adoption of modern managerial and analytical methods? My observation is that most fall in the laggards category. One might presume that laggards are at the tail end of a distribution curve. However, I view the distribution curve as being similar to cyclists in a race. There are high-performing cyclists out in front, and then much further behind is a large pack of riders – called the peloton. The distribution of cyclists doesn’t follow a bell curve, but rather one shaped like a camel with two humps: a short one followed by a broad, tall one with a long tail.

So what are the differences between organizations that lead the pack and those that fall behind? My observation is that innovators and early adopters quickly move forward for two reasons:

1. They are having financial  difficulties and will try anything that might help them survive; or
2. They are very progressive and  driven to continuously seek a competitive edge.

On the other hand, the late majority and laggard organizations are either risk averse with resistance to change, or have weak leadership with little vision. But I believe there is another possible explanation for the laggards – they are too distracted.

Worldwide Volatility Distractions

There is no doubt that volatility is on the rise, and it results in distractions to an organization. Examples of volatility include changes in consumer preferences, foreign currency exchange rates, commodity prices, etc. New trends contributing to volatility can develop quickly, such as oil dependence, emergence of national economies (e.g., India and Brazil) and instantaneous, Internet-based global communications.

Unanticipated shocks can come from occurrences like the Asian tsunami in 2004, H1N1 virus outbreak, Euro currency shocks, civil unrest in North African Arab countries, and the Japan tsunami and subsequent nuclear crisis. The Internet,  global communications, social networks and relaxation of international trade barriers have introduced big, sine wave vibrations and turbulence compared to relatively smooth rises and falls of the past decades.

But is increased worldwide volatility reason enough to not adopt or at least test modern managerial methods? I am not talking about innovation, where you have to come up with new ideas. I am talking about implementing proven methods  and techniques, such as pilot projects, rapid prototyping and the balanced scorecard.

Modern Management Examples

Business analytics and enterprise performance management methodologies can be adopted by late majority and laggard organizations, regardless of the volatilities they are experiencing.

In my December 2010 column “Geeks are Chic,” I described how popular Harvard Business School Professor Michael Porter’s accepted, generic strategies for a company (i.e., cost leadership, differentiation and focus) are all vulnerable today because competitors can more quickly take actions (such as reduce costs), imitate a company or invade a company’s market niche.

An organization’s best defense against the competition is the ability to quickly make smart decisions, which can easily be accomplished by implementing business analytics. Organizations that achieve competency with business analytics are
able to sustain a long-term competitive advantage. But late majority and laggard organizations are often too distracted to recognize this.

A similar case can be made for adopting enterprise performance management methodologies. The early adopters are already well ahead. Their executives have properly communicated their strategy to employees through appropriate key performance indicators and achievable targets to align behavior. They have robust predictive analytics that reduce uncertainty and allow them to take smarter and quicker actions.

Early adopters understand their cost and profit margins by product, service, channel and customer – as well as optimal actions needed to retain and grow customers and acquire the best target customers. They have driver-based budgets and
rolling financial forecasts using modeling techniques.

Take the Lead – or Follow Way Behind

When executive teams are distracted with firefighting, reacting to surprises or internal politics, then the urgent crowds out the important. Business and government face a vastly different environment than they did 30 years ago. The Internet was not part of our daily lives. The Cold War was on. China was a small economic player. European countries had their own currencies. During that time, the global environment didn’t play as much of a factor in their decision-making. Today, organizations operate as though perpetual crises are the new normal, so multiple distractions when trying to react are a given.

But such distractions aren’t an excuse for always being reactive instead of proactive. A focus on solving problems is not mutually exclusive to seeking opportunities to improve management and performance. Organizations that want to move beyond the laggards category must take on the mentality of the early adopters; they understand the importance of using business analytics and enterprise performance management methods to enhance decision-making and align employee behavior and priorities to execute the executive team’s strategy. Most importantly, remember that it’s never too late to go from being in the middle of the pack to taking a commanding lead over your competitors.

Gary Cokins is the global product marketing manager for Performance Management solutions at SAS, a market leader in data management, business intelligence and analytical software.

Information Management Magazine, 03/01/2011

Jane Griffin

Business intelligence is not enough anymore. Data volumes, global sourcing models and an ever-changing regulatory environment have combined in a perfect storm to render traditional BI capabilities inadequate at capturing deep insight into business performance. Deep analytic insight comes from looking deeper and further out toward the horizon to help answer the most challenging, even unknown, questions, and to build those answers into the business itself.

