In: Categories » Business » Customer services » The Human Component in the CRM Process
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Take data quality, for example, also known as data cleansing or data hygiene. Allow me to discuss briefly our particular offering here, the Trillium Software System®. Trillium Software System is a software solution, and is one of a few enterprise-wide data quality solutions available in the marketplace. In short, it is technology. Through an automated process, it standardizes, enhances and links elements in a marketing organization's database, or in operational transactions for use in an enterprise data warehouse, operational data store or worldwide master file. It is unique, in that it has years of application history, and ROI demonstrations that are often tenfold with clients. However, Trillium Software System also is based on human intelligence. Its more than 200,000 modifiable business rules understand the context of data. Included are best practices that are learned from human capital, just as there are time-tested business rules identifying and linking customers, there are specific cultural and linguistic rules that enable human-like understanding of data in application specific environments. So, yes, Trillium Software can be thought of as a solution of IT professionals, but in reality, critical business applications such as RM, e- business, and enterprise resource planning suites all benefit from high data quality. Enterprise data quality, done right, is proof positive that "technology" is an enabler for the people who will make it happen. Related to data quality is data management. Today's smart marketers are everywhere the customer is – using the medium or media of a customer's preference to dialogue, to transact, and to deliver service. Multichannel communication now comes with the territory, demanding data accuracy and customer recognition no matter what channel the customer is happening to use a particular day. Thus, we now have to ensure we have a technology in play to validate customer files so we know that they contain accurate and up-to-date contact information. We may need to verify and enhance physical addresses so the U.S. Postal Service will deliver. We may need to correct a faulty, outdated or incomplete address, or to verify or add a valid phone number – even if the customer mistakenly provided an error. There may need to be an e- mail address validation. All this is technology at work in a marketing data center, and it is driven in part by a timely, "we need it now" demand that originates with our clients and their customers, most of whom expect immediate, relevant communication in this interactive age with those businesses with which they choose to patronize. So, while data centers deliver this quality and speed, so do the data management account teams – again, human capital – who make sure each channel of communication is optimized and that the marketing organization has a full "360-degree" view for each of its customers. Another example is analytics. Again, analytics is now touted in many CRM software suites by software vendors – a technology-based solution. That's all well and good, but only if the user understands the discipline, capabilities, pitfalls and reality checks of segmenting customer databases, building predictive models, evaluating those models, and interpreting the results. A common plea from our clients is, "What do the data say?" and "What is the knowledge that is hidden in our data?" Human expertise – experience as a data professional, statistician and industry expert – is vital to answer these questions. It makes productive, optimal use of such software, to help derive substantive action based on accurate data reads. In effect, the software is a tool of the data professional, and not a replacement of the data professional. Analyzing data is a specialty. Where we engage analytics with customers, for example, we do so using program development teams who interpret data and deliver solutions that make full use of the software and technology toolbox. And these teams have rich vertical industry experience. That means these professionals are adept at recognizing and avoiding "garbage-in, garbage-out" that can fault many segmentation and model-building assignments. Even a trained eye may not spot a model's deficiency until after its first test. These teams understand most diligently the build-test-read-and-refine process. Thus, once again, the most productive analytics engagements, and the unique contributions of a professional's perspective, cannot (yet) be automated fully for any one company. Technology is the enabler, not the deliverer. Data quality, data management and multiple channel communications are and will be critically important in the foreseeable future. Also real-time monitoring to detect behavior change and the use of predictive modeling that help identify on the fly all the people who need to be communicated with is very young in its implementation. The technology of analytics is not young, but it is not old either. The IT professionals drive things, but technology increasingly will have more normative and “productionized” capabilities over time, which will be exciting to see. The IT department's partnerships with other professionals inside an organization must be strong to capitalize on these capabilities
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