A comprehensive, 360-degree view of every customer’s relationship with your company forms a foundation for more than marketing campaigns. This guide will explain what CDPs are, why they matter, and how a data analytics platform can streamline your Customer 360 initiative.
What is customer data?
The customer journey leaves a trail of data in its wake. Companies can use these data trails to enhance an individual customer’s lifetime value. But first, they need to know what kinds of data are available and what sources to turn to.
Types of customer data
Customer data can be highly technical, such as the transactional and real-time data people generate while using a product or navigating a website.
Customer data, such as names, ID numbers, and addresses, can be highly personal.
Demographics, psychographics, and other data types are less specific to the individual but help predict customer behavior.
Sources of customer data
Companies that limit themselves to data generated through their ecommerce sites will never get a complete picture of their customers. A 360-degree view of a customer requires casting a wider net.
First-party data sources go far beyond the marketing department’s campaign records and social media accounts. The product may generate data through the use of desktop and mobile apps. Sales and customer support organizations often have their own customer databases.
Third-party data provides more nuanced demographic and psychographic data. For example, credit card companies, data brokers, and other third-party providers can help identify customer preferences and buying patterns that can inform marketing campaigns.
What is customer 360?
Customer 360 is a strategic effort to understand each customer, from how they interact with different parts of the organization to why and when they make their purchases. These initiatives do more than enhance marketing campaigns. A 360-degree view ensures customers enjoy consistent, delightful experiences with every interaction, leading to higher conversions and lower churn.
What is a customer data platform?
Typically offered through a software-as-a-service (SaaS) model, customer data platforms unify internal and third-party sources. From this central hub, companies can manage every aspect of the customer experience to maximize customer value. Some of the features of a CDP include:
Data collection – Connectors integrate data from internal and external sources.
Identity resolution – Identifiers from different sources must match individual customers.
Machine learning – Algorithms automatically generate insights into customer behavior.
Integrations – CDPs can send data to campaign orchestration and other systems.
Why use a customer data platform?
While there are many ways to consolidate data in a central location, CDP providers have already done the heavy lifting by developing a platform optimized for customer data management.
1. 360 Customer View
Unified customer profiles make it easier to generate actionable insights. Marketing teams can tailor promotions based on a customer’s readiness to buy. Service teams have more context for support calls when they can see every interaction. Inbound sales teams can use this comprehensive view of their customers to drive upsell conversions.
2. Improved Personalization and Targeting
Understanding each customer’s relationship lets marketing teams create seamless and relevant customer experiences. Adopting a CDP turns customer segmentation from a mass marketing tool to a customer engagement engine. Personalized campaigns can tailor emails and promotions for each customer’s preferences, buying histories, and purchasing intent. This level of detail wouldn’t be possible without marketing automation solutions that can use the CDP’s single customer view to generate targeted campaigns at scale.
3. Scalability, data-driven decision-making, and competitive advantage
Through the unification of disparate data sources and the automation of customer insights, customer data platforms provide scalable solutions for data-driven decision-making. These decisions result in improved customer experiences, greater brand loyalty, and more profitable businesses than the competition.
Examples of customer data platforms
Companies can choose from various CDP vendors that offer focused solutions or complete marketing platforms. Here are five examples worth considering:
Segment – Part of Twilio’s portfolio of marketing automation tools, Segment collects, unifies, and activates customer data to enhance customer communications.
Tealium – A standalone CDP solution, Tealium’s platform has more than 1,300 connections to data sources and marketing systems.
Salesforce Data Cloud for Marketing – Salesforce evolved its customer relationship management (CRM) platform to support Customer 360 strategies.
SAP Customer Data Platform – The enterprise software company’s CDP lets corporations unlock customer information across their global infrastructure.
Oracle Unity Customer Data Platform – Oracle ties together an enterprise’s internal and external data sources and provides machine learning features to create what they call “just for me” customer experiences.
