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ROI for customer data integration

In this excerpt from Customer Data Integration: Reaching a Single Version of the Truth (Wiley, 2006), authors Jill Dyche and Evan Levy discuss how customer data hubs can drive ROI and how ING Insurance Americas reaped the rewards.

This article originally appeared on the BeyeNETWORK.

In this excerpt from Customer Data Integration: Reaching a Single Version of the Truth (Wiley, 2006), authors Jill Dyché and Evan Levy discuss how customer data hubs can drive ROI and how ING Insurance Americas reaped the rewards.

What can’t your business do that it needs to do, and how can integrated customer data help? Understanding what’s missing in your company’s business and IT capabilities is a great first step in pitching an initial customer data integration (CDI) project. However, as with most strategic IT endeavors, customer data integration requires a deliberate approach to explaining the problem, educating stakeholders, laying out the financial costs and benefits, and continuously communicating progress.

CDI’s promise lies in its ability to deliver integrated customer information operationally. Where CRM, data warehouses, custom applications, and even emerging technologies like enterprise information integration (EII) can provide a window into customer information, they usually rely on an incomplete view of the customer. CDI provides customer information as of right now. It serves a class of business problems in need of immediate, current, accurate, and complete customer data.

Justifying CDI is a delicate dance, and will probably have as much to do with your organization’s political environment as it will with formal business case development. This article provides some practical advice on how to make the CDI pitch, and how to deflect the inevitable arguments from those in your organization that have a firm stake in the status quo.

Insurance is a complex industry, made even more so by the fact that independent agents and subsidiaries usually have their own data and systems. Consolidating individual customer data across these business boundaries isn’t easy. Despite the fact that most insurance companies have implemented centralized customer databases or data warehouses – largely for claims analysis and other business intelligence work – many insurance companies remain mired in paper-based enrollment forms and re-sellers resistant to sharing their data.

The customer lifecycle within an insurance company can be very complex, from the initial application for insurance through actuarial services through to underwriting, payment, and claims management. Some insurance companies are renowned for their delays in shepherding new customers through the policy enrollment process. Tracking customers at a point-in-time across customer-centered business processes is a key, but largely unmet, goal for insurers.

Indeed, at each point in a customer’s interaction with an insurance carrier, his status with the firm can change. This workflow touches people and systems both inside and outside the insurance company, rendering the monitoring of current and accurate customer information difficult. Add to this the multi-channel environment that may encourage a customer to purchase a term life policy at his agent’s office while securing auto insurance online and his homeowner’s insurance through a subsidiary. It’s a tangled web of policies, forms, and payment details and linking the relevant customer data is a complex and intricate task.

Individualizing customers drives smarter target marketing. While many insurance firms had initially relied on new CRM and data warehouse technologies to solve some of these problems, they linger due to an inability to perform effective customer matching across systems and business divisions. Through newly funded customer data integration and master data deployment programs, insurers are finding ways to reconcile their customer data, and save money in the bargain.

Master Data Management Drives Cost Savings

Irrespective of your company’s industry, the most straightforward way to estimate the ROI CDI is what we call the pre-facto approach. When you look at the quantity of customer-centric applications on your company’s radar for the next 18 months, it’s likely that the integration development work for those applications will comprise 30 percent (a conservative estimate) of total development. The cost saving benefits of the aggregate value of 30 percent for each application make the payback promise of CDI relatively straightforward. This will likely be your best bet for financial justification of a CDI project.

If you don’t have the facts about what your IT development pipeline looks like, consider the post-facto approach and extrapolate from past projects. Look at what your annual investment in application maintenance and new application development is specific to customer-focused projects. Most IT organizations are hyper-aware of their budgets, and can get their arms around past projects and what they cost. How much data integration and cleansing work has been involved to deliver your business intelligence, campaign management, and sales force automation projects? Apply 30 percent of those costs and you’ll have a reliable idea of what data integration has cost you in maintenance.

You can also look at maintenance activities centered around customer data correction, data cleanup, and data reconciliation. Consider the maintenance associated with one-off data correction issues, data inaccuracies, and source system changes. This work is replicated across multiple applications, and can be tied back to the lack of sustainable data integration. A business case can usually be made for CDI based on the maintenance cost savings alone.

It’s worth noting that CDI represents a remedy for companies badly in need of integrated customer data that assume a data warehouse is their only option. In many instances, the integrated view of the customer doesn’t require acquisition and loading of all associated customer data, but merely calls for the linkages to that data via the registry type of CDI or a homegrown “index” approach. This approach can address business problems such as:

  • Discovering whether someone is already a customer

  • Searching for the customer while she’s on the phone

  • Identifying a customer at an airline check-in kiosk

  • Knowing how recently a customer has contacted the company

For instance, you don’t need the customer’s entire purchase history to understand his most recent purchase. Such business problems don’t require exhaustive information about the customer – just the unique attributes that characterize that individual.

The heart of CDI’s value is the eventual eradication of redundant data integration processing across a company’s various application systems. Combined with increased staff productivity, these are the two “low hanging fruit” justifications of CDI.

