Building a complete, real-time understanding of each customer is vital for more personalized engagement—but that will only happen when your data is clean, connected, and consistent. That’s when data mapping for the Customer Data Platform can help.
A CDP will pull data together from multiple sources, process and standardize the customer records, and remove duplicates to create one master single profile. Each view of the customer adds data to observe all activities and interactions, and layers behavioral and predictive analytics on top of that—providing your marketing and customer experience teams with smarter, more actionable customer insights.
Data Mapping and Integration: Connecting the Dots Across Systems
Today, enterprises leverage a variety of digital tools (marketing automation platforms, ERPs, sales CRMs, loyalty programs, and mobile apps). Each of these systems stores data in its own way and can often create a spectrum of disconnected data silos.
Data mapping and integration in a CDP enables businesses to connect this disparate data and associate it with a singular customer identity. Mapping maps data, for example, matching an email address from an e-commerce site with that user’s purchase history in the POS system, and describing all that user’s transactions (from both mobile and web) against data like their behavioral patterns or advertising profile.
This process is crucial for enterprise data mapping, where accuracy and consistency are essential to maintain trust, compliance, and performance across business units.
Data Mapping Automation: The Backbone of Scalable Insights
With the rising volume and complexity of customer data, manually mapping everything becomes increasingly impractical and error prone. Instead, CDPs with data mapping
automation functionality can dynamically recognize fields, recommend schema alignments, identify and notify you of inconsistencies in real time, and on and on.
Automation diminishes the load of repetitive work on IT teams and locks in quality data at scale. AI is capable of recognizing these fields and will automatically map them to a single unified field without further human involvement.
AI-powered data mapping combines machine learning to improve the quality of the mappings continually, quickly align with new data structures, and recommend adjustments. This allows for faster onboarding of new data sources, while providing a seamless cross-channel customer experience.
Data Mapping for Digital Transformation: Fueling Enterprise Agility
Digital transformation is not just a buzzword; it is a strategic shift in how an organization operates, uses technology, and engages customers to meet new expectations. The ability to act on trusted data in real-time is at the heart of these changes.
Data mapping for digital transformation enables an organization to constantly capture, process, and act on customer data via any channel, or in any combination of channels, whether it be online, offline, or hybrid. When customer data via a Data Management Platform (DMP) or Customer Data Platform (CDP) is mapped to help inform marketing campaigns, product recommendations, or real-time support responses, they are enabling those customers to have a seamless experience based on the relevance and speed of their experience.
For organizations which are transforming, data mapping for ETL (Extract, Transform and Load) becomes a very important exercise to help ensure that their new systems are getting clean, standardized and usable data from their legacy systems.
Data Mapping for Analytics and Insights: Driving Business Intelligence
While unified customer profiles enhance the ability to personalize, the true power of a CDP is that it takes those profiles and creates actionable insights.
When mapping data for analytics and insights, customer behavior – buying habits, clicks and browsing, and interactions or content consumption – must be consistently mapped and related back to a resolute profile. This ensures consistency can be used to accurately
forecast, benchmark marketing effectiveness, and uncover areas of growth. Unified and consistent mapped data allows enterprises to confidently segment customers, forecast patterns, and, ultimately, optimize spend.
Best Practices for Data Mapping Optimization
To help ensure the success of a Customer Data Platform (CDP) project, consider some of the following best practices:
Assess Your Data Ecosystem
Take stock of all relevant customer data sources, the formats they are in, and how they are being utilized.
Create a Canonical Data Model
Establish a standard data schema that can be a reference point for all incoming data.
Use AI to Map Data
Use intelligent tools to automate field mappings, identify possible errors, and provide potential schema.
Emphasize Identity Resolutions
Utilize deterministic (e.g., email, phone) and probabilistic (e.g., location, behavior) indicators of each customer to bring together their identities across touchpoints.
Test and Iterate
Data sources do not remain stagnant. Examine your mappings frequently to ensure they are fitting, functional, and compliant.
Organizations can create accurate, 360-degree profiles from any touchpoint, and leverage that data directly for real-time personalization and enhanced insights, by integrating, organizing and unifying customer data. Whether you want to improve marketing effectiveness, better understand customer journeys or accelerate your digital transformation, a solid mapping of data is the first step to transforming data into intelligence – and intelligence into action.
Reach out to us to learn how the right CDP and data mapping strategy can transform your customer insights.
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