Copyright © 2014 http://jtonedm.com James Taylor
IBM recently introduced a Predictive Customer Intelligence solution. Like the counter fraud solution earlier, this is one of IBM’s multi-product and services signature solutions. The focus of this solution is the increasing need to be truly customer-centric, focusing marketing on individual customers not on segments or campaigns, not on pre-existing mental models or ideas. This matters because customers are changing with 75% disbelieving ads, only 8% thinking that organizations are providing a superior experience for instance. This is challenging due to multiple channels, incomplete information, problems delivering personalization on every channel and doing all this in real-time. Success requires putting customers at the center.
IBM believes that by connecting more data, and doing more sophisticated analysis of this data, a clearer view of customers emerges allowing for better customer engagement. Predictive Customer Intelligence is solution designed to deliver real-time, optimized recommendations at the point of decision and has emerged from the individual products and GBS projects using them. It can be combined with digital analytics like Coremetrics, customer behavior analytics like TeaLeaf and social media analytics to leverage all these analytics to deliver next best action decisions at the point of contact.
The solution has three capabilities:
Predictive capabilities to analyze data
Industry specific content to accelerate delivery in retail, telco, insurance and banking
Operational connections into things like Unica EMM as well as other contact systems like Mobile, web etc.
The solution uses a wide range of analytic techniques and approaches from acquisition to churn, propensity and affinity to lifetime value and segmentation. A wide range of SPSS products are included in the solution along with the pre-built connectors based on IBM’s integration technology and content.
From IBM’s point of view this is a powerful stack because it combines:
Predictive and other advanced analytics
Decision Management to deploy and operationalize these analytics using rules and optimization
Cross-channel, real-time support
Industry specific templates
IBM Watson foundations
It would have been nice to learn more about how this works but it is certainly targeted at a key decision management use case (probably the most dynamic decision management market out there) and has the right technology components.
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