IBM® SPSS® Customers Honored for Innovative Use of Predictive Analytics

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SPSS, an IBM Company and the leading global provider of predictive analytics software and solutions, today announced the recipients of its annual Insight Awards presented at the 2009 Directions User Conference in Las Vegas.

Advance Auto Parts, Bancolombia and Nu Skin Recognized for Improving Market Basket Analysis, Combating Money Laundering and Reducing Churn

CHICAGO, 11/16/09—SPSS, an IBM Company and the leading global provider of predictive analytics software and solutions, today announced the recipients of its annual Insight Awards presented at the 2009 Directions User Conference in Las Vegas.

Advance Auto Parts, Bancolombia and Nu Skin Enterprises Inc. were recognized for their innovative use of IBM® SPSS® predictive analytics software to improve market basket analysis and supply chain optimization, monitor and identify suspicious money laundering activities and increase marketing campaigns to reduce churn, respectively. These organizations join commercial, government and academic customers worldwide that are deploying predictive analytics to acquire, grow and retain customers, while mitigating risk and reducing fraud.
Established in 2004, the Insight Awards recognize customers that have successfully delivered exceptional business value and gained significant return on investment (ROI) — in quantitative results or objective observations. The Insight Awards honor customers for the compelling and pioneering use of predictive analytics contributing to their organizations’ successes.

Deepak Advani, now responsible for the recently acquired SPSS business, and vice president of predictive analytics at IBM, said, “Smart and strategic companies worldwide recognize the value of applying IBM SPSS predictive analytics software to gain insight from the wealth of data for better, faster decisions and actions, optimized processes, and more predictable outcomes. We are excited to shine the spotlight on these deserving companies and showcase the impressive results gained through using IBM SPSS predictive analytics software.”

Advance Auto Parts Increases Loyalty, Improves Sales
Advance Auto Parts, the second-largest retailer of automotive parts in the United States, uses IBM SPSS predictive analytics software for market basket analysis and supply chain optimization to accurately manage inventory at warehouses and individual stores. The organization chose IBM SPSS predictive analytics to quickly and easily leverage all data assets to produce, evaluate and deploy analytical models on SKU association modeling, market basket and cluster analysis when processing sales transaction data.

Using IBM SPSS predictive analytics software, Advance Auto Parts analyzes and better understands merchandise affinities to identify customer preferences, improve cross-selling and optimize products. This has led to improved availability, overall profitability, higher propensity to move SKUs at stores, and, most importantly, increased customer loyalty and satisfaction.

Bancolombia Combats Money Laundering
Bancolombia, the largest commercial bank in Colombia and one of the largest in Latin America, uses IBM SPSS predictive analytics software to monitor the transactional patterns of its customers. Bancolombia is committed to prevent money laundering activities in its organization, implementing pattern recognition, anomaly detection, risk analysis and predictive modeling capabilities that IBM predictive analytics software offers. Employing this technology, the bank reduced the number of suspicious operations that required auditing by more than 90 percent. The bank also increased accuracy in identifying positive cases of money laundering, helping investigators to easily adapt models to the changing tactics of those engaged in this criminal activity.

Additionally, Bancolombia improved the efficiency of its auditing process as it increased the number of investigations that finally ended in reports to the authorities from less than 25 percent to more than 60 percent. Bancolombia slashed the administrative costs of the investigations of suspicious operations by 60 percent after implementing IBM SPSS predictive analytics software, allowing it to change from a distributed investigation process to a centralized one.

Nu Skin Enhances Marketing Campaigns, Reduces Churn
Nu Skin Enterprises Inc., a $1 billion global direct selling company, markets premium anti-aging personal care and nutrition products through a global network of more than 750,000 active independent distributors. The organization selected IBM SPSS predictive analytics software to quickly build, validate and deploy predictive models to improve its marketing campaigns and segmentation and, most importantly, improve distributor retention.

Using IBM SPSS predictive analytics software, Nu Skin's business development department built highly effective propensity models that allowed the company to identify customer and distributor segments to significantly improve retention in a key market by 40 percent. With these models, Nu Skin was able to understand customer and distributor trends, and then successfully re-engage distributors – on an individual basis – with event-based marketing activities.

About SPSS, an IBM Company
SPSS, an IBM Company, is a leading global provider of predictive analytics software and solutions. The Company’s complete portfolio of products – data collection, statistics, modeling and deployment – captures people’s attitudes and opinions, predicts outcomes of future customer interactions, and then acts on these insights by embedding analytics into business processes. SPSS solutions address interconnected business objectives across an entire organization by focusing on the convergence of analytics, IT architecture and business process. Commercial, government and academic customers worldwide rely on SPSS technology as a competitive advantage in attracting, retaining and growing customers, while reducing fraud and mitigating risk. SPSS was acquired by IBM in October 2009. For more information, visit http://www.spss.com.

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