Gaining Customer Insights and Segmentation Using Artificial Intelligence

Efficiently modeling segments in machine learning for personalization by using tensor train decompositions instead of matrices. This involves converting the input matrix to an N-dimensional tensor where each dimension corresponds to a segment property. The tensor is then approximated using tensor train decompositions to enable efficient training and scoring of segment-specific models without needing large fully-connected layers. This allows scaling segment modeling with many segments and segment properties.

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Autonomously determining and resolving a customer's perceived discrepancy during an automated customer service interaction. The system uses NLP to extract perceived and expected states from customer utterances. It determines discrepancies and verifies them. It then generates responses with fact patterns and corrections/confirmations based on the discrepancy and verifiable assertions. The system refines itself by monitoring responses and acquiring missing info/evidence.

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A system for accurately attributing the value of marketing touchpoints that influence customer orders. The system uses machine learning to analyze a user's touchpoints (e.g., ads viewed) over time and predict the probability of an order during a specific time period based on the touchpoint vector. When an order is placed, the system determines the contribution of each touchpoint to the order using the trained model. It then allocates a percentage of credit for the order to each touchpoint based on their respective contributions. This allows measuring the long-term and short-term effects of marketing touchpoints on customer orders separately.

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Automatically personalizing digital channels like websites, apps, and email without manual intervention. The system uses machine learning techniques to analyze visitor behavior, context, and content attributes to autonomously cluster users into behavioral segments. It then generates personalized content recommendations for each user based on maximizing mutual information between segments and content. This provides automated personalization that adapts to users over time and leverages behavioral insights to optimize content selection.

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Automated analysis of customer interaction text to generate customer intent information and a hierarchy of customer issues using neural networks and clustering techniques. The method involves training a neural network on customer interaction transcripts and notes. It then generates summaries of each interaction using the trained network. These summaries are converted into multidimensional vectors and clustered based on similarity. The clusters are aligned with interaction attributes to generate a hierarchical mapping of customer issues.

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System for efficiently identifying target customer subgroups without requiring complex Boolean queries. It generates bitmap indexes from customer data streams and allows users to select conditions via a GUI. The system automatically generates Boolean expressions from the selected conditions and queries the bitmap indexes to find matching subsets. This allows targeting customers based on conditions like credit score thresholds and likelihood of product applications.

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Objectively evaluating data from a business and relating it to customized lists of merchant services. The technique involves using machine learning models to analyze business data and merchant service data, then deriving tailored recommendations of relevant services for each merchant. The models transform numerical representations of merchants and services into a common vector space to compare and correlate them. The models are trained on merchant profile data including statistics, categorical info, historical usage, and search history. This enables intelligent, near-real-time service recommendations based on aggregated merchant data.

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Method to improve customer satisfaction and resource allocation in call centers by prioritizing call-backs based on predicted customer value. When a customer hangs up or uses an option to call back, the call center retrieves customer data, analyzes factors like purchase history, and predicts the likelihood of sale or payment. High-value customers are given priority call-back slots while lower-value ones use a subordinate queue. This ensures important customers get prompt attention versus waiting.

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Generating more informative user profiles to better understand and analyze user needs. The method involves acquiring user data from a primary user and their connected secondary users. Clustering the secondary users into sets based on similarity. Determining key user characteristics from each set. Then generating the primary user profile using their own data and the key characteristics from their connected users. This leverages the connected user data to improve the primary user profile when their own data is insufficient.

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A system to provide personalized product recommendations based on customer interactions captured in video. The system analyzes video of customers interacting with products to identify areas containing objects representing the customer and product. It then selects a relevant model from a library of models to output based on the relationship between the customer and product objects identified in the video. This allows providing targeted information correlated to the customer-product interaction seen in the video.

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Targeting users for products and services using machine learning to identify potential customers and their interests. The method involves determining affinity levels between users and entities based on their data. A machine learning model is trained on entity and user data to predict affinity levels. This allows generating personalized campaign strategies targeting users to entities with high affinity.

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Determining user affinities for digital content to predict what content will engage specific users. The method involves tracking user interactions with tagged digital content, aggregating reports to generate user affinity profiles, and evaluating new content against profiles to predict user engagement. It provides real-time feedback to marketers on content attractiveness based on affinity similarity/difference.

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Personalized messaging protocol that improves engagement by segmenting users based on their historical behavior. The protocol calculates user propensity scores for taking different actions using feature vectors of historical data. It segments users into groups based on their propensity scores for different actions. Messages are then delivered to users based on their segment. This targeted segmentation aims to increase interaction with messages compared to generic messaging.

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A retail assistance system using AI to provide personalized recommendations to customers as they shop. The system detects customers entering the store, determines if they are new or returning, analyzes their facial expressions to determine emotions and personality, and leverages past purchases and store history to recommend items tailored to each customer. The system can also track real-time in-store purchases for ongoing recommendations. The personalized recommendations are presented to the customer for choice.

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Automated pairing of customers with relevant businesses using machine learning models to predict customer needs based on historical activity and optimization algorithms to minimize cost, distance, and time. The system analyzes customer purchase patterns, identifies anticipated activities, matches them with relevant offerings, and provides tailored recommendations. It leverages ML to detect patterns, identify resources, and optimize matches.

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Using clustering and machine learning to automatically route customer service tickets to the most relevant troubleshooting guidebooks. The method involves extracting features from a new ticket, clustering it based on similarity to other tickets, and using the associated guidebook from that cluster to process the ticket. The guidebooks are generated by applying machine learning to historical tickets in each cluster.

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Assessing product concepts using agent-based simulation and machine learning to rank and select winning products that meet consumer requirements. The method involves extracting ingredient and theme data from candidate product descriptions, simulating consumer responses using connected agents, and determining relevance and originality scores. A market model provides relevance metrics and a clustering model originality metrics. The ranked list of products is generated based on the scores. The simulation allows testing product concepts against consumer needs without actual product launches.

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Content recommendation system that uses continuity and grouping information of attributes to improve the accuracy of predicting user interaction with content items. The system trains a machine learning model using historical user interaction data, user attributes, and content attributes. It encodes continuity and grouping information into the model to take into account similarity of user behavior beyond isolated discrete values. This allows the model to consider continuity like age ranges or time intervals instead of just discrete ages or hours. It improves the precision and accuracy of predicting user interaction with content items.

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Training and applying machine learning models in a centralized database system to identify accounts likely to interact with a content item when presented. The system trains a model using historical data on account characteristics, content item characteristics, and interactions. It then applies the model to a target set of accounts to predict which ones will interact with a specific content item. The content item is presented to those accounts with high interaction likelihood. The model allows targeted content presentation to increase interaction rates in the database system.

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Data-driven segmentation and clustering method for retail customers that dynamically creates customer segments based on transaction histories instead of manual segment definitions. It involves mapping item codes to multidimensional space, calculating aggregate consumer-item vectors from transactions, clustering them, and providing the segments to promotions engines. The segments adapt as transactions change.

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