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Software // Information Management

Get Your Customer In Focus

Many now see BI as the lens CRM systems must use to bring customers into view, but are businesses prepared to overcome the data headaches? To choose the right technology, consider management and integration needs, potential performance penalties and the scope of development.

Cover ImageMike Biwer, vice president of product and technology services at Power Information Network, has learned a lesson that many organizations are just now discovering: The more strategic CRM becomes, the more it's about data.

Executive Summary
CRM has blossomed in its sales force automation, customer service and marketing management roles and now churns out data that can be tapped for deeper insight. In fact, sales and marketing executives now routinely request new query and reporting capabilities to get a clearer picture of customers and internal performance. As the analyses multiply and move from tactical to strategic questions, there's a need for more historical data, more information from outside of CRM and more powerful query and analysis tools.

Can you balance the needs of data-hungry managers with IT cost and performance constraints? And should you choose add-on tools and technologies supplied by CRM vendors, or are you better off investing in a business intelligence (BI) infrastructure that can be applied to CRM as well as other needs across the enterprise? The answers depend on your environment. If CRM is focused on a narrow business area and departmental use, the incumbent CRM vendor is likely to offer extensions that will meet your data integration and analysis needs. Conversely, if your data needs and user population are broad and disparate, a BI infrastructure makes it easier to gain meaningful customer intelligence across departments and user communities.
An affiliate of J.D. Power and Associates, the research firm best known for its automotive surveys, Power Information Network gathers daily point-of-sale transaction data from more than 6,000 car dealers in 40 major markets in North America. Biwer's organization has to clean, manage and deploy all that data, and Biwer has become a veritable evangelist. "Data is one of the pillars on which our company was founded," he says. "Our solutions are critically dependent on it."

For some time now, data has been bulging in data warehouses, OLAP servers, packaged applications and other technology platforms. The terabyte threshold, once the exclusive domain of a few big banks, retailers and telecommunications providers, hardly draws notice anymore. A good deal of this data continues to populate enterprise dashboards that enable companies to monitor sales, track profitability and understand customer segments. All the while, CRM has been blossoming in front-line sales force automation, customer service and marketing management.

Many now see BI as the essential component of successful CRM. But what's been a boon to many companies' bottom lines may represent a headache for CRM developers unaccustomed to the challenges of data. Let's examine what kind of data management and integration problems you'll confront in bringing BI into CRM and where you should turn for the required technology. The companion article "Intelligence in the Here and Now," considers how IT organizations are addressing key issues of performance, data integration and predictive analytics to support CRM objectives.

From Tactical to Strategic

In the early days of CRM (circa 1998), "CRM Secrets of Success" and its cousin, "The Seven Deadly Sins of CRM," were two staple presentations at nearly every related conference. They were variations on a theme emphasizing CRM as a business initiative, enriching customer-facing business processes with more relevant interactions or better service, while at the same time driving efficiencies through automation. "People, process, technology" was the catchphrase back then, and business sponsors and practitioners put the secrets into practice (see "Secrets of CRM Success, Debunked").

Today, everybody's automating customer-facing processes in sales, marketing and customer care, and e-business has entered the mainstream. The enticement was not only happier customers, it was cost savings, and thus so-called operational CRM has become standard practice. Those who have delivered operational CRM are now confronting the difficult truth that their new CRM systems not only process data but also actually generate new data. The accelerated creation of customer data presents a host of new business opportunities, not the least of which is greater customer insight. As priorities shift from tactical to strategic, the need to transcend the canned reports that accompany most CRM products is growing more urgent. CRM experts coined the term "analytic CRM," but I'd argue that it's business intelligence, replete with BI's challenges.

Here's a typical scenario. A sales executive is delighted that salespeople are maintaining their lead lists and managing their pipelines with the company's new CRM tool. As customer activities make their way into a database, the executive starts thinking: With the canned reports telling us which salespeople are above quota, wouldn't it be great to factor in their gross margin for this year? And for last year? A report on who closed the most new accounts would be nice.... And who made the most prospect calls.... Are the two related?

So begins grassroots BI. The sales executive asks the CRM team to build some reports. And the team confronts a crop of brand-new problems that have likely been mowed elsewhere in the enterprise.

Sowing the Seeds of BI

In their quest to establish the infrastructure to support sustained customer analytics, most development teams accustomed to operational CRM find thorny BI issues lurking in the tall grass:

  • Historical data. New business programs requiring customer behavior patterns and service trends mandate ever-greater volumes of historical data. But history can be the proverbial fly in the ointment for CRM systems that are measured on response times, such as call center systems supporting waiting customers. Unfortunately, the data volumes that accompany historical data will bring most operational systems to their knees, slowing down data searches and delaying the processing of customer transactions.
  • Ad hoc queries. As business users grow more sophisticated, so do their needs for customized, parameterized or real-time reports. But ad hoc capabilities aren't easy to deliver — the resulting formatting alone can get hairy — nor are they optimized to the static schemas inherent in most CRM toolsets.
  • Data integration. It's no secret that data silos persist even in best-practice IT shops. (Indeed, the administrative nightmare of disparate spreadsheets, databases and standalone data marts is often cited as one of the seminal justifications for exploring enterprise CRM.) Nevertheless, solving the silo crisis — and the inherent data inventory, quality and metadata efforts — is routinely underestimated. Most organizations confront the data integration nightmare when users, building customer reports, want to include data that's not owned by their CRM application.

When it comes to CRM reporting, many IT organizations still struggle with the system contention issues inherent in growing data volumes and more complex analytics. The temptation is to reduce the quantity of reports, cut back on user access or trim data volumes to avoid performance problems. Users assert that BI is less about efficient processing and more about efficient decision-making. But as the diagram below shows, the more sophisticated the reporting and analysis capabilities, the more burdened the system resources.

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