Published on 30-Jun-2012
"The company didn’t want the people building analytic applications worrying about data distribution while the algorithms were running. Netezza takes that worry away." - Brad Terrell, Vice President and General Manager, Netezza and Big Data Platforms, IBM
A financial software company
Big Data, Data Warehouse
A financial software company is well known for high-quality products and effective multi-channel advertising programs. In part this is due to the way it uses user behavior data to provide a more personalized and relevant customer experience that delights customers. To achieve higher customer satisfaction results and keep improving its products to meet its customers’ demands, the company needed the ability to load huge volumes of data into its data warehouse—hundreds of millions of rows daily and 100 million clicks—and to run complex queries very fast.
A financial software company sought to analyze customer engagements to improve product quality and increase retention. It also wanted to increase marketing return on investment and targeting precision using behavioral variables.
The company deployed an IBM Netezza data warehouse appliance, which enables it to perform next-generation analytics in order to consistently and continuously improve its service.
Higher customer satisfaction scores; projected USD10 million revenue lift within 12 months; ability to detect and correct cross-channel cannibalization.
A financial software company owes its success to high-quality products, innovative distribution channels and effective multichannel advertising programs. The company was an early adopter of the software as a service (SaaS) delivery model, while also reaching customers through promotions, retail channels and call centers. But credit is also due to the way it uses granular user behavior data to provide a more personalized and relevant customer experience.
This financial software company uses the Net Promoter Score, a popular methodology for gauging the loyalty of customer relationships, to confirm its customers are satisfied by asking them a simple question: Would you recommend us to a friend? There is no way for any company to fake its way to a good score: It must provide first-rate service.
This financial software company employs next-generation analytics to consistently and continuously improve its service through:
- In-channel optimization
- Cross-channel/platform engagement measurement
- In-product behavioral analysis
- Predictive analytics
- Testing for impact
Easier said than done
There are several obstacles to achieving success in each of the areas noted above. First, digital media data pours in with unprecedented speed. The company needs the ability to load huge volumes of data into its data warehouse—hundreds of millions of rows and 100 million clicks daily—and to run complex, high-speed queries.
This has been accompanied by an explosion in the number of variables business users want to analyze thereby making segmentation, classification and regression exponentially complex. Then there are the related tasks of running complex analytics and building predictive models, both the regression and classification varieties.
The company created a five-point program for using data to create a better customer experience:
- Intelligent tracking of user behavior
- A scalable analytic platform
- Predictive modeling
- Hypothesis testing and validation
- Intelligent intercept interaction
One major component that would enable these action items: technology that would give the company a single view of the customer, and at the same time allow it to analyze petabytes of data.
After evaluating several leading data warehouse and analytics platforms, the software company selected and deployed the IBM Netezza® data warehouse appliance, an architecture that can rapidly query large volumes of data and handle computational complexity with low total cost of ownership (TCO) and operational simplicity. The company promptly put the IBM Netezza data warehouse appliance to work on three critical projects:
- Website analysis
- In-product discovery
- Analysis of Google DoubleClick advertising data
The goal of this project was to increase the company’s customer satisfaction score for its products with thorough website analysis. This software company’s customers have the ability to use its products online and submit payment when they are finished with a given project. But there’s a high price that the company pays if it fails to satisfy the customer: Frustrated users abandon the product, leaving the site without submitting payment. Thus, the company has to anticipate behavior and offer help before the customer asks for it. That means flagging screens, screen clusters, application flows and issues with higher-than-usual abandonment rates.
The company collects behavioral data from multiple sources. But those tools don’t enable staff to track and analyze individual web behaviors. Nor do they facilitate behavioral segmentation, time-series analysis, predictive modeling or scoring. Furthermore, the company wanted to unite its user behavior and demographics data to gain deeper insights through analysis.
The IBM Netezza data warehouse appliance allows the company to combine these varied digital media data types and data sources, while also offering access to in-database analytic functions. It can now look at user paths through the product or the website, identify places where customers have trouble or drop-off, and use that information to optimize website navigation.
This granular understanding of user behavior on its site has allowed the company to make small changes that enhance the user experience. As a result, the company has achieved a higher customer satisfaction score and increased subscription revenues.
One of the software company’s online products serves as the main channel for two critical revenue streams. Managing these revenue streams requires advanced analytics which provide detailed usage information for that product.
The company collects in-product discovery (IPD) data using custom instrumentation built into its software. Prior to using the IBM Netezza data warehouse appliance, it could only get data from one percent of users, and it had trouble analyzing the data it had because the average query took five hours to run.
As a result, the product team faced challenges when making roadmap decisions. They couldn’t base offers on “an understanding of the important characteristics and behaviors of that target audience,” says Brad Terrell, vice president and general manager for Netezza and Big Data Platforms at IBM. “Response rates were low, and they were not achieving their goals.” The company also needed to filter outliers and spot erroneous data coming from fraudulent clicks and click bots.
