One of the hottest segments in the IT software market today concerns software for "customer analytics." The demand for customer-analytic software is driven somewhat by the current conventional wisdom that a company will receive a considerable return on its IT investment by analyzing customer data and acting on that analysis. Achieving this benefit, however, requires several varieties of analytic software which fall into four broad categories:

  • Generic analytic tools. Software tools for online analytic processing (OLAP) or data mining can be applied to customer data analysis, assuming that other tools have collected and consolidated customer data. Designed for specially trained business analysts, many of these generic tools are too difficult for the increasing number of marketers who are charged with improving sales in their campaigns or on their Web sites. Hence, many vendors of generic analytic tools have been building high-level packaged functionality for these not-so-powerful users.
  • Packaged analytic applications. The goal of many of these packages is to supply marketing campaign management (sometimes called e-marketing). The best of these packages provide a closed-loop system that pulls together customer data analysis (often with OLAP or mining technologies embedded under an easy user interface), the development of archetypal customer profiles (sometimes called 1-to-1 segments), campaign rollout and campaign analysis. Note that these analytic functions are sometimes packaged together with operational CRM or e-commerce functions.
  • Data movement tools. Collecting and cleansing customer data (or data about related business entities like outstanding orders) is a prerequisite for all other categories of customer analytics. Customer data – typically strewn across IT systems for order entry, shipping, CR M, e-commerce, etc. – must be extracted, transformed and loaded into a central database for analysis. The consolidation of customer data often demands cleansing, matching and householding.
  • Customer profiling. Much of customer analytics revolves around the creation or analysis of customer profiles. Profiles are integral because they are the basis for up-selling, cross-selling, 1-to-1 promotions and customer retention. The categories of software previously listed create, analyze and act upon customer profiles – or provide data for them. Furthermore, customer analytics also involves exchanging customer profiles between partnering companies as well as buying and selling customer profiles (representing individuals or demographics) on the Internet.

THE HURWITZ TAKE: Many companies are entering a life cycle stage in which a variety of operational systems are in place (whether for CRM, e- commerce or ERP), and now it's time to enhance and leverage the sales and marketing potential of these systems by adding an analytic component. Also, many companies are finally abandoning their ancient database marketing techniques in favor of the best practices of electronic marketing campaign management. Both of these trends drive the need for customer analytics software.

IT organizations, marketers and others evaluating software for customer analytics should beware of generic tools (especially for data mining) that are difficult to learn and force you to build customer analytic functionality yourself. Many application suites now include high-level functionality that can help organizations get up and running fast without the resource drain of having to build tools. Even so, be aware that some application suites have weak data movement capabilities and may need to be augmented by a serious tool for data extraction and transformation. Organizations should look for advanced customer profiling functions since these are basic to most customer-analytic tasks.

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