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Unlocking the Full Potential of Customer Data Integration Through Complete Product and Service Lifecycle Management
Information Management Special Reports, October 2007
In today's business environments, access to rich customer information can provide a significant competitive advantage and be the determining factor in revenue growth and margin expansion. Having detailed knowledge of the customer is not only paramount to extracting the maximum amount of revenue, but also to delivering the highest levels of service throughout the lifecycle of each customer relationship. The challenge is that customer data is often arranged in silos of access - relegated to individual systems based upon the purpose of the data/information and the nature of the transaction, rather than centralized around the customer.
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Historically, companies have focused their efforts on capturing and storing transactions in specific but isolated data environments. Examples of these isolated data environments include shipping, ordering, accounting, technical assistance, point-of-sale (POS), and incentive and rebate data systems. Loosely coupled to the customer, they are also strictly governed by the transaction, which they are commissioned to track. For example, looking at simple metrics such as product sales and service sales, these values are likely to be obtained from two different systems while falling under the responsibility of two different business units.
Customer interactions are organized by transactions more often than not because of the distributed organizations that are charged with managing different segments of the overall business. The quality of the information contained in these transactions and, ultimately, the registration processes following their occurrence, must allow reconnection with the customer in order for future actionable renewal and sales opportunities to ensue. In the technology supply chain - made up of manufacturers, distributors and resellers - the likelihood of reconnection has traditionally been less than stellar. Although all of these entities are ultimately selling to the same end customer, the manufacturer and distributor often do not have the necessary information related to that end customer to facilitate future sales and service renewals. Again, these businesses are aligned according to the transaction, with each of them taking from the transaction the data elements that are critical to their respective businesses. Yet, in most cases, they are either neglecting or failing to adequately capture the intelligence that is necessary for future interaction with the end customer in order to maximize the lifecycle revenue opportunity.
Take, for instance, the lifecycle revenue opportunity of maintenance service contracts within the technology hardware and software industry, Figure 1 depicts an overview of the revenue lifecycle, starting with the initial hardware purchase, followed by the purchase of the original service contract associated with the hardware asset, and then the subsequent service renewals (year one, year two) and ultimately, the replacement of the original product (otherwise known as technology refresh).
Figure 1
Organizations can optimize the value of their customer data by ensuring that it evolves in parallel with the actions of customers over the lifecycle of the relationship, particularly at strategic points, such as at the initial point of sale, at product registration, in the post-sales phase when customer changes occur; when service and warranty contracts are up for renewal, and when technology refresh makes sense. It all begins with gathering, managing and assimilating important customer data at the time when a product is initially purchased. On average, however, only 60 percent of technology products sold with service get registered with the manufacturer. When proper registration does not take place, record of the service purchase is not appropriately entered into the manufacturer's entitlement system. This creates the possibility that no record of the contract actually exists, leading to major setbacks in the delivery of the service, much less in the tracking of expiration notifications. The result is lost service renewal opportunities as well as the risk of noncoverage for the customer on critical assets. Even worse, lack of registration can lead to loss of the customer due to dissatisfaction.
As illustrated in Figure 2, the primary reason for low service registration and service renewal rates is poor data quality as a result of disparate multi-party systems that track purchases. This poor data quality is the principal driving force behind new service contract management practices emerging today.
Figure 2
With CDI, data quality is achieved through normalization, correlation, and standard and advanced matching techniques designed to create a homogenous environment where detailed quality measures can also be applied. Matching in CDI is the process of comparing similar records, removing duplicates and uniting them into the best possible version of a record. Weighted quality scoring and specific automated scans can then be implemented to measure the quality and completeness of the resulting data pool. The CDI environment then must be connected to all business processes, customer touchpoints and other systems that interface with the customer in order to maintain a unified front of information. No matter which system or touch point may be in play, in an effective CDI initiative, the customer information is always derived from the same place.
This approach requires dynamic intelligence that develops with the customer relationship over time. It is based upon the premise that all data eventually becomes stale or inactionable. Most service contract management systems in play today lack the ability for secure customer interaction or they require manual customer interaction and do not embrace the channel partner. As a result, customers and channel partners cannot effectively update the information with critical moves, adds or changes (MACs), further compromising the value of their data as time passes and MACs occur. This ultimately results in lost opportunities for renewal. The most advanced CDI-based service contract management platforms, however, automate many of these functions by leveraging multiple "static" existing systems without disrupting or displacing current systems (i.e., POS systems) or processes, resulting in increased data integrity, and ultimately increased revenue. Further, through use of service-oriented architecture (SOA) technology, they can provide flexible, automated and dynamic multiparty interaction to allow the management of each product, service and registration as a transactional/dynamic property.
Best practice solutions for service contract management also bring together disparate customer data systems via CDI techniques that ensure that the entire supply chain can adequately connect with customers through multiple data touch points. In the case of product and service registration, streamlining the registration process and eliminating manual intervention significantly improves the quality of registration information and yields increased customer satisfaction and improved revenues for the entire supply chain.
Bridging the gap across the supply chain to produce complete customer records is imperative for a successful product and service contract lifecycle management strategy. Advanced CDI techniques, which include aggregation, normalization and mapping of customer data from various independent systems, are driving the success of new lifecycle management initiatives. Mapping, in particular, is key. Creating a complete picture of the customer requires mapping of that customer's shipping information to a manufacturer's entitlement system and also to the channel's point-of-sale systems and order records. The challenge is mapping the relevancy of the data in order to establish a complete registration or customer record. By first identifying the sources of data and bringing them into a centralized location, it's then possible to map the relevancy of that data. Understanding and adhering to deterministic and probabilistic matching techniques will allow the blending and normalization of customer information. By matching relevant fields, an organization is able to build a quality data set that completes a registration record and creates a complete customer data environment.
Using the principals of CDI, leading companies are now creating customer-centric data environments that blend together customer information from multiple data sources. Rather than being beholden to the disparate requirements of transaction management, their customer information is organized around the lifecycle of the customer revenue opportunity. These organizations also recognize that the principals of CDI need to extend beyond the walls of traditional data capture paradigms. Data needs to be obtained from multiple members of the supply chain, not only through the normal course of transactions that occur, but also through specific post-transaction data extraction efforts intended to bring together important customer-facing elements from all members of the supply chain - including those that are often left out of the transaction.
By tracking customer dynamics throughout product and service lifecycles, manufacturers are better positioned to maximize service and product sales with customers. As a result, they are better prepared to anticipate the customer's every need, which ultimately translates into an opportunity for increasing revenues. To unlock the full potential of CDI, organizations must embrace strategies, improve business processes and deploy technologies that unite silos of information into single, centralized repositories of actionable business intelligence.
Andrew Africa, chief operating office for MaintenanceNet, Inc., has more than 10 years of business operations experience. With a background in defining and managing highly efficient operations frameworks, Africa steers the design, production and delivery of advanced software technologies for the company.
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