Customer Master Data Management – Key to Personalizing the Customer Journey


No matter your product or service, modern customers expect an Amazon-like experience, complete with personalized recommendations. So to stay competitive, you must deliver curated messages and relevant, timely recommendations. (Hint: you need a customer master data management plan)

According to McKinsey, 76% of consumers claim that receiving personalized communications was key to prompting their consideration of various brands. Additionally, 78% of respondents said that receiving personalized content increased the likelihood that they would continue doing business with a particular brand.

To deliver personalized content and experiences that align with consumer expectations, you need access to an abundance of high-quality customer master data. This kind of data is all the various information you gather about your customers — including contact information, addresses, demographic insights, and purchasing history. 

That said, simply having a wealth of information about your target audience isn’t enough.

To put this information to use, you need a cohesive customer master data management (MDM) strategy that outlines when and how to apply your data.

With the right customer master data management plan in place, you’ll be able to tailor messaging based on customer preferences, customize your product and service recommendations, and engage in dynamic content personalization that moves you toward your growth goals. 

Here’s everything you need to know about customer MDM to effectively put your data to work for your business.

In this article, we will cover:

Understanding Customer Master Data
Customer master data is all available information about your consumer base, including purchasing and browsing history, contact information, etc. Naturally, then, customer master data management is the process of creating and maintaining a centralized, accurate view of this information.

The objective of customer MDM is to ensure that your business has a single source of truth through which it can glean insights about its various audience segments and guide decision-making to yield personalized experiences.

Think of customer master data as a window into the consumer’s mind. If this database is incomplete, inaccurate, or otherwise unreliable, your personalization efforts will be misaligned with actual customer preferences.

Conversely, if your organization excels at customer master data management, you can monitor consumer interactions in real-time and deliver engaging, impactful experiences that build loyalty and boost revenue. 

To effectively manage customer master data, you must first implement processes for continuously integrating information from various business sources into a centralized database.

In other words, data from all of your company’s various applications and software need to be brought together so they can push information to a single source of truth. Otherwise, data will be compartmentalized and largely inaccessible, creating blind spots in your customer MDM strategy. 

While this process may seem relatively straightforward, it can actually be incredibly challenging. Duplicate records and inaccuracies across software can lead to discrepancies in your unified database. In turn, these errors can hinder decision-making and make it difficult to deliver personalized experiences.

Fortunately, you can set the stage for effective customer master data management by adheing to some best practices and thoroughly addressing all facets of MDM, which we’ll examine below.

Collecting & Managing Customer Master Data
Regardless of the industry you operate within, customer master data management always begins with the same step: customer data collection.

E-commerce brands and organizations that provide services through digital customer portals gather much of their customer data at the point of purchase. However, hybrid brands that rely on brick-and-mortar interactions and online purchases typically employ several data collection strategies.

A few common tools for gathering customer master data include:

  • Web-tracking tools and cookies
  • Customer relationship management (CRM) software
  • Third-party data about customers/members
  • Social media channels
  • Transaction and purchase history records
  • Chatbots
  • Surveys
  • Email newsletters
  • Blog subscriptions
  • Gated content

Ensuring data quality at the point of collection is difficult. However, there are some steps businesses can take to improve data quality.

For instance, if you’re using gated or subscription-based content to collect information, send a verification link to the user’s email address so they can’t input fictitious email addresses to access your blog, newsletter, or white paper.

collecting data

In addition to proactively improving data quality, you’ll need to institute data governance processes to ensure that consumer information is handled safely and ethically.

These governance protocols must be applied from the moment data is collected to keep you from inadvertently violating any regulatory acts or eroding customer trust in your brand.

Once you’ve addressed data quality and governance, you can begin to put all the information you collect to use.

With that in mind, let’s examine some customer MDM best practices and explore the key tenets of master data management in the post-collection phase, such as ongoing data governance and quality optimization.

Customer Master Data Management Best Practices
Although your customer master data management strategy should be tailored to the needs of your business, there are a few best practices every organization should adhere to.