Since deploying the IBM Netezza data warehouse appliance, the company has increased its sample size from one percent of its users to 10 percent while reducing the average query time to a fraction of what it took before. Moreover, the product team now has a much deeper understanding of how customers are using the product. And with a new targeting analysis tool fueled by the IBM Netezza data warehouse appliance, the company conducts monthly digital media cross-sell campaigns generating additional revenues for that product.
“The company projected USD10 million in new revenue within 12 months,” says Terrell. “That’s a non-trivial lift.” And with a market sizing tool powered by the IBM Netezza data warehouse appliance, the company can target even more precisely based on behavioral variables: who saw or clicked on an ad, the amount of time they have in business and whether they use competitors’ online services.
Advertising data analysis
The software company is a major display advertiser, and that’s likely to continue. Its ad spend includes display impressions, television, email, affiliate marketing, mobile and search advertising. And this leads to staggering online traffic. The company received 18 billion impressions in the last year, and recently collected 16 billion records in 15 days. But when there are so many clicks, it’s difficult to analyze them all, and the clicks that DoubleClick records can’t be sampled. The company had to determine through regression analysis which exposures led to conversion and the impact of those conversions on the bottom line.
In its incumbent environment, the company had trouble detecting and correcting cross-channel cannibalization. But with transaction-level analysis facilitated by the IBM Netezza data warehouse appliance, this is no longer a challenge. In one campaign, the company justified additional advertising spend after proving that cannibalization had not occurred where the company thought it had.
In another case, the company discovered that people had been served digital media ads after they had converted. The company stopped these post-conversion impressions, leading to substantial savings while preventing customers from getting annoyed.
The company uses the IBM Netezza data warehouse appliance to track the full customer lifecycle from exposure to conversion (E2C). The IBM Netezza data warehouse appliance helps this company field more effective campaigns and make better data-driven decisions based, as indicated above, on in-channel optimization, cross-channel/platform engagement measurement, predictive analytics and testing for impact.
Big data, big math
Thanks in part to the IBM Netezza data warehouse appliance, the company is solving its core advertising challenge: Navigating through a complex ecosystem to achieve great outcomes.
The IBM Netezza data warehouse appliance enables the company to handle big data and big math. For example, customer experience analytics can be run without headaches thanks to massively parallel algorithms. “The company didn’t want the people building analytic applications worrying about data distribution while the algorithms are running,” says Terrell. “The IBM Netezza appliance takes that worry away.”
The company had 15 terabytes of data in its IBM Netezza data warehouse appliance in 2011, and that number is constantly growing. For example, about 20 million impressions flowed through the IBM Netezza data warehouse appliance in one recent fiscal quarter.
Why the IBM Netezza data warehouse appliance? It combines storage, server and a high-performance database in a simple appliance that works well with other platforms and reporting tools. The IBM Netezza data warehouse appliance outperforms traditional systems on query execution, and it’s scalable. It can be run with minimal management overhead. Freed from worrying about technology, the company can focus on attracting and serving customers.
The company’s big data analytics platform complements its high performance IBM Netezza data warehouse appliances with a multinode Hadoop cluster. By moving data back and forth between the two systems, the company is able to take advantage of both technologies. Hadoop helps in establishing relationships between unstructured data elements before they are loaded into the IBM Netezza data warehouse appliances. There, the ability of the IBM Netezza data warehouse appliance to execute high-speed complex queries and integrate with a broad array of extract, transform and load (ETL) and reporting tools with minimal management gives the software company the power, flexibility and scalability it requires.
What’s next? The company will surely have more complex analytical needs as time goes on. “People expect instant gratification—with good reason,” says Terrell. “Real-time access to data is very critical in many applications. So that puts a premium on many new types of analytics.”
The company will also find new ways to leverage what IBM’s Terrell, calls “the digital exhaust trail of human activity” across its growing product suite.
It’s no small task to conduct customer experience analytics and take action on the results when your customer base is as large as this company’s. But IBM helps solve these challenges in ways previously not possible, and the results for the company can be easily summed up: better products, a more delightful customer experience and increased return on investment.
About IBM Netezza data warehouse appliances
IBM Netezza data warehouse appliances revolutionized data warehousing and advanced analytics by integrating database, server and storage into a single, easy-to-manage appliance that requires minimal set-up and ongoing administration while producing faster and more consistent analytic performance. The IBM Netezza data warehouse appliance family simplifies business analytics dramatically by consolidating all analytic activity in the appliance, right where the data resides, for industry-leading performance. Visit: ibm.com/software/data/netezza to see how our family of data warehouse appliances eliminates complexity at every step and helps you drive true business value for your organization. For the latest data warehouse and advanced analytics blogs, videos and more, please visit: thinking.netezza.com
About IBM Data Warehousing and Analytics Solutions
IBM provides the broadest and most comprehensive portfolio of data warehousing, information management and business analytic software, hardware and solutions to help customers maximize the value of their information assets and discover new insights to make better and faster decisions and optimize their business outcomes.
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Products and services used
IBM products and services that were used in this case study.
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