Most companies probably already have at least a subset of the capabilities they need to engage in deep, sustainable analytics. The challenge is to push those analytic capabilitie seven deeper into the organization – from business executives to  he front lines – in a coordinated fashion. This requires changing the way companies look at the business and at information itself.

To meet the challenge of implementing deep, sustainable analytics – and capture more value from their analytics investments – leading companies often focus on seven key principles.

1) Start where you are. The first step in understanding your current capabilities and the gaps you’ll need to close in order to get more value from your analytics investments is an honest self assessment. This extends to all your analytic  apabilities, from both a technical and organizational depth perspective. Focus on how well (or not) you’re employing those capabilities, and on which capabilities you need to acquire. Set goals for improvement based on what you find. For  example, if your analytic capabilities are divided into business function-specific silos, set a long-term goal of having them integrated across the business functions, and develop a roadmap to get there.

2) Ask crunchy questions. Crunchy questions are the ones that help you find out what really drives value in your business processes. They focus on finding out what happened in the past and what the root cause of outcomes might have been. They help you understand what’s happening now and why, and whether these events are consistent with forecasts and expectations. They also help you watch for expected events and predict what might happen in the future, based on past and current experience. They drive hindsight, insight and foresight.

3) Enhance signal strength. The data in your structured databases is not enough to fully understand your business performance. With the volume of unstructured data on pace to overwhelm traditional, structured data, it’s critical to develop capabilities that leverage the unstructured content that inundates you on a daily basis. Email, video, customer interaction data and social media all provide a depth of understanding that traditional data simply can’t. You need to capture it and exploit it, or fall behind.

4) Accelerate insights. You can do this by embracing automation, where it’s practical. Implementing deep analytics requires automation across all dimensions of your analytics operations. In short, automate information delivery to people and processes and automate responses as much as is practicably possible, so that action can be taken with certainty, and at its lowest cost. Examples of automation include the use of price optimization models, developing compensation plans aligned with profitability data and optimizing vendor contract terms based on volumes and margins.

5) Engage the users and visualize. Give people power. Design data visualization capabilities and interfaces that help business users ask complex questions and get answers quickly. Then, give them the authority to act on the results, based on role and responsibility levels. Involve users in the design of the interface. They’ll tell you what they need to do their jobs and when you’ve got it right.

6) Build a fact-based culture. Shooting from the hip is no longer in vogue. There is simply too much complexity in the business environment and too much competitive pressure to base decisions solely on gut feelings. Instinct is valuable, but it needs to be supported by data. Start at the top. For example, a CFO who says, “These are the inputs I use to make decisions,” will go a long way to facilitate the adoption of those inputs throughout the organization.

7) Practice right-fit analytics. There’s no silver analytics bullet that’s right for every business. Businesses are unique entities that require unique analytics infrastructures. Just because a toolset is out there doesn’t mean it’s the right fit for your needs. Therefore, it’s critical to set an overall analytics strategy that’s right for your particular business and assemble a disciplined, experienced team to implement the strategy.

Sustainable analytics is not a project with a nice deliverable that you put on a shelf. It’s a continuous cycle of understanding and improving business process performance. It should focus on high-impact areas and ask hard questions. It  requires embarking on a journey to help move your organization beyond the hindsight of traditional business intelligence towards deeper insight and distant foresight. Are you ready to go?

This publication contains general information only and is based on the experiences and research of Deloitte practitioners. Deloitte is not, by means of this publication, rendering business, financial, investment or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte, its affiliates and related entities shall not be responsible for any loss sustained by any person who relies on this publication.

Jane Griffin is a Deloitte Consulting LLP partner. Griffin has designed and built business intelligence solutions and data warehouses for clients in numerous industries.

A study by the credit bureau TransUnion shows that when choosing which bills they can afford to pay, consumers are more likely to pay their credit card obligations and fall behind on their mortgage payments.

And this trend is not surprising, according to the Chicago-based company. TransUnion says this trend has continued for the past three years, and while the number of consumers current on credit cards but delinquent on their mortgage has declined slightly, it is more than 70 percent higher than it was at the beginning of the “Great Recession.”

“The percentage of consumers current on their credit card payments and delinquent on their mortgages first surpassed the percentage of consumers current on their mortgages and delinquent on credit cards in the Q1 2008,” the company said in a statement. “Although many industry analysts believed that a reversion to the conventional payment hierarchy would ensue once the recession had concluded, this has not been the case.”