When engaging in customer MDM, make sure you do the following:

  • Establish a single source of truth for your data: To effectively manage your data, you must first consolidate it in a centralized, secure database.
  • Implement robust data governance protocols to promote data completeness and accuracy: A great data governance strategy will help you create and maintain accurate datasets and detect/prevent errors before they can negatively impact decision-making.
  • Regularly update and maintain customer data: Customer master data must be continuously maintained and adjusted. Old, irrelevant records must be updated or removed, and new information should be added so you can stay apprised of the latest consumer trends.
  • Implement data privacy and security protocols: Data privacy and security protocols not only protect your business from a breach but also help you stay in compliance with relevant consumer protection laws.
  • Integrate your data with other systems: Integrating your single source of truth with other data sources and systems yields a more complete and comprehensive dataset. In this way, a master customer data source can eliminate data silos by allowing applications to reference a single source of truth. In turn, this will make it easier to manage and maintain high-quality customer data.
  • Implement a system for auditing your data: You must have a system in place for auditing your data, demonstrating compliance with regulatory requirements, and identifying potential instances of misuse.
  • Provide customers with self-service tools: Giving customers control over their data with self-service tools promotes trust and demonstrates your commitment to transparency.
  • Regularly evaluate and improve your customer MDM processes: Excelling at customer master data management requires you to embrace the concept of continuous improvement. Routinely evaluate your customer MDM strategy and examine ways to make better use of your consumer data.

By adhering to these best practices, you can cultivate a customer master data management strategy that supports your organization’s growth goals, respects consumer privacy, and promotes compliance. 

Customer Segmentation & Profiling
Customer segmentation and profiling are precursors to experience personalization. 

Customer segmentation involves dividing your target audience into subgroups. Traditionally, customer segmentation divides audiences based on demographic factors. While these variables shouldn’t be ignored, grouping audiences based on behavioral factors is more pragmatic for journey personalization.

customer segmentationOnce you’ve segmented your audience, you can engage in customer profiling, which involves creating a series of fictitious customer profiles based on your master data. 

Each profile should outline a customer’s behaviors, preferences, pain points, needs, and goals. These templates will serve as a guide when creating personalized content for each audience sub-group.

Segmenting and subsequently profiling customers helps marketers and sales teams systematically address each archetype’s concerns and eliminate conversion barriers. 

For instance, let’s say one customer type prioritizes convenience above all else and is even willing to pay more for products or services if the process is frictionless.

When interacting with this type of customer, team members could emphasize your company’s self-service tools, user-friendly website, and other features that make the end user’s life easier. 

Data Integration & Centralization
Another key facet of customer MDM is data integration and centralization. Data integration involves creating connections between various sources and applications. By integrating key business software solutions, you can tear down data silos and begin creating a consolidated database of customer information. 

Integrating data from all major sources paints a more detailed picture of the customer journey. For instance, you can combine purchase history from your e-commerce platform, browsing data from your main website, and contact information collected when a consumer signed up for an email newsletter. 

After integrating your platforms, you need a place to send all that data. Enter customer data platforms (CDPs).

A CDP is a set of software and tools that creates a consolidated customer database. A CDP can integrate with and make data accessible to your other systems, including your CRM and marketing platform. 

Data Enrichment & Augmentation
Data enrichment, also known as data augmentation, is the process of enhancing customer master data with additional insights or missing information. You can perform data enrichment by tapping into third-party data sources. Enriching your data fills in the gaps to make the information more useful.

In addition to using third-party sources, you can also use machine learning for data augmentation. Machine learning technology analyzes and “learns” from available data. It then draws conclusions based on this analysis, thereby reducing or eliminating the need for third-party data.

Using machine learning for data augmentation is a relatively new process, but it’s becoming increasingly prevalent for two primary reasons.

For one thing, several platforms, including Google, are preparing to ban third-party cookies. Additionally, machine learning has advanced significantly over the last few years, making the technology more accessible and effective.

Data Security & Privacy
No customer master data management strategy is complete unless it addresses security and privacy. Amassing an abundance of customer information in a centralized location makes it easy to put your data to use. The flip side is that it also creates an attractive target for hackers.

data securityWhen you collect customer data, you have a responsibility to safeguard it from breaches. Failing to do so can tarnish your brand reputation and cost you your customers’ trust. A breach can also have long-lasting — and potentially expensive — impacts on your organization.

After you’ve addressed security, you must also put protocols in place to promote customer data privacy.

According to legal frameworks such as the EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), your business must obtain customer consent prior to collecting certain types of data, allow them to manage data collection preferences, and take other steps to protect their privacy rights. 

It’s also important to stay abreast with emerging policies, regulations, and best practices. After all, privacy rights are relatively new, and they’re routinely modified to ensure they align with the ever-changing digital landscape. For example, the California Privacy Rights Act (CPRA) will go into effect in 2023, superseding the CCPA and implementing several new regulations that businesses must be aware of.

