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customer behavioral data

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Published By: Oracle OMC     Published Date: Nov 30, 2017
Since the theme of this brief is to keep things simple, letís be as simple and plain as possible: The customer experience (CX) is broken because the marketer experience is broken. However, itís not entirely marketingís fault. Legacy technology provides only a distorted view of the customer. Thatís what creates the broken experiences. Itís the same technology thatówhile providing rich behavioral, demographic, and preference dataódoes not allow for silos of data to be shared across channels.
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Oracle OMC
Published By: Adobe     Published Date: Oct 11, 2018
Adobe offers powerful personalization tools that help you give your customers custom experiences every time they interact with you. With Adobe, you can take control of your data, use AI to achieve scale, and see incredible results. Adobe Target helps marketers deliver relevant, personalized experiences to highly targeted audiences based on behavioral analytics and audience data. Powered by AI and machine learning, users can deliver individualized customer experiences at massive scale.
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Adobe
Published By: Unica     Published Date: Sep 23, 2010
If you knew more about your customers, wouldn't you tailor their experience to their unique profile and online behavioral history? Download Three Key Integrations for Interactive Marketing Success and learn how to integrate online data into your interactive marketing strategy.
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unica, interactive marketing, behavioral marketing, personalization, email marketing, target marketing, aberdeen
    
Unica
Published By: SPSS     Published Date: Jun 30, 2009
This paper describes why and how Enterprise Feedback Management (EFM) is a critical component in solving the problem of enhancing customer-driven innovation and improving the predictive capabilities of the IT organization.
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predictive enterprise, spss, enterprise feedback management, efm, customer-driven innovation, crm, customer relationship management, customer experience, consumer behavior, actionable insight, centralized system, feedback programs, push orientation, lifecycle-based approach, customer retention, churn rate, compliance, internal r&d, behavioral data, descriptive data
    
SPSS
Published By: SPSS, Inc.     Published Date: Mar 31, 2009
This paper describes why and how Enterprise Feedback Management (EFM) is a critical component in solving the problem of enhancing customer-driven innovation and improving the predictive capabilities of the IT organization.
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predictive enterprise, spss, enterprise feedback management, efm, customer-driven innovation, crm, customer relationship management, customer experience, consumer behavior, actionable insight, centralized system, feedback programs, push orientation, lifecycle-based approach, customer retention, churn rate, compliance, internal r&d, behavioral data, descriptive data
    
SPSS, Inc.
Published By: Oracle     Published Date: Jan 07, 2014
Most enterprises understand the importance of listening to customer comments and conversations through social channels, and engaging and developing relationships with influencers and customer communities. But as the variety, volume, and velocity of social data continues to grow, many organizations are looking for cost-effective ways to use this data to get a better understanding and more holistic view of their customer. Social provides a unique channel to learn about your customer, and offers real-time insights like interests, actions, likes and dislikes that can provide invaluable behavioral and predictive data patterns. This social data (when aggregated with enterprise CRM data) reveals a more complete picture and understanding of customers.
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social crm social roi, social media analytics, cmo and cio collaboration, socially enabled enterprise, social business, social data, social marketing, social media management, social relationship management, marketing and technology collaboration, cmo and cio relationship, customer insight, customer experience
    
Oracle
Published By: Q2 eBanking     Published Date: Jul 30, 2017
Many financial institutions see their customer data as one of their most valuable assets. Unlocking insights from that data helps FIs understand, anticipate and offer account holders the products and services they truly need. A major trend to unlocking customer insights is using machine learning to surface the behavioral intelligence buried in the large amount of account holder transactional data captured each and every day. In this paper, learn how a group of talented, enthusiastic analysts with an open approach to data can yield some very interesting and extremely valuable and actionable results. This approach, championed by Q2 Executive Vice President and CTO Adam Anderson, has led to a new platform, Q2 SMART, which provides powerful behavioral analytics for financial institutions, enabling growth while providing account holders with real value.
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Q2 eBanking
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