Published Jan 30, 2024

What is customer data integration?



Some of the most important data businesses gather and use is customer data. This helps manage relationships, understand customer trends, and build future products and services existing customers will love, and hopefully attract new customers. In today’s IT landscape, businesses are dealing with many different applications and systems causing customer interactions to change rapidly.

On average, high-growth SaaS companies spend roughly $2.5 million a year on a tech stack including nearly 200 SaaS apps, causing an issue called SaaS sprawl. This constant change often means ever-important customer information is fragmented, hard to access, and doesn’t provide a clear story. This is detrimental to a business’s relationship with its customers. However, this data fragmentation doesn’t need to be the norm. Customer data integration can address these issues and offers many benefits. To best understand customer data integration, we will explore what it is, the different types of customer data integration, their benefits, and best practices to keep in mind as customer data integration is implemented.

What is customer data integration?

Customer data integration is the process of combining and organizing all customer data from multiple databases and applications providing a unified platform to connect and synchronize data from various customer touchpoints and systems. One method of customer data integration, iPaaS, or integration platform as a service allows businesses to integrate data from various sources such as CRM systems, marketing automation tools, sales platforms, customer service software, and social media channels.

Customer data comes from many different sources and may have different or similar information. The fact this information may not be easily accessible to every party in an organization that needs it can cause serious problems when it comes to creating cohesive strategies. Customer data integration aims to solve these problems by automatically collecting and analyzing all customer data into a single source of truth for everyone in the organization that uses customer data to complete their tasks.

There are many examples where customer data integration can be useful. For one, if an organization wants to run ads about a new product or service and plans to target both existing customers and new customers. To target properly, the marketing department will need to access key customer information such as what platforms existing customers use most, what their pain points might be,  and more specific information like email addresses. It’s possible, and likely, the marketing department doesn’t have access to all the information they need and will need to track it down from the various departments and applications. This means waiting on departments to email back and forth, and hoping all the information sent over is still relevant to the customers. This wastes time, money, and ultimately, means campaigns are unable to run as efficiently as they should. Employing customer data integration in this instance would help remove roadblocks. The marketing team could simply tap into the information collected and stored by the customer data integration and create a campaign and customer journey map that is sure to drive tremendous results.

Additional use cases of customer data integration include e-commerce which allows businesses to combine data from each customer touchpoint. CRM which allows customer information consolidation from multiple sources to create a more comprehensive view of a customer profile. Sales can use customer data integration to see a holistic view of a customer’s likes and dislikes and use that information to predict what new products and services they may be interested in. The finance department uses this data integration to minimize spending on ineffective front-end processes. Finally, data warehousing is able to easily and securely store all data collected from the customer data integration.

Types of customer data integration

Now that we know what customer data integration is, we can explore some of the different ways it is handled. There are generally three different types of customer data integration. Each works a little differently, however, they all end up having roughly the same results; easier access to the data you need.


  • Data consolidation: The first type of customer data integration is called data consolidation. This is the most common when discussing customer data integration. As the name suggests, it consolidates customer data from multiple sources to one single source. This means there are fewer places departments need to look when attempting to find information, making analyzing this information far easier. It’s important to note when data consolidation is used as the integration process, it physically moves where that data is stored to the new centralized repository.
  • Data propagation: The second type of customer data integration is called data propagation. The best way to think about data propagation is as a copy/paste process between two applications. When two applications need access to the same information, data propagation will take the data from one of the source applications, copy it, and then paste it into the other application. When doing this the original application maintains the data, and the destination application has a copy, which means there are now two instances of that data. Data propagation doesn’t provide a single source of information but still allows applications to speak and share when needed. Due to the lack of consolidation, data propagation is best used when there are fewer sources of data being shared.
  • Data federation: The third type of customer data integration is called data federation. Data federation is fairly similar to data consolidation in that it gathers data into a central hub that can be accessed by those who need it, however, how that data is consolidated is slightly different and is more useful for different cases. While data consolidation moves data to a central location as soon as it’s gathered, a data federation approach won’t gather the data until an end user has requested it. Once a request for data has been made, the system will find it, analyze it, then deliver the requested information. This type of customer data integration is most useful for very large organizations that have many sources of data that each contain large amounts of information. Storing all this data in one place can be expensive. Data federation presents only the necessary data to allow for a centralized approach without the memory needs of storage all in one place.

