It's the moment of truth: Your customer is about to complete a
transaction. Will this be the beginning, middle or end of a beautiful
customer relationship? Does this transaction — and this customer — fit
your organization's strategic vision of how it will prosper in the
marketplace?
The pressing need to answer such questions has become a huge
driver behind data warehousing and business intelligence (BI). As Jill
Dyche discusses in "Get
Your Customer in Focus," BI analytics are redefining customer
relationship management (CRM); companies are finding that, unless coupled
with fruits of timely data analysis, CRM applications fall short of
delivering competitive advantage. "Without analytics, how can I say which
of our customers is most likely to come back to us?" says Atique Shah,
vice president of CRM and technology solutions at Churchill Downs, one of
the largest owners of race tracks and off-track betting parlors in the
United States. "CRM by itself can't give you the expected return on
investment."
Just as important as analytics' contribution to CRM, however, is how
CRM is altering the priorities of the supporting data warehousing
infrastructure. Strong interest in marketing applications — software
purchases are rising 13 percent annually, making it the fastest-growing
segment of the CRM market, according to Gartner — is increasing the focus
on BI, predictive analytics and other means of applying data smarts to
improve the efficiency and effectiveness of marketing campaigns.
Conventional historical analysis and the extract, transform and load (ETL)
activities essential to gathering and preparing the data are obviously
important. But the drive to improve the timeliness and lasting value of
marketing applications is becoming a magnet pulling BI, data warehousing
and data integration toward actionable, "real-time" intelligence.
Let's look at how IT organizations are addressing key issues of
performance, data integration and predictive analytics to support CRM
objectives.
Less Latency, More Opportunity
The time lag between market analysis and action in the form of new
campaigns or other activities adds up to lost opportunity. Credit
marketing organizations even have a measure for campaign latency, called
"days not yet in the mail." Reducing the time by a day or two can mean
thousands if not millions of dollars, both in savings due to process
efficiency and potential revenue gained by reaching customers at just the
right time.
Automation is changing the face of marketing, giving organizations the
ability not only to coordinate a larger number of campaigns, but also to
better allocate resources to the processes most important to
customer-centric business goals. Multichannel campaign management is
increasingly blurring the distinctions between offline and online
channels; automated processes enable more event-driven marketing, where
customer behavior triggers relevant offers that are delivered through the
most appropriate channel. And, lest we forget, latency is the target of
grander, extra-enterprise visions of end-to-end process management.
Organizations would like to transmit event information — that is, the
customer's response to an offer — to demand-driven manufacturing
operations, which then "pull" products and services from suppliers and
business partners in real time to fulfill orders.
Therefore, customer data analysis must keep pace with the new marketing
machine. Some organizations are using business rules to develop customer
intelligence and express that knowledge in a way that process management
understands. Faced with a daunting number of account holders and records
on file, for example, a large financial services company might use
customer data to inform business rules management software, such as Fair
Isaac's Blaze Advisor, Ilog's JRules or Pegasystems' PegaRules. The rules
engine would manage the automated logic behind the modification of
marketing and sales strategies. Without rules and process automation, it
could be hugely labor intensive to repeatedly query the data through BI
tools to decide on the most profitable business strategy. Along with time
and cost savings, the rules would express in process logic the metrics
defined by the company to achieve business performance goals. In this way,
the business rules help align business goals with the overall execution of
CRM strategy.
Speed for the Masses
Customer and market analysis involve both a large volume of queries as
well as significant query complexity to gain insight into multiple
attributes. The speed with which analytic tools can identify, profile and
segment customers often depends on the computing power available. Thus,
the cost of reducing latency has been high, usually requiring massive
investment in an array of data warehousing software, servers and often a
parallel processing infrastructure. Typically, this price tag gives a
decisive competitive edge to large organizations with big IT budgets. It's
clear that Wal-Mart, for example, owes a good deal of its continuing
growth and success to strategic investments in customer intelligence. The
company reportedly has more than 450 terabytes of data stored on
NCR/Teradata systems.
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