Making Marketing Decisions Just in Time
To remain competitive in this dynamic business environment, many companies are shifting from a focus on products and distribution channels to an emphasis on the customer. These dramatic shifts in corporate focus and information sources have placed tremendous demands on existing IT resources, because today's static-software technologies simply cannot keep pace with the dynamic change required in managing customer relationships in business today.
While log-file analysis provides insight specific to the online buyer, true customer intelligence requires just-in-time analytic solutions that link customer information across all customer touch points - points that include e-commerce along with data from traditional bricks-and-mortar business channels - enabling a comprehensive, cross-channel view of customer behavior and preference.
Businesses have invested billions in software applications that reduce costs by automating transaction-oriented business processes such as sales, accounting, support, manufacturing and finance. These applications have allowed companies to collect and store enormous volumes of customer data, which is often augmented by marketing data from third-party providers.
Despite the vast amounts of data generated, these applications remain focused on automating business processes, rather than analyzing data to help companies better understand their customers. Moreover, because this data resides in disparate computer systems, integrating and analyzing the data to provide a comprehensive view of the customer is a significant challenge requiring substantial amounts of time and expense to integrate, deploy and maintain.
To succeed, companies can no longer afford to wait months or years to build and deploy applications that "manufacture knowledge" regarding customer behavior and preference. The same market forces that caused the transformation of rigid, in-line manufacturing lines into highly flexible, adaptive, just-in-time systems will demand the conversion of rigid, inflexible decision-support systems into flexible-knowledge manufacturing systems.
The digital speed in which businesses operate today emphasizes "time to market" and "first-mover advantage" as critical success factors. This is true of nearly every business and no more so than with Web-related initiatives. Because customer behavior is so dynamic, companies must be able to deploy just-in-time principles to build and deploy applications that accept data across all customer touch points, and that accept newly discovered sources of customer data automatically.
Analysis for E-business. Given the recent emphasis on Internet commerce, one might assume that new systems could be designed that would alleviate the problem of linking all data necessary for a complete customer view. Remarkably, this assumption would be incorrect.
Understanding online customer attitudes, preferences and behaviors remain the biggest challenge facing e-commerce success. The data needed for in-depth analysis of the online shopper is buried deep within a Web site's log files and e-commerce transaction database. Many products exist today that can extract this click-stream data from Web site log files and tell you how many people came to your site, where they came from and which pages had the most activity.
Other products take the analysis further by combining the log-file analysis with e-commerce transaction data, allowing shopping-cart analytics, path analysis of buyers vs. browsers, banner ad effectiveness and online customer profitability. While this information provides marketers with a feel for who their online customers are, what they buy and how they shop, such analysis is specific only to the online channel - so the derived insight falls short of providing a complete customer view.
When "clicks" meet "bricks." With the increase in online initiatives by traditional retailers, marketers can no longer afford to view the Web as a separate and distinct channel. They must learn to integrate it with existing ones. Customer purchase activity is no longer specific only to one channel but is leveraged across multiple channels.
For example, a customer may receive a direct mail piece prompting him to browse a company's Web site and, in turn, buy through a retail store. Or the customer may browse and purchase online, and take delivery at home or the local retail store. Each example generates customer-centric data across separate channels and systems.
Furthermore, as companies continue to serve the customer along multiple-channel fronts, new issues in customer behavior arise such as channel migration, cannibalization and profitability of one channel at the expense of another. Analytic solutions must recognize this problem, integrate the necessary data and allow the business to answer certain specific questions. Some queries may include:
* What is the effect of e-commerce on my traditional business channels?;
* Are certain products better suited for one channel over another?;
* Has my e-commerce initiative cannibalized any of my traditional channel customers?;
* Is my Web site driving traffic to my retail stores and vice versa?;
* And, overall, has my enterprise benefited from my e-commerce channel?
As stated earlier, the data required for such insight resides in disparate computer systems. Significant market advantages will go to those companies employing dynamic, flexible applications offering the ability to integrate disparate customer data quickly and easily.
David Frankland is president/CEO of customer analysis software firm Digital Archaeology Corp., Lenexa, KS. His e-mail address is firstname.lastname@example.org.