IBM announced that Nedbank Limited, a leading financial services provider throughout Southern Africa, is using IBM’s predictive analytics solutions to improve the customer experience and provide more responsive real-time services. As a result of this technology implementation, the bank has reduced social media monitoring costs by over a million rand a year (US$105,000 per year), while boosting customer service productivity levels by 20 percent.
Nedbank partnered with IBM and its Business Partner Olrac spSolutions to develop a predictive modeling solution that integrates social media analytics into the bank’s systems. By doing so, decision makers across the bank’s operations now have access to deep, near-real-time analysis of social media data through flexible dashboards. This gives marketers potent insights regarding preferences, sentiment and satisfaction that help to craft and deliver more effective sales promotions and customer messages.
Nedbank recognizes the growing importance of social media as a marketing tool, actively reaching out to customers through Facebook, LinkedIn and Twitter, as well as through blogs and customer forums.
“By improving our social media analytics capabilities, we’re engaging with our customers on a 1:1 basis, to address and anticipate their needs more cohesively,” says Eugene Liebenberg, head of Retail Business Intelligence Solution Science at Nedbank.
Gordon Barnes, Industry Solutions Leader at IBM South Africa says, ”Where before the bank used a number of tools and external resources to mine social media for a 360 view of their customers, today the analytics provide a visual dashboard of customer feedback and opinion in near-real time from social media. This has helped Nedbank transform the process of customer engagement and response by embedding information-based insights into every process, decision and action.”
“By using advanced analytics, Nedbank is in a prime position to sharply focus marketing campaigns, promote high profit margins and increase cross-selling opportunities.” concludes Barnes.
The solution used a combination of IBM predicative analytics SPSS Collaboration and Deployment Services, SPSS Modeler.
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