These frameworks, and others like them, place stringent demands on organizations that collect and handle data. They also include severe penalties for entities that knowingly or negligently violate customer data privacy laws. As such, you must familiarize yourself with relevant regulations and account for them in your customer MDM strategy. 

Leveraging Customer Master Data for Personalization
With a comprehensive customer master data management strategy in place, you’ll be able to derive timely, relevant insights about your target audience. You can use these insights to create targeted marketing campaigns that engage existing customers and help you acquire new ones.

When creating your personalized content, make it a point to target each customer segment based on their unique attributes and preferences. Some factors to consider include past shopping history, age, location, income, marital status, and profession. 

By keeping your master data up to date, you can engage in real-time personalization, delivering customized ads and marketing content based on an individual’s most recent activities. 

If they’ve just begun searching for a new type of product or service, you can use your customer master data to target them with personalized recommendations related to their recent queries. Doing so allows you to capitalize on their high buying intent and drive revenue for your business.

Engaging in real-time personalization allows you to stay ahead of seasonal trends, such as consumers buying new jackets in preparation for winter or credit union members taking out small personal loans to buy holiday gifts. 

Measuring & Iterating Personalization Efforts
You’ve instituted a cohesive customer master data management strategy and begun serving up personalized experiences. But how do you know if your efforts are making a measurable impact on your bottom line?

Simple — you need to monitor several key personalization metrics. The following are some of the most important:

  • Average revenue per customer: Average revenue per customer is a great metric for providers that offer subscription-based products and services. It tracks how much average revenue you generate across your user base. If your personalization efforts are yielding the desired result, your average revenue should be on the rise.
  • Cart abandonment: Cart abandonment occurs when users add products or services to their digital carts and leave your site before finishing their purchases. Personalized ads should help reduce abandonment rates by presenting customers with items that best align with their needs and interests.
  • Churn rate: Churn rate is the percentage of customers that cancel their subscription or otherwise cut ties with your business. If you want to increase revenue from recurring customers, churn rate is one of the personalization metrics you must monitor closely.
  • Click-through rate: Click-through rate measures the number of clicks an ad or call-to-action (CTA) button receives in relation to the number of times it’s displayed. The higher the CTR, the more successful your ad or CTA. Personalized content will have a higher CTR because the customer is being presented with products or services that are relevant to them.
  • Conversion rate: Conversion rate reveals what percentage of prospective customers actually sign up for a service or make a purchase. Monitoring conversion rates across all marketing channels can help you pinpoint gaps in your sales strategy.
  • Customer lifetime value (LTV): Customer lifetime value projects how much revenue you’ll generate from the average customer over their entire relationship with your company. LTV provides a glimpse of the big picture and can reveal how your personalization efforts are impacting customer loyalty.
  • Average order value (AOV): Average order value demonstrates how much the average customer spends when making a purchase from your business. If your personalization strategy is helping you cross-sell and upsell customers during the shopping process, your AOV should go up.
  • Site traffic: An uptick in site traffic indicates that users are responding well to your personalized content. An increase in first-time visitors is an especially good sign, but any significant boost in traffic should be considered a win for your personalization strategy.

You should use the insights gleaned from these personalization metrics to continuously improve your customer MDM plan.

Additionally, conduct A/B testing and other forms of experimentation to ensure that your personalized content resonates with your target audience. Combine the information these metrics provide with customer feedback to elevate content quality and deliver memorable journeys to your clients. 

Elevate Your Customer Master Data Management Capabilities
An effective customer master data management strategy lays the foundation for experience personalization and enables your organization to connect with its target audience on a deeper level.

Moreover, it will promote data security, insulate your business from cyber threats, and empower decision-makers to capitalize on emerging growth opportunities.

A winning strategy involves much more than simply adopting new technologies and collecting massive amounts of customer master data. In order to deliver truly personalized and engaging experiences, your business needs a game plan to structure and analyze its customer master data. Enter UDig.

At UDig, we enable your team to thrive in the data-driven world of business by providing tailor-made strategies and custom software purpose-built for your organization. 

If you’d like to learn more about what we do or how we can take the hassle out of customer master data management, schedule a consultation with our team today.



About The Author

Reid, SVP of Data and Analytics at UDig, is a long-time data professional with experience at multiple Fortune 500 companies. Most recently, he was the Chief Data and Analytics Officer at Markel. Prior to that he held multiple roles at Capital One including VP of Data Engineering.