Single Customer View Accelerator - 8 Wk Implementation

Customer data is one of the most valuable assets for any business, yet it can also be one of the most challenging to manage and leverage to achieve its full potential. You may have multiple customer databases from different systems, channels, and departments, each having its own unique identifier, format, quality and completeness. This can result in data silos, inconsistencies, duplicates and errors that prevent you from gaining a holistic and reliable view of your customers and their needs, preferences, and behaviours.

To overcome these challenges, you need a Single Customer View (SCV) solution that can match and deduplicate your customer data across multiple sources to create a single, trusted, consistent, enriched record for each customer. This can help you improve your customer service, marketing, sales, and analytics, and deliver personalised, relevant experiences to your customers.

Azure Machine Learning is a cloud-based platform that provides end-to-end capabilities for building, training, deploying and managing data products. Working alongside your existing database solution, BizData uses Azure Machine Learning to implement a SCV solution using techniques such as:

• Data ingestion, preparation and profiling: Extract your customer data from various sources and profile to gain an understanding of data quality issues that could affect the SCV solution
• Data matching and deduplication: Apply ML algorithms such as fuzzy matching, unique individual signatures, record linkage and entity resolution to identify and merge duplicate customer records using classification models developed in Azure Machine Learning alongside your custom matching rules
• Unique common identifier: Create a lookup table to link your existing systems and databases using a common identifier
• Customer graph: Link all records for the same customers across and within multiple systems to help understand changes to their circumstances and behaviours, and highlight data quality issues
• Manual matching: Manually review potential matches to improve the solution’s accuracy and refine classification models
• Data delivery and consumption: Publish and consume your SCV data in various formats and destinations

SOLUTION ENGAGEMENT:

This unique 8-week engagement will include:

• Four 2-hour workshop to assess your current customer data landscape, identify your SCV goals and challenges and articulate your desired future state
• Designing a customized SCV solution and architecture to integrate selected customer data sources based on your specific requirements
• Implementing the SCV solution using Azure Machine Learning
• Implementing an application to manage the manual identification of potential matches
• Automating the SCV process at your desired frequency such as monthly, weekly or even daily
• Validating the functionality and accuracy of the SCV solution

On completion, a comprehensive handover session will be conducted with the team and a SCV Operations Guide detailing the configuration and operation of the SCV solution as delivered will be provided.

*NOTE: All terms, conditions and pricing are subject to each engagement.