Customer Data Management (CDM) Best Practices
Two kinds of organizations dominate in nearly every industry today. Those which have already employed some system or platform (whether successfully or not is a different matter) to improve their customer experience, and those that are seriously considering one. As a result, only a miniscule number are sitting on the fence or unclear about what excellent customer experience can do for their business. Even those with tight budgets are trying hard to employ some technology that can boost their customer experiences. Customer Data Management (CDM) is one such system that stitches together the fragments of your customer data strewn across departments and allows you to make the best out of it.
I. Succeeding in CDM
Deploying CDM technology is one piece of the puzzle, just as another one is how it functions and the technical expertise to manage it.
However, the real success in CDM lies in developing a vision, etching out a customer strategy, bridging the gap between significant departments, and communicating the desired change across the board. Similarly, building collaborative approaches in the company culture, striving to get rid of poor customer data management practices, and achieving consistent customer data across systems can have a snowballing effect.
Apart from the above, clear segregation of goals—such as ‘nurturing customer relationships, ‘closing leads better,’ and ‘enhanced targeting of customers’—must be the focus. And finally, it is about constantly analyzing how your customers perceive their experiences with your enterprise and your products or services.
i. What Does it Take?
In broad terms, it starts with detecting your most significant customer information across various systems, cleansing it (fixing inconsistencies and flaws), creating integrated and trustworthy customer profiles, and placing them in centralized locations. Also, it is essential to tie customer data to other significant data points like other customers in the same household, average ticket size of products, buying channels in use (in-store or online), along with merging customer data with their social and clickstream data, focusing on data governance, and comprehending other related patterns. Finally, it attempts to approach customers from not an ad-tech, but a mar-tech perspective, by shifting the focus to first-party customer data derived from a host of sources.
ii. The ‘real’ Role of Customer Data Management Platform
A customer data platform is a marketing technology that unifies a company’s customer data from marketing and other channels to enable customer modeling and optimize the timing and targeting of messages and offers. It has four primary functions:
|Data Collection||Profile Unification||Segmentation||Activation|
|Refers to ingesting 1st party, user-level data from a variety of offline and online sources possibly in real-time, including but not limited to (data browsing, cookies, names, demographic data, emails, device addresses, page visits, purchase history, etc.)||Refers specifically to unifying customer profiles by matching up duplicate profiles of the same customer. It also pertains to linking (or negating) various devices, email ids of the same customers, even aggregating customers to the same household accounts.||CDP works like an interface that enables marketers to develop and manage segments. It is about developing rule-based segmentation, including automated segment discovery, proclivity models, predictive analytics, and importing and deployment of custom models.||Activation is all about sending across the segments having instructions to execute campaigns that include email, mobile messages, advertising, suggesting recommendations, dynamic, and self-optimization.|
II. Setting the Foundation for CDM
Acknowledging the Siloed Customer Data Management Ecosystem
Before enterprises take even a single step further, they must realize that the data they may be looking for might be scattered around multiple departments. For example, some of it may be lying with customer engagement departments (like CRMs, customer support), some at the point of transaction, i.e., with sales and commerce (e.g., at check-outs, payment gateways), some in the back-office systems (like the ERPs), and some with marketing (digital personalization engines, customer identity and access management, multichannel marketing hubs).
Therefore, it is critical to acknowledge the expanse of the customer data ecosystem and that any CDM project that is undertaken will need the support, coordination, and approval from all these systems as well as external partners and agencies.
Identify Objectives, Data Needs, and Plan Cross-departmental Collaboration
III. Preparing for CDP implementations
- Finding the ‘Needed’ Customer Data
Apart from finding out the kind of data needed, there is also a need to know how much of it is required. For instance, sometimes, the full 360-degree view of data is not only out of reach but is also not recommended, especially after comparing the data acquisition costs with the returns. And many times, it isn’t feasible for the CDP implementation plan to go after every application and technology in search of data. However, on the other hand, enterprises sometimes get complacent in going out of the way to look for the data that is clearly ‘needed.’ Therefore, a balance must be maintained in collating the data—i.e., neither insistence on getting all the pieces of customer data nor complacency in compiling the data that’s essential, must be exercised. Therefore a ‘why’ aspect must be strictly addressed by enterprises at the data collection stage. Only customer data that is relevant, use-worthy, and valuable makes CDP implementations successful.
- Standardizing Capabilities Vital to Customer Data Management Practice
By standardizing CDM practices, leaders in data and analytics need to develop a robust and collaborative relationship with marketing, sales, and customer service stakeholders. Therefore, similar standards must apply for activities like:
- Data capturing: The capability to unearth and extract customer data from all the relevant sources
- Data Preparation: The capability to make the data suitable for use cases through processing-transformation-ingestion-transference and loading
- Data Integration: The capability to connect data from multiple sources and develop a single cohesive data view.