Instead, the number of borrowers delinquent on mortgages and current on credit cards was at 7.24 percent in the last quarter of 2010, which was a slight drop from 7.4 percent in Q3. The percentage of consumers who are delinquent on credit cards but current on their mortgages dropped to its lowest level in Q4 2010, to 3.03 percent.

TransUnion says that the current economic and housing environment has consumers reevaluating their priorities.

“The reversal of the traditional payment hierarchy was driven in large part by home value depreciation and rising unemployment, both of which speak to consumer willingness and ability to pay their mortgages versus their credit cards,” said Ezra Becker, vice president of research and consulting in TransUnion’s financial services business unit.

He continued, “Home value concerns and stubbornly high unemployment continue to drive this dynamic, though the decline in the number of consumers delinquent on mortgages and current on credit cards may be a sign that the divergence in the payment hierarchy has peaked.”

What is surprising is that when polled, consumers claim if they could only make one payment in a month, they would chose to pay their mortgage over their credit cards.

In a February study commissioned by TransUnion, 79 percent of adults said they would rather be delinquent on their credit cards than their mortgages. Despite this, TransUnion reports that of the consumers who defaulted in Q4 2010, 52 percent defaulted on their mortgages and kept their credit cards current, compared with 22 percent who defaulted on their credit cards but kept their mortgage current.

Details
about household debt and credit developments in the fourth quarter of 2010.

38 page / 288 kb

Information Management Magazine, 03/01/2011

Don Haderle

Enterprises have massive amounts of structured and unstructured data, and to be truly useful, this information has to deliver a 360-degree view of the business so that decision-makers can understand its implications across all of the business disciplines. Unfortunately, it is almost impossible to make sense of all the different ongoing discussions captured in enterprise data today – and the amount and types of data that enterprises will be expected to manage in the next few years will be orders of magnitude higher than what exists today. Coordinating this exponentially increasing amount of data, eliminating data silos through standardization and consolidation, and plugging all the data together across multiple disciplines is a secret weapon for organizations that want to maintain their competitive advantage.

In other words, the problem of information overload is a business problem, not just a technical problem. This is something that many organizations overlook, mainly because it’s up to CIOs and CTOs to find a solution. But the effect of poorly managed data has a direct impact on productivity and the bottom line, and smart companies (as well as forward-thinking government agencies and nonprofit entities) know that they need to be ahead of the curve if they want to benefit from the information avalanche rather than be buried under an infinite pile of ones and zeroes.

The Problem We Face

It’s no secret that today’s organizations in every sector – from government agencies to manufacturers to law firms to insurance companies – are faced with the challenge of managing more data than even a decade ago. Back in the 1970s, we used to talk about megabytes of information – then it became gigabytes and terabytes, and now we’re dealing with exabytes. Information storage has gotten so inexpensive (Moore’s Law strikes again!) that the per-byte cost to save and  access a particular piece of data is pretty close to free – it’s like dealing with pennies or subatomic particles because there’s always a smaller and smaller unit in play.

Having information is good, but as more and more data becomes available, we have no idea how to harvest it. And sometimes more information actually just confuses us. Companies that used to plan by analyzing one year of information now have the ability to scrutinize 50 years of data. It may sound like a good thing until you start having to deal with the crush of millions of information fields. We’re no longer managing information – we have become collectors. The issue is that we need to synthesize the information into something useful to improve our business.

Structured and Unstructured Data

To complicate matters, it’s not just the amount of data that’s increased over the years – it’s the kind of information that systems are expected to store. Having a spreadsheet filled with tables of numbers is one thing, but having photographs, scanned documents, audio and video and other information in play creates an entirely new kind of animal. In order to deal with this, data experts divided the world into structured and unstructured data. Much has been written on these two categories of data, but from my perspective, the standard delineation isn’t quite right because it doesn’t adequately deal with the shades of gray that I believe are important to understand.

I define structured data as information that fits nicely in a relational database. This is pretty self-explanatory: financial data, sales tables and profit-and-loss information fit neatly into databases and are easy to access and manage. Everything that doesn’t fit into tabular form is usually lumped into the unstructured category, but in my experience, very little data is truly unstructured. The problem is that most databases don’t know how to deal with it.

