BENEFITS AND CHALLENGES OF CRM, MDM, CDP FOR CUSTOMER DATA
Introduction
Many valid options exist in the marketplace when seeking a solution to manage customer data. Organizations commonly consider Customer Relationship Management(CRM), Master Data Management (MDM) and Customer Data Platform (CDP) solutions. They each offer some significant differences but confusingly are marketed in a manner that causes them to appear to be the same. They are not the same. An instinctive choice might fulfill initial needs but could be at high risk for challenges or re-implementation as needs for new capabilities emerges. The recommended approach is to complete an internal discovery process before engaging with vendors and analysts.
First Glance at CRM, MDM, CDP
Modern companies have many information systems that contain valuable information about their customers. Discovering the best way to derive value from this often duplicated customer data is challenging. Many companies have implemented purpose-built software such as Master Data Management (MDM), Customer Relationship Management (CRM) or a Customer Data Platform (CDP).
At first glance, the potential contribution from MDM, CRM or a CDP is the same: Increase the understanding of customer need and increase the quality of each interaction. To differentiate among the solution types is not as easy as it might seem, especially if the organization is not fully aware of what requirements exist and are most important. Determining when and how to employ each of these solutions effectively adds to the challenge.
Someone investigating solution options to manage customer data in the recent past would have evaluated MDM platform vendors as well as newer, analytic focused big-data solutions. When reviewing customer data in the context of onboarding, journey management or service management, CRM solutions would have been the focus.
Most installed MDM vendors can provide the basics to address common needs and solve common challenges. These basics would include standards-based methods for acquiring data, cleanse-match-resolve, and a framework for configuring rules to maintain and make the results available to other systems in a variety of modes. The technical deployment platform, flexibility for changes to the underlying data or application model, breadth of capabilities and references are the differentiators. The selection should not be prolonged or complicated; it should focus on the specific context and fit for purpose.
Master Data Management – Platform vs. Practice
One should differentiate between MDM the software and MDM the practice. The capabilities above are specific to the technical platform. The method of MDM includes broader change management topics that include a lifestyle of governance and discipline that not all organizations plan for appropriately. There are also additional tools and technologies to support initial and ongoing governance such as discovery, quality, workflow and analytics. Most vendors also employ some artificial intelligence (AI) and machine learning (ML). Depending on the vendor, these capabilities are enough to get started while others will depend on integration with specialty tools. MDM will commonly include a variety of methods to facilitate the ongoing maintenance and distribution of the data within the platform.
Recently, there has been a rapid increase in the number of CDP offerings. These are marketed as rapid deployment, identification of similar data (entity resolution) and integration with many familiar tools such as email, campaign management and analytics.
CRM vs. MDM
CRM is different from MDM since it supports business functions such as sales and service versus prioritizing the technology to perform data management. With CRM, one is managing the processes and lifecycle from prospect to purchase, service, may be an initial or first significant step into that style of solution.
The most common error made related to a customer data effort is to select, design and implement a technical solution before fully understanding the needs. This error becomes evident when a company seeks to advance beyond a simple, initial implementation.
Successful Customer Data Management Solutions Evaluation and Implementation
The following are some proven steps towards managing this risk and performing adequate discovery.
Complete a documented assessment of capabilities related to customer data, including integration and distribution with source and target systems. Use or reference established methods such as the Data Management Capability Assessment Model (DCAM) or the DAMA-DMBOK© framework and include participation from the data producers and consumers.
A documented and validated set of what is currently limiting growth and what the business has planned to change in the next two to five years. These should first be written in business terms then mapped into terms of customer data. Use these in preparation for conversations with the broader company to show how the target solution will address their needs.
Identify gaps between the business needs and the current and emerging capabilities. Work hard to include the quantified impact of not meeting or improving on the need.
Prioritize the gaps and begin to develop a plan for improving against the current state.
Depending on the size of the organization, urgency and the available experience, these discovery steps could take three months to a year or more to complete. Be disciplined about the process. It will be challenging to limit the scope. There will be a period of definition to determine what customer data is and what attributes matter. Inevitably, some related topics such as agreements, products and reference data will be included.
Once the understanding is known, a fact-based selection process can occur to research and evaluate an appropriate solution partner.
To illustrate, below is a scenarios with a sample of typical capabilities across the three solution types. These are not intended to reflect all capabilities of all vendors and with enough time and effort (money), any solution can fulfill any requirement.
This illustration is for a fictional, multi-line company with five systems per business line. It is seeking to evolve from selling exclusively through third-party retailers into direct to consumer as they expand to new countries.
| NEED | CRM | MDM | CDP> |
| Identify which customer data records from across all systems are really the same unique person. | Y | Y | Y |
| Manage N number of types of names and addresses and other attributes for the unique person | N | Y | N |
| Manage and maintain other data specific to the unique person such as account and location | Y | Y | N |
| Provide history of where contributing data elements were sourced and updated | N | Y | N |
| Provide facility for authorized users to update data elements and distribute those changes to interested systems | Y | Y | N |
| Identify unique persons to be contacted for a campaign and track their preference to participate and intent to purchase | Y | Y | Y |
| Maintain unique person data as it changes in its source systems and share changes with interested systems | N | Y | N |
| Maintain localized and translated values for use by systems or campaigns | Y | Y | N |
| Define and maintain analytics based on customer’s journey and interaction across all channels | Y | N | Y |
| SAMPLE TOTAL Y/N | 6/3 | 8/1 | 3/6 |
Conclusion
An organization may reasonably justify having all three types of solutions. Do not get hung up on trying to fulfill all requirements with a single solution. If one solution could address all needs, the market would not offer so many.
Complete the discovery and prioritize which needs will matter most to the business and to what extent the capability is needed. Leverage the discovery work to help govern when and how to employ each capability to maximize impact.
Having an understanding of the need is critical to avoid a false start.