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.

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).

It is not uncommon for companies the 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.

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.

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.

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.