But don’t ignore other potential CDI benefits. Many will be company or industry specific or solve political issues like data ownership and processing problems that have plagued development organizations for a long time.

For instance, tying CDI to specific and high-visibility company strategies is a legitimate tact, but probably more difficult to quantify financially. Consider your company’s culture and reward mechanisms when requesting budget and support for CDI. Be ready to argue the consequences of not doing CDI. This will help you make a much more persuasive argument.

A riskier move is the “If We’d Had CDI…” approach, as in, “If we’d had CDI before we’d launched the Corporate Compliance Program we wouldn’t have had to spend the $600,000 it took to research and profile our existing customer data sources, and we would have saved $1.2 million in data extraction programming.”

The more effectively you can make the case for customer master data management, the better your chances are for convincing management to fund it. Moreover, the more thorough the business case, the higher the likelihood that IT management’s behaviors will match its claims of support, ensuring that CDI and MDM teams will be adequately staffed and managed.

Case Study: ING Insurance Americas

 “The main driver for CDI is a shift in corporate strategy.”

For a CIO to begin a conversation with the strategy angle is unexpected, but refreshing. For David Gutierrez, Chief Information Officer for ING Insurance Americas, his company’s recent strategic shift was significant, both for the business and for the programs his IT organization was planning to deliver.

“Our corporate culture is changing to become more customer-oriented, less product-oriented,” Gutierrez explains. “We’re promising to treat our customers as individuals. Our main objective is to bring all of our customer data together so that when you call us, we know you.”

When Gutierrez joined ING Americas he not only had deep IT and insurance industry expertise, he brought 16 years of advertising background. All this added up to a vision for managing customers across a global landscape while supporting the company’s brand integrity. “We’re in 62 different countries,” he says. “But we developed our overarching approach in the U.S., since it represents one of the more complex business models. We figured if it could work here, it could work anywhere.”

Add to that the fact that ING is comprised of many different companies, a product of a divisional organization and a global presence combined with an aggressive growth-through-acquisition strategy. Each of ING’s subsidiaries and divisions has its own systems, its own data and its own challenges.

Gutierrez and his team addressed these challenges with their Enterprise Information Platform (EIP) program, the foundation not only for integrated customer data, but also for re-use of other data across the various ING companies. The goal of EIP was to create business value based on three dimensions:

  1. Delivery speed of data-enabled business programs

  2. Minimization of risks around data usage and realization

  3. Cost reduction through technology and data re-use

With EIP, Gutierrez’ team took a “leave it where it is” strategy, providing federated query functionality and bi-directional updates of distributed customer data via a centralized hub interchange. The hub supports both distributed and centralized data from across ING’s numerous data sources, including its enterprise resource planning, mainframe, and document management systems.

The hub is flexible enough to store persistent information when it needs to, but extensible enough to support the addition of new data sources and client applications as they become available. “We toyed with the idea of just having a centralized data warehouse,” Gutierrez explains, “but we realized that wouldn’t be enough to support the real-time needs of our business. The hub interchange is really our engine for integrated customer data.”

The results have been impressive. Before EIP, ING relied on custom-built extracts from siloed systems and latent information from dozens of legacy applications. Post-EIP, the company has seen dramatic improvements in data quality and data provisioning turnaround time. Customer survey data is available on demand and in near-real time across all lines of business. The Security and Exchange Commission (SEC) and insurance regulations can be more tightly managed, since policies can be more strictly enforced and tracked.

And the financial return is noteworthy. The company estimates savings of $450,000 for each application that requires customer profiles, and $1.2 million for each system requiring transaction details.

Moreover, EIP has a global reach for ING, serving as the foundation for diverse programs across different countries, including data migration in Brazil, a common information architecture in Canada, customer segmentation in Chile, and business performance management in Mexico. It’s the intended standard for different development processes across the company, including data quality, data modeling, and knowledge management.

Gutierrez takes the benefits of EIP full-circle, back to the company’s strategy. His team mapped EIP’s benefits back to several of the company’s key strategic objectives, including maintaining brand integrity, achieving a reputation for regulatory compliance, and increasing marginal revenue and asset retention. “We just launched a campaign that celebrates how easy we are to do business with. ING wants to be a different kind of company. To be innovative with our customers, we needed to be innovative with our technology.”

Mission accomplished.

Jill Dyché

Jill is a partner co-founder of Baseline Consulting, a technology and management consulting firm specializing in data integration and business analytics. Jill is the author of three acclaimed business books, the latest of which is Customer Data Integration: Reaching a Single Version of the Truth, co-authored with Evan Levy. Her blog, Inside the Biz, focuses on the business value of IT.

Evan Levy

Evan is a partner and co-founder of Baseline Consulting, a professional services firm concentrating on enterprise data issues. In addition to his executive management responsibilities at Baseline, Evan is actively involved in managing project delivery teams and guiding client solution delivery. He also advises vendors and VC firms on new and emerging product strategies. Considered an industry leader on the topic of data integration and management, Evan is a faculty member of The Data Warehousing Institute. He is co-author of the new book, Customer Data Integration: Reaching a Single Version of the Truth (John Wiley and Sons, 2006).

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