How to Build an Efficient Customer Data Integration

Why is Customer Data Integration Essential for Business?

How do you improve your business and make it fit your customer needs? The typical answer is learn from user experience, analyze it, and provide data-driven solutions. But how do we collect all the data from customers in one place for analysis?

For example, if you need to target specific clients in your marketing campaign, where can you find the details about them? Here are some of the typical places:

• social media
• market research
• website visits
• surveys
• app downloads and other places

As you can see, the information you need is stored in different locations.

To solve this problem and get a full view of customer relations, you need customer data integration software to consolidate all client information from various business units in order to have continuous access to all customer data 24/7.

At the same time, customer data integration can not help the company if this process is built incorrectly. Only 3% of integrated customer data fit quality standards. The reason is insufficient time to process deployment.

Reasons to Use Customer Data Integration for Enterprises

The reasons for implementing integration software are pretty obvious. But we underline some specific benefits that can help big enterprises and small businesses stay competitive and learn from their customers.

1. Automate the process and reduce the cost and time efforts. CDI tools help make the enterprise more productive, as it makes a lot of analytical techniques and offers a disruption-free rollout with lower cost and time efforts.
2. Improve customer service experience. You will have real-time updates on each customer issue and will be able to respond quickly.
3. Reduce risks. The customer data integration system provides stewardship and data governance to ensure efficient access control, data encryption and management.
4. Effective and quick response to data security leaks. CDI tools increase the protection level and allow you to respond immediately to security threats. It becomes easier to protect customer data if you have a general view of all processes.
5. Get one access point for all data. There is no need to collect the data from various sources and analyze them. With integration software, you will have one place for all the data.
6. Have everyone on one page. All departments and managers will have access to all data. The company reduces data duplicates and knows what data was collected and why.
7. Open new business opportunities. After you have a holistic view of all the data, it is possible to identify new possibilities for development.
8. Predict trends. A valuable data analysis is key to marketing forecasts and allows you to follow industry trends.

What Type of Customer Data Integration Should You Use?

After the company determines that data integration is necessary, they need to check the most effective type of integration based on their company-specific size and needs.

There are three types of CDI:

• Consolidation. The most common type of CDI. It takes data from various sources, unifies it and stores it on one platform or warehouse.
• Propagation. This type of CDI ensures copying data from all possible sources and passes them into one solution. The data will exist in those sources, and a copy of data will be in the developed integration solution.
• Federation. In this case, the data will be taken from different sources, but the company will have access from one point.

What type of customer data integration process is better for your organization? You will use data consolidation in most cases, as it is the fastest and easiest way to build an advanced solution. But it can be expensive if you are dealing with a significant amount of data. In this case, it might be better to use a data federation. It is a standard solution for big enterprises with a lot of customer data.

If you have a small business and not a lot of data, you can use a data propagation type of CDI. It will be the most efficient way to improve customer experience.

Customer Data Integration Methodology

You may wonder how to choose the strategies for your specific case. We would recommend determining if this methodology is easy to implement, whether it will improve data security for your organization, and you should consider how many organizations have used this method before.

We define the three most often used methods to integrate customer data:

1. Manual integration. The in-house IT department can create coded integration software. It can take time, but it will be designed for specific needs. At the same time, this works only for small businesses. Also, it became less popular because of the variety of integration tools the market has these days.
2. Automated integration. This type of CDI applies a ready solution for running the process. It can be an easy tool that performs an integration in one click or a more sophisticated solution.
3. Custom integration. This method involves creating a custom integration platform to fit the company’s needs. Usually, it applies when a ready solution is not enough for complicated integration. You also need to have a reliable development partner to have an efficient custom platform.

Customer Data Integration Steps: How to Perform the Process

The next thing to discuss is step-by-step instructions to help with the integration process. Let’s look at each step.

Define Your Data Sources

First, define what data you need to integrate for the platform or app. It can be information on customer transactions, website visits and data from different cloud sources. It can include various data from hundreds of sources that will help achieve business goals in the future.

