All you need to know about Customer Data Integration (CDI) and its impact

Customer data sits in numerous places: from transaction systems and CRM to social media analytics and email marketing platforms.

Each silo contains a piece of the puzzle that is the customer journey, and deciphering each one can lead to a real headache. This is where CDI comes in, which helps to have a holistic view of customers and understand them, their needs, and buying preferences, which is a fundamental part of the success of any business. We tell you what it is and how you can integrate it into your processes.

What is Customer Data Integration

Customer Data Integration (CDI) is the process of collecting, defining, and managing customer data from numerous sources and organizing it so that it can be easily shared among company members and departments.

This record is generated by integrating information from all available source systems, such as contact data, rating data, or information gathered through interactions such as direct marketing.

This type of customer data integration can improve business processes and enable better information sharing between departments. As a result of this focus on improving customer service, CDI initiatives have become an essential element of customer relationship management (CRM).

Why is CDI important?

It is not uncommon for companies to have been collecting customer data for years, but not always effectively. As a result, companies may maintain obsolete or redundant customer data that provides nothing useful.

CDI policies therefore help to establish order in the data generated by these disparate source systems. The main benefits are:

• Improved sales: More accurate customer data enables organizations to better understand customers and focus on personalizing cross-sell and up-sell opportunities.
• Improved customer service: When responding to service calls, customer service agents can better understand the entire customer journey. With these tools, they can more accurately assess whether a customer is satisfied or dissatisfied and why, and use that information to determine when, where, and how to interact with them in the future.
• More efficient data management on an ongoing basis: Once CDI policies and data quality efforts are in place, it becomes easier to update customer records, manage real-time data collection and consolidate data silos.
• Eliminating data silos: These silos cause employee misalignment, force employees to re-enter data into different applications, or prevent them from accessing the information they need to do their jobs. CDI ends this and solves any relationship problems, as employees can access all the information they need in one centralized location.
• Critical data security: Having a single source of customer information facilitates effective data governance. In addition, permissions can be easily managed, and information can be administered in a way that respects critical privacy and data protection measures.
• Identification of new business opportunities: customer data integration provides the data needed to identify additional sales opportunities (product usage data, emails, activities on third-party review sites, etc.). Once these leads are identified, you can also use that information to evaluate which products to sell to which contacts.
• Higher-performing marketing campaigns: when marketers have more data to work with, they can further segment the customer base and create more targeted campaigns. With segmented customers, more personalized messages can be shared with each type of audience, which translates into a greater likelihood of generating interaction.
• Time savings: by being able to access the data you need in real-time and in one place, you can avoid having to move between applications to find information or having to ask other colleagues for it. This results in time savings and the ability to focus more on activities that add value to the organization and customers.

Data Integration Types

Understanding the types of data integrations helps you determine the best way to integrate them for your specific business case. All three will go a long way toward achieving the goal of unifying and understanding your data, but the ways in which they do so are different:

1. Consolidation: is the most common and involves taking data from several sources, unifying them, and storing them in a central data warehouse. It is most widely used when you simply want to reduce the number of places where data is stored. As an end user, consolidated data can be easily accessed for analysis.
2. Propagation: This is an automatic copy-and-paste operation. The data is still at the source location, but there is also a copy of it at the destination. It is mostly used when there are two tools that need to share data with each other, such as a marketing automation tool and CRM. It does not consolidate data in the same way as the first, which is why it is so useful when you have a small number of data sources.
3. Federation: is a form of data virtualization, which takes data from multiple sources and makes it accessible from a central point. To the end user, it looks similar to the first type, but the federation does not perform any consolidation, as the data is kept separate until the end user makes a request for the data.

At this point, the question may arise as to which of the three types is suitable for my case. As a general rule, in most CDI cases it is convenient to use consolidation. It is usually the simplest way to standardize data, maximize its functionality and ensure that data silos are eliminated. However, its disadvantage is that, if you are working with massive amounts of data, it can be costly.

On the other hand, propagation is more useful when you are a small company that does not collect a lot of data.

Finally, federation is used in cases where consolidation is too costly or when dealing with large companies that collect a large amount of data.

Customer data integration tools

There are thousands of data integration tools, but the choice of one or the other depends on the needs and expectations of each company. For example, if you plan to use data consolidation, you will need a customer data platform and a data warehouse.

The customer data platform will help collect and standardize data, and the data warehouse will store it securely.

To do this, we also have different ways of integrating the data. To start doing this, we need to ask ourselves some questions such as: Does this method improve the security of our data? Can it continue to be used in the future? Is it simple?

With all this, the most common is the following:

• Manual data integration: just as it sounds, it is a hand-coded integration created manually by the internal IT team. It is time-consuming, can be costly and can have many critical errors. In addition, if something changes in the data collection strategy, the integration must be rebuilt.
• Automated integration: These are usually very quick and easy to set up. The only disadvantage is that they are usually limited by the company that created them, so there may be a limitation on the number of tools that can be integrated.
• Customer Data Platforms (CDP): this takes automated integration one step further. CDPs allow you to connect two or more tools that would not otherwise work together, and create an automated integration where a manual integration would otherwise be required. They also keep your data as clean as possible by giving you a central way to control all data sources.

Customer data integration best practices

To ensure seamless implementation and continuous operation of the CDI platform, some best practices include:

Develop a comprehensive data-tracking strategy

Data silos, while restrictive, offer a level of organization that is important in information management.

When multiple data sources are combined, it can lead to a chaotic data lake if not managed properly. Therefore, a well-thought-out data tracking plan ensures that data is securely organized, easily accessible, and maximizes the value of the CDI.

Designate a data manager

Assigning a data manager or small team to oversee the entire CDI process can significantly reduce the risk of errors.

This person or team should have a thorough understanding of the company’s data management and tracking plan. For example, a digital marketing agency should be able to ensure seamless integration of customer data from social media platforms, email marketing campaigns, or web analytics.

Conduct regular audits

Audits are crucial to ensure that the data collected is relevant and useful.

Redundant or irrelevant data can be safely discarded during these reviews, resulting in optimized storage space and improved data hygiene.

Automate

Automation can significantly reduce the risk of human error in data entry, especially when large volumes are involved. By automating most of the process, companies can achieve greater accuracy and reliability of their data.

For example, an e-commerce platform can benefit from automation to accurately track customer interactions across multiple channels, providing a more precise understanding of customer behavior and preferences.

Customer Data Integration Solutions

Data integration is one of the most important steps in becoming a data-driven company. Without it, you will have inaccessible data silos everywhere, which will result in not having a complete view of your customers and your decision-making will be based on unbiased data.

At Plain Concepts we propose a data strategy in which you can get value and get the most out of your data.

We help you discover how to get value from your data, control and analyze all your data sources, and use data to make intelligent decisions and accelerate your business:

• Data analytics and strategy assessment: we evaluate data technology for architecture synthesis and implementation planning.
• Modern analytics and data warehouse assessment: we provide you with a clear view of the modern data warehousing model through understanding best practices on how to prepare data for analysis.
• Exploratory data analysis assessment: we look at the data before making assumptions so you get a better understanding of the available data sets.
• Digital Twin and Smart Factory Accelerator: we create a framework to deliver integrated digital twin manufacturing and supply chain solutions in the cloud.

We will formalize the strategy that best suits you and its subsequent technological implementation. Our advanced analysis services will help you unleash the full potential of your data and turn it into actionable information, identifying patterns and trends that can condition your decisions and boost your business.

Extract the full potential of your data now!