Customer data management (CDM) is one of the most important aspects of customer experience management. It helps companies get to know their customers, understand their preferences and needs, and deliver personalized services and experiences that keep customers happy and loyal. A key element of CDM is data quality.
Data quality refers to how accurately your company’s information about its customers reflects reality. If you don’t have good-quality customer data, your efforts will be wasted on inaccurate information about those customers—including ones who can’t be found when they need help with a problem or question!
Data quality is critical for Customer MDM.
Data quality is critical for Customer MDM. In fact, it’s important to all aspects of the business. Let’s take a look at how data quality affects just three business areas while recognizing that the customer master is important to many areas of the business. Let’s look at marketing, sales, and customer service.
How do you ensure data quality?
Data quality is not just a technical issue. It’s also a business issue. Customer data must be accurate, complete, consistent, and timely to satisfy your business needs successfully.
Data quality must be measured in order to be managed with accuracy, completeness, and consistency. Measuring where you need to improve will enable you to make improvements in those areas that most affect your customers’ satisfaction levels with your products or services;
Managing the quality of customer-related data means understanding what it takes for it to be considered of sufficient quality for various organizational initiatives such as implementing Customer MDM or other improving other operational processes that rely on customer-centric information;
Improving data quality is one of the most important activities when developing an effective system for managing ERP/CRM/CDP/POS integration between systems, the back office, and across multiple customer-facing channels (eCommerce sites and bricks and mortar stores).
As companies move toward more customer-centric operations models, they need systems that can help them manage their relationships better so they can serve all kinds of customers better too!
What is a data quality management plan and why do you need one?
The first step to creating a data quality management plan is to track your organization’s current state with regard to data quality.
This will help you understand where you are now and what needs improvement. To do this, you need to analyze the following:
- What type of customer data exists in your organization?
- How much of it there is and where it is located?
- Can you assign customer IDs across channels or do you have a common external key?
- Is there an easy way to match customer IDs across channels, such as email addresses or phone numbers?
- Are customer IDs unique enough that they don’t overlap with other customers within that channel (for example, if John Smith buys something on Amazon and later comes back for another purchase—is he counted as two different customers)?
- How often does new information get added into the system versus how often does old information need updating or deleting from the system due to customer cancellations or other reasons?
You could also ask, how long should your organization wait before updating customer records based on a new set of data such as payment status changes.
Decisions like this can vary widely depending on industry standards so there may not be one “right answer” here—the key point is just making sure everyone agrees on when, why, and how updates are done so everyone knows how quickly results should appear in systems once the change is complete
What does a data quality management plan include?
A data quality management plan is a document that describes how you will manage the data quality process. It may include a number of different components, such as:
- A review of the current data quality processes used by your organization. This can help identify any problems with current practices and establish goals for improvement.
- The creation of a new or updated standard set of guidelines and standards for data collection, processing, and storage within your company. These guidelines should specify what types of information should be collected from customers, how often it should be collected, and other relevant details related to customer contact management best practices.
Defining and adhering to key, measurable data quality guidelines will help you successfully implement a customer MDM solution.
In order to decide whether to pursue MDM, you need to examine your current data quality and determine how it can be improved. Are there any issues with data integrity? How much manual work is involved in maintaining key records?
Do you have sufficient resources available to support the process of implementing a Customer MDM solution?
If you’ve answered “yes” to all of these questions, it’s time for business owners like yourself to look at their goals and determine whether or not they’re realistic. If they are, then great! You’re ready for this next step.
But if they’re not…don’t worry! It’s okay if your goals aren’t quite where they need to be right now; there is always time, just don’t leave it too long! And don’t let someone else dictate what those goals should or shouldn’t be: only you know what works best for your business model.
Data quality is critical for Customer MDM. It’s important to ensure that your data is of high quality before you perform any analytics or analysis on it. The Pretectum CMDM can help in your journey to improving customer data quality. This will help ensure that your results are accurate and not skewed by bad data. If you don’t have a plan in place to check and monitor the quality of your data, then it probably won’t be very helpful in achieving key business goals like improving customer satisfaction or increasing sales revenue.
Learn more about Pretectum CMDM