Benefits of customer data integration

With a better understanding of what customer data integration is, and what different types you may encounter, we can further explore the benefits of this data handling strategy. As we’ve discussed, customer data integration allows information to be accessed quickly and easily. While this in of itself is a clear benefit, several others provide even more value for businesses.


  • Removes data silos: The first benefit we should talk about is the fact customer data integration removes data silos from your organization. This is an incredible benefit as it allows your organization to work together in a more efficient environment. When data silos exist, departments within the same organization run into huge roadblocks that prevent collaboration. Even more than that, it can hinder the work they do in their areas. A silo means that anytime a department needs a bit of information housed by another department, or in a different application, they are going to face a far harder time retrieving that information. This wastes time and means even the work they can complete may not be the best it can be.Breaking down these data silos removes the roadblocks to retrieving important data, and means the work will take less time, save money, and be of higher quality as none of it is rushed or missing data. This benefit alone makes customer data integration worth the investment, however, several others further show how important customer data integration is.
  • Data accuracy: The second benefit is data accuracy. Data accuracy is critical to businesses, especially as that information is transferred between departments and used for projects. Without customer data integration, the only way to transfer data is through manual methods. Manual methods can be hindered by human errors. These human errors may seem small, but any amount of inaccurate data is detrimental to business operations. The best way to avoid human error is to remove the need to transfer data manually. In other words, remove that human element. With customer data integration, no matter which type is used, data is collected automatically directly from the source applications before being stored or sent to the application or user that needs it. This helps guarantee that all data collected is accurate and relevant.
  • Enhanced customer experience: The third major benefit of customer data integration is an enhanced customer experience through automation. Customer relations is an incredibly important part of any business. Ensuring all of a customer’s information is easily accessible is key to consistent interactions with the organization. Through customer data integration nearly all customer data can be seen including how they entered the funnel, how long they’ve been a customer, their buying habits, email, and if there were ever any complaints solved. Customer support is especially important. If a customer calls with a problem, they shouldn’t be led through the same steps and departments they’ve already been through. Furthermore, knowing how someone became a customer, and their buying habits helps create personalized campaigns to help maintain the relationship and opens the door for them to be interested in new products and services.

Customer data integration best practices

With so many uses and benefits of customer data integration, it’s important to understand the best practices when considering implementing one of these solutions.

Identify Source Points

The first step to customer data integration is to identify the source points that data will be coming from, what format that data will take, and where the data will be sent. iPaaS solutions allow easy data pipelines to be built with numerous prebuilt integrations between applications that allow data to flow automatically between source points. Additionally, data is automatically formatted for ease of use and compatibility.

Assign a data leader

Next, assign one person, or several people, to be a “data leader.” The data leader’s job is to keep regular tabs on the data being transferred, where it’s coming from, and its accuracy. While customer data integrations are far more accurate, there can’t be absolutely zero errors 100% of the time. With data leaders doing regular checkups the continued accuracy of the integration systems can be upheld.

A successful data leader should be highly competent in data analytics as they are supervising the data transfer from beginning to end. Additionally, they should be able to identify new integrations and processes that can save time and money. Their job expands that of integrations as well. They should stay up-to-date with teams, their needs, points of pain, and how they and the organization’s data integrations can transform their processes for the better.

Conduct Regular Data Audits

Data accuracy is massively important to a business and how it operates. Despite the automation and accuracy of a customer data integration it’s important to conduct regular data audits to ensure data remains accurate. Mistakes can be made, data can be entered into a source incorrectly, or a number of other things can happen. Regular data audits will catch any potential inaccuracies or reporting problems early and greatly mitigate the impact they have on the organization.

Monitor and Measure Performance

In order to ensure integrations are successful it’s important to identify specific goals and performance indicators. This allows an organization to follow up on how a customer data integration is functioning.This includes monitoring for data quality, accuracy, and customer satisfaction metrics.

Plan for growth

For most businesses, growing is the ultimate goal. When creating a customer data integration solution, this growth needs to be accounted for. As an organization grows, more people and more departments will need access to this data and the solution you choose must be able to handle this kind of scalability. If potential growth is ignored, there are growing pains organizations will run into as they scale customer data integrations. New integrations will need to be built, or existing ones expanded, and if the wrong solution is chosen, integrations, APIs, or entire applications may break when these new integrations are built.

Customer data integration is a great tool to remove data silos and create a better customer experience. These benefits make customer data integration a near must-have for any business. If you want to learn more about how customer data integration from Celigo can revolutionize your organization contact us today to request a demo!