A Customer Data Platform that is built for speed
Manthan CDP helps marketers deliver higher ROI by aggregating customer data
from all sources and managing it effectively
Why bring customer data together?
Your business descisions are as good as your understanding of the customer.
A Customer Data Platform
- Is critical for seamless customer engagement, providing you the ability to deliver a consistent customer experience across all touchpoints
- Provides better context, richer analytics and personalization based on cross-channel behavioral data
- Impacts multiple parts of your business, not just marketing. e.g. customer service, location analytics, pricing, assortment planning
Manthan improves the quality of customer data and brings in third party data sets, to help surface opportunities and enhance the quality of insights
Having all the data is pretty useless if business users can’t access it seamlessly
Manthan CDP serves the needs of enterprise users, and makes it easy for you to comply with GDPR and other data privacy requirements
- Serves as a repository of all your first party data organized with customer at the center
- Core infrastructure for analytical workloads
- End to end data ingestion, data management and data serving capabilities
- Puts business users in control, not the IT department
- Can be onboarded within 4-6 weeks, unlike others that take over 8 months
How does Manthan CDP work
Data Ingestion
- Capture operational, technical and business metadata
- Batch & real-time data sync
Data Management
- Optimized data model specific to your industry
- Data quality checks and corrections
- Cost effective, hybrid and real-time
- Create complete customer identity graph
Data Serving
- Serve various user groups: business, data analysts, data scientists
- Support marketing, customer journeys, enterprise analytics
- Applications in personalization, customer service, business intelligence
Manthan CDP meets the data management needs of different user groups
Raw data
As was, unclean
Long-term data storage
Curated/ Transformed data zone
Data structures and relationships defined
Most relevant for data scientists and analysts who are trying to understand long term business trends, and run heavy algorithmic workloads
Business storage
Summarized, purpose built
- Most relevant for business users (marketers, clientelling apps)
- Data marts that store last 2-3 years of data necessary for business planning, comparisons, customer micro-segmentation
In-memory
Very recent or recently used
- Temporary or in-memory cache
- Data extracts designed for highly interactive what if analyses and quick responses
Real-time
Personalization engines
- NoSQL lookup of customer data, most relevant for API to API integration with personalization engines
- Not always a component of CDP
METADATA
Semantic layer, Authorization
Semantic layer that provides data governance capabilities, and allows business users to interact with the data without knowing SQL
