Why should we use AI for data analysis?

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Natural language processing
Natural language processing enables an AI analytics tool to understand and interpret human language so it can process and analyze it for meaningful information. NLP is pivotal to accurately analyzing social luxembourg b2b leads media listening data, customer reviews, blogs, news articles and other text-based data, including numbers and emojis.

Neural networks
Neural networks are algorithms built to understand relationships between data points identified by machine learning. They are inspired by how the human brain functions and comprise interconnected layers of nodes or neurons that remember relationships in data and learn from them. As a result, the AI tool progressively becomes smarter and more accurate with time, thus also making it cost-effective in the long run.

Deep learning
Deep learning algorithms are advanced NNs that process complex raw data using multiple layers of interconnected nodes. This enables it to adapt to a variety of data types and datasets and extract deeper insights for far more sophisticated analysis than traditional data analytics.

AI analytics empowers companies to automate repetitive tasks and process complex data more efficiently. Thus making scaling easy and frictionless. These tools can process thousands of API calls in minutes, giving you critical information about your brand, customer care and competitive analysis in a matter of minutes. This helps your team focus on more strategic aspects like providing stellar customer service and brand experience.

Sample this—per The 2023 Sprout Social Index ™, 76% of consumers notice and appreciate when companies prioritize customer support. Customers expect personalized service that’s more than just resolving an issue quickly. They want a personalized interaction that’s authentic and engaged.