Marry Customer Data Platforms and Master Data Management for Your Customer Data Needs
By Ajay Khanna published in CMSWire at https://www.cmswire.com/digital-experience/marry-customer-data-platforms-and-master-data-management-for-your-customer-data-needs/
Managing customer data is a task that requires capabilities of both master data management (MDM) and customer data platform (CDP) technologies.
Wikipedia defines a customer data platform as “a marketer-based management system [that] creates a persistent, unified customer database that is accessible to other systems.” Wikipedia’s CDP entry goes on to explain that, in a CDP, “data is pulled from multiple sources, cleaned and combined to create a single customer profile. This structured data is then made available to other marketing systems.” Moreover, a CDP “provides real-time segmentation for personalized marketing,” according to Wikipedia.
Wikipedia’s MDM entry states that “in business, master data management is a method used to define and manage the critical data of an organization to provide, with data integration, a single point of reference. The data that is mastered may include reference data, [or the] the set of permissible values, and the analytical data that supports decision-making.”
Typical MDM systems do not provide insight into customer relationships and lack information about omnichannel interactions, web clickstream data and historical transactions. For their part, most packaged customer data platforms lack advanced data-matching, merging, profiling, stewardship and governance capabilities.
Here are seven steps to take when you build your next platform for managing enterprise customer data if you want to meet the customer expectations of a connected experience and enable compliant engagement.
1. Build Reliable Customer Profiles
A comprehensive customer data management system must put all information about customers (people and organizations) in one place and make it accessible in real time. Data from internal, external and third-party sources is blended to create comprehensive and reliable profiles of people and accounts. In addition to consolidating customer master records, the data platform must also create exhaustive profiles with correlated transactions and interactions in order to provide organizations with the complete picture. All customer information must also be continuously cleansed and organized for ongoing insight and engagement.
2. Uncover Relationships and Hierarchies
The task of managing customer data involves more than just bringing data together from multiple sources. It also involves uncovering relationships between consumers, products, organizations, stores, locations, family members, channels and transactions. Using graph technology to understand such many-to-many relationships between all data entities provides deeper customer understanding and enables more accurate segmentation.
In a B2B customer scenario, you can model and manage custom hierarchies outside of formal legal structures and add business logic, such as sales territory alignments, to create visual, actionable views of your customers. Retailers can also address householding challenges by uncovering relationships using graph technology and then grouping individuals into households based on their relationships and profile attributes.
3. Correlate Master, Interaction and Transactional Data
Marketers want to understand customer needs as customers evaluate, compare or buy products in today’s multichannel engagement environment. Omnichannel support for marketing, sales and service activities requires uniform and complete understanding of customer behaviors and preferences as well. This is where many MDM systems fall short. Blending omnichannel information with master profiles helps organizations understand customer product preferences and channel preferences, and this information can be provisioned to integrated analytics to determine next-best actions for future engagement.
4. Infuse Analytics and Recommendations
With data blended into one place, marketing, ecommerce and support teams can get visibility into customer preferences, behaviors, product interests and channel choice. Organizations can understand customers’ needs and provide personalized experiences based on the complete information. Data-driven customer applications deliver insights like churn propensity, lifetime value and abandonment rates. You can use machine learning and predictive analytics to suggest next-best actions to send relevant and consistent information to customers and find opportunities for upselling and cross-selling.
5. Consistent Information With Personalized Views
Make all customer information available across the enterprise and offer it in applications that employees can easily use. This is where MDM systems excel. Delivering consistent information across departments and channels, and throughout the consumer’s journey, is essential for a good customer experience. Consistency is possible only when there is a single source of truth for reliable customer data across the organization offered to business users in personalized applications. Role-based, contextual and consumer-grade applications for business users across marketing, finance, call centers, order management and field service ensure that everyone has access to reliable and consistent customer data. Industry-specific data-driven applications that bring together data and insights that are relevant to the role and task at hand improve operational efficiency and the customer experience. With systems that are nearly as easy to use as popular consumer platforms like LinkedIn, Google and Facebook, teams can get productive very quickly.
6. Collaborative Curation of Data
With a multi-team, multi-customer focus, it’s critical to allow teams to collaborate for ongoing data stewardship and to formulate customer engagement strategies. Customer data platforms must provide built-in annotating, tagging and voting, so every team member can contribute and continuously improve enterprise knowledge. Structured collaboration, in the form of well-defined workflows, helps manage master data life cycle tasks and handle change requests, delete requests or other custom processes. Workflow capabilities help ensure compliance with data management rules and governance policies. A more ad-hoc form of collaboration offers users an ability to comment anywhere on a profile and direct that comment to another user to allow for faster and more interactive social stewarding. Such collaboration capabilities must be an integral part of any customer data platform.
7. Ensure Compliance
Your platform should organically support compliance with regulations like the EU’s General Data Protection Regulation (GDPR) by giving you the ability to manage your customer’s profile information, lineage and succession as part of day-to-day data management activities.
It is critical to have a system that enables you to honor individual customers’ rights (such as the GDPR’s right to be informed, right of access, right to rectification, right to restrict processing, right to object, right to erasure and right to data portability) and ensure that you secure customers’ consent to use their data (whether it’s via informed consent, explicit consent or explicit parental consent). A customer data platform with graph technology can help you better understand relationships among people, products, locations and consents. You will also need integrated workflow capabilities in order to have the ability to, for example, purge all of the information about customers who invoke their right to be forgotten.
Enterprises must develop a holistic customer data management strategy while implementing customer data platforms. Investigate and document how various departments within your organization use customer data, identify who the users are, and then determine what the analytics, reporting and compliance requirements are. In addition, make sure that your data platform can grow and adapt to your changing business needs.
About the Author
Ajay Khanna is Vice President, Marketing at Reltio, the creator of data-driven applications. Prior to joining Reltio he held senior positions at Veeva Systems, Oracle and other software companies including KANA, Progress and Amdocs.