Trillion Rows of Data

When you buy an item at a retail grocer, chances are you’ve seen the point- of- sale coupons that emerge from the register, enticing you to return and save on items you’re likely to buy or may be interested in. As the largest consumer- behavior marketing company in the world, Catalina Marketing predicts shoppers’ buying behaviors to generate customized point- of- sale coupons, ads, and informational messages at 23,000 retailer stores and 14,000 pharmacies across the United States, as well as another 7,000 stores worldwide by analyzing more than 250 million transactions every week.

But Catalina aspired to an even greater level of sophistication and pre- cision. Its recent initiative stores transaction histories over a three- year period on 140 million consumers and uses high- performance analytics to generate more- targeted messages and offers based on that historical knowledge. Eric Williams, Catalina’s former executive vice president and chief information officer, explained the rationale to us.

“A hundred years ago, a merchant knew all about you—your purchases, preferences, and tastes,” he said. “Today, it’s very challenging for a retailer to make the right recommendation for additional products or services to a specific individual based on historical purchases—the volumes have just grown too large. Instead, we’ve settled for segments of demographically similar customers. But cheap data storage and high- performance analyt- ics are changing that. Now we can arm sales associates with timely and prescient information about what you’ve purchased previously and what’s coming in the next inventory refresh. Now you can have your floor staff equipped with mobile devices displaying that information to give every shopper a personalized experience.”

Today, Catalina can build new models in a day, not a month, that enable it to acquire new clients. Those models can more accurately gauge cus- tomer preferences—especially for the hundreds of new products that come out every week. Using in- database scoring, the company processes databases with as many as 1.2 trillion rows of information. “We’ve been

helping clients reach the right people with the right messages for 25 years,” Williams said. “But with the predictive capabilities of high- performance analytics tapping into the historical purchasing data of almost every gro- cery shopper in the country, we’re able to achieve a greater level of preci- sion than ever before—a level no competitor can touch.”

Knowing Which Relationships to Court

With millions of dollars on the line—as well as crucial customer rela- tionships—mobile- phone service providers need to make the right call on past- due accounts. On one hand, late- paying customers will generate profit as long as they are happy. On the other, some delinquent accounts will never pay, so why bother trying to hold onto them?

The trick is separating one from the other—in real time—while they’re engaged with the call center. Applying in- database analytics to a model that predicts a customer’s propensity to pay, a major U.S. telecommunica- tions service provider brings in millions of dollars each month by know- ing which relationships to cultivate—and which ones to hang up on.

Before adding in- database analytics to its IT mix, the provider was already generating $7–$10 million a month from an older version of its propensity model that identified customers more likely to churn. After refining the model and applying in- database analytics, the company added $1 million in revenue.

With in- database analytics, the model comes to the data—stored in a single enterprise data warehouse—instead of moving the data to the model. By eliminating hundreds of steps involved in the process of mov- ing the data and doing the required transformations for analysis, the pro- vider has results in minutes, not hours.

With high- performance analytics, the provider can predict payment, nonpayment, or delinquency for each of its 40 million accounts—not just for a segmented subset, allowing it to make the right decisions.

Call- center representatives access real- time payment predictions about each customer they’re talking to, whether by phone or online chat. Based on those insights, the reps can immediately identify the best offer to give each customer. Bringing its refined model to 40 million records— versus extracting, transforming, and loading 350,000 records from dif- ferent sources and applying the former model—the provider reports an incremental lift of 13 percent, an additional $900,000 to $1.4 million in recouped bad debt each month.

Faster Execution, Greater Efficiency

These kinds of results and financial advantages are happening thanks to

in- database technologies. This technique uses a massively parallel pro-

cessing database architecture for faster execution of key data management and analytic development and deployment tasks. The analytical algo- rithms move closer to the data by running inside the database as native routines to avoid time- consuming data movement and conversion. This HPA architecture provides several advantages by helping to

• Ensure data governance—In- database analytical processing can reduce or even eliminate the need to replicate or move large amounts of data between data warehouses and the analytical environment or data marts.

• Increase IT efficiency and decrease costs—You can use the existing infrastructure and resources, which protects investments and increases operational efficiency, yielding a faster time to value and reducing total cost of ownership.

• Improve model- scoring performance—By eliminating the need to move data between modeling environments and the database for analytic scoring, you can more efficiently deploy processing- intensive predictive models and achieve results faster.

Ideally, in- database analytics should support a wide range of third- party data warehouses and databases, including EMC Greenplum, IBM DB2, IBM Netezza, Oracle Exadata, Teradata, and Teradata Aster.