What we’re really dealing with is what I think of as formal and informal worlds of information drawn from many different perspectives and viewpoints. Unstructured data (which, as I said before, makes up a small portion of the information that organizations deal with) truly has no shape or form that is externally discernable. Most data is what I call semistructured, meaning that it actually has attributes that can be managed.

A perfect example of this is email, which is often categorized as unstructured because it’s not as neat and tidy as some might like. In fact, emails have plenty of attributes that advanced data management systems can deal with just as easily as they handle charts of numbers. For starters, a good data management system can parse emails based on their language, what system they come from, when they were sent, who sent them and who received them. By the way, this also works for memos, faxes and other documents.

So what about pictures and videos, which are often dropped into the unstructured data bucket? Just because there is no single bit in a relational database that includes a photo (which are stored as binary large objects, or BLOBs, but are really black boxes within the database), and there is no field called “mpeg,” doesn’t mean that these items can’t be categorized and stored for analysis. In my mind, when data is classified as unstructured, what it tells me is that someone said,
“We have no understanding of what this is.”

How We’ve Tried To Fix The Problem

Traditional relational databases are wonderful things, but they only go so far, and there have been several alternative approaches that have found success in the marketplace. The original content management systems that started popping up in the late 1980s focused on eliminating paper. They allowed organizations to scan documents and images and store them away for recall, which reduced the need for file cabinets. At the time, this was a leap forward because for the first time organizations were able to use technology to manage what had been a physical process.

One of the first industries to embrace this approach was insurance, which is a document-intensive industry. Insurance companies used the information for call center and customer self-service, claims handling and payer operations. Insurer USAA started with a focus on customer actions driven by paper mail – in fact, the daily mail was scanned and queued for action.

Insurers gradually converted nearly all of their documents and moved to the second phase – business process management – where the business process is automated and driven by the document system. In the case of USAA, the claim request kicks the process off and drives the information to the investigator, then to the claims adjuster and finally through the payment system. The resultant cost savings are enormous: USAA, Blue Cross, the U.S. Social Security agency, State Farm and Alliance all benefited from this approach, and in the last 10 years, BPM has become the de facto standard in industries that have repeatable, document-heavy processes.

The Future

The rate of information growth is not going to slow down any time soon. Faster computer systems, cheaper storage and better analytical tools are making it easier than ever for organizations to collect virtually unlimited quantities of data. The Holy Grail is being able to use the information effectively. In the insurance vertical, the benefits of these alternative approaches have been breathtaking, and they offer a roadmap for helping organizations in all industries manage the ever-increasing flow of data. From a technical standpoint, insurers are able to deal with all data equally well, including so-called structured and unstructured information. Photographs may not fit natively into traditional relational database systems, but CMS/BPM tools allow insurers to store accident pictures, memos and emails just as easily as they archive claim information.

Given all the information that insurance companies now have at their fingertips, analysis for fraud and increased operational efficiency have been huge areas of improvement because information crosses business function areas to create a consolidated perspective. Many insurance solutions have also added records management and use compliant storage in response to regulatory issues, and it is reasonable to expect them to continue expanding the kinds of data that can be categorized and acted upon.

More recently, enterprise content management systems have started housing and managing collaborative content,  such as emails, blogs and social networking posts, as employees communicate in-house and to outside audiences via these channels. The motivation for these capabilities is a recognition that all collaborative content needs to be sustained, immutable and available for legal discovery. These systems integrate text search/patterns with transactional analysis to detect fraud, improve efficiency of operation and improve service for customers.

So what is the key to information management in the future? Certainly increasing the kinds of data that can be stored and analyzed within a data system is important, but the major change needs to be one of attitude, not technology. This may sound odd coming from a database lifer, but I believe that it is true. Twenty years ago, different departments within an organization had fundamentally different views of life: finance folks looked at P&L numbers, human resources saw the world in terms of headcount, and engineers focused on building their products. Today’s organizations have much better communication between groups and are able to assimilate information from lots of different perspectives.

In order to cure the information disconnect, people need to embrace technologies that allow them to share their insights and collaborate in real time. Today those insights are mostly private – held in private spreadsheets, Word documents or yellow sticky notes – meaning that there’s no effective way to share information through a single technology interface. Instead, everyone brings his or her private data and analysis to the cross-company meeting so it can get sorted out in person. Talk about inefficient! For organizations to be successful, this information needs to be captured, shared and managed to really move the needle on real-time collaboration.