What sources to consider:

• loyalty programs
• search data
• surveys
• reviews
• emails and phones

It is also essential to define what customer data integration tools you need at this stage. For instance, you may need a specific tool to extract data from multiple sources or perform accurate time data extraction.

Cleaning the Data

After extracting the data, you will need to clean it and organize it. What does that include? Different sources store data in various formats. Also, some of them collect the same data. For instance, transaction information can be stored in a few databases. After this information is extracted, you need to clear it and delete duplicate data.

The data cleaning process has a few phases:

• Validation
• Unification
• Normalization
• Categorization

Unification of the Data

The next step in the CDI process is data unification or identity resolution. It ensures data connection from various sources and links it to individual customer profiles. In this case, you will access data history and past customer activities. You also can use different automated tools for data unification.

There are three common concepts of data unification in CDI:

• Identity graph to identify the connection between each of the data sets.
• Deterministic matching to identify the same user in different databases.
• Probabilistic matching uses a statistical approach to measure the probability that two or more customer records represent the same user.

Each concept of data unification can be applied to different business goals. That is why you should focus on communication with customers first, as it will determine the choice of approach.

Data Enrichment

The last step is to transform the data you have into accessible information or a platform that can be used by your team. Data enrichment can fill in some missing details or standardize some data types to have efficient and improved data grouping. It is also helpful to learn more about your clients and improve communication with them.

Customer Data Integration Challenges

The process of data integration can look quite simple, but this process also has a lot of challenges we need to outline.

Creation of a reliable integration plan. As we mentioned before, you will extract the data from various sources, and the first challenge you will face is the standardization of all the data. To perform it successfully, you need to have a robust plan for integration.

Dealing with a vast amount of data. It will be a huge challenge to manage and accumulate such data. It can also become a reason for changing storage capacity, as the data will have unexpected needs. In most cases of integrating big data, you will need to develop custom integration software.

Protecting clients’ data. Data integration can provide additional levels of security for the data. At the same time, it poses a risk of losing customer data or compromising it. That is why businesses need to pay extra attention to data protection while performing integration.

Integrating historical customer data with new software solutions. This type of integration can have some data losses, but the goal is to minimize them. Even if you have advanced software, you still need to save access to all the historical data from customers. Another problem is that this data can be stored in outdated software.

Customer Data Integration Best Practices

Here are some best practices to make customer data integration easier for you.

1. Use a data tracking plan. It will protect you from a mess with data integration from various sources.
2. Automate all possible data movements.
3. Identify a responsible person. This person will be the owner of your tracking plan and will be aware of all the data integrations in the company.
4. Be transparent with the data you need to keep. Delete all duplicates.
5. Audit and monitor the integration platform after the process is done. You need to ensure that everything works correctly.
6. Be aware of where your data is stored and how it is used.
7. Decide on an integration timeline. It will help you manage the process more efficiently.
8. Start with integration goals. It should be your guide for planning the whole process.
9. Delineate data categories before integration. It will help to clean the data after extraction.

Customer Experience Automation Tool With Slack: Integration Case Study

For one of our clients, Cprime Studios integrated Slack and a customer experience automated tool to improve communication and data tracking.

The tool we updated with Slack combined email marketing, marketing automation, sales automation and CRM tooling. We used Slack for integration as it offers many IRC-style features, including persistent chat rooms organized by topic, private groups and direct messaging.

Integration with Slack brought many benefits to the company, such as managing tasks in customer experience automation directly from Slack. It boosted the activity of managers and decreased the time spent on solving customer issues. Also, it became possible to send notifications about changes in customer experience software to Slack channels.

Customer data integration in this case allowed users to view the contact’s history of marketing activities and retrieve the data about the customer or account using Slack’s custom commands.

The integration process can change the way of data analysis and open new perspectives on understanding clients’ needs. You can learn the best practices, steps of integration or customer data integration benefits for your type of company through integration. Cprime Studios provides custom data integration solutions for any organizational goal and type of client data.