Background

A global retail group with millions of customers across e-commerce, physical stores, and mobile apps faced a fragmented view of customer interactions, data showed reduced customer conversion rates, ie lost sales, combined with eroding customer loyalty.  By implementing a Single Customer View powered by advanced customer data platforms, AI-driven analytics, and cloud-native integration tools, the company unified all customer touch-points into one seamless profile.

Customer Outcome

Customers now experience intuitive navigation, personalized recommendations, and faster issue resolution creating ease of use, trust, and loyalty.

Business Outcome

SCV reduced duplication of records by 45%, improved cross-sell and upsell conversions by 30%, and enabled real-time insights into customer journeys. Leveraging the latest in AI, data orchestration, and privacy-compliant platforms, the company achieved faster innovation cycles and simplified governance. The result: improved customer satisfaction, sustained growth, and a scalable framework that positions the enterprise for leadership in customer experience excellence.

Technology Platform Options

1. Customer Data Platforms (CDPs)

• Examples: Adobe Experience Platform, Salesforce Data Cloud, Oracle CX Unity, Treasure Data, Segment (Twilio).

• Why: Purpose-built to unify customer data from multiple sources (CRM, ERP, web, mobile, POS) into a single profile, with real-time segmentation and activation.

2. Master Data Management (MDM) Platforms

• Examples: Informatica MDM, SAP Master Data Governance, IBM InfoSphere MDM, Reltio.

• Why: Ensures data consistency and governance across enterprise systems by creating a “golden record” of customer data.

3. Data Integration & Middleware

• Examples: MuleSoft (Salesforce), Dell Boomi, Talend, Informatica Intelligent Cloud Services.

• Why: Connects siloed systems and automates real-time data flows into the SCV platform.

4. Cloud-Native Data Lakes & Warehouses

• Examples: Snowflake, Databricks, AWS Redshift, Google BigQuery, Microsoft Fabric.

• Why: Centralizes structured/unstructured data at scale, enabling AI/ML-driven analytics for personalization.

5. AI & Analytics Layers

• Examples: SAS Customer Intelligence, Microsoft Azure AI, AWS SageMaker, Google Vertex AI.

• Why: Applies machine learning to SCV data to predict behavior, personalize experiences, and drive insights.
  
  
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