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Unstructured Data is the Key to Improving the Customer Experience: Here’s Why

InMoment XI

Other examples of unstructured data sources include social media posts, call transcriptions, and customer reviews. Examples of Unstructured Data The best example of unstructured data is customer reviews. Another example of unstructured data is a call transcript. Unstructured data can come from various sources.

Data 260
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Conduent CX Research Finds Strong CX Fundamentals Are Essential in the Consumer Relationship

CSM Magazine

For example, 42% of survey respondents said the care provided today is “Better” or “Much Better” than that of three years ago. The good news for Retail/e-Commerce brands is that 62% of their customers think their customer care needs and expectations are generally being met, versus 35% of consumers across all industries.

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Beyond Profit: The Ascendancy of Brand Purpose in B2B

ECXO

They are judging companies on environmental, social, and governance (ESG) claims, and more importantly the action they take. Price and quality matters but consumers and buyers are increasingly making decisions driven by values-based preferences aligned with customer experience. Business customers care about what your brand stands for.

B2B 353
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A Comprehensive Analysis of AI’s Impact on the Employee Experience by Ricardo Saltz Gulko

ECXO

This transformation, driven by advanced data analytics, machine learning, and predictive technologies, is ushering in a new era of workplace efficiency and personalization. Automated resume screening, AI-powered interviews, and predictive analytics streamline the hiring process, making it faster and more efficient.

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6 Killer Applications for Artificial Intelligence in the Customer Engagement Contact Center

If Artificial Intelligence for businesses is a red-hot topic in C-suites, AI for customer engagement and contact center customer service is white hot. This white paper covers specific areas in this domain that offer potential for transformational ROI, and a fast, zero-risk way to innovate with AI.

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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

For example, in an application that recommends a music playlist, features could include song ratings, listening duration, and listener demographics. SageMaker Feature Store now allows granular sharing of features across accounts via AWS RAM, enabling collaborative model development with governance.

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Build and train ML models using a data mesh architecture on AWS: Part 1

AWS Machine Learning

For example, in the financial services industry, you can use AI and ML to solve challenges around fraud detection, credit risk prediction, direct marketing, and many others. Large enterprises sometimes set up a center of excellence (CoE) to tackle the needs of different lines of business (LoBs) with innovative analytics and ML projects.