Customer data platform use cases
A comprehensive understanding of customers is as essential to enterprise business models as it is to omnichannel retail and other B2C models. That’s why industries as diverse as financial services, healthcare, and telecommunications rely on CDPs to enhance their marketing efforts. CDP use cases include:
- Customer segmentation
- Customer journey mapping
- Retargeting
- A/B testing and experimentation
- Cross-channel marketing
- Conversion rate optimization
- Product recommendations
- Lead scoring
- Customer support and service optimization
- Compliance and data governance
Customer data platforms and other data architectures: The differences
Marketing technology (MarTech) is designed for various aspects of sales and marketing. However, a CDP’s role is more holistic in the data it collects and the teams it supports.
CDP (customer data platform) vs. CRM (customer relationship management)
CRM software tracks leads, prospects, opportunities, and existing customers in a traditional B2B sales funnel. However, it does not include data that a CDP collects effortlessly.
CDP (customer data platform) vs. DMP (data management platforms)
Data management platforms (DMPs) collect data from tracking cookies to support online advertising campaigns. A DMP only helps one part of the marketing team, whereas a CDP’s benefits spread across the business.
CDP vs. Data Warehouse
Data warehouses are centralized databases structured to support efficient data access and analysis. By design, they cannot match a CDP’s ability to handle unstructured social media, clickstreams, and other real-time data.
CDP vs. Data Lake
Data lakes are repositories of structured and unstructured data but do not offer the customer data management tools and machine learning algorithms a CDP provides.
Challenges to customer 360
While companies aspire to Customer 360, several challenges stand in their way. Unifying disparate data sources is never straightforward. Once companies overcome that hurdle, they must balance the benefits of seamless data access with the demands of data governance.
Data silos
Every department develops solutions for gathering and storing customer data that meet their internal needs. These data silos inhibit the free flow of data within the company and lock away essential aspects of the customer relationship.
Data integration
Internal and external data sources are inherently inconsistent. Engineers must develop custom pipelines to ingest, clean, and process data. Updating these pipelines to account for changes at the source adds to the burden on resource-constrained data teams.
Data access
The vast quantities of structured and unstructured customer data create complexities that limit access. Data teams must create pipelines to support every request. As a result, marketing analysts may have to wait while data teams address higher-priority business needs.
Data privacy
Data privacy regulations such as GDPR and CCPA limit who a business can grant access to customer data. These restrictions will vary based on the data user’s role as well as the location of the customer, the data, and the data user.
Starburst data lake analytics platform for Customer 360
Starburst streamlines your journey to Customer 360 by unifying every data source in a single point of access. The Starburst data lake analytics platform abstracts data sources to eliminate the burden of pipeline development. Virtualizing your storage infrastructure lets authorized users access data through any SQL-enabled analytics software without compromising governance standards.
As you implement your Customer 360 initiative, you can use Starburst to:
1. Activate Customer 360 faster
Starburst speeds data discovery and unification so your data teams can uncover once-siloed customer data faster.
2. Unlock data
With over 50 connectors, Starburst gives customer teams access to data no matter where it lives — on-premises, in the cloud, locally, or globally.
3. Enforce strict security and governance
Create granular access control rules based on user roles and source attributes to enforce governance policies systematically.
4. Increase productivity and lower costs
Giving users direct SQL access to customer data reduces burdens on data teams and generates actionable insights faster.
Steps to implementing a customer data platform with Starburst
Your Starburst data lake analytics platform becomes a single source of customer data for your CDP, getting you to Customer 360 faster.
1. Set up Starburst
Whether you use our managed Starburst Galaxy solution or self-manage with Starburst Enterprise, you can quickly get started on AWS, Azure, or Google Cloud.
2. Connect your data sources
Add connectors to link Starburst with your data lakes, relational databases, streaming stores, and other sources.
3. Connect your CDP
Starburst offers dedicated connectors for Salesforce and other data platforms. Or let your CDP query Starburst with standard ANSI SQL.
4. Create access control rules
Define access control rules to enforce governance policies based on geography, role, and data source.
