July 29, 2021

How to build a CDP: 4 steps involved in a customer data integration

minute read

Customer experience is fast becoming a top priority for businesses looking to gain an edge over the competition—and the best CX starts with clean and integrated customer data. Here’s a breakdown of the customer data integration process in creating a CDP.

A recent study reveals 84 percent of companies that work to improve their customer experience report an increase in their revenue. Consequently, most companies now spend a considerable amount of resources to gather extraordinarily rich consumer data that helps them improve the customer experience. This data-driven retail approach has given many companies an unprecedented competitive advantage.

However, most organizations that collect and store data about their customers don't use it efficiently to improve their business due to a lack of effective customer intelligence capabilities. One way to maximize your customer data is by integrating it into a streamlined, easy-to-understand single customer view.

This blog details the 4 main steps involved in the customer data integration process, including:

  • Integrating the data based on a common variable
  • Data cleaning and organizing
  • Data unification and identity resolution
  • Data enrichment

What is a customer data integration?

Customer data integration refers to a process of combining and organizing customer data from different sources into a single, more accessible, and usable form for enhanced analytical capabilities. Customer Data Integration is an integral part of customer engagement that guarantees all departments in an organization gain constant access to the most critical and complete view of customer information.

When you have a comprehensive and singular view of your customers across your company, you are empowered to create personalized customer service for improved customer experience and retention. You also achieve a more tailored marketing and sales communications journey that increases your chances of converting your prospects.

How to build a CDP: 4 steps involved in customer data integration

A successful customer data integration process must go through the following four steps that enable the creation of the essential single customer view:

1. Integrating the data based on a common variable

Bringing together all crucial customer data, including transactions, product choices, engagement data, and retail data from every source is the first step to achieve a single customer view.

Some of the critical data sources to focus on at this stage include surveys, reviews, email, ecommerce, retail POS, website, and loyalty. Each of these sources provides vital customer data, including demographics, purchase histories, customer service interactions, web and mobile browsing activities, email engagement, and more.

To create an excellent relationship with your customers, collect all the valuable data from these sources and integrate them together using common linkage variables like phone numbers, email addresses, and names.

2. Data cleaning and organizing

After collecting and combining all the data, you need to clean and organize it for ease of calculation and analysis. This process involves removing errors and duplicates across data formats and systems to guarantee the highest quality information in your single customer view.

Typically, preparing and analyzing data takes more time and effort before moving to higher impact activities. Streamlining this process is an integral part of a successful Customer Data Platform building process that makes it easier for you to compare records and get the best results. Data cleaning and organizing takes the following steps:

  • Validation: The validation process guarantees that all your data is correct, reliable, and consistent.
  • Unification: This process involves linking records and the removal of duplicates.
  • Data normalization: Normalization is a process of transforming all data into one consistent and accessible format.
  • Categorization: Your final step in data cleaning involves categorizing data for easy customer segmentation, customer insight, and activation.

Clean data is key! Without it, it’s impossible to accurately measure and analyze your performance.

3. Data unification and identity resolution

Data unification or identity resolution merges data from multiple sources and links it to individual customer profiles, enabling you to access valuable insights into past customer activities. You can leverage automated systems to collect and interpret data from multiple sources and merge them into a cohesive data set. There are three fundamental principles of data unification within a CDP:

  • Identity graph: An identity graph is a collection of known customer identifiers associated with one another. The goal of data unification is to draw a link between each of the data sets.
  • Deterministic matching: Deterministic matching helps identify the same user across different devices by matching user profiles together. The process looks for an exact match between two pieces of data through a precise matching on known variables such as email and phone number. If your data is cleansed and standardized, you can get a 100 percent match easily.
  • Probabilistic matching: Probabilistic matching utilizes a statistical approach to measure the probability that two customer records represent the same user. This method leverages weights to calculate the match scores and thresholds to arrive at a match, non-match, or possible match. Matching using this field may involve using a range of user variables such as the last name and postal address.

In essence, your CDP building process may take either a deterministic, probabilistic or combined approach to data unification, depending on your goals and preferences. The choice you make will depend on how you understand and communicate with your customers.

4. Data enrichment

After cleaning and matching your data into a standardized format, your next step is to transform it to become ready and accessible information for everyone in your team. Data enrichments may involve filling in missing details and the standardization of all merging data to achieve better data quality. Data enrichment is an effective way to get a refined, improved, and enhanced data grouping. Enriched data also enables you to be more knowledgeable about your business and customers to enhance your brand's presence.

Once you have created your single customer view, use it to fuel all the other Customer Data Platform use cases with built-in tools such as measurement and reporting dashboards, cross-channel campaigns, automation, surveys for data collection, and audience segmentation.

Get technical and strategic guidance from Lexer to achieve a successful customer data integration

Lexer is the Customer Data Platform of choice for leading brands like Igloo, Quiksilver, Rip Curl, Nine West, Supergoop!, and more. As the only CDP built for retail, we help leading brands drive incremental sales from enhanced customer engagement.

Once you've integrated your data into a single customer view, what changes can you expect to see to your teams, processes, customers, and performance? Click here to read "Post-Deployment Timeline: What Changes Can You Expect with a CDP."

Feel free to contact us for any questions or more information about our solutions.

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Elizabeth Burnam
Content Marketing Specialist
Elizabeth Burnam is a content marketer and a poet at heart. She has a degree in Professional Writing and experience developing high-impact marketing assets for a broad range of industries.Outside of work, she enjoys reading, painting, people-watching, and exploring the natural wonders of Vermont.