- Data storage: The capability to ensure data maintenance, efficiency, and availability.
- Data Analysis: The ability to discover insights from the data to decide the next course of action.
- Differentiating between Various Customer Frameworks and Data Management Technologies
Before implementing a CDP, enterprises must understand that many customer data software and platforms are often mistaken as data management software. In other words, many of them extract and make good use of customer data but are not data management software or tools. Therefore, comprehending the difference between CDP with customer frameworks like Customer relationship management (CRM), Digital experience platform (DXP), Multichannel marketing hub (MMH), Customer engagement hub (CEH) is an essential part of ensuring that the implementation stays on the right track and delivers what CDP promises to deliver. Though all these frameworks lend support during a CDP implementation and may have some overlapping capabilities, their purposes are quite different.
IV. Customer Data Management Strategy Essentials
While weaving an all-inclusive view of customers is at the core of a successful CDP strategy, certain essential considerations must be made right at the start.
- Preventing a few common pitfalls
To let your CDP implementations make maximum impact, a few things need to be paid close attention to:
- Following the diktats of a single department should be avoided.
- A realistic sense of expectation must be kept from the implementation and must be mapped with possible outcomes.
- Instead of assuming to get a ‘perfect’ 360-degree customer view, specific use cases and data sources must be defined beforehand.
- Study the commonly deployed types of CDP in the industry but avoid following the competition blindly.
- Reluctance from making ‘essential investments’ to empower teams must be avoided.
- Employing Smarter Data Governance Processes
Data governance not done appropriately can pose enormous challenges and undermine the effort to execute a CDP project. Cross-functional data strategy remains central to enterprise data governance endeavors. Both data and analytics leaders within the enterprise ecosystem must clearly define the data usage scope, governance standards, policies, procedures and create standardized governance frameworks. Measures need to be taken to manage customer data for creating dynamic customer profiles led by the specific enterprise use case so that CDP can be managed dynamically. Once data stewards are appointed, capabilities must be built for creating adaptive governance rules and styles to fit the needs of enterprise CDP.
Governance in digital marketing can also be supported through tag management. In addition, it can play a pivotal role in collecting customer data while carrying out data management and data modeling endeavors.
V. Aiming for a ‘Zero-Data-glitch’ Approach Post-implementation
By creating a comprehensive view of customers and their association across the entire business ecosystem, enterprises make sure that every department, especially the data-dependent ones, gets benefitted through the CDP implementation. Therefore, a constant effort must be made to improve customer data quality with every passing day. Every gap must be stitched, as even a single data glitch can affect several departments. To ensure that your customer data is serving you efficiently in your CDP implementations, you can keep track of a few markers:
- Higher conversion and lead closing rate
- Increased net promoter score (NPS)
- Lesser customer acquisition cost
- Better upselling and cross-selling
- Superior productivity of marketing, sales, and customer service
- Decreased IT-related OpEx
- Improved check-outs, invoicing, and collections
- Better selling margins
VI. Pimcore Can Help You Implement a CDP Keeping its Best Practices
Pimcore follows best practices while implementing a CDP. Apart from building a strong customer data foundation that includes customer data integration, data-modeling, automation, real-time personalization, and executing all the steps systematically, Pimcore’s best practices ensure that enterprise internal and external relations improve through better collaboration. Pimcore understands that good customer data management is at the heart of enterprise sales numbers or marketing effectiveness. We ensure that every enterprise strategy is backed by customer datasets and subsets relevant to targeted customer segments, demographics, or individual customers.
We also assure that every enterprise CDP strategy includes the right CX stakeholders who have deep and intricate knowledge of enterprise customers, data, and analytics. Lastly, we strongly advocate that CDP should not remain IT-managed but should instead come under the purview of data management.
VII. Pimcore Use Case
Pimcore Helped Northgate markets, a supermarket and a fresh-food industry giant in North America with customer data management
Northgate markets was not able to maintain its customer data, due to no central repository. Multiple profiles of same custmers were present, which hindered the possibility of providing personalized experience to customers. It also severely affected their marketig activities due to no segmentation. No insights could be culled out too. Besides, Northgate had no online presence, therefore targeted brand communication could not be shared with customers.
Pimcore implemented customer data management by introducing real-time data integration with end-user touchpoints for data aggregation at a single location. It also executed data verification and registration by integrating with 3rd party channels such as OFAC for facial as well as address recognition. Apart from this, an online and in-store ordering system was developed for Northgate’s grocery business.
The result was an astounding success in terms of customer engagement through better marketing opportunities, including higher cross-selling and up-selling possibilities, better segmentation, and personalized content for customers, while ensuring lesser time and effort got spent in data gathering, cleansing, and enrichment.