Campaign Universe Expansion -
Lending products - We leverage risk models, test vs. control incremental results, response models, and an overview of past decisions to identify new segments that can be targeted for lending products.
Response modeling -
Carmel develops machine learning models to predict the propensity of prospects and existing customers to respond to specific offers. While machine learning models are highly effective in predicting propensity, they do not provide business owners an intuitive understanding of the drivers that are most important for a customer or prospect to respond.
Customer level optimization -
Customer-level profitability is the most robust indicator of customer management. It represents complex interaction among multiple revenue and cost drivers. Creating customer level P&L, predicting cost and revenue drivers, and optimizing profitability under multiple constraints can help an organization move the needle in terms of top-line and bottom-line growth
ETL and data integration -
Data is the heart of digital. Integrating data across multiple sources and resolving data quality challenges is one of the most important foundations for developing digital and AI solutions. We provide an extensive set of ETL, ELT, Master Data Management and Data Quality Management solutions for an organization to fast track its data integration journey and integrate data across all sources in real-time or near-real-time frequency. Also provide pre-built connectors to all sources of structured and unstructured data as well as pre-built utilities to leverage scanned document, image, voice, video and text data.
Data quality and master data management -
Data quality is one of the major challenges that hinder an organization’s ability to leverage data. We provide pre-built applications and advisory for helping resolve the data quality challenges within the organization. Our solution consists of pre-built data quality rules for specific data types and rules for correlating accuracy across data fields. The pre-built applications and data quality consulting allow data stewards to identify the root cause of data quality issues and possible suggestions for addressing current data quality concerns.
Optimizing Hadoop stack -
Optimizing setup and processing parameters for optimal processing time batch size and scheduling frequency. Identify the right processing layer by choosing the right mix of processing tools: Spark, Hive, MapReduce, Impala, KUDU, etc.
Branch Network Optimization -
Branch network optimization - Carmel uses geospatial data, external data geo-demographic data, Google Maps data, and competition data to identify the trade area of a store or branch and the influence of competition. This allows organizations to leverage the power of machine learning to optimize their physical distribution thereby enhancing sales and new customer acquisition.
Real-time sensor data has ushered in a new paradigm across multiple applications. We use Kafka, Flume, and Spark-based technologies to harness real-time sensor data from in-car OBD devices, wearables, smartphone sensors, moving assets, and equipment. We also help organizations finetune the Hadoop-based processing engine that is required to process such massive data and leverages cutting-edge machine learning algorithms and pre-built feature libraries to build ML models to predict failure, anomalies, usage patterns, etc.
Digital Analysis -
Our web and app analytics suite allow organizations to track digital data using open source technologies and use it to perform digital market mix analysis, attribution analysis, finetune digital assets, perform A/B testing and optimize the digital funnel. We also leverage extensive image, text, and video analytics to identify key objects, individuals and mine sentiment from text, audio and video data.
Data integration & loyalty data lake -
Integrating data across transactions, demographics, interactions, campaigns, etc. is the key to drive a customer-centric loyalty program. We can help in creating a loyalty data lake to integrate all sources of structured and unstructured data and leverage the data lake to build customer experience management, hyper-personalization, offer management, and campaign management solutions. The loyalty data lake is also the key foundation for developing customer segmentation, churn, cross-sell, customer lifetime value, and offer optimization models. We also help organizations integrate external data like Acxiom data, Lexis Nexis data, etc.
Effective micro-segmentation is the driver of all marketing and customer management strategies. We create micro-segments (also referred to as Customer DNA), by combining demographic, transaction, interaction, and campaign response information. The micro-segmentation is used for hyper-personalization, cross-sell, and all customer management initiatives.
Customer lifecycle marketing & engagement strategy -
Effective customer lifecycle management starting from acquisition to usage management, cross-sell, personalization, and churn prevention is critical to enhancing customer level profitability and customer satisfaction. We help organizations develop machine learning models to predict various customer behavior like cross-sell and churn. Carmel’s micro-segmentation helps in creating customer specific hyper personalization. And the customer 360 application (IVOC) helps organizations obtain a customer-centric view.