Bringing this information – made of structured, unstructured and semistructured data – together is really puzzling, but organizations are already coming up with exciting ways to do it. In the manufacturing world, real-time demand systems allow companies to amalgamate information all the way from the customer’s initial request, through the ordering and manufacturing process, all the way to final delivery. This affects every department within the organization, but we tend to still think of the whole process as a glorified supply-chain approach. Effective companies are already building systems and processes that draw information in many forms and from many sources to produce a magical, well-oiled solution that converts mountains of disparate information into successful outcomes.

Don Haderle retired from IBM in 2005 after 35 years, and since 2006 has been on the Technical Advisory Board for ANTs Software Inc., a company that helps accelerate migration to leading database vendors.

In order to determine the state of the business analysis function in the financial services industry, Celent teamed with the International Institute of Business Analysis (IIBA) to survey banks and insurance firms and take a snapshot of current performance in key business analysis skill areas. The survey was designed to hold a mirror up to the industry and reflect its perception of its performance. These are reviewed in a recently published report – “Business Analysis Competencies: Mirror, Mirror: A Self-assessment by Banks and Insurers.”

The data confirms what many people say in discussion on this topic — that skill development in business analysis is often insufficient to maintain sustained performance at sufficient levels. Very few companies rate their performance in any business analysis area as a strength.

The highest rating was for the elicitation skill in banks, and this level was assigned by only 21 percent of the banking participants. The study also collected opinions on which BA competencies are most critical to successful implementations.

The good news for insurers is that both life/annuities/health and P&C insurers report a close match between their highest ranked importance areas (elicitation and requirements analysis, respectively) and their performance. That is, delivery in these areas is ranked at or above average, indicating that investments that have been made are beginning to pay off and deliver benefits.

Many financial services companies are updating automation systems and continuing to improve business processes. For organizations that are modernizing their platforms, an expected benefit is to reduce the dependency on IT and move maintenance and some development into business analyst areas.

Many software vendors are producing products that are designed to be configured, not programmed. This is intended to increase flexibility and speed in system development and maintenance. Neither group will realize its goals without solid business analysis skills.

The Celent/IIBA survey identifies the specific gaps in business analysis skills in banks and insurance companies. Celent encourages financial services firms to use the results of this survey to examine their current approach to business analysis, place their bets on which areas are most important, and invest in skill improvement.

This blog has been reprinted with permission from Celent. It originally appeared on Insurance Networking News.

InformationManagement Newsletters, March 21, 2011

Margaret Dawson, Jasmine Basrai

Remember that commercial where two guys are walking toward each other, one eating chocolate and the other eating peanut butter, and then crash into each other, forcing their favorite foods to “merge”? In the ad, they’re confused at first until one of them actually tastes the combination and voila – they discover a match made in heaven.

Sometimes in technology we overthink things and fail to see natural connections that might enable IT and business to taste great together – or at least find mutual benefit. (We’re not going to insinuate which is chocolate and which is peanut butter.)

We’ve seen it recently with business process management, service-oriented architecture and integration. Taken alone, each of these has a simple definition and role in IT and business. But taken amid the multiple architectures, frameworks and solutions, it is hard to know how to fit the pieces together, even though there are natural synergies.

Let’s start with just one of these, business process management, and look at what it is and how it might work with these other areas. We recently attended a Gartner summit on architecture and, while simplicity is not usually Gartner’s trademark, we have to give kudos to analyst Michelle Canterra, who gave a great, straightforward definition of BPM. She called it “the work” we do.

That’s it. The work. We’d take that one step further and say it’s how we do that work. It’s identifying the work that could be more efficient and then basically mapping the ideal way for that work to be done across people, technology and processes.

It helps to align BPM (and any technological framework) to the end goal – business impact. So, for a more formal definition: BPM is a systematic approach of streamlining tasks and activities in order to achieve a specific organizational goal, typically to deliver value to customers and drive more revenue.

Since we threw Gartner a bone, we’ll do the same for Forrester, whose definition is also good: the discipline of BPM strives to coordinate people, and process information and technology to streamline and continuously improve business processes that create customer value while raising the overall quality of the work.

Even with our attempt to simplify BPM, it can still be challenging, if for no other reason than that it requires close coordination between IT and the line of business. As with any IT initiative, if the business side does not feel “pain” or understand why you need to do something, your chance of getting funding or approval remains low. And, let’s face it, some of us don’t actually like peanut butter with our chocolate.

So, start with a simple process that can quickly show increased efficiency and business impact. (This is true, by the way, with just about any new IT initiative, as incremental changes are always easier to both absorb and implement, not to mention fund. With identifying the process, get business buy-in that they have pain or goals around that process and then set specific goals of what success would look like.)

Let’s say your company is doing manual invoicing, which many companies still do, in spite of a huge market spend in basic-to-enterprise ERP and accounting systems. How could you start automating invoicing across the technology, people and process? Does it require new technology, or, for an easier fix, is there an existing system you could leverage for phase one to start the ball rolling?

Once you’ve agreed on the process, outline what the results should be. Maybe time to invoicing goes from two weeks to three days, which would expedite time to payment, meaning your company would have more cash flow. It could also provide other benefits, such as the accounts payable manager becoming more of a customer relationship manager instead of an envelope stuffer.

Many good business process modeling tools allow you to map the process and engage the business side early by leveraging simple Web-based technologies. Many of these tools are delivered as software as a service, meaning you do not need to install any software or hardware in your infrastructure, and the maps and processes are easily shared via Web-based tools.

Once you have the process mapped, there is an important question to ask: What data is involved and where does that data sit? With invoicing, you are dealing with multiple pieces, such as ordering information, accounting information and customer contact information.This information is most likely scattered across disparate applications, servers, etc. To make this business process work, you need to make sure the data is available.

Here is where service-oriented architecture and integration come into play.

Think of SOA as just a way to bring together disparate information into a streamlined fabric of shared services. Many people simplify it to just call it a Web service layer that everybody can plug into. If the process you are automating and improving crosses multiple information sources, departments and systems, then establishing a shared repository or “service” where all the people and systems can gather the same version of that information is ideal.

If, in contrast, you have just two application sources within that business process, then application integration might be the answer. There are an increasing number of plug-and-play or SaaS-based application integration solutions that provide data transfer, transformation and synchronization across two applications. Application integration allows different applications to share information even though they “speak” different languages.

No matter which of these approaches you choose, a combination of both, the goal here is ensuring a consistent data flow to support your new business process.

Going a step further, let’s take that invoice process beyond your organization to a multi-enterprise business process. You’ve coordinated the data and the information within your company, but now you need to automate the process of sending the invoices to your customer community externally. This is what we call business-to-business, or B2B, integration.

B2B integration allows you to send and receive messages electronically across disparate systems, formats and companies, enabling both organizations to work directly from their existing procurement systems and internal business
processes. The integration solution does the transformation of the message – in this case an invoice – to align with the required standards and formats for each company.

As this market matures, business integration is also folding in many aspects of business process management, enabling companies to dictate sophisticated business rules or security policies as part of a multi-enterprise transaction. For example, if a company must adhere to strict compliance mandates such as HIPAA or PCI DSS, a business integration process should be able to maintain compliance with data protection, encryption, security auditing, access control rules and other orchestration.

BPM is a great place to start. However, it should not be done in a vacuum, because it fits together perfectly with other initiatives, including SOA and integration. Integration provides the interoperability and real-time exchange of information related to the business process, while SOA enables centralized services and access.

While no single solution does it all, an enterprise should look for vendors that are moving in this direction through either organic growth or partnerships. In the end, what matters is that together they are all allowing you to “interact” with
applications, users and information in a more systematic, aligned way that delivers value to customers and drives revenue.

Margaret Dawson is Vice President of Product Management for Hubspan. She’s responsible for the overall product vision and roadmap and works with key partners, such as IBM, in delivering innovative solutions to the market. She
has more than 20 years experience in the IT industry, working with leading companies in the network security, semiconductor, personal computer, software, and e-commerce markets, including Microsoft and Amazon. Dawson has worked and traveled extensively in Asia, Europe and North America, including ten years working in the greater China region, consulting with many of the area’s leading IT companies and serving as a BusinessWeek magazine foreign correspondent.

Jasmine Basrai is Senior Product Manager for IBM, focused on business process management and the BlueWorks Live solution. In this role, she drives product leadership in the areas of business modeling, process design and dynamic business interfaces. She has more than a decade experience in BPM consulting and working with development teams to build innovative products. Prior to IBM, she was the Director of Consulting Services at Holosofx. Jasmine holds a Bachelor’s of Science degree in Business Administration from